Contract No: ED-IES-12-C-0007

Supporting	Justification	for	OMB	Clearance	of	a	Study	of	Feedback	in	Teacher	

Evaluation	Systems

Section A

June 14, 2013

Submitted to: Submitted by:

Institutes of Education Sciences Marzano Research Laboratory

U.S. Department of Education 9000 E. Nichols Ave., Ste. 210

555 New Jersey Ave., NW Centennial, CO 80112

Washington, DC 20208 (303) 766-9199

(202) 219-1385

Project Officer: Project Director:

Ok-Choon Park Trudy L. Cherasaro
1

Table	of	Contents

Section A. Justification..................................................................................................................................2

A1. Circumstances Necessitating Collection of Information.....................................................................2

Statement of Need................................................................................................................................ 2

Overview of the Study Design...............................................................................................................4

Overview of the Specific Data Collection Plan...................................................................................... 5

Timeline for the Data Collection...........................................................................................................6

A2. How, by Whom, and for What Purpose Information is to Be Used ...................................................6

Pilot Study .............................................................................................................................................6

Full Study...............................................................................................................................................6

A3. Use of Automated, Electronic, Mechanical or Other Technological Collection Techniques.............7

A4. Efforts to Identify Duplication. ...........................................................................................................7

A5. Sensitivity to Burden on Small Entities............................................................................................... 7

A6. Consequence to Federal Program or Policy Activities if the Collection is Not Conducted or is 

Conducted Less Frequently....................................................................................................................... 8

A7. Special Circumstances ........................................................................................................................ 8

A8. Federal Register Announcement and Consultation............................................................................8

Federal Register Announcement ..........................................................................................................8

Consultations Outside the Agency........................................................................................................8

A9. Payment or Gift to Respondents........................................................................................................9

A10.Confidentiality of the Data ................................................................................................................9

A11. Additional Justification for Sensitive Questions.............................................................................10

A12. Estimates of Hour Burden...............................................................................................................10

Pilot Study ...........................................................................................................................................10

Full Study.............................................................................................................................................11

A14.Estimates of Annualized Costs to the Federal Government............................................................12

A15.Reasons for Program Changes or Adjustments...............................................................................13

A16. Plans for Tabulation and Publication..............................................................................................13

Tabulation Plans..................................................................................................................................13

Publication Plans.................................................................................................................................21

Timeline...............................................................................................................................................22

A17. Approval to Not Display the Expiration Date for OMB Approval ...................................................22
2

A18. Exception to the Certification Statement.......................................................................................22

References ..............................................................................................................................................23

Section	A.	Justification	

The U.S. Department of Education (ED) requests OMB clearance for data collection related to the 

Regional Educational Laboratory (REL) program. ED, in consultation with Marzano Research Laboratory 

under contract ED-IES-12-C-0007, has planned a study of feedback to teachers in teacher evaluation 

systems in three of the states served by REL Central. OMB approval is being requested for data 

collection including a pilot survey of teachers, interviews with teachers about the pilot survey, a survey 

of teachers (revised based on the pilot), and collection of extant teacher evaluation ratings. 

A1.	Circumstances	Necessitating	Collection	of	Information

This data collection is authorized by the Educational Sciences Reform Act (ESRA) of 2002 (see 

Attachment A). Part D, Section 174(f)(2) of ESRA states that as part of their central mission and primary 

function, each regional educational laboratory “shall support applied research by ... developing and 

widely disseminating, including through Internet-based means, scientifically valid research, information,

reports, and publications that are usable for improving academic achievement, closing achievement 

gaps, and encouraging and sustaining school improvement, to— schools, districts, institutions of higher 

education, educators (including early childhood educators and librarians), parents, policymakers, and 

other constituencies, as appropriate, within the region in which the regional educational laboratory is 

located.”

Statement of Need 

The importance of teacher effectiveness has been well-supported by studies demonstrating that 

teachers vary in their ability to produce student achievement gains; all else being equal, students taught 

by some teachers experience greater achievement gains than those taught by other teachers

(Konstantopoulos & Chung, 2010; Aaronson, Barrow, & Sander, 2007; Nye, Konstantopoulos, & Hedges, 

2004; Wright, Horn, & Sanders, 1997). These findings have encouraged wide interest in identifying the 

most effective programs and practices to enhance teacher effectiveness. 

All seven states in the Central Region have either developed performance-based teacher evaluation 

systems (Colorado, Kansas, and Missouri), are in the process of developing or adopting a model system 

(Nebraska and South Dakota), or are exploring the development or adoption of a model system (North 

Dakota and Wyoming). Because states in the Central Region are local control states, districts can choose 

to adopt the state system or develop or adopt a different model that is aligned with the state guidelines 

for educator evaluation. Colorado, Kansas, and Missouri are in the process of piloting their state 

systems. Table 1 provides a timeline of the development of the Colorado, Kansas and Missouri teacher 

evaluation systems. States intend to use these models to serve both measurement and development 

purposes. In order for evaluation systems to improve teaching effectiveness, it is recommended that the 
3

first priority for such system design be promotion of teacher development (Papay, 2012). As states and 

districts prepare to develop and implement teacher evaluation systems, they are exploring ways to use 

evaluation findings to provide individualized feedback that will facilitate improved teaching and learning 

practices, which will lead to increased student performance (Kane & Staiger, 2012). Because such new 

evaluation systems that focus on a system of observation and feedback have not been fully established 

and tested, research on feedback in teacher evaluation systems is limited. The majority of research on 

performance feedback is conducted in industries outside of the education profession. The current study 

will address this gap in the current research by examining theories of performance feedback that have 

limited research in the education field. 

Table 1. Development Timeline of Teacher Evaluation Systems in Colorado, Kansas, and Missouri

Colorado Kansas Missouri

Timeline

2012‒2013:

Pilot

2013‒2014:

Validation study

2014‒2015:

Full implementation

2011‒2012: 

Pilot

2012‒2013: 

Pilot

2013‒2014: 

Validity study

2014‒2015: 

Full implementation

2011‒2012:

Field test

2012‒2013/2013–2014:

Pilot

2014‒2015:

Full implementation

State 

Requirements

State developed model 

system for voluntary 

adoption by districts.

State developed model 

system for voluntary 

adoption by districts.

State developed model 

system for voluntary 

adoption by districts.

Components

Fifty percent of the 

evaluation is based on 

evidence of professional 

practice.

Fifty percent of the 

evaluation is based on 

student growth measures.

Evidence for the four 

standards will be the basis 

of teacher evaluation 

ratings.

Student growth will 

significantly inform the 

evaluation.

Evaluation scores are 

determined based on 

evidence collected for 

each of the standards. 

Student growth is a 

significant factor in the 

scores. 
4

Colorado Kansas Missouri

Teacher 

Standards

State-developed Teacher

Quality Standards

1. Know content

2. Establish environment

3. Facilitate learning

4. Reflect on practice

5. Demonstrate 

leadership

6. Student growth

InTASC standards (10 

standards grouped into 4

categories) 

1. The learner and 

learning

2. Content knowledge

3. Instructional practice

4. Professional 

responsibility

State-developed 

standards aligned with 

InTASC

1. Content knowledge

2. Student learning, 

growth, and 

development

3. Curriculum 

implementation

4. Critical thinking

5. Positive classroom 

environment

6. Effective 

communication

7. Student assessment and 

data analysis

8. Professional practice

9. Professional 

collaboration

Educators throughout the Central Region have expressed strong interest in this area. For example, 

district superintendents in Colorado want to know what types of feedback are best, what the right 

evidence is to gather about teacher performance, and how to incorporate feedback into educator 

evaluation systems (Central States Education Laboratory, 2011; Colorado Association of School

Executives, n. d.). REL Central’s Governing Board members also identified the need to develop teacher 

evaluation systems that both hold teachers accountable and improve professional practice as one of 

their top educational challenges (REL Central Governing Board Meeting Minutes, March 19–20, 2012). 

Conversations with each state liaison have indicated that states want to ensure that their newlydeveloped educator evaluation systems are supportive in nature, and that they specifically provide for 

constructive, timely feedback offered to all evaluated individuals.

Overview of the Study Design 

The purpose of the proposed study is to describe teacher experiences with feedback as part of a teacher 

evaluation system and identify factors that may influence their use of feedback and their performance.

Specifically, our study will examine inputs, mediating variables, and outcomes that have been identified 

in previous research on performance feedback. Figure 1 presents a theoretical model that depicts 

relationships between inputs, mediating variables, and outcomes. The proposed model of performance 

feedback (Figure 1) was developed based on seminal work (Ilgen, Fisher, & Taylor, 1978) and emerging 

research on performance feedback. The theoretical model suggests that the feedback characteristics 

(usefulness, accuracy, and credibility) affect teachers’ responsiveness to feedback and their access to 

learning opportunities, which, in turn, mediate the effect of feedback characteristics on teachers’
5

performance. The relationship between the inputs, mediating variables, and outcomes in the theoretical 

model will be examined to gain a deeper understanding of feedback characteristics that may be 

necessary for teachers to move from receiving feedback to improved performance.

Figure 1. Theoretical model for performance feedback based on previous research

The study will address the following research questions:

1. What are teachers’ perceptions of the usefulness, accuracy, and credibility of the feedback they 

receive as a part of their performance evaluation?

2. What is the relationship among the characteristics of performance feedback (usefulness, 

accuracy, and credibility)?

3. What is the relationship between feedback characteristics and teacher responsiveness?

4. What is the relationship between teacher responsiveness and teacher performance?

Overview of the Specific Data Collection Plan

This proposed study includes a pilot test of the survey instrument and implementation of the full study. 

During the 2013–14 school year, researchers will conduct a pilot test of the survey instrument. The pilot 

test will be used to gather data to refine the survey and assess reliability and validity. This refined survey 

will then be used to conduct the full study in the 2014–15 school year.

Pilot	Study	Data	Collection

The pilot serves two purposes: (1) to ensure that the survey items are understood and easy to answer;

and (2) to examine the psychometric properties of the survey instrument through confirmatory factor 

analysis (CFA). The pilot test includes two data collection instruments: (1) a researcher-developed 

survey, and (2) a teacher interview protocol. The online survey consists of a set of items about feedback 

as part of a district’s teacher evaluation system to be asked of all respondents. The survey includes 

questions about the usefulness of feedback, accuracy of feedback, credibility of the person providing 

feedback, learning opportunities related to the feedback, and responsiveness to feedback. The online 

survey will be administered once in the pilot study to approximately 450 teachers, in 4 school districts

Inputs 

•Feedback characteristics:

•Teachers receive feedback 

that they perceive as useful. 

(Usefulness)

•Teachers receive feedback 

that they believe accurately 

represents their 

performance. (Accuracy)

•Teachers believe that the 

person providing feedback 

is qualified to do so. 

(Credibility)

Intermediate Outcomes 

(Mediating Variables)

•Teachers have access to the 

necessary opportunities to 

learn about or plan for 

implementation of feedback. 

(Opportunities)

•Teachers implement changes 

or seek professional 

development based on 

feedback. (Responsiveness)

Final Outcomes 

•Supervisors/evaluators 

rate teachers’ 

performance as proficient, 

distinguished, or 

accomplished. 

(Performance)
6

for the CFA. During the pilot study, interviews will also be conducted with approximately 20 teachers to 

gather their feedback on the clarity of the survey questions. Teachers participating in the interview will 

have already completed the online survey. During the interview, teachers will be provided with a hard 

copy of the survey so that they can review the items and directions in order to respond to questions 

about clarity, such as “In question 1, what does the term ‘designated evaluator’ mean to you?”. The 

interviews will be transcribed. Copies of the teacher survey and the teacher interview protocol are 

provided in Attachments B and C, respectively.

Full	Study	Data	Collection

The full study involves two data collection instruments: (1) a teacher evaluation instrument already in 

use in the districts, and (2) a researcher-developed teacher survey. The revised survey from the pilot 

study will be administered once, to approximately 2,446 teachers in approximately 134 districts, in the 

full study. In addition to the survey, researchers will collect existing teacher evaluation ratings from the 

teacher evaluation instrument in use in each state to provide information about the teachers’

performance as related to standards identified in the each state. Tables 8–10 in Section A.16 present the 

performance standards for each state.

Timeline for the Data Collection

Instrument Pilot Test

Timeframe Data Collection

Spring 2014 Administer pilot survey

Spring 2014 Conduct interviews about pilot survey

Full Study

Timeframe Data Collection

Spring 2015 Administer revised survey

Spring 2015 Collect existing teacher evaluation ratings

A2.	How,	by	Whom,	and	for	What	Purpose	Information	is	to	Be	Used

Pilot Study

The findings from the pilot study will be used by the researchers to refine the survey. The results will 

inform changes to the survey used in the full study, such as determining that certain indicators or survey 

items are not good indicators of the factors (usefulness, credibility, accuracy, opportunities, and 

responsiveness), or determining that there are more or fewer factors than originally conceived.

Full Study

The findings will be used by state and district leaders to prioritize needs both at the state and district 

level for training and guidance on providing feedback as part of teacher evaluation systems. This study 

will result in a report intended for district and state leaders who are responsible for selecting, 

developing, and implementing teacher evaluation systems and overseeing support for teachers’ 

professional growth and effectiveness. The report will provide the findings in an accessible format 
7

describing possible interpretations and implications of the results of the model testing. Technical 

information such as the correlation matrix used to test the model, the fit statistics for proposed and 

adjusted models, and significant standardized coefficients for each of the parameters estimated in the 

model will be provided in an appendix. The report will summarize findings based on results of the model 

testing, provide a detailed description of methods in an appendix, and discuss limitations and 

implications for practice and further research. The report will provide findings from each state as well as 

the synthesized findings from the multi-analytic structural equation modeling to inform leaders across 

the region. The synthesized findings will provide information about the relationship of the 

characteristics of feedback from three different evaluation contexts within the Central Region. These 

findings may be helpful for states and districts in the region who are implementing teacher evaluation 

systems, because they will identify which variables account for a significant amount of variance in other 

variables across three different evaluation systems. Researchers will also facilitate discussions about the 

results with state and district leaders to examine and interpret the results from this study. For example, 

to identify which variables account for a significant amount of variance in other variables, we will 

examine the coefficients of the hypothesized relationships (that the feedback characteristics 

[usefulness, accuracy, and credibility] affect teachers’ responsiveness to feedback and their access to 

learning opportunities, which, in turn, mediate the effect of feedback characteristics on teachers’

performance).

A3.	Use of Automated, Electronic, Mechanical or Other Technological Collection 

Techniques

The survey will be administered online using survey software such as SurveyMonkey in order to reduce 

burden on respondents. Respondents will be sent a unique link via email that will lead them to the 

online survey. SurveyMonkey allows the creation of a unique link for each email address in the invite list 

that prevents duplicate responses, because the link cannot be used after the survey is completed. 

Researchers will also pre-code items using the online survey software which will reduce the time needed 

to prepare and merge the teacher survey and teacher evaluation rating data files. Reminders will also be 

sent via email using the survey software. 

Participants’ contact information (for sending the online survey) will be collected electronically from a 

district contact using a secure file sharing software such as Dropbox. Teacher evaluation rating data will 

also be collected in electronic format and stored in electronic databases created by MRL. 

A4.	Efforts	to	Identify	Duplication.

This project will use existing teacher evaluation rating data. The survey data is unique data that 

addresses the need for providing targeted feedback as part of a teacher evaluation system. REL Central 

reviewed the literature and determined that there is no alternative source for the information to be 

collected through the survey. 

A5.	Sensitivity	to	Burden	on	Small	Entities

The data collection does not involve small entities. 
8

A6.	Consequence to	Federal	Program	or	Policy	Activities	if	the	Collection	is	Not	Conducted	or	

is	Conducted	Less	Frequently

If the proposed data were not collected, the goals of the IES Regional Education Laboratory program

may not be met. REL Central would not be able to “conduct and support high quality studies on key 

regional priorities,” resulting in decision makers in the region not having access to the research needed 

to build “an education system in which decisions are firmly grounded in data and research.”1

Additionally, if the proposed data were not collected, the states would not have timely and relevant 

research on teacher feedback in evaluation systems to inform training and guidance to evaluators on 

providing feedback to teachers. The data will be collected only one time; thus, there is no “frequency” 

component to the data collection.

A7.	Special	Circumstances

There are no special circumstances. 

A8.	Federal	Register	Announcement	and	Consultation

Federal Register Announcement

ED will publish Federal Register Notices to allow both a 60-day and 30-day public comment period. The 

REL will assist ED in addressing any public comments received. 

The 60 day Federal Register notice was published on 7/10/13, Vol. 78, page 41386. No comments were 

received. The 30-day Federal Register Notice was published on [DATE] (Appendix X).”

Consultations Outside the Agency

REL Central has consulted with research alliance members on the availability of data, the clarity of the 

instructions, and the survey instrument. Additionally, we have consulted with content and methods 

experts to develop the study. The following individuals were consulted on the statistical, data collection, 

and analytic aspects of this study:

Name Title Organization Contact Information

Dr. Fatih Unlu Senior Scientist Abt Associates Fatih_Unlu@abtassoc.com

617-520-2528

Katy Anthes Executive Director of 

Educator 

Effectiveness

Colorado Department 

of Education

Anthes_K@cde.state.co.us

Britt Wilkenfeld Strategic Data Fellow Colorado Department 

of Education

Wilkenfeld_B@cde.state.co.us

Dr. Jean Williams Evaluation Design 

Specialist

Colorado Department 

of Education

Williams_J@cde.state.co.us

 

1

REL goals were described on the program website: http://ies.ed.gov/ncee/edlabs/about/.
9

As appropriate, we will consult with representatives of those from whom information is to be obtained, 

such as research alliance members, and Technical Working Group members to address any public 

comments received on burden. The Technical Working Group members are experts in content and 

methodology and they provide consultation on the design, implementation, and analysis of the study. 

The research alliance members are educators and stakeholders in the Central Region who share a 

specific education concern. The research alliance members have a shared interest in the proposed 

study, and they provide feedback on the design and implementation of the study. 

A9.	Payment	or	Gift	to	Respondents

Teachers participating in the pilot will be offered a $30 Visa gift card to participate in the study. Teachers

participating in the full study also will be offered an incentive of a $30 Visa gift card to participate in the 

study. This incentive was determined using NCEE guidance, which suggests a $30 incentive for a 30-

minute survey about interventions (NCEE, 2005). Additionally, the results of the Reading First Impact 

Study (RFIS) incentives study were considered, which found that incentives significantly increase 

response rates in surveys (Gamse, Bloom, Kemple, & Jacob, 2008). Teachers will receive the incentive 

upon completion of the survey.

A10.Confidentiality	of	the	Data

REL Central will be following the new policies and procedures required by the Education Sciences 

Reform Act of 2002, Title I, Part E, Section 183: "All collection, maintenance, use, and wide 

dissemination of data by the Institute" are required to "conform with the requirements of section 552 of 

title 5, United States Code, the confidentiality standards of subsection (c) of this section, and sections 

444 and 445 of the General Education Provision Act (20 U.S.C. 1232g, 1232h)." These citations refer to 

the Privacy Act, the Family Educational Rights and Privacy Act, and the Protection of Pupil Rights 

Amendment. Subsection (c) of section 183 referenced above requires the Director of IES to "develop 

and enforce standards designed to protect the confidentiality of persons in the collection, reporting, and 

publication of data." Subsection (d) of section 183 prohibits the disclosure of individually identifiable 

information, and makes any publishing or communicating of individually identifiable information by 

employees or staff a felony.

REL Central will protect the confidentiality of all information collected for the study and will use it for

research purposes only. No information that identifies any study participant will be released. 

Information from participating institutions and respondents will be presented at aggregate levels in 

reports. Unique identification numbers will be assigned to each participating teacher and used to 

identify all survey responses. The ID number/name association files will be kept secure in a confidential 

file separate from the data analysis file. No information identifying respondents will be included in the 

study data files or reports. Data from the online survey software system will be downloaded and deleted 

from the online system within one week after the survey window closes. These data files will then be 

stripped of any identifying information. Information will be reported in aggregate so that individual 

responses are not identifiable. All identification lists will be destroyed at the end of the project. The 

research team is trained to follow strict guidelines for soliciting consent, administering data collection 
10

instruments, and preserving respondent confidentiality. All members of the research team have 

successfully completed the Collaborative Institutional Training Initiative (CITI) course in the Protection of 

Human Research Subjects through Liberty IRB. 

REL Central will collect consent forms from study participants which will also be stored in a locked file.

Copies of the consent form for the pilot and full study are provided in Attachments D and E,

respectively. A copy of the affidavit of non-disclosure is provided in Attachment F for each researcher 

who will have access to the data. 

All study materials will include the following language:

Per the policies and procedures required by the Education Sciences Reform Act of 2002, Title I, Part E, 

Section 183, 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. Any willful disclosure of such information for nonstatistical purposes, except 

as required by law, is a class E felony.

A11. Additional	Justification	for	Sensitive	Questions	

No questions of a highly sensitive nature are included in the survey or interview. 

A12.	Estimates	of	Hour Burden	(total	burden	is	being	divided	by	the	two	years	of	actual	

burden	collection	to	establish	an	annual	figure)

Pilot Study

The total reporting burden associated with the data collection for the pilot study is 672 hours (see Table 

1). A stratified random sample of 900 teachers in four districts from one state will be drawn from 

approximately 6,887 records. Sampled teachers will be invited to attend a 30-minute in-person meeting 

in which they will learn about the study and sign consent forms if they agree to participate. Of the 900 

sampled teachers, we expect that approximately 450 will agree to participate in the study. Of the 450 

teachers, we expect that approximately 180 (40%) will complete the survey without any email 

reminders; the remaining 270 teachers will require email reminders. Each email reminder is estimated 

to take 2.5 minutes (0.04 hours), with an estimated average of 2 additional emails sent per teacher. The 

first email notification is estimated as part of the survey completion time. Of the 450 who agree to 

participate in the survey, an estimated 360 (80%) will complete the survey, with an estimated burden of 

30 minutes (0.50 hours) per teacher. In addition to the survey, 20 participants will agree to participate in 

a 1-hour interview.
11

Table 1. Estimated annualized burden hours (Pilot)

Instrument

Person 

Incurring 

Burden

Number of 

Respondents

Responses 

per 

Respondent

Total 

Responses

Hours 

per 

Response

Total 

Burden 

Hours

Recruitment 

and Consent 

Teacher 900 1 900 .5 450

Email 

reminder

Teacher 270 2 540 .04 22

Pilot Survey Teacher 360 1 360 .5 180

Interview Teacher 20 1 20 1 20

Total 672

Table 2. Estimated annualized cost to respondents (Pilot) 

Instrument

Person Incurring 

Burden

Total 

Burden 

Hours

Hourly Wage 

Rate

Total 

Respondent 

Costs

Recruitment and 

Consent

Teacher 450 $28 $12,600

Email reminder Teacher 22 $28 $616

Pilot survey Teacher 180 $28 $5,040

Interviews Teacher 20 $28 $560

Total 672 $18,816

Bureau of Labor Statistics, Occupational Employment Statistics, estimates annual teacher salary to be

$57,810. To calculate an hourly wage, a 40-hour work week was assumed, resulting in an average hourly 

wage of $27.79.

Full Study

The total reporting burden associated with the data collection for the full study is 3,541 hours (see Table 

3). A stratified random sample of 4,892 teachers in 134 districts on three Central states will be drawn 

from approximately 32,500 records. Sampled teachers will be invited to attend a 30-minute in-person 

meeting, in which they will learn about the study and sign consent forms if they agree to participate. Of 

the 4,892 sampled teachers, we estimate that approximately 2,446 will agree to participate in the study 

and therefore will be invited to take the survey. Of the 2,446 teachers, we estimate that approximately 

978 (40%) teachers will complete the survey without any email reminders; the remaining 1,468 teachers 

(60%) will require up to two email reminders. Each email reminder is estimated to take 2.5 minutes 
12

(0.04 hours), with an estimated average of 2 additional emails sent per teacher. Of the 2,446 teachers

who agree to participate in the survey, an estimated 1,956 will complete the survey, with an estimated 

burden of 30 minutes (0.50 hours) per teacher.

Table 3. Estimated annualized burden hours (Full Study)

Instrument

Person 

Incurring 

Burden

Number of 

Respondents

Responses 

per 

Respondent

Total 

Responses

Hours 

per 

Response

Total 

Burde

n 

Hours

Recruitment 

and Consent 

Teacher 4,892 1 4,892 .5 2,446

Email 

reminder

Teacher 1,468 2 2,936 .04 117

Survey Teacher 1,956 1 1,956 .5 978

Total 3,541

Table 4. Estimated annualized cost to respondents (Full Study) 

Instrument

Person Incurring 

Burden

Total 

Burden 

Hours

Hourly Wage 

Rate

Total 

Respondent 

Costs

Recruitment and 

Consent

Teacher 2,446 $28 $68,488

Email reminder Teacher 117 $28 $3,276

Pilot survey Teacher 978 $28 $27.384

Total 3,541 $99,148

Bureau of Labor Statistics, Occupational Employment Statistics, estimates Annual Teacher Salary to be

$57,810. To calculate an hourly wage, a 40-hour work week was assumed, resulting in an average hourly 

wage of $27.79.

Annual Responses and Burden

The annual reporting burden is 2,106 hours with 5802 responses. 

A13. Estimate	for	the	Total	Annual	Cost	Burden	to	Respondents	or	Record Keepers

There are no start-up costs for this collection. 

A14.Estimates	of	Annualized	Costs	to	the	Federal	Government.	
13

The total estimated cost for this study is $373,250 over three years. The average yearly cost is $124,417. 

The costs in the pilot study include developing, administering, analyzing, and refining the survey, and 

conducting and analyzing the interview results. The costs in the full study include administering the 

survey, collecting and cleaning teacher evaluation rating data, and analyzing and reporting results.

In addition to the evaluation costs, there are personnel costs of several Federal employees involved in 

the oversight and analysis of information collection that amount to an annualized cost of $5,000 for 

Federal labor. The total annualized cost for the evaluation is therefore the sum of the annual contracted 

evaluation cost ($124,417 and the annual Federal labor cost ($5,000), or a total of $129,417 per year.

A15.Reasons	for	Program	Changes	or	Adjustments	

This is a new study.

A16. Plans	for Tabulation	and	Publication.

Tabulation Plans

Pilot	Study

The pilot test is intended to test the survey prior to full study implementation on a small sample of 

teachers in four districts that are implementing a teacher evaluation system that has the intention of 

informing teacher development through feedback. The pilot study will include the administration of an 

online survey and interviews. 

Researchers will examine the clarity and psychometric properties of the survey. Data from the 

interviews will be analyzed to help determine if the respondents interpreted the directions and 

questions as the researchers intended. Interview transcripts will be coded using MAXQDA, a qualitative 

analysis program, to identify patterns in responses to the interview questions. 

Researchers will conduct item analysis to identify problematic items and to examine the internal 

consistency of the survey. Cronbach’s alpha coefficients will be computed for each scale and correlation 

analysis will be used to determine the extent to which the items correlate with the scales and the effect 

on Cronbach’s alpha if the item were removed. Items will be deleted if removal of the items would 

increase Cronbach’s alpha and increase the average inter-item correlation. Following the criterion 

proposed by Nunnally & Bernstein (1994), alpha coefficients of 0.70 or higher will be considered as 

reliable. 

Following the item analysis, researchers will analyze the proposed measurement model (see Section A, 

Figure 3) using confirmatory factor analysis (CFA). The factors in this measurement model will be 

assumed to covary, with no hypothesis that any one factor causes the other. The methods for CFA 

proposed by Kline (2005) will be followed. First, researchers will test for a single factor. This step allows 

researchers to examine the feasibility of testing a more complex model, and to determine if the fit of the 

single-factor model is comparable. Selected fit indices will be examined to determine if the results 

indicate a poor fit, which would substantiate the need for a more complex model. Next, the five-factor 

model will be examined. Researchers will examine the model fit indices, the indicator loadings, and the 
14

factor correlations to determine not only if the model fits the data, but also if the indicators have 

significant loadings on the associated factors, and if the estimated correlations between the factors are 

not “excessively high (e.g., > .85)” (Kline, 2005; p. 73). If the model does not fit the data, the model will 

be respecified based on an examination of modification indices, correlation residuals, and practical and 

theoretical considerations. To determine if the added constraints five-factor model significantly reduces 

model fit, Chi-square difference tests will be performed.

Full	Study

The full study will address the following research questions:

1. What are teachers’ perceptions of the usefulness, accuracy, and credibility of the feedback they 

receive as a part of their performance evaluation?

2. What is the relationship among the characteristics of performance feedback (usefulness, 

accuracy, and credibility)?

3. What is the relationship between feedback characteristics and teacher responsiveness?

4. What is the relationship between teacher responsiveness and teacher performance?

The study will use descriptive methods, as described below, to address research question 1. For survey 

items 2 and 3, the median response and the percentage of teachers selecting each response choice will 

be reported. The median response will be reported because the scales are ordinal and the median is the 

most appropriate central tendency for ordinal data. 

The principal investigator considered different analytic strategies that would provide the best 

information toward understanding the role of various feedback variables in teacher performance in a 

teacher evaluation system (research questions 2, 3, and 4). The principal investigator determined that 

an exploration of the structure of the relationship of the feedback variables as they relate to 

performance would be most relevant and therefore necessitated the use of structural equation 

modeling (SEM).

SEM techniques will be used to examine a model of performance feedback within each state. The 

structural model (Figure 2) and the underlying measurement model for all of the latent variables except 

the teacher performance variable will be the same for each state (Figure 3). However, because each 

state collects slightly different evidence of teacher performance in their teacher evaluation system, the 

indicators will vary as presented in Figure 4. In the model, straight arrows represent a direct effect, 

while curved arrows represent covariance of the latent variable due to common predictors, which lie 

outside of the model. The latent variables in the model are the input, mediating, and outcome variables 

that were identified previously based on research on effective feedback. Table 5 presents the name of 

each latent variable, the name of each latent variable as identified in the proposed model, and the 

instrument that will be used to provide a measure of each latent variable. The model suggests that 

performance (PERF) is directly influenced by both access to learning opportunities (OPP) and 

responsiveness to feedback (RESP), and also indirectly by usefulness (USE), accuracy (ACC), credibility 

(CRED), and opportunities (OPP), which influence performance via responsiveness (RESP). The survey 

instruments and teacher evaluation ratings described in the data collection section are used as 

indicators of the latent variables present in the proposed structural model. Table 6 presents example 
15

teacher survey items aligned to the variables in the model (variable names are included in parentheses)

that are linked to survey data. For the performance variable, teacher evaluation rating data will be 

collected from the districts. Tables 7 through 9 present a brief description of the indicators on which 

teachers are rated for each state’s teacher evaluation system. Ratings on each of the states’ indicators 

will be collected to serve as the indicators for teacher performance. The loading of indicators on latent 

variables is presented in the proposed measurement model. As appropriate, based on an examination of 

comparing descriptive statistics for the variable groupings, background information will also serve as 

covariates in the model analysis. The measurement model may be modified once the final survey 

instrument is developed to include all items related to the latent variables. SEM allows for the 

examination of the latent structure underlying a set of observed variables (Byrne, 1998). 

Figure 2. Hypothesized structural model of performance feedback

Opportunities

(OPP)

Usefulness

(USE)

Accuracy

(ACC)

Credibility

(CRED)

Responsiveness

(RESP)

Performance

(PERF)
16

Figure 3. Hypothesized measurement model except performance variable

Figure 4. Hypothesized measurement model for performance variable by state

Table 5: Data collection instrument for each variable

Latent Variable Latent Variable Name

in the Model Instrument

Usefulness of Feedback Usefulness (USE) Usefulness items from the 

researcher developed teacher 

survey

Accuracy of Feedback Accuracy (ACC) Accuracy items from the 

researcher developed teacher 
17

survey

Credibility of the Person Providing 

Feedback

Credibility (CRED) Credibility items from the 

researcher developed teacher 

survey

Responsiveness to Feedback Responsiveness (RESP) Responsiveness items from the 

researcher developed teacher 

survey

Access to Opportunities to Learn Opportunities (OPP) Opportunities items from the 

researcher developed teacher 

survey

Performance Ratings Performance (PERF) Ratings on the state’s teacher 

evaluation standards, including 

professional standards and the 

student growth standard

(provided by the district)

Table 6: Example teacher survey items by model variable

Model Variable Example Survey Items (model variable names provided in parenthesis)

Usefulness

(USE)

- My evaluator recommended next steps for finding professional development to 

improve my teaching. (REC)

- My evaluator provided feedback within an appropriate timeframe. (TIME)

- My evaluator provided feedback as frequently as I needed it. (FREQ)

- My evaluator’s feedback included specific suggestions to improve my 

content/subject knowledge. (CIMP)

- My evaluator’s feedback included specific instructional strategies that I could use 

to improve my teaching. (SIMP)

- My evaluator’s feedback included specific classroom management strategies that I 

could use to improve my teaching. (MIMP)

Accuracy

(ACC)

- The feedback I received was an accurate portrayal of my teaching. (ACCU)

- The classroom observations that informed the feedback I received represented a 

typical day in my classroom. (REP)

- I would receive the same feedback from a different evaluator. (EVAL)

- I would receive the same feedback if my evaluator had examined different 

evidence (for example, if they observed a different lesson, or reviewed additional 

evidence). (EVID) 

Credibility

(CRED)

- My evaluator had sufficient knowledge of my subject/content. (CONT)

- My evaluator had sufficient knowledge of instructional strategies. (INST)

- My evaluator had sufficient knowledge of how my students learn. (STUD)

- My evaluator had a sufficient understanding of the curriculum being observed. 

(CURR)

- My evaluator had a sufficient understanding of the established teacher evaluation 

criteria. (CRIT) 

Opportunities

(OPP)

- I had access the professional development that I needed in order to implement 

suggestions provided in my feedback. (PD)

- I had access to a coach/mentor who supported me in implementing suggestions 
18

Model Variable Example Survey Items (model variable names provided in parenthesis)

provided in my feedback. (COACH)

- I was able to observe expert teachers modeling skills that related to my feedback. 

(MODEL)

- I had time during the school day to plan for implementing new strategies based on 

my feedback. (PLAN)

Responsiveness

(RESP)*

- Because of the feedback I received from my evaluator, I tried new instructional 

strategies in my classroom. (INSTRA)

- Because of the feedback I received from my evaluator, I tried new classroom 

management strategies in my classroom. (CMSTRA)

- Because of the feedback I received from my evaluator, I sought advice from a 

coach or mentor. (ADVICE)

- Because of the feedback I received from my evaluator, I sought professional 

development opportunities. (PROF)

* The responsiveness items were based on items used in Tuytens & Devos (2011), and Geijsel, Sleegers, Stoel, & Krüger (2009).
19

Table 7: Indicators of performance in the Colorado Model Evaluation System

Name of 

Indicator for 

Performance in 

the Model

Colorado 

Standard Description of Indicator

PED

Demonstrate 

pedagogical 

expertise in the 

content 

Rating using a rubric based on observations and at least one 

of the following measures: (a) student perception measures 

(such as surveys), (b) peer feedback, (c) feedback from 

parents or guardians; and/or (d) reviews of teacher lesson 

plans or student work samples

ENV

Establish a safe 

and respectful 

learning 

environment

INST

Plan and deliver 

effective 

instruction 

REF Reflect on 

practice

LEAD Demonstrate 

leadership 

SAG Student academic 

growth

Rating based on multiple measures of student academic 

growth, such as median student growth percentiles

ALL Overall

Weighted average of the ratings received for each of the 

professional practice standards (50%) and the student growth 

standard (50%)

Table 8: Indicators of performance in the Kansas evaluation system

Name of Indicator 

for Performance 

in the Model

Kansas Standard Description of Indicator

LEARN The Learner and 

Learning

Rating using a rubric based on observations and other 

evidence, such as student work samples, lesson plans and 

professional learning plans.

CONT Content 

Knowledge

INST Instructional 

Practice

PROF Professional 

Responsibility

SAG N/A

Student growth is a significant factor in the teacher 

evaluation rating. Kansas is currently determining how 

student growth will be measured and how it will be 

incorporated into the evaluation system
20

Table 9: Indicators of performance in the Missouri evaluation system

Name of Indicator 

for Performance 

in the Model

Description of Indicator

EOY End-of-year rating using a rubric on three selected indicators based on evidence

including data on professional commitment (such as credentials and professional 

development plans), professional practice (such as classroom observation data), 

and/or professional impact (such as student performance measures)

GROW Growth rating based on difference between the end-of-year and baseline ratings 

on three selected indicators

ALL Overall rating on the rubric based 50% on overall growth and 50% on end-of-year 

ratings

SAG Student growth is identified as a significant factor in the Missouri Educator 

Evaluation System, and the system is structured such that a teacher cannot 

receive a proficient or distinguished rating if student growth is low. The plans for 

including student growth are currently under development.

Each state’s model will be examined separately using a two-step modeling process. First, researchers 

will examine the fit of the measurement model. Researchers will examine multiple fit indices (2, 

goodness-of-fit index [GFI], parsimony goodness-of-fit index [PGFI], root mean square error of 

approximation [RMSEA], and comparative fit index [CFI]) and whether or not factor loadings are 

significant to determine the degree to which the model fits the data. If it is determined that the 

measurement model fits the data, analysis will proceed with the structural portion of the model. If it is 

determined that the model does not fit the data, researchers will examine the modification indices, 

residuals, factor loadings, factor correlations, and theoretical considerations to potentially re-specify the 

model. The model would only be re-specified only if the re-specification is in line with the theory that 

supports the model. Re-specification may include allowing indicators to load on more than one factor 

and allowing measurement errors to covary.

To determine the relationship among the variables proposed in each model of performance feedback,

the fit of the structural model will be tested and the path coefficients within the model will be 

examined. Information about the model for each state is presented in Table 10. Maximum likelihood will 

be used as the estimation method. In maximum likelihood, “the estimates are the ones that maximize 

the likelihood (the continuous generalization) that the data (the observed covariances) were drawn from 

this population”(Kline, 2005, p. 112). Once the model is tested, the path coefficients will be examined, 

and several goodness-of-fit indices (2, goodness-of-fit index [GFI], parsimony goodness-of-fit index 

[PGFI], root mean square error of approximation [RMSEA], and comparative fit index [CFI]) will be 

examined to determine the degree to which the model fits the data. Multiple-model fit indices are 

needed to accurately examine model fit (Tabachnick & Fidell, 2001). Modification indices, which are 

reported in most SEM statistical output, will be examined to determine the extent to which the overall 

model fit would increase if particular paths are freely estimated.
21

Table 10: Information about each state’s structural equation model2

Colorado Kansas Missouri

Parameters 69 65 63

Variances of Measurement Error 30 28 27

Factor Loadings 30 28 27

Factor Correlations 6 6 6

Factor Covariances 3 3 3

Indicators 30 28 27

Data Points 435 378 351

Degrees of Freedom 366 313 288

The assumptions of multivariate normality and linearity will be evaluated by analyzing the multivariate 

and univariate skewness and kurtosis. The distribution and shape of all survey data and evaluation 

ratings will be examined to screen for outliers, skewness, and kurtosis. If significant skewness or kurtosis 

is found (alpha ≤ .01), estimation techniques that address non-normality, such as robust maximum 

likelihood (RML), will be used. RML gives standard errors with unspecified distributional assumptions,

yielding the least biased standard errors when multivariate normality assumptions are false (Bentler & 

Dijkstra, 1985; Chou & Bentler, 1995).

Researchers will synthesize the findings across the states using meta-analytic structural equation 

modeling (MASEM) techniques such as the two-stage structural equation modeling (TSSEM) approach 

(Cheung and Chan, 2005). Using the TSSEM approach, researchers will first use multiple-group 

confirmatory factor analysis approaches to test the homogeneity of the correlation matrices across the 

states. Next, the pooled correlation matrix and its asymptotic covariance matrix (ACM) will be calculated 

from the state matrices. The pooled correlation matrix will be analyzed and its ACM will be used as the 

weight matrix using the asymptotic distribution-free method as the estimation method. Fit indices from 

this analysis will be examined to determine the extent to which model fits the pooled data.

Publication Plans

All results for REL Central’s rigorous studies will be made available to the public through peer-reviewed 

evaluation reports that are published by IES on the IES website. The datasets from these rigorous studies 

will be turned over to the REL’s IES project officer. These data will become IES restricted use datasets 

requiring a user’s license that is applied for through the same process as NCES restricted use datasets. 

 

2

These numbers were calculated using methods proposed by Tabachnick & Fidell (2001). The number of 

parameters equals the total number of regression coefficients, variances, and covariances to be estimated in the 

model. Each of the indicators in the models have an associated variance of measurement error and factor loading. 

The six straight arrows between the factors represent factor correlations and the three curved arrows in the model 

represent factor covariances. Data points equals n(n– 1)/2, where n is the number of indicators. Degrees of 

freedom equals the number of data points minus the number of parameters. 
22

Even the REL contractor would be required to obtain a restricted use license to conduct any work with 

the data beyond the original evaluation.

Timeline

The timeline for data collection, analysis and reporting is shown in Table 11.

Table 11: Schedule of activities

Study Activity Schedule

Pilot Study Hold in-person recruitment meeting November 2013–February 2014

Administer survey instrument April 2014–May 2014

Conduct interviews May 2014

Analyze and report May 2014–July 2014

Full Study Hold in-person recruitment meeting August 2014–January 2015

Administer survey instrument April 2015–May 2015

Collect existing teacher evaluation rating data May 2015–June 2015 

Analyze and report May 2015–November 2015

A17. Approval	to	Not	Display	the	Expiration	Date	for	OMB	Approval

No request is being made for exemption from displaying the expiration date.

A18. Exception	to	the	Certification	Statement	

No exceptions to the certification statement are requested or required.
23

References

Aaronson, D., Barrow, L., & Sander, W. (2007). Teachers and student achievement in the Chicago public 

schools. Journal of Labor Economics, 25(1), 95–135.

Bentler, P. M., & Dijkstra, T. (1985). Efficient estimation via linearization in structural models. In P. R. 

Krishnaiah (Ed.), Multivariate analysis VI (pp. 9–42). Amsterdam: North Holland.

Byrne, B. M. (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, 

applications, and programming. Mahwah, NJ: Erlbaum.

Central States Education Laboratory (2011). Technical Proposal – Final Proposal Revisions, Regional 

Educational Laboratories 2012-2017 Central Region, RFP ED-IES-11-R-0036. Centennial, CO: 

Marzano Research Laboratory.

Cheung, M. W.-L., Chan, W. (2005). Meta-analytic structural equation modeling: A two-stage approach. 

Psychological Methods, 10(1), 40–64.

Chou, C. P., & Bentler, P. M. (1995). Estimates and tests in structural equation modeling. In R. H. Hoyle 

(Ed.), Structural equation modeling (pp. 37–55). Thousand Oaks, CA: Sage.

Colorado Association of School Executives. (n. d.). Colorado School Leaders Speak Out on Teacher 

Evaluation. Retrieved July 18, 2012 from http://www.cocase.org/associations/779/files/2%200%20Colorado%20School%20Leaders%20Speak%20Out%2

0On%20Teacher%20Evaluation.pdf.

Geijsel, F., Sleegers, P., Stoel, R., & Krüger, M. (2009). The effect of teacher psychological, school 

organizational and leadership factors on teachers’ professional learning in Dutch schools. The 

Elementary School Journal, 109, 406–427.

Gamse, B.C., Bloom, H.S., Kemple, J.J., Jacob, R.T., (2008). Reading First Impact Study: Interim Report 

(NCEE 2008-4016). Washington, DC: National Center for Education Evaluation and Regional 

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

Ilgen, D. R., Fisher, C. D., & Taylor, M. S. (1979). Consequences of individual feedback on behavior in 

organizations. Journal of Applied Psychology, 64(4), 349–371.Kane, T. J., & Staiger, D. O. (2012). 

Gathering feedback for teaching: combining high-quality observations with student surveys and 

achievement gains. Methods of Effective Teaching project, Bill & Melinda Gates Foundation, 

Seattle, WA. Retrieved July 18, 2012 from 

http://www.metproject.org/downloads/MET_Gathering_Feedback_Practioner_Brief.pdf.

Kline, R.B. (2005). Principles and practices of structural equation modeling (2nd ed.). New York: Guilford.

Konstantopoulos, S., & Chung, V. (2010). The persistence of teacher effects in elementary grades. 

American Educational Research Journal 48(2), 361–386.

National Center for Education Evaluation (2005).Guidelines for incentives for NCEE impact evaluations. 

Washington, DC: Author.
24

Nye, B., Konstantopoulos, S., & Hedges, L. V. (2004). How large are teacher effects? Educational 

Evaluation and Policy Analysis, 26(3), 237–257.

Papay, J. P. (2012). Refocusing the debate: Assessing the purposes and tools of teacher evaluation. 

Harvard Educational Review, 82(1), 123–141.

Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Boston: Allyn and Bacon.

Tuytens, M., & Devos, G. (2011). Stimulating professional learning through teacher evaluation: An 

impossible task for the school leader? Teaching and Teacher Education 27, 891–899.

Wright, S. P., Horn, S. P., & Sanders, W. L. (1997). Teacher and classroom context effects on student 

achievement: implications for teacher evaluation. Journal of Personnel Evaluation in Education 

11: 57–67.