NCEE 2010–4014 U.S. DEpartmENt of EDUCatioN

The Effectiveness of a Program to 

Accelerate Vocabulary Development 

in Kindergarten (VOCAB)

Kindergarten Final Evaluation Report
The Effectiveness of a Program to Accelerate 

Vocabulary Development in Kindergarten 

(VOCAB)

Kindergarten Final Evaluation Report

November 2010

Authors:

Barbara Goodson, Principal Investigator 

Abt Associates Inc.*

Anne Wolf, Director of Evaluation 

Abt Associates Inc. 

Steve Bell, Task 2 Methodological Leader 

Abt Associates Inc. 

Herb Turner, Technical consultant 

ANALYTICA 

Pamela B. Finney, Regional Educational Laboratory Southeast Research Management 

Leader 

SERVE Center at the University of North Carolina at Greensboro 

*Joan McLaughlin, Original Principal Investigator, Abt Associates

Project Officer:

Sandra Garcia 

Institute of Education Sciences 

NCEE 2010–4014 

U.S. Department of Education 

 
ii

U.S. Department of Education 

Arne Duncan 

Secretary 

Institute of Education Sciences 

John Q. Easton 

Director 

National Center for Education Evaluation and Regional Assistance 

Rebecca A. Maynard 

Commissioner 

November 2010 

This report was prepared for the National Center for Education Evaluation and Regional 

Assistance, Institute of Education Sciences, under contract ED-06C0-0028 with Regional 

Educational Laboratory Southeast administered by the SERVE Center at the University of North 

Carolina at Greensboro. 

IES evaluation reports present objective information on the conditions of implementation 

and impacts of the programs being evaluated. IES evaluation reports do not include conclusions 

or recommendations or views with regard to actions policymakers or practitioners should take in 

light of the findings in the report. 

This report is in the public domain. Authorization to reproduce it in whole or in part is 

granted. While permission to reprint this publication is not necessary, the citation should read: 

Goodson, B., Wolf, A., Bell, S., Turner, H., and Finney, P.B. (2010).The Effectiveness of a 

Program to Accelerate Vocabulary Development in Kindergarten (VOCAB). (NCEE 2010-

4014). Washington, DC: National Center for Education Evaluation and Regional Assistance, 

Institute of Education Sciences, U.S. Department of Education. 

This report is available on the Institute of Education Sciences website at 

http://ncee.ed.gov and the Regional Educational Laboratory Program website at 

http://edlabs.ed.gov. 

Alternate Formats Upon request, this report is available in alternate formats, such as 

Braille, large print, audiotape, or computer diskette. For more information, please contact the 

Department’s Alternate Format Center at 202-260-9895 or 202-205-8113. 

 
Disclosure of potential conflict of interest1 

None of the authors or other staff involved in the study from Abt, ANALYTICA, 

Empirical Education, the Regional Educational Laboratory-Southeast, SERVE Center at the 

University of North Carolina at Greensboro, or the University of Georgia has financial interests 

that could be affected by the content of this report.1 No one on the nine-member Technical 

Working Group, convened twice annually by the research team to provide advice and guidance, 

has financial interests that could be affected by the study findings. 

1

 Contractors carrying out research and evaluation projects for IES frequently need to obtain expert advice and 

technical assistance from individuals and entities whose other professional work may not be entirely independent of 

or separable from the tasks they are carrying out for the IES contractor. Contractors endeavor not to put such 

individuals or entities in positions in which they could bias the analysis and reporting of results, and their potential 

conflicts of interest are disclosed. 

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CONTENTS

SUMMARY ...........................................................................................................................................................1

CHAPTER.1:.INTRODUCTION.AND.STUDY.OVERVIEW............................................................................................4

ROLE!OF!VOCABULARY!KNOWLEDGE!IN!READING!COMPREHENSION .......................................................................................... 4

SELECTING!A!VOCABULARY!INSTRUCTION!PROGRAM.............................................................................................................. 4

THEORY!OF!CHANGE!FOR!K:PAVE .................................................................................................................................... 7

EXPERIMENTAL!AND!QUASI:EXPERIMENTAL!EVIDENCE!OF!IMPACTS!OF!VOCABULARY!INSTRUCTION.................................................. 8

STUDY!TARGETS!THE!MISSISSIPPI!DELTA!REGION!TO!ADDRESS!LOW!STUDENT!READING!ACHIEVEMENT ........................................... 12

STUDY!DESIGN ............................................................................................................................................................ 13

REPORT!OVERVIEW ...................................................................................................................................................... 15

CHAPTER.2:.STUDY.DESIGN.AND.METHODOLOGY .............................................................................................. 16

OVERVIEW ................................................................................................................................................................. 16

SAMPLE!RECRUITMENT!AND!RANDOM!ASSIGNMENT............................................................................................................ 17

DATA!COLLECTION ....................................................................................................................................................... 28

ANALYTIC!SAMPLE........................................................................................................................................................ 40

DATA!ANALYSIS!METHODS ............................................................................................................................................. 45

CHAPTER.3:.IMPLEMENTATION.OF.THE.INTERVENTION...................................................................................... 52

OVERVIEW ................................................................................................................................................................. 52

DESIGN!OF!THE!K:PAVE!PROGRAM................................................................................................................................. 52

K:PAVE!PROGRAM!COMPONENTS!AND!TEACHING!STRATEGIES ............................................................................................. 54

K:PAVE!TEACHER!TRAINING!AND!SUPPORT....................................................................................................................... 57

PROCEDURE!FOR!ASSESSING!FIDELITY!OF!IMPLEMENTATION!OF K:PAVE ................................................................................. 60

FINDINGS!ON!FIDELITY!OF!K:PAVE!IMPLEMENTATION!IN!THE!INTERVENTION!CLASSROOMS......................................................... 65

SUMMARY!OF!FINDINGS!AND!CONCLUDING!OBSERVATIONS .................................................................................................. 68

CHAPTER.4:.IMPACT.RESULTS............................................................................................................................. 69

SUMMARY!OF!K:PAVE!IMPACTS .................................................................................................................................... 69

RESEARCH!QUESTIONS .................................................................................................................................................. 70

IMPACTS!ON!KINDERGARTEN!STUDENTS’!EXPRESSIVE!VOCABULARY......................................................................................... 70

IMPACTS!ON!KINDERGARTEN!STUDENTS’!ACADEMIC!KNOWLEDGE!AND!LISTENING!COMPREHENSION ............................................. 71

IMPACTS!ON!CLASSROOM!INSTRUCTION ........................................................................................................................... 73

CHAPTER.5:.SUMMARY.OF.FINDINGS.AND.STUDY.LIMITATIONS ........................................................................ 78

EFFECT!OF!K:PAVE!ON!EXPRESSIVE!VOCABULARY .............................................................................................................. 78

EFFECT!OF!K:PAVE!ON!OTHER!VOCABULARY:RELATED!LITERACY!SKILLS .................................................................................. 78

EFFECT!OF!K:PAVE!ON!CLASSROOM!INSTRUCTION............................................................................................................. 78

STUDY!PARAMETERS..................................................................................................................................................... 79

STUDY!LIMITATIONS ..................................................................................................................................................... 80

APPENDIX.A..MISSISSIPPI.COUNTIES.WITH.STUDY.SCHOOLS,.BY.COUNTY .......................................................... 81

APPENDIX.B..STATISTICAL.POWER.ANALYSIS...................................................................................................... 82

STATISTICAL!POWER!FOR!DETECTING!IMPACTS!ON!STUDENTS ................................................................................................ 82

ACTUAL!MINIMUM!DETECTABLE!EFFECT!SIZES!FOR!STUDENTS’ EXPRESSIVE!VOCABULARY.......................................................... 84

STATISTICAL!POWER!FOR!DETECTING!IMPACTS!ON!CLASSROOM!INSTRUCTION........................................................................... 85
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ACTUAL!MINIMUM!DETECTABLE!EFFECT!SIZES!FOR!CLASSROOM!INSTRUCTION!OUTCOMES........................................................... 86

APPENDIX.C..RANDOM.ASSIGNMENT................................................................................................................. 88

MATCHING!OF!SCHOOLS!WITHIN!BLOCKS!FOR!RANDOM!ASSIGNMENT ..................................................................................... 88

PROCESS!OF!RANDOM!ASSIGNMENT:!SEQUENCE!GENERATION............................................................................................... 89

APPENDIX.D..RECRUITMENT.AND.RANDOM.SELECTION.OF.THE.STUDENT.SAMPLE ............................................ 93

APPENDIX.E..COMPARISON.OF.STUDENTS.MISSING.AND.NOT.MISSING.BASELINE.ASSESSMENT........................ 94

APPENDIX.F..CLASSROOM.OBSERVATION.MEASURES.FOR.IMPACT.EVALUATION............................................... 95

CLASSROOM!ASSESSMENT!SCORING!SYSTEM...................................................................................................................... 95

READ!ALOUD!PROFILE–KINDERGARTEN ........................................................................................................................... 98

VOCABULARY!RECORD.................................................................................................................................................. 99

CREATION!OF!VOCABULARY!AND!COMPREHENSION!SUPPORT!COMPOSITE ............................................................................. 100

APPENDIX.G..TEACHER.SURVEY........................................................................................................................ 105

APPENDIX.H..KFPAVE.FIDELITY.OBSERVER.HANDBOOK.AND.TRAINING.FIDELITY.CHECKLIST ............................. 109

KINDERGARTEN!PAVED!FOR!SUCCESS!(K:PAVE)!FIDELITY!OBSERVATIONS ............................................................................ 109

TRAINING!FIDELITY!CHECKLIST!DETAILED!DIRECTIONS!AND!IMPLICATIONS!FOR!NOTE!TAKING ...................................................... 110

FORM!COMPLETION−TRAINING!FIDELITY!CHECKLIST ......................................................................................................... 115

TEACHER!OBSERVATION!FOLLOW:UP!MEETING!PROTOCOL—DETAILED!DESCRIPTION.............................................................. 119

TEACHER!OBSERVATION.............................................................................................................................................. 122

FOLLOW:UP!MEETING!PROTOCOL................................................................................................................................. 122

FREQUENTLY!ASKED!QUESTIONS................................................................................................................................... 124

APPENDIX.I..DATA.COLLECTION.PROCEDURES.................................................................................................. 125

PROTOCOL!FOR!CHILD!ASSESSMENTS:!QUICK!REFERENCE ................................................................................................... 125

PROTOCOL!FOR!CLASSROOM!OBSERVATIONS .................................................................................................................. 126

APPENDIX.J..DATA.QUALITY.ASSURANCE.PROCEDURES.................................................................................... 128

APPENDIX.K..MODEL.SPECIFICATIONS .............................................................................................................. 130

THREE:LEVEL!MODEL!USED!TO!ESTIMATE!IMPACTS!ON!KINDERGARTEN!STUDENTS!(FOR!SINGLE!OUTCOME!MEASURES).................... 130

MODELS!SPECIFICATION!FOR!GLOBAL F:TEST!OF!JOINT!IMPACT!ON!MULTIPLE!STUDENT!OUTCOMES!WITHIN!A!DOMAIN ................... 132

TWO:LEVEL!MODEL!USED!TO!ESTIMATE!IMPACTS!ON!CLASSROOM!INSTRUCTION!(FOR!SINGLE!OUTCOME!MEASURES)...................... 134

MODELS!SPECIFICATION!FOR!GLOBAL!F:TEST!OF!JOINT!IMPACT!ON!MULTIPLE!CLASSROOM!INSTRUCTION!

OUTCOMES!WITHIN!A!DOMAIN .................................................................................................................................. 136

APPENDIX.L..FLOWCHART.ILLUSTRATING.SAMPLE.ATTRITION.FROM.DATA.COLLECTION ................................. 139

APPENDIX.M..MISSING.DATA.IMPUTATION...................................................................................................... 140

DUMMY!VARIABLE!ADJUSTMENT!FOR!MISSING!COVARIATES................................................................................................ 140

SINGLE!STOCHASTIC!REGRESSION!IMPUTATION!FOR!MISSING!PRETEST!AND!POSTTEST!DATA ....................................................... 142

APPENDIX.N..SENSITIVITY.ANALYSES................................................................................................................ 146

IMPACTS!ON!STUDENTS .............................................................................................................................................. 146

IMPACTS!ON!CLASSROOM!INSTRUCTION ......................................................................................................................... 153

APPENDIX.O..SCHOOL,.TEACHER,.AND.STUDENT.COVARIATES ......................................................................... 158
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APPENDIX.P..LIST.OF.KFPAVE.MATERIALS.PROVIDED.TO.TEACHERS.................................................................. 161

APPENDIX.Q..SAMPLE.WEEKLY.UNIT.FROM.KFPAVE.PROGRAM........................................................................ 162

TRANSPORTATION...................................................................................................................................................... 162

CAR TALK................................................................................................................................................................ 163

PAIRED!WORDS!FOR!TRANSPORTATION ......................................................................................................................... 163

EXTENSION!ACTIVITIES ................................................................................................................................................ 164

APPENDIX.R..LIST.OF.THE.240.KFPAVE.TARGET.WORDS .................................................................................... 166

APPENDIX.S..KFPAVE.TEACHER.TRAINING.AGENDA .......................................................................................... 168

DAY!1 ..................................................................................................................................................................... 168

DAY!2 ..................................................................................................................................................................... 168

K:PAVE ASSISTANT!TEACHER!SCHEDULE!(DAY!2!ONLY).................................................................................................... 168

APPENDIX.T..KFPAVE.TEACHER.PHONE.FOLLOWFUP.AGENDA........................................................................... 170

I. GENERAL!PROBLEMS!(ASK!EACH!FOLLOW:UP) ............................................................................................................... 170

II. PROGRAM!AREAS ................................................................................................................................................... 170

III. WHAT!CAN!WE!DO!TO!HELP!YOU!WITH!THE!PROGRAM? ................................................................................................ 173

IV. REMINDER!OF!WHEN!NEXT!PHONE!CALL!WILL!BE.......................................................................................................... 173

APPENDIX.U..SAMPLE.MEANS.AND.STANDARD.DEVIATIONS.FOR.STUDENT.AND.CLASSROOM.OUTCOME.

MEASURES,.BY.INTERVENTION.STATUS ...................................................................................................... 174

APPENDIX.V..CHECKING.MODEL.ASSUMPTIONS ............................................................................................... 175

MODEL!EXAMINING!IMPACTS!ON!STUDENTS’!EXPRESSIVE!VOCABULARY!(EVT–2) ................................................................... 175

MODELS!EXAMINING!IMPACTS!ON!CLASSROOM!INSTRUCTION ............................................................................................. 178

PROPORTION!OF!CLASSROOM!OBSERVATION!CYCLES!SPENT!ON!NONVOCABULARY!LITERACY!INSTRUCTION.................................... 183

APPENDIX.W..TRANSLATING.IMPACTS.ON.STUDENTS.INTO.AGEFEQUIVALENT.DIFFERENCES.IN.POSTTEST.

OUTCOMES ................................................................................................................................................... 186

REFERENCES..................................................................................................................................................... 188

BOXES

Box 2.1 Summary of kindergarten evaluation design ................................................................................. 16

Box F1. Read Aloud Profile–Kindergarten coding form ............................................................................ 99

Box F2. Vocabulary Record coding form ................................................................................................. 102

FIGURES

Figure 2.1. District and school recruitment process and timeline............................................................... 20

Figure 2.2. Outcome of random assignment of schools and random selection of classrooms in the 

study sample.............................................................................................................................. 23

Figure 2.3 Flow of students through the study............................................................................................ 39

Figure D1. Recruitment and random selection of the student sample ........................................................ 93

Figure V1. Studentized residuals at the student level plotted against a normal distribution .................... 176

Figure V2. Raw residuals versus normal distribution at classroom and school levels ............................. 176
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Figure V3. Studentized residuals at the student level plotted against EVT posttest score for 

students in treatment (red) and control group (blue) schools ................................................. 177

Figure V4. Studentized residuals at the classroom level (left panel) and school level (right panel) 

plotted versus EVT posttest score for students in treatment (red) and control group (blue) 

schools..................................................................................................................................... 177

Figure V5. Plot of residuals versus a normal distribution at the classroom and school levels, 

from the model testing the impact on vocabulary and comprehension support ..................... 178

Figure V6. Residuals at the classroom level and school level versus vocabulary and comprehension 

support posttest score for students in treatment (red) and control group (blue) schools........ 179

Figure V7. Plot of residuals versus a normal distribution at the classroom and school levels, 

from the model testing the impact on instructional support ................................................... 180

Figure V8. Residuals at the classroom level and school level versus instructional support 

posttest score for students in treatment (red) and control group (blue) schools..................... 181

Figure V9. Plot of residuals versus a normal distribution at the classroom and school levels, 

from the model testing the impact on emotional support ....................................................... 182

Figure V10. Residuals at the classroom level and school level versus emotional support 

posttest score for students in treatment (red) and control group (blue) schools..................... 183

Figure V11. Plot of residuals versus a normal distribution at the classroom and school levels, 

from the model testing the impact on nonvocabulary literacy instruction.............................. 184

Figure V12. Residuals at the classroom level and school level versus nonvocabulary literacy 

instruction for students in treatment (red) and control group (blue) schools.......................... 185

MAP

Map A1. Mississippi counties with study schools, by county .................................................................... 81

TABLES

Table 2.1 Comparison of schools that agreed to participate and all eligible schools ................................. 25

Table 2.2 Description of school sample ...................................................................................................... 26

Table 2.3 Reading programs in place at baseline in intervention and control schools ............................... 28

Table 2.4 Timeline of study activities: data collection and intervention implementation .......................... 29

Table 2.5 Child measures, outcome variables, and designations................................................................ 30

Table 2.7 Response rates for data collected from schools, classrooms, teachers, and students for 

intervention and control groups (percent)................................................................................. 38

Table 2.8 Nonparticipants in intervention and control groups.................................................................... 40

Table 2.9 Comparison of initial sample at randomization and final analytic sample ................................. 41

Table 2.10 Characteristics of teachers in the analytic sample, by intervention condition .......................... 42

Table 2.11 Characteristics of students in the analytic sample, by intervention condition .......................... 43

Table 2.12 Baseline pretest scores on student outcomes for intervention and control groups ................... 44

Table 2.13 Baseline measures of classroom instruction for intervention and control groups (n = 64 

schools, 128 classrooms) .......................................................................................................... 45
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Table 2.14 Primary research question, outcome variable, and need for adjustment for multiple tests....... 48

Table 2.16 Number and percentage of students, teachers, and schools missing covariate data ................. 51

Table 2.17 Number and percentage of students missing either pretest or posttest assessment, by 

intervention and control group.................................................................................................. 51

Table 3.1 Methodologies for addressing questions on implementation of K-PAVE.................................. 53

Table 3.2 Schedule of K-PAVE teacher training and support activities..................................................... 58

Table 3.3 Methodology for assessing fidelity of K-PAVE training and support for teachers 

in intervention schools.............................................................................................................. 61

Table 3.4 Coding K-PAVE classroom fidelity: training fidelity checklist items and coding protocol....... 63

Table 3.5 Proportion of intervention teachers participating in K-PAVE training and support................... 65

Table 3.6 Participation of intervention teachers in K-PAVE teacher training and support activities......... 66

Table 3.7 Number of teaching strategies implemented............................................................................... 66

Table 3.8 Presence of instructional strategies by K-PAVE program components...................................... 67

Table 4.1 Estimated regression-adjusted impact of K-PAVE on kindergarten students’ expressive 

vocabulary................................................................................................................................. 71

Table 4.2 Estimated regression-adjusted impact of K-PAVE on kindergarten students’ listening 

comprehension and academic knowledge................................................................................. 73

Table 4.3 Estimated regression-adjusted impacts of K-PAVE on classroom instruction 

in kindergarten .......................................................................................................................... 74

Table 4.4 Sample control group means and standard deviations for four components of vocabulary 

and comprehension support composite measure (n = 128 classrooms).................................... 75

Table 4.5 Estimated regression-adjusted impact of K-PAVE on amount of literacy instruction in 

areas other than vocabulary and comprehension in kindergarten............................................. 77

Table B1. Power analysis summary: minimum detectable effect sizes for student outcomes, 

by number of schools................................................................................................................ 83

Table B2. Comparison of assumed and observed factors related to minimum detectable effect size 

for the Expressive Vocabulary Test–2 posttest......................................................................... 84

Table B3. Power analysis summary: minimum detectable effect sizes for classroom 

instruction outcomes, by number of schools............................................................................. 86

Table B4. Minimum detectable effect sizes and estimated impacts in the observed sample 

for classroom instruction outcomes.......................................................................................... 86

Table B5. Comparison of assumed and observed factors related to minimum detectable 

effect sizes for classroom instruction outcomes ....................................................................... 86

Table C1 Random assignment for a hypothetical list of Reading First Schools, ordered based 

on school characteristics........................................................................................................... 91

Table E1. Characteristics of students missing and not missing baseline assessment ................................. 94

Table M1. Missing data on school covariates in treatment and control samples...................................... 141

Table M2. Missing data on student pretest and posttest assessments, for treatment and 

control groups (596 treatment students, 700 control students)............................................... 142

Table N1. Estimated impact on kindergarten students’ expressive vocabulary (EVT–2) in 

models fit for sensitivity analysis compared with final impact model in Chapter 4............... 148
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Table N2. Results of sensitivity analysis conducted on joint test of K-PAVE impact on academic 

knowledge and listening comprehension ................................................................................ 149

Table N3. Estimated impact on kindergarten students’ listening comprehension in models fit for 

sensitivity analysis compared with final impact model presented in Chapter 4..................... 151

Table N4. Estimated impact on kindergarten students’ academic knowledge in models fit for 

sensitivity analysis compared with final impact model presented in Chapter 4..................... 152

Table N5. Results of sensitivity analysis conducted on joint test of K-PAVE impact on 

vocabulary and comprehension support, instructional support, and emotional support......... 154

Table N6. Results of sensitivity analyses of estimated K-PAVE impact on vocabulary and 

comprehension support ........................................................................................................... 156

Table N7. Results of sensitivity analyses of estimated K-PAVE impact on instructional support........... 156

Table N8. Results of sensitivity analyses of estimated K-PAVE impact on emotional support............... 157

Table N9. Results of sensitivity analyses of estimated K-PAVE impact on proportion of 

cycles spent on nonvocabulary literacy instruction ............................................................... 157

Table O1. Student-level covariates ........................................................................................................... 158

Table O2. Teacher-level covariates........................................................................................................... 159

Table O3. School covariates...................................................................................................................... 160

Table U1. Sample intervention and control group means for student outcome measures........................ 174

Table U2. Sample intervention and control group means for classroom instruction 

outcome measures................................................................................................................... 174
SUMMARY 

Improving the ability of at-risk children to read and comprehend text has been a high 

priority in education policy over the last two decades. Low levels of reading achievement have 

been related to low academic performance, and one critical factor in reading achievement is 

adequate vocabulary knowledge. Children from disadvantaged backgrounds often lack general 

and academic vocabulary to enable them to acquire knowledge and comprehend text when they 

learn to read. 

State education departments, in discussions with Regional Educational Laboratory (REL) 

Southeast, identified low reading achievement as a critical issue for their students and expressed 

an interest in identifying effective strategies to promote the foundational skills in young students 

that might improve reading achievement. The Mississippi State Department of Education has 

focused specifically on interventions that might enhance students’ vocabulary knowledge. The 

Mississippi state legislature established a high priority on meeting the early education needs of 

students in or near the Delta, a primarily rural area of the state with a high level of poverty and 

historically low performance on reading achievement. To address these concerns, the current 

study tests the impact of a kindergarten vocabulary instruction program on students’ expressive 

vocabulary when used across a range of districts and schools in the Mississippi Delta by 

kindergarten teachers as a supplement to their regular instructional program. Previous research 

on the program showed that a preschool version of the curriculum was associated with improved 

student vocabulary acquisition but did not provide a test using methodologies that can establish 

causal relationships. 

Kindergarten PAVEd for Success (K-PAVE) was selected to be tested in Mississippi for 

three reasons. First, there were only a small number of vocabulary interventions appropriate for 

this age group to be considered. Second, among these, PAVE—the preschool version of the 

intervention—was the only one for which an impact study had been completed that provided 

some evidence of effects. Third, K-PAVE was the only curriculum that had developed teacher 

training materials and a training protocol, which meant that it could be implemented with 

sufficient fidelity across a variety of districts and school settings. The experimental design of this 

evaluation addresses limitations of earlier research and ensures a valid basis for estimating the 

effect of K-PAVE, implemented across a range of settings in the real world, on vocabulary 

knowledge of students in kindergarten. 

K-PAVE is built around three components that support the acquisition of vocabulary in 

young students: instruction on a large set of thematically related target words through provision 

of definitions, examples, and visual images and through embedded instruction using storybook 

reading, extension activities, and teacher conversation; Interactive Book Reading to build 

vocabulary and comprehension skills; and Adult-Child Conversations to build vocabulary and 

oral language skills. 

K-PAVE was implemented as a supplement to the regular classroom literacy instruction. 

Teachers were given broad latitude to choose how to integrate K-PAVE into their classroom 

instruction, including conducting K-PAVE activities in multiple curriculum areas across the 

classroom day and week. Fidelity of K-PAVE implementation was evaluated using a rating 

system provided by the program developer and administered based on classroom observation. 

Results showed that there was substantial variation in fidelity of implementation across 

1 
classrooms, which is typical of interventions implemented across a range of settings in the real 

world. At the same time, most classrooms were observed to be implementing K-PAVE with 

sufficient fidelity to support impacts on students. 

The primary research question for the study addressed the impact of K-PAVE on 

kindergarten students’ expressive vocabulary. Secondary research questions addressed the 

impacts on kindergarten students’ academic knowledge and listening comprehension. Although 

the study was concerned primarily with the impacts of K-PAVE on students, impacts on 

intermediate classroom instruction outcomes were also assessed to provide context for 

understanding potential impacts on students. The study addressed research questions about 

impacts on classroom instruction in vocabulary and comprehension support, instructional 

support, and emotional support. Finally, the study examined whether the introduction of KPAVE had the unintended consequence of reducing the time spent on areas of literacy instruction 

other than vocabulary and comprehension (such as phonological awareness, alphabet knowledge, 

print concepts, and decoding). 

The experimental design of this evaluation addresses limitations of earlier research and 

provides a valid basis for estimating the effect of K-PAVE on the vocabulary knowledge of 

kindergarten students. The study employs a cluster random assignment design that randomly 

assigned schools to an intervention or control condition. Two kindergarten classrooms in each 

school were randomly selected to be in the study. The final sample included 65 schools, 130 

kindergarten teachers, and approximately 1,300 kindergarten students distributed across 35 

districts in the Mississippi Delta region and surrounding area. All districts, schools, teachers, and 

students volunteered to participate in the study and were not randomly sampled from the 

universe of eligible schools in the region. 

The study sample did not differ significantly from all eligible schools in the Mississippi 

Delta and surrounding areas on a set of measured characteristics (including region, percentage of 

students eligible for free and reduced-price meals, and school accountability measures relating to 

school performance classification and meeting annual expectations for growth in achievement). 

Study schools did differ from eligible schools on two characteristics: study schools had a greater 

percentage of African American students and were more likely to be located in small towns and 

less likely to be located in rural areas. 

To be eligible for the study, schools had to have at least two kindergarten classes, at least 

two consenting kindergarten teachers willing to be selected for data collection, and at least 40% 

of students eligible for free and reduced-price meals. The random assignment produced two 

groups of schools that did not differ significantly on pre-intervention measures of socioeconomic 

mix, state classifications of school-level student achievement, reading initiatives, racial 

composition of student body, region, and locale. In addition, students in the two groups of 

schools did not differ significantly on pre-intervention measures of expressive vocabulary, 

academic knowledge, listening comprehension, and other characteristics, including 

socioeconomic status, race, age, special education status, and gender. 

After receiving training on the K-PAVE intervention, kindergarten teachers in treatment 

schools implemented it in the 2008/09 school year. The K-PAVE program was designed as a 24­

week supplement to the core language arts program used in each school. Kindergarten teachers 

in control schools also implemented their core language arts curriculum and received their 

district’s regular professional development during 2008/09. Teachers in the intervention 

2 
condition received two days of initial group training in fall 2008 (one month into the school 

year), three follow-up telephone conference calls to discuss implementation issues and reinforce 

key aspects of the K-PAVE program, and two rounds of classroom observation and feedback on 

how to improve their implementation of K-PAVE—one immediately after the initial training and 

one two-thirds of the way through the 24-week intervention. 

The impact of K-PAVE at the end of kindergarten was assessed for one primary student 

outcome—expressive vocabulary. The estimated impact of K-PAVE on expressive vocabulary 

was 1.60 points on a scale with a mean of 100 and a standard deviation of 15,2 and the impact 

was statistically significant. The standardized effect size for this impact is 0.14. Translating this 

effect size into a difference in age-equivalent scores, students who received the K-PAVE 

intervention were one month ahead in vocabulary development at the end of kindergarten 

compared with students in the control group (See Chapter 4, p. 85, and Appendix W for a 

discussion of how impacts on students can be translated into differences in age-equivalent 

scores). 

The impact of K-PAVE at the end of kindergarten was also assessed for two secondary 

student outcomes—academic knowledge and listening comprehension. The impact of K-PAVE 

on academic knowledge was found to be statistically significant, with a magnitude of 1.95 

points3 on an item response theory-based scale with a sample mean of 455 points and standard 

deviation of 13.5 points in the control group. The standardized effect size for this impact is 0.14. 

Translating this effect size into a difference in age-equivalent scores, students who received the 

K-PAVE intervention were one month ahead in academic knowledge at the end of kindergarten 

compared with students in the control group. K-PAVE did not cause a statistically detectable 

impact on kindergarten listening comprehension. 

The impact of K-PAVE at the end of kindergarten was also assessed for three classroom 

instruction outcomes hypothesized to foster students’ vocabulary development: vocabulary and 

comprehension support, instructional support, and emotional support. K-PAVE caused a positive 

and statistically significant impact on vocabulary and comprehension support, which includes the 

introduction of vocabulary words throughout the school day and the use of comprehension 

supports and open-ended questions during book reading. The magnitude of this impact, 0.82 

standard deviations, is equivalent to providing comprehension support during book reading 12 

more times, asking three more higher order questions during book reading, introducing three 

more words during book reading, and introducing one more word during other times of the day. 

K-PAVE did not cause a statistically detectable impact on instructional support or emotional 

support in the classroom. 

K-PAVE did not cause a statistically detectable impact on the amount of instructional 

time spent on literacy in areas other than vocabulary and comprehension. Thus, K-PAVE did not 

cause teachers to provide more vocabulary and comprehension support at the expense of time 

spent on other areas of literacy instruction. 

2

 The 95% confidence interval around the impact estimate is 0.4–2.8 points. 3

 The 95% confidence interval around the impact estimate is 0.2–3.7 points. 

3 
CHAPTER 1: INTRODUCTION AND STUDY OVERVIEW 

ROLE OF VOCABULARY KNOWLEDGE IN READING COMPREHENSION 

The importance of vocabulary in reading achievement has long been recognized. The 

National Reading Panel (2000) has identified vocabulary as one of five key aspects of literacy 

involved in the reading comprehension of skilled readers. Oral vocabulary occupies an important 

middle ground in learning to read, as students move from oral to written forms of words 

(National Reading Panel 2000). To understand the meaning of the text, the beginning reader 

must be able to map an oral representation of a known word to the written word (or what is 

sometimes called reading vocabulary). Thus, learners who have a larger set of oral vocabulary 

are more likely to be able to apply that knowledge to print material. Evidence from studies of 

students with reading problems indicates that language and vocabulary deficits are critical 

factors underlying reading problems (Catts, Hogan, & Adolf 2005; Storch & Whitehurst 2002; 

Vellutino, Tunmer, Jaccard, & Chen 2007). Many children who successfully learn to read in 

grade 1 or grade 2 are unable to understand the books they need to read by grade 3 or grade 4 

because they lack adequate vocabulary (Chall, Jacobs, & Baldwin 1990; Chall & Conard 1991; 

Spira, Bracken, & Fischel 2005; Storch & Whitehurst 2002). Conversely, high-knowledge grade 

3 students have been reported to have vocabularies about equal to the lowest performing grade 

12 students, and high school seniors near the top of their class are reported to know about four 

times as many words as their lower performing classmates (Beck, McKeown, & Kucan 2002). 

These data are the underlying rationale for the two decades of research investigating the 

effectiveness of different instructional interventions to enhance young children’s vocabulary 

knowledge. 

As stated by researchers in the field: 

It is essential that teachers engage struggling readers in activities that foster 

vocabulary development. Although wide reading should be encouraged and 

facilitated, struggling readers need more than just time to read. They seem to have 

difficulty gleaning the meanings of words from context and benefit from having new 

words and concepts that are critical to their learning taught directly to them. 

(Strickland, Ganske, & Monroe 2002) 

SELECTING A VOCABULARY INSTRUCTION PROGRAM 

The current study provides a rigorous test of the effectiveness of the vocabulary 

instruction program Kindergarten PAVEd for Success (K-PAVE) (Hamilton & Schwanenflugel, 

under contract) at increasing the vocabulary knowledge of low-income kindergarten students. 

Identifying effective strategies that schools can use to promote vocabulary acquisition in young, 

at-risk students is a critical challenge, based on the research showing that many low-income 

children enter school with limited vocabulary, which has consequences for their subsequent 

literacy development, especially reading comprehension (Biemiller & Slonim 2001; Coyne, 

Simmons, Kame’enui, & Stoolmiller 2004). 

K-PAVE is a recently developed program to promote students’ knowledge of vocabulary 

through multiple pathways, including explicit and embedded instruction of a set of target 

4 
vocabulary words and incidental exposure to other, novel vocabulary words. The program is 

designed to train teachers to use enhanced vocabulary instructional practices regularly and 

systematically. It is a modification of the original preschool PAVE program (Schwanenflugel et 

al., in press), which was designed to enhance early literacy skills in preschool children. 

K-PAVE has three key components, each with a set of recommended teaching strategies. 

The first component, Explicit Vocabulary Instruction (labeled “New Vehicles”), involves 

explicit instruction of the target vocabulary words using word-learning strategies, exposure to 

the vocabulary words embedded in storybooks through repeated reading, and hands-on activities 

to extend student understanding of the meaning of the target vocabulary words. The second 

component of the K-PAVE program, Interactive Book Reading (labeled “CAR Talk”), involves 

teacher engagement of children during story reading through questions that promote 

comprehension and oral language skills. The third component, Adult-Child Conversations 

(labeled “Building Bridges”), involves frequent teacher conversations with individual or small 

groups of students to provide an opportunity for the teacher to use new vocabulary and for the 

students to increase their productive use of new vocabulary and their oral language skills 

generally. 

The preschool program, PAVE, includes strategies not only to support vocabulary 

learning but also to enhance print knowledge and phonological awareness. K-PAVE does not 

include all of the components of PAVE that were implemented in the previous quasiexperimental study of preschool classrooms in Georgia public schools (Schwanenflugel et al. in 

press). The components that focused on the alphabet, phonological awareness, and uses of print 

were not included as part of K-PAVE. Schwanenflugel and her colleagues at the University of 

Georgia noted that, unlike in preschool, nearly all kindergarten teachers emphasize these 

components as part of general literacy instruction.4

 Other alterations to make PAVE appropriate 

for kindergarten included adaptations to the target vocabulary words and associated storybooks 

and to teacher training and support activities, to make it practical to take K-PAVE to scale. 

These changes are described in detail in Chapter 3. 

K-PAVE was selected for testing for three primary reasons. First, there were only a small 

number of vocabulary interventions appropriate for this age group to be considered. Second, 

among these, PAVE—the preschool version of the intervention—was the only one for which an 

impact study had been completed that provided some evidence of effects (see below). Third, KPAVE was the only curriculum that had developed teacher training materials and a training 

protocol. Therefore, to address the expressed desire of the Mississippi State Department of 

Education for a kindergarten vocabulary intervention, K-PAVE was selected for testing even 

though it represents an untested modification of the tested curriculum (PAVE) and its evidence 

of effectiveness was based on a quasi-experimental evaluation with some design limitations 

(described below). The earlier study of PAVE lacked the appropriate control groups to generate 

strong evidence about the effects of the program. As a randomized trial, the current study will 

provide a rigorous test of the effectiveness of K-PAVE in schools serving primarily low-income 

students. 

In the current study, K-PAVE was implemented as a supplement to the regular 

kindergarten curriculum. Each week of the 24-week curriculum is organized around a 

4

 In fact, the phonological awareness program adopted by PAVE was a popular kindergarten program called 

Phonological Awareness for Young Children (Adams, Foorman, Lundberg, & Beeler 1998). 

5 
vocabulary unit consisting of 10 thematically linked target words. The target words were 

selected to align with the themes in the Mississippi state science and social studies frameworks 

for kindergarten. Teacher training included initial group training, three follow-up telephone 

conferences over the 24-week program, and classroom visits to observe teachers implementing 

the curriculum and to provide remediation for teachers who were not implementing the 

curriculum practices with fidelity to the model. In this study, the group training and follow-up 

telephone conferences were led by the curriculum developer and a team from the University of 

Georgia. The classroom observations and remediation were conducted by a team from Regional 

Educational Laboratory Southeast, overseen by the curriculum developer. 

This study is the first randomized test of K-PAVE. The previous study of the original 

PAVE preschool program evaluated the impact of PAVE using a quasi-experimental design that 

compared the intervention group with a comparison group and reported statistically significant 

differences in vocabulary knowledge (Schwanenflugel et al., in press). The intervention group 

students were from 18 volunteer schools (31 classrooms) in two counties. The 18 schools were 

randomly assigned to one of four PAVE treatment conditions, two that included the three 

components of the current K-PAVE intervention and two that did not include the Explicit 

Vocabulary Instruction component. Student participants were 180 boys and 165 girls (n = 3505

) 

attending a full-day program for 4-year-olds (mean age = 4 years, 6 months, standard deviation = 

4 months). According to parental report, 56% were African American, 40% European-American, 

2% multiracial, 1% Hispanic, 1% Asian-American, and 1% not reported. Sixty percent of the 

children received free or reduced-price meals based on the federal formula for eligibility. Only 

children who were native English speakers according to parental report were included in the 

study. The sample included approximately 9% of children with identified special needs. One 

school in a neighboring county identified as being demographically similar to the treatment 

counties was recruited as the comparison. No data were reported on the baseline equivalence of 

the schools in the four treatment conditions and the comparison school; however, differences in 

children’s baseline performance level were controlled in models examining the effects of PAVE 

on student outcomes.6 On average, children in treatment schools that received the vocabulary 

components of the PAVE intervention scored significantly higher on expressive vocabulary than 

did children in the comparison school (p < .01). The standardized effect size ranged from 0.29 to 

0.41, depending on which other PAVE components were part of the condition. 

The findings suggesting an impact of PAVE on the vocabulary knowledge of young 

students from low-income households provide some evidence that the model builds vocabulary 

in young students. These results have to be interpreted cautiously since the design of the impact 

study raises concerns of potential selection bias in the assignment of schools to condition, 

statistical conclusion validity because of the small sample sizes, and internal validity because 

only a single school served as the control group. Nevertheless, in light of the paucity of 

appropriate curricula for kindergarten vocabulary instruction, the current study selected K-PAVE 

as the best candidate for an effectiveness trial because of the positive effects found in the earlier 

5

 Data on gender were missing for five students. 

6

 Pretest scores were entered as a level 1 predictor to adjust for the within-classroom variance among children in 

their initial skills on each outcome measure. Analyses of the equivalence of the groups indicated that there were 

baseline differences in child ethnicity and economic status (as determined by free and reduced-price meal rates) 

across the test conditions. Level 1 variables included these demographic variables and pretest scores (grand mean 

centered). Level 1 variables were dropped from the model if they did not account for statistically significant 

classroom-level variation. 

6 
study and because of the stage of development of the curriculum and associated materials. The 

current study provides a stronger test of the curriculum approach when implemented in 

kindergarten classrooms. 

The current study is an effectiveness trial of K-PAVE because the curriculum is being 

tested under typical, real-world conditions rather than under optimal conditions. The study tests 

the impact of K-PAVE when implemented by kindergarten teachers as a supplement to their 

regular kindergarten instructional environment. Thus, school districts that are interested in 

implementing the curriculum could replicate the conditions under which the impacts of K-PAVE 

were tested in the current study, including the instructional materials and professional 

development. Even though the targeted vocabulary and associated materials and books were 

selected to align with the Mississippi science and social studies frameworks, the vocabulary is 

appropriate for kindergarten children, is likely to be consistent with kindergarten standards in 

other districts, and could be easily adapted by other districts. K-PAVE is not currently available 

from a publisher, but the developer has negotiated a contract with a publisher to make it 

available. Those who are interested in K-PAVE should contact the developer for more 

information. 

THEORY OF CHANGE FOR K-PAVE 

Although improvement in students’ vocabulary knowledge at the end of kindergarten is 

the primary outcome for the study, the intervention model extends beyond explicit vocabulary 

instruction. K-PAVE’s focus on conversation and interactive book reading is hypothesized to 

affect students’ academic knowledge and comprehension as well as their vocabulary knowledge. 

Learning vocabulary and acquiring general knowledge are related; each supports the other. For 

example, when explaining the meaning of words to children, adults often make connections to 

students’ existing knowledge. At the same time, learning new information, perhaps as part of a 

school unit on new academic content, provides opportunities for learning new vocabulary. 

Furthermore, increasing students’ vocabulary and knowledge about the world is a pathway to 

skills in comprehending spoken language. Also, as students learn to read, their vocabulary and 

background knowledge support their comprehension of print. For these reasons, K-PAVE also is 

hypothesized to have impacts on secondary student outcomes of academic knowledge and 

listening comprehension. 

Further, K-PAVE is hypothesized to lead to sustained advantages in vocabulary 

knowledge through grade 1—increased vocabulary acquisition in kindergarten is assumed to 

have continuing positive effects on vocabulary acquisition in the next school year, even in the 

absence of the intervention in grade 1. The logic model for sustained effects of K-PAVE in grade 

1 assumes that increased vocabulary knowledge at the end of kindergarten will lead to impacts at 

the end of grade 1 that go beyond vocabulary to comprehension and academic knowledge. 

The pathway by which the K-PAVE intervention is hypothesized to enhance students’ 

vocabulary knowledge is through impacts on kindergarten teachers’ instructional practices. 

Teachers are trained to implement specific K-PAVE instructional strategies aimed at building 

vocabulary, which are hypothesized to result in stronger vocabulary knowledge for students. To 

test this theory of change, the study includes estimates of impacts on teachers’ instructional 

practices. 

7 
EXPERIMENTAL AND QUASI-EXPERIMENTAL EVIDENCE OF IMPACTS OF VOCABULARY

INSTRUCTION

K-PAVE builds on the body of literature about effective strategies for promoting 

vocabulary knowledge in young students. Although research on the impacts of Explicit 

Vocabulary Instruction has focused on students in grade 3 and higher (Baumann, Kame’enui, & 

Ash 2003), gaps in vocabulary evident at school entry (Biemiller & Slonim 2001) underscore the 

importance of vocabulary instruction before grade 3. The research suggests that vocabulary can 

be improved through systematic instructional strategies to promote vocabulary learning in 

students below grade 3. In general, vocabulary interventions in preschool and kindergarten 

embed vocabulary instruction in storybook reading, part of most programming for students in 

this age group and a primary source of new vocabulary for young children. Although many of 

the interventions embed vocabulary instruction in storybook reading, the studies test such 

variants as whether stories are read once or multiple times, whether meanings are provided 

directly for vocabulary words encountered in reading, and whether students have opportunities to 

interact with the book during reading. 

The studies also share design characteristics. Typically, the studies used pre-post designs, 

which test students on their knowledge of instructed words that they were exposed to in the 

storybooks and present results as gains in student knowledge of word meanings. Few studies also 

assess the effect of vocabulary instruction on untaught vocabulary, for example, by using a 

standardized receptive or expressive vocabulary measure. Also, the sample sizes in the studies 

tend to be small. These design features have limited the strength of the evidence on the impacts 

of vocabulary instruction on these outcomes for young students. 

The research on vocabulary interventions has been summarized in two meta-analyses— 

the National Reading Panel (2000), which reviewed studies published before 1998, and the 

National Early Literacy Panel (2009), which reviewed studies published through 2003—and by 

individual researchers (see Biemiller & Boote 2007). Findings have been reported for different 

approaches to vocabulary instruction in storybook reading. Studies testing the impact of repeated 

reading of a story without explicit explanations of word meanings reported gains of 5%–9% in 

instructed words learned at the end of the intervention (Elley 1989; Robbins & Ehri 1994; 

Hargrave & Senechal 2000; Penno, Wilkinson, & Moore 2002; Brabham & Lynch-Brown 2002; 

Biemiller & Boote 2006). 

A second set of studies tested the effect of augmenting the reading aloud with explicit 

explanation of the meaning of the words during the story reading. In general, these studies 

indicate that this strategy increases the effectiveness of the vocabulary instruction. For example, 

a set of studies that tested the effectiveness of a single reading of a story with explicit 

explanations of word meaning reported an average gain of 12% in instructed words learned 

(Senechal 1997; Senechal & Cornell 1993). Studies that examined the impact of repeated 

readings of a story with explanations of word meaning reported an average gain of 17% in word 

meanings known (Robbins & Ehri 1994; Senechal 1997; Senechal, Thomas, & Monker 1995; 

Hargrave & Senechal 2000; Penno, Wilkinson, & Moore 2002; Brabham & Lynch-Brown 2002; 

Biemiller & Boote 2006). Some of these studies compared immediate posttests with posttests six 

weeks to three months after the end of the vocabulary instruction and reported word knowledge 

to be about the same or higher at delayed testing (Senechal & Cornell 1993; Senechal, Thomas, 

& Monker 1995). Finally, studies that tested the effectiveness of involving students in interactive 

8 
word discussions during repeated story readings report average gains of 12% in instructed words 

learned (Brabham & Lynch-Brown 2002; Hargrave & Senechal 2000). 

It has been pointed out that repeated reading of the same story does not expose students 

to additional contexts for increasing their understanding of target words (Beck & McKeown 

2007). More recent studies have included follow-up extension activities or vocabulary reviews as 

means to increase vocabulary knowledge. Wasik and Bond (2001) randomly assigned four 

preschool teachers to two vocabulary instruction conditions: the intervention teachers were 

trained to use Interactive Book Reading techniques combined with book reading extension 

activities, and the control teachers continued with the regular classroom programming. The four 

classes in the study included 127 four-year-olds. At the end of the 15-week intervention, students 

taught by the intervention teachers had significantly higher scores on a standardized vocabulary 

test (p < .001) and on tests of receptive and expressive vocabulary developed for the study (p < 

.01). 

Coyne et al. (2004) conducted an experiment to test the impact of embedded instruction 

with 96 at-risk kindergarten students from seven schools. Students were randomly assigned to 

one of three groups: a storybook intervention with embedded instruction, an intervention that 

focused on increasing phonologic and alphabetic skills, or a control group that received a module 

on sounds and letters from a commercial reading program. Students in all groups received 30 

minutes of small group intervention each day for seven months, for a total of 108 instructional 

periods. The storybook intervention consisted of lessons developed to accompany 40 storybooks. 

Three target vocabulary words were taught explicitly from each storybook, and two storybooks 

were read twice each week. In addition, each week children were given opportunities to retell the 

stories using selected illustrations as prompts, with encouragement from teachers to use target 

vocabulary. At posttest, students in the storybook group scored significantly higher than students 

in the code-based and control groups on an experimenter developed expressive measure of 

explicitly taught vocabulary. The effect size was 0.73 for the contrast of the storybook and the 

code-based interventions and 0.85 for the contrast of the storybook intervention and the control 

group.7 

A recent group of studies tested an instructional approach different from the embedded 

instruction using storybook reading. Extended vocabulary instruction or rich instruction is 

hypothesized to help students develop greater depth of vocabulary knowledge through exposure 

to multiple examples of vocabulary words in multiple contexts. This, in turn, is expected to help 

students process words more deeply by identifying and explaining appropriate and inappropriate 

uses and generally by providing extended opportunities to discuss and interact with words in 

multiple contexts in addition to story reading. 

Beck and McKeown (2007) conducted two quasi-experimental studies to test the 

effectiveness for students in kindergarten and grade 1 of a treatment called Text Talk, which 

provided opportunities for rich language development through discussion of complex narratives 

in storybooks selected to be conceptually challenging. In the first study, the intervention 

consisted of book read-alouds and associated vocabulary instruction over a 10-week period. The 

7

 The effect sizes reported in many of the studies on vocabulary instruction are large, some more than one standard 

deviation. The large effect sizes may be related to the fact that the outcomes are based on gain scores or on 

experimenter-developed measures, often without a comparison group, and to the fact that research has shown that 

quasi-experiments, on average, produce more optimistic estimates of effect sizes than do experiments. 

9 
study was conducted in eight classrooms in an urban school district with a low-income, 

predominantly African American population. Two classrooms from each grade were designated 

as intervention (implementing Text Talk) or as control (implementing daily read-alouds as part 

of the regular school reading curriculum). The student sample included 98 students in the eight 

classrooms. In both grades the intervention students learned significantly more of the 22 

instructed words. The mean gain for the students in the intervention classes was 5.6 words, 

compared with 1.0 word for students in the control group classes (p < .000; effect size = 1.17). 

Comparable gains in grade 1 were 3.6 words for students in intervention classes and 1.7 words 

for students in control classes (p < .01; effect size = 0.74). 

The second study tested whether students learned more vocabulary if teachers spent more 

time on the intervention. The sample consisted of three kindergarten classrooms (36 students) 

and three grade 1 classrooms (40 students), half designated to implement the same intervention 

as in the first study and half designated to implement the intervention but with additional 

instructional time. (Observations showed that Text Talk alone resulted in five encounters per 

vocabulary word, while the enhanced version of the intervention resulted in 20 encounters.) In 

both grades, students in the enhanced instruction classrooms made significantly greater gains in 

the number of instructed words learned (p < .001). Average gains in kindergarten were 8.2 of the 

42 instructed words for students in the enhanced intervention classrooms and 2.5 words for 

students in the regular intervention classrooms; comparable gains in grade 1 were 6.9 words and 

3.8 words. 

Coyne and colleagues have conducted a set of experimental studies comparing different 

methods of vocabulary instruction with kindergarten students. Both the embedded and extended 

instruction conditions involved direct teaching of the meaning of target vocabulary words in the 

context of story reading, but the extended instruction also included opportunities for students to 

interact with and discuss target words in varied contexts outside of the story as a way to extend 

their understanding of the words. Students were taught three vocabulary words using each of 

these methods—embedded, extended, and incidental exposure. At the end of the intervention 

students were tested on their receptive knowledge, expressive knowledge, and depth of 

knowledge of the target words in multiple contexts. In one study, 32 kindergarten students 

received extended and embedded instruction. Students scored significantly higher at immediate 

posttest on all three measures—expressive and receptive definitions and context—on words that 

received extended instruction compared with words that received incidental exposure (p < .001 

for the three outcomes, with effect sizes of 2.27 for expressive definitions, 1.00 for receptive 

definitions, and 1.02 for context) (Coyne, McCoach, and Kapp 2007). In a second study with 

another sample of 32 kindergarten students that compared extended and embedded instruction, 

students scored significantly higher on all three outcome measures on words that received 

extended instruction compared with words that received embedded instruction ( p < .001 on all 

three outcomes, effect sizes of 1.70, 0.99, and 1.12, respectively) (Coyne, McCoach, and Kapp 

2007). 

A third study that compared all three instructional conditions with the same sample of 42 

kindergarten students reported findings that were consistent with the earlier studies (Coyne, 

McCoach, Loftus, Zipoli, and Kapp 2009). On tests of receptive and expressive definitions, 

students had significantly higher mean scores for words taught using extended instruction than 

for words taught with embedded instruction (p < .01; effect size of 0.70 for receptive definitions 

and 1.34 for expressive definitions). Mean scores were significantly higher on vocabulary tests 

10 
of words taught using either of the intervention approaches than for words taught through 

incidental exposure. For extended instruction, the difference was significant at p < .01 for both 

receptive and expressive definitions, with effect sizes of 0.97 and 2.57 respectively. For 

embedded instruction, the difference was statistically significant at p < .05 for receptive 

definitions (effect size of .24) and at p < .01 for expressive definitions (effect size of 0.87). On 

the measure of word knowledge at the end of the intervention, mean scores were significantly 

higher for extended instruction than for embedded instruction, for both full knowledge (p < .01, 

effect size of 0.38) and for partial knowledge (p < .01, effect size of 0.56). Mean scores also 

were significantly higher for each of the intervention approaches compared with incidental 

exposure. For extended instruction, the difference was significant for measures of full and partial 

knowledge (effect sizes of 0.91 and 1.07). For embedded instruction, the difference also was 

significant for the measures of full and partial knowledge (effect sizes of 0.63 and 0.49). 

Another recent study examined a language and literacy intervention that shares a number 

of instructional components with K-PAVE and reported impacts on children’s vocabulary 

acquisition (Landry, Anthony, Swank, & Monseque-Bailey 2009). The study was primarily a test 

of four professional development programs, all of which focused on improving the language and 

literacy instruction among teachers of at-risk preschool children. The study employed a large 

sample of 262 classrooms in 158 schools across four states, to test the feasibility of taking 

intensive professional development to scale. Classrooms were randomly assigned to business as 

usual or to one of four professional development conditions, which varied in terms of receipt of 

in-class mentoring and detailed instructionally linked feedback concerning children’s progress in 

language and literacy. On a measure of expressive vocabulary, the children whose teachers 

received the most intensive professional development (i.e., both mentoring and feedback) had 

statistically significantly higher scores at posttest (effect size = 0.19). 

There is a growing research base on instructional strategies that have been found to 

promote vocabulary learning. The small set of experimental and quasi-experimental studies in 

this area, in addition to the pre-post studies, suggest that early elementary school students’ 

vocabulary can be improved through interventions involving instructional strategies to promote 

vocabulary learning. Effective strategies include explicitly instructing students in vocabulary 

words, embedding vocabulary instruction in repeated reading of the same book and providing 

opportunities for students to actively participate in book reading, extending students’ 

opportunities to engage with word meanings beyond book reading (explaining meanings, using 

words in new contexts, discriminating among examples of word meanings, allowing students to 

provide their own meanings) and combining strategies for embedded and extended strategies for 

teaching word meanings. 

With the exception of the recent work by Landry et al. (2009), research on instructional 

strategies tends to involve small-scale efficacy studies, however, testing short-term interventions 

under ideal conditions, with developer support to ensure optimal implementation fidelity, and 

often using developer-created outcome measures that test students only on intervention target 

words. The current study of K-PAVE is the first large-scale effectiveness trial testing a 

vocabulary intervention in kindergarten under typical conditions over the course of a full school 

year, using a standardized vocabulary outcome measure. 

11 
STUDY TARGETS THE MISSISSIPPI DELTA REGION TO ADDRESS LOW STUDENT READING

ACHIEVEMENT

Research supports that vocabulary acquisition is related to a child’s early environment. 

Children from households that live in poverty are more likely to enter school with poorly 

developed language skills, including vocabulary, than are children from households with more 

resources (Smith, Brooks-Gunn, & Klebanov 1997). A large disparity in vocabulary is apparent 

at least as early as age 3. At this age children from middle-class households have vocabularies of 

approximately 1,000 words, while children from households living in poverty have vocabularies 

half that size (Hart & Risley 1995). The initial disparity continues through elementary school. By 

grade 1, children from middle-class households know approximately 5,000 words, while children 

from households living in poverty know only about 3,000. By grade 4, the respective 

vocabularies are 16,000 words and 11,000 words (White, Graves, & Slater 1990; Beck, 

McKeown, & Kucan 2002). These trends highlight the need for instructional interventions to 

accelerate vocabulary acquisition in young children (Biemiller 2001; Catts et al. 2005). 

The Mississippi Delta region was selected as the target area for the study for three 

reasons: Students in the Delta are at increased risk for poor reading outcomes based on the high 

levels of family poverty in the region; students in the Delta have a history of low achievement 

scores, and the state legislature had established a high priority on meeting the early education 

needs of students in the Delta.8 

The Delta area is primarily rural with a poverty rate more than twice the national rate 

(Bishaw & Iceland 2003).9

 According to the U.S. Bureau of the Census (2008), the poverty rate 

in 2007 for children under age 18 in the Delta region counties averaged 45.3%, with poverty 

rates in most Delta counties above 36% and as high as 57.6%. The overall poverty rate in 2007 

for children under age 18 was 29.7% in Mississippi and 18.0% nationally. Mississippi’s poverty 

rate is the highest of any state in the U.S. 

For the Southeast Region as a whole, data from several psychometric studies find that in 

this region, children from households living in poverty have particularly low vocabulary scores, 

at about one standard deviation below the national average (Campbell, Bell, & Keith 2001; 

Restrepo et al. 2006). Oral language deficits manifest themselves in academic difficulties as 

these children transition from learning to read to reading to learn. On average, 18% of grade 3 

and 4 students in Alabama, Florida, Georgia, Mississippi, and South Carolina do not meet state 

reading standards. By middle school the proportion is 32%. The rate in middle school is more 

pronounced for African American students (41%) and economically disadvantaged students 

(40%). Mississippi ranks 50th among U.S. states on grade 4 reading scores and 49th on grade 8 

reading scores on the National Assessment of Educational Progress (Quality Counts 2008). It is 

in this context that Mississippi passed the Delta Revitalization Act of 2006, which focuses on 

revitalizing the Delta region on several fronts, including an emphasis on meeting the early 

education needs of students through grade 3. 

8

 Mississippi is also the focus of the study because it is one of the target states in the region served by the Regional 

Educational Laboratory-Southeast, 1 of 10 laboratories funded by the U.S. Department of Education’s Institute of 

Education Sciences. Southeast Region states include Alabama, Florida, Georgia, Mississippi, North Carolina, and 

South Carolina. 

9

 See Appendix A for map of Mississippi showing the Delta region. 

12 
STUDY DESIGN 

Using an experimental design, the study addresses whether K-PAVE has impacts when 

implemented under real-world rather than optimal conditions. The study was conducted in a 

sample of kindergarten classrooms in multiple school districts in the Mississippi Delta and 

surrounding areas. Based on a statistical power analysis, the sample includes 33 school districts, 

65 schools, 130 kindergarten classrooms (two per school), and 1,296 students (about 20 per 

school). The study used a cluster random assignment design that assigned schools either to the 

K-PAVE intervention as a supplement to the usual classroom instructional program or to the 

usual instructional program not supplemented by K-PAVE. 

The study is also designed as an effectiveness study, to test whether the intervention can 

be successfully implemented and can improve student vocabulary acquisition under real-world 

conditions. Kindergarten teachers in the intervention group received the K-PAVE intervention 

training in fall of the 2008/09 school year. Kindergarten teachers in the control group received 

their district’s usual professional development during the intervention year and were offered the 

K-PAVE training the following school year. Kindergarten students in both intervention and 

control schools were assessed in fall 2008, prior to intervention implementation, and in spring 

2009, to examine impacts of K-PAVE on vocabulary development and related outcomes. 

Classroom instruction in both intervention and control schools was observed in fall 2008, prior 

to intervention implementation, and in spring 2009, to examine the impacts of K-PAVE on 

kindergarten instructional practices. 

Study objectives 

The primary objective of the K-PAVE intervention is to improve students’ expressive 

vocabulary in kindergarten. Because the intervention seeks to enhance students’ vocabulary not 

only through explicit instruction but also through teachers’ informal conversations with students 

and interactive book reading, K-PAVE is also hypothesized to have a secondary impact on 

children’s general knowledge and listening comprehension. To test these hypotheses, the study 

addresses one primary research question about the impact of K-PAVE on kindergarten students’ 

expressive vocabulary and a secondary research question on the impacts on kindergarten 

students’ academic knowledge and listening comprehension: 

1. What is the impact of K-PAVE on students’ expressive vocabulary in kindergarten? 

2. What is the impact of K-PAVE on students’ listening comprehension and academic 

knowledge in kindergarten? 

The pathway by which K-PAVE is hypothesized to improve children’s expressive 

vocabulary and, secondarily, their general knowledge and comprehension skills is through 

impacts on teachers’ instructional practices. Impacts on intermediate classroom instruction 

outcomes are examined to provide context for understanding potential impacts on students. The 

study addresses two secondary research questions about impacts on classroom instruction. One 

question focuses on impacts on instruction in areas hypothesized to foster impacts on students— 

vocabulary and comprehension support, instructional support, and emotional support. The other 

question examines whether the introduction of K-PAVE has the unintended consequence of 

reducing the time spent on areas of literacy instruction other than vocabulary and comprehension 

(such as phonological awareness, alphabet knowledge, print concepts, and decoding). 

13 
3. What are the impacts of K-PAVE on kindergarten instructional practices, specifically 

vocabulary and comprehension support during book reading, instructional support, and 

emotional support? 

4. Does implementation of explicit and embedded vocabulary teaching reduce classroom time 

for instruction in other areas of early literacy (such as concepts of print, phonological 

awareness, alphabetic knowledge, reading, writing, fluency, and spelling)? Specifically, do 

K-PAVE teachers spend less time on these nonvocabulary literacy teaching practices 

compared with control teachers? 

The study also addresses a secondary research question about the hypothesized sustained 

effects of K-PAVE on vocabulary knowledge and other reading-related skills in grade 1. This 

component of the study will be conducted only if K-PAVE is found to have impacts on students’ 

expressive vocabulary at the end of kindergarten. The associated research question asks: 

5. What is the impact of K-PAVE on students’ expressive vocabulary, listening comprehension, 

academic knowledge, and passage comprehension in grade 1? 

Exploratory research questions to be addressed and reported separately 

In a second report, the study will investigate several exploratory questions involving 

student outcomes that K-PAVE is also hypothesized to affect but for which there is no strong 

research basis for the hypotheses: 

1. What is the impact of K-PAVE on students’ lexical diversity in kindergarten? 

2. Do intervention effects on children in kindergarten or in grade 1 vary with student 

characteristics (such as age, gender, and entry-level pretest standard score)? 

3. What are the impacts of K-PAVE on kindergarten teachers’ lexical diversity and 

instructional practices during book reading? 

These analyses are intended to suggest directions for future research on vocabulary 

interventions. The analyses of impacts on the lexical diversity measures for both students and 

teachers are considered exploratory because the measure itself and the procedures for its 

measurement are relatively new. 

The study will also explore differential impacts for subgroups of students, such as males 

and students with below average pretest scores. These analyses are considered exploratory not 

only because the study does not have a priori hypotheses about subgroup differences but also 

because the study is not powered to calculate reliable estimates of impacts on subgroups of 

students. Finally, exploratory analyses will be conducted of the impacts of K-PAVE on 

instructional practices embedded within the instructional practices examined in the confirmatory 

analyses. For example, we examine impacts on a broad measure of vocabulary and 

comprehension support in the classroom as part of the confirmatory impact analysis. Exploratory 

analyses will examine elements of broader vocabulary and comprehension support, such as 

asking higher-order questions during book reading. 

14 
Contribution of the current study to research on vocabulary instruction 

The current study is the first randomized study of the K-PAVE vocabulary intervention 

and a rigorous test of the impacts of vocabulary instruction on young students who have not yet 

encountered formal reading instruction. The results provide policymakers with important 

evidence on whether K-PAVE can produce impacts when implemented in school districts under 

the same real-world conditions. 

The study provides evidence on two topics on which there is little empirical evidence: 

whether vocabulary interventions have impacts not only on vocabulary knowledge but also on 

broader student skills, including academic knowledge and comprehension, which are considered 

foundational skills for later reading achievement, and whether vocabulary instruction in 

kindergarten has sustained effects beyond the intervention period, in grade 1. 

REPORT OVERVIEW 

The remainder of the report details study design and methodology, implementation of the 

intervention, and impact results. Chapter 2 describes the study design and methodology, 

including sample recruitment, random assignment, data collection, outcome measures, response 

rates, analytic sample sizes, and data analysis methods. Chapter 3 discusses implementation of 

the intervention, including delivery and receipt of the intervention, variation in implementation, 

and fidelity to the program model. Chapter 4 presents the results of the impact analysis, which 

include estimated intent to treat impact findings for primary and secondary student outcomes. 

Impacts on intermediate classroom instruction outcomes are also presented in Chapter 4. Chapter 

4 includes only analyses specified prior to any examination of outcome data, which are intended 

to address a priori hypotheses about intervention impacts. Chapter 5 summarizes the major 

findings of the study and design features that limit the generalizability of the results. In addition, 

technical appendices are included, which provide more information about measures, statistical 

models, imputation of missing data, and sensitivity analyses. 

15 
CHAPTER 2: STUDY DESIGN AND METHODOLOGY 

OVERVIEW 

This study examines the impact of the Kindergarten PAVEd for Success (K-PAVE) 

intervention on students’ vocabulary using a cluster random assignment design that randomly 

assigns schools to intervention or control conditions (see Box 2.1 for a design summary). The 

rigorous experimental design addresses limitations of earlier research noted in Chapter 1 and 

provides a valid basis for answering the study’s key research questions. 

Box 2.1 Summary of kindergarten evaluation design 

Intervention. The K-PAVE vocabulary intervention was selected as the study intervention based on positive 

outcomes found in preschool and the identified need in the region for improving children’s vocabulary outcomes. 

The K-PAVE intervention involved 24 weekly units that supplemented the core language arts program. The KPAVE intervention was implemented October 2008–April 2009. 

Participants. Study participants included 35 districts, 65 schools, 130 kindergarten teachers, and approximately 

1,300 kindergarten students. Schools were recruited from districts in the Mississippi Delta region and surrounding 

area that were willing to allow schools to participate and be randomly assigned to intervention or control conditions. 

Schools were recruited from among those with at least two kindergarten classes, at least two consenting 

kindergarten teachers willing to be selected for data collection, and at least 40% of students eligible for free or 

reduced-price meals. Students were eligible to participate if they were enrolled in the kindergarten classes within the 

first month of the 2008/09 school year, had parental permission to be randomly selected for data collection, were not 

learning English as a second language, and did not have a language or hearing impairment that would prevent them 

from being assessed. 

Research design. Schools were randomly assigned for the 2008/09 school year to the intervention group that would 

use K-PAVE or to a control group that would not have access to K-PAVE until the following school year. All 

kindergarten teachers in each school were assigned to the same condition. Since K-PAVE was a supplemental 

program, all intervention and control teachers were using some other language arts program. The study used 

multistage random sampling, which randomly selects two kindergarten classrooms from each school from a pool of 

consenting teachers before random assignment. Subsequently, a random sample of 10 students from each randomly 

selected classroom was selected for data collection from the pool of students with parental permission. Students in 

the intervention and control schools were assessed before the start and after the completion of the 24-week 

intervention. In both intervention and control schools, classroom instruction was observed by raters who were part 

of the evaluation team and not involved in K-PAVE implementation; observations took place before the start and at 

the end of the 24-week intervention period. Additional data on intervention and control schools were collected from 

teacher questionnaires and district student records, and K-PAVE intervention staff from the SERVE Center at the 

University of North Carolina, Greensboro, provided data on participation in intervention training and observed 

classrooms to measure intervention implementation fidelity. 

Outcomes. Impact estimates focused primarily on student expressive vocabulary, as measured by the Expressive 

Vocabulary Test–2nd edition (Williams 2007). Secondary student outcomes included academic knowledge, assessed 

using the Woodcock-Johnson III/Normative Update academic knowledge test (Woodcock, McGrew, Shrank & 

Mather 2007) and listening comprehension, assessed using the Kaufman Test of Educational Achievement listening 

comprehension test (Kaufman & Kaufman 2004). Intermediate classroom instruction outcomes included four 

measures: a measure of vocabulary and comprehension support created for this study from two classroom 

observation measures (Read Aloud Profile–Kindergarten; Vocabulary Record); instructional support (Classroom 

Assessment Scoring System, or CLASS; Pianta, LaParo, Hamre 2008); emotional support (CLASS); and the 

proportion of observed instructional time devoted to nonvocabulary-related language arts instruction (CLASS). 

According to the cluster random assignment design, all kindergarten classes in each 

sample school that agreed to participate were assigned to the same condition. The intervention 

was implemented from October 2008–April 2009; kindergarten teachers in intervention schools 

16 
were trained on and implemented the K-PAVE intervention in the 2008/09 school year. The KPAVE program was designed to supplement the core language arts program being used in each 

school. Kindergarten teachers in intervention schools implemented K-PAVE along with their 

core language arts curriculum. Kindergarten teachers in control schools implemented their core 

language arts curriculum and received their district’s regular professional development during 

the intervention year.10 Control teachers were offered the K-PAVE intervention training the 

following school year.11 Because of the experimental design of the study, differences between 

intervention schools and control schools in student outcomes and in classroom instruction that 

exceed chance (that are statistically significant) can be attributed to the K-PAVE intervention. 

Assigning all classes in a school to the same condition eliminates concerns about 

potential diffusion of effects across classrooms within a school that could occur when 

classrooms are randomly assigned within each school (intervention practices could diffuse from 

intervention teachers to other teachers in a school through discussion, observation, or other 

forms of cross-teacher communication). Furthermore, having the intervention in all kindergarten 

classrooms in an intervention school is hypothesized to help support implementation. In the 

intervention schools, both kindergarten teachers and assistant teachers received the K-PAVE 

intervention training in the first year. 

The study tests whether K-PAVE is effective when districts volunteer to participate and 

schools and teachers volunteer to implement the intervention. District superintendents, school 

principals, and teachers were all informed about the random assignment process and agreed to 

participate before schools were randomly assigned to the intervention or control condition. 

Consequently, the decision to participate was not influenced by whether a school received the 

intervention in 2008/09 or a year later. Eligible districts were under no obligation to participate; 

86% (38 of 44) of districts recruited agreed to do so.12 Once district superintendents agreed to 

participate, school principals were invited to participate; 85% of eligible schools in participating 

districts agreed to participate. Individual teachers could also decline to participate in the study. 

On average, 91% of teachers in each study school agreed to participate. The voluntary nature of 

the study and the fact that teachers, schools, and districts were participating by choice could 

mean that the impacts might differ from those that would result if a district mandated K-PAVE. 

SAMPLE RECRUITMENT AND RANDOM ASSIGNMENT 

The study took place in a rural area of the Mississippi Delta and in surrounding areas 

sharing characteristics with the Delta (see map in Appendix A), including high rates of poverty, 

low student achievement, and predominantly rural and African American communities. The 

target population for the study was all elementary schools in the Mississippi Delta with at least 

two kindergarten classes. The Delta Revitalization Act of 2006 identified the counties that 

comprise the Mississippi Delta region. 

10 Control group teachers received no monetary compensation or instructional materials during the intervention year. 11 K-PAVE is a 24-week intervention for kindergarten classrooms. The fact that control teachers may implement KPAVE in their kindergarten classrooms during the year following the intervention year is not expected to 

contaminate grade 1 impacts. Students in control schools will be in grade 1, and the grade 1 teachers will not receive 

the K-PAVE training. Although teachers in the same grade often plan instruction together, teachers for different 

grades rarely do. Grade 1 teachers are not likely to implement the K-PAVE program in grade 1 classrooms. 12 See the section of this chapter on sample recruitment for more details on the eligibility criteria for schools and 

districts. 

17 
Recruitment of eligible districts, schools, and teachers 

Based on the statistical power analysis, the recruitment target was 60–70 schools, with 

two classrooms per school and 10 students per classroom. (See Appendix B for details on the 

statistical power analysis.) All school districts in the Delta were recruited to participate in the 

study. 

To be eligible for recruitment, districts were originally required to meet the following 

criteria (based on information from school year 2007/08): 

1. The district is part of the geographic area known as the Mississippi Delta, a region of highpoverty and low-achieving schools, with some pockets of relative affluence. (The median 

percentage of students eligible for free or reduced-price meals is 91%; however, in some 

schools, the percentage is as low as 40%.) 

2. The district has at least one school with two kindergarten classes and is in a high-poverty 

community (with at least 40% of students receiving free or reduced-price lunch). 

3. The district has at least one school that meets these criteria and is willing and able to allow 

two consenting kindergarten teachers to be randomly selected for observation and 20 

students to be randomly selected for testing from among students with parental permission. 

4. The district is willing to allow schools that are eligible and willing to participate in the study 

to be randomly assigned to intervention or control conditions. 

In the Delta region at the time of recruitment, there were 32 school districts in highpoverty communities with 74 schools that had at least two kindergarten classrooms. To ensure 

that a sample of at least 60 schools could be obtained (the sample size goal based on power 

calculations), the sampling frame was expanded to include schools from districts contiguous to 

and sharing demographic characteristics with the Mississippi Delta region.13 After including 

schools from districts neighboring the Delta, the sampling universe comprised 44 school districts 

with 94 schools that met the eligibility criteria. All kindergarten classes in targeted schools were 

full-day programs. 

The characteristics of the final sample of schools in the Delta and those in the 

surrounding area did not differ significantly on any measured characteristics except that schools 

in the Delta had a higher percentage of African American students (median of 97%), on average, 

than did schools in the surrounding region (median of 84% outside; t = –2.28; p=.03). 

Figure 2.1 presents the sample recruitment process and timeline. Recruitment of districts, 

schools, and teachers took place over January–August 2008. Of the 44 districts in the sampling 

13 The counties bordering the Delta region include 24 districts with 86 elementary schools. After a set of exclusions 

because of nonsimilarity of schools with the Delta sample, 12 districts and 20 schools remained for recruitment to 

the study. Exclusions were made for three reasons. First, 18 schools were excluded because they served fewer lowincome children than the Delta schools (fewer than 40% of students eligible for free or reduced-price meals). 

Second, the 35 schools in the Jackson Public School District were excluded because Jackson, as a mid-size city, is a 

more urban setting than anywhere in the Delta. Third, the Mississippi Department of Education evaluated the match 

between schools in contiguous counties and those in the Delta. The department started with a list of districts and 

elementary schools in contiguous counties with at least 40% of students eligible for free or reduced-price meals, and 

they indicated, based on their insider’s perspective, whether each school was similar to those in the Delta in 

demographic, poverty, and achievement data. Based on their ratings, 13 more schools were eliminated from the 

potential sampling frame. 

18 
universe, 38 superintendents (86%) agreed to participate in the study. Once a district 

superintendent agreed to participate, principals of all eligible schools in the district were 

recruited for the study. Once schools agreed to participate, all kindergarten teachers in the 

sample of schools were recruited. Teachers were recruited to participate before random 

assignment to ensure that their decision was not influenced by the school’s random assignment 

status. 

School recruitment took place in two phases, one phase before random assignment and 

one phase after random assignment but before schools were notified of their random assignment 

status. Schools were initially recruited from February 25, 2008 to June 2008. Random 

assignment of schools occurred at the end of July 2008. All eligible schools that agreed to 

participate and had begun the consent process were randomized. Schools that were in the 

randomization pool were contacted in July to confirm willingness to participate; obtain consent 

for all kindergarten teachers, including any teachers hired since the first phase of recruitment; 

and obtain any outstanding written consent forms not yet submitted by the school. 

Of the 81 eligible in participating districts, 70 schools had indicated a willingness to 

participate in the study prior to randomization. All 70 schools were included in the pool of 

schools to be randomized, regardless of whether they had submitted all the written consent 

forms. Schools were randomized on July 24, 2008; 35 were assigned to the intervention group 

and 35 to the control group. 

At the time of randomization, 33 schools had submitted all written consent forms and 

confirmed all staffing. For these schools, two kindergarten teachers from each school were 

randomly selected for data collection and, on July 25, 2008, letters were sent notifying these 33 

schools of their random assignment status and the names of the teachers selected for data 

collection. 

For the remaining 37 schools, efforts to confirm and complete the school and teacher 

consent process continued through August 7, 2008. None of the 37 schools was notified of its 

random assignment status before all written consent forms were obtained. By August 7, 2008: 

•	 For 32 of the remaining 37 schools, all staffing was confirmed, and all the necessary written 

consent forms were submitted. 

•	 The other five schools were excluded from the study because they did not meet eligibility 

criteria or because they were unwilling to participate in the study. 

19 
Notification of

Schools 

School

Recruitment

(Stage 2) 

School

Recruitment

(Stage 1) 

District

Recruitment 

Eligible

Schools

Mississippi Delta Contiguous Counties 

32 districts 12 districts 

74 schools 20 schools 

Sampling Universe 

44 districts 

94 schools 

Agreed 

38 districts (81 schools) 

Declined 

6 districts (13 schools) 

Agreed 

70 schools 

Declined 

11 schools 

Random Assignment 

70 schools 

35 intervention & 35 control 

33 schools 37 schools 

Eligibility confirmed and consent Eligibility to be confirmed and 

finalized final consents to be obtained 

32 schools 5 schools 

Eligibility confirmed and consent Ineligible or consents not finalized 

finalized Never notified of RA status 

65 schools

Random selection of classrooms Study Sample of Schools 

(see detail in Figure 2.2) 65 schools (35 districts) 

Notification of schools 31 intervention & 34 control 

Figure 2.1. District and school recruitment process and timeline 

20 
Although all these schools were randomized, recruitment was not considered complete 

until schools confirmed their willingness and eligibility to participate and submitted all necessary 

written consent forms. All schools, including the five that were excluded based on unwillingness 

or ineligibility, went through the same confirmation process before being notified of their 

random assignment status. Because the schools were excluded before being notified of their 

assignment status, the integrity of the random assignment was maintained. That is, the school’s 

decision not to participate was not influenced by its status as an intervention or control school, 

since the schools did not know to which group they were assigned. 

The final sample included 65 schools: the 33 schools with complete written consent as of 

July, 24, 2008 and the 32 schools that had complete written consent as of August 7, 2008. The 

sample included 31 intervention schools and 34 control schools.14 

Random assignment within blocks 

Schools were placed into three blocks based on previous participation in reading 

initiatives.15 Among the 65 schools in the study sample, 17 were Reading First schools, 5 had a 

Mississippi state reading initiative (Barksdale Reading or Mississippi Sufficiency), and 43 had a 

local district initiative or no reading initiative. Within the reading initiative blocks, schools were 

matched based on a set of school characteristics: School Performance Classification,16 

percentage of students receiving free or reduced-price meals, percentage of students who are 

African American, locale, and region (Delta or contiguous county). (Appendix C, which details 

the process of random assignment, includes a description of the matching within blocks.) Once 

14 Randomization did not result in equal numbers of schools in the intervention and control conditions because five 

schools that were randomized became nonparticipants (for ineligibility or incomplete consents) before notification 

of random assignment.

15 Although within-district random assignment would have controlled for district characteristics, doing so would not 

have resulted in a sufficient sample size. Almost half the districts did not have enough schools to assign one to the 

intervention group and one to the control group. Of the 32 school districts in the Mississippi Delta region, 15 had 

only one elementary school with kindergarten. A within-district random assignment design would have reduced the 

sampling frame in the Delta from 74 schools to 59 schools. Furthermore, blocking based on substantive features of 

schools was preferred over blocking based on school districts, because experience with other reading initiatives is 

expected to be associated with differences in baseline classroom instructional practices. The strategy was thus 

intended to minimize differences between intervention and control schools in areas that are likely to be related to 

classroom instructional practices and student outcomes. Factors likely to drive impact levels can be quite 

heterogeneous within districts. The comparatively small number of schools and potentially large amount of variation 

within districts would tend to result in less comparable intervention and control groups than would random 

assignment with blocks based on prior experience with reading initiatives.

16 The School Performance Classification is an annual classification based on students’ performance on the state 

accountability test (Mississippi Curriculum Test [MCT]) administered to students in grades 3 and higher. 

Classifications include low performing, underperforming, successful, exemplary, and superior. Data on student 

achievement in the current year and on patterns of student growth from the prior year both contribute to the 

performance classification. A student’s level of proficiency (i.e., minimal, basic, proficient, or advanced) in a given 

content area is determined based on his or her score on the MCT, with threshold scores dividing levels of 

proficiency. “Basic” proficiency is defined as “partial mastery of the content area knowledge and skills required for 

success at the next grade,” and “proficient” is defined as “solid academic performance and mastery of the content 

area knowledge and skills required for success at the next grade” (http://www.mde.k12.ms.us/acad/OSA/gltp.html). 

A school’s achievement level is classified based on the percentage of students scoring basic or higher and the 

percentage of students scoring proficient or higher. Data for students in all grades and all subject areas are 

combined. Schools are also rated on whether students meet or do not meet growth expectations on average. 

21 
matched based on these school characteristics, schools were randomly assigned to intervention 

or control conditions. 

Random selection of classrooms 

Figure 2.2 summarizes the schools that were recruited into the final study sample and 

randomized, recruitment of teachers, and random selection of classrooms. The 65 schools had a 

total of 256 full-day kindergarten classrooms. The average consent rate for teachers across the 65 

schools was 91.5%.17 Because at least two teachers from all 65 schools consented to participate 

in the study, no schools were lost due to lack of teacher consents. A total of 218 teachers 

consented to participate in the study. 

From the pool of consenting teachers in each school, two were randomly selected to 

participate in data collection if there were more than two kindergarten classrooms in the 

school.18 This random selection ensures that selected teachers are representative of teachers in 

their schools who are willing to participate in the study. For schools with only two kindergarten 

classrooms or with only two consenting kindergarten teachers (40% of the schools), both 

classrooms were selected. Schools were not notified of their assignment condition until after the 

random selection of teachers was completed. 

17 Teacher consent rates range from 20% to 100%. In 74% of schools, all kindergarten teachers consented to be in 

the study.

18 There were more than two kindergarten classrooms in 66% of the schools—31% of schools had three 

kindergarten classrooms, 29% had 4–8 classrooms, and 6% had 11–15 classrooms. All kindergarten classes were 

full day. 

22 
23 

Figure 2.2. Outcome of random assignment of schools and random selection of classrooms 

in the study sample 

Study Sample of Schools

65 schools

(35 districts)

Intervention Group

31 schools

135 kindergarten classes

Recruitment of Teachers

31 schools

112 kindergarten classes

Random Selection of 

Classrooms

62 kindergarten classes

31 schools

Control Group

34 schools

121 kindergarten classes

Recruitment of Teachers

34 schools

106 kindergarten classes

Random Selection of 

Classrooms

68 kindergarten classes

34 schools

33 schools 

with eligibility 

confirmed & 

consent finalized as 

of 7/24/08 

32 schools 

with eligibility 

confirmed & 

consent finalized as 

of 8/7/08 

In the intervention schools all consenting teachers were offered the K-PAVE training, 

whether selected for data collection or not. All teachers who participated in the K-PAVE training 

received continuing education credits from Delta State University, based on the number of hours 

that teachers participated in the professional development activities. 
Random selection of students 

Based on statistical power calculations, the study set a goal of sampling 20 students from 

each participating school, equally distributed as 10 students per classroom. In the first month of 

the school year19 the study sought permission from parents of all kindergarten students enrolled 

in the study classrooms to be assessed individually on their vocabulary and literacy skills in the 

fall and the spring. Theoretically, parents’ decision to permit their child to be assessed could 

have been influenced by the school’s intervention status, since recruitment of students occurred 

after the random assignment. However, implementation of the K-PAVE program had not yet 

begun at the time permission was sought from parents, and recruitment letters to parents did not 

provide information about schools’ use of an intervention program. The study was described as 

attempting “to better understand how to help kindergarten children in Mississippi to develop the 

vocabulary skills they need to become successful readers.” Therefore, it was considered unlikely 

that parents’ willingness to permit their child to participate would have been affected by the 

school’s assignment status. 

If 20 or more students in a school had parental permission to participate, 20 students per 

school were randomly selected, preferably 10 students per classroom. If there were 20 or more 

students with parental permission but fewer than 10 students with parental permission in one of 

the study classrooms in a school, more students were randomly selected from the other study 

classroom in the school to achieve a total sample of 20 students in the school. If there were fewer 

than 20 total students with parental permission in the school, all students with permission to 

participate were selected with certainty. No adjustment for unequal numbers of students in 

classrooms or schools was made in the analysis. 

The two study classrooms in a school were full-day classrooms, enrolling an average of 

40 students, with enrollment ranging from 24 to 58 students. Parental permission was received 

for an average of 28 students per school (average permission rate of 73%), with the number of 

permissions ranging from 12 to 48 students per school. Only 11 schools (17%) received 

permission from fewer than 20 students. On average, 20 students per school were randomly 

selected to be assessed (average selection rate per school of 74%). From the 1,849 eligible 

students with parental permission, 1,276 students were randomly selected to be assessed—598 

students in intervention schools and 678 students in control schools. 

Appendix D illustrates the steps in recruiting and randomly selecting the student sample 

and shows the numbers of students involved at each step. (Also see Figure 2.3 in the section on 

response rates for data collection, which illustrates the flow of students through the study.) At 

the time of baseline data collection, 46 of the randomly selected students (23 intervention and 23 

control) were not in school.20 In their place 43 alternates (21 in intervention schools and 22 in 

control schools) were randomly selected for testing. The final analytic sample includes all of the 

19 Although school begins the first week of August in Mississippi, schools indicated that only about 60% of 

kindergarten students are enrolled by the start of school, with 20% of students arriving by the end of August and 

another 20% arriving after Labor Day. The study extended the period for obtaining parental permission forms to 

include late-arriving kindergarteners in the sample.

20 Appendix E compares the characteristics of students who were not assessed at baseline with those who were. 

There were no statistically significant differences between the groups in terms of gender, eligibility for free or 

reduced-price meals, Individualized Education Programs status, and age. However, students who were not assessed 

at baseline were less likely to be African American than those who were tested (t = 2.49; p = .007). Of the 46 

students not tested at baseline, 29 were tested at posttest. 

24 
students who were originally randomly selected as well as the 43 randomly selected alternates. 

In total, there were 1,319 students in the analytic sample—619 students in intervention schools 

and 700 students in control schools. 

Characteristics of schools in the final study sample 

The sample of schools that volunteered for the study was similar to the pool of eligible 

schools in the Delta and surrounding counties in terms of School Performance Classification 

(low performing, underperforming, successful, exemplary, and superior performing), 

expectations for annual growth in student achievement, and percentage of students eligible for 

free or reduced-price meals (Table 2.1). However, the schools that volunteered for the study 

differed from the pool of eligible schools on two characteristics. Sample schools had a greater 

percentage of African American students (mean of 85%) than all eligible schools (mean of 73%; 

t = –2.17; p = .03). In addition, a greater percentage of sample schools were located in small 

towns and a smaller percentage were located in rural areas than the pool of eligible schools (x

2

 = 

6.53; p = .03). 

Table 2.1 Comparison of schools that agreed to participate and all eligible schools 

Agreed Declined Sampling 

(study sample) frame Test of 

65 schools 28 schools 93 schools differencea 

Region 

Delta 83.1% 67.9% 78.5% x

2 = 2.69 

Outside 16.9% 32/1% 21.5% p = .10 

School Performance Classification (grades 3 and higher) 

Low or underperforming 20.7% 30.8% 23.8% x

2 = 4.30 

Successful 55.2% 30.8% 47.6% p = .12 

Exemplary or superior 24.1% 38.5% 28.6% 

Annual growth expectation (from the previous school year for grades 3 and higher) 

Met or exceeded 17.2% 11.5% 15.5% p = .75 

Not met 82.8% 88.5% 84.5% 

Eligibility for free or reduced-price meals 

96% - 100% 32.3% 21.4% 29.0% 

90% - 95% 29.2% 17.4% 25.8% 

70% - 89% 21.5% 28.6% 23.7% 

Less than 70% 16.7% 32.1% 21.5% 

t = –1.71, 

Mean (standard deviation) 85.2% (16.3) 78.5% (19.0) 83.2% (17.3) p = .09 

Percent of students who are African 

American 

96% - 100% 53.9% 32.1% 47.3% 

81% - 95% 21.5% 17.9% 20.4% 

Less than 81% 24.6% 50.0% 32.2% 

t = –2.17, 

Mean (standard deviation) 85.0 (22.8) 73.1% (27.0) 81.4% (24.6) p = .03 

Locale 

Rural 47.7% 67.9% 53.8% x

2 = 6.53 

Small town 36.9% 10.7% 29.0% p = .03 

Large town or fringe of mid-size city 15.4% 21.4% 17.2% 

25 
Note: Distributions of school characteristics are assumed to be the same for cases with missing data as for cases with 

nonmissing data. Rates of missing data range from 0.0% to 10.8%. 

a. Chi-square tests were used to test for differences between study schools and other eligible schools in School 

Performance Classification and school locale. T-tests were used to test for differences in eligibility for free or 

reduced-price meals and in the percentage of students who are African American; although categories are presented 

for these variables in the table, they are continuous variables. Fischer’s exact test was used to test for differences in 

annual growth expectation because of small cell sizes. 

Among the 65 schools in the sample, 26% were Reading First schools, 8% participated in 

a state reading initiative or pilot program (Mississippi Reading Sufficiency Program or 

Barksdale Reading Initiative), and 66% had either a local reading initiative or none (Table 2.2). 

There were 21% of schools that were classified as low performing or underperforming on the 

School Performance Classification for 2006/07, 55% that were classified as successful, and 

24%that were classified as exemplary or superior. The majority of schools (83%) did not meet 

state expectations for annual growth in student achievement. The composition of the schools 

reflects the largely poor, African American population of the Delta and surrounding region. The 

median percentage of African American students in a school was 96% (not shown), and the 

median percentage of students who were eligible for free or reduced-price meals was 92% (not 

shown). Some 48% of the schools were in rural areas, 37% in small towns, and 15% in large 

towns or on the fringe of a city. The intervention and control schools were not significantly 

different at baseline on any of the characteristics in Table 2.2. 

Table 2.2 Description of school sample 

Control Intervention Full sample Test of 

34 schools 31 schools 65 schools differencea 

Reading initiatives 

Reading First/state reading initiative 35.3% 32.3% 33.9% p=.99 

Local or no initiatives 64.7% 67.7% 66.2% 

School Performance Classification (grades 3 and higher) 

Low or underperforming 19.4% 22.2% 20.7% 

Successful 58.1% 51.9% 55.2% p = .92 

Exemplary or superior 22.6% 25.9% 24.1% 

Annual growth expectation (from the previous school year for grades 3 and higher) 

Met or exceeded 16.1% 18.5% 17.2% p = .99 

Not met 83.9% 81.5% 82.8% 

Eligibility for free or reduced-price meals 

96% - 100% 26.5% 38.7% 32.3% 

90% - 95% 32.4% 25.8% 29.2% 

70% - 89% 23.5% 19.4 21.5% 

Less than 70% 17.7% 16.1% 16.7% t = 0.96, 

Mean (standard deviation) 84.8% (15.5) 85.6% (17.4) 85.2% (16.3) p = .34 

Percent of students who are African American 

96% - 100% 52.9% 54.8% 53.9% 

81% - 95% 20.6% 22.6% 21.5% 

Less than 81% 26.5% 22.6% 24.6% t = –0.34, 

Mean (standard deviation) 84.0% (15.2) 86.0% (20.3) 85.0 (22.8) p = .74 

Locale 

Rural 47.0% 48.4% 47.7% 

Small town 41.1% 32.3% 36.9% p = .62 

Large town or fringe of mid-size city 11.8% 19.4% 15.4% 

Region 

Delta 79.4% 87.1% 83.1% 

26 
Outside 20.6% 12.9% 16.9% p = .41 

Note: Distributions of school characteristics are assumed to be the same for cases with missing data as for cases with 

nonmissing data. Rates of missing data range from 0.0% to 12.9%. 

a. Fischer’s exact test was used to test for intervention and control group differences in reading initiatives, School 

Performance Classification, and annual growth expectation because of small cell sizes. T-tests were used to test for 

intervention and control group differences in eligibility for free or reduced-price meals and in the percentage of 

students who are African American; although categories are presented in the table for these variables, they are 

continuous variables. Chi-square tests were used to test for differences in school locale. 

As noted above, we blocked schools by reading initiative to ensure that intervention and 

control schools were balanced on this factor. We took this step because we expected that 

experience with Reading First or a state reading initiative would be related to classroom 

instructional practices and/or student outcomes. By ensuring that intervention and control 

schools were balanced with regard to reading initiatives, we were able to conclude that any 

impacts that we detect can be attributed to K-PAVE rather than to the Reading First program or a 

state reading program that may be present in certain schools. In addition, because experience 

with other reading initiatives may interact with how teachers implement K-PAVE and with its 

effectiveness for improving students’ vocabulary outcomes, we plan to conduct a subgroup 

analysis in a future report to examine whether impacts of K-PAVE vary based on whether 

schools have the Reading First program or do not. 

As noted above, K-PAVE was implemented as a supplement to literacy programs already 

in use in the intervention classrooms. According to reports from the schools, all study classrooms 

were using a commercial reading program. There was no statistically significant difference 

between intervention and control schools in the reading series used (χ

2

 = 2.51, p=.47; see Table 

2.3). In both groups, more than 40% of schools reported using Trophies, 2005 Edition (Houghton 

Mifflin Harcourt School Publishers), in their kindergarten classrooms. 

27 
Table 2.3 Reading programs in place at baseline in intervention and control schools 

Publisher/reading series 

Intervention schools 

(percent) 

Control 

schools (percent) 

Test of 

difference 

Trophies 2005 Edition (Houghton Mifflin 

Harcourt School Publishers) 43.3 41.2 

x

2 = 2.51 

p = .47 

 Treasures, a Reading Language Arts 

Program, Grade K, Kindergarten System 

(MacMillan/McGraw-Hill, 2008) 

or 

 Houghton Mifflin Reading (Houghton 

Mifflin Harcourt School Publishers, 

2008) 

26.7 23.5 

a Other 30.7 35.3

Note: Sample includes all intervention schools (n = 30) and all control schools (n = 34). 

a. Includes nine other curricula. 

Source: Telephone survey of schools. 

DATA COLLECTION 

Overview of the units of data collection 

To address the research questions, the study collected information on student 

performance, classroom instructional practices, student characteristics, teacher characteristics, 

school characteristics, and K-PAVE implementation. Table 2.4 provides a timeline of study 

activities, including data collection and intervention implementation. Data on student 

performance and classroom instructional practices were collected before the start of the K-PAVE 

intervention (for use as covariates in the impact analysis) and at the end of the school year. 

Student outcomes were measured at the end of the school year to estimate the impacts of KPAVE on student expressive vocabulary and other vocabulary-related outcomes (academic 

knowledge and listening comprehension). Information about instructional practices at the end of 

the school year was used to examine how K-PAVE changed practices in literacy instruction and 

how teachers allocated their literacy instruction time (among vocabulary, comprehension, and 

other areas of literacy instruction). Data on student, teacher, and school characteristics were 

collected for use as covariates in the impact analysis. Information about the implementation of 

K-PAVE was used to assess the fidelity of implementation to program design. 

28 
Table 2.4 Timeline of study activities: data collection and intervention implementation 

Date 

Student 

assessments 

Observation 

of 

intervention 

and control 

classrooms 

Student 

characteristic 

s (district 

administrative 

records) 

Survey of 

teacher 

character 

istics 

School 

characteristics 

(online state 

database) 

K-PAVE 

fidelity rating 

(intervention 

classrooms 

only) 

Spring 2008 X 

August 2008 X X 

September 2008 X X X 

October 2008 Xa 

November 2008 

December 2008 

January 2009 

February 2009 X Xb 

March 2009 Xc X Xb 

April 2009 Xa Xc X 

May 2009 X X 

June 2009 X 

Note: Shaded cells correspond to implementation of K-PAVE. The 24-week intervention period was staggered by 

one week for four groups of schools: October 6, 2008–April 10, 2009; October 13, 2008–April 17, 2009; October 

20, 2008–April 24, 2009; October 27, 2008–May 1, 2009. 

a. Baseline student assessments were completed before the start of the K-PAVE, and all posttest student assessments 

took place after completion of the K-PAVE intervention. 

b. K-PAVE fidelity rating was conducted in intervention classrooms only, during weeks 17–20. 

c. Posttest classroom observations were conducted in both intervention and control classrooms during weeks 22–24. 

All baseline classroom observations were completed before the start of the K-PAVE intervention. 

Student assessment measures. Table 2.5 summarizes the student measures, the outcome 

variables derived from the measures, and the designation of each outcome as primary or 

secondary.21 The primary research question examines the impact of K-PAVE on the outcome 

most directly targeted—students’ expressive vocabulary in kindergarten. Other student outcomes 

in kindergarten and sustained student outcomes in grade 1 are considered secondary because 

they extend beyond the primary target of the intervention. Impacts on classroom instruction 

(discussed below) are considered secondary because they are the intermediate outcomes through 

which K-PAVE is hypothesized to affect students’ expressive vocabulary. 

Student outcomes were assessed for expressive vocabulary, academic knowledge, and 

listening comprehension. At baseline and again at the end of the kindergarten year, students were 

individually assessed for about 45 minutes by trained members of the evaluation team who were 

independent of the intervention and unaware of the school assignment to intervention or control 

conditions. Baseline assessments of all students in intervention schools and nearly all students in 

21 Outcomes are related to research questions that are driven by a priori hypotheses about the impacts of K-PAVE on 

student outcomes and classroom instruction. The research questions were specified prior to observation of the 

experimental outcomes and are not informed by any knowledge about the actual data. 

29 
control schools were completed before K-PAVE intervention training. There were 3.9% of 

students in control schools who were assessed one to three weeks later.22 Posttest assessments 

for all students were conducted after completion of the 24-week intervention period. 

Table 2.5 Child measures, outcome variables, and designations 

Study area Measure Outcome variable 

Status in 

analysis 

Vocabulary knowledge Expressive Vocabulary Test–2 

(EVT–2) 

Standard score Primary 

Academic knowledge Woodcock-Johnson III/Normative 

Update (WJ–III/NU), Academic 

Knowledge subtest (science, 

humanities, social studies) 

W-score, an item response 

theory–based scale score 

(all three content areas 

combined) 

Secondary 

Listening comprehension Kaufman Test of Educational 

Achievement–II (KTEA–II), 

Listening Comprehension subtest 

Standard score Secondary 

Expressive vocabulary. The primary measure of vocabulary acquisition in the study is the 

Expressive Vocabulary Test–2 (EVT–2; Williams 2007). Expressive vocabulary is the primary 

outcome in the impact analysis. 

Assessing vocabulary development is a complex issue, because there are different kinds 

of vocabulary knowledge. Henriksen (1999) and Melka (1997) describe a receptive-expressive 

continuum where each word passes from receptive into productive use, with increasing exposure 

to the word (see Zareva, Schwanenflugel, & Nikolova 2005). In addition, Vermeer (2001) makes 

a distinction between breadth of vocabulary knowledge (number of words in the lexicon) and 

depth of such knowledge (how well those words are known). Expressive vocabulary captures 

both breadth and depth of vocabulary knowledge. Theoretically, receptive vocabulary represents 

the shallowest level of vocabulary knowledge. For example, children are likely to be able to 

select correctly from a multiple choice (such as the Peabody Picture Vocabulary Test, PPVT) 

with only partial word knowledge (Curtis 1987), whereas expressive vocabulary measures 

require a greater depth of knowledge not only to recognize but also to recall a word. 

Although the PPVT, which assesses students’ receptive vocabulary, is a commonly used 

vocabulary measure, we tested expressive vocabulary rather than receptive vocabulary because 

K-PAVE is intended to give students many opportunities to practice their expressive skills in 

addition to receptive word learning. K-PAVE instructional practices encourage students’ oral 

language use during Interactive Book Reading and extended Adult-Child Conversations. In 

addition, there was evidence from the previous quasi-experimental evaluation of the preschool 

PAVE intervention of statistically significant positive effects on students’ expressive vocabulary. 

Furthermore, the National Early Literacy Panel (NELP 2009) found an average correlation, 

across 30 randomized studies between expressive language comprehension outcomes and later 

reading performance of .48. Measures of receptive vocabulary had relatively weak relationships 

with both decoding and reading comprehension. Consequently, we concluded that a measure of 

22 Baseline testing was delayed for 3.9% of students in control schools because parental permissions, although 

collected by the school earlier, were provided to the study team late. 

30 
expressive vocabulary was better aligned with the immediate, primary goals of the program, as 

well as with the secondary goal of an extended impact on later reading comprehension. 

The recently updated version of the EVT has been co-normed and standardized on a 

representative sample of American children with attention to gender, race/ethnicity, geographic 

region, socioeconomic status, and special education needs. The EVT–2 has been used in criterion 

studies with other language assessments such as the Comprehensive Assessment of Spoken 

Language (CASL; Carrow-Woolfolk 1999) and Group Reading Assessment and Diagnostic 

Evaluation (GRADE; Williams, 2001). The EVT–2 has internal consistency (split half) 

reliabilities of .94–.95 and has a test-retest reliability of .95 (Williams 2007). Correlations 

between the EVT–2 and other tests are .84 for the Peabody Picture Vocabulary Test–4 (Dunn & 

Dunn 2007) for children ages 5–6; .68–.80 for the receptive language, expressive language, and 

core language scales on the Clinical Evaluation of Language Fundamentals, Fourth Edition 

(CELF–4; Semel, Wiig, & Secord, 2003) for children ages 5–8; and .59–.76 for the GRADE 

total reading score in kindergarten. 

The EVT–2 raw scores were used to calculate a standard score using student age. The 

standard scores allow the performance of students in intervention classrooms to be compared 

with that of a national sample of children of the same age. Such comparisons can be used to 

address the policy implications of any changes in the achievement gap between the study sample 

of at-risk children and a national sample. 

Academic knowledge. The study also measured general or background knowledge, a 

secondary outcome, using the Woodcock-Johnson III/Normative Update (WJ–III/NU) Academic 

Knowledge subtest (Woodcock, McGrew, Schrank, & Mather 2007). The Academic Knowledge 

subtest is a suggested outcome measure for interventions that provide a language-rich 

environment, frequent exposure to words, reading aloud to children, and text talk (Wendling, 

Schrank, & Schmidt 2007). The test focuses on background knowledge in science, social studies, 

and humanities: 

•	 Science items include naming or pointing to body parts and pictures of animals and 

vegetables and answering questions such as, “What does a tadpole, or polliwog, become 

when it grows up?” 

•	 Social studies items include naming or pointing to items of clothing, furniture, grooming, 

tools, and buildings and answering questions such as, “What is the person called who fixes 

teeth?” 

•	 Humanities items include naming or pointing to arts and crafts supplies, colors, and musical 

instruments and completing the phrase, “Once upon a ....” or answering, “What is a large 

group of singers called?” 

A single item response theory–based scale score (W-score) was calculated based on a 

nationally representative norm group. Reported technical characteristics of the WJ–III/NU 

Academic Knowledge test indicate that internal consistency (split half) reliability is .92 for 

students age 5 and .82 for children age 6 (Woodcock, McGrew, Schrank, & Mather 2007). 

Listening comprehension. The study also measured students’ comprehension, since 

increasing children’s vocabulary and knowledge about the world is a pathway to stronger skills 

31 
in comprehending spoken language and print. For kindergarten students, most of whom have not 

yet learned to read connected text, comprehension is most validly measured through listening to 

speech rather than through reading. Listening comprehension at the end of kindergarten was 

measured using the Kaufman Test of Educational Achievement–II (KTEA–II) Listening 

Comprehension subtest (Kaufman & Kaufman 2004). This test assesses listening ability and 

understanding, without assessing reasoning or memory for details. The assessment involves 

listening to short passages read orally and answering comprehension questions. Student age was 

used in calculating a single standardized score based on a nationally representative norm group. 

Listening comprehension is a secondary outcome in the impact analysis. 

Reported technical characteristics of the subtest indicate that the internal consistency 

(split-half) reliability for this measure in kindergarten is .84 (Kaufman & Kaufman 2004). 

Results from a confirmatory factor analysis with students in grade 1 and higher indicate that the 

correlation between the oral language factor (listening comprehension and oral language 

subtests) and the reading factor is .91 and that the errors for the listening comprehension subtest 

and the reading comprehension subtest are correlated. The correlation in grades 1–5 between the 

KTEA–II Listening Comprehension subtest and the WJ–III Listening Comprehension test is .77. 

Observations of instruction in intervention and control classrooms. Teachers’ instructional 

behavior was assessed through direct classroom observation at baseline and at the end of the 

school year. Half-day observations focused on vocabulary and literacy instruction. Baseline 

observations were completed before the K-PAVE intervention training. Posttest observations 

were timed for weeks 22–24 of the 24-week intervention period.23 Observations were conducted 

by trained members of the evaluation team who were independent of the K-PAVE intervention 

and unaware of the school assignment to intervention or control conditions. 

Classroom observation measures. Three classroom observation measures were 

administered and are discussed below (see Table 2.6 for a summary and Appendix F for detailed 

descriptions). 

•	 Classroom Assessment Scoring System. The Classroom Assessment Scoring System (CLASS 

K–3; Pianta, La Paro, & Hamre 2008) assesses the quality of interactions and instruction and 

yields ratings on instructional support, emotional support, classroom organization, and a 

combined global quality rating. 

The CLASS, a time-sampling observation tool, was based on at least two hours of classroom 

observation. Coding involved 30-minute cycles, during which 10 dimensions of classroom 

instruction were rated on a seven-point Likert scale. Observers also documented the 

academic content covered (vocabulary/comprehension, other language arts, or other 

academic content). 

Two of the three CLASS domains—instructional support and emotional support—more 

closely reflect the goals of the K-PAVE intervention. K-PAVE trains teachers on techniques 

for vocabulary instruction, Interactive Book Reading, and informal conversations with 

students, techniques that can be expected to affect dimensions of instructional support 

(concept development, quality of feedback, and language modeling). The emphasis on 

23 The initial K-PAVE intervention training workshop was staggered over four weeks, so not all schools began the 

intervention at the same time. For this reason, weeks 22–24 of the intervention period spanned a five-week period, 

depending on when the intervention training was delivered. 

32 
informal conversations about topics of students’ interest and choosing may be expected to 

influence emotional support (positive classroom climate, teachers’ regard for student 

perspectives, and teacher sensitivity). Therefore, scores for these two domains were used as 

secondary outcomes for the study. In addition, data from the CLASS observation on 

academic content were used to describe the distribution of classroom time across content 

areas. This variable was used to examine whether implementation of the K-PAVE 

supplementary program reduced the time spent on other literacy-related instruction. 

•	 Vocabulary Record. The Vocabulary Record, which documents the vocabulary support 

provided by teachers during a book read-aloud and during each CLASS cycle, yields two 

measures: the number of words defined by the teacher or assistant teacher during book 

reading and the average number of words defined during other instructional time. The 

Vocabulary Record is coded throughout the entire classroom observation; the observer 

documents every word that the teacher or assistant teacher introduces—by providing a 

definition or synonym, illustrating with a picture, providing a contrast, or asking a student for 

word meaning—during the book reading when the Read Aloud Profile–Kindergarten is being 

coded and during each CLASS cycle. The total number or words documented during the 

read-aloud was tallied for a measure of the number of words introduced during the book 

reading. The total number of words documented during each CLASS cycle was tallied and 

averaged (i.e., divided by the number of CLASS cycles) for a measure of the average 

number of words introduced during other instructional time. The Vocabulary Record created 

for this study is an adaptation of the vocabulary component of the Instructional Practice in 

Reading Inventory (IPRI; Smith et al. 2005). 

•	 Read Aloud Profile–Kindergarten. The Read Aloud Profile–Kindergarten (RAP–K) 

documents teachers’ comprehension support and questions while reading aloud to students. 

Comprehension supports include providing background information, making connections to 

students’ experiences, and asking concrete or factual questions to clarify meaning. Higher 

order questions include questions asking students to analyze, explain, predict, imagine, make 

inferences, or generate hypotheses. Two measures were generated: number of comprehension 

supports and number of higher order questions. When teachers did not read aloud to students 

during the classroom observation, the reading behaviors measured by the RAP-K were coded 

as not occurring (i.e., occurring zero times) rather than as missing. The RAP-K, created for 

this study, is an adaptation of the Read Aloud Profile (RAP) instrument from the Observation 

Measures of Language and Literacy Instruction (OMLIT; Goodson, Layzer, Smith, & 

Rimdzius 2004). 

Vocabulary and comprehension support composite created from RAP-K and Vocabulary 

Record variables. The two variables created from the Vocabulary Record—the number of words 

introduced during book reading and the average number of words introduced during other 

instructional time—and the two variables created from the RAP-K—number of comprehension 

supports and number of higher order questions—are all measures of the broader construct of 

vocabulary and comprehension support. The K-PAVE intervention is intended to improve 

teachers’ vocabulary and comprehension instructional behaviors and thereby improve students’ 

vocabulary development. In order to test the impact of K-PAVE on the vocabulary and 

comprehension support construct overall rather than test its impact on the specific behaviors that 

comprise the construct, we created a single composite measure from the four variables. (Creation 

of the composite is described in Appendix F.) 

33 
In addition to combining the four variables into a single composite for substantive 

reasons, doing so also has the benefit of minimizing the number of hypothesis tests being 

conducted. The risk of type I error (i.e., false positives) when we test the impact of K-PAVE on a 

single composite measure of vocabulary and comprehension support is lower than if we were to 

test the impact of K-PAVE on each of the four variables that comprise the construct. 

Table 2.6 Classroom/teacher measure and outcome variables 

Study area Measure 

Outcome 

variable 

Status in 

analysis 

Instructional support 

Classroom 

Assessment Scoring 

System (CLASS) 

Overall rating on instructional support (1–7) Secondary 

Emotional support CLASS Overall rating on emotional support (1–7) Secondary 

Amount of literacy-related 

instruction in areas other 

than vocabulary and 

comprehension 

CLASS 

Proportion of CLASS cycles in which literacyrelated content other than vocabulary, 

comprehension, and oral language is covered 

Secondary 

Vocabulary and 

comprehension support 

• Vocabulary Record 

• Read Aloud Profile– 

Kindergarten 

Vocabulary and comprehension support 

composite: 

• Number of words teacher/assistant introduces 

or asks students to define during book readaloud 

• Average number of words teacher or assistant 

teacher introduces or asks students to define 

during other instructional time 

• Number of comprehension supports during 

reading 

• Number of higher order questions during 

reading 

Secondary 

Covariate measures of student, teacher, and school characteristics. Data were collected 

on the characteristics of students, teachers, and schools. Data on student demographics and 

school characteristics were included as covariates in the analysis of impacts on students, and data 

on school characteristics and teacher background were included as covariates in the analysis of 

impacts on classroom instruction. 

Student demographic characteristics were collected from district administrative records 

on date of birth, gender, race/ethnicity, special education status, status as an English language 

learner, retention in kindergarten, eligibility for free or reduced-price meals, and whether 

attended preschool. 

For teachers, baseline data were collected using a short (approximately 10 minutes) 

survey on race/ethnicity, age, training/education, and number of years of teaching experience 

and of teaching kindergarten. (The teacher survey is presented in Appendix G.) The survey was 

completed by teachers in both intervention and control classrooms as part of the baseline 

classroom observation visit (when the CLASS, RAP-K, and Vocabulary Record instruments 

34 
were collected, as described above). Observers gave teachers the paper-and-pencil survey to 

complete during the classroom visit. 

Data on school characteristics gathered from the Mississippi Department of Education, 

districts, and schools included the percentage of students in the school receiving free or reducedprice meals, the school’s Achievement Level Index24 (a score based on student performance in 

grades 3 and higher on the Mississippi Curriculum Test in 2006/07), school racial/ethnic 

composition, and the literacy curricula used in kindergarten classrooms. 

Fidelity of implementation. Fidelity of implementation was based on two factors: the degree to 

which intervention teachers participated in the K-PAVE teacher training and support, and 

whether or not intervention teachers implemented the 12 K-PAVE instructional strategies as 

intended. The participation in training and support was based on attendance records, while 

fidelity of implementation was based on classroom observations by members of the training 

team. 

Fidelity of implementation was based on observation of intervention classrooms in late 

February or early March (corresponding to weeks 17–20 of the 24-week intervention period), 

after teachers had the opportunity to participate in all of the planned K-PAVE training and 

support. The measure of fidelity, the Training Fidelity Checklist, was provided by the K-PAVE 

developer (see Appendix H for the K-PAVE Training Fidelity Checklist and coding 

instructions). Data collectors trained by the developer scored fidelity of implementation in each 

intervention classroom based on a 90-minute observation. Teachers were assigned a fidelity 

score ranging from 1 to 12, indicating the number of strategies to which they demonstrated 

fidelity. Based on the K-PAVE developer’s guidelines about the number of strategies 

implemented with fidelity, teachers were then assigned a fidelity rating. (Fidelity ratings are 

discussed in further detail in Chapter 3.) 

Observers also kept detailed field notes to indicate the evidence they used to score 

implementation. Inter-rater agreement on the fidelity scoring was not assessed during the 

training. However, as part of the fidelity observation, observers were trained to make detailed 

field notes, describing the evidence on which they based their fidelity scoring. A member of the 

developer’s team, who did not observe the teacher directly, subsequently scored the fidelity of 

implementation based solely on the field notes. There was a high level of agreement between the 

fidelity scores assigned by the observers and by the independent scoring based on the field notes 

(Cohen’s Kappa = .98 across the 12 individual instructional strategies). 

24 A school’s Achievement Level Index is created based on student performance on the Mississippi state 

accountability test (Mississippi Curriculum Test, MCT), which is administered to all students in grade 3 and higher. 

The Achievement Level Index (ALI) corresponds to the School Performance Classification (described above); the 

ALI is measured on a continuous scale ranging from 100 to 600, while School Performance Classification is a 

categorical rating (i.e., low performing, underperforming, successful, exemplary, and superior). The ALI score is 

created based on the percentage of students in the school who scored basic or higher on the MCT and the percentage 

of students in the school who scored proficient or higher on the MCT. Schools with scores in the 100 range are rated 

as having a School Performance Classification of “low performing”; schools with scores in the 200 range are rated 

“underperforming”; schools with scores in the 300 range are rated “successful”; schools with scores in the 400 range 

are rated “exemplary”; and schools with scores in the 500 range are rated “superior.” 

35 
Training of data collectors to ensure reliable data 

The study required two types of data collectors—student assessors and classroom 

observers. Their recruitment and training are described below. 

Qualifications of data collectors. Data collectors were recruited from local universities and 

community colleges in Mississippi and through contacts at the Mississippi Department of 

Education. Data collectors included students with a background in education and retired 

teachers, school counselors, and school administrators. Data collectors were independent of the 

intervention implementation and unaware of intervention status. Data collection procedures were 

identical in intervention and control schools, since data collectors did not know schools’ 

intervention status. (Student assessment and classroom observation procedures are described in 

Appendix I.) 

Training data collectors to level of research reliability. Each group of data collectors— 

student assessors and classroom observers—attended one week of training and had to pass 

reliability testing before being hired to collect data. Child assessors were trained by senior Abt 

Associates staff experienced with the child assessments used in this study. Trainees received 

thorough instruction on test administration and scoring rules and had numerous opportunities to 

practice mock test administrations.25 Classroom observers were trained to use the CLASS 

instrument by staff from the University of Virginia Center for the Advanced Study of Teaching 

and Learning (CASTL), where the instrument was developed. Observers were trained to 

administer the RAP-K and Vocabulary Record instruments by senior Abt Associates staff 

involved in the development of or with extensive experience using the instruments. Training on 

all observation tools involved thorough instruction on coding rules, illustration of codes using 

video examples of classroom instruction, and numerous opportunities to practice coding video 

segments. 

Criteria developed before training were used to determine whether trainees had met the 

required standards. Student assessors were required to conduct a mock administration of each 

student assessment without any major administration errors (such as scoring errors or incorrectly 

establishing basal or ceiling criteria) and with fewer than two minor errors (such as neglecting to 

point at a picture when reading a prompt). Classroom observers were required to code five video 

segments of classroom interactions for the CLASS instrument. For the RAP–K and the 

Vocabulary Record, classroom observers were required to code two video segments of 

classroom read-alouds. All videotapes had been coded in advance by master coders, and 

observers were required to have at least 80% agreement for all coded video segments. The 

25 For all child assessments—Expressive Vocabulary Test-2; Woodcock-Johnson III/NU Academic Knowledge test; 

and Kaufman Tests of Educational Achievement-II Listening Comprehension test—assessors were trained to follow 

administration and scoring guidelines outlined by the test publisher. A substantial amount of training time was 

devoted to learning criteria outlined in test manuals for judging student responses as correct, incorrect, or requiring 

further prompting. In mock administrations of each test, trainers provided increasingly complex responses to test 

items, to give trainees experience scoring a range of responses. 

36 
average rate of agreement with the master codes was 82% on the CLASS,26 88% on the RAP– 

K,27 and 85% on the Vocabulary Record.28 

For student assessment, 21 of 34 trainees (62%) were certified to collect data, and for 

baseline data collection, 10 of 14 trainees (71%) were certified as reliable. For posttest data 

collection, both returning and new data collectors were trained and certified. Among student 

assessors, all 13 returning assessors (100%) and six of nine new trainees (67%) were certified to 

collect data. Among classroom observers, all four returning observers (100%) and five of eight 

new trainees (63%) were certified as reliable. 

In addition, data collectors received close oversight during the first weeks of data 

collection to identify any problems before extensive data were collected. Experienced data 

collectors accompanied new ones on early data collection visits to provide guidance and answer 

questions. Within the first week of data collection, trainers held conference calls with data 

collectors to discuss questions and challenges. No measure of interrater reliability was collected 

during these visits. 

Quality assurance process for data. Student assessments and classroom observation data 

underwent a thorough quality assurance protocol (described in Appendix J). 

Response rates for data collection 

As shown in Table 2.7, the study achieved high response rates at all levels—schools, 

classrooms, teachers, and students. 

School-level and classroom-level attrition. During the study one intervention school dropped 

out; no data were collected on student demographics or on posttest classroom instruction or 

student outcomes. Without posttest data or student-level covariates, the school could not be 

included in the analysis. The school that dropped out of the study has characteristics similar to 

the average school in the intervention group and the average school in the sample overall.29 The 

school was in the block of intervention schools with either a local reading program or no reading 

program. The remaining schools in the block were weighted to adjust for the loss of the school 

from this block (see Appendix K for a discussion of the weighting used), and sensitivity models 

were estimated without weights to examine sensitivity to weighting (see Appendix N). 

Because one school dropped out of the study, two classrooms and 23 students were lost 

from the intervention group. A total of 64 schools (30 intervention and 34 control), 128 

classrooms (60 intervention and 68 control), and 1,296 students (596 intervention and 700 

control) remained in the analytic sample. Appendix L illustrates the attrition from data collection 

26 Each observer’s average rate of agreement with master codes across all five coded CLASS video segments ranged 

from 80% to 84%. 

27 Each observer’s average rate of agreement with master codes across both coded RAP-K video segments ranged 

from 80% to 96%. 

28 All observers were within one word of the number of words identified for the master coders on each of the two 

coded video-recordings. The rate of agreement ranged from 63% to 100%. The low incidence of new vocabulary 

words being introduced contributed to a lower than 80% rate of agreement for three classroom observers.

29 To maintain confidentiality, we do not report specific characteristics of the school that dropped from the study in 

comparison to the remaining schools. 

37 
at the school, classroom, and student levels. There was no other attrition at the school or 

classroom levels. 

Student-level attrition. The flow of students through the study—including recruitment, random 

selection, and attrition from data collection—is shown in Figure 2.3 for both intervention and 

control groups. At posttest, student attrition was 8.1% in the intervention group and 5.9% in the 

control group. The higher attrition rate in the intervention group reflects the loss of students from 

the school that dropped out of the study. Excluding the students from that school brings the 

attrition rate in the intervention group down to 4.5%. There is no statistically significant 

difference between these two rates (χ

2

 = 1.14, p = .29) or between the two rates when students 

from the school that dropped out are included (χ

2 = 2.19, p = .14). With the exception of the one 

intervention school that dropped out of the study, the reasons for attrition were that students 

moved out of state, transferred to a nonstudy school in Mississippi, and were absent during the 

data collectors’ visits. (See section later in the chapter on data analysis methods and Appendix M 

for details on the imputation of missing data. Appendix N reports sensitivity analyses examining 

the influence of missing data imputation on impact estimates and their standard errors.) 

Table 2.7 Response rates for data collected from schools, classrooms, teachers, and students for 

intervention and control groups (percent) 

Baseline response rate Posttest response rate 

Level and variable Overall Intervention Control Overall Intervention Control 

Schools (number) 65 31 34 65 31 34

Demographics 100 100 100 na na na

Classrooms (number) 130 62 68 130 62 68

Reading program 100 100 100 a na na na

Classroom Assessment Sorting System 100 100 100 98.5 96.8 100 

Read Aloud Program–Kindergarten 100 100 100 97.7 96.8 98.5 

Vocabulary Record 100 100 100 98.5 96.8 100

Teachers (number) 130 62 68 130 62 68

Teacher demographic survey 99.2 98.4 100 na na na

Language sample 93.8 88.7 98.5 93.8 93.4 94.1

Students (number) 1,319 619 700 1,319 619 700

Demographics 97.5 95.5 99.9 na na na

Expressive vocabulary (EVT–2) 96.3 96.0 96.6 93.5 92.2 94.6

Listening comprehension (KTEA–II) 94.4 93.7 95.0 93.4 92.2 94.4

Academic knowledge (WJ–III/NU) 95.1 93.9 96.1 94.3 92.1 94.6

Passage comprehension (WJ–III/NU) 96.3 96.0 96.6 93.5 92.2 94.6 

na is not applicable because the variable was collected only once during the school year, rather than at baseline and 

posttest. 

Note: One school (two classrooms) dropped out of the study after baseline data collection, which means all posttest 

measures and student demographic data are missing for one intervention school, two intervention 

classrooms/teachers, and 23 intervention students. 

38 
Figure 2.3 Flow of students through the study 

Intervention schools Control schools 

Total student enrollment 

62 randomly selected classrooms in 31 

intervention schools 

n = 1,251 

Total student enrollment 

68 randomly selected classrooms in 34 

control schools 

n = 1,331 

Excluded: n = 632 

No parental permission: 388 

Not randomly selected 

or not eligible: 244 

Excluded: n = 631 

No parental permission: 345 

Not randomly selected 

or not eligible: 286 

Randomly selected 

students 

n = 619 

Randomly selected 

students 

n = 700 

Posttest 

collected 

n = 569 

(91.9%) 

Lost at posttest 

n = 50 (8.1%) 

Moved, crossover, or absent at 

posttest 27 

School dropped out 23 

Posttest 

collected 

n = 659 

(94.1%) 

Lost at posttest 

n = 41 (5.9%) 

Moved, crossover, or absent at 

posttest 41 

Analyzed n = 596 Analyzed n = 700 

Excluded from analysis: 23 Excluded from analysis: 0 

Note: missing posttest scores Note: missing posttest scores 

were imputed, except for students in were imputed. 

school that dropped out. 

39 
Nonparticipants and crossovers 

Students. Forty-five students were categorized as nonparticipants—students who moved out of 

state or transferred to a nonstudy school in Mississippi either before baseline testing or between 

pretest and posttest and therefore have no posttest data. Of the 45 students, 21 were in 

intervention schools and 24 in control schools (Table 2.8). To maintain the statistical 

equivalence of the two control groups, all students—both participants and nonparticipants—were 

included in the analysis (except students in the school that dropped out of the study). To retain 

nonparticipants in the analysis, values were imputed for missing pretest and posttest data (see 

Appendix M on missing data imputation). The inclusion of nonparticipants in the analysis could 

lead to underestimation of the effect of the K-PAVE intervention, depending on the number of 

nonparticipants. Sensitivity analyses examining the influence of nonparticipants on impact 

estimates and standard errors are reported in Appendix N. 

Table 2.8 Nonparticipants in intervention and control groups 

Intervention Control Test of 

Condition (n = 596) (n = 700) difference 

Transferred out of state or to a nonstudy school (nonparticipants) 21 (3.4%) 24 (3.4%) 

Tested at pretest only 10 (1.6%) 18 (2.6%) χ

2 = 3.56 

Not tested at pretest or posttest 11 (1.8%) 6 (0.9%) p = .06 

There were only five crossover students—students who were initially enrolled in a 

control school but switched to an intervention school or students who were initially enrolled in 

an intervention school but switched to a control school during the study. The crossover rate was 

not significantly different for the intervention and control schools (p=.99).30 If there are many 

crossovers, this kind of movement can compromise the integrity of the impact estimates. The 

low rate of crossovers in this study is unlikely to threaten the validity of the estimates. To 

preserve the integrity of the random assignment, crossover students were analyzed in their 

original assigned condition. This could bias the analysis against finding significant effects of the 

K-PAVE intervention. (See Appendix N for sensitivity analyses examining the influence of 

student crossovers.) 

Teachers. No intervention teachers left midyear. Because there was no teacher turnover in the 

intervention schools, there was no need to offer the K-PAVE workshop training to midyear 

replacement teachers. 

ANALYTIC SAMPLE 

Because one intervention school dropped out of the study, the analytic sample included 

one fewer school, two fewer classrooms, and 23 fewer students than the initial sample after 

randomization. The loss of that one school resulted in an overall attrition rate of 2% and a 

30 Fischer’s exact test was used to test for differences in crossovers between intervention and control groups because 

of small expected values for cells. 

40 
differential attrition rate of 3%.31 The analytic sample had 64 schools (30 intervention and 34 

control), 128 classrooms (60 intervention and 68 control), and 1,296 students (596 intervention 

and 700 control). Table 2.9 compares the initial sample and the analytic sample. 

Table 2.9 Comparison of initial sample at randomization and final analytic sample 

Initial sample at 

Level and condition randomization Final analytic sample 

Schools 65 64 

Intervention 31 30 

Control 34 34 

Classrooms 130 128 

Intervention 62 60

Control 68 68

Students 1,319 1,296 

Intervention 619 596

Control 700 700

Characteristics of teachers and students in intervention and control schools 

There are no statistically significant differences in characteristics between teachers in the 

intervention and control groups (Table 2.10) or between students in the intervention and control 

groups (Table 2.11). Nonetheless, to obtain more precise estimates of K-PAVE impacts on 

classroom instruction, all teacher characteristics were included as covariates in the analysis of 

impacts on classroom instruction because teacher characteristics might relate to classroom 

instructional practices. Similarly, to obtain more precise estimates of K-PAVE impacts on 

students, all student characteristics were included as covariates in the analysis of impacts on 

students because these characteristics are likely related to student performance. 

31 The loss of 1 of 31 intervention schools gives an attrition rate in the intervention group of 3%. The attrition rate in 

the control group is 0% so the differential attrition rate is 3% (intervention group attrition rate) minus 0% (control 

group attrition rate). 

41 
Table 2.10 Characteristics of teachers in the analytic sample, by intervention condition 

Interventiona Control Overall Test of 

Characteristic (60 classrooms) (68 classrooms) (128 classrooms) differenceb 

Race 

African American 40.7% 48.5% 44.9% t = –0.81, p = .42 

White 59.3% 51.5% 55.1% 

Education t = 0.03, p = .98 

College 39.4% 36.8% 38.1% 

Some graduate courses 22.4% 27.9% 25.4% 

Graduate degree 37.9% 35.3% 36.5% 

Certifications 

Early childhood 71.7% 79.4% 75.8% t = –0.96, p = .34 

Reading 11.7% 13.2% 12.5% t = –0.30, p = .76 

Years teaching t = 1.05, p = .30 

Mean 17.0 years 14.6 years 15.7 years 

Standard deviation 12.3 years 11.3 years 11.8 years 

Years teaching kindergarten t = 0.91, p =. 37 

Mean 10.9 years 9.4 years 10.1 years 

Standard deviation 9.1 years 8.7 years 8.9 years 

a. Sample includes all intervention classrooms (n = 60) and control classrooms (n = 68). Distributions of teacher 

characteristics are assumed to be the same for cases with missing data as for cases with non-missing data. Rates of 

missing data range from 0.0% to 3.3%. 

b. A two-level model was used to test for baseline differences between intervention and control groups on teacher 

characteristics at the school level to account for the nesting of teachers within schools. The test of baseline 

differences was adjusted for the multilevel structure of the data but not for covariates. A linear model was used to 

test for differences in dichotomous and ordinal variables—gender, race, education, early childhood certification 

(yes/no), and reading certification (yes/no)—to account for the nested data structure. Thus, t-tests rather than chisquare tests were conducted. 

42 
Table 2.11 Characteristics of students in the analytic sample, by intervention condition 

Characteristic 

Intervention 

(n = 596) 

Control 

(n = 700) 

Overall 

(n = 1,296) 

Test of 

differencea 

Gender 

Female

Male

 49.2% 

 50.8% 

50.6% 

49.4% 

50.0% 

50.0% 

t = –0.47, p = .64 

Race 

African American 

White

Other

87.6%

 11.5% 

 0.8% 

 82.8% 

14.8% 

2.4% 

85.0% 

13.3% 

1.7%

t = 0.81, p = .42 

Eligibility for free or reduced-price meals 

Yes

No

 93.4% 

 6.6% 

92.5% 

7.5% 

92.9% 

7.1% 

t = 0.26, p = .80 

Has an Individualized Education Program 

Yes 8.8% 

No 91.2% 

7.7% 

92.3% 

8.2% 

91.8% 

t = 0.55, p = .58 

Age at posttest 

Mean 

Standard deviation 

6 yrs, 1.9 mo 

4.8 mo 

6 yrs, 1.8 mo 

4.6 mo 

6 yrs, 1.9 mo 

4.7 mo 

Note: The distribution of student characteristics is assumed to be the same among missing data as among 

nonmissing data. Rates of missing data range from 0.1% to 1.8%. 

a. A three-level model was used to test for baseline differences between the intervention and control groups in 

student characteristics at the school level to account for the nesting of students within classrooms and classrooms 

within schools. The test of baseline differences was adjusted for the multilevel structure of the data but was not 

adjusted for covariates. A linear model was used to test for differences in dichotomous variables—gender, race, 

eligibility for free or reduced-price meals, and having an Individualized Education Program—to account for the 

nested data structure. Thus, t-tests rather than chi-square tests were conducted. 

Student outcomes and classroom instruction at baseline for intervention and control 

schools 

Table 2.12 shows the baseline pretest scores on student outcome measures for 

intervention and control groups. Before the start of the K-PAVE intervention, students were 

administered standardized assessments of expressive vocabulary (EVT–2), academic knowledge 

(WJ–III/NU Academic Knowledge subtest), and listening comprehension (KTEA–II Listening 

Comprehension subtest). Scores for each assessment were standardized based on a norming 

sample and have a mean of 100 points and a standard deviation of 15 points. In both intervention 

and control groups, students in the sample have mean baseline standard scores approximately 

two-thirds of a standard deviation below the norm for their age on expressive vocabulary and 

academic knowledge and slightly lower for listening comprehension. There were no statistically 

significant differences at baseline between intervention and control groups on any of the student 

outcomes. 

43 
Table 2.12 Baseline pretest scores on student outcomes for intervention and control groups 

Test of baseline differences in 

Unadjusted pretest means student outcomesa 

Control group 

Intervention group mean Estimated 

mean (standard (standard difference Test of 

deviation) deviation) (standard error) difference 

Student outcome measure n n n (p-value) 

Expressive vocabulary 91.1 (12.3) 90.7 (11.4) 0.40 (1.10) .72 

574 676 1250 

Academic knowledge 90.5 (11.0) 90.6 (11.3) –0.05 (1.05) .96 

574 675 1,249 

Listening comprehension 88.2 (12.8) 87.0 (13.4) 1.16 (1.52) .44 

560 665 1,225 

Note: The sample includes 596 intervention group students in 60 classrooms in 30 schools and 700 control group 

students in 68 classrooms in 34 schools. 

a. A three-level model, with student, classroom, and school levels, was used to test for the baseline difference 

between intervention and control group means in student outcome scores at the school level. The model had the 

same multilevel structure as the model used to test K-PAVE impacts on student outcomes at posttest. (See Appendix 

K for impact model specifications.) The test of baseline differences was adjusted for the multilevel structure of the 

data but was not adjusted for covariates. 

Table 2.13 shows the baseline classroom instruction measures for intervention and 

control groups. The average rating of instructional support of approximately 2.6 points in both 

intervention and control classrooms indicates quality at baseline at the high end of low quality.32 

The average rating of emotional support of 5.2 points in intervention groups and 5.1 points in 

control groups indicates quality at baseline at the high end of moderate quality. 

Intervention and control groups do not differ significantly at baseline on these two 

classroom instruction measures or on the measure of vocabulary and comprehension support. 

However, the percentage of CLASS cycles observed at baseline during which the teacher spent 

time on nonvocabulary literacy instruction (such as print concepts, phonological awareness, and 

alphabet knowledge) was 10 percentage points higher in intervention schools than in control 

schools (t = 2.08, p = .04). This difference is assumed to occur by chance rather than as the result 

of any bias in the random assignment of schools or the random selection of classrooms for data 

collection. Because there were no missing data on the classroom observation measures, the 

difference is not the result of nonresponse bias. The analysis of the impact of K-PAVE on 

instructional time included the baseline measure for each outcome to increase the precision of 

the impact estimate. In this case, inclusion of the baseline measure in the impact analysis also 

reduces variability in the impact estimates that can result from chance nonequivalence between 

the intervention and the control groups. 

32 The Classroom Assessment Scoring System (Pianta et al. 2008), used to rate instructional support and emotional 

support, defines ratings of 1 and 2 as low quality, ratings of 3–5 as moderate quality, and ratings of 6–7 as high 

quality. 

44 
Table 2.13 Baseline measures of classroom instruction for intervention and control groups 

(n = 64 schools, 128 classrooms) 

Test of baseline differences in a Raw pretest means classroom instruction outcomes

Intervention group Control group Estimated Test of 

mean (standard mean (standard difference difference 

Classroom instruction measure deviation) deviation) (standard error) (p-value) 

Vocabulary and comprehension –0.002 0.002 –0.007 .97 

support (1.00) (1.01) (.183) 

2.62 2.67 –0.049 Instructional support .75 (0.73) (0.69) (0.156) 

Emotional support 5.21 5.06 0.152 .26 (.67) (.81) (.133) 

Instructional time on literacy .56 .46 .103* 

areas other than vocabulary and .04 (.23) (.24) (.049) comprehension 

* p < .05.

Note. The sample includes all intervention classrooms (60 classes in 30 schools) and all control classrooms (68 

classrooms in 34 schools). Vocabulary and comprehension support is measured as a z-score with a mean of 0 and a

standard deviation of 1. Instructional support and emotional support are measured on a seven-point Likert scale. 

Instructional time on other literacy areas is measured as a proportion, with a 0–1 possible range. 

a. A two-level model, with classroom and school levels, was used to test for the baseline difference between

intervention and control group means on measures of classroom instruction at the school level. The model had the 

same multilevel structure as the model used to test K-PAVE impacts on classroom instruction at posttest. (See 

Appendix K for impact model specifications.) The test of baseline differences was adjusted for the multilevel 

structure of the data but not for covariates.

DATA ANALYSIS METHODS 

This section describes the analytic approach used to address research questions on the 

impact of the K-PAVE intervention primarily on kindergarten students’ expressive vocabulary 

and secondarily on kindergarten students’ academic knowledge and listening comprehension. It 

also describes the analytic approach used to address research questions on classroom instruction 

in vocabulary and comprehension support, instructional support, and emotional support and the 

extent to which aspects of literacy other than vocabulary and comprehension received 

instructional focus. 

Based on the presence of a positive impact of K-PAVE on students’ expressive 

vocabulary in kindergarten, a subsequent report will examine the impacts of exposure to KPAVE in kindergarten on students’ expressive vocabulary, academic knowledge, listening 

comprehension, and passage comprehension in grade 1. A separate exploratory report will 

investigate impacts of K-PAVE on kindergarten students’ lexical diversity and variation in 

impacts on students in kindergarten and grade 1 depending on student characteristics (such as 

age, gender, and entry-level pretest scores). The exploratory report will also investigate impacts 

on kindergarten teachers’ lexical diversity and other instructional practices. 

Impacts on students 

A three-level hierarchical linear model (HLM), with school, classroom, and student 

levels, was used to model the impact of the K-PAVE intervention on students (the model is 

45 
presented in Appendix K). The model provides an estimate of the average impact of the 

intervention on students across all schools at a given time (at the end of kindergarten) and an 

estimate of this impact’s standard error. The HLM is appropriate for this analysis because the 

study used a multilevel design with students nested within classrooms and classrooms within 

schools. The multilevel modeling also parses the variance among students, classrooms, and 

schools to produce more accurate estimated standard errors (Raudenbush & Bryk 2002). 

The student outcome variable (EVT–2 posttest score), student baseline test score, and 

student covariates were included in the model at the student level, with residual variation 

between students within a classroom also represented. Variation between classrooms within a 

school in the average student outcome score was modeled at the classroom level. Included at the 

school level were school covariates and an indicator variable indicating whether a school is in 

the intervention group or the control group; this level also models residual variation between 

schools. The parameter for the intervention variable indicates the impact of the K-PAVE 

intervention on the specified student outcome. A t-test with a .05-level of significance as the 

criterion was used to test the null hypothesis that the intervention effect is zero. A positive and 

statistically significant parameter estimate means that the K-PAVE intervention improves the 

student outcome by the magnitude of the parameter estimate. A standardized effect size was 

calculated by dividing the estimated impact from the model by the standard deviation of the 

outcome variable in the control group. The control group standard deviation was used, as 

recommended in Burghardt et al. (2009), because the intervention could affect the standard 

deviation in the intervention group.33 

Student-level covariates used in the analysis included (see Appendix O for definitions): 

• Score on the student outcome measure at baseline (pretest score). 

• Gender. 

• Race. 

• Eligibility for free or reduced-price meals. 

• Having an Individualized Education Program. 

All covariates were included in the model when analyzing each student outcome variable. 

However, for each student outcome measure, only the corresponding pretest score for that 

measure was included. 

School-level covariates used in the analysis included: 

• Reading initiatives (Reading First, a Mississippi state reading initiative, or other). 

• Achievement Level Index. 

• Percentage of students who are African American. 

33 Our view is that standardized effect sizes should be used to help interpret the magnitude of impacts. However, we 

believe that tests of statistical significance should be conducted on impact estimates measured in nominal units 

rather than on the standardized effect sizes. Therefore, we do not conduct tests of statistical significance on the 

standardized effect sizes. 

46 
• Percentage of students eligible for free or reduced-price meals. 

• Locale (rural, small town, or large town/fringe of city). 

• Location in or out of the Mississippi Delta region. 

All school covariates were included when analyzing each student outcome variable. 

Impacts on classrooms 

The impacts of the K-PAVE intervention on instructional practices, controlling for 

teacher and school characteristics, were estimated using a multilevel model to account for the 

clustering of two classrooms per school. The model included a classroom level (level 1) and a 

school level (level 2). Because of the limited degrees of freedom at the classroom level (due to 

sampling only two classrooms per school), teacher characteristics were controlled for at the 

school level. The average value for the school was calculated for each teacher characteristic. The 

multilevel model used to test for K-PAVE impacts on classroom instruction is specified in 

Appendix K. 

The same school-level covariates included in the models estimating impacts on students 

were included in the models estimating impacts on classroom instruction. 

The following teacher characteristics were included in the analysis as covariates in the 

school-level model (and are defined in Appendix O):34 

• Gender. 

• Race. 

• Highest level of education. 

• Number of years teaching. 

• Number of years teaching kindergarten. 

• Having teaching certification in early childhood. 

• Having teaching certification in reading. 

A dummy variable indicating whether a school was assigned to the intervention or 

control group was included at the school level. The parameter estimate for the intervention 

indicator estimates the impact of K-PAVE on the specified classroom instruction outcome. A 

positive and statistically significant parameter estimate indicates that K-PAVE affects instruction 

in the desired direction. The magnitude of the parameter indicates the estimated magnitude of the 

average impact. Effect sizes of classroom impacts were calculated following the approach 

described above for student impacts. 

34 There were plans to include covariates for teacher ethnicity (Hispanic or not) and certification in special 

education, but no teachers were Hispanic, and none was certified in special education. 

47 
Adjustments for multiple comparisons 

In testing for an impact on an outcome, there is some chance that the null hypothesis of 

no impact will be rejected when there is no true impact (type I error). To limit the likelihood of 

such false positives to an acceptable level, the criterion for rejecting the null hypothesis was set 

to p = .05 for a single hypothesis test. However, the chance of one or more false positives 

increases as tests of impacts are conducted on more outcomes. To reduce the heightened chance 

of error introduced by conducting multiple tests, a single global F-test was conducted to test 

simultaneously whether the impact of any one or more of the outcome measures within a domain 

is statistically different from zero. Impact models for each of the multiple outcomes within a 

single domain were estimated simultaneously, and the null hypothesis that the impacts on all of 

the multiple outcomes are zero was then tested. 

Although we adjust for multiple comparisons within a single domain, we do not make 

adjustments across substantive domains. In other words, we do not combine all outcomes in the 

study in order to conduct only one single hypothesis test for the study, nor do we combine all 

three student outcomes (expressive vocabulary, academic knowledge, and listening 

comprehension) in order to conduct a single test of the K-PAVE impact on students. Schochet 

(2008b) does not recommend adjusting for multiple comparisons across domains, because doing 

so produces unnecessarily large reductions in statistical power. Consequently, we maintain the 

substantive distinction between expressive vocabulary as the primary target of the K-PAVE 

intervention and the other student outcomes as secondary targets, and we do not adjust for 

multiple comparisons across the primary and secondary student outcomes. 

Because the one primary research question was addressed using a single outcome 

measure—the EVT–2 standard score—no adjustments for multiple comparisons were required 

(Table 2.14). 

Table 2.14 Primary research question, outcome variable, and need for adjustment for 

multiple tests 

Adjustment for 

Primary research question Outcome variable multiple tests required 

1. What is the impact of K-PAVE on • Expressive Vocabulary Test–2 standard 

students’ expressive vocabulary in score No 

kindergarten? 

Each of the three secondary research questions addressed in the study represents a 

separate domain. For some domains, impacts were tested on multiple outcome variables as 

shown in Table 2.15. 

48 
Table 2.15. Secondary research questions, outcome variables, and need for adjustment for 

multiple tests 

Secondary research question Outcome variables 

Adjustment for 

multiple tests required 

2. What is the impact of K-PAVE on 

students’ listening comprehension and 

academic knowledge in kindergarten? 

•  Listening comprehension standard scor

•  Academic knowledge standard score Ye

3. What are the impacts of K-PAVE on 

kindergarten vocabulary and 

comprehension instruction? 

•  Vocabulary and comprehension suppor

composite 

•  Instructional suppor

•  Emotional suppor

Yes 

4. What is the impact of K-PAVE on 

instructional time use in kindergarten? 

Proportion of observation cycles in which 

language arts content other than 

vocabulary, comprehension, or oral 

language is covered 

No 

For research questions 2 and 3, if the null hypothesis of zero impact of K-PAVE on all of 

the outcomes is not rejected (for example, no impact on listening comprehension and no impact 

on academic knowledge), then impacts on individual outcomes within the domain will not be 

investigated. However, if the null hypothesis of no impact on all outcomes within the domain is 

rejected, based on the global F-test, then the impact on each individual outcome measure will be 

tested. This within-domain exploratory analysis does not provide confirmatory evidence about 

the domain as a whole, but it provides information that can help interpret the global findings 

(Schochet 2008b). 

Sensitivity analyses 

The following sensitivity analyses were conducted (see Appendix N for details): 

•	 Estimating a baseline model with three levels (school, classroom, student) and no covariates 

other than the intervention indicator in the level 3 equation and the baseline outcome 

measure (pretest score) in the level 1 equation. 

•	 Estimating impacts without imputing missing values for outcome variables and pretest scores 

by single stochastic regression imputation, to test the sensitivity of findings to regression 

imputation compared with casewise deletion. 

•	 Estimating impacts without imputing missing values for covariates other than pretest scores 

imputed by dummy variable estimation (see below for an explanation of this method), to test 

the sensitivity of findings to the dummy variable approach compared with casewise deletion. 

•	 Estimating impacts without imputing student test scores for tests that were incomplete due to 

administration errors, to test the sensitivity of findings to the imputation of incorrectly 

administered tests compared with not scoring incomplete tests and allowing values to be 

missing. 

49 
•	 Estimating impacts without students whose baseline assessments were conducted one to 

three weeks late,35 to test the sensitivity of findings to late baseline testing compared with 

baseline testing conducted prior to K-PAVE training. 

•	 Estimating impacts without outliers, to test the sensitivity of findings to a few influential 

cases compared with the exclusion of values with large studentized residuals36 (with an 

absolute value greater than three). 

•	 Estimating impacts without including students with a raw score of 0 on either a pretest or a 

posttest, to test the sensitivity of the findings to treating a raw score of 0 as student nonresponse rather than student inability to answer the test items. For raw scores of 0, it is 

impossible to know whether the student was unable to answer the test items, in which case a 

raw score of 0 is valid, or if the student was refusing to complete the test, in which case the 

score would be treated as missing rather than 0. 

•	 Estimating impacts without weighting schools to adjust for the loss of one school that 

dropped out of the study. 

Chapter 4 reports on cases where the results of the sensitivity analyses deviate from the 

finding of the main model. (Appendix N contains tables comparing impact estimates from all 

models.) Sources of sensitivity are reported for impacts on individual outcomes, providing 

transparency about analytic decisions (missing data imputation) or other factors (delayed 

baseline testing) that may influence whether an impact is found to be statistically significant. 

In conducting global F-tests for multiple outcomes within a single domain, sensitivity 

analysis results were considered when deciding whether to conduct separate tests of impacts on 

individual outcomes. Specifically, if a global F-test was not statistically significant in our main 

model, but we found that the null hypothesis was rejected in the preponderance of models 

estimated in the sensitivity analyses, we separately estimated the impact of K-PAVE on the 

individual outcomes within the domain. In other words, if the results of a global-F test were 

sensitive to different procedures for handling covariate adjustment, imputation of missing data, 

delayed baseline testing, and student crossovers, we conducted separate tests of impacts on 

individual outcomes. In this scenario, the Bonferroni correction was applied to the individual 

significance tests. If the null hypothesis was rejected for the global F-test, tests of the individual 

impacts were conducted without a Bonferroni correction. However, if the p-value for the global 

F-test in the main model was above .05 but below .05 in the preponderance of sensitivity 

models, a Bonferroni correction to the individual hypothesis tests was added to ensure adequate 

protection against the type I error introduced by conducting separate hypothesis tests for the 

multiple outcomes. 

Methods for handling missing data 

The following types of missing data occurred: missing information for teachers and 

students on demographic covariates, missing test scores for students because they were absent 

35 Late baseline testing occurred only in the control group. 

36 Studentized residuals are calculated by dividing a raw residual (i.e., measured in nominal units of the variable) by

the estimated standard deviation for the distribution of raw residuals. Studentized residuals measure the deviation 

between observed and predicted values in standard deviation units rather than in the nominal units of the variable.

50 
during test administration, incomplete or incorrect administration of the test battery, absence of a 

classroom observation in the school that dropped from the study, a failure to submit a completed 

read-aloud observation instrument for one classroom, and a refusal by some teachers to be audiorecorded. Missing data imputation was done separately for intervention and control groups, as 

described in Appendix M. 

For missing covariates other than pretest scores (such as student characteristics and 

teacher characteristics), a dummy variable adjustment was employed for imputation (Puma, 

Olsen, Bell, & Price 2009). Missing cases of a variable were all set to a constant value. In 

addition, the analysis included an indicator variable identifying observations for which the true 

value of the covariate was missing. The dummy variable adjustment was applied to all missing 

student, school, and teacher covariates except missing the pretest scores. Table 2.16 shows the 

percentage of students, teachers, and schools for which at least one covariate was missing. 

Table 2.16 Number and percentage of students, teachers, and schools missing covariate 

data 

Missing covariate data 

Missing at least 1 student covariate 

Analytic sample 

n (%) 

21 students (1.6) 

Missing at least 1 teacher covariate 4 teachers (3.1) 

Missing at least 1 school covariate 7 schools (10.9) 

For student assessments both missing pretest scores and missing posttest scores were 

imputed using single stochastic regression (Puma, Olsen, Bell, & Price 2009). A multiple 

regression model, adjusted for the multilevel structure of the data, was used to estimate predicted 

values for each pretest or posttest with missing values. Predictors included all other information 

collected (including pretest scores, posttest scores, and covariates). For each missing score a 

randomly selected residual was added to the predicted value from the regression model to obtain 

an imputed value. The total number of cases missing either a pretest or a posttest score for each 

student assessment is shown in Table 2.17. 

Table 2.17 Number and percentage of students missing either pretest or posttest 

assessment, by intervention and control group 

Intervention group 

(n = 596) 

Control group 

(n = 700) 

Assessment variable 

Expressive vocabulary 

Academic knowledge 

Listening comprehension 

n (%) 

36 (6.0) 

47 (7.9) 

50 (8.4) 

n (%) 

58 (8.0) 

59 (8.4) 

67 (9.6) 

The same single stochastic regression imputation approach was used for a classroom 

instruction posttest variable—vocabulary and comprehension support—with a single missing 

value. There were no missing values for any other baseline or posttest classroom instruction 

variables. 

51 
CHAPTER 3: IMPLEMENTATION OF THE INTERVENTION 

OVERVIEW 

The study addresses two important questions about the implementation of K-PAVE: 

1. What was the fidelity of implementation of K-PAVE, as implemented by regular 

kindergarten teachers as a supplement to their ongoing literacy instruction? 

2. What impacts did K-PAVE have on classroom instruction? 

The first question is about documenting the extent to which the training and support that 

was provided for K-PAVE teachers resulted in implementation of the instructional strategies that 

accurately represented the objectives of the curriculum. The second question is about whether KPAVE, at the level with which it was actually implemented in the treatment classrooms, resulted 

in significant and meaningful differences in instruction between treatment and control 

classrooms. 

The study used classroom observations to address both of these questions, but the 

methodologies in each case were different, in terms of measures, observers, data collection 

systems, and analysis. Table 3.1 provides an overview of our approach to each question. The 

findings on fidelity of implementation are presented in Chapter 3. The findings on impacts on 

classroom instruction are presented in Chapter 4. 

DESIGN OF THE K-PAVE PROGRAM 

K-PAVE is a kindergarten vocabulary instruction program designed to supplement 

regular classroom literacy instruction. K-PAVE has a standard protocol and materials for 

training and supporting teachers. (See Appendix P for a list of K-PAVE materials provided to 

teachers and Appendix Q for a sample K-PAVE weekly instructional unit.) As described in 

Chapter 1, K-PAVE was adapted for kindergarten from PAVEd for Success (PAVE), an earlier 

preschool version. The adaptations involved eliminating instructional components that 

kindergarten classrooms were presumed to cover and adapting the curriculum materials to focus 

on vocabulary appropriate for kindergarten, using the Mississippi social studies and science 

frameworks as a source of vocabulary words. (Appendix R includes a list of the 240 K-PAVE 

target vocabulary words.) The teacher training and support protocol was also revised. The initial 

training was shortened, since K-PAVE covered fewer instructional components. PAVE used 

district literacy specialists trained in the curriculum to provide follow-up support through inschool coaching. Follow-up implementation support in the K-PAVE study did not include such 

coaches. Instead, members of the implementation team visited each teacher twice to observe and 

provide feedback and coaching, and telephone conferences were held between the developer and 

teachers. 

52 
Table 3.1 Methodologies for addressing questions on implementation of K-PAVE 

 Sample Measure 

Alignment of measures with KPAVEa Reliability Observers Data colle

Fidelity of K-PAVE K-PAVE Fidelity Completely aligned Not assessed K-PAVE At weeks implementation classrooms Observation training and 

implementation support teamb 

of 24-weePAVE pro

Classroom instruction impacts 

K-PAVE and control classrooms 

Vocabulary Record Highly aligned with KPAVE Explicit 

Vocabulary Instruction component 

Observers trained to agreement with master coding 

Independent observers 

At weeks 24-week Kprogram 

Read Aloud Profile Highly aligned with 

PAVE Interactive Book Reading component 

K- Observers 

trained to agreement with master coding 

Independent observers 

At weeks 24-week Kprogram 

Classroom Assessment Scoring System (CLASS K--3): Instructional Support 

Aligned with all 3 KPAVE components Observers trained to 

agreement with master coding 

Independent observers 

At weeks 24-week Kprogram 

Classroom Assessment Scoring System (CLASS K--3): Emotional Support 

Aligned with K-PAVE adult conversations component 

Observers trained to agreement with master coding 

Independent observers 

At weeks 24-week Kprogram 

a. Alignment is defined as the extent to which the constructs being measured are similar to or overlap with the key components 

curriculum. Completely aligned indicates that the instrument measures all teaching strategies explicitly identified as part of the K-P

Highly aligned indicates that the instrument measures one or more but not all key teaching strategies explicitly identified as part curriculum and also may measure other teaching behaviors that are consistent with but broader than the K-PAVE components and teAligned indicates that the instrument does not measure K-PAVE teaching behaviors but focuses on other teaching behaviors that are c

PAVE but are more broadly focused.

b. The team included the K-PAVE developer and staff from the University of Georgia and from the Regional Educational Laboratory S
K-PAVE is designed to be implemented by kindergarten teachers as a supplement to the 

regular instructional environment. K-PAVE is not currently available from a publisher, but the 

developer has negotiated a contract with a publisher, and persons interested in the program 

should contact the developer for more information. 

The K-PAVE vocabulary instruction program and teacher training protocol are described 

briefly below. More detailed descriptions are provided in Appendices P–T. 

K-PAVE PROGRAM COMPONENTS AND TEACHING STRATEGIES 

K-PAVE is designed to increase students’ vocabulary knowledge.37 The program is built 

around 240 target words introduced in 24 weekly units of 10 target words per unit. The target 

vocabulary words are explicitly taught to students and then reinforced through repeated exposure 

in multiple contexts: storybook reading, extension activities, and classroom conversations and 

discussion. Storybooks are selected that include the target words; interactive reading techniques 

are used to engage students in active discussion of the meaning of the words. Small group 

extension activities use the target words in literacy, math, or other games or exercises to 

reinforce students’ understanding of the meaning of the target words. Finally, teachers are 

encouraged to use the target words in conversations and discussions with students throughout the 

day. 

K-PAVE is built around three instructional components to increase student knowledge of 

targeted and nontargeted vocabulary and promote oral language skills: Explicit Vocabulary 

Instruction (labeled “New Vehicles”), Interactive Book Reading (labeled “CAR Talk”), and 

Adult-Child Conversations (labeled “Building Bridges”). The K-PAVE teacher training 

materials specify teaching strategies for delivering each component, including guidance on their 

intensity and frequency. 

Explicit Vocabulary Instruction (New Vehicles) 

The 10 target words introduced each week are associated with a common theme. The 

themes for the current study were selected to align with the state kindergarten thematic units 

from the Mississippi science and social studies frameworks to respond to state concerns that the 

vocabulary instruction be consistent with other state education objectives. The target words are 

part of the general academic knowledge that all students are expected to learn. 

As part of K-PAVE, teachers are trained in five strategies for the Explicit Vocabulary 

Instruction component: quick definitions, novel-name nameless-category (N3C) strategy, 

repeated exposure to the words embedded in book reading, extension activities, and teacherstudent conversations or discussions using the words. Each is defined below. K-PAVE also uses 

the family to reinforce vocabulary learning by sending home a list of the week’s target 

vocabulary words for families to use in their conversations with their children. 

37 Information describing K-PAVE is from the Kindergarten PAVEd for Success Teacher’s Guide (SERVE Center at 

the University of North Carolina at Greensboro & University of Georgia, June 2008). 

54 
Quick definitions. Quick definitions are simple, age-appropriate definitions of the target words. 

Teachers are trained to provide these definitions when introducing the words using picture cards 

or when encountering the words in the book reading. 

Novel-Name Nameless-Category (N3C) strategy. The N3C strategy involves placing a picture 

or object that is unknown to the students among pictures or objects with which the students are 

already familiar. For example, a teacher introducing the new target vocabulary word kiwi would 

show students pictures of an apple, an orange, and a kiwi and ask, “Which one is the kiwi?” 

Because the students are familiar with the other two fruits and know that their names are not 

kiwi, they would point to the correct picture. In this way, students begin to associate the new 

word with a new object or picture. The repeated use of the N3

C strategy not only helps students 

learn the meaning of the target words, but it also is hypothesized to motivate students to adopt 

this strategy in their independent learning of new vocabulary. 

Book reading and re-reading to reinforce target vocabulary words. For each of the 24 units, 

K-PAVE materials include two books, one fiction and one nonfiction, that include the target 

words. Embedding the new vocabulary in stories provides context to help students learn the 

meaning of the words. Teachers are trained to read the same selected texts with their students at 

least twice during the week. 

Extension activities to reinforce target vocabulary words. Teachers are trained to use 

extension activities that integrate each unit’s 10 weekly target vocabulary words into learning 

centers in the classroom. These activities enable students to practice the new words and make 

them part of their permanent vocabulary. Each unit includes suggestions for extension activities, 

and teachers are encouraged to develop their own activities that embed vocabulary practice into 

free play, center activities, and other ongoing classroom procedures. Consider an example for the 

target word radish. Early in the week, the teacher would introduce the word by showing students 

a picture of a radish and providing a short definition. Following up later with the N3

C strategy, 

the teacher would ask students to point to the correct picture among pictures of a radish, carrot, 

and tomato (the last two being words students already know). During the week, the teacher 

would read the books for that week’s unit, which would include the word radish. In small group 

activities, the teacher might use radishes in the math center as manipulatives. In conversations 

with students during the week, the teacher might talk about having put a radish in her salad. The 

teacher could also bring in radishes for students to taste. 

Interactive Book Reading (CAR Talk) 

K-PAVE trains teachers in a method of reading aloud to students—called Interactive 

Book Reading—that is intended to enhance students’ comprehension of the words and concepts 

in the texts. K-PAVE also provides guidance to teachers on the ideal frequency of reading aloud 

with students. 

Interactive reading. K-PAVE methods of Interactive Book Reading are intended to actively 

engage students in discussion and promote their understanding of the concepts and vocabulary in 

the books. In K-PAVE, Interactive Book Reading involves repeated reading of the same book. 

As the same book is read more than once, the teacher is trained to move from introducing the 

contents of the book to asking students complex comprehension questions about the book. KPAVE Interactive Book Reading has three steps: 

55 
1. When introducing a new book, the teacher looks through the book with the students without 

reading the text and may ask students to predict what the book will be about by looking at the 

pictures. 

2. The teacher talks with students about the book during the read-aloud, using three types of 

questions at least twice for each type (for a total of at least six questions). 

•	 Questions that ask students to label, recall, describe, and locate allow students to demonstrate 

competence with skills they have already mastered. The goal is to give students a sense of 

success and confidence. 

•	 Questions that require students to practice abstract thinking by asking students to judge, 

contrast, compare, summarize, predict, define, take another point of view, or solve problems. 

•	 Questions that build comprehension by relating the book to students’ experiences. 

3. Teachers are instructed to use more complex questions as the students become more familiar 

with the material. 

Frequency of reading aloud. K-PAVE guidance directs teachers to read the same book at least 

twice during the week, whether with the whole class or with small groups or individual students. 

K-PAVE training recommends that each student be read to at least three times a week, either 

individually or in small groups. A tracking tool enables teachers to record when they read with 

students, to ensure that they have read at least three times each week to each student. 

Adult-Child Conversations (Building Bridges) 

K-PAVE trains teachers to engage in extended conversations with individual, pairs, or 

small groups of students to help them develop strong oral vocabulary skills. Teachers are trained 

to engage students in extended talk on topics chosen by the students and to be responsive, active 

listeners, showing interest in and acceptance of students’ contributions. Teachers are also trained 

to use a broad array of new words, to help students increase their word knowledge. As teacherstudent conversations accumulate, exposure to more frequently used words may contribute to 

students’ own productive use of such words, offering students extensive opportunities to develop 

an expressive vocabulary as well as a receptive vocabulary. 

Strategies for teacher-student conversations. K-PAVE provides explicit guidance on teacherstudent conversations that build oral language skills and vocabulary: 

•	 The student is the conversation leader (teachers show interest and encouragement; time 

between conversational exchanges is increased to provide more time for the student to 

speak). 

•	 Teachers model complex language through extension and linguistic recasting of students’ 

statements. 

•	 Teachers reinforce vocabulary development by introducing words that are not part of the 

students’ typical working vocabulary into the conversation. 

•	 Teachers encourage students by using open-ended questions. 

56 
•	 Teachers model good conversational skills and encourage students to use them (not 

interrupting, showing interest in topics others bring up, including everyone in the 

conversation). 

Frequency of teacher-student conversations. Teachers are encouraged to find opportunities 

throughout the day for conversations of at least five minutes’ duration. Each student is expected 

to have at least three opportunities each week to engage in conversation with an adult in the 

class, alone or as part of a small group. K-PAVE offers teachers a tracking tool to ensure that 

every child meets this goal. 

K-PAVE TEACHER TRAINING AND SUPPORT 

The K-PAVE model of teacher training and support includes three activities (Table 

3.2):38 

•	 An initial large group training of intervention teachers and assistant teachers, led by the KPAVE developer and a team from the University of Georgia. 

•	 Three small-group follow-up telephone conference calls with teachers, led by the developer, 

to reinforce key elements of classroom instruction and to discuss implementation challenges 

and strategies for overcoming them. 

•	 Up to three rounds of classroom observation to document whether the key elements of KPAVE were being implemented as intended, with follow-up remediation discussions with 

teachers about elements rated as not fully implemented. The classroom observations and 

remediation support were conducted by an implementation team composed of the developer, 

University of Georgia trainers, and members of the Regional Educational Laboratory 

Southeast (who attended the initial K-PAVE training but were not involved in collecting or 

analyzing data for the impact study). All of the intervention teachers were observed in the 

first two rounds of classroom observations. The third round was offered only to teachers who 

had low fidelity scores on the second observation and who had participated in all three of the 

follow-up phone calls. 

38 To encourage teachers to participate fully in all training and support activities, the study provided continuing 

education credits for participation in the training activities. 

57 
Table 3.2 Schedule of K-PAVE teacher training and support activities 

Activity/responsibility Sample Dates 

Initial group training on K-PAVE/ 

Developer and staff from the 

University of Georgia (UGA) 

Teachers, aides from all 

intervention classrooms 

• September–October 2008 intervention 

schools assigned to attend one of three 

training sessions 

• After all consents are completed and after 

baseline student assessments 

First fidelity observation using 

Fidelity Checklist and remediation 

based on scoring of Fidelity Checklist/ 

K-PAVE training and implementation 

support team 

All intervention classrooms 

September–October 2008 immediately 

following initial group training on K-PAVE 

Follow-up telephone conferences 

(three calls per teacher)/ Developer 

and staff from UGA 

Teachers, aides from all 

intervention classrooms 

invited to attend in small 

groups (10 or fewer teachers 

per call) 

• October 2008, December 2008, January 

2009 

• Approximately weeks 4–5, 9–10, and 14– 

15 of K-PAVE 

Second fidelity observation and 

remediation (see above) All intervention classrooms • February–March 2009 

• Approximately weeks 17–20 of K-PAVE 

Third fidelity observation and 

remediation (see above) 

Intervention classrooms with 

low fidelity scores in second 

observation 

• March 2009 

• Approximately week 21 of K-PAVE 

Initial training workshop 

The training workshop was designed to last two days for teachers and one day for 

assistant teachers (see Appendix S for the agenda). The workshop was organized around the 

three key components of K-PAVE (New Vehicles, CAR Talk, and Building Bridges). The 

workshop also included time for teachers and assistant teachers to prepare sample units for 

implementing K-PAVE when they returned to their classrooms; the trainers were available to 

provide technical assistance. Teachers received a teacher’s guide and materials, including 48 

children’s books, picture cards for the 240 target vocabulary words, and cards for the N3C 

strategy. Assistant teachers were invited to attend the training with the classroom teachers. When 

assistant teachers could not attend the training, teachers decided whether and how to enlist the 

help of assistant teachers in implementing the K-PAVE instructional elements. 

The initial training workshop was scheduled for the beginning of the 2008/09 school 

year, immediately following random assignment of schools. The workshop was conducted three 

times, each time for teachers from a different set of the intervention schools (see Table 3.2).39 

As a supplement to the regular literacy curriculum, K-PAVE is expected to be integrated 

into ongoing classroom instruction. Teachers have broad latitude to determine the best strategy 

39 Staggering the initial workshop was necessitated by the fact that the consent process for and selection of 

kindergarten teachers within schools occurred on a rolling basis. Schools were invited to one of the three 

workshops, depending on the date when the school completed its consent and selection process. 

58 
for adding K-PAVE activities to their instructional schedule. For example, the training on the 

teacher-student conversations in the Building Bridges component indicates only that the 

conversations should take place whenever the teacher has time to focus on an individual student 

or small group of students for at least five minutes. Similarly, teachers are encouraged to 

schedule the large and small group read-alouds at any time. Although the direct instruction on 

the target vocabulary and the associated extension activities would logically fit into the 

scheduled literacy block, teachers are left to determine the best times for these K-PAVE 

activities. 

Follow-up telephone conference calls 

Three one-hour follow-up conference calls led by the developer provided continuing 

support for teachers. Scheduled to occur every four to five weeks after the initial training 

workshop, the conference calls were voluntary. Each teacher was offered a time slot to 

participate in the three conference calls so that no more than 10 teachers were scheduled for each 

call. The three phone calls focused on the three major components of K-PAVE: Explicit 

Vocabulary Instruction (New Vehicles), Interactive Book Reading (CAR Talk), and Adult-Child 

Conversations (Building Bridges). Each telephone call included discussion about general 

implementation challenges, implementation issues related to the specific component that was the 

focus of the call (e.g., Building Bridges), and any additional assistance teachers wanted from the 

developer. (Appendix T describes the protocol for the conference calls, including specific 

questions that were asked in the call.) The telephone conference calls were not explicitly tied to 

the fidelity observations. 

Classroom observations and associated remediation 

Up to three rounds of coaching on K-PAVE were tied to the results of a 90-minute 

observation of the classroom by a member of the K-PAVE training and implementation support 

team.40 Observers completed a structured Training Fidelity Checklist and Teacher Observation 

Follow-Up Meeting Protocol (copies in Appendix H). 

Immediately following the observation and scoring, teachers were offered a 20–30- 

minute one-on-one meeting with the observer for follow-up technical assistance or remediation. 

As stated in the protocol, the purpose of the remediation meeting was to “use timely and 

elaborative feedback to reinforce good performance, shape and strengthen more tenuous 

performance, and investigate, explain, and possibly remediate poor or absent behavior” based on 

items of the Training Fidelity Checklist (Follow-up Meeting Protocol, p. 1). The observers were 

trained to do the following: 

•	 Provide feedback on any practice that was missing from the teacher’s performance or that 

was performed incorrectly. 

•	 Review with the teacher the relevant section in the Teacher’s Guide. 

40 The team included the developer and staff from the University of Georgia and from the Regional Educational 

Laboratory Southeast. 

59 
•	 Ask the teacher to identify any difficulties encountered or to discuss ideas that were unclear 

or that presented the greatest challenge. 

•	 Elaborate or provide additional examples to illustrate concepts that appear unclear to the 

teacher. 

•	 Demonstrate any procedure that the teacher appears to be having a problem with and give the 

teacher a chance to practice it. 

The first observation and remediation session was scheduled to immediately follow the 

initial group training. The second session was scheduled to follow the last of the three telephone 

conference calls for a teacher (around 15 weeks into the 24-week intervention period). The third 

round of fidelity observations was planned only for teachers who, based on the second 

observation, were still not implementing with fidelity but who had chosen to participate in all 

three of the conference calls and who wanted a third observation and remediation. This third 

round was scheduled to occur shortly after the second observation, at approximately week 17. 

PROCEDURE FOR ASSESSING FIDELITY OF IMPLEMENTATION OF K-PAVE 

Methodology for evaluating fidelity of implementation 

The study evaluated the fidelity of implementation separately for the K-PAVE teacher 

training and support activities and classroom activities. 

Fidelity of K-PAVE teacher training and support. Assessment of the fidelity of 

implementation of the K-PAVE teacher training and support was based on records of attendance 

and participation of intervention teachers and assistant teachers in the initial training, teacher 

participation in the three rounds of follow-up telephone conference calls, and teacher 

participation in the first two rounds of classroom observations and remediation. Table 3.3 

summarizes the methods and data sources used to measure fidelity of implementation for 

training and support activities. Seven training and support activities were open to teachers: (1–2) 

initial training for teachers and assistant teachers, (3–5) three telephone conference calls, and (6– 

7) the first two in-class observation and remediation sessions.41 Each classroom received a 

fidelity score (ranging from 0-7), indicating the number of training and support activities in 

which they participated. 

41 Participation in the third round of classroom observations and remediation was voluntary and therefore was not 

counted as part of the fidelity score. 

60 
61 

Table 3.3 Methodology for assessing fidelity of K-PAVE training and support for 

teachers in intervention schools

Implementation component Data source Responsible staff

Initial workshop Teacher and assistant 

attendance records

teacher 

Developer training staff and 

Regional Educational 

Laboratory Southeast staff

Follow-up telephone conference calls Teacher participation records Developer training staff

First two rounds of classroom 

observation and remediation sessions

Records of completed 

remediation sessions (two 

rounds) based on Training 

Fidelity Checklist and Teacher 

Observation Follow-Up Meeting 

Protocol

Developer training staff and 

Regional Educational 

Laboratory Southeast staff

Fidelity of implementation of K-PAVE classroom activities. Assessment of the 

fidelity of K-PAVE implementation was based on classroom observations using the Training 

Fidelity Checklist. The checklist was designed by the K-PAVE developer to rate implementation 

of the 12 key teaching strategies for the three main program components (New Vehicles, CAR 

Talk, and Building Bridges). Table 3.4 lists these teaching strategies and the criteria for 

determining fidelity of implementation. Each teaching strategy is rated on a 0/1 scale, where a 

score of “1” indicates that there was evidence that the strategy was implemented. As shown in 

Table 3.4, for 5 of the 12 K-PAVE strategies, ratings are based on the presence or absence of the 

specific instructional behavior. For the other seven strategies, ratings involve an assessment of 

the quantity or duration of specific activities. Although the fidelity checklist does not include 

ratings of the quality of implementation of instructional strategies, the checklist does indicate 

whether a teacher demonstrates at least the minimum level of implementation of key elements of 

K-PAVE.

The Training Fidelity Checklists were completed based on direct classroom observation. 

The fidelity observations were scheduled to coincide with the regular literacy instruction block. 

Intervention teachers provided the observers with a class schedule that detailed when the literacy 

block of instruction was typically taught. The observations were scheduled at a time that would 

allow the fidelity observers to evaluate whether teachers were implementing the K-PAVE 

components. Teachers were occasionally asked to carry out their literacy block at a time other 

than their typical time to allow observers to maximize the number of observations that could be 

carried out at a given school. In the second round of observations, because some teachers 

reported in follow-up telephone conference calls that they were integrating K-PAVE into their 

science and social studies blocks in the afternoon, fidelity observations were split across both 

mornings and afternoons. This means that if K-PAVE was being implemented during afternoon 

instructional activities, the K-PAVE instruction would be captured during the observation. As 

shown in Table 3.4, for 5 of the 12 items on the Fidelity Checklist, teacher reports rather than 

direct observation could be used as partial or complete evidence of implementation. Based on the 

classroom visit, an observer assigned each intervention classroom a score of 0-12, corresponding 

to the number of teaching strategies rated as being implemented. 
Classroom observers were trained to record detailed field notes to describe the evidence 

on which they based their fidelity ratings, including recording verbatim any instances of teacher 

use of linguistically complex language, different question types, elaborations of students’ speech 

or vocabulary recasting, and any mention of vocabulary as a topic. (Copies of the coding form 

and full coding instructions for the Fidelity Checklist are provided in Appendix H.) Subsequent 

to the observation-based fidelity rating, a member of the developer’s team reviewed the field 

notes from each classroom observation and independently assigned a score for the 

implementation of each of the 12 teaching strategies. The classroom receives a score of 0–12 

based on the number of teaching strategies rated as being implemented. Agreement between the 

implementation ratings based on the classroom visit and those based on the field notes was 

calculated using Cohen’s kappa statistic. Kappa was .98 across the 12 instructional strategies, 

and ranged from .92 to 1.00 for the individual strategies. Discrepancies in the fidelity scores on 

the individual instructional strategies that were assigned by the observer and by the reviewer 

arose in 1% of the ratings (7 of 720 ratings across 60 intervention teachers). According to the 

scoring protocol, differences in the fidelity scores assigned by the observer and the reviewer are 

resolved by the program developer, based on her review of the field notes. The final fidelity 

score reflected the developer’s resolutions. 

62 
Table 3.4 Coding K-PAVE classroom fidelity: training fidelity checklist items and coding protocol 

K-PAVE component and teaching strategy Definitions 

Criterion for scoring fidelity based on 

observationa 

Explicit Vocabulary Instruction (New Vehicles) 

1. Quick definitions Teacher supplied definitions of target words 

or of any words. This would generally occur early in the unit when words are being introduced. 

Score as “1” if any instance of a target or nontarget vocabulary word is observed. 

May definthe 1

2. Sufficient small-group book 

reading quantity 

Small group book readings with seven or fewer students. 

Score as “1” if two small group book readings are observed. One small group book reading must be observed. 

May insta

3. N3C introduction of 

 vocabulary 

• In introducing new words, teacher presents 

target word picture card in the context of several “known” (nontarget) picture cards. 

• Teacher should query each by saying 

“show me [target]” or “who can show me [target]?” or something similar. 

Score as “1” if one instance of N3C 

instruction is observed. 

May usin

begi

4. Presence of vocabulary  targets 

• Teacher posts lists, pictures, or props of 

targets as a group in the room. 

• Teacher mentions that there are vocabulary 

words. 

Score as “1” if one instance of either of these is observed. 

5. Vocabulary extension activity 

• Students sent to centers where they were 

encouraged to process vocabulary words through some activity. 

Score as “1” if one instance of small group extension activities is observed. Notation in lesson plan or child product from the extension activity can be evidence. 

6. At least two extension 

activities that target 

 vocabulary 

• Extension activities target vocabulary. Score as “1” if at least two groups of 

children participating in this type of small group activity are observed. One group must be observed. 

May instaexten
K-PAVE component and 

teaching strategy Definitions 

Criterion for scoring fidelity based on 

a observation

OtheDocu

Interactive Book Reading (CAR Talk) 

7. Competence questions Concrete questions to which students can be 

expected to know the answer. 

Score as “1” if two instances of this type of question are observed. 

8. Abstract questions Questions asking students to summarize, 

define, explain, judge, compare, contrast, predict, take another point of view, solve problems, how and why, and questions about students’ internal state. 

Score as “1” if two instances of this type of question are observed. 

9. Questions related to 

students’ lives 

Questions that relate contents of book to students’ lives. Score as “1” if two instances of this type 

of question are observed. 

Adult-Child Conversations (Building Bridges) 

10. Child-centered  conversation 

• Conversations should be about students’ 

interests or topics. 

• Students should be allowed a significant 

opportunity to talk. 

Score as “1” if both characteristics are true for majority of the time in each fiveminute conversation observed. 

11. Duration • Count number of small group or 

individual conversations of at least five minutes in groups of seven or fewer. 

• These can be carried out at a variety of 

times such as following small group academic times, small group book readings or extension activities, or they can be scheduled separately. 

Score as “1” if at least two small group conversations that meet criteria are observed. At least one conversation must be observed. 

May evidof onconvconv

12. Linguistic complexity • Complex talk includes complex 

cognitively complex questions and linguistic recasts. 

• Vocabulary emphasis includes rare 

vocabulary and vocabulary recasts. 

Score as “1” if at least one instance of both complex talk and vocabulary emphasis is observed. 

a. The fidelity ratings took into account the behavior of both the teacher and the assistant teacher, if there was one present in the classro

observation. Observers rotated between the teacher and assistant teacher, focusing for five minutes on the teacher, then for five minutes throughout the observation period. Source: Training Fidelity Checklist and Handbook for Fidelity Observer.
FINDINGS ON FIDELITY OF K-PAVE IMPLEMENTATION IN THE INTERVENTION CLASSROOMS 

Teacher training and support activities 

The records of teacher participation in training and support activities show that 40% of 

teachers attended all training and support activities, and 12% participated in 6 of 7 training and 

support activities (Table 3.5). However, 27% of classrooms participated in only 1–4 of 7 training 

and support activities. 

Table 3.5 Proportion of intervention teachers participating in K-PAVE training and 

support 

Number of training and support 

activities attended Percent of teachers 

0 0

1–4 26.7

5 21.7

6 11.7

7 40.0

Note: Sample is teachers in all intervention classrooms (n = 60) 

Source: Records from implementation team. 

All intervention teachers attended the initial training and received two rounds of fidelity 

observation, but just 48% participated in all three follow-up conference calls, and 43% missed 

either two or all three of the calls (Table 3.6). Remediation was indicated for 85% of teachers 

following both classroom observations, and all teachers who needed remediation received it. At 

the end of the second fidelity observation, five teachers were identified as implementing fewer 

than 8 of the 12 instructional strategies with fidelity but also having participated in all three 

telephone conference calls. The five teachers were offered an additional remediation visit from 

the developer; two of the teachers accepted the offer. 

65 
Table 3.6 Participation of intervention teachers in K-PAVE teacher training and support 

activities 

Participation 

Activity (percent) 

Initial workshop 

Attendance by lead teacher 100 

Attendance by assistant teacher 75 

Conference calls 

Participation in three conference calls 48.3 

Participation in two conference calls 8.3 

Participation in one conference call 21.7 

Participation in no conference calls 21.7 

Classroom fidelity 

Participation in both sessions 100 

Participation in optional third session (five teachers eligible) 40 

Remediation 

Received remediation following one observation 8.3 

Received remediation following both observations 85.0 

Did not need remediation 6.7

Note: Sample is all 60 intervention teachers. 

Source: Records from implementation team. 

Classroom teaching strategies 

Based on the Training Fidelity Checklist completed at the second round of classroom 

observations (after all training and ongoing support for teachers had been completed), a third of 

teachers (32%) were rated as demonstrating 10-12 of the 12 K-PAVE teaching strategies (Table 

3.7). Another third of the teachers (37%) were rated as demonstrating 8 or 9 of the 12 teaching 

strategies. There were 6.7% of teachers who implemented four or fewer strategies. 

Table 3.7 Number of teaching strategies implemented 

Number of instructional strategies implemented Percent of intervention classrooms 

≤ 4 strategies 6.7 

5–7 strategies 25.0 

8–9 strategies 36.7 

10–12 strategies 31.7 

Note: Sample is teachers in all intervention classrooms (n = 60). 

Source: Training Fidelity Checklists. 

Examination of the implementation scores by K-PAVE components indicates that 

Interactive Book Reading was the only component in which teachers were consistently 

implementing all of the teaching strategies (Table 3.8). About three-quarters of teachers were 

66 
rated as implementing all of the instructional strategies associated with Interactive Book 

Reading, compared with approximately one-quarter of teachers for the instructional strategies 

associated with Explicit Vocabulary Instruction and one quarter of teachers for the strategies 

associated with Adult-Child Conversations. Overall, 13% of teachers implemented all 12 

instructional strategies. 

Table 3.8 Presence of instructional strategies by K-PAVE program components 

Intervention classrooms implementing with 

K-PAVE component and teaching strategy fidelity (percent) 

Explicit Vocabulary Instruction (New Vehicles) 

Individual instructional strategies 

Quick definitions 96.7 

Two small-group book readings 43.3 

N3

C vocabulary introduction 50.0 

Vocabulary targets posted 93.3 

Vocabulary extension activity 86.7 

At least two extension activities related to 71.7 vocabulary 

All 6 teaching strategies 26.7 

Interactive Book Reading (CAR Talk) 

Individual instructional strategies 

Competence questions 88.3 

Abstract questions 88.3 

Relate questions 78.3 

All 3 teaching strategies 76.7 

Adult-child conversation (Building Bridges) 

Individual instructional strategies 

Child-centered 43.3

Sufficient duration 31.7 

Linguistically complex 68.3 

All 3 teaching strategies 25.0 

All 12 teaching strategies 13.3 

Note: Sample is teachers in all intervention classrooms (n = 60). The results shown indicate the percentage of 

classrooms rated as having the strategy present, as defined by the developer.

Source: Training Fidelity Checklist. 

Explicit Vocabulary Instruction. Four of the six teaching strategies within Explicit Vocabulary 

Instruction were implemented in a majority of classrooms (Table 3.8). The two teaching 

strategies not implemented in the majority of classrooms were the novel-name namelesscategory (N3

C) strategy for introducing vocabulary and twice-daily book readings with small 

67 
groups of students. Just over a quarter of classrooms (27%) were rated as implementing all six 

teaching strategies for Explicit Vocabulary Instruction. 

Interactive Book Reading. Each of the three Interactive Book Reading teaching strategies was 

implemented in a majority of classrooms. Overall, 77% of classrooms were rated as 

implementing all three teaching strategies. 

Adult-Child Conversations. Only one of the three adult-child conversation strategies— 

“linguistically complex” discussions—was implemented in a majority of intervention classrooms 

(68%). In more than half of the classrooms, neither of the other two teaching strategies was 

implemented. Overall, a quarter of classrooms were rated as implementing all three teaching 

strategies. 

SUMMARY OF FINDINGS AND CONCLUDING OBSERVATIONS 

All of the elements of the planned K-PAVE teacher training and support were in place, 

but participation by intervention classroom teachers and assistant teachers varied across the 

activities. All teachers and 75% of assistant teachers in the intervention group attended the KPAVE training workshop. All intervention teachers received feedback and remediation twice on 

K-PAVE implementation, once after group training and again 17 weeks into the 24-week 

intervention. Participation by teachers was lower for the three follow-up telephone conference 

calls—43% of the intervention teachers participated in one or no calls. 

Based on the Training Fidelity Checklist from the K-PAVE developer, 68% of 

intervention classroom teachers were rated as implementing at least 8 of the 12 core K-PAVE 

instructional strategies. However, for two of the broad K-PAVE components (Explicit 

Vocabulary Instruction and Adult-Child Conversations), fewer than 30% of intervention teachers 

demonstrated evidence of all the teaching strategies. The variation in the implementation across 

K-PAVE classrooms, even with the support offered through classroom observations and 

remediation, is not inconsistent with an effectiveness trial, where the goal is to evaluate impact 

under typical implementation conditions. In an effectiveness study like this one, teachers are 

offered all components of the intervention training and support, but the extent to which they 

implement the program with fidelity is left to the teachers’ discretion. The level of support 

provided to promote high fidelity implementation by all teachers in an efficacy study is not 

realistic when an intervention is implemented on a larger scale. The goal in an effectiveness 

study is to test the intervention under conditions similar to those under which a district might 

adopt and implement the intervention on a larger scale. 

One question for the study concerns whether this implementation of K-PAVE is 

appropriately conceived of as an effectiveness study. Did the version of the curriculum that was 

tested and the teacher training and support activities represent a “typical” implementation— 

would implementation of the curriculum be similar if it were taken to scale? This question 

focuses especially on the teacher training and support activities. The K-PAVE curriculum 

materials—target vocabulary words, storybooks, and prepared lesson units—are the same as 

those available to any district that wanted to implement K-PAVE. The training manual is 

standard and would be provided to districts that purchased K-PAVE. The kind of in-person 

support provided to teachers in the current study might or might not be implemented by districts 

adopting K-PAVE. 

68 
CHAPTER 4: IMPACT RESULTS 

The primary goal of K-PAVE is to improve students’ expressive vocabulary in 

kindergarten. Because the intervention seeks to enhance students’ vocabulary not only through 

Explicit Vocabulary Instruction but also through Interactive Book Reading and teachers’ 

informal conversations with students, K-PAVE is also hypothesized to have a secondary impact 

on students’ academic knowledge and listening comprehension. This chapter reports the results 

of the analyses of the primary research question on the impact of K-PAVE on kindergarten 

students’ expressive vocabulary, and a secondary research question on the impacts on 

kindergarten students’ academic knowledge and listening comprehension. The rationale for the 

hypotheses and for the structuring of the research questions as primary and secondary is 

discussed in Chapter 1 and Chapter 2. Since K-PAVE is hypothesized to achieve these 

improvements through impacts on teachers’ instructional practices (as described in the theory of 

change presented in Chapter 1), the study also examined impacts on intermediate classroom 

instruction outcomes. 

SUMMARY OF K-PAVE IMPACTS 

K-PAVE had a positive and statistically significant impact on the student outcome most 

directly targeted by the intervention: students’ expressive vocabulary at the end of kindergarten. 

The standardized effect size was 0.14 standard deviation, which corresponds to an additional 

month of growth in vocabulary during kindergarten (see p. 85 and Appendix W for a discussion 

of how impacts on students can be translated into differences in age-equivalent scores). 

K-PAVE also had a positive and statistically significant impact on one of the two 

secondary student outcomes: students’ academic knowledge at the end of kindergarten. The 

standardized effect size was 0.14 standard deviation, which corresponds to an additional month 

of growth in academic knowledge during kindergarten. K-PAVE did not have a statistically 

significant impact on the other secondary student outcome: students’ listening comprehension at 

the end of kindergarten. 

The intervention also had a positive and statistically significant impact on the classroom 

instruction outcome that was most closely aligned to K-PAVE—vocabulary and comprehension 

support during reading aloud and other classroom activities. The standardized effect size of the 

impact on this outcome was 0.82. There was no impact of K-PAVE on two other classroom 

instruction measures, which were less highly aligned with K-PAVE and represent more general 

quality constructs—instructional support and emotional support from the CLASS. The fact that 

K-PAVE resulted in significant differences in instructional practices that support vocabulary and 

comprehension provides a rationale for why K-PAVE resulted in impacts on students. The 

theory of action suggests that classroom instructional practices are important mediators through 

which K-PAVE affects student outcomes, and K-PAVE was found to have an impact on an 

outcome measuring classroom practices that are hypothesized to be closely linked with fostering 

students’ vocabulary development. 

69 
RESEARCH QUESTIONS 

Specifically, this chapter presents the results of analyses that address four research 

questions, one primary: 

1. What is the impact of K-PAVE on students’ expressive vocabulary in kindergarten? 

In addition, there are three secondary research questions: 

2. What are the impacts of K-PAVE on students’ listening comprehension and academic 

knowledge in kindergarten? 

3. What are the impacts of K-PAVE on kindergarten instructional practices, specifically 

vocabulary and comprehension support during book reading, instructional support, and 

emotional support? 

4. Does implementation of K-PAVE vocabulary teaching come at the expense of time for 

instruction in other areas of early literacy (such as concepts of print, phonological awareness, 

alphabetic knowledge, reading, writing, fluency, and spelling)? Specifically, do K-PAVE 

teachers spend less time on these nonvocabulary literacy teaching practices compared with 

control group teachers? 

IMPACTS ON KINDERGARTEN STUDENTS’ EXPRESSIVE VOCABULARY 

This section presents the results of the analysis addressing the primary research question: 

What is the impact of K-PAVE on students’ expressive vocabulary in kindergarten? 

The Expressive Vocabulary Test–2 (EVT–2) is a one-on-one, normed, and standardized 

test for measuring students’ expressive vocabulary. Students provide a single-word response to 

describe a pictured stimulus. Scores were normed using a national sample of children of the 

same age and were standardized in the norming sample to have a mean of 100 and a standard 

deviation of 15 points. (Sample means and standard deviations for all outcome measures are 

presented in Appendix U.) 

The estimated impact of K-PAVE on kindergarten students’ expressive vocabulary is 

1.60 points, which is a standardized effect size of 0.14 standard deviation (Table 4.1).42 The 95% 

confidence interval around the impact estimate is 0.43–2.77 points.43 The estimated magnitude 

and standard error of the impact remain consistent in all sensitivity analyses (see Appendix N). 

42 Model assumptions of residual normality and homoscedasticity were met for this model and all other models 

presented in this chapter. Information on the evaluation of model assumptions and examination of influence 

statistics for all models is in Appendix V. The standardized effect size is calculated by dividing the parameter 

estimate (measured in the units of the EVT standard score scale) for the intervention indicator by the standard 

deviation of the control group posttest score. 

43 Although the a priori statistical power analysis suggested that the study would have 80% power to detect an 

impact on students of 0.26–0.28 standard deviation or higher, the actual minimum detectable effect size for the 

EVT–2 was 0.14. The study’s actual power for detecting impacts on students was higher than anticipated, primarily 

because the school-level R2 was higher than assumed in the initial statistical power analysis (see Appendix B). 

70 
Table 4.1 Estimated regression-adjusted impact of K-PAVE on kindergarten students’ 

expressive vocabulary 

 Regression-adjusted 

posttest means 

Estimated 

impact 95% 

Focus of research Intervention Control (standard Confidence Effect a question group group error) p-value interval size

Expressive Vocabulary 1.60** 

Test–2 (EVT–2) 93.22 91.62 .006 0.43–2.77 0.141 (0.59) standard score 

** p < .01. 

Note: The intervention group includes 596 students in 60 classrooms in 30 schools; the control group includes 700

students in 68 classrooms in 34 schools. A three-level model was used to estimate impact, controlling for schoollevel and student-level covariates. 

a. The effect size was calculated by dividing the estimated impact by the standard deviation (11.35) of the control 

group posttest EVT–2 score. 

Students’ posttest EVT–2 scores were translated into age-equivalent scores based on the 

norming sample (see Williams 2007). At posttest the regression-adjusted mean EVT–2 score in 

the intervention group was equivalent to the average score for children age 5 years and 7 months; 

the regression-adjusted posttest EVT–2 score in the control group was equivalent to the average 

score for children age 5 years and 6 months. (See Appendix W for a discussion of how the ageequivalent scores were determined.) Thus, students who received the K-PAVE intervention were 

one month ahead of students in the control group in vocabulary development. 

Hill, Bloom, Black, & Lipsey (2008) suggest examining the magnitude of an impact in 

comparison to the size of performance gaps. In this study, the average baseline EVT score 

among students in the study is 0.60 standard deviation below the norm for students their age.44 

By the end of the intervention, students in the control group, in the absence of K-PAVE, are still 

.56 standard deviations below the norm, while students in the K-PAVE intervention group are 

.45 standard deviation below the norm. Although students in the intervention group are still 

nearly half a standard deviation below the norm, they are 20% closer to the norm than students in 

the control group. In other words, the performance gap has been reduced by 20%. 

IMPACTS ON KINDERGARTEN STUDENTS’ ACADEMIC KNOWLEDGE AND LISTENING 

COMPREHENSION 

This section presents results of the analysis addressing the secondary research question: 

•	 What are the impacts of K–PAVE on students’ listening comprehension and academic 

knowledge in kindergarten? 

The Kaufman Test of Educational Achievement, Second Edition (KTEA–II) Listening 

Comprehension subtest tests students’ ability to answer comprehension questions about short 

passages read aloud to them. Scores were normed using a national sample of students of the 

44 The average baseline EVT score is 91 points on a normed scale with a mean of 100 and a standard deviation of 

15. On average, students score 9 points below the norm, which is 0.60 standard deviations (9 points /15 points) 

below the norm. 

71 
same age and standardized to have a mean of 100 and a standard deviation of 15 points. 

Individual student scores in the sample range from 56 points to 123 points, with a mean of 89.1 

points and a standard deviation of 13.0 points. 

The Woodcock-Johnson III/Normative Update (WJ–III/NU) Academic Knowledge 

subtest measures students’ background knowledge in science, social studies, and humanities. An 

IRT-scale score (W-score) was calculated based on a nationally representative norm group. 

Student scores in the sample range from 396 points to 559 points, with a mean of 455.7 points 

and a standard deviation of 13.0 points. The W-scores correspond to scores on a standard score 

scale (with normed mean of 100 and standard deviation of 15) that range from 43 points to 131 

points, with a mean of 91.7 points and a standard deviation of 11.5 points. 

We conducted a global F-test to test the null hypothesis that K-PAVE’s impacts on 

kindergarten students’ academic knowledge and listening comprehension are both zero. We did 

not reject the null hypothesis (F2, 96 = 3.08, p = .051). Sensitivity analyses (described in Chapter 

2 and Appendix N) revealed that this finding is highly sensitive to four of six issues examined. 

The four issues involved the handling of: incorrectly administered baseline tests (1 model); late 

baseline testing (2 models); student crossovers and transfers (3 models); and posttest raw scores 

of 0 (1 model). Findings were not sensitive to covariate adjustment (1 model) or the handling of 

missing test scores and covariates (2 models). In 7 of the 10 sensitivity models estimated, we did 

reject the null hypothesis of zero impact on both outcomes, with p-values below .050. Thus, the 

results of the majority of the sensitivity models were not consistent with the result from the main 

model. 

We could not reject the null hypothesis of zero impact on both academic knowledge and 

listening comprehension in the main model; however, it was rejected in most of the sensitivity 

models. Therefore, we examined the impact of K-PAVE on each of the two outcomes separately. 

However, because the p-value for the global F-test in the main model was above .05, a 

Bonferroni correction was added to the hypothesis tests to protect against the type I error 

introduced by conducting separate hypothesis tests for the two outcomes. For each of the two 

hypothesis tests, a criterion of p < .025 was used to identify an impact as statistically significant. 

Table 4.2 shows the estimated impact of K-PAVE on listening comprehension and 

academic knowledge in kindergarten. The estimated impact on listening comprehension of 1.4 

points, which is an effect size of .11 standard deviation, is not statistically significant (t = 1.60; p 

= .11). The 95% confidence interval, which ranges from -0.35 point to 3.17 points, includes zero. 

This finding is consistent in all sensitivity analyses (see Appendix N). 

There is a statistically significant impact of K-PAVE on academic knowledge (t = 2.29; p 

= .02245). The impact estimate is 1.95 points, with a 95% confidence interval of 0.25–3.65 

points. The standardized effect size of 0.14 indicates that students in intervention schools score 

0.14 standard deviation higher on the WJ–III/NU academic knowledge test than do students in 

control schools. The magnitude and standard error of the estimate remain consistent in the 

sensitivity analyses.46 

45 The Bonferroni adjustment for multiple comparisons sets the threshold of statistical significance at .025 when two 

outcomes are examined within the same domain. A p-value of .022 meets this threshold. 46 In 4 of the 12 sensitivity models testing the impact of K-PAVE on academic knowledge, the p-value is above the 

.025 threshold for statistical significance set by the Bonferroni adjustment. The p-values range from .026 to .043, 

indicating some sensitivity to covariate adjustment, imputation of missing covariate data (two models), and 

72 
Students’ posttest academic knowledge scores were translated into age-equivalent scores, 

which were developed on the norming sample (McGrew, Schrank, & Woodcock 2007). At 

posttest, students in intervention schools had an average age-equivalent score of 5 years and 10 

months, and students in control schools had an average age-equivalent score of 5 years and 9 

months. (See Appendix W on how the age-equivalent scores were determined.) Thus, 

kindergarten students who received the K-PAVE intervention were one month ahead of students 

in control schools in academic knowledge. 

Table 4.2 Estimated regression-adjusted impact of K-PAVE on kindergarten students’ 

listening comprehension and academic knowledge 

Regression-adjusted 

posttest means 

Estimated 

Control impacta 95% 

Focus of research Intervention group (standard confidence Effect b question group mean mean error) p-value interval size

Listening 1.41 

comprehension 90.13 88.72 .11 –0.35–3.17 0.109 (0.88) standard score 

Academic 

knowledge IRT- 1.95* 456.48 454.53 .022 0.25–3.65 0.144 

based (0.85) 

W-score 

* p < .025 (p-value thresholds reflect the Bonferroni adjustment for testing two outcomes in a domain). 

Note: The intervention group includes 596 students in 60 classrooms in 30 schools; the control group includes 700

students in 68 classrooms in 34 schools.

a. A three-level model was used to estimate impacts, controlling for school-level and student-level covariates. 

b. Calculated by dividing the estimated impact by the standard deviation of the control group posttest (13.0 for 

listening comprehension and 13.48 for academic knowledge).

IMPACTS ON CLASSROOM INSTRUCTION 

•	 This section addresses the secondary research question about the impacts of K-PAVE on 

kindergarten classroom instruction, which is hypothesized to improve students’ vocabulary 

and vocabulary-related outcomes: What are the impacts of K-PAVE on kindergarten 

instructional practices, specifically vocabulary and comprehension support during book 

reading, instructional support, and emotional support? 

The question was addressed in two steps. First, the null hypothesis of no impact of KPAVE on vocabulary and comprehension support, instructional support, or emotional support 

was tested. The global F-test found a statistically significant impact of K-PAVE on one or more 

of these measures of classroom instruction (F3,132 = 5.26, p = .002). This finding remained 

consistent in all sensitivity analyses (see Appendix N). Second, because the null hypothesis of 

zero impacts on all three outcomes was rejected, the impact on each type of instructional practice 

was tested individually. 

imputation of scores for incorrectly administered tests; however, for all models, the magnitude of impact estimates 

(effect sizes ranging from .12 to .16) and standard errors are similar to those in the main model (see Appendix N). 

73 
Table 4.3 shows the estimated impact of K-PAVE on the three classroom instruction 

outcomes in kindergarten.47 K-PAVE has a statistically significant impact on classroom 

instruction focused on vocabulary and comprehension support (t = 3.50, p = .0009). The impact 

estimate is 0.73 point, with a 95% confidence interval of 0.31–1.14 points. The standardized 

effect size is 0.82, indicating that, on average, classrooms in intervention schools provided 

students with vocabulary and comprehension support that was .82 standard deviations higher 

than classrooms in control schools.48 

Table 4.3 Estimated regression-adjusted impacts of K-PAVE on classroom instruction in 

kindergarten 

Regression-adjusted 

posttest means 

Estimated 

Focus of impact a 95% 

research Intervention Control (standard confidence Effect a b b question group group error) p-value interval size

Vocabulary and 0.498 –0.228 ***0.726*** .0009 0.31–1.14 0.823 

(0.21) comprehension 

support 

3.69 3.09 ~0.60~ .097 –0.11–1.31 0.470 Instructional 

(0.36) support 

Emotional 5.47 5.31 0.156 .439 –0.24–0.56 0.196 

(0.20) support 

*** p< .001 ~ p<.10 

Note: The intervention group includes 60 classrooms in 30 schools; the control group includes 68 classrooms in 34 

schools. 

a. A two-level model was used to estimate impact, controlling for both school and teacher covariates at the school 

level. 

b. Calculated by dividing the estimated impact (the raw parameter estimate for the intervention indicator) by the 

standard deviation of the control group posttest score. The control group standard deviation was 0.882 for 

vocabulary and comprehension support, 1.28 for instructional support, and 0.80 for emotional support. 

As described in Chapter 2, the composite measure of vocabulary and comprehension 

support was created from four variables: number of words introduced during a book read-aloud, 

average number of words introduced during other instructional time, number of comprehension 

supports (such as background information, connections to children’s experience, and 

clarifications) provided during the read-aloud, and number of higher order questions asked 

during the read-aloud. This composite measure was created for this study, so there is no basis for 

comparing schools’ scores on the measure other than for schools within this sample. In addition, 

the measure cannot be interpreted in the original units because the four variables were converted 

47 The vocabulary and comprehension support composite has a z-score scale, which was standardized on the study 

sample to have a mean of 0 and a standard deviation of 1. A positive score indicates more vocabulary and 

comprehension support than the average for the sample, and a negative score indicates less. The instructional 

support and emotional support variables are measured on a seven-point Likert scale.

48 The estimated magnitude and standard error of this intervention effect remained consistent across most of the 

sensitivity analyses (see Appendix N). In all models except one, the standardized effect size was 0.79–0.82; the 

exception (a model with just two covariates: intervention status and the baseline vocabulary and comprehension 

support score) suggested a somewhat larger standardized effect size of 0.97. This difference suggests that not 

controlling for covariates runs the risk of attributing variation in classroom instruction associated with teacher and 

school characteristics to the K-PAVE intervention. 

74 
to a scale with a mean of zero and standard deviation of one in order to weight them equally 

when summed. 

The nominal units of the four component measures of the composite vocabulary and 

comprehension support can provide some context for interpreting the 0.82 standard deviation 

difference in classroom vocabulary and comprehension support between intervention and control 

classrooms (Table 4.4). For example, control group teachers provided comprehension support 

(provided background information related to the book, made connections to students’ 

experiences, clarified meaning, or asked students to clarify the text) an average of 17 times 

during a book read-aloud. With a control group standard deviation of 14.5 instances of 

comprehension support, 0.82 standard deviation would imply 12 instances of providing 

comprehension support during a read-aloud. If this were the only component of vocabulary and 

comprehension support measured, an impact of 0.82 could be interpreted to mean that teachers in 

intervention schools were providing comprehension support to students during book reading 12 

times more than teachers in control schools. 

Table 4.4 Sample control group means and standard deviations for four components of 

vocabulary and comprehension support composite measure (n = 128 classrooms) 

Component Mean Standard deviation 

Comprehension support during read-aloud (frequency) 17 times 14.7 times 

Higher order questions during read-aloud (frequency) 3 questions 3.3 questions 

Number of word meanings introduced during read-aloud 4 words 4.2 words 

Number of word meanings introduced during other times 1.8 words 1.4 words 

For higher order questions during book reading (asking students to analyze, explain, 

predict, imagine, make inferences, or generate hypotheses), the sample mean for the control 

group was 3 questions, with a standard deviation of 3.3 questions. If higher order questions were 

the only component measured, an impact of 0.82 could be interpreted to mean that teachers in 

intervention schools were asking almost three more higher order questions during book reading 

than were teachers in control schools. 

For the introduction of word meanings—during both book reading and other instructional 

periods—the sample mean for the group was 4 word meanings introduced during book readings, 

with a standard deviation of 4.2 word meanings and nearly 2 word meanings at other times, with 

a standard deviation of 1.4 word meanings. A difference of 0.82 standard deviation translates 

into three (3.4 words) additional words during book reading and an additional word (1.2 words) 

during other times of the day. 

K-PAVE had no statistically significant impact on the level of instructional support or 

emotional support provided in the classroom (see Table 4.3). For emotional support, findings 

remained consistent in the sensitivity analyses (see Appendix N). For instructional support, the 

findings were sensitive to covariate adjustment and to the imputation of missing values for 

teacher and school characteristics.49 Although the null hypothesis of zero impact on instructional 

49 In a model with no covariates other than intervention status and baseline instructional support, the effect size of 

0.59 standard deviation is statistically significant (t = 2.46, p = .02). As noted previously, the difference in 

magnitude of the impact estimate suggests that without the covariates, the variation in instructional support 

75 
support is not rejected in the main model, models without covariate adjustment or without 

imputation of missing values for covariates offer some indication that K-PAVE may affect 

instructional support. 

In both intervention and control classrooms, the level of instructional support can be 

considered of moderate quality according to the Classroom Assessment Scoring System 

(CLASS), with regression-adjusted mean ratings of 3.8 and 3.2.50 The level of emotional support 

can be considered to be on the high end of moderate quality, with regression-adjusted mean 

ratings of 5.5 in intervention classrooms and 5.3 in control classrooms. 

Impacts of K-PAVE on instructional time use in kindergarten 

•	 The study also tested whether K-PAVE had an unintended impact on instruction in other 

areas of early literacy: Does implementation of K-PAVE vocabulary teaching come at the 

expense of time for instruction in other areas of early literacy (such as concepts of print, 

phonological awareness, alphabetic knowledge, reading, writing, fluency, and spelling)? 

Specifically, do K-PAVE teachers spend less time on these nonvocabulary literacy teaching 

practices compared with control teachers? 

The study measured the proportion of 20-minute CLASS observation cycles that included 

literacy instruction in areas other than vocabulary and comprehension support. Time spent on 

areas of literacy instruction other than vocabulary and comprehension support was an estimated 

1.1 percentage points higher in control classrooms than in intervention classrooms, which is not 

a statistically significant difference (t = -0.14, p = .89). Thus, the null hypothesis that the impact 

of K-PAVE on instructional time spent on nonvocabulary literacy instruction is zero cannot be 

rejected. The magnitude and standard error of the impact estimate remained consistent in 

sensitivity analyses (see Appendix N). 

associated with teacher and school characteristics would be attributed to the K-PAVE intervention. In a model 

estimated without imputing missing values for teacher and school characteristics, the impact was statistically 

significant (t = 2.14, p =.04), with an effect size of 0.55 standard deviation. 50 Ratings in the 1–2 range are considered poor quality, in the 3–5 range moderate quality, and in the 6–7 range high 

quality (Pianta, LaParo, & Hamre 2008). 

76 
Table 4.5 Estimated regression-adjusted impact of K-PAVE on amount of literacy 

instruction in areas other than vocabulary and comprehension in kindergarten 

Focus of research 

question 

Regression-adjusted 

posttest means 

Intervention Control 

group group 

Estimated 

impact 

(standard 

error)a p-value 

95% 

confidence 

interval 

Effect b size

Proportion of 

instructional cycles 

focused on areas of 

literacy other than 

vocabulary or 

comprehension 

0.502 0.513 –0.011 

(0.07) .89 –0.16–0.14 –0.03 

Note: The intervention group includes 60 classrooms in 30 schools; the control group includes 68 classrooms in 34 

schools. 

a. A two-level model was used to estimate impact, controlling for both school and teacher covariates at the school 

level. 

b. Calculated by dividing the estimated impact by the standard deviation of the control group posttest measure 

(standard deviation = 0.329). 

77 
CHAPTER 5: SUMMARY OF FINDINGS AND STUDY LIMITATIONS 

This chapter summarizes the findings on the impact of K-PAVE on kindergarten 

students’ expressive vocabulary and related literacy skills and on classroom instruction in ways 

hypothesized to improve students’ vocabulary outcomes. The chapter also describes the main 

design parameters for the study and the study’s strengths and limitations. 

EFFECT OF K-PAVE ON EXPRESSIVE VOCABULARY 

The study found that kindergarten students in schools using K-PAVE as a supplement to 

the regular literacy instruction performed better than kindergarten students in control schools on 

the Expressive Vocabulary Test–2 administered at the end of the school year.51 The estimated 

impact on expressive vocabulary is 1.60 points, which represents a standardized effect size of 

0.14 standard deviation. The impact is statistically significant, and the 95% confidence interval 

around the impact estimate is 0.4–2.8 points. An impact of this magnitude means that students 

who received the K-PAVE intervention are one month ahead in vocabulary development at the 

end of kindergarten compared with students in the control group. 

EFFECT OF K-PAVE ON OTHER VOCABULARY-RELATED LITERACY SKILLS 

The study also tested the impact of K-PAVE on two secondary student outcomes related 

to vocabulary knowledge: academic knowledge (measured with the Woodcock-Johnson 

III/Normative Update) and listening comprehension (measured with the Kaufman Test of 

Educational Achievement II). The study found that kindergarten students in K-PAVE schools 

performed better than students in control schools on the measure of academic knowledge 

administered at the end of the year.52 The estimated impact on expressive vocabulary is 1.95 

points, which represents a standardized effect size of 0.14 standard deviation. The impact is 

statistically significant, and the 95% confidence interval around the impact estimate is 0.2–3.7 

points. An impact of this magnitude means that students who received the K-PAVE intervention 

are one month ahead in academic knowledge development at the end of kindergarten compared 

with students in the control group. The study did not find a statistically significant difference 

between kindergarten students in K-PAVE schools and students in control schools on the 

measure of listening comprehension administered at the end of the school year. 

EFFECT OF K-PAVE ON CLASSROOM INSTRUCTION 

The theory of action for K-PAVE posits that effects on students will be mediated through 

effects on their teachers’ instructional practices. K-PAVE caused a positive and statistically 

significant impact on one of the three kindergarten classroom instructional practices examined: 

vocabulary and comprehension support, which includes introducing vocabulary words during 

51 The study conclusions are not sensitive to the inclusion or exclusion of student and school covariates, alternative 

approaches for imputing missing data, or to the handling of extreme values (outliers). 52 The study conclusions are not sensitive to the inclusion or exclusion of student and school covariates, alternative 

approaches for imputing missing data, or to the handling of extreme values (outliers).

78 
read-alouds, introducing vocabulary words throughout the school day, asking higher order 

questions during read-alouds, and providing comprehension support during book read-alouds. 

The estimated impact on vocabulary and comprehension support was 0.82 standard deviation. 

Although the relative contribution to this impact of each of the four components of the measure 

is unknown, if all contributed equally, the size of this impact is equivalent to introducing three 

more words during book reading, introducing one more word during other times of the day, 

asking three more higher order questions during book read-alouds, and providing comprehension 

support during book read-alouds 12 more times than in control classrooms. 

The study tested the impact of K-PAVE on two other classroom instruction outcomes 

through which K-PAVE is hypothesized to affect students—instructional support and emotional 

support, as measured by the Classroom Assessment Scoring System (CLASS). K-PAVE 

classrooms were not significantly different than control classrooms on either outcome measured 

at the end of the year. 

Finally, K-PAVE did not have an unintended effect of reducing the amount of time spent 

on areas of literacy instruction other than vocabulary and comprehension (such a phonological 

awareness, alphabet knowledge, print concepts, and decoding) Thus, the positive impact of KPAVE on vocabulary and comprehension support did not come at the expense of time spent on 

other literacy instruction. 

STUDY PARAMETERS 

These results come from the first randomized controlled trial testing the effectiveness of 

K-PAVE in enhancing expressive vocabulary of kindergarten students. The study employed a 

cluster randomized design, with approximately 1,300 students nested in 64 schools in 35 

districts. The design has strong internal validity, based on the randomization procedure and the 

low levels of attrition from the sample at all levels over the outcome period. The study is well 

powered to detect impacts on students, and the multilevel modeling used in the analysis 

appropriately accounts for the nesting of students in classrooms and schools. The study 

generated statistically unbiased estimates of the impact of K-PAVE under typical 

implementation conditions for the Mississippi Delta region for schools that volunteer for this 

type of study. 

The study evaluated the implementation of K-PAVE to assess whether the intervention 

delivered to students represented a strong version of the program as designed. Results on fidelity 

of implementation indicated that the teacher training and support activities were implemented as 

planned and that, as expected in an effectiveness trial, there was substantial variation in 

implementation across K-PAVE classrooms. A total of 68% of teachers implemented at least 8 

of the 12 instructional strategies; 25% of teachers implemented 5–7 strategies, and 7% 

implemented 1–4 strategies. 

The study tests K-PAVE as implemented under typical rather than optimal conditions. KPAVE was implemented by regular kindergarten teachers as a supplement to their ongoing 

literacy instruction. This suggests that schools that want to implement K-PAVE could replicate 

the study’s implementation conditions. As indicated in Chapter 1, the developer has negotiated a 

contract with a publisher to offer K-PAVE and PAVE, which would be called PreK-PAVE. 

Persons interested in the program should contact the developer for more information. The impact 

results represent effects when the teacher training and support model includes the elements 

79 
described previously – group training; curriculum materials and sample lesson plans; follow-up 

training; and in-class observation and support. There is no way to know whether this full menu 

of training and support represents what districts would “typically” implement. 

STUDY LIMITATIONS 

Although the study is a rigorous test of K-PAVE, with strong internal validity, there are 

limitations to the generalizability of the findings. First, the results apply to K-PAVE 

implemented at the kindergarten level. Second, because the sample districts, schools, and 

students were all in the Mississippi Delta and surrounding counties, findings are not 

generalizable to other areas. Moreover, the study was not a random sample of eligible schools in 

the Delta and surrounding counties but of schools that volunteered to participate. However, 

schools that volunteered were similar to the pool of all eligible schools on a set of measured 

characteristics (including region, school performance classification, extent of meeting annual 

expectations for growth in achievement, and eligibility for free or reduced-price meals). 

Compared with schools that declined to participate, volunteer schools tended to have a higher 

percentage of African American students, were more likely to be in small towns, and less likely 

to be located in rural areas. 

At this point, the study does not provide evidence on the important question of whether 

the effects of K-PAVE in kindergarten provide a sustained advantage for the students in 

intervention schools. Does a significant boost in vocabulary in kindergarten have long-lasting 

effects on students’ vocabulary and related literacy skills in first grade and the formal reading 

instruction that typically begins in that grade? The evidence of the positive impact of K-PAVE 

on students’ expressive vocabulary and academic knowledge at the end of kindergarten allows 

the study to address this question, since the study design stipulates that the kindergarten cohort 

will be followed into first grade if there is evidence of a positive impact of K-PAVE on these 

outcomes at the end of kindergarten. This question will be examined in a follow-up study. 

80 
APPENDIX A. MISSISSIPPI COUNTIES WITH STUDY SCHOOLS, BY COUNTY

Map A1. Mississippi counties with study schools, by county 

81 
APPENDIX B. STATISTICAL POWER ANALYSIS

This appendix presents results from a priori statistical power analyses conducted to determine 

the sample size targets for recruitment. 

STATISTICAL POWER FOR DETECTING IMPACTS ON STUDENTS 

The a priori statistical power analysis suggested that the study would have 80% power to detect 

impacts on students’ expressive vocabulary of 0.26–0.28 standard deviation or higher. In 

addition to the details of the statistical power analysis conducted in designing the study, the 

study’s actual statistical power, which was determined based on knowledge of the impact results, 

is reported. 

The study has a cluster randomized design in which schools were randomly assigned to the KPAVE intervention or a control condition. Two kindergarten classrooms were randomly selected 

from each school, and 10 students were randomly selected from each classroom. The 

hierarchical structure of the data led to the use of the following equation to estimate minimum 

detectable effect sizes (Schochet 2008a): 

(B1) 

σ 2 

school (1− R2 ) σ2 (1− R2 ) σ 2school class class (1− R2

+ + child child ) 

^ sp(1− p) (sp(1− p))c (sp(1− p))cn

MDES(β 1 treatment) = Factor(α, β,df )* 

σ 

^ 

where MDES(β1 TREATMENT) is the estimated minimum detectable effect size for the 

treatment impact; Factor (α, β, df ) is a constant that is a function of the significance level (α), 

statistical power (β), and the number of degrees of freedom (df); σ 2

school is the school-level 

2 2 variance in the outcome; σclass is the classroom-level variance in the outcome; σ child is the 

student-level variance in the outcome; 2 R is the amount of school-level variance in the school

2 outcome explained by covariates; Rclass is the amount of classroom-level variance in the outcome 

explained by covariates; 2 R is the amount of student-level variance in the outcome explained child

by covariates; s is the number of schools; p is the proportion of schools assigned to the treatment 

condition; c is the number of classrooms sampled from each school; n is the average number of 

students sampled from each classroom; and σ is the standard deviation of the outcome measure 

for the control group. 

Based on previous quasi-experimental evaluations of the PAVEd for Success program, which 

found standardized effect sizes ranging from 0.20 to 0.43, the goal was to recruit a sample that 

would enable effect sizes at the lower end of this range to be detected. The following 

assumptions were thus in the statistical power analysis: 

82 
•	 The proportion of total variation in student outcomes at the school level is between .10 and 

.15 because of the project’s focus on recruiting schools exclusively within high-poverty 

communities (see Hedges and Hedberg 2007).53 

•	 The proportion of total variation in student outcomes at the classroom level is .05. It was 

assumed that variation in student outcomes between classrooms would be relatively low 

given that kindergarten classrooms are generally not grouped based on ability. 

•	 The pretest explains 50% of the variation between schools in posttest scores (that is, schoollevel R2

 = .50). 

•	 The correlation between any other covariates and the outcome measure, conditional on the 

value of the pretest measure, is .00 (so these covariates do not influence the school-level 

R2

).54 

•	 The classroom-level R2 and student-level R2 were both 0, in order to be conservative in the 

power calculation. 

•	 By the end of the study, the student attrition rate will equal approximately 20%. 

•	 A two-tailed test of significance will be conducted at the .05 level. 

•	 The desired power to detect effects is .80. 

Based on the statistical power analysis, the recruitment target was 60–70 schools, with two 

classrooms per school55 and 10 students per classroom.56 Table B1 indicates the minimum 

detectable effect sizes for student outcomes anticipated for 60–70 schools, with an intraclass 

correlation of .10–.15 and based on the above assumptions. The estimated minimum detectable 

effect size ranges from 0.25 to 0.29. 

Table B1. Power analysis summary: minimum detectable effect sizes for student outcomes, 

by number of schools 

School-level 

intraclass correlation 

.10 

70 schools 

0.25 

65 schools 

0.26 

60 schools 

0.27 

.15 0.27 0.28 0.29 

53 Based on a compilation of interclass correlations in group randomized evaluations of student achievement,

Hedges and Hedberg (2007) find that the intraclass correlation for low–socioeconomic status schools is 

approximately .19 and for low-achievement schools is approximately .09. 54 A higher school-level R2 (that is, additional explanatory power for school-level covariates other than for pretest)

would reduce minimum detectible effect sizes. It is possible that the school-level R2

 could be higher than assumed, 

but we erred on the side of being more conservative in the power calculation to ensure sufficient power to detect

hypothesized effects. 55 Two classrooms per school were sampled for three reasons. First, we wanted school-level estimates to be based

on a sample size of more than one classroom. Second, we wanted to allocate resources to maximize the number of 

schools more than the number of classrooms per school. Third, although some schools in the Mississippi Delta have 

many kindergarten classrooms, some schools have only two classrooms, and we did not want to eliminate those

schools from the sample. 56 Based on the assumption of a 20% student attrition rate, eight students per classroom were assumed in the 

statistical power analysis.

83 
ACTUAL MINIMUM DETECTABLE EFFECT SIZES FOR STUDENTS’ EXPRESSIVE VOCABULARY 

The actual analytic sample size at the end of kindergarten was 64 schools, 128 classrooms, and 

1,296 students (or 10 students per classroom). Once the data were collected, the actual minimum 

effect size that could be detected with 80% power was calculated using the standard error of the 

estimated treatment effect from the fitted impact models. The following equation was used to 

calculate the actual minimum detectable effect size in the sample for the Expressive Vocabulary 

Test–2nd Edition (EVT–2) posttest: 

(B2) 

^ 

MDES (β 

^

1 treatment ) = Factor(α , β , df ) * S.E.(β 1 treatment ) 

SDCONTROL 

^ 

where MDES(β1 treatment) is the estimated minimum detectable effect size for the treatment 

impact; Factor(α , β , df ) is a constant that is a function of the significance level (α), statistical 

power (β), and the number of degrees of freedom (df); and SDCONTROL is the standard deviation 

of the EVT–2 posttest in the control group, in the units of the EVT–2 posttest score. This 

calculation indicated that a standardized effect size of 0.144 standard deviation could be detected 

for the EVT–2. 

Because the minimum detectable effect size was lower than anticipated, the assumptions were 

compared with the observed data for the EVT–2 (Table B2). Appendix K presents a multilevel 

model estimated to test the impact of K-PAVE on students’ expressive vocabulary, including a 

list of the student and school-level covariates. 

Table B2. Comparison of assumed and observed factors related to minimum detectable 

effect size for the Expressive Vocabulary Test–2 posttest 

Assumed Observed 

Proportion of variation between schools .10–.15 .11 

Proportion of variation between classrooms .05 .01 

Student attrition rate .20 .07 

School-level R2 .50 .99 

The observed between-school variation in EVT–2 posttest scores was within the assumed range; 

however, for the other factors, the observed data indicated that the assumptions about statistical 

power were too conservative. The proportion of variation between classrooms was lower than 

assumed (.01 compared with .05). Most notable, however, was the difference between the 

observed school-level R2 of .99 and the assumed R2 of .50. The school and student covariates in 

the impact model accounted for nearly all the between-school variation in students’ EVT–2 

posttest standard scores.57 

57 The student-level covariates in the model were baseline EVT–2 score, gender, race/ethnicity, eligibility for free or 

reduced-price meals, and special education status (having an individualized education plan). The school-level 

covariates in the model were previous reading initiative (Reading First, a state initiative, or other), state rating of 

school achievement level index (created based on student performance on the Mississippi state accountability test 

administered to students in grades 3 and higher), percentage of students in the school who are African American, 

84 
The higher school-level R2

 and the lower classroom-level variation contributed to the study’s 

power to detect a lower effect size than had been assumed. Although the study was thought to 

have 80% power to detect an effect size of 0.26 to 0.28 or higher, the actual minimum detectable 

effect size for EVT–2 was 0.149, and the observed standardized effect size of 0.141 was found to 

be statistically significant (see Table 4.1 in Chapter 4). 

STATISTICAL POWER FOR DETECTING IMPACTS ON CLASSROOM INSTRUCTION 

Given the recruitment target of 60–70 schools, a statistical power analysis was conducted to 

determine the minimum detectable effect sizes for classroom instruction outcomes (table B3).58 

The following equation (Schochet 2008a) was estimated with terms defined as described for 

equation B2: 

(B3) 

σ 2 

school (1− R2 2 2school ) σ class (1− Rclass ) + 

^ sp(1− p) (sp(1− p))c

MDES(β 1 treatment) = Factor(α, β,df )* 

σ 

with the following assumptions: 

•	 A two-tailed test of significance will be conducted at the .05 level. 

•	 The proportion of total variation in classroom outcomes at the school level is .15.59 

•	 The school-level R2 and classroom-level R2 were both 0, in order to be conservative in the 

power calculation. 

•	 The desired power to detect effects is .80. 

With a sample of 60–70 schools, the minimum detectable effect size was estimated to be 

0.51–0.56.60 Although this range is substantially larger than those for impacts on students, this 

range is appropriate for examining impacts on classrooms. Because the intervention is intended 

to directly impact instructional practices, larger effects are expected on classrooms than on 

students. 

percentage of students in the school who are eligible for free or reduced-price meals, locale (rural, small town, large 

town/fringe of city), and location in or out of the Delta region.

58 The statistical power analysis for classroom instruction outcomes was not conducted to determine the sample size 

but rather to estimate minimum detectable effect sizes, given the targeted sample size. The study was designed to 

detect impacts on students.

59 We intended to make a conservative assumption about the intraclass correlation, so we assumed one at the upper 

range of the assumption in the statistical power analysis for detecting impacts on students. We were unaware of 

empirical evidence for guiding this assumption. 60 Because there is higher measurement error in the classroom outcomes than is typically the case for standardized 

student outcome measures, our ability to detect effects on classroom instruction is more limited than these 

calculations suggest. Even so, the number of schools in the study need not be increased, since the project's primary 

purpose is to measure impacts on students in a sufficiently precise manner. 

85 
Table B3. Power analysis summary: minimum detectable effect sizes for classroom 

instruction outcomes, by number of schools 

Intraclass 

correlation 

.15 

70 schools 

0.51 

65 schools 

0.53 

60 schools 

0.56 

The results of the a priori statistical power analysis and the known analytic sample of 128 

classrooms and 64 schools suggested that the minimum detectable effect size for classroom 

instruction outcomes with 80% power was 0.53. 

ACTUAL MINIMUM DETECTABLE EFFECT SIZES FOR CLASSROOM INSTRUCTION OUTCOMES 

Once the data were collected, the actual minimum effect size that could be detected with 80% 

power was calculated using the standard error of the estimated treatment effect from the impact 

models for each of the classroom instruction outcomes. Just as with the EVT–2, the actual 

minimum detectable effect size was calculated using equation B2. Table B4 shows the minimum 

detectable effect sizes in the observed data for each of the four classroom instruction outcomes. 

Table B4. Minimum detectable effect sizes and estimated impacts in the observed sample 

for classroom instruction outcomes 

Minimum detectable Estimated treatment 

effect size impact 

Vocabulary and comprehension support 0.67 0.82*** 

Instructional support 0.77 0.47 

Emotional support 0.74 0.20 

Time on non–vocabulary literacy instruction 0.61 –0.03 

*** p < .001. 

For impacts on classroom instruction, the minimum detectable effect sizes were not as low as 

anticipated. Table B5 compares the assumptions with the observed data for classroom 

instruction. 

Table B5. Comparison of assumed and observed factors related to minimum detectable 

effect sizes for classroom instruction outcomes 

Intraclass correlation School-level R2 

Assumed Observed Assumed Observed 

Vocabulary and comprehension support .15 .41 .00 .10 

Instructional support .15 .66 .00 .00 

Emotional support .15 .33 .00 .00 

Time on non–vocabulary literacy instruction .15 .60 .00 .06 

Although the observed school-level R2

 values were close to the assumption, the observed 

intraclass correlations were higher than assumed, which contributed to higher minimum 

detectable effect sizes for classroom instruction than anticipated.61 That is, more of the variation 

61 The finding that the school-level variation in classroom instruction measures is higher than typically found for 

student outcomes may not be unusual. Schochet (2009) reviewed studies testing impacts on classroom instruction 

86 
in classroom instruction than assumed was between schools rather than within schools, resulting 

in less power than estimated. Although the study was thought to have 80% power to detect 

effects on classroom instruction of 0.53 standard deviation or higher, the minimum detectable 

effect sizes ranged from 0.63 to 0.78. 

outcomes and found that intraclass correlations ranging from 0.20 to 0.33 for a study evaluating reading 

comprehension curricula in elementary school. 

87 
APPENDIX C. RANDOM ASSIGNMENT 

MATCHING OF SCHOOLS WITHIN BLOCKS FOR RANDOM ASSIGNMENT 

Seventy schools were blocked into three groups based on previous participation in reading 

initiatives: Reading First (n = 17 schools), Mississippi state reading initiative (n = 7 schools), or 

local district initiative or no reading initiative (n = 46 schools). Within these three blocks, 

schools were matched based on a set of school characteristics (see Table 2.1 in Chapter 2 for 

sample distributions): 

• School performance classification62 

o Low or underperforming 

o Successful 

o Exemplary or superior 

• Percentage of students receiving free or reduced-price meals 

o 96%–100% 

o 90%–95% 

o 70%–89% 

o Less than 70% 

• Percentage of students that are African American 

o 96%–100% 

o 81%–95% 

o 21%–80% 

o Less than 20% 

• Locale 

o Rural 

o Small town 

o Large town or fringe of city 

• Region 

o Delta 

o Outside 

School characteristics were ordered such that those hypothesized to be more strongly associated 

with student outcomes were prioritized in the matching process. 63 Within reading initiative 

62 The School Performance Classification is an annual classification based on students’ performance on the state accountability 

test (Mississippi Curriculum Test) administered to students in grades 3 and higher. Classifications include: low performing, 

underperforming, successful, exemplary, and superior. 

88 
blocks, schools were sorted by all five characteristics in the order listed above—from school 

performance classification to region. Specifically, within the three reading initiative blocks, 

schools were sorted into the three school performance classifications from lowest performing to 

highest performing (that is, low or underperforming, successful, and exemplary or superior). 

Within each school performance classification, schools were sorted into four categories from 

highest to lowest percentage of students receiving free or reduced-price meals. Within each of 

these four categories, schools were sorted into four categories from the highest to lowest 

percentage of African American students. Within each category regarding the percentage of 

African American students in the school, schools were sorted into three categories from most to 

least rural. Within each locale category, schools were sorted into two regions–Delta and outside. 

Once schools were ordered, the first school within each reading initiative block was randomly 

assigned to either the intervention or control condition. The next school on the ordered list—the 

first school’s match—was assigned to the other condition. The third school on the list was 

assigned to the first condition, and the fourth school—the third school’s match—was assigned to 

the same condition as the second school. The assignment of schools alternated between each 

condition until all schools were assigned to either the intervention or control condition; 35 

schools assigned to the K-PAVE intervention and 35 schools assigned to the control condition. 

By sorting and then assigning schools in this manner, each school was matched to the school 

most similar to it in terms of these five school characteristics. However, characteristics that were 

given lower priority in the matching process (used later in the sorting order) had less influence 

on the matches than characteristics that were given higher priority. A hypothetical example can 

be used to illustrate the relative influence of school characteristics in the matching process. For 

example, if within the Reading First block, there are six schools classified as low or 

underperforming, they are grouped together. Next, these six schools would be ordered based on 

the percentage of students eligible for free or reduced-price meals, four of which have 96% or 

more students eligible. In two of those schools, 96% or more students are African American. By 

the time the last two characteristics—locale and region—are factored in to the matching process 

in this example, there are only two Reading First schools that have been grouped together based 

on the three higher priority school characteristics—school performance classification, eligibility 

for free or reduced-price meals, and percentage of African American students. Regardless of the 

values of locale and region, these two schools cannot be sorted any further; they have already 

been matched with each other. One school will be randomly assigned to the intervention 

condition, regardless of the values for locale and region. 

PROCESS OF RANDOM ASSIGNMENT: SEQUENCE GENERATION 

Random assignment was conducted by the evaluation team. The evaluation team was 

independent of the intervention team, which was responsible for intervention training and 

support, and was independent of the school districts and schools, which were responsible for 

intervention delivery. 

The process of generating a random sequence of numbers in order to randomly assign schools to 

either the K-PAVE intervention condition or the control condition was conducted using SAS 

63 We needed to rely on hypotheses because at the time of random assignment we did not have data with which to estimate the 

relationship between each school characteristic and the student outcomes examined in the study. 

89 
computer software (version 9.2 for Windows). As noted above, schools were ordered within 

reading initiative blocks based on a set of school characteristics. With the ordered dataset for any 

block, we used the RANUNI function in SAS, which returns a number that is generated from a 

uniform distribution on the interval (0, 1) using a prime modulus multiplication generator with 

modulus 231 and multiplier 397204094 (Fishman & Moore, 1982). Specifically, we used the 

following SAS code: 

Treatment = INT ((RANUNI (0) * 2) + 1). 

The RANUNI function requires a seed value, which is a numeric constant that provides an initial 

starting point for generating a stream of random numbers. By specifying zero as the seed, as we 

did, the computer clock initializes the stream. Including a multiplier (2, in this case) changes the 

length of the interval, and adding a constant (1, in this case) moves the interval. In this case the 

RANUNI function returns a number that is generated from a uniform distribution on the interval 

(1, 2). The INT function truncates the decimal portion of the number generated by the RANUNI 

function, thus yielding an integer, either 1 or 2, with a 50 percent probability. For a value of 2, 

the school was assigned to the K-PAVE intervention; for a value of 1, the school was assigned to 

the control condition. 

However, because schools were ordered based on school characteristics within reading initiative 

blocks, we used the random number generated by the RANUNI function only for the first school 

within the block. Once the assignment of the first school was complete, the next school—its 

matched-pair mate—was assigned to the other condition. The remaining schools on the ordered 

list were assigned to their condition in an alternating sequence, as described above. In other 

words, the assignment of schools on the ordered list alternated between each condition until all 

schools within a block were assigned to either the intervention or control condition. 

Table C.1 illustrates the alternating sequence for a hypothetical list of schools within the 

Reading First block. Based on the RANUNI function in SAS, the first school in the block was 

randomly assigned to the K-PAVE intervention. The remaining schools were alternately 

assigned to the control and intervention conditions. 

90 
Table C1 Random assignment for a hypothetical list of Reading First Schools, ordered based on 

school characteristics 

School 

ID 

Reading 

initiative 

School performance 

classification 

Percent eligible 

for free/ 

reduced price 

lunch 

Percent 

African 

American 

Locale Region Treatment 

1 Reading First Under-performing 96%-100% 96%-100 Rural Delta 1 

2 Reading First Under-performing 96%-100% 96%-100 Small town Outside 0 

3 Reading First Under-performing 90%-95% 81%-95% Rural Delta 1 

4 Reading First Under-performing 70%-89% 81%-95% Small town Delta 0 

5 Reading First Under-performing 70%-89% 21%-80% Large town Outside 1 

6 Reading First Successful 96%-100% 81%-95% Rural Delta 0 

7 Reading First Successful 90%-95% 96%-100 Rural Outside 1 

8 Reading First Successful 90%-95% 81%-95% Small town Delta 0 

9 Reading First Successful Less than 70% 96%-100 Large town Delta 1 

10 Reading First Exemplary/Superior 90%-95% 81%-95% Rural Delta 0 

11 Reading First Exemplary/Superior 90%-95% 21%-80% Large town Delta 1 

12 Reading First Exemplary/Superior Less than 70% 21%-80% Small town Delta 0 

13 Reading First na 96%-100% 96%-100 Rural Outside 1 

na is not applicable. 

Allocation concealment 

For random assignment to be successful, the process by which study units are randomly 

allocated to the intervention or control condition must be concealed from both the study 

participants and the evaluators as the allocation takes place (Forder, Gebski, & Keech, 2005). If 

either study participants or the evaluators are able to influence the random allocation process in 

progress, then the equivalence of the intervention and control groups may be compromised. If 

the evaluator has influence over which units are assigned to a given condition, then the process 

of assignment is not random. Similarly, if participants’ willingness to enroll in the study is 

influenced by whether they are assigned to the intervention or control condition, then selfselection bias creating treatment/control differences is introduced. 

91
In this study, allocation remained concealed from both evaluators and study participants during 

the randomization process. As described above, the process for blocking schools based on 

reading initiative and ordering schools within blocks based on school characteristics was defined 

prior to initiating the random assignment process and was not influenced by any examination of 

school characteristics data. In addition, once schools were ordered through an automated 

computer sorting of the schools, an automated computer process for generating the random 

allocation was used in order to ensure that there was a 50 percent chance of assignment to either 

the intervention or control condition. The evaluator had no control in determining the allocation 

of schools apart from initiation of randomization by the computer. 

For study participants, the decision to enroll in the study was not influenced by random 

assignment (i.e., by whether a school would receive the intervention in 2008-09 or a year later). 

Districts, schools, and teachers were not notified of random assignment status until all required 

paperwork for study participation was submitted. Concealing random assignment status from 

schools until enrollment was completed guards against the introduction of selection bias as part 

of the study participation process. 

92 
Figure D1. Recruitment and random selection of the student sample 

Treatment group Control group 

22 districts 21 districts

31 schools 34 schools

62 kindergarten classrooms 68 kindergarten classrooms 

1,251 enrolled kindergarten students 1,331 enrolled kindergarten students 

Parent permission to participate sought for all 

students 

22 districts 

31 schools 

62 classrooms 

863 students with parental permission 

25 schools had 20 or more students 

with permission 

6 schools had fewer than 20 students 

with permission 

21 districts 

34 schools 

68 classrooms 

986 students with parental permission 

29 schools had 20 or more students with 

permission 

 5 schools had fewer than 20 students 

with permission 

Student sampling 

22 districts 

31 schools 

62 classrooms 

598 students + 21 alternates 

25 schools: 20 students 

6 schools: < 20 students 

21 districts 

34 schools 

68 classrooms 

678 students + 22 alternates 

29 schools: 20 students 

 5 schools: < 20 students 

APPENDIX D. RECRUITMENT AND RANDOM SELECTION OF THE STUDENT SAMPLE 

93 
APPENDIX E. COMPARISON OF STUDENTS MISSING AND NOT MISSING BASELINE 

ASSESSMENT

Table E1. Characteristics of students missing and not missing baseline assessment 

Missing baseline 

assessment Not missing Test of a n=46 n=1250 difference

Student gender 

Female 47.4% 50.0% t = -0.32, p=.75 

Male 52.6% 50.0% 

Student race/ethnicity 

African American 74.4% 85.3% t = 2.49, p=.007 

Other 25.6% 14.7% 

Eligibility for free or reduced-price meals 

Eligible /free or reduced-price 92.1% 92.9% t=0.37, p=.71 

Not eligible 7.9% 7.1% 

Has an Individualized Education Plan 

Yes 17.5% 7.9% t= 1.90, p=.06 

No 82.5% 92.1% 

Age at posttest 

Mean 6 yrs, 2.8 mo 6 yrs, 1.9 mo t= 1.08, p=.28 

Standard deviation 4.8 mo 4.7 mo 

Note: The distribution of student characteristics for cases with missing data is assumed to be the same as for cases 

with non-missing data. The rate of missing data ranges from 0.1% to 21.7%. 

a. Differences in student characteristics between the missing and nonmissing groups were tested using a model with 

a three-level error structure to account for the nesting of students within classrooms and classrooms within schools; 

no covariates were included in the model other than presence or absence of a baseline assessment. Although four of 

the student characteristics are dichotomous (gender, race/ethnicity, eligibility for free or reduced-price meals, and 

special education status), tests were conducted using a linear model, which yielded a t-test of the mean difference 

between students with and without a baseline assessment (where the mean for each characteristic equals the 

percentage of students in each group that are female, African American, eligible for free or reduced-price meals, or 

in special education, respectively). A chi-square test, which is usually used to test group differences in categorical 

variables, would not take account of the multilevel structure of the data. 

94 
APPENDIX F. CLASSROOM OBSERVATION MEASURES FOR IMPACT EVALUATION 

This appendix discusses three classroom observation measures for impact evaluation: Classroom 

Assessment Scoring System (CLASS), Read Aloud Profile–Kindergarten (RAP–K), and 

Vocabulary Record. In addition, the last section describes the vocabulary and comprehension 

support composite, which is created from two RAP-K variables and two Vocabulary Record 

variables. 

CLASSROOM ASSESSMENT SCORING SYSTEM 

The CLASS is a time-sampling observation tool used to rate the quality of interactions and 

instruction in kindergarten to grade 3 classrooms. The CLASS has been widely used in 

evaluations of education interventions, has established interrater and internal consistency 

reliability, and has established construct and predictive validity (Pianta, La Paro, & Hamre 

2008). The CLASS shows statistically significant correlations with the Early Childhood 

Environment Rating Scale–Revised interactions factor (r = 0.45–0.63 for various CLASS 

domains). In addition, in the National Center for Early Development and Learning Multi-State 

Pre-Kindergarten Study, children in preschool classrooms with higher CLASS scores 

demonstrated higher performance on vocabulary, prereading, and mathematics outcomes at the 

end of preschool and showed greater gains during preschool on these outcomes as well as fewer 

behavior problems, than did children in classrooms with lower CLASS scores (Howes et al. 

2008; Mashburn et al. 2008). 

The CLASS is a theoretically and empirically based classroom observation tool. As stated in the 

CLASS manual (Pianta, LaParo, & Hamre 2008, p. 1): 

The CLASS dimensions are based on developmental theory and research suggesting that 

interactions between students and adults are the primary mechanism of student 

development and learning (Greenberg, Domitrovich, & Bumbarger 2001; Hamre & 

Pianta 2007; Morrison & Connor 2002; Pianta 2006; Rutter & Maughan 2002). The 

CLASS dimensions are based solely on interactions between teachers and students in 

classrooms; this system does not evaluate the presence of materials, the physical 

environment or safety, or the adoption of a specific curriculum. ... The CLASS focuses 

on interactions between teachers and students and what teachers do with the materials 

they have [emphasis in original]. 

The CLASS measures three domains of classroom quality: instructional support, emotional 

support, and classroom organization. 

Instructional support 

The instructional support domain examines the extent to which interactions effectively support 

cognitive and language development and provide opportunities for children to gain useable 

knowledge—learning how facts are interconnected and organized—and develop metacogntive 

skills. The dimensions of instructional support are: 

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•	 Concept development. How teachers use instructional discussions and activities to promote 

students’ higher order thinking skills in contrast to a focus on rote instruction: 

o Analysis and reasoning 

o Creating 

o Integration 

o Connections to the real world 

•	 Quality of feedback. How teachers extend students’ learning through their responses to 

students’ ideas, comments, and work: 

o Scaffolding 

o Feedback loops 

o Prompting through processes 

o Providing information 

o Encouragement and affirmation 

•	 Language modeling. The extent to which teachers facilitate and encourage students’ 

language: 

o Frequent conversation 

o Open-ended questions 

o Repetition and extension 

o Self- and parallel-talk 

o Advanced language 

Emotional support 

Teachers’ ability to support children’s social and emotional functioning is considered central to 

effective classroom practice, as children who are motivated and connected to others in the early 

years of schooling are much more likely to have positive social and academic outcomes. The 

dimensions of emotional support are: 

•	 Positive climate. The emotional connection, respect, enjoyment demonstrated between 

teachers and students and among students: 

o Relationships 

o Positive affect 

o Positive communication 

o Respect 

•	 Negative climate. The level of expressed negativity such as anger, hostility, or aggression 

exhibited by teachers and or students in the classroom: 

o Negative affect 

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o Punitive control 

o Sarcasm/disrespect 

o Severe negativity 

•	 Teacher sensitivity. Teachers’ awareness of and responsivity to students’ academic and 

emotional concerns: 

o Awareness 

o Responsiveness 

o Addresses problems 

o Student comfort 

•	 Regard for student perspectives. The degree to which teachers’ interactions with students and 

classroom activities place an emphasis on students’ interests, motivations, and points of 

view: 

o Flexibility and student focus 

o Support for autonomy and leadership 

o Student expression 

o Restriction of movement 

Classroom organization 

The classroom organization domain assumes that students are able to monitor, regulate, and 

control their learning, motivation, and behavior in well regulated classroom environments. The 

dimensions of classroom-level regulation measured by the CLASS are: 

•	 Behavior management. How effectively teachers monitor, prevent, and redirect behavior: 

o Clear behavior expectations 

o Proactive 

o Redirection of misbehavior 

o Student behavior 

•	 Productivity. How well the classroom runs with respect to routines and the degree to which 

teachers organize activities and directions so that maximum time can be spent in learning 

activities: 

o Maximizing learning time 

o Routines 

o Transitions 

o Preparation 

•	 Instructional learning formats. How teachers facilitate activities and provide interesting 

materials so that students are engaged and learning opportunities are maximized: 

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o Effective facilitation 

o Variety of modalities and materials 

o Student interest 

o Clarity of learning objectives 

he CLASS is completed based on at least two hours of observation of a classroom. Coding 

nvolves 30-minute cycles (20 minutes for observing and 10 minutes for rating each dimension). 

he 10 dimensions described above are rated on a seven-point Likert scale. Ratings of the 10 

imensions are used to compute the three domain scores and a total score, in accordance with 

uidelines outlined by the instrument developers. 

READ ALOUD PROFILE–KINDERGARTEN 

T

i

T

d

g

The RAP–K was adapted from the Read Aloud Profile–Revised (RAP) instrument from the 

Observation Measures of Language and Literacy Instruction (Goodson, Layzer, Smith, & 

Rimdzius 2004). The original instrument was adapted to focus during book readings primarily on 

teachers’ comprehension support statements and questions, teachers’ open-ended questions, and 

teachers’ emphasis on word meanings. For this study the instrument was modified to eliminate 

the focus on other aspects of literacy, such as book concepts and print concepts (including letter 

names, letter sounds, decoding, punctuation, and spelling). The process of adapting the RAP into 

the RAP–K involved multiple iterations during which coders jointly and then later independently 

coded a series of video recordings of teacher-child book readings. Once a near-final version of 

the instrument was completed, the instrument was pilot-tested in five kindergarten classrooms in 

May 2008. Slight modifications were made to the format of the coding form based on the pilot, 

but the coding rules remained unchanged. 

The reading instructional strategies captured by the RAP-K include how the reader reads the 

book and how the reader interacts orally with children during the text reading to build their 

comprehension and vocabulary. This measure focuses on reading aloud because of the 

widespread recognition that reading aloud to children is one of the “most important activities for 

building the knowledge required for eventual success in reading” (Anderson, Hiebert, Scott, & 

Wilkinson, 1985, p. 23). The RAP-K is designed to measure interactive instructional practices in 

reading aloud (sometimes called “dialogic reading”) that research has shown to promote 

children’s comprehension and higher order thinking abilities (see review of shared reading 

interventions in Chapter 4 of NELP 2009).64 

The RAP-K focuses on the behavior of the reader during the read-aloud and provides 

information on the characteristics of the book being read. The RAP-K describes two aspects of 

the reader’s strategies during the read-aloud: the reader’s use of comprehension supports before, 

during, and after the text reading and the reader’s use of higher order, cognitively challenging 

questions (Box F1). 

During the classroom observation, the RAP-K was coded the first time that a teacher read aloud 

to a group of students, at which time the observer stopped coding the CLASS until the read aloud 

64 The research on effective practices for reading with children is based primarily on a teacher or parent reading with 

an individual child (see chapter 4 of NELP 2009). The RAP-K is based on the assumption that many of the same 

practices that have been shown to be effective in one-on-one contexts may also be effective with groups of children. 

98 
ended. Throughout the entire book reading, from the time the teacher announces that she is going 

to read a book until any post-reading discussion of the book is concluded, the observer 

documents the number of comprehension supports provided during reading, including providing 

background information related to the book, making connections to children’s experiences, and 

asking concrete or factual questions to clarify meaning, and the number of higher order questions 

asked during reading, including asking students to analyze, explain, predict, imagine, make 

inferences, or generate hypotheses. If the teacher does not read aloud to a group of students 

during the classroom observation, then all behaviors are coded as occurring zero times; they are 

not coded as missing. If the teacher reads aloud to a group of students more than one time during 

the observation, the observer continues coding the CLASS for all additional instances of reading 

aloud; the RAP-K is coded only the first time that the teacher reads aloud. 

Box F1. Read Aloud Profile–Kindergarten coding form 

VOCABULARY RECORD 

The Vocabulary Record was adapted from the vocabulary component of the Instructional 

Practice in Reading Inventory (Smith, Dwyer, Dixon, Schimmenti, Boulay, Khalil, Blocklin, & 

Gamse 2005). Although the Instructional Practice in Reading Inventory was designed to 

document a broad array of literacy instruction in kindergarten to grade 3 classrooms, the 

Vocabulary Record focuses exclusively on teachers’ attention to word meaning. Strategies for 

communicating word meaning were discussed and combined into like categories. For example, 

providing word meaning through a definition, an example, or a synonym were all considered a 

single strategy, while using a picture or physical demonstration of word meaning was considered 

a distinct strategy. Stating what a word is not or does not mean was considered a third distinct 

99 
strategy for communicating word meaning. The process of adapting the Instructional Practice in 

Reading Inventory into the Vocabulary Record involved multiple iterations, involving both 

piloting early versions in kindergarten classrooms and coding of video recordings of teachers. 

The initial piloting and video coding informed the instrument development. A second pilot test 

in five kindergarten classrooms was conducted during which two raters independently coded the 

Vocabulary Record. One modification was made to the instrument based on the pilot: a code for 

the teacher asking the student to define a word was added. 

The Vocabulary Record is completed during the CLASS observation cycle and during the readaloud coded with the RAP-K. The observer records all words defined by the teacher or assistant 

teacher and all words that the teacher or assistant teacher ask a student to define (Box F2). For 

each word entered on the Vocabulary Record, the observer indicated how meaning was 

elaborated by marking all the check boxes that apply for that word: 

• Asks student for meaning 

• Definition, example, or synonym 

• Picture or demonstration 

• Contrast 

For the current study, this instrument yielded two variables. The total number of words 

documented during the read aloud was tallied for a measure of the number of words introduced 

during the book reading. The total number of words documented during each CLASS cycle was 

tallied and averaged (i.e., divided by the number of CLASS cycles) for a measure of the average 

number of words introduced during other instructional time. 

CREATION OF VOCABULARY AND COMPREHENSION SUPPORT COMPOSITE 

A composite measure of vocabulary and comprehension support was created from the two 

variables from the Vocabulary Record (number of words introduced during the read-aloud and 

the average number of words introduced during other instructional time) and the two variables 

created from the RAP-K (number of comprehension supports during reading and number of 

higher order questions during reading). The four variables were standardized to ensure that each 

would be equally weighted in the composite total. The standardized variables were summed, and 

the total composite score was then standardized to a mean of 0 and a standard deviation of 1. The 

standardized composite score units are standard deviation units, such that a score of 0 indicates 

an average amount of vocabulary and comprehension support was provided in the classrooms 

and a score of 1 indicates that the amount of vocabulary and comprehension support is one 

standard deviation higher than average. A positive score indicates that a higher than average 

amount of vocabulary and comprehension support is provided, while a negative score indicates 

that a lower than average amount of comprehension support is provided. 

Cronbach’s alpha for the vocabulary and comprehension support composite is 0.62. Each 

variable created from the book reading—comprehension support, higher order questions, and 

number of vocabulary words—has a correlation, with the composite ranging from .45 to .55. 

Removing any of the variables from the composite reduces the internal consistency of the 

composite (that is, Cronbach’s alpha becomes smaller). The correlation between the average 

number of words introduced during other instructional time—collected throughout the rest of the 

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three-hour observation other than the book reading coded with the RAP–K—and the composite 

variable was .16. Removing this variable from the composite would increase the internal 

consistency (that is, Cronbach’s alpha would be 0.71). However, all four variables were retained 

in the composite for analysis. The composite was determined based on the theorized relation 

among the four variables as part of a single domain prior to any examination of the relationships 

observed in the sample data. An empirically defined composite that may have been overly 

influenced by sampling variation was to be avoided. 

101 
Box F2. Vocabulary Record coding form 

3 

CLASS cycle # 1 

Vocabulary support 

Asks for meaningDefinition, example, or synonym Picture or demonstration Contrast

3 Vocabulary support 

Asks for meaningDefinition, example, or synonym Picture or demonstration Contrast 

a   

b   

c   

d   

e   

f   

g   

3 

CLASS cycle # 2 

Vocabulary support 

Asks for meaningDefinition, example, or synonym Picture or demonstration Contrast

3 Vocabulary support 

Asks for meaningDefinition, example, or synonym Picture or demonstration Contrast 

a   

b   

c   

d   

e   

f   

g   

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3 

CLASS cycle # 3 

Vocabulary support 

Asks for meaningDefinition, example, or synonym Picture or demonstration Contrast

3 Vocabulary support 

Asks for meaningDefinition, example, or synonym Picture or demonstration Contrast 

a 

•  

b 

•  

c 

•  

d 

•  

e 

•  

f 

•  

g 

•  

3 

CLASS cycle # 4 

Vocabulary support 

Asks for meaningDefinition, example, or synonym Picture or demonstration Contrast

3 Vocabulary support 

Asks for meaningDefinition, example, or synonym Picture or demonstration Contrast 

a 

•  

b 

•  

c 

•  

d 

•  

e 

•  

f 

•  

g 

•  

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3 

CLASS cycle # 5 

Vocabulary support 

Asks for meaningDefinition, example, or synonym Picture or demonstration Contrast

3 Vocabulary support 

Asks for meaningDefinition, example, or synonym Picture or demonstration Contrast 

a   

b   

c   

d   

e   

f   

g   

3 

CLASS cycle # 6 

Vocabulary support 

Asks for meaningDefinition, example, or synonym Picture or demonstration Contrast

3 Vocabulary support 

Asks for meaningDefinition, example, or synonym Picture or demonstration Contrast 

a   

b   

c   

d   

e   

f   

g   

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APPENDIX G. TEACHER SURVEY 

OMB Number: 1850-0846 

Expiration Date: 12/31/2010 

Responses to this data collection will be used only for statistical purposes. The reports prepared for this 

study will summarize findings across the sample and will not associate responses with a specific district 

or individual. We will not provide information that identifies you or your district to anyone outside the 

study team, except as required by law. 

Teacher Name School Name 

Teacher Number _________ School Number________

Birth Date (Month, Day, Year): ___/_____/____

1. What is your gender? O Female O Male 

2. What is your race? (Select one or more) 

O African American O American Indian 

O White O Pacific Islander/Hawaiian 

O Asian O Multiracial 

O O Unknown 

3. What is your ethnicity? O Hispanic O Non-Hispanic O Unknown 

4. EDUCATIONAL BACKGROUND AND PROFESSIONAL EXPERIENCE 

Please check and complete for all that apply. 

Education Major Year Completed 

O High School ____________________ _______________ 

O GED ____________________ _______________ 

O Non-degree program (e.g. Montessori, CDA) ____________________ _______________ 

O Some college/university ____________________ _______________ 

O Bachelor’s degree ____________________ _______________ 

O Some graduate level classes ____________________ _______________ 

O Master’s degree ____________________ _______________ 

O Education Specialist ____________________ _______________ 

O Doctorate ____________________ _______________ 

5. Please check all areas in which you have a current teaching certificate. 

O Early Childhood O Gifted/Talented 

O Middle Childhood O Administration 

O Secondary O Reading 

O ESOL O Other 

O Special Education 

6. Do you have any other special training? O Yes O No 

Please describe. __________________________________________________________ 

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We would like to learn about teachers’ experiences collaborating with other teachers in their schools. 

Please think about both formal activities at your school intended to encourage collaboration and informal 

conversations you have with other teachers. 

7. Not including the current school year and not including student teaching, how many years 

have you been a teacher? If this is your first year teaching, answer “zero.” _____ years 

8. Not including the current school year and not including student teaching, how many years 

have you been teaching kindergarten? 

If this is your first year teaching, answer “zero.” _____ years 

9. Not including the current school year and not including student teaching, how many years 

have you taught in your current school? If this is your first year in this school, answer 

“zero.” _____ years 

10. Some teachers work independently while other teachers prefer to get input from other 

teachers. Would you say you get... 

O No input 

O Minimal input 

O Moderate input 

O A great deal of input 

11. How comfortable are you receiving advice from other teachers? 

O Not at all comfortable 

O Slightly comfortable 

O Moderately comfortable 

O Completely comfortable

12. How comfortable are you offering advice to other teachers? 

O Not at all comfortable 

O Slightly comfortable 

O Moderately comfortable 

O Completely comfortable

13. How supportive are other teachers at your school when you need help or advice with 

 teaching? 

O Virtually no teachers are supportive 

O Some teachers are supportive, but a majority are not 

O A majority of teachers are supportive, but some are not 

O Nearly every teacher is supportive 

14. How receptive are other teachers at your school when you offer help or advice with 

teaching? 

O Virtually no teachers are supportive 

O Some teachers are receptive, but a majority are not 

O A majority of teachers are receptive, but some are not 

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O Nearly every teacher is receptive 

15. In general, how often do you participate in any organized group activities or meetings 

involving other teachers at your school... 

...that primarily focus on administrative issues, such as schedules, upcoming events, and 

teachers work assignments? 

Number of times: ________ O per week

 O per month 

O per year 

...that primarily focus on issues pertaining to student instruction/behavior? 

Number of times: ________ O per week

 O per month 

O per year 

16. Think of changes that you have made over the past year that were due to a suggestion from 

another teacher in your school OR due to your having observed another teacher in your 

school. 

Do NOT include changes that were due to a principal, or to someone outside of your school, 

that you were required to make, or that occurred as a regular part of the school calendar (for 

example, changes that always occur when switching from fall to spring semesters). 

Changes in... Mark all that apply 

...classroom materials that you use 

O Handouts 

O Books 

O Hands-on learning materials 

O Computer software 

O Assessments (tests) 

O Behavior charts 

O Parent communication product (for example, daily reports) 

O Other (please describe)______________________________________________________ 

_________________________________________________________________________ 

O how you teach lessons that you’ve taught in the past 

O curriculum that involve teaching new lessons 

O the homework you assign to students 

O how you handle behavior problems involving an individual student 

O your overall approach to managing student behavior in your class 

O classroom management unrelated to discipline 

O strategies for communicating with parents 

O the classroom setting (physical environment) 

O your own understanding of materials/procedures that you currently use 

O your own understanding of the content of what you teach 

O your approach to teaching specific groups of students (for example, students who are less 

proficient in English than they are in another language) 

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O your approach to any aspect of extra-curricular activities that you might be involved with 

(for example, coaching, tutoring or helping in an after school program) 

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APPENDIX H. K-PAVE FIDELITY OBSERVER HANDBOOK AND TRAINING 

FIDELITY CHECKLIST

KINDERGARTEN PAVED FOR SUCCESS (K-PAVE) FIDELITY OBSERVATIONS 

Scheduling observations/protocol 

Observations of the intervention classrooms will be conducted within one week of the training. 

As Bowman, Donovan, and Burns (2001) suggest “effective in-service education must be 

intensive and continuous, with opportunities to apply knowledge and receive individualized 

feedback and mentoring in order to support improved teaching practices and positive outcomes 

for children” (p. 276). The intervention designers, their assistants, and REL-SE staff will visit 

each K-PAVE focal teacher’s classroom to assess the degree to which teachers are implementing 

the intervention as taught in the training. A 90-minute observation will be conducted in each 

focal teacher’s class. So that the observation is conducted during the typical literacy block, focal 

teachers will provide the designers/fidelity observers with a class schedule that details when the 

literacy block of instruction is typically taught. The observations will be scheduled ahead of time 

to allow the fidelity observers to evaluate the degree to which teachers have learned the 

vocabulary intervention components. Teachers may be asked to carry out their literacy block at a 

time other than their typical time to allow observers to maximize the number of observations that 

can be carried out at a given school. 

Forms used 

There are two forms that need to be completed after each 90-minute observation−the Training 

Fidelity Checklist and the Teacher Observation Follow-Up Meeting Protocol. This handbook 

provides details on how to complete each. Observers will need to use their field notes to 

complete these forms. These 2 forms, along with the field notes, will be sent to Paula 

Schwanenflugel at UGA. 

Field notes overview 

Observers will be present for a 90-minute time period during the literacy block of instruction. So 

that reliabilities can be determined later by a fidelity checker, who was not present, observers 

will take extensive field notes on all teacher activities. If there is an assistant in the room, they 

will also be observed with equal weight to the teacher on a five-minute on-teacher and fiveminute on-teacher assistant rotation. It is important that the notes are descriptive enough so that 

complex observations such as question types, linguistically complex talk, and extension 

activities can be fully coded. It is incumbent upon the classroom fidelity observer to provide the 

necessary details so that items can be properly coded. 

Field notes will be taken on numbered/lined paper or using a computer generated line document, 

so that evidence of elements observed can be provided on the Checklist. All observers should 

have the numbered paper available in case they are unable to use their computer (battery dead, 

too disruptive to classroom, etc.). 

109 
Particular emphasis will be noted on questions, elaborations for children’s speech, and 

vocabulary recasting; these will be quoted to the extent possible. Further, if children are sent to a 

particular center by the teacher or assistant, what the children are asked to do in that center 

should be noted. Any mention of vocabulary as a topic should be quoted to the extent possible. 

Make note of group size and the length in minutes of any activities observed. 

TRAINING FIDELITY CHECKLIST DETAILED DIRECTIONS AND IMPLICATIONS FOR NOTE TAKING 

Below you will find a description of each numbered component of the Checklist. In this 

description, we have provided a description and brief rationale for the intervention component so 

that ambiguous cases can be better decided. Each numbered component on the Checklist is 

scored individually. (i.e., a teacher may need remediation only on intervention component #1 

and have no need of remediation on component #’s 2 and 3 even though all three items are part 

of the Building Bridges component). We have also provided recommendations for how field 

notes should be taken. Keep this information handy as a reference while you are in the 

classroom. 

Building Bridges—child-centered conversation 

The purpose of this component is to promote conversations that encourage children to build good 

relationships with their teachers through talk. When children recognize that these conversations 

are on topics of interest to them, they can take risks with talk. 

a. Children’s interests or topics should be dealt with in these conversations. The topics 

should be non-didactic. That is, teachers should not be using these conversational groups 

as a way of re-teaching other topics within the curriculum. If academic topics are brought 

up, they should be brought up by the children first. These topics should be pursued by 

teacher only to the extent that children seem interested in knowing more. This should be 

indicated by children continuing to respond to this topic. They should not be cut off so 

that the teacher can redirect them to her own topic. 

b. Children should be allowed a significant opportunity to talk. Teacher should attempt to 

get child to talk further when limited responses are made. Teacher may emphasize good 

turn-taking skills among children. 

c. The above characterization should be true for the majority of time spent in any fiveminute conversation segment observed. 

d. These components must be observed. 

Field notes should note whether teachers maintain children’s topics. They should indicate 

attempts by teacher to get child to talk more. They should note attempts to turn these into 

didactic moments on teacher’s part (the latter to be viewed as evidence for need for remediation). 

Building Bridges—sufficient conversation duration 

The goal of this component is to ensure that sufficient amount of teacher-child conversation 

occurs throughout the school year to encourage the development of language skills. 

110 
a. Small group or individual conversations that are five minutes in duration or longer. 

Group size should be seven or fewer. Minor interruptions (< 1 min.) by children outside 

the small group may occur during these sessions, but the teacher must demonstrate a 

good attempt to hold an extended conversation. Note # of children in the group. 

b. These can be carried out at a variety of times such as following small group academic 

times, small group book readings or extension activities, or they can be scheduled 

separately. 

c. At least one conversation must be observed; the second may be counted based on selfreport only if one five-minute conversation was observed during the observation. If these 

conversations occur outside the observation, the second can be self-reported by teacher as 

having occurred during play times or recess, during early arrivals or late departures, 

during family-style meals with teacher, during nap time for non-sleepers, or before or 

after school or with “early finishers” for other academic times. 

d. Ideally, two sessions per observations will be observed. However, if one session is 

observed then you may accept a self-report of the second. 

Field notes should indicate time stamp so that duration can be determined. Count the number of 

children in the group. If your attention shifts to the other adults, please note if the conversation 

continues. Each instance of extended conversations should be noted. If there is at least one 

observed during the 90-minute observation period, teacher can self-report a second one at some 

specific other point in the day. This should be evident in her lesson plans, tracking tool, or if the 

teacher is able to describe the conversation with sufficient detail. 

Building Bridges—linguistically complex talk 

The purpose of this component is to encourage teacher speech in forms known to be related to 

the development of oral language skills and vocabulary in children. It takes a variety of forms 

including: 

a. Complex talk— 

i. Cognitively complex questions that query internal states (What do you think he was 

feeling? Thinking?), clarifying and hypothesizing (What do you mean? What do you 

think happened?), and open-ended questions (How, why, what else?) 

ii. Linguistic expansions/grammatical recasts which take what the child says and adds 

the missing grammatical elements to it; or extensions which add elaborative 

information into the child’s sentences. The teacher should be non-corrective in 

providing these expansions and recasts. The child should not really be aware that he 

is being corrected. Some examples are: 

“My daddy, he go Wal-Mart.” Æ

T: “Your daddy went to Wal-Mart? (grammatical recast) 

T: “Has your daddy gone to the Wal-Mart near the mall?” (extension) 

b. Vocabulary emphasis—Teacher’s language should include vocabulary recasting or 

rare vocabulary words. 

111 
i. Rare vocabulary (words that are used infrequently) and/or vocabulary recasts 

introduced by the teacher in the conversation. 

“There a big car.”Æ

 T: “Yes, that’s an enormous car.” (vocabulary recast); or 

T: “Is that a sports utility vehicle?” (rare vocabulary) 

c. Each component must be observed at least once in order to check the box as present. 

In field notes, observers should write down teacher talk that sounds like a modified repetition. 

They should write down teacher talk that sounds like a deliberate attempt to use a rare word. 

They should write down as many teacher questions during these conversations as possible. 

CAR Talk—competence questions 

The purpose of these questions is to allow children to have a successful experience among the 

various questions that may be asked by the teacher during book reading. These success 

experiences are designed to encourage child participation during storybook reading and 

comprehension. 

a. Classify question as competence if children would be expected to know the 

information being queried. This is often information that is right in the book either just 

previously stated or in the book’s picture. A vocabulary word might be embedded, 

however. 

b. Some examples are: Find the (object); What color is the (object)? Where is the 

(object)? What shape is the (object)? What is this? Tell me about (object, person). What 

is (character)’s name? What else do you see? What did you just find out? What are 

(names) doing? Where is (person) (action)? Who said (phrase)? Who is (name) talking 

to? When did (person) (action)? And (person) said.... 

c. Questions should be concrete. 

d. Two of this type question must be observed. 

For field notes, record which book was used and write down all the questions asked during the 

book reading. The question type can be identified later. Please note, this is not about pacing of 

questions or if the teacher provides sufficient time for students to respond. If the teachers are 

“question machines” that can be noted and addressed in the teacher debriefing session but it is 

not relevant to the implementation assessment. 

CAR Talk—abstract questions 

The purpose of these questions is to encourage cognitively and linguistically complex talk which 

has been shown to encourage the development of vocabulary and comprehension. They will also 

encourage child participation. 

a. These are questions which ask students to summarize, define, explain, judge, compare, 

contrast, predict, take another point of view, or solve problems. How and why questions 

and questions querying about the internal states of characters are always abstract. 

112 
b. Some examples are: Why do you think (person) did (action)? What is (character) 

thinking? Who does this remind you of? What does (word) mean? How are (two objects) 

the same? What will happen next? How are (two objects) different? How do you think 

(person) feels about (action)? Where else do (people, animals) live? Why is (person) 

(action)? What was the story about? 

c. Two of this type question must be observed. 

For field notes, record which book was used and write down all the questions asked during the 

book reading. The question type can be identified later. Please note, this is not about pacing of 

questions or if the teacher provides sufficient time for students to respond. If the teachers are 

“question machines” that can be noted and addressed in the teacher debriefing session but it is 

not relevant to the implementation assessment. 

CAR Talk—relate questions 

The purpose of these questions is to encourage children to relate the contents of books to their 

own lives and prior knowledge. This has been shown to encourage comprehension. 

a. These are questions that ask children to relate the contents of books to their own lives. 

b. Some examples are: Have you ever had a really horrible day? What do you do when 

you have a bad dream? What would you do if you could (action) like Hager? Who has on 

a blue shirt like Oliver’s? Whose birthday was it last week? What does your mom like to 

cook? 

c. Two of this type question must be observed. 

For field notes, record which book was used and write down all the questions asked during the 

book reading. The question type can be identified later. Please note, this is not about pacing of 

questions or if the teacher provides sufficient time for students to respond. If the teachers are 

“question machines” that can be noted and addressed in the teacher debriefing session but it is 

not relevant to the implementation assessment. 

New Vehicles—quick definitions 

The purpose of these questions is to get children used to listening to and thinking about 

definitions for words, which is a skill which is under development at this age. 

a. Teacher supplied definitions of target words or of any words. 

b. This would generally occur early in the unit when words are being introduced. 

c. This can be self-reported. 

For field notes, note any verbal definitions supplied by teacher for target words either 

within the initial presentation of the targets or later. Count this as observed if you see the teacher 

providing any quick definitions−even if this is not of a target word. If it is not the first day of the 

unit, the observer may not see teacher defining targets, so you can rely on self-report for this. 

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New Vehicles—sufficient small-group book reading quantity 

The purpose of the small group book readings is to allow children to have ample opportunity to 

learn vocabulary words by listening to books being read because written language emphasizes 

different vocabulary than spoken language. 

a. Two small group book readings should be present in any 90 minute observation. 

b. Group size should be seven or fewer. 

c. If only one is observed, the second can be self-reported. 

For field notes, make note of any time a teacher is reading to a small group or individual. Count 

and make note of the number of children in the small group. There should be two instances of 

small group readings per day. Do not count any small grouping where children are reading book 

to teacher or reading silently to themselves. If there is at least one small group reading observed 

during the 90-minute observation period, the teacher can self-report a second one at some 

specific other point in the day. This should be evident in her lesson plans or tracking tool. 

New Vehicles—N3

C introduction of vocabulary 

The purpose of this practice is to provide children with a chance to exercise a strategy for 

learning to words that is already likely to be in their repertoire. This should provide an 

opportunity for children to look and feel smart in the learning of new words. 

a. The teacher should present target word picture card in the context of several “known” 

(non-target) picture cards. 

b. The teacher should query each by saying “show me (target)” or “who can show me 

(target)?” or something similar. 

c. If not observed, this can be self-reported. 

For field notes, if this is not at the beginning of the vocabulary units, observer will not see it. 

Observer may rely on teacher self-report for this. Do not count any presentation where teacher 

merely presents the target picture and names it, although this can be done in conjunction with 

N3

C introduction. 

New Vehicles—presence of vocabulary targets 

The purpose of this practice is to communicate explicitly to children that there is a list of words 

that they should be learning. 

a. Teacher posted lists or pictures or props of targets as a group somewhere in the room; 

OR 

b. Teacher mentions that there are vocabulary words. 

c. One of these must be observed. 

For field notes, postings of target list or pictures, Table of target props should be noted. Direct 

mention of the topic of vocabulary words should be noted. If the teacher has sent home a parent 

letter with the weekly target vocabulary targets make note of that. 

114 
New Vehicles—extension activity 

The purpose of this practice is to allow children to have the opportunity to practice the 

vocabulary words in some activity. Ideally, this activity would be a somewhat authentic context 

that will enable the children to learn when, where, how, and why particular words might be used. 

a. Children were sent to centers where they were encouraged to process vocabulary 

words through some activity. 

b. This must be observed. 

For field notes, write down any statements given by teachers regarding what the purpose of a 

small group activity is. Write down any statements about a center being for vocabulary. Write 

down directions given to children regarding a center activity that possibly seems related to a 

vocabulary theme. Do not observe the group activity if there is no adult present, rather note the 

directions students were given when sent to the center. Do not evaluate the quality of the 

activity-which may be addressed in the debriefing session-but focus on the presence of the 

activity, no matter how boring, etc. 

New Vehicles—sufficient small group extension activity quantity 

The purpose of this practice is to ensure that children have enough practice using the vocabulary 

words in an activity. 

a. At least two groups of children participating in small group activities is necessary. 

b. Activities should target vocabulary. 

c. If only one is observed the second can be self-reported. 

For field notes, note if there are at least two groups sent to these activities. If there is at least one 

observed during the 90 minute observation period, teacher can self-report a second one at some 

specific other point in the day. This should be evident in her lesson plans or tracking tool. 

FORM COMPLETION−TRAINING FIDELITY CHECKLIST 

Complete as much of the Training Fidelity Checklist as possible using your field notes. (Attach 

notes to the checklist for reference.) There are some components of the observations where 

teacher self-report is allowable. These instances are marked within the Fidelity Checklist. In the 

case where an intervention activity is not observed during the course of the observation, and the 

Fidelity Checklist for this item allows self-report, the teacher will be asked to provide evidence 

on how that activity was included in a previous lesson during the after school meeting. If the 

component must be observed to be counted that is noted in the Fidelity Checklist. 

Use column 2 of the Checklist to document whether or not you observed the intervention 

component. Some items ask for group size or conversation length. Evidence for each 

intervention component will be noted on the Checklist by noting the line number in the field 

notes where the item was observed. 

115 
Use the third column of the Checklist to indicate whether remediation may be required (by 

circling the appropriate designation) and to write any comments, notes, or suggestions that you 

want to share with the teacher. 

As you review the Checklist, please remember that each numbered item on the Checklist is 

scored independently—that is, each item stands on its own. For example, although the Building 

Bridges component consists of three separate practices (child-centered conversation, sufficient 

quantity, and linguistically-complex talk), a teacher may only need remediation on the item 

related to linguistically complex talk not the entire Building Bridges component. 

A teacher must receive all positive marks (Yes, observed) for each numbered item on the 

Checklist to be considered not to need remediation. For each item that the teacher does not show 

evidence in implementation (Not observed) remediation will be provided. In order to preserve 

reliability, the observer should always remember to keep in mind that the presence of a 

component is what is scored—rather than judgments about the quality of the activity. Each 

numbered component on the Checklist is scored individually (i.e., a teacher may need 

remediation only on intervention component #1 and have no need of training on component #2 

and #3 even though all three items are part of the Building Bridges component). 

116 
Teacher: _________________________ School: ___________________________________ 

Observer: ________________________ Date:__________________ Time Observe

(Line to be completed by data manager) Teacher ID#_____ School ID #_

Training Fidelity Checklist 
Teacher: _________________________ School: ___________________________________ 

Observer: ________________________ Date:__________________ Time Observe

(Line to be completed by data manager) Teacher ID#_____ School ID #_

a Teacher in all instances means either lead or assistant teacher. Analysis is at classroom level, so any instance by either teachb Small group in all instances means group size of 7 or fewer. Page numbers noted refer to K-PAVEd for Success Teacher’s Gu
TEACHER OBSERVATION FOLLOW-UP MEETING PROTOCOL—DETAILED DESCRIPTION 

Fidelity observers will meet individually with each teacher observed immediately after 

school (for approximately 20-30 minutes per teacher) on the day of observation. Teacher 

assistants are not required to be in the follow-up debriefing but are welcome to participate if they 

wish. 

Preparing for the follow-up/debriefing session 

The second form, the Teacher Observation Follow-Up Meeting Protocol is used during 

the after school meeting with the teacher. Prior to the teacher debriefing, observers should 

carefully review the classroom observation Checklist and field notes taken during the 

observation, and complete the Follow-Up Meeting Protocol form. 

The purpose of this debriefing is to provide the teachers with feedback on the 

intervention delivery in their classroom. Our goal is to use timely and elaborative feedback to 

reinforce good performance, shape and strengthen more tenuous performance, and to investigate, 

explain, and possibly remediate poor or absent behavior. 

Suggestions for conducting the follow-up/debriefing session 

As you complete the Protocol, remember, the more specific feedback that teachers 

receive the better opportunity they have to change or correct their teaching. Try to provide 

teachers with enough detail so that they may either continue their current teaching behaviors or 

work to improve those behaviors. (The following directions are numbered the same as the 

Protocol form for ease of use.) 

I. Thank You−Begin the debriefing session by thanking the teacher for their participation 

in the study. The Protocol contains a suggested paragraph (Item I) you can read at this 

time. It may be helpful to explain to the teacher the process you used to collect 

information and how the Checklist is used to assess the teachers’ effectiveness in 

implementing the K-PAVE program. 

II. Follow-Up/Debriefing−Item II of the Protocol has six items to discuss during this 

meeting. Use the Protocol in conjunction with the Checklist to guide the discussion on 

these six items. 

1. Begin by noticing a positive that is not be related to the intervention. (e.g., “I really 

liked the way you had children’s artwork exhibited” or “You did a great job of 

holding the children’s interest during circle time this morning). 

2. Focus on an element that was particularly strong. Provide detail on how this aspect 

was done especially well. 

3. Go through those areas that appeared to be strengths for the teacher. Hopefully, 

this will encourage him/her to continue to use practices that are appropriate and 

effective. Move through each item that was marked as observed (No – Remediation 

119 
required) on the Checklist and briefly discuss those items with the teacher offering as 

many positive illustrations as possible. 

4. Go through the aspects of the program that they got almost right and re-explain 

where needed. This would pertain to items that were marked as observed on the 

Checklist but needed some improvement or could have been done with a higher level 

of quality. (e.g., “You asked the right type of questions during your book reading, but 

it might have been better if you had allowed the children a little more time to answer 

your questions.”) Try to include specific examples of where and how the teacher’s 

practices could be improved. Try to determine if you are dealing with a practice for 

which the teacher understands the underlying concepts and principles but has not yet 

had sufficient practice, versus a situation in which the teacher has not mastered the 

prerequisite skills necessary to successful performance of the desired behavior. This 

distinction can guide the nature of the feedback and/or remediation you offer, namely 

additional practice versus a more basic revisitation of the foundational skills and 

concepts (Item 6 below). 

5. Ask about the aspects you didn’t see that allow for teacher self-report. The items 

that allow for teacher self-report are clearly marked in the Checklist. If they selfreport any items, be sure to check if remediation is needed for that item. 

6. The observer should focus on components of the intervention that were marked as 

needing remediation. Remediation simply means that there were some items the 

teacher could use a second explanation on how to make this part of the intervention 

work. Below are some specific suggestions that you can use to help teachers better 

understand the skills that were taught during the training: 

•	 Provide feedback on what particular practice might have been missing from the 

teacher’s performance or what practice might have been performed incorrectly. 

•	 Ask the teacher to open to the section in question within the Teacher’s Guide. 

•	 Together, you and the teacher move through the concepts and ideas that were 

presented during the training. 

•	 Ask the teacher to indicate where difficulty was encountered or to discuss which 

ideas were unclear or which presented the greatest challenge. 

•	 If the difficulty appears to be a failure to understand or comprehend a particular 

concept (e.g., child-centered conversation), then the observer may need to provide 

further elaboration on the content or provide additional examples to illustrate the 

concept. 

•	 If the difficulty appears to be more of a problem with procedures (e.g., presenting 

N3C pictures), then it may be helpful for the observer to demonstrate the practice 

and then allow the teacher practice. 

III. Implementation Challenges−There are six questions that you should ask the teachers 

during the after-school debriefing session. Write the teacher responses in the space 

provided. The Protocol form and the Checklist will both be sent to Paula Schwanenflugel 

at the University of Georgia in order to help guide the telephone follow-ups with 

teachers. 

120 
IV. Reminder of Follow-ups and Contact Information (Both in person and via telephone 

with the University of Georgia.)—Ask the questions and obtain contact information for 

this item on the Protocol. Give the teacher your contact information in case he/she would 

like to contact you at a later date. 

121 
Teacher: _____________________________ School _______________________ 

Observer: ____________________________ Date: ________________________

TEACHER OBSERVATION 

FOLLOW-UP MEETING PROTOCOL 

Thank You (please read this or rephrase as you wish) 

“Thanks for participating in this study and attending the training. We know that there are 

many things that teachers are asked to do surrounding literacy. Without teachers willing to 

take what they learned in training and try it out in their classrooms, we couldn’t make 

progress in designing effective programs for young children. We know it is hard to 

implement this program just a day or so after training. You probably haven’t had the chance 

to fully digest the teachers’ manual and thoroughly examine the vocabulary units yet. I am 

here to help get you started with both tasks. I am not here to provide an in-depth critique of 

your teaching, but rather to get a sense of what I can do to support your work in 

implementation of the K-PAVE materials. I also want you to know that my observation and 

this conversation will not be relayed to any of your school or district staff.” 

II. Training Follow-up 

1. Notice something in his/her teaching outside of the intervention. “First of all, I really loved 

______________________________________________ (e.g., her nice manner with the 

children, her good classroom management, her effective organization of the room). 

(elaborate) 

2. Focus on an element of the intervention that was particularly strong. “With regards to the 

program, I thought the way you carried out ______________________________________ 

(some aspect of the program) was just terrific.” (elaborate) 

3. Go through the positive aspects of the Fidelity Checklist. “Even though it has only been 

hours since you were introduced to this new curriculum, I notice that you did a great job in 

_____________________________________________________________ (some practice)” 

4. Go through the aspects of the program that they got almost right and re-explain. “I saw 

you _______________________________________ (some practice). This was pretty close 

to what we had in mind, but____________________________________________________ 

(Describe fully correct practice).” 

122 
5. Ask about the aspects you didn’t see. “We were looking for 

_________________________ (some aspect of program), but we didn’t see it. Did you carry 

this out at some other time when we weren’t there? (yes or no). What did you do? 

_________________________________________ 

__________________________________________________________________________ 

__________________________________________________________________ (teacher 

response). 

For items marked as Remediation Needed (See Follow-up/Debriefing and Remediation – Detailed 

description II.7 above for suggestions on how to address these items.) 

Re-explain any marked items.____ (check, if second explanation was all that was needed) 

If they admit to not carrying out a particular practice, re-explain the rationale for the practice 

and go over part of the teacher manual that indicates what to do ______ (check, if 

appropriate). 

III. Implementation challenges: 

(a) Are there aspects of this program you had difficulty implementing? 

(b) Are there aspects of this program in which you don’t see a purpose? 

(c) Are there aspects of the program you really like? 

(d) Are there aspects of the program you really can’t envision using? If so, what 

might make it easier for you to work on including those practices? 

(e) Are there any aspects of the program you think are developmentally inappropriate 

for children of this age? 

Do you have any suggestions for how we can make the program easier for you to use? 

IV. Reminder of follow-ups: 

1. We will be coming back to visit again within the next few weeks. Is that okay with 

you? __________ (yes/no; teacher response). Would you be willing to carry out all 

the aspects of the program at that time so we can see that you know what to do?” 

_________ (yes/no; teacher response). 

2. For all teachers, “We will be carrying out some follow-ups by phone where you 

can ask us questions, listen to experiences of the other teachers, and so forth. These 

will occur about once a month after school. We handed you a personalized schedule 

yesterday that let you know when your phone calls have been scheduled. Can we 

have some basic contact information such as your phone number and email?” 

Contact Information for Teacher: _____________________ 

Phone number: ___________________ 

123 
____________________________________________________________________ 

Email address: 

Do you monitor this email fairly often? Yes/No 

FREQUENTLY ASKED QUESTIONS 

(As we get more questions we will continue to add them and distribute to all.) 

1. What do I do if there is no teacher assistant in the classroom or the teacher assistant 

didn’t show up for training? 

a. Rather than rotating 5 minutes on teacher and 5 minutes on teacher assistant 

you will observe the teacher for the entire 90 minutes. Be sure to clearly mark on 

your forms that only the teacher was observed. 

2. What do you tell the principal if they want to know details about your observation? 

a. Due to the nature of the research we are not able to report out on any particular 

classrooms or teachers. You should also let the teachers know that you will not be 

discussing your observation or your follow-up discussion with anyone else at 

their school or district. 

3. What do I do if the 90-minute period is interrupted by a special class (i.e., gym, 

music, art) or by any other interruption (fire drill, etc.)? 

a. Go back and continue you observation so that you have a full 90 minutes in 

each classroom. Make sure that your field notes state that there was an 

interruption. 

4. What do I do if the teacher can’t stay after school for the follow-up meeting? 

a. Hopefully this won’t happen, but if it does see if you can reschedule it another 

time during the same day. Perhaps the principal or someone else could watch the 

class while you conduct this follow-up as it only takes a few minutes to conduct. 

124 
APPENDIX I. DATA COLLECTION PROCEDURES 

PROTOCOL FOR CHILD ASSESSMENTS: QUICK REFERENCE 

Introducing yourself to the student 

Required: 

Hi, (student’s first name), my name is (your name). 

I’d like to talk with you today and show you some pictures and ask you some 

questions and listen to some stories together. I’ll be talking with some other kids today too. 

And I have some stickers to give you when we’re finished. 

Required: 

When I ask you questions, I am going to write down your answers, but I’m not going to tell your 

teacher or the other kids what you say. I just want to learn more about what kids your age know. 

Would it be okay for you to come with me [to the library, my room, the place designated by the 

school]? I’d like to learn more about what you know about words and stories. You don’t have to go 

with me if you don’t want to. You can let me know anytime when you want to go back to your room. 

If child says “YES” Proceed to the testing location. 

If child says “NO” Say, “Okay, I’ll check back with you later.” 

*************************** 

Would you feel better if [name of assistant teacher] walked over with us or would 

you like to walk over with me yourself? 

Build rapport while walking to the assessment location 

Choose one or two: 

How old are you? What did you do on your last birthday? 

Do you have any brothers and sisters? What are their names? How old are they? 

What kinds of things do you like to do when you’re not in kindergarten/school? 

Do you have any pets? What pets do you have? 

I see you have Hulk/ Spiderman/Wall-E/dinosaurs... etc. on your T-shirt/ shoes... etc.. 

Do you like Hulk/ Spiderman/Wall-E/dinosaurs? 

I see you have ribbons in your hair today/special shoelaces/etc. 

Explain the assessment processI’ll need you to sit up straight and tall in that chair. 

I’m going to show you some pictures and ask you some questions. Listen carefully and give 

your best answer. Sometimes the questions might seem hard, but that’s okay, some of these 

125 
questions are for older kids. If you are not sure what to answer it’s okay to take your best 

guess? 

(Continue with the following.) Also, I have to follow some rules, too. One of the 

rules says that I’m not allowed to tell you whether you’re right or wrong. Do you have any 

questions before we get started? Do you need to go to the bathroom first? Ok, if you need to 

use the restroom, just let me know. Let’s get started. 

• WJ-III Passage Comprehension 

• WJ-III Academic Knowledge 

• EVT–2 

• KTEA Listening Comprehension 

• If applicable, Language Elicitation Task 

Protocol for Classroom ObservationsWhen you arrive at the classroom, you will 

begin by talking with the teacher (see “General Guidelines for Observing in Classroom” tab). 

At this time, you will remind the teacher and assistant teacher that they have each agreed to 

complete a short, self-administered survey. The teacher and assistant teacher will each be 

given a survey to complete on their own – the Teacher Survey and the Assistant Teacher 

Survey, respectively (see “Teacher Surveys” tab). You will collect the surveys from the 

teacher and assistant teacher when the observation is complete, and you will take the surveys 

with you when you leave the school. 

In addition to giving the teacher the Teacher Survey, you will also remind the teacher 

that we will be recording her speech during the observation (see “Teacher Speech Sample” 

tab). You should show the teacher the digital recorder, explaining that the lanyard enables the 

recorder to be worn around the neck and that the clip secures the recorder so that it does not 

swing. You can work with the teacher to adjust the length of the lanyard. Be sure to turn the 

recorder on before giving it to the teacher to wear. 

Turn the recorder on by pushing the red “REC” (record) button on your digital 

recorder. You will begin by providing the identifying information for that session. Speak 

clearly as you give the following information: 

• Teacher’s name 

• Teacher ID number 

• Date of observation 

• “Conducted by, (your name and your observer ID number)” 

Repeat this information a second time to ensure that it will be clearly understood by 

other research staff listening to your recording. DO NOT STOP THE RECORDER, BUT 

LET IT CONTINUE TO RECORD. Then ask the teacher to wear the recording device. Ask 

the teacher to slip the lanyard holding the recorder around his or her neck and to clip the 

device to a piece of clothing so that it does not bounce around during instruction. 

Once you have given the teacher and assistant teacher the surveys and given the 

teacher the digital recorder to wear, get prepared to start your observation session. 

126 
Familiarize yourself with the classroom layout, and identify areas that will allow you to 

easily see and hear what is going on in the class while causing minimal distraction to the 

teacher and students. Make sure that your recording sheets are organized in a manner that it 

will be easy for you to change between them quickly. 

Since you will be standing and moving around the classroom during the observation 

session, it is useful to have a clipboard for writing. It is easy to shift between instruments 

efficiently using the following order of materials on the clipboard from top to bottom: 

CLASS booklet, folded to the current observation sheet; the Vocabulary Record sheet; the 

RAP-K; and finally, the laminated CLASS dimensions overview fold-out. Underneath 

everything you should have your CLASS manual with you; you may need to consult the 

manual when scoring CLASS cycles. The CLASS form sits on top since most of the 

observation session will be recorded on it. The Vocabulary Record sheet is next, so when 

vocabulary instruction occurs it is easy to flip to the sheet and record. When it is time to use 

the RAP-K, it can be pulled out and placed on top while coding and then returned in the 

stack once completed. The dimensions overview stays on the bottom of the stack, since it 

will only be pulled out to use while you are scoring the CLASS. 

If you are scheduled to start your observation at a specific time, begin at that time. 

Most observations will be scheduled to begin at the start of the school day as students arrive. 

(You should always arrive at the classroom well in advance of the start of your observation.) 

You will most likely begin observing using the CLASS – not the RAP-K. If beginning your 

observation at the start of the school day, wait until at least four students arrive and then start 

the first cycle of the CLASS. Even if formal instruction is not taking place yet, remember 

that the CLASS dimensions reference many other aspects of the classroom experience that 

would be a part of other morning activities. 

127 
APPENDIX J. DATA QUALITY ASSURANCE PROCEDURES 

Student assessments and classroom observation data underwent a thorough quality 

assurance protocol. Data quality monitors checked each individual completed instrument, 

using protocols developed by data collection trainers to ensure that forms were completed 

correctly. The quality control process eliminated any out-of-range values for student and 

classroom outcomes by examining the raw data and correcting scoring errors. 

When the quality control monitor discovered more serious errors that could not be 

easily corrected, such as a child assessment for which a basal was not established, the 

completed score sheets were sent to data collection trainers for further review. Trainers 

determined whether an estimate of the total raw score could be made based on the completed 

items or whether a raw score could not be imputed. If possible, a raw score was imputed 

based on the completed test items. All tests with administration errors for which raw scores 

were imputed based on the completed items were flagged, and, as a sensitivity analysis, 

impacts were estimated both with and without tests with imputed scores. 

For classroom measures (Classroom Assessment Scoring System [CLASS], Read 

Aloud Profile–Kindergarten [RAP–K], and Vocabulary Record), no individual items used to 

create the total scores were missing. Either the instrument was completed as part of the 

classroom observation or the entire instrument was missing. (Specifically, if the observation 

was conducted, there were three to five CLASS cycles, and there was one completed RAP-K. 

If a book read-aloud did not occur during the observation, the classroom had valid scores on 

the RAP-K of 0 for comprehension support, 0 for open-ended questions, and 0 words 

introduced.) 

Standardized student assessment scores were calculated from raw scores 

electronically to eliminate computation errors. For the two subtests of the WoodcockJohnson III/Normative Update, academic knowledge and passage comprehension (which will 

be examined in an exploratory analysis presented in a later report), raw scores were 

converted to W-scores, which are item response theory–based scale scores, using the 

Woodcock-Johnson Compuscore Program. Students’ raw scores, dates of birth, dates of 

testing, and gender were entered into the program, which generated the W-scores. To check 

the accuracy of the data entry, W-scores were compared with raw scores from the original 

data file, and any inconsistencies were flagged, double-check, and rectified, if necessary. 

Classroom instruction variables, which were created by summing and averaging observer 

ratings or tallies, were all cross-checked to ensure that variables were created correctly. 

Once analytic variables were created, descriptive analyses of all outcome and 

covariate measures were conducted. The distribution of each measure was examined for any 

out-of-range values and outliers. Although missing values were present (their handling is 

described in Chapter 2 and Appendix M), out-of-range values were not observed for any 

classroom instruction pretest or posttest variables or for any student covariates, teacher 

covariates, or school covariates. For student outcomes, seven students had a raw score of 0 

either at pretest or posttest on at least one assessment of expressive vocabulary or academic 

knowledge.65 Hard copy score forms and assessor notes were examined to confirm that the 0 

65 Raw scores of 0 for listening comprehension were present and were not unusual. 

128 
raw scores were accurate for each of these students. Because there were no notes from 

assessors indicating that the student refused to complete the assessment, the 0 scores were 

retained. A sensitivity analysis was conducted to examine the influence of students who 

provided no responses when tested (see Appendix N). 

129 
APPENDIX K. MODEL SPECIFICATIONS

THREE-LEVEL MODEL USED TO ESTIMATE IMPACTS ON KINDERGARTEN STUDENTS (FOR 

SINGLE OUTCOME MEASURES) 

To model the impact of the K-PAVE intervention on kindergarten students, a 

hierarchical linear model was estimated. This model provides an estimate of the average 

impact of the intervention on students across all schools at a given time point (for example, 

at the end of the kindergarten year) as well as an estimate of this impact’s standard error. In 

the evaluation data, students are nested within classrooms, and classrooms are nested within 

schools. Therefore, a three-level hierarchical linear model was specified, with students nested 

within classrooms within schools. The multilevel modeling also parses the variance among 

students, classrooms, and schools to produce both more precise point estimates of 

intervention impact and more accurate standard errors (Raudenbush and Bryk 2002). 

The model used to test impacts on students is written in hierarchical form, as shown 

below. Student and school covariates are defined in Appendix O. 

Sampling weights were used to adjust for the loss of one school that dropped out of 

the study. The weighting results in estimates that represent the sample of 65 schools that 

were randomly assigned rather than the analytic sample of 64 schools—missing the one 

school that dropped out. Sampling weights were constructed for the block of intervention 

schools without either Reading First or a Mississippi reading initiative (that is, the school had 

a local reading program or no reading program). The 20 schools remaining in this block after 

the loss of one school were weighted 1.05, so that estimates would represent the full sample 

of schools that were randomly assigned. All schools in the other blocks were assigned a 

weight of 1.0. 

The level 1, or student-level model is: 

(K1) 

Yijk = β 0 jk + β1 jk ( preijk − pre) + β 2 jk ( femaleijk − female) 

+ β 3 jk (StudentIEPijk − StudentIEP) + β 4 jk (FreeReducedLunchijk − FreeReducedLunch) 

+ β 5 jk (AfricanAmericanijk − AfricanAmerican) + ε ijk 

where Y is an outcome measure (for example, score) of the i

th 

ijk Expressive Vocabulary Test–2 

student in the j

th classroom in the kth school; preijk is the baseline version of the outcome 

measure for the i

th student in the jth classroom in the kth school (centered at the grand mean, 

pre ); femaleijk is a dummy variable taking the value 1 if the i

th student in the jth classroom 

in the k

th school is female and 0 otherwise (centered at the grand mean, female ); 

StudentIEP is a dummy variable taking the value 1 if the i

th student in the jth

ijk classroom in 

the k

th school has an receives special education services (that is, has an Individualized 

Education Plan) and 0 otherwise (centered at the grand mean, StudentIEP); 

FreeReducedLunchijk is a dummy variable taking the value 1 if the i

th student in the jth 

130 
classroom in the k

th school is eligible for free or reduced-price meals and 0 otherwise 

(centered at the grand mean, FreeReducedLunch); AfricanAmericanijk is a dummy variable 

taking the value 1 if the i

th student in the jth classroom in the kth school is African American 

and 0 otherwise (centered at the grand mean, AfricanAmerican); β0jk is the covariate adjusted 

mean value of the outcome measure for classroom j in the k

th school, β1jk through β5jk are 

regression coefficients indicating the effect of each student-level covariate on the outcome 

measure Yijk ; and ε ijk is the student-level residual or error term of the i

th student in the jth 

classroom in the k

th school (the assumed distribution of these residuals is normal, with mean 

0 and variance φ 2 ). 

The level 2 or classroom-level model is: 

(K2) 

β 0 jk = π 00 k + rjk 

where π 00k is the covariate adjusted mean value of the outcome measure for school k, and rjk

is the error term of the j

th classroom in the kth school (the assumed distribution of these 

residuals is normal, with mean 0 and variance σ 2 ). 

The level 3 or school-level model is: 

(K3) 

π 00k = γ 000 + γ 001 (Tk ) + γ 002 (ReadingFir st k − ReadingFir st) 

+ γ 003 (MSStateIni t k − MSStateIni t) + γ 004 ( AchLvlIndexk − AchLvlIndex) 

+ γ 005 (PctAfrAm k − PctAfrAm ) + γ 006 (PctFreeLun chk − PctFreeLun ch) 

+ γ 007 (SmallTown k − SmallTown ) + γ 008 (L arg eTownk − L arg eTown) 

+ γ 0 09 (Deltak − Delta) +υ k 

where γ 000 is the covariate adjusted mean value of the outcome measure across control 

schools; γ 001 is the mean difference in the covariate-adjusted outcome between treatment 

and control schools (main effect of treatment); Tk is treatment status dummy variable taking 

the value 1 for a school assigned to the K-PAVE treatment and 0 for a school assigned to the 

control group; ReadingFirstk is a dummy variable taking the value 1 if the k

th school 

participated in the Reading First program in the 2008/09 school year and 0 otherwise 

(centered at the grand mean ReadingFirst ); MSStateInit k is a dummy variable taking the 

value 1 if the k

th school has a Mississippi reading initiative and 0 otherwise (centered at the 

grand mean, MSStateInit ); AchLvlInde xk is the Achievement Level Index66 for the k

th 

66 The Achievement Level Index is created by the Mississippi Department of Education for each school in the 

state, based on student performance on the Mississippi state accountability test (that is, Mississippi Curriculum 

131 
school (centered at the grand mean, AchLvlIndex ); PctAfrAmk is the percentage of students 

in the k

th school that are African American (centered at the grand mean, PctAfrAm ); 

PctFreeLunchk is the percentage of students in the k

th school eligible for free or reducedprice meals (centered at the grand mean, PctFreeLunch); SmallTownk is a dummy variable 

taking the value 1 if the k

th school is in a small town and 0 otherwise (centered at the grand 

mean, SmallTown); LargeTownk is a dummy variable taking the value 1 if the k

th school is 

in a large town or on the fringe of a city and 0 otherwise (centered at the grand mean, 

L arg eTown )

67; Deltak is a dummy variable taking the value 1 if the kth school is in the Delta 

and 0 otherwise (centered at the grand mean, Delta); γ through γ are regression 002 009 

coefficients indicating the effect of each school-level covariate on the covariate-adjusted 

mean value of the outcome measure; and υk is the error term for the k

th school (the 

distribution is assumed to be normal, with mean 0 and variance τ 

2

). 

The parameter γ001 indicates the impact of the K-PAVE treatment on the specified 

student outcome. A t-test was conducted to test the null hypothesis that the treatment effect is 

0 using a .05 level criterion to reject the null hypothesis. A positive and statistically 

significant estimate of γ001 indicates that there is compelling scientific evidence (at the 5 

percent level) that the K-PAVE intervention improves student vocabulary (or 

comprehension) outcomes. The magnitude of γ001 estimates the magnitude of the impact— 

that is, school participation in K-PAVE is estimated to have, on average, a γ001 point effect 

on the scores of the students in participating schools. 

The standardized effect size is calculated by dividing the estimated impact from the 

model by the standard deviation of the outcome variable, Yijk , in the control group, as 

recommended in Burghardt, Deke, Kisker, Puma, & Schochet (2009) because the 

intervention might affect the standard deviation in the treatment group. Letting Sc be standard 

γˆ001 

deviation of the outcome measure in the control group means that the effect size is Sc . 

MODELS SPECIFICATION FOR GLOBAL F-TEST OF JOINT IMPACT ON MULTIPLE STUDENT 

OUTCOMES WITHIN A DOMAIN

The secondary confirmatory analysis examines two student outcomes—academic 

knowledge and listening comprehension—in a single domain (that is, vocabulary-related 

student outcomes). Before analyzing either measure individually, a joint test of the null 

Test), which is administered to all students in grade 3 or higher. Scores at all grade levels and for all subject 

areas are included in the index. The percentage of students in the school who score basic or higher and the 

percentage of students in the school scoring proficient or higher are used to create an Achievement Level Index 

score ranging from 100 to 600, with scores in the 100 range corresponding to a school performance level of 

“low” and scores in the 500 range corresponding to a school performance level of “superior.” 

67 Locale is represented by a series of three dummy variables, indicating whether the school is in a small town 

(SMALLTOWN), is in a large town or on the fringe of a midsize city (LARGETOWN), or is in a rural area 

(RURAL). The reference category in the model is RURAL. 

132 
hypothesis that the impact of K-PAVE on both outcomes is 0 was conducted. To do this, the 

following ordinary least squares regression model for both outcomes within the domain was 

simultaneously estimated. Values for all student-level variables (that is, posttest, pretest, and 

covariates) were averaged for each school, and an ordinary least squares regression model 

was estimated using school mean values.68 School means were weighted based on the 

number of students sampled in each school. In addition, weights were included to adjust for 

the loss of the school that dropped out of the study (as described in the previous section). 

(K4) 

Yk = β 0 + β1Tk + β 2 PREk + β3 femalek + β 4 StudentIEPk + β5 FreeReducedLunchk 

+ β 6 AfricanAmericank + β 7 ReadingFirstk + β8 MSStateInitk + β9 AchLvlIndexk 

+ β10 PctAfrAmk + β11PctFreeLunchk + β12SmallTownk + β13LargeTownk + β14 Deltak +ε k 

where Yk is an average value of a student outcome measure (for example, an academic 

knowledge test score) for the k

th school; Tk is a dummy variable for treatment status taking 

the value 1 for a school assigned to the K-PAVE treatment and 0 for a school assigned to the 

control group; PREk is the average baseline achievement test score for the k

th school; 

femalek is the proportion of students sampled from the k

th school that are female; 

StudentIEPk is the proportion of students sampled from the k

th school that receive special 

education services; FreeReducedLunchk is the proportion of students sampled from the k

th 

school who are eligible for free or reduced-price meals; AfricanAmericank is the 

proportion of students sampled from the k

th school who are African American; 

ReadingFirstk is a dummy variable taking the value 1 if the k

th school participated in the 

Reading First program in the 2008/09 school year and 0 otherwise; MSStateInit k is a dummy 

variable taking the value 1 if the k

th school has a Mississippi reading initiative and 0 

otherwise; AchLvlIndexk is the Achievement Level Index for the k

th school; PctAfrAmk is 

the percentage of all students in the k

th school that are African American; PctFreeLunchk is 

the percentage of all students in the k

th school that are eligible for free or reduced-price 

meals; SmallTownk is a dummy variable taking the value 1 if the k

th school is in a small town 

and 0 otherwise; LargeTownk is a dummy variable taking the value 1 if the k

th school is in a 

large town or on the fringe of a city and 0 otherwise69; Deltak is a dummy variable taking the 

value 1 if the k

th school is in the Delta and 0 otherwise; β0 is the covariate adjusted mean 

value of the outcome measure for control schools whose covariate values are all 0; β1 is the 

68 Because we could not estimate a series of multilevel models simultaneously, models were estimated using 

school means for each variable. Using PROC SYSLIN in SAS, we estimated multiple ordinary least squares 

equations simultaneously and tested the null hypothesis that the impact in all models is 0.

69 As in the multilevel model, the omitted category for locale is RURAL, which indicates that the school is located in a rural 

area. 

133 
average treatment effect; β2 through β14 are regression coefficients indicating the effect of 

each covariate on the outcome; and ε k is the error term for the k

th school (the assumed 

distribution of these residuals is normal, with mean 0 and variance σ 2 ). 

This model was estimated simultaneously for both outcomes in the domain— 

academic knowledge and listening comprehension—using two outcome equations. 

Correlations among error terms in the two equations were assumed to be 0. The equations 

were estimated simultaneously in order to be able to impose the null hypothesis that the 

treatment effect in both equations is 0 and to be able to conduct a joint F-test of the 

hypothesis, which is stated as 

(K5) 

H0 = β1( ACADEMIC_ KNOWLEDGE) = 0 and β1(LISTENING _ COMPREHENSION ) = 0 

If the p-value for this hypothesis test was less than .05, then the null hypothesis was 

rejected and a multilevel model (as described above) was estimated to test the impact of KPAVE on each of the individual outcomes. If the p-value for this hypothesis test was greater 

than .05, then the null hypothesis was not rejected and the impact of K-PAVE on the 

individual vocabulary-related student outcomes was not examined. 

TWO-LEVEL MODEL USED TO ESTIMATE IMPACTS ON CLASSROOM INSTRUCTION (FOR 

SINGLE OUTCOME MEASURES) 

The impact of the K-PAVE intervention on classroom instructional practices, 

controlling for teacher and school characteristics, was estimated using a multilevel model, to 

account for the clustering of classrooms within schools. The model includes a classroom 

level (level 1) and a school level (level 2). Because of the limited degrees of freedom at level 

1 (due to sampling only two classrooms per school), teacher characteristics were controlled 

for at the school level. For each teacher characteristic, the average value for the school is 

calculated. 

The multilevel model used to test impacts on classroom instruction took the following 

form. Teacher and school characteristics used as covariates are defined in Appendix O. 

As with the models estimating impacts on students, sampling weights were used to 

adjust for the loss of one school that dropped out of the study (see above). The weighting 

results in estimates that represent the sample of 65 schools that were randomly assigned 

rather than the analytic sample of 64 schools—missing one school that dropped out. 

Level 1, classroom-level model: 

(K6) 

Yij = β0j + εij 

where Yij is an outcome measure for the i

th classroom in the jth school, β0j is the mean value 

of the outcome Y for school j, and εij is the residual for the i

th classroom in the jth school (the 

level 1 residuals are assumed to be normally distributed with mean 0 and variance σ2

). 

Level 2, school-level model: 

134 
(K7) 

β 0 j = γ 00 + γ 01 (Tj ) + γ 02 (Tch _ Female j - Tch_Female) + γ 03 (Tch _ AfrAm j − Tch _ AfrAm)

+ γ 04 (College j − College) + γ 05 (GradDegree j − GradDegree ) 

+ γ 06 (CertEC j -CertEC ) + γ 07 (Cert Re ad j -Cert Re ad ) 

+ γ 08 (YrsTchj -YrsTch) + γ 09 (YrsTchKg j - YrsTchKg ) 

+ γ 10 (ReadingFir st j − ReadingFir st) + γ 11 (MSStateInit j − MSStateIni t) 

+ γ 12 (AchLvlIndex j − AchLvlIndex) + γ 13 (PctAfrAm j − PctAfrAm ) 

+ γ 14 (PctFreeLun ch j − PctFreeLun ch) + γ 15 (SmallTown j − SmallTown ) 

+ γ 16 (L arg eTown j − L arg eTown) + γ 17 (Delta j − Delta) +υ 0 j 

where γ00 is the grand mean of the outcome measure for control schools; γ01 is the average 

treatment effect on the classroom outcome; Tj is a dummy variable for treatment status 

taking the value 1 for a school assigned to the K-PAVE treatment and 0 for a school assigned 

to the control group; γ 02 through γ 09 are regression coefficients indicating the effects of 

teacher characteristics on the outcome, averaged for school j; γ 10 through γ 17 are regression 

coefficients indicating the effects of school characteristics on the outcome, for school j; 

70 th Tch_ Femalej −Tch_ Female is the proportion of focal teachers in the j school who are 

female, centered at the grand mean; Tch_ AfrAmj −Tch_ AfrAm is the proportion of focal 

teachers in the j

th school who are African American, centered at the grand mean; 

College j − College is the proportion of focal teachers in the j

th school whose highest level 

of education71 is a bachelor’s degree, centered at the grand mean; 

GradDegreej − GradDegree is the proportion of focal teachers in the j

th school who have a 

graduate degree, centered at the grand mean; CertECj −CertEC is the proportion of focal 

teachers in the j

th school with a teaching certificate in early childhood, centered at the grand 

mean; CertReadj −CertRead is the proportion of focal teachers in the j

th school with a 

teaching certificate in reading, centered at the grand mean; YrsTchj −YrsTch is the average 

number of years that focal teachers in the j

th school have been teaching children (that is, total 

70 “Focal teachers” refers to the two teachers in the classrooms randomly selected in each school for data 

collection. All schools in the study had at least two kindergarten classrooms. In schools with only two 

kindergarten classrooms, both classrooms were selected for data collection with certainty. In schools with more 

than two kindergarten classrooms, two classrooms were randomly selected for data collection. In this case, 

teacher characteristics are averaged for the two teachers from whom data were collected. Data on other 

kindergarten teachers in the school were not collected and thus are not included. 71 Highest level of education is represented by a series of dummy variables: College indicates that teachers’ 

highest level of education is a bachelor’s degree, and GradDegree indicates that teachers’ highest level of 

education is a graduate degree. The reference category is teachers with some graduate coursework but no 

degree obtained. In the sample, 38% of teachers have a bachelor’s degree, 25% have some graduate 

coursework, and 36% have a graduate degree (see table 2.10 in chapter 2.) 

135 
136

years of experience teaching), centered at the grand mean; is the 

average of the total number of years that focal teachers in the j

th school have been teaching 

kindergarten, centered at the grand mean; is a dummy variable taking the value 

1 if the j

th school participated in the Reading First program in the 2008/09 school year and 0 

otherwise, centered at the grand mean; ; is a dummy variable 

taking the value 1 if the j

th school has a Mississippi reading initiative and 0 otherwise, 

centered at the grand mean, ; is the Achievement 

Level Index for the jth school, centered at the grand mean; is the 

percentage of students in the j

th school who are African American, centered at the grand 

mean; is the percentage of students in the j

th school eligible 

for free or reduced-price meals, centered at the grand mean; is a dummy variable 

taking the value 1 if the j

th school is in a small town and 0 otherwise, centered at the grand 

mean, ; is a dummy variable taking the value 1 if the j

th school is in 

a large town or on the fringe of a city and 0 otherwise, centered at the grand mean, 

72; is a dummy variable taking the value 1 if the jth school is in the Delta and 

0 otherwise, centered at the grand mean, ; and is the error term for the j

th school 

(school-level error terms are assumed to be normally distributed with mean 0 and 

variance ). 

The parameter γ01 indicates K-PAVE’s impact on a specified classroom outcome. 

Thus, a positive and significant estimate of γ01 indicates that there is compelling scientific 

evidence that the K-PAVE intervention influences classroom instructional practice. The 

statistical significance is assessed and the effect sizes of classroom impacts calculated 

following the same approach described for student impacts. 

MODELS SPECIFICATION FOR GLOBAL F-TEST OF JOINT IMPACT ON MULTIPLE 

CLASSROOM INSTRUCTION OUTCOMES WITHIN A DOMAIN 

The secondary confirmatory analysis examined three classroom instruction outcomes 

within a single domain: vocabulary and comprehension support, instructional support, and 

emotional support. All three involve instructional practices hypothesized to foster impacts on 

students’ vocabulary and vocabulary-related outcomes. Before analyzing any of these 

measures individually, a joint test of the null hypothesis that the impact of K-PAVE on all 

three outcomes is 0 was conducted. The following ordinary least squares regression model 

was simultaneously estimated for all three outcomes within the domain. Values for all 

classroom-level variables (that is, posttest outcome, baseline outcome, and teacher 

covariates) were averaged for each school, and an ordinary least squares regression model 

72 As in the models testing impacts on students, the omitted category for locale is RURAL, which indicates that 

the school is in a rural area.
was estimated using school mean values and including weights to adjust for the loss of the 

school that dropped out of the study (as described above). 

(K8) 

Yk = β 0 + β1Tk + β 2 PREk + β 3 tch _ femalek + β 4 Tch _ AfrAmk + β 5 Collegek + β 6 GradDegreek 

+ β 7 CertECk + β 8 Cert Re adk + β 9 YrsTchk + β10 YrsTchKgk 

+ β11 ReadingFirst k + β12 MSStateInit k + β13 AchLvlIndexk + β14 PctAfrAmk + β15 PctFreeLunchk 

+ β16 SmallTownk + β17 L arg eTownk + β18 Deltak + ε k 

where Yk is an average value of a classroom outcome measure (for example, vocabulary and 

comprehension support) for the k

th school; Tk is a dummy variable for treatment status taking 

the value 1 for a school assigned to the K-PAVE treatment and 0 for a school assigned to the 

control group; PREk is the average baseline classroom instruction score for the k

th school; 

tch_ femalek is the proportion of teachers sampled from the k

th school that are female; 

Tch_ AfrAm is the proportion of teachers sampled from the k

th

k school that are African 

American; Collegek is the proportion of teachers sampled from the k

th school whose highest 

level of education is a bachelor’s degree; GradDegreek is the proportion of teachers 

sampled from the k

th school whose highest level of education is a graduate degree; CertECk 

is the proportion of teachers sampled from the k

th school who have a teaching certificate in 

early childhood; CertReadk is the proportion of teachers sampled from the k

th school with a 

teaching certificate in reading; YrsTchk is the average number of years that teachers sampled 

from the k

th school have been teaching children (that is, total years of experience teaching); 

YrsTchKgk is the average number of years that teachers sampled from the k

th school have 

been teaching kindergarten; ReadingFirstk is a dummy variable taking the 1 if the k

th school 

participated in the Reading First program in the 2008/09 school year and 0 otherwise; 

MSStateInit k is a dummy variable taking the value 1 if the k

th school has a Mississippi 

reading initiative and 0 otherwise; AchLvlIndexk is the Achievement Level Index for the k

th 

school; PctAfrAmk is the percentage of all students in the k

th school that are African 

American; PctFreeLunchk is the percentage of all students in the k

th school that are eligible 

for free or reduced-price meals; SmallTownk is a dummy variable taking the value 1 if the k

th 

school is in a small town and 0 otherwise; L arg eTownk is a dummy variable taking the 

value 1 if the k

th school is in a large town or on the fringe of a city and 0 otherwise73; Deltak 

is a dummy variable taking the value 1 if the k

th school is in the Delta and 0 otherwise; β0 is 

the covariate adjusted mean value of the outcome measure for control schools whose 

73 The reference category for locale is RURAL, which indicates that the school is located in a rural area. 

137 
covariate values are all 0; β1 is the average treatment effect; β2 through β18 are regression 

coefficients indicating the effect of each covariate on the outcome; and ε k is the error term 

for the k

th school (the assumed distribution of these residuals is normal with mean 0 and 

variance =σ 2 ). 

This model was estimated simultaneously for all three outcomes—vocabulary and 

comprehension support (Voc_Comp), instructional support (Instr), and emotional support 

(Emot)—in the domain, using three outcome equations. Correlations among error terms in 

the three equations were assumed to be 0. The equations were estimated simultaneously in 

order to be able to impose the null hypothesis that the treatment effect in all three equations 

is 0 and to be able to conduct a joint F-test of the hypothesis, which is stated as 

(K9) 

H0 = β1(VOC _ COMP) = 0 and H0 = β1(INSTR) = 0 and H0 = β1(EMOT ) = 0 

If the p-value for this hypothesis test was less than .05, then the null hypothesis was 

rejected and a multilevel model (as described above) was estimated to test the impact of KPAVE on each of the individual classroom instruction outcomes. If the p-value for this 

hypothesis test was greater than .05, then the null hypothesis was not rejected and the impact 

of K-PAVE on the individual classroom instruction outcomes is not estimated. 

138 
APPENDIX L. FLOWCHART ILLUSTRATING SAMPLE ATTRITION FROM DATA COLLE

Note: There were 23 students in the treatment school that dropped out of the study, and they are excluded from this figure and the analy

students, there were 619 students selected for the treatment group, and 594 were tested at baseline. In other words, 20 of the 23 student

dropped were tested at baseline, and none was tested at posttest.
APPENDIX M. MISSING DATA IMPUTATION 

Two approaches were employed to impute missing data: dummy variable adjustment 

for missing covariates other than pretest scores and single stochastic regression imputation 

for missing student assessments and classroom instruction measures. Missing data 

imputation was done separately for treatment and control groups. For each type of missing 

data (covariates, test scores), the extent of the missing data in the treatment group and in the 

control group is described first followed by the imputation approach used. 

DUMMY VARIABLE ADJUSTMENT FOR MISSING COVARIATES 

Missing data on student covariates 

Data were collected for the following student covariates: 

•	 Age at posttest (age) 

•	 Gender 

•	 Race 

•	 Eligibility for free or reduced-price meals 

•	 Special education status 

In the treatment group 1.5% of students (9 of 596) were missing data on at least one of the 

covariates, and in the control group 0.7% of students (5 of 700) were. 

Missing data on school covariates 

Data were collected for the following school covariates: 

•	 Reading initiative in place prior to K-PAVE (Reading First, a Mississippi state initiative, 

or other) 

•	 Achievement Level Index74 

•	 Percentage of students who are African American 

•	 Percentage of students eligible for free or reduced-price meals 

•	 Locale (rural, small town, or large town/fringe of city) 

•	 Delta region (or neighboring county) 

74 The Achievement Level Index is created by the Mississippi Department of Education for each school in the 

state, based on student performance on the Mississippi state accountability test (that is, Mississippi Curriculum 

Test), which is administered to all students in grade 3 or higher. Scores at all grade levels and for all subject 

areas are included in the index. The percentage of students in the school who score basic or higher and the 

percentage of students in the school scoring proficient or higher are used to create an Achievement Level Index 

score ranging from 100 to 600, with scores in the 100 range corresponding to a school performance level of 

“low” and scores in the 500 range corresponding to a school performance level of “superior.” 

140 
There were no missing data in either the treatment or control group on any of the 

school covariates except for Achievement Level Index (Table M1). 

Table M1. Missing data on school covariates in treatment and control samples 

Treatment Control 

Covariate 

(30 schools) (34 schools) 

n % n %

Reading initiative 0 0.0 0 0.0 

Achievement Level Index 4 13.3 3 8.8 

Percentage of students who are African 0 0.0 0 0.0

American 

Percentage of students eligible for free or 

reduced-price meals 0 0.0 0 0.0

Locale 0 0.0 0 0.0

Delta region 0 0.0 0 0.0 

Missing data on teacher covariates 

Data were collected on the following teacher covariates: 

• Gender 

• Race 

• Highest level of education 

• Number of years teaching 

• Number of years teaching kindergarten 

• Has teaching certification in early childhood education 

• Has teaching certification in reading instruction 

There were 3.1% of teachers (4 of 128 teachers) that were missing data on at least one 

covariate. 

Applying the dummy variable adjustment 

The dummy variable adjustment was applied to all missing student, school, and 

teacher covariates, except for missing student and classroom instruction pretest scores. In this 

approach, missing values were all set to a constant value of 0. In addition, the analysis 

included an indicator variable identifying observations for which the value of the covariate is 

missing. The indicator variable for a given covariate was set to one in cases where the 

covariate was missing and was set to 0 in cases where the covariate was not missing. While 

some articles have shown that this method will generally produce biased estimates (for 

example, Jones 1996), a recent National Center for Education Evaluation and Regional 

Assistance Technical Methods report found that this method performed well in simulations 

that mirrored the randomized study design and analysis plan of this evaluation (Puma, Olsen, 

Bell, & Price 2009). 

141 
This approach is described here using the missing data on gender (GENDER) as an 

example. A new variable, GENDER_IMP, was created, which was set to GENDER for all 

nonmissing cases and to 0 for all missing cases. In addition, a second new variable, 

GENDER_IMP _FLAG, was created, which was set to 1 for all students for whom GENDER 

was missing and to 0 for all students for whom GENDER was not missing. Both new 

variables were included in place of the original variable (GENDER) in the multilevel model 

used to estimate impacts of K-PAVE on students. The same approach was used for each 

student, teacher, and school covariate with missing values, and each pair of new variables 

was included in the impact model simultaneously in place of the original variables that had 

missing values. No distinctions were made between treatment and control group observations 

in applying this procedure. 

SINGLE STOCHASTIC REGRESSION IMPUTATION FOR MISSING PRETEST AND POSTTEST DATA 

Missing pretest and posttest data 

The percentage of students missing pretest assessment data is 3.7%–6.0% in the treatment 

group and 3.4%–3.6% in the control group. The percentage of students missing posttest 

assessment data is 4.2%–4.4% in the treatment group and 5.4%–5.6% in the control group 

(Table M2). 

Table M2. Missing data on student pretest and posttest assessments, for treatment and 

control groups (596 treatment students, 700 control students) 

Expressive Vocabulary 

Test–2nd Edition 

Listening 

comprehension 

Academic 

knowledge 

Treatment 

group Control group Treatment group Control group Treatment group 

Control 

group 

Missing pretest only 11 (1.9%) 18 (2.6%) 25 (4.2%) 28 (4.0%) 11 (1.9%) 19 (2.7%) 

Missing posttest only 14 (2.4%) 32 (4.6%) 14 (2.3%) 32 (4.6%) 15 (2.5%) 32 (4.6%) 

Missing both 11 (1.9%) 6 (0.9%) 11 (1.8%) 7 (1.0%) 11 (1.9%) 6 (0.9%) 

Total missing pretest 22 (3.7%) 24 (3.4%) 36 (6.0%) 25 (3.6%) 22 (3.7%) 25 (3.6%) 

Total missing posttest 25 (4.2%) 38 (5.4%) 25 (4.2%) 39 (5.6%) 26 (4.4%) 38 (5.4%) 

For the four measures of classroom instruction—vocabulary and comprehension 

support, instructional support, emotional support, and time on literacy areas other than 

vocabulary and comprehension—one posttest measure was missing for only one classroom. 

No other measures were missing. 

142
Applying single stochastic regression imputation 

For student assessments and the classroom instruction measure, missing pretest 

scores and missing posttest scores were both imputed using single stochastic regression75 

(see Puma, Olsen, Bell, & Price 2009 for a description). To impute missing values using 

single stochastic regression, a multiple regression model adjusted for the multilevel structure 

of the data was used to estimate predicted values for each pretest or posttest with missing 

values. Predictors in each imputation model included all other available information 

collected (including pretest scores, posttest scores, and covariates). For each pretest and 

posttest measure with missing values, an imputation model was estimated using cases with 

complete data. For each missing score, a randomly selected residual was added to the 

predicted value from the regression model to obtain an imputed value (that is, imputed value 

= predicted value + a randomly selected residual). The residual error was added to predicted 

values in an effort to have the same variation among imputed values as among observed 

values. As noted above, imputation models were estimated separately for treatment and 

control groups. 

All known variables were included as predictors of variables with missing values. 

When imputing pretest scores, posttest scores were included on the same measure as 

predictors, as well as pretest and posttest scores for other student outcomes (or other 

classroom instruction outcome measures). For example, for students missing expressive 

vocabulary pretest scores, all covariates were used to obtain predicted values, and students’ 

expressive vocabulary posttest scores were included as predictors of the missing pretest 

scores. Although it seems unusual to use a posttest score to impute a pretest score, which will 

in turn be used to predict the posttest outcome in the impact model, this approach is 

recommended by experts (Little & Rubin, 2002; Moons et al, 2006; and Allison, 2002, as 

cited in Puma et al, 2009). 

In addition to using posttest scores on the same measure to predict a pretest with 

missing values, pretest and posttest scores for other student outcomes were also included. In 

the example above with students missing Expressive Vocabulary Test pretest scores, pretest 

and posttest measures of academic knowledge and listening comprehension were included as 

predictors in the imputation model, as were all the covariates and the Expressive Vocabulary 

Test posttest measures. Following the recommendations of Puma et al. (2009), the 

imputation models included any measured variables that may be associated with missing 

data. 

Equation M1 shows the model used to predict missing student assessment pretests. In 

this example, the Expressive Vocabulary Test–2nd Edition pretest is predicted for students in 

treatment schools. The same approach was used for all student assessments. A series of 29 

treatment school dummy variables were included in the model to adjust for the nesting of 

75 The literature suggests that in general single stochastic regression imputation produces standard error 

estimates that are biased downward and that this problem can be addressed by multiple stochastic regression 

imputation (see, for example, Allison 2002). However, Puma et al. (2009) found that when schools were 

randomized but data were missing at the student level, single stochastic regression imputation did not yield 

standard error estimates that were biased downward. Therefore, multiple imputation would seem to be 

unnecessary in this context. 

143 
students in 30 treatment schools.76 The same model was estimated separately for control 

schools, with 33 control school dummy variables (for the 34 control schools) instead of the 

treatment school indicators. 

(M1) 

EVT _ pretest = β 0 + β1EVT _ posttest 

+ β 2 AcadKnow_ pretest + β 3 AcadKnow_ posttest 

+ β 4 ListeningComp _ pretest + β 5 ListeningComp _ posttest 

+ β 6 Stud _ PosttestAge + β 7 Stud _ Female 

+ β8 Stud _ AfrAm + β 9 Stud _ EligFreeReducedLunch+ β10 Stud _ IEP 

+ β11Sch _ ReadingFirst + β12 Sch _ MSStateInit + β13Sch _ AchLvlIndex 

+ β14 Sch _ PctAfrAm+ β15Sch _ PctEligFreeReducedLunch 

+ β16 SmTown+ β17 LgTown + β18 Delta 

+α1TreatmentSch1+ ...+α 29TreatmentSch29 + ε 

This single stochastic regression imputation approach was also used to impute 

missing posttest data. As with missing pretest scores, imputation was done separately for 

treatment and control groups, and all covariates from the impact analysis model were used in 

the imputation model, as were pretest and posttest scores for other student assessments. 

Equation M2 shows the model used to predict missing student assessment posttest scores, 

using the Expressive Vocabulary Test posttest for the control group as an example. Again, 33 

control school dummy variables were included in the model to adjust for the nesting of 

students in 34 control schools. The same model was used to predict missing posttest scores in 

the treatment group; however, 29 treatment school dummy variables were used in the model 

instead of the control school dummy variables. 

(M2) 

EVT _ posttest = β 0 + β1EVT _ pretest 

+ β 2 AcadKnow _ pretest + β 3 AcadKnow _ posttest 

+ β 4 ListeningComp _ pretest + β 5 ListeningComp _ posttest 

+ β 6 Stud _ PosttestAge + β 7 Stud _ Female 

+ β 8 Stud _ AfrAm + β 9 Stud _ EligFreeReducedLunch + β10 Stud _ IEP 

+ β11Sch _ ReadingFirst + β12 Sch _ MSStateInit + β13 Sch _ AchLvlIndex 

+ β14 Sch _ PctAfrAm + β15 Sch _ PctEligFreeReducedLunch 

+ β16 SmTown + β17 LgTown + β18 Delta 

+α1ControlSch1+ ... +α 33ControlSch33 + ε 

The same approach was used to impute the one missing value on the vocabulary and 

comprehension support posttest measure (see equation M3). Predictors in the model were 

school covariates, teacher covariates, baseline vocabulary and comprehension support, other 

classroom instruction baseline and posttest measures, and a series of 33 control school 

76 A series of dummy variables was used to adjust for the nested structure of the data rather than estimating a 

multilevel model for imputing missing values. 

144 
dummy variables to adjust for the nesting of classrooms in 34 control schools. There was no 

imputation model for the treatment group, as there were no missing classroom data for the 

treatment group. 

(M3) 

VocCompSup_ posttest = β 0 + β1VocCompSup_ pretest 

+ β 2 InstrSup _ pretest + β 3 InstrSup _ posttest 

+ β 4 EmotSup_ pretest + β 5 EmotSup_ posttest 

+ β 6OthLit _ pretest + β 7OthLit _ posttest 

+ β8Tch _ Female + β 9Tch _ AfrAm + β10College 

+ β11GradDegree+ β12CertEC + β13Cert Read 

+ β14YrsTch+ β15YrsTchKg + β16 Sch _ ReadingFirst 

+ β17 Sch _ MSStateInit + β18 Sch _ AchLvlIndex 

+ β19 Sch _ PctAfrAm+ β 20 Sch _ PctEligFreeReducedLunch 

+ β 21SmTown + β 22 LgTown + β 23 Delta 

+α1ControlSch1+ ...+α33ControlSch33 + ε 

Once all missing values were imputed for student assessment pretests and posttests 

and for the one missing classroom instruction posttest measure using the single stochastic 

regression approach, the student and classroom impact models were estimated using imputed 

values. As a form of sensitivity analysis, impact models were also estimated using only cases 

with nonmissing pretest and posttest data and with missing covariates imputed using the 

dummy variable method, using only cases with nonmissing covariates but with missing 

pretest and posttest data imputed using single stochastic regression, and using only cases 

with nonmissing data for pretests, posttests, and covariates (that is, listwise deletion). 

Sensitivity analyses (reported in Appendix N) indicate that the magnitude and standard errors 

of impact estimates were similar regardless of whether missing pretest data, missing posttest 

data, or missing covariate data were imputed. The imputation of missing data did not affect 

whether impact estimates were statistically significant. 

145 
APPENDIX N. SENSITIVITY ANALYSES 

IMPACTS ON STUDENTS 

Expressive vocabulary 

Sensitivity analyses were conducted to examine the robustness of the impact estimates. The 

results of the sensitivity analyses were compared with the models presented in Chapter 4. For the 

Expressive Vocabulary Test–2nd Edition (EVT–2), the impact model reported in Chapter 4 is a 

three-level model (school, classroom, and student; see Appendix K on model specifications), with 

a treatment indictor included at the school level to estimate the average impact of K-PAVE on 

students’ EVT–2 posttest score. The impact estimate is adjusted for students’ baseline EVT–2 test 

score, other student covariates, and school characteristics. Missing values for pretest and posttest 

scores are imputed using single stochastic regression, and missing values for school and student 

covariates are imputed using the dummy variable adjustment. 

Models were estimated to examine the sensitivity of the findings from the final impact 

model to covariate adjustment, missing data imputation, delayed baseline student testing, student 

crossovers, nonparticipation, students with a score of 0 on the EVT–2, and outliers. The sensitivity 

analysis models (described below) were compared with the final impact model in Chapter 4: 

•	 One model testing sensitivity to covariate adjustment was estimated with no covariates other 

than the treatment indicator and EVT–2 pretest score. All other models estimated as part of the 

sensitivity analyses had the same structure and covariates as the final model. 

•	 Four models testing sensitivity to missing data imputation were estimated: 

o without imputing any missing values—posttest, pretest, or covariates (that is, with a 

sample including only complete cases, or listwise deletion), without imputing missing 

values for the EVT–2 posttest and pretest (that is, with a sample including only students 

with both posttest and pretest scores) but using the dummy variable method for missing 

covariates without imputing missing values for covariates other than pretest scores (that 

is, with a sample including only students with no missing covariates) but imputing 

missing posttest values using single stochastic regression imputation, and without 

imputing scores for incorrectly administered tests and instead dropping these cases from 

the analysis. 

o Two models testing sensitivity to delayed baseline testing were estimated. One excluded 

29 students tested more than one week after K-PAVE training was completed, and the 

other excluded only the 12 students tested three weeks after all other student testing was 

completed. 

•	 One model testing sensitivity to crossovers was estimated and excluded the five students that 

transferred to another study school and crossed conditions, for whom posttest data were 

available. 

o Two models testing sensitivity to movement to non-study schools were estimated. One 

excluded 17 students—11 from the treatment group and 6 from the control group—that 

transferred to another school and were not tested at pretest and not tested at posttest, and 

the other excluded 45 students—21 from the treatment group and 24 from the control 

146 
group—that transferred to another school and were not tested at posttest (this group 

includes the 17 students that were also not tested at pretest). 

•	 One model testing sensitivity to scoring 0 on the EVT pretest was estimated and excluded 

students who had a 0 score. 

•	 One model testing sensitivity to outliers was estimated and excluded six outliers identified in 

Appendix V. For the EVT–2, observations with studentized residuals that were more than four 

standard deviations from the mean were considered outliers. 

•	 One model testing sensitivity to weighting schools to adjust for the school that dropped out was 

estimated. 

In all models, impact estimates from the sensitivity analysis models are statistically 

significant, with magnitudes ranging from 1.2 to 1.7, standard errors ranging from 0.56 to 0.64, and 

standardized effect sizes ranging from 0.11 to 0.15 (Table N1). Findings were found to be robust. 

Other vocabulary-related student outcomes: academic knowledge and listening 

comprehension 

To minimize the amount of type I error introduced by multiple hypothesis testing, a global 

F-test was conducted of the null hypothesis that the impact of K-PAVE on the two vocabularyrelated student tests – academic knowledge and listening comprehension – was 0 for both 

outcomes. Impacts on each of the individual outcomes measured within a domain were examined 

only if we were able to reject the null hypothesis of no impact on both outcomes, which was tested 

by the global test. 

The approach to conducting the global F-test involved simultaneously estimating ordinary 

least squares regression models—one for academic knowledge and one for listening 

comprehension—in which school mean posttest scores were regressed on school mean pretest 

scores, school means for student covariates, and school covariates (see Appendix K on model 

specifications). In addition, school means were weighted to reflect the number of students sampled 

in each school. 

147 
Table N1. Estimated impact on kindergarten students’ expressive vocabulary (EVT–2) in models fit for se

compared with final impact model in Chapter 4. 

Impact Standard 95% conf

estimate error t-statistic p-value interv

Final model (reported in Chapter 4) 1.60 0.585 2.74 .006 0.43 Covariate adjustment No covariates except treatment status and pretest 1.19 0.595 1.99 .05 –0.004 Missing data imputation Excluding cases with any missing data (listwise deletion) 1.45 0.624 2.32 .02 0.20 Excluding cases with missing test scores 1.67 0.568 2.94 .003 0.53 

Excluding cases with missing covariates (other than pretest) 1.47 0.643 2.29 .02 0.19 

Excluding cases with incorrectly administered tests 1.55 0.588 2.64 .008 0.38 

Delayed baseline testing Excluding 27 students with baseline testing at least one week late 1.74 0.576 3.02 .003 0.58 Excluding 12 students with baseline testing three weeks late 1.63 0.580 2.80 .005 0.47 

Crossovers and out-migrants Excluding five crossovers 1.66 0.592 2.81 .005 0.48 

Excluding transfers missing pre- and posttest 1.66 0.580 2.85 .004 0.49 

Excluding transfers missing posttest 1.66 0.580 2.86 .004 0.50 

Zero raw score Excluding students with zero raw score 1.52 0.568 2.68 .007 0.39 

Outliers Excluding six outliers 1.43 0.556 2.57 .01 0.32 

Weighting schools Excluding school weights adjusting for school drop out 1.60 0.585 2.74 .006 0.43 
Sensitivity analyses were conducted to examine whether the findings from the global 

F-test were robust. Models were estimated to examine the sensitivity of the findings to the 

same factors examined for the EVT: covariate adjustment, missing data imputation,77 

delayed baseline student testing, student crossovers, out-migration, and students with a score 

of 0 on the academic knowledge test.78 The sensitivity of findings to outliers was not tested 

because no outliers were identified. Results of the sensitivity analyses are presented in Table 

N2. Findings were found to be sensitive to the procedures used to address imputation of 

incorrectly administered baseline tests, delayed baseline testing, student crossovers, 

imputation of values for students transferring out of study schools, and students with a 0 

posttest raw score. In 7 of 10 models estimated by varying these factors, the null hypothesis 

of 0 impact on both listening comprehension and academic knowledge was rejected (see 

Table N2). For this reason, the impact of K-PAVE on listening comprehension and academic 

knowledge was estimated separately using a Bonferroni correction to limit the heightened 

type I error associated with conducting multiple comparisons within a domain. 

Table N2. Results of sensitivity analysis conducted on joint test of K-PAVE impact on 

academic knowledge and listening comprehension 

F-statistic p-value 

Final model (reported in Chapter 4) F2,96 = 3.08 .0508 

Covariate adjustment 

No covariates except treatment status and pretest F2,122 = 2.48 .09 

Missing data imputation 

Posttest and pretest school means calculated without missing values F2,96 = 2.87 .06 

School covariates not imputed and posttest and pretest school means 

calculated without missing values (7 of 64 schools lost) F2,84 = 1.69 .19 

Excluding cases with incorrectly administered tests from school means F2,96 = 3.24 .04 

Delayed baseline testing 

Excluding 27 students with baseline testing at least one week late F2,94 = 4.57 .01 

Excluding 12 students with baseline testing three weeks late F2,96 = 3.53 .03 

Crossovers and out-migrants 

Excluding five crossover students from school means F2,96 = 3.25 .04 

Excluding student transfers from school means: missed pretest and posttest F2,96 = 3.56 .03 

Excluding student transfers from school means: missed posttest F2,96 = 3.77 .03 

Zero raw score 

Excluded from school mean, students with a 0 academic knowledge raw 

score 

F2,96 = 3.21 .045 

Results of these sensitivity analyses are presented in Table N3 for the model testing 

the impact of K-PAVE on listening comprehension and in Table N4 for the model testing the 

77 In the models used to conduct the global F-test, missing values for student covariates were not imputed using 

the dummy variable adjustment but were based only on nonmissing values for the school. School means for 

student pretest and posttest scores were calculated with missing values imputed using single stochastic 

regression, and sensitivity analyses included school means calculated based only on nonmissing pretest and 

posttest values. For missing school covariates, the dummy variable adjustment was applied, and sensitivity 

analysis examined models excluding schools with missing values for school covariates. 78 Raw scores of 0 on the listening comprehension test were common; thus we did not examine sensitivity of 

findings to the presence of 0 scores on the listening comprehension test. 

149
impact on academic knowledge. Findings regarding listening comprehension were found to 

be robust (see Table N3). Impacts were not found to be statistically significant in any 

models. Unstandardized impact estimates ranged from 1.0 to 1.6, with standard errors 

ranging from 0.84 to 0.94 and standardized effect sizes ranging from 0.08 to 0.12. 

The positive and statistically significant impact of K-PAVE on academic knowledge 

was also found to be robust in the sensitivity analysis (see Table N4). Across all sensitivity 

models, the impact estimates ranged from 1.8 to 2.2, standard errors ranged from 0.81 to 

0.90, and standardized effect sizes ranged from 0.13 to 0.16. However, using the 0.025 

threshold for statistical significance set by the Bonferroni adjustment, the impact was not 

statistically significant in 4 of the 12 sensitivity models. The p-values range from.026 to 

.043, which indicates some sensitivity to covariate adjustment, imputation of missing 

covariate data (two models), and imputation of scores for tests that were incorrectly 

administered. Nonetheless, the magnitude of impact estimates and standard errors are similar 

to those in the main model. In three of the four models, the impact estimate ranges from 1.84 

to 2.02, compared with 1.95 in the main model, with standard errors ranging from 0.89 to 

0.90, compared with 0.85. Effect sizes for all four sensitivity models with p-values between 

.025 and .05 were .13 to .14. The impact estimate was lower in the model without covariate 

adjustment, at 1.71. Although these four models had p-values above .025, the preponderance 

of the evidence indicates an impact occurred, even when judged by the conservative 

standards of the Bonferroni test for significance when dealing with multiple comparisons. 

150 
Table N3. Estimated impact on kindergarten students’ listening comprehension in models fit for sensitivity ana

with final impact model presented in Chapter 4 

Impact Standard t- p- 95% con

estimate error statistic value inter

Final model (reported in Chapter 4) 1.41 0.881 1.60 .11 –0.35 

Covariate adjustment No covariates except treatment status and pretest 1.00 0.845 1.19 .23 –0.69 

Missing data imputation Excluding cases with any missing data (listwise deletion) 1.39 0.937 1.48 .14 –0.48 

Excluding cases with missing test scores 1.46 0.865 1.69 .09 –0.28 

Excluding cases with missing covariates (other than pretest) 1.32 0.939 1.41 .16 –0.55 

Delayed baseline testing Excluding 27 students with baseline testing at least one week late 1.57 0.877 1.79 .07 –0.18 

Excluding 12 students with baseline testing three weeks late 1.51 0.869 1.74 .08 –0.22 

Crossovers and out-migrants Excluding five crossovers 1.38 0.882 1.56 .12 –0.39 

Excluding transfers missing pretest and posttest 1.44 0.872 1.65 .10 –0.30 Excluding transfers missing posttest 1.47 0.844 1.74 .08 –0.22 

Weighting schools Excluding school weights adjusting for school drop out 1.42 0.90 1.61 .11 -0.38 
Table N4. Estimated impact on kindergarten students’ academic knowledge in models fit for sensitivity analys

final impact model presented in Chapter 4 

Impact Standard t- p- 95% confidence 

estimate error statistic value interval 

Final model (reported in Chapter 4) 1.95 0.851 2.29 .022 0.25 3.65 Covariate adjustment No covariates except treatment status and pretest 1.71 0.819 2.08 .037 0.07 3.35 Missing Data Imputation Excluding cases with any missing data (listwise deletion) 1.99 0.892 2.23 .026 0.21 3.78 Excluding cases with missing test scores 2.01 0.824 2.44 .015 0.36 3.65 Excluding cases with missing covariates (other than pretest) 2.02 0.903 2.24 .026 0.21 3.83 Excluding cases with incorrectly administered tests 1.84 0.904 2.03 .043 0.08 3.65 Delayed baseline testing Excluding 27 students with baseline testing at least one week late 2.19 0.813 2.70 .007 0.57 3.82 Excluding 12 students with baseline testing three weeks late 2.05 0.820 2.50 .013 0.41 3.69 Crossovers and out-migrants Excluding five crossovers 1.98 0.853 2.32 .020 0.28 3.69 Excluding transfers missing pretest and posttest 1.99 0.847 2.35 .019 0.29 3.68 Excluding transfers missing posttest 1.93 0.835 2.31 .021 0.26 3.60 Zero raw score Excluding students with 0 raw score 2.02 0.868 2.32 .020 0.28 3.75 Weighting schools Excluding school weights adjusting for school drop out 1.95 0.85 2.29 .022 0.24 3.65 
IMPACTS ON CLASSROOM INSTRUCTION 

Sensitivity analyses were conducted to examine the robustness of the estimates of 

impacts on classroom instruction outcomes. The domain of classroom instructional practices 

hypothesized to foster impacts on students’ vocabulary was measured by three outcomes: 

vocabulary and comprehension support, instructional support, and emotional support. 

Sensitivity analyses were conducted on the models estimated to conduct the global F-test, 

which tested the null hypothesis that impacts on all three types of instructional support were 

0. Three ordinary least squares regression models—one for each type of instructional 

support—were estimated, in which school mean posttest scores were regressed on school 

mean pretest scores,79 school means for teacher covariates,80 and school covariates81 (see 

Appendix K on model specifications). Because data were from the same number of 

classrooms (two) in each school, school means did not need to be weighted. The sensitivity 

of the findings to covariate adjustment and missing data imputation were examined by fitting 

four models and comparing the results of their global F-tests to the test from the model 

reported in Chapter 4: 

•	 Models estimated with no covariates other than the treatment indicator and baseline 

measure of the outcome. 

•	 Models estimated with all covariates but without imputation of missing values for school 

covariates (that is, with a sample including only the 57 schools with no missing school 

covariate data). 

•	 Models estimated with all covariates, but school means for posttest82 vocabulary and 

comprehension support were calculated without imputation of the one missing value.83 

•	 Models estimated with all covariates, but without imputation of missing school covariates 

and with school posttest means calculated without imputation of one missing value. 

The findings from the sensitivity analyses for the joint test of impacts on classroom 

instructional practices were found to be robust (Table N5). For all models the null hypothesis 

that the impacts on all three instructional practices hypothesized to foster students’ 

vocabulary were 0 was rejected. 

79 There were no missing values for pretest scores, so school means were calculated based on complete cases. 80 School means for teacher covariates were calculated based on non-missing values only. 81 Missing values for school covariates were imputed using the dummy variable adjustment.

82 No other classroom instruction pretest and posttest measures had missing values.

83 The sample size of 64 schools is not affected by the inclusion or exclusion of the single imputed posttest

value for the vocabulary and comprehension support composite. When the imputed value is included, the school 

mean is calculated based on posttest scores for 64 schools. When the imputed value is excluded, the school

mean is calculated based on posttest scores for 63 schools.

153 
Table N5. Results of sensitivity analysis conducted on joint test of K-PAVE impact on 

vocabulary and comprehension support, instructional support, and em

F-statistic 

Final model (reported in Chapter 4) F3,132 = 5.26 

Covariate adjustment 

otional support 

p-value 

.002 

No covariates except treatment and pretest 

Missing data imputation 

F3,183 = 8.97 < .0001 

School covariates not imputed 

Vocabulary and comprehension support posttest mean calculated 

without imputing missing value 

School covariates not imputed and vocabulary and 

comprehension posttest mean calculated without missing value 

F3,114 = 5.14 

F3,132 = 5.19 

F3,114 = 5.09 

.002 

.002 

.002 

Because the null hypothesis was rejected, impacts on each of the three instructional 

practices—vocabulary and comprehension support, instructional support, and emotional 

support—were examined. As reported in Chapter 4, for each outcome and for the fourth 

classroom instruction outcome—proportion of observation cycles spent on non-vocabulary 

literacy instruction—a two-level model (classrooms, schools) was estimated, with a 

treatment indicator included at the school level to estimate the average impact on the 

classroom instruction outcome measure (see Appendix K on model specifications). The 

impact estimate was adjusted for baseline classroom instruction score, teacher characteristics, 

and school characteristics. Missing values for covariates other than the pretest were imputed 

using the dummy variable adjustment, and the one missing posttest value (vocabulary and 

comprehension support score for one classroom) was imputed using single stochastic 

regression. 

The results of the sensitivity analyses were compared with those from the final 

models reported in Chapter 4 that examine impacts on classroom instruction. Models were 

estimated to examine the sensitivity of the findings from the impact models to covariate 

adjustment, missing data imputation, and weighting to adjust for school drop out. The same 

sensitivity analyses were conducted for all four classroom instruction measures—the three 

types of instructional practices hypothesized to foster students’ vocabulary (vocabulary and 

comprehension support, instructional support, and emotional support) and the amount of time 

spent on non-vocabulary literacy instruction. For all four models, the results from three 

sensitivity analysis models were compared with those from the final models: one model 

estimated with no covariates other than the treatment indicator and baseline measure of the 

outcome; another model estimated with all covariates but without imputation of any missing 

covariates;84 and a third model that was the same as the final model but without weights to 

adjust for school drop out. 

84 One school covariate was missing for 7 schools, reducing the sample size to 57 schools for models without 

imputation of missing school covariates. For missing teacher covariates and for the one classroom missing 

vocabulary and comprehension support posttest, excluding imputed values from the calculation of school means 

did not alter the sample size of schools. 

154 
In addition, for vocabulary and comprehension support, which was missing one 

posttest value, two additional models examining the sensitivity of the impact finding to 

missing data imputation were estimated: 

•	 A model with all covariates but without imputation of the missing posttest score. 

•	 A model with all covariates but without imputation of either missing covariates or the 

missing posttest score. 

For all models of the sensitivity analyses for vocabulary and comprehension support, 

the null hypothesis that the impact on vocabulary and comprehension support was 0 was 

rejected (Table N6). The results were found to be robust. In the model with no teacher and 

school covariates, the impact estimate was higher, with an unstandardized impact of 0.85 

compared with 0.73 (on a scale with a mean of 0 and a standard deviation of 1) and a 

standardized effect size of 0.97 compared with 0.82. 

The results from the sensitivity analyses for instructional support were found to be 

sensitive to covariate adjustment and the imputation of missing values for covariates (Table 

N7). In both models the magnitude of the impact estimate was higher, with unstandardized 

impacts of 0.76 and 0.71, respectively, compared with 0.60 (on a 1–7 scale) and standardized 

effect sizes of 0.59 and 0.55, respectively, compared with 0.47. The standard errors were 

smaller85 (0.31 and 0.33, respectively, compared with 0.36), and the impacts were found to 

be statistically significant. 

The results from the sensitivity analysis for emotional support were found to be 

robust, and the null hypothesis of no impact on emotional support in any of the models could 

not be rejected (Table N8). The unstandardized impact ranged from 0.16 to 0.22, and the 

standardized effect size ranged from 0.20 to 0.27. 

The results from the sensitivity analysis for time spent on nonvocabulary literacy 

instruction were found to be robust, and the null hypothesis of no impact on the proportion of 

observation cycles spent on nonvocabulary literacy instruction could be rejected in any of the 

models (Table N9). The unstandardized impact ranged from –0.01 to 0.02, and the 

standardized effect size ranged from –0.03 to 0.07. 

85 Although we would expect the covariates to increase the precision of the impact estimate, models examining 

K-PAVE impacts on classroom instruction that include covariates consistently have higher standard errors for 

the impact estimate than models without covariates (other than the baseline measure of the outcome). The 

standard errors increase with the inclusion of covariates because of covariates that—in the data (though not in 

expectation)—are correlated with treatment status. Random assignment of schools usually results in a sample 

that is balanced on school characteristics. However, as noted in chapter 2, a greater percentage of control 

schools than treatment schools are located in rural areas. The inclusion of covariates that are correlated with 

treatment status, although required to adjust for the imbalance between groups, causes the standard error of the 

impact estimate to increase. 

155 
Table N6. Results of sensitivity analyses of estimated K-PAVE impact on vocabulary and comprehension s

Standard 95% confidence 

 Estimate error t-stat p-value interval 

Final model (reported in Chapter 4) 0.726 0.208 3.50 .0009 0.31 1.14 Covariate adjustment No covariates except treatment status and pretest 0.853 0.191 4.46 <.0001 0.47 1.24 Missing data imputation Missing covariates and posttest not imputed 0.699 0.190 3.67 .0006 0.32 1.08 Missing covariates not imputed 0.701 0.190 3.68 .0006 0.32 1.08 

Missing posttest not imputed 0.726 0.207 3.51 .0009 0.31 1.14 Weighting schools Excluding weights adjusting for school drop out 0.727 0.201 3.50 .0009 0.32 1.13 

Table N7. Results of sensitivity analyses of estimated K-PAVE impact on instructional support 

Standard 95% confidence 

 Estimate error t-stat p-value interval 

Final model (reported in Chapter 4) 0.60 0.36 1.69 .097 –0.11 1.32 

No covariates except treatment status & pretest 0.758 0.31 2.46 .0169 0.14 1.36 

Final model without imputing missing covariates 0.705 0.33 2.14 .04 0.05 1.36 

Excluding weights adjusting for school drop out 0.60 0.36 1.68 .10 -.11 1.32 
Table N8. Results of sensitivity analyses of estimated K-PAVE impact on emotional support 

Standard 95% confidence 

 Estimate error t-stat p-value interval 

Final model (reported in Chapter 4) 0.156 0.200 0.78 .44 –0.24 0.56 

No covariates except treatment status and pretest 0.194 0.178 1.09 .28 –0.16 0.55 

Final model without imputing missing covariates 0.184 0.182 1.01 .32 –0.18 0.55 

Excluding one outlier 0.217 0.193 1.12 .27 –0.17 0.60 

Excluding weights adjusting for school drop out 0.157 0.200 0.78 .44 -0.24 0.56 

Table N9. Results of sensitivity analyses of estimated K-PAVE impact on proportion of cycles spent on nonvocabulary literacy instruction

Standard 95% confidence 

 Estimate error t-stat p-value interval 

Final model (reported in Chapter 4) –0.0107 0.074 –0.14 .89 –0.16 0.137 

No covariates except treatment status and pretest 0.0159 0.069 0.23 .82 –0.12 0.154 

Final model without imputing missing covariates –0.0025 0.066 –0.04 .97 –0.14 0.130 Excluding weights adjusting for school drop out -0.0108 0.074 –0.15 .88 -0.16 0.137 
APPENDIX O. SCHOOL, TEACHER, AND STUDENT COVARIATES 

Student-level covariates used in the analysis are defined in Table O1.86 

Table O1. Student-level covariates 

Covariate How measured 

Baseline standardized score on standardized student outcome measure 

Score on student outcome measure at • Expressive Vocabulary Test–2 

baseline (Pre) • Academic knowledge 

• Listening comprehension 

Female (female) 1 =student is female 

0 =student is male 

1 = student has an Individualized Education Plan and receives special 

education services for speech/language, occupational therapy, physical 

Student has an Individualized therapy, social skills, reading/literacy, mathematics, behavior 

Education Plan (StudentIEP) modification, adaptive behaviors, self-care skills or other special 

educational needs 

0 = student does not have an Individualized Education Plan 

Eligibility for school meal program 1 = student is eligible for free or reduced-price meals 

(FreeReducedLunch) 0 = student is not eligible for free or reduced-price meals 

African American 1 = student is African American 

(AfricanAmerican) 0 = student is not African American 

86 We planned to include two other covariates in the analysis: whether students had been retained in kindergarten 

and whether students had attended preschool prior to entering kindergarten. However, issues of data quality 

precluded their use. In the case of data on kindergarten retention, schools reported inconsistent information. Some 

schools reported whether students had been retained in the previous year (that is, whether they were attending their 

second year of kindergarten); other schools reported whether students would be retained in the current school year 

(that is, whether they would be attending a second year of kindergarten in the subsequent year). Because we did not 

have comparable data on all students, kindergarten retention status could not be included in the analysis. Schools 

also reported inconsistent information regarding whether students attended preschool prior to kindergarten. Some 

schools reported information about the type of arrangement that children attended prior to kindergarten (such as 

prekindergarten, Head Start, daycare, family childcare, public/private), but others reported either “yes” or “no,” 

making it unclear what types of arrangements were included in the “yes” category and what types were not. For 

example, some schools may have included family childcare in their definition of attending preschool, while others 

may have included only prekindergarten and Head Start, excluding even programs in daycare centers. Furthermore, 

for 9.3% of the sample, schools reported that they did not know whether students attended preschool or they did not 

collect information on preschool. For another 10.2% of students, schools did not report preschool information and 

did not indicate why. Given that the information reported was not consistently defined and that no information was 

reported for 19.5% of students, data on preschool attendance were not included as a covariate. 

158 
Teacher characteristics—defined in Table O2—were averaged for each school and treated as 

covariates in the school-level model. 

Table O2. Teacher-level covariates 

Covariate measure How measured 

Female (Tch_Female) 1 = teacher is female 

0 = teacher is male 

African American 

(Tch_AfrAm) 

1 = teacher is African American 

0 = teacher is not African American (for this sample, teacher is 

White) 

Level of education 

Teacher’s highest level of education completed, which is 

represented as a series of dummy variables, where 1 = specified 

level is highest level of education and 0 = specified level is not 

highest level of education. The reference category is “some 

graduate courses” (SomeGrad). 

• Bachelor’s degree (College) 

• Some graduate courses (SomeGrad) 

• Graduate degree (GradDegree) 

Teaching certification in 

early childhood (CertEC) 

1 = teacher has a teaching certificate in early childhood education 

0 = teacher does not have a teaching certificate in early childhood 

education 

Teaching certification in 1 = teacher has a teaching certificate in reading instruction 

reading instruction 0 = teacher does not have a teaching certificate in reading 

(CertRead) instruction 

Years teaching (YrsTch) Number of years that the teacher has been teaching children. Values 

are on a continuous scale. 

Years teaching kindergarten Number of years that the teacher has been teaching kindergarten. 

(YrsTchKg) Values are on a continuous scale. 

159 
School-level covariates used in the analysis are defined in Table O3. 

Table O3. School covariates 

Covariate measure How measured 

Treatment status (T) 

1 = school was randomly assigned to receive the K-PAVE treatment 

0 = school was randomly assigned to the control group 

Reading initiatives 

Achievement Level Index 

(AchLvlIndex) 

Percentage of students who 

are African American 

(PctAfrAm) 

Represented in the model by a series of two dummy variables. The 

reference category is OthRead. 

• Reading First (ReadingFirst): 

o 1 = school participates in the Reading First program 

o 0 = school does not have Reading First program 

• State reading initiative (MSStateInit) 

o 1 = school has a state reading initiative (for example, 

Barksdale, Reading Sufficiency) 

o 0 = school does not have a state reading initiative 

• Other (OthRead) 

o 1 = school has a reading program/curricula other than Reading 

First or a state reading initiative 

o 0 = school has either Reading First or a state reading initiative 

Measure of school-level achievement based on average scores on the 

Mississippi Curriculum Test, given annually to all students in grades 

3 and higher. Scores are on a continuous scale from 100 to 600. 

Percentage of students at school who are African American. Values 

range from 6% to 100%. 

Percentage of students 

eligible for school meal 

program (PctFreeLunch) 

Percentage of students at the school who are eligible for free or 

reduced-price meals. Values range from 40% to 100%. 

Locale Represented by a series of two dummy variables. The reference 

category is Rural. 

• Rural (Rural) 

o 1 = school is in a rural area 

o 0 = school is not in a rural area 

• Small town (SmallTown) 

o 1 = school is in a small town 

o 0 = school is not in a small town 

• Large town/fringe of city (LargeTown) 

o 1 = school is in a large town or on the fringe of a city 

o 0 = school is not in a large town or on the fringe of a city 

Delta region (Delta) 1 = school is in the Delta region 

0 = school is in a county contiguous to the Delta region 

160 
APPENDIX P. LIST OF K-PAVE MATERIALS PROVIDED TO TEACHERS 

•	 48 children’s books 

•	 Teacher guide 

o Description of three K-PAVE components (Building Bridges, Interactive Book 

Reading [“CAR Talk”], Explicit Vocabulary Instruction) and individual instructional 

strategies. 

o Template for weekly K-PAVE lesson plans. 

o Suggested tracking tools for monitoring conversations and reading with individual 

children. 

o Suggested schedule for integrating small group activities. 

o Instructions for creating vocabulary units on own. 

•	 24 K-PAVE teaching units 

o List of 10 target words for each unit to be posted in the classroom. 

o Quick definitions for 240 target words. 

o Sample CAR Talk questions for each book in the unit. 

o Brief description of two suggested extension activities per unit. 

o 360 laminated picture cards with pictures of 240 target words and 120 common words 

for use in Novel-Name Nameless-Category activity. 

161 
Books 

Morris, A. (1990). On the go. New York: Scholastic Inc. 

Ziefert, H. (2005). From Kalamazoo to Timbuktu. Maplewood, NJ: Blue Apple Books. 

Quick definitions 

On the Go

cargo things that are carried by a ship or an airplane 

helicopter it flying machine with long blades that spin around on top 

motor a machine that makes things go 

pedal a part of a bicycle, car, or piano that you push with your foot 

From Kalamazoo to Timbuktu 

submarine a boat that goes under the water 

oar a flat piece of wood you use to steer boats 

sailboat a boat that that uses the wind to moves with 

tire wheels on a car, truck or bicycle 

APPENDIX Q. SAMPLE WEEKLY UNIT FROM K-PAVE PROGRAM

TRANSPORTATION

Additional words 

scooter something that has two wheels and a tall handle 

Target Vocabulary

162 

cargo helicopter motor pedal 

submarine oar taxi sailboat

tire scooter 
you ride on taxi a car that you pay someone to take you somewhere 

CAR TALK 

On the Go 

“Wheels make things go easier and faster. 

They can be pedaled or pushed. . .” 

Competence: Where are the pedals on this bike? 

Relate: When have you used pedals? What sorts of things have 

you pedaled? 

“Or people. Some wheels are powered by motors?” 

Abstract: What do you think the motor does? 

“All aboard! Trains switch from track to track.” 

Competence: Where are the tracks for the train? Where are thetracks for the trolley? What 

about the monorail? 

Abstract: How are all these tracks the same? How are they different? 

“You can go straight up in a helicopter or a rocket. . . Liftoff!” 

Relate: Has anyone seen a helicopter before? What does it look like? Would you ever want 

to take a ride in a helicopter? 

From Kalamazoo to Timbuktu 

“The bus blew tires in Butte, Montana. Millie took out her red bandana.” 

Competence: What happened to the bu s tires? 

Competence: What do you think the bus tires are filled with? 

Abstract: What might make the tires of a bus to blow out? 

Relate: Have you ever been on a bus or in a car when a tire popped? What happened? 

“They set out across the Pacific Ocean with cases of food and suntan lotion.” 

Relate: Would you want to use an oar to get all the way across the ocean? 

Abstract: Do you think it would be easier for them to use a sailboat? 

PAIRED WORDS FOR TRANSPORTATION 

Find the pictures for a ll the target words listed for this unit as well as the two words that are paired 

for each target word . The “paire d words” are words that are knownby the children. In the large 

group setting show all 3 pictures to the children and ask them which one is the “Target Word.” 

This helps “map” the new or “novel” word to the unknown object. That is, children can begin to 

associate the new word with a new object or picture of an object. 

163 
164 

Target words Paired N3C words

Cargo Book, Circle

Helicopter Bike, Truck

Motor Train, Swing

Oar Carrot, Saw

Pedal Sock, Hand

Sailboat Swing, Plane

Scooter Orange, Jump

Submarine Kitchen sink, Hammer

Taxi Bed, Train

Tire Apple, TV

EXTENSION ACTIVITIES 

Activity 1: Sorting transportation by characteristics 

Materials:

Pictures cards of types of transportation (e.g., helicopter, scooter, submarine, taxi)

Pictures cards of words associated with different types of transportation (e.g., carries cargo, 

has pedals, has motors, has oars) 

•

Description: 

The purpose of the activity is to have children practice discussing the attributes of various 

modes of transportation and sorting by the presence or absence of the attribute. Have children 

sort the transportation pictures into each classification (i.e., does it carry cargo? does it have 

pedals, does it have wheels, etc). Have children carry it out in pairs or as a group. They should 

talk together about what they are doing and why they are doing it. Repeat a number of times 

using each of the sorting classifications. If there is time, children can to offer their own ideas of 

attributes by which they could sort the transportation by (e.g., has seats, windows, goes fast, 

slow, etc.) 
165 

Activity 2: How would you get there from here? 

Materials:

Transportation pictures from Activity 1.

Description: 

Talk with children about the kinds of transportation they have used to get from one place 

to another and about the kinds of transportation they have seen in their communities. Show 

students the picture cards for transportation. Have children in the group take turns thinking of a 

destination to which he or she would like to go. Explain to children that we will be looking at the 

different ways that we get from one place to another. Make sure 

they understand that different children in the group 

might use different modes of transportation to get 

from one place to another. Place the pictures of modes 

of transportation within reach so that children can use 

them if they need to refer to them. Have children 

discuss the travel destination offered by the child and 

whether they would like to go there, too. Have them 

discuss how they might get from here to the 

destination. Some destinations might require multiple 

modes of transportation, so encourage the children to consider all the different types of 

transportation they might use. 
APPENDIX R. LIST OF THE 240 K-PAVE TARGET WORDS

angry castle fireplace letter plantation star 

antennae cave fishing rod library plastic steam 

ants cavity flames lighthouse plow stem 

aphid cello flood lightning poison submarine 

appear centers floss liquid pole supplies 

arch chick flour litter police officer sword 

architect chipmunk flute locomotive praying mantis tack 

armor cinnamon foal lunchbox proud tambourine 

artist clarinet forecast lure puddle tarantula 

athlete claws French horn magnet radish taxi 

atlas cliff frustrated map rake temperature 

attract cloud fur mask recipe thermometer 

backpack compass garden measuring cup recycling bin thermos 

bacteria compost globe melt repel tide pool 

bait conductor gold mitten rise tire 

baker confused gravel mole root track 

barge container gum monsoon route trench 

barn cork gymnasium moon sailboat trestle 

battery crossing guard hall motor saliva trombone 

bay cub harp mouthwash sand dune trumpet 

beetle cucumber hatch mower scooter tunic 

blackberry delta hay needle seashore tunnel 

boil desert helicopter nest seeds twig 

bored desk helmet nozzle shade veterinarian 

boulder disappear highway nurse shed volcano 

166 
braces disappointed hoe oar shell waist 

branch disgusted hook ocean shovel wasp 

bright dragon horseshoe orchard shrimp waves 

bud dragonfly hose orchestra shy web 

burner drill hydrant owl sieve wheat 

burrow dusk hygienist paperclip sink whiskers 

cable earth icicle parents siren whistle 

caboose earthworm ingredients passageway skates wool 

cafeteria engineer iron pasture sled wreath 

calendar equator judge pedal slingshot x-ray 

calf eraser junkyard penny sliver 

candle excited knight pickax smoke 

canoe exhaust ladybug picnic snowflake 

canyon exhausted lake pill bug soil 

cargo fawn landfill planet spatula 

carpenter feathers legend plant stall 

167 
APPENDIX S. K-PAVE TEACHER TRAINING AGENDA

DAY 1 

7:30–8:00 am Sign-in and Introduction to Materials and Badges 

Continental Breakfast 

8:00–9:45 am Welcome and Introduction 

Background and Orientation to the Day 

9:30–9:45 am Go to Breakout Rooms 

9:45–11:45 am Building Bridges Conversation Strategies: Lecture, Followed by Small Group Activities 

11:45–12:30 pm Lunch (Practice Using Building Strategies) 

12:30–2:30 pm CAR Talk Book Reading Followed by Small Group Activities 

2:30–3:30 pm 

Introduce Homework Activity, Brainstorm Topics, Pick Two Books, and Put it Down in Form. 

(Think of a Topic or Two That has Some Good Vocabulary Potential. Consider [Bring] Possible 

Materials [Books, Activities, etc.] for the Next Day.) 

DAY 2 

7:30–8:00 am Continental Breakfast 

Questions Regarding Homework 

8:00–10:15 am New Vehicles: Explicit Vocabulary Practices for Kindergarten Classrooms 

10:15–12:00 noon Putting it All Together: Scheduling, Tracking, Communication with Families, Using Assistants 

12:00–12:45 pm Lunch (Talk About Small Group Classroom 

Management) 

12:45–2:00 pm Preparing Your First K-PAVE Unit, Using Your Lesson Plan, and Creating Your Own Unit Using 

Homework Materials 

2:00–2:45 pm Meet with TA and Plan 

2:45–3:30 pm 

Overview of the Fidelity Observation, Questions and Schedule for the Classroom Observation(s), 

Remediation Schedule, Schedule for Telephone Follow-ups, Schedule for Child Assessments 

or for Non-Focal Teachers 

Continue to Plan; Schedule for Telephone Follow-ups 

K-PAVE ASSISTANT TEACHER SCHEDULE (DAY 2 ONLY) 

Continental Breakfast 

7:30–8:00 am Welcome and Introduction 

Introduction of Teacher Assistant Trainers 

168 
8:00–9:45 am Building Bridges: Strategies for Carrying Out Conversations with Kindergarten Children 

9:45–11:45 am CAR Talk: Book Reading Strategies 

11:45–12:30 pm Lunch (Small Group Classroom Management) 

12:30–2:00 pm New Vehicles: Engaging Children in the Use of Vocabulary Words 

2:00–2:30 pm Meet with Teachers to Plan 

169 
APPENDIX T. K-PAVE TEACHER PHONE FOLLOW-UP AGENDA 

Attending: 

Absent: 

Moderator: 

Assistant: 

I. GENERAL PROBLEMS (ASK EACH FOLLOW-UP) 

•	 Which is the most difficult aspect of the program for you to carry out? Why? 

o Follow-up: Does anyone have a solution to this problem they would like to share? 

o Add our own suggestions. 

o Reiterate these two questions until teachers’ questions are tapped. 

• Which practices do you feel will be difficult to carry out in the long term? 

o Does anyone have an idea that might be helpful to enable you to carry out this part of 

the program in the long term? 

o Add our own suggestions 

o Reiterate these two questions until teachers’ questions are tapped. 

II. PROGRAM AREAS 

Building Bridges (1st follow-up) 

•	 Is anyone having trouble with the Building Bridges aspect of the program? Can you describe 

the problem? 

o Follow-up: Does anyone have a solution to this problem they would like to share? 

•	 Add our own suggestions. 

•	 Reiterate these two questions until teachers’ questions are tapped. 

•	 Could someone share some vocabulary recasting or introducing rare words they have managed 

to carry out in small group conversations? 

170 
•	 Has expanding on children’s speech started to feel natural yet? Can someone recall a particular 

instance where you have done this? Has anyone heard a child use this expansion in their own 

speech? 

•	 Can someone share an open-ended question that seemed to yield a lot of spontaneous talk from 

the children? 

•	 Can someone describe a strategy they have used to initiate child-centered conversations with 

children? 

•	 Has anyone had success in scheduling the Building Bridges conversations? When are good 

times for scheduling this? 

•	 Has anyone had success using the Building Bridges Tracking tool to schedule the small group 

conversations? 

171 
New Vehicles (2nd follow-up) 

•	 Is anyone having problems with the NEW Vehicles aspect of the program? Can you 

describe the problem? 

•	 Follow-up: Does anyone have a solution to this problem they would like to share? 

o Add our own suggestions. 

o Reiterate these two questions until teachers’ questions are tapped. 

•	 Can someone describe to me how you are using Quick Definitions in your classroom? 

•	 Can someone describe to me how you have fit the N3C presentation of new words into 

your teaching? 

•	 Can someone describe to me how you have emphasized vocabulary words so that the 

children understand that there is a list to be learned? 

•	 Does anyone have a good extension activity that you are using to support vocabulary, 

even if it is not one that is presented in the unit we provided you? Are there any that 

haven’t worked very well? 

•	 Has anyone had success in scheduling the extension activities? Where works best? 

•	 Has anyone had success keeping track of which child has spontaneously used a particular 

vocabulary word using the New Vehicles Extension Activity Tracking Tool? 

CAR Talk (3rd follow-up) 

•	 Is anyone having problems with the CAR Talk aspect of the program? Can you describe 

the problem? 

o Follow-up: Does anyone have a solution to this problem they would like to share? 

o Add our own suggestions. 

o Reiterate these two questions until teachers’ questions are tapped. 

•	 Has anyone tried to use the concrete questions we provided with the units? What are 

some of the successes or challenges that you have had creating concrete questions for 

your own lessons or other books you are reading to the class? Could someone share a 

concrete question you have used this week? 

•	 Have you used the relational questions we have provided with the units? What are some 

of the successes or challenges that you have had creating relational questions for your 

own lessons or other books you are reading to the class? Could someone share a 

relational question you have used this week? 

•	 Have you used the abstract questions we have provided with the units? What are some of 

the successes or challenges that you have had creating abstract questions for your own 

172 
lessons or other books you are reading to the class? Could someone share an abstract 

question you have used this week? 

•	 Could someone share their difficulties or success strategies for the two whole-group book 

readings that you do with your class? 

•	 Can someone share how they have scheduled three small-group sessions for each child in 

their classroom? Has anyone had success using the CAR Talk Tracking Tool? 

•	 Is anyone having trouble finding new books to use in the development of their own 

lessons? Could someone share how they have been selecting their own books? What 

aspects of good books have you been searching for when you are selecting new texts to 

use with your children (this can be a time to remind them about depictable words, rare 

words etc.; the elements that we specified when we talked about the creation of the 

teacher's own units)? 

III. WHAT CAN WE DO TO HELP YOU WITH THE PROGRAM? 

IV. REMINDER OF WHEN NEXT PHONE CALL WILL BE. 

173 
APPENDIX U. SAMPLE MEANS AND STANDARD DEVIATIONS FOR STUDENT AND 

CLASSROOM OUTCOME MEASURES, BY INTERVENTION STATUS

Table U1. Sample intervention and control group means for student outcome measures 

K-PAVE 

intervention group Control group 

(n = 596 students) (n = 700 students) 

Standard Standard 

Measure 

Expressive vocabulary 

Mean 

93.18 

deviation

11.26 

 Mean 

91.95 

deviation 

11.35 

Listening comprehension 89.93 12.91 88.42 13.00 

Academic knowledge 456.51 12.54 455.05 13.48 

Table U2. Sample intervention and control group means for classroom instruction outcome 

measures 

Measure 

K-PAVE 

intervention group 

K-PAVE(n = 60 

classrooms) 

Mean 

Standard 

deviation

Control group 

(n = 68 classrooms) 

Mean 

Standard 

deviation 

Vocabulary and comprehension support 0.44 0.94 –0.41 0.88 

Instructional support 3.66 1.49 2.93 1.28 

Emotional support 5.49 0.95 5.25 0.80 

Time spent on literacy in areas other than 

vocabulary and comprehension 0.52 0.27 0.50 0.33 

174 
APPENDIX V. CHECKING MODEL ASSUMPTIONS

MODEL EXAMINING IMPACTS ON STUDENTS’ EXPRESSIVE VOCABULARY (EVT–2) 

The model in Appendix K assumes error terms for the student-, classroom-, and schoollevel equations that are normally distributed with mean 0. The tests of the statistical significance 

of estimated treatment impacts depend on this property. So testing the normality assumption by 

looking at the residuals from the fitted equations is worthwhile. 

Normality assumption 

Whether the assumption of normally distributed errors had been met was evaluated by 

examining the distribution of studentized residuals at the student level and by examining 

residuals at each level plotted against a normal distribution. Examination of the residuals 

indicates that the normality assumption was met. The shape of the distribution of studentized 

residuals appears normal, although its tails are long, with values ranging from –6.6 to 6.9. Even 

with some values that appeared extreme, 4.3% of residuals were more than two standard 

deviations from the mean, which is consistent with a normal distribution. The plot of the 

studentized residuals at the student level versus a normal distribution shows a straight line with 

values at the tails deviating from the line somewhat (see left panel of Figure V1). The plot 

remains straight with the removal of six potential outliers, which have studentized residuals from 

the student-level equation that are four or more standard deviations from the mean (see right 

panel of Figure V1). 

Findings from the impact model remained consistent when these six potential outliers 

were excluded from the analysis (see sensitivity analyses in Appendix N). 

175 
Figure V1. Studentized residuals at the student level plotted against a normal distribution 

Figure V2 shows a plot of raw residuals versus a normal distribution at the classroom 

level (left panel) and at the school level (right panel). In both plots, the residuals fall 

approximately on a straight line indicating that they are approximately normally distributed. 

Figure V2. Raw residuals versus normal distribution at classroom and school levels 

Homoscedasticity assumption 

Whether the homoscedasticity assumptions in the hierarchical model were met was 

evaluated by examining plots of residuals versus the outcome (expanded vocabulary test [EVT] 

posttest) at the student, classroom, and school levels, for both treatment and control classrooms. 

176 
Tests of the statistical significance of the impact estimate depend on meeting these assumptions. 

Heteroscedasticity (the lack of homoscedasticity) would appear as wider dispersion of 

studentized residuals at some values of the EVT posttest outcome measure than at other values. 

The plots indicate that the homoscedasticity assumption was met (Figure V3). The full plot (left 

panel of Figure V3) suggests that there are four potential outliers in the lower range of EVT 

posttest scores that are spread apart from the point cloud. The plot without these four cases (right 

panel of Figure V3) shows an even distribution of residuals across the range of EVT posttest 

scores. Figure V4 presents plots of the residuals for the classroom level (left panel) and school 

level (right panel), which also show equal variation across the range of EVT posttest scores. 

Figure V3. Studentized residuals at the student level plotted against EVT posttest score for 

students in treatment (red) and control group (blue) schools 

Figure V4. Studentized residuals at the classroom level (left panel) and school level (right 

panel) plotted versus EVT posttest score for students in treatment (red) and control group 

(blue) schools 

177 
MODELS EXAMINING IMPACTS ON CLASSROOM INSTRUCTION 

Vocabulary and comprehension support 

Normality assumption 

The residuals at each level versus a normal distribution were plotted to evaluate whether the assumption 

of normally distributed errors had been met (Figure V5). At the classroom level (left panel) and the 

school level (right panel), the residuals fall approximately along a straight line, indicating that they are 

approximately normally distributed. The shape of the distribution of studentized residuals at the 

classroom level appears normal, with no indication of extreme values. Studentized residuals ranged from 

–2.3 to 2.5, and 3% of the residuals had values more than two standard deviations from the mean. There 

was no indication of any outliers. 

Figure V5. Plot of residuals versus a normal distribution at the classroom and school levels, from 

the model testing the impact on vocabulary and comprehension support 

Homoscedasticity assumption 

Plots of residuals versus the outcome (vocabulary and comprehension support posttest) at the classroom 

and school levels were examined to evaluate whether the homoscedasticity assumption was met (Figure 

V6). Both plots indicate that the homoscedasticity assumption was met. Residual variation is equal across 

the range of posttest scores, and no curves or funnel shapes are apparent. 

178 
Figure V6. Residuals at the classroom level and school level versus vocabulary and comprehension 

support posttest score for students in treatment (red) and control group (blue) schools 

Instructional support 

Normality assumption 

The normality assumption was evaluated by examining residuals at each level plotted against a 

normal distribution (Figure V7). At the classroom level (left panel) and the school level (right 

panel), the residuals fall along a straight line, indicating that they are normally distributed. No 

data points in the tails warrant further investigation. The shape of the distribution of studentized 

residuals at the classroom level appears normal. However, 7.8% of residuals have values greater 

than two standard deviations from the mean, which is above the 5% expected in a normal 

distribution. Studentized residuals ranged from –2.8 to 2.6. There was no indication of any 

outliers. 

179 
Figure V7. Plot of residuals versus a normal distribution at the classroom and school levels, from 

the model testing the impact on instructional support 

Homoscedasticity assumption 

Plots of classroom- and school-level residuals versus the outcome (instructional support posttest) 

were examined to evaluate whether the homoscedasticity assumption was met (Figure V8). Both 

plots indicate that the homoscedasticity assumption was met. Residual variation is equal across 

the range of posttest scores, and no curves or funnel shapes are apparent. 

180 
Figure V8. Residuals at the classroom level and school level versus instructional support posttest 

score for students in treatment (red) and control group (blue) schools 

Emotional support 

Normality assumption 

The normality assumption was evaluated by examining residuals at each level plotted against a 

normal distribution (Figure V9). At the classroom level (left panel) and the school level (right 

panel), most of the residuals fall along a straight line; however, data points in both plots show 

some curve in the tails. Although the plots suggest that the normality assumption was met, the 

data points in the tail were investigated further. Studentized residuals ranged from –3.5 to 2.4. 

The shape of the distribution of residuals was normal. There were 9.4% of residuals more than 

two standard deviations from the mean, which is slightly more than the 5% expected for a 

normal distribution. 

Based on an examination of the distribution of the studentized residuals, one potential 

outlier was identified, with a studentized residual of –3.45. Findings from the impact model 

remained consistent when this potential outlier was excluded from the analysis (see sensitivity 

analyses in Appendix N.) 

181 
Figure V9. Plot of residuals versus a normal distribution at the classroom and school levels, from 

the model testing the impact on emotional support 

Homoscedasticity assumption 

Plots of classroom- and school-level residuals versus the outcome (emotional support 

posttest) were examined to evaluate whether the homoscedasticity assumption was met (Figure 

E10). Both plots indicate that the homoscedasticity assumption has been met. Residual variation 

is equal across the range of posttest scores, and no curves or funnel shapes are apparent. 

182 
Figure V10. Residuals at the classroom level and school level versus emotional support posttest 

score for students in treatment (red) and control group (blue) schools 

PROPORTION OF CLASSROOM OBSERVATION CYCLES SPENT ON NONVOCABULARY LITERACY 

INSTRUCTION 

Normality assumption 

The normality assumption was evaluated by examining residuals at each level plotted 

against a normal distribution (Figure V11). At the classroom level (left panel) the residuals fall 

along a straight line. At the school level (right panel), some curve is apparent in the middle and 

the upper end of the line. Even with the curve at the school level, the assumption of normality 

appears to have been met. The shape of distribution of the classroom level residuals appears 

normal. The studentized residuals range from –2.8 to 2.4, and 4.7% of classrooms have residuals 

that are more than two standard deviations from the mean, which is consistent with a normal 

distribution. 

Examination of the distribution of the studentized residuals did not indicate that there 

were any extreme values. However, because some curve was observed in the extremes of the 

normal probability plot (see Figure V11), the six classrooms with studentized residuals greater 

than +/– 2.0 from the impact model were removed as a sensitivity analysis (see Appendix N). 

Due to the slight curviness at the center of the plot of the school level residuals, a Wilks-Shapiro 

test was conducted, and the null hypothesis that the residuals were not normally distributed (W = 

0.99, p = .70) could not be rejected. Findings remained consistent when the six observations 

were excluded. 

183 
Figure V11. Plot of residuals versus a normal distribution at the classroom and school levels, from 

the model testing the impact on nonvocabulary literacy instruction 

Homoscedasticity assumption 

Plots of classroom- and school-level residuals versus the outcome were examined to 

evaluate whether the homoscedasticity assumption was met (Figure V12). Both plots indicate 

that the homoscedasticity assumption was met. Residual variation is equal across the range of 

posttest scores, and no curves or funnel shapes are apparent. 

184 
Figure V12. Residuals at the classroom level and school level versus nonvocabulary literacy 

instruction for students in treatment (red) and control group (blue) schools 

185 
APPENDIX W. TRANSLATING IMPACTS ON STUDENTS INTO AGE-EQUIVALENT 

DIFFERENCES IN POSTTEST OUTCOMES

To interpret the practical meaning of the magnitude of the impact of K-PAVE on 

students’ expressive vocabulary and academic knowledge, posttest differences in scale scores 

between the intervention and control group were translated into age-equivalent differences. 

Ideally, regression-adjusted posttest means for both groups could be translated into ageequivalent scores, but the scale scores used in this particular analysis cannot be directly 

translated into age-equivalent scores. They must be translated into raw scores (the number of 

correct items) for each test, and the raw scores then converted into age-equivalent scores. Raw 

score conversion into age equivalents is possible because, based on the norming sample for each 

test, the average raw score was determined for students at each month of age. Hence, each raw 

score has an age equivalent. 

Raw scores were not analyzed in the test for impacts. For the Expressive Vocabulary 

Test–2nd Edition (EVT–2), standard scores were analyzed—that is raw scores transformed (for 

students at each age in months) into a scale with a mean of 100 and a standard deviation of 15, 

based on the norming sample. For academic knowledge, W-scores, which are item response 

theory–scaled scores, were reviewed. 

The two-stage process of converting the regression-adjusted mean posttest scores first 

into raw scores and then into age-equivalent scores is not precise. The statistical precision 

advantage of basing the difference on regression-adjusted scores may be lost in the process. For 

this reason, two approaches to generating age-equivalent scores were examined: converting 

regression-adjusted mean scores into raw scores and then using those raw scores to generate ageequivalent scores and using sample mean raw scores for each group without regression 

adjustment to generate age-equivalent scores. For both tests, both approaches yield consistent 

results. 

For the EVT–2, the standard-to-raw-score conversion table for the spring of kindergarten 

in the EVT–2 manual (Williams 2007, p. 178) was used to convert regression-adjusted mean 

standard scores to raw scores. The regression-adjusted mean standard scores of 93 for the 

intervention group and 92 for the control group can be converted to raw scores of 69 for the 

intervention group and 67–68 for the control group for test form A. These regression-adjusted 

raw scores closely approximate the observed posttest raw score means of 69.3 (for the 

intervention group) and 68.0 (for the control group). Using the raw score to age-equivalent score 

conversion table in the EVT–2 manual (pp. 192–93), both the regression-adjusted mean posttest 

score and the sample mean raw score in the intervention group are equivalent to the average 

score for a child who is 5 years, 7 months, and the regression-adjusted mean posttest score and 

the sample mean raw score in the control group are equivalent to the average score for a child 

who is 5 years, 6 months. Hence, the regression-adjusted mean difference is one month. 

For the Woodcock-Johnson III/ Normative Update academic knowledge test, W-scores 

map closely to raw scores but do not match exactly. For example, in the current sample, students 

with raw scores of 30 have W-scores ranging from 454 to 456, and students with raw scores of 

31 have W-scores ranging from 456 to 459. The age-equivalent score for a raw score of 30 is 5 

years, 9 months, and the age equivalent score for a raw score of 31 is 5 years, 11 months. If the 

186 
regression-adjusted mean scores for the intervention and control groups are converted to raw 

scores and then to age-equivalent scores, the age-equivalent score for the control group is 5 

years, 9 months, and the age-equivalent score for the intervention group is between 5 years, 9 

months and 5 years, 11 months—or approximately 5 years, 10 months. Alternatively, each 

individual student’s raw score can be converted into its age-equivalent score and the ageequivalent scores for the intervention and control groups averaged. This approach—although not 

regression-adjusted—yields a similar result: the average age-equivalent score is 5 years, 9.3 

months for the control group and 5 years, 10.5 months for the intervention group. Using either 

approach, the intervention group is ahead of the control group in academic knowledge by an 

average of one month. 

187 
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