U . S . D E P A R T M E N T O F E D U C A T I O N

��

Study of the Effect of the Talent 

Search Program on Secondary and 

Postsecondary Outcomes in Florida, 

Indiana and Texas
A Study of the Effect of the Talent Search Program 

On Secondary and Postsecondary Outcomes in 

Florida, Indiana and Texas 

Final Report From Phase II 

Of the National Evaluation 

By: 

Jill M Constantine 

Neil S. Seftor 

Emily Sama Martin 

Tim Silva 

David Myers 

Prepared for: 

U.S. Department of Education 

Office of Planning, Evaluation and Policy Development 

Policy and Program Studies Service 

2006 
This report was prepared for the U.S. Department of Education under Contract Number ED98-CO-0073 with 

Mathematica Policy Research Inc. The project monitor was Sandra Furey in the Policy and Program Studies 

Service. The views expressed herein do not necessarily represent the positions or policies of the Department of 

Education. No official endorsement by the U.S. Department of Education is intended or should be inferred. 

U.S. Department of Education 

Margaret Spellings 

Secretary 

Office of Planning, Evaluation and Policy Development 

Tom Luce 

Assistant Secretary 

Policy and Program Studies Service 

Alan Ginsburg 

Director 

Program and Analytic Studies 

David Goodwin 

Director 

June 2006 

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 suggested citation is: U.S. Department of Education, Office of 

Planning, Evaluation and Policy Development, Policy and Program Studies Service, A Study of the Effect of the 

Talent Search Program on Secondary and Postsecondary Outcomes in Florida, Indiana and Texas: Final Report 

From Phase II of the National Evaluation, Washington, D.C., 2006. 

To order copies of this report, write: 

ED Pubs 

Education Publications Center 

U.S. Department of Education 

P.O. Box 1398 

Jessup, MD 20794-1398; 

via fax, dial (301) 470-1244; 

or via electronic mail, send your request to: edpubs@inet.gov. 

You may also call toll-free: 1-877-433-7827 (1-877-4-ED-PUBS). If 877 service is not yet available in your area, 

call 1-800-872-5327 (1-800-USA-LEARN). Those who use a telecommunications device for the deaf (TDD) or a 

teletypewriter (TTY) should call 1-800-437-0833. 

To order online, point your Internet browser to: www.edpubs.org. 

This report is also available on the Department’s Web site at http://www.ed.gov/offices/list/opepd/ppss/index.hmtl. 

On request, this publication is available in alternate formats, such as Braille, large print or computer diskette. For 

more information, please contact the Department’s Alternate Format Center at 202-260-9895 or 202-205-8113. 
iii

ACKNOWLEDGMENTS 

This report completes Phase II of the National Evaluation of Talent Search and reflects the 

contributions of many people. The authors would like to thank Project Officer Sandra Furey of 

the Policy and Program Studies Service (PPSS) in the U.S. Department of Education (ED) for 

her technical guidance, support, and encouragement throughout all phases of the study. Other 

PPSS staff providing guidance and support at key times were David Goodwin and Maggie 

Cahalan. The study also benefited from feedback and assistance from staff members in the 

Office of Federal TRIO programs including Larry Oxendine and Frances Bergeron. 

The compilation of the data described in this report would not have been possible without 

assistance from individuals across the country. Talent Search project directors and staff 

members in Florida, Indiana, Minnesota, Texas, and Washington provided volumes of data on 

the students they served. Data on applications for federal financial aid were generously and 

expertly provided by Joe Mike from the Office of Postsecondary Education in ED and Elise 

Brand from Computer Business Methods Inc. Finally, we owe special thanks to dedicated staff 

and researchers in the study states, including Glenda Droogsma Musoba, Edward St. John, and 

Jack Schmit at Indiana University, Darlene Gouge and Shannon Housson of the Texas Education 

Agency, Kathy Benson of the Texas Higher Education Coordinating Board, and Jay Pfeiffer, 

Murray Cooper, Kathy Peck, Jeff Sellers, and Duane Whitfield at the Florida Department of 

Education, whose heroics in battling through the four-hurricane summer of 2004 to provide data 

were greatly appreciated. 

The study was performed under contract to Mathematica Policy Research Inc. The 

successful compilation of the data for the study was due to the efforts and skills of several 

Mathematica project team members: Justin Humphrey and Missy Thomas led the effort to 

collect data from Talent Search projects, Jackie Agufa provided technical supervision in 

processing the data, Carol Razafindrakoto and Miriam Loewenberg provided expert 

programming to compile millions of records across all of the states, and Annette Luyegu 

conducted phone interviews with all Talent Search project directors providing data for this 

report. Patricia Ciaccio and Roy Grisham edited numerous drafts of the report, and Bill Garrett 

was responsible for word processing and the layout of the report. Paul Decker and Mark 

Dynarski provided a constructive quality assurance review at Mathematica. 

v 

CONTENTS 

Chapter Page 

 Acknowledgments................................................................................iii 

 List of Tables......................................................................................ix 

 List of Figures..................................................................................... xiii 

 Executive Summary......................................................... ...................xv 

 I Introduction.....................................................................................................................1 

A. Program Description ................................................................................................1 

1. Context for Studying Talent Search.................................................................2 

2. Services Offered by Talent Search ..................................................................4 

3. Study Description and Report Overview .........................................................4 

 II Study Design and Methodology......................................................................................7 

A. Study Design............................................................................................................7 

1. Research Questions..........................................................................................7 

2. State and Project Selection...............................................................................7 

B. Analytic Approach...................................................................................................9 

1. The Preferred Comparison Group....................................................................9 

2. Using Propensity Score Models to Identify the Comparison Group .............11 

C. Estimating the Effect of Talent Search on Secondary and Postsecondary 

Outcomes ...............................................................................................................14 

1. Empirical Specification..................................................................................14 

2. Standard Errors ..............................................................................................15 

3. Reliability of Propensity Score Matching Methods.......................................15 
CONTENTS 

Chapter Page

vi

 III Texas .............................................................................................................................17 

A. Introduction............................................................................................................17 

B. Data ........................................................................................................................18 

1. Strengths and Weaknesses of the Data ..........................................................20 

2. Characteristics of Talent Search Participants and Potential 

Comparison Students .....................................................................................22 

C. Comparison Groups ...............................................................................................25 

D. Results....................................................................................................................29 

1. High School Completion................................................................................31 

2. Application for Financial Aid ........................................................................32 

3. Postsecondary Enrollment .............................................................................34 

4. Postsecondary Persistence .............................................................................37 

E. Discussion of Results.............................................................................................43 

 IV Indiana...........................................................................................................................49 

A. Introduction............................................................................................................49 

B. Data ........................................................................................................................49 

1. Strengths and Weaknesses of the Data ..........................................................52 

2. Characteristics of Talent Search Participants and Potential 

Comparison Students .....................................................................................54 

C. Comparison Groups ...............................................................................................57 

D. Results....................................................................................................................63 

1. Application for Financial Aid ........................................................................63 

2. Postsecondary Enrollment .............................................................................64 

E. Discussion of Results.............................................................................................68 
CONTENTS 

Chapter Page

vii

 V Florida ...........................................................................................................................71 

A. Introduction............................................................................................................71 

B. Data ........................................................................................................................72 

1. Strengths and Weaknesses of the Data ..........................................................73 

2. Characteristics of Talent Search Participants and Potential 

Comparison Students .....................................................................................75 

C. Comparison Groups ...............................................................................................77 

D. Results....................................................................................................................79 

1. High School Completion, Application for Financial Aid, and 

College Entrance Test Taking........................................................................81 

2. Postsecondary Enrollment .............................................................................82 

3. Postsecondary Persistence and Completion...................................................87 

E. Discussion of Results.............................................................................................90 

 VI Comparing Findings Across the States .........................................................................95 

A. Implications of the Data for Studying Talent Search.............................................95 

B. Findings .................................................................................................................96 

C. Implications..........................................................................................................100 

 References...................................................................................................................103 

Appendix A: Chapter Tables ....................................................................................105 

Appendix B: Compilation of Data Sources and Feasibility of Evaluations 

Based on Administrative Records.......................................................131 

ix 

TABLES 

Table Page 

I Number of Talent Search Projects, Talent Search Participants, and 

Comparison Students, by State .................................................................................. xvi 

I.1 Services Most Commonly Offered by Talent Search Projects in 2000 .........................5 

III.1 Texas Data Sources......................................................................................................19 

III.2 Baseline Characteristics of Talent Search Participants and All Other 

Students, All of Texas..................................................................................................23 

III.3 Below Grade and Persistence of Talent Search Participants and All Other 

Students in Texas .........................................................................................................25 

III.4 Assessing Balance Between Talent Search Participants and Nonparticipants 

in the Same High Schools in Texas .............................................................................28 

III.5 Assessing Balance Between Talent Search Participants and Nonparticipants 

at Different High Schools in the Same Districts in Texas ...........................................30 

III.6 Percentage of Talent Search Participants and Comparison Students Who 

Completed High School in Texas, by Project..............................................................33 

III.7 Percentage of Talent Search Participants and Comparison Students Who 

Were First-Time Applicants for Federal Financial Aid in 1999 or 2000 in 

Texas, by Project..........................................................................................................35 

III.8 Percentage of Talent Search Participants and Comparison Students Who 

Enrolled in Any Public Postsecondary Institution in Texas, by Project......................38 

III.9 Percentage of Talent Search Participants and Comparison Students Who 

Enrolled in a Four-Year Public Postsecondary Institution in Texas, 

by Project .....................................................................................................................39 

III.10 Percentage of Talent Search Participants and Comparison Students Who 

Enrolled in a Two-Year Public Postsecondary Institution in Texas, 

by Project .....................................................................................................................40 

III.11 Continuous Enrollment in Four-Year Institutions and Total Credits Earned 

by Talent Search Participants and Comparison Students in Texas..............................42 

IV.1 Indiana Data Sources ...................................................................................................51 
TABLES 

Table Page

x

IV.2 Baseline Characteristics of Talent Search Participants and All Other 

Students in Ninth Grade in Fall 1995, All of Indiana..................................................55 

IV.3 Assessing Balance Between Talent Search Participants and Nonparticipants 

in the Same High Schools in Indiana...........................................................................60 

IV.4 Percentage of Talent Search Participants and Comparison Students Who 

Applied for Financial Aid or Enrolled in a Postsecondary Institution in 

Indiana, by Project Group............................................................................................67 

V.1 Florida Data Sources....................................................................................................73 

V.2 Baseline Characteristics of Talent Search Participants and All Other 

Students, All of Florida................................................................................................76 

V.3 Below Grade and Persistence of Talent Search Participants and All Other 

Students in Florida .......................................................................................................77 

V.4 Assessing Balance Between Talent Search Participants and Nonparticipants 

in the Same High Schools in Florida ...........................................................................78 

V.5 Assessing Balance Between Talent Search Participants and Nonparticipants 

at Different High Schools in the Same Districts in Florida .........................................80 

V.6 Percentage of Talent Search Participants and Comparison Students Who 

Completed High School and Applied for Financial Aid in Florida, 

by Project .....................................................................................................................83 

V.7 Percentage of Talent Search Participants and Comparison Students Who 

Took College Entrance Exams in Florida, by Project..................................................84 

V.8 Percentage of Talent Search Participants and Comparison Students Who 

Enrolled in Any Public Postsecondary Institution in Florida, by Project....................86 

V.9 Percentage of Talent Search Participants and Comparison Students Who 

Enrolled in a Four-Year Public Postsecondary Institution in Florida, 

by Project .....................................................................................................................88 

V.10 Percentage of Talent Search Participants and Comparison Students Who 

Enrolled in a Two-Year Public Postsecondary Institution in Florida, 

by Project .....................................................................................................................89 

V.11 Percentage of Talent Search Participants and Comparison Students Who 

Persisted in Public Postsecondary Institutions in Florida, by Project..........................91 
TABLES 

Table Page

xi

V.12 Percentage of Talent Search Participants and Comparison Students Who 

Completed a Two-Year Degree in Florida, by Project ................................................92 

A.III.1 Variable Descriptions—Texas...................................................................................107 

A.III.2 Baseline Characteristics of Talent Search Participants and Nonparticipants in 

Texas, by Project........................................................................................................109 

A.III.3 Below Grade and Persistence of Talent Search Participants and 

Nonparticipants in Texas, by Project.........................................................................115 

A.IV.1 Variable Descriptions—Indiana.................................................................................117 

A.IV.2 Baseline Characteristics of Talent Search Participants and Nonparticipants in 

Ninth Grade in Fall 1995, in Indiana, by Project Group............................................119 

A.V.1 Variable Descriptions—Florida.................................................................................123 

A.V.2 Baseline Characteristics of Talent Search Participants and Nonparticipants in 

Florida, by Project......................................................................................................125 

A.V.3 Below Grade and Persistence of Talent Search Participants and 

Nonparticipants in Florida, by Project.......................................................................129 

B.1 Summary of Talent Search Project Record Data Collection .....................................136 

xiii 

FIGURES 

Figure Page 

1 Talent Search Participants and Comparison Students Who Were First-Time 

Applicants for Federal Financial Aid, 1999–2000, by State.................................... xviii 

2 Talent Search Participants and Comparison Students Who Enrolled in a 

Public Postsecondary Institution, 1999–2000, by State............................................. xix 

3 Talent Search Participants and Comparison Students Who Enrolled in a 

Public Two-Year or Four-Year Institution, 1999–2000, by State ...............................xx 

III.1 Talent Search Participants and Comparison Students Who Completed High 

School and Were First-Time Applicants for Financial Aid from Texas 

in 1999 or 2000 ............................................................................................................32 

III.2 Talent Search Participants and Comparison Students Who Enrolled in a 

Public Postsecondary Institution in Texas in 1999, 2000, or 2001..............................36 

III.3 Talent Search Participants and Comparison Students Who Enrolled in a 

Public Postsecondary Institution in Texas in 1999, 2000, or 2001, 

by Institution Type.......................................................................................................37 

IV.1 Talent Search Participants and Comparison Students Who Applied for 

Financial Aid in Indiana...............................................................................................64 

IV.2 Talent Search Participants and Comparison Students Who Enrolled 

in College in Indiana....................................................................................................65 

IV.3 Talent Search Participants and Comparison Students Enrolled in College 

in Indiana, by Degree Program ....................................................................................66 

V.1 Talent Search Participants and Comparison Students Who Completed High 

School, Applied for Financial Aid, and Took College Entrance Exams 

in Florida......................................................................................................................82 

V.2 Talent Search Participants and Comparison Students Who Enrolled in a 

Public Postsecondary Institution in Florida .................................................................85 

V.3 Talent Search Participants and Comparison Students Who Persisted 

in Public Postsecondary Institutions in Florida ...........................................................90 

VI.1 Talent Search Participants and Comparison Students Who Were First-Time 

Applicants for Federal Financial Aid, 1999–2000, by State........................................97 
FIGURES 

Figure Page

xiv

VI.2 Talent Search Participants and Comparison Students Who Enrolled in a 

Public Postsecondary Institution, 1999–2000, by State...............................................98 

VI.3 Talent Search Participants and Comparison Students Who Enrolled in a 

Public Two-Year or Four-Year Institution, 1999–2000, by State ...............................99 
xv

EXECUTIVE SUMMARY 

Many studies have documented the fact that college graduates earn significantly more 

money during their lifetimes than high school graduates (U.S. Department of Education 2004). 

Research also shows that college graduates demonstrate greater civic involvement and are more 

likely to vote and assume leadership roles in their communities (Astin 1993; Bowen and 

Bok 1998). 

Low-income students and students whose parents have not attended college typically are 

less likely than middle- and upper-income students to complete high school and attend college, 

and are thus less likely to reap the benefits of attending college. Lack of information, resources, 

and exposure to others who have navigated the college process may be substantial hurdles for 

these students. Federal financial aid is available through Pell Grants, college tuition tax credits, 

and student loan programs, but low-income students may not be taking full advantage of these 

sources. Even low-income students with high educational aspirations may find the financial aid 

and college application processes overwhelming and discouraging. 

In 1965, Congress established the Talent Search Program as one of the original federal 

TRIO programs. The others include Upward Bound, created in 1964, and Special Services (later 

renamed Student Support Services), established in 1968. Today, TRIO includes five other 

programs. Collectively, these programs help low-income, potentially first-generation college 

students prepare for and gain access to college. The Talent Search program primarily provides 

information on the types of high school courses students should take to prepare for college and 

on the financial aid available to pay for college. The program also helps students access 

financial aid through applications for grants, loans, and scholarships, and orients students to 

different types of colleges and the college application process. In fiscal year (FY) 2004, the 

Talent Search program received approximately $144 million to serve 382,500 students in 

470 projects across the country; on average, each project spent approximately $375 per 

participant served. The program has grown since FY 2000, the period relevant to this study, 

when Talent Search served 320,000 students and spent approximately $313 per student. 

After a two-year implementation study, the U.S. Department of Education’s Policy and 

Program Studies Service selected Mathematica Policy Research Inc. (MPR) in 2000 to assess the 

effect of Talent Search in selected states. A variety of designs were considered and ultimately 

the study team opted to compile data from administrative records from many sources, including 

program, state, and federal records, to evaluate the effectiveness of federal education programs, 

partly as a test of whether such an evaluation was feasible. The study also yielded useful 

information about the effectiveness of the Talent Search program. 

METHODOLOGY 

The study included an analysis of the effectiveness of the Talent Search program in Florida, 

Indiana, and Texas. We based our analysis on administrative data compiled in these three states 

and a quasi-experimental design to create matched comparison groups (Rosenbaum and Rubin 
xvi

1985). Outcomes of students who participated in Talent Search were compared with outcomes 

of similar students at the same schools or other schools who did not participate. We restricted 

our analysis to the cohort of students who were in ninth grade in 1995–96 to allow us to collect 

information on high school completion and postsecondary enrollment, which occurred as late as 

2002. Although all students were in ninth grade in 1995–96, Talent Search participants may 

have received services through the program at any point from grades six through twelve. We 

compared secondary and postsecondary outcomes between Talent Search participants and 

comparison groups within each state. 

We received data on Talent Search participants from at least 60 percent of all Talent Search 

programs operating in Florida, Indiana, and Texas in 1995–96. As shown in Table 1, samples 

included large numbers of Talent Search participants and matched comparison students in 

each state. 

TABLE 1 

NUMBER OF TALENT SEARCH PROJECTS, TALENT SEARCH PARTICIPANTS, 

AND COMPARISON STUDENTS, BY STATE 

State 

Total Number of 

Talent Search 

Projects Operating 

in 1995–96 

Number of Projects 

Providing Data 

Number of Talent 

Search Participants 

in Study Cohort 

Number of Matched 

Comparison 

Students in Study 

Cohort 

Florida 7 5 908 13,843 

Indiana 8 7 1,166 9,844 

Texas 16 10 4,112 30,842 

KEY FINDINGS 

The main research questions addressed in this report are as follows. 

1. Is it possible to rely on administrative records to compile a retrospective record of 

participation in Talent Search, characteristics of students in secondary school, and 

secondary and postsecondary outcomes? 

2. Can administrative data and quasi-experimental techniques be used to identify 

students who are not participating in the program but have characteristics similar to 

the Talent Search students in order to estimate the relationships between 

participation in Talent Search and secondary and postsecondary outcomes? 
xvii

The Efficacy of Using Large State Databases to Inform Policy 

The compilation of data from administrative sources to study the effect of Talent Search on 

participants was successful in Florida, Indiana, and Texas. We could not compile a suitable data 

file for analysis in Minnesota (due to a lack of access to state secondary school records) and 

Washington (due to missing or poor-quality Talent Search project data). Obtaining student level 

data which included information identifying students to facilitate merging records across data 

sources was challenging to obtain for the years of interest, 1995–2000. Data from recent years 

should be easier to attain as more states develop systems for compiling secondary and 

postsecondary school records, and federal programs are more consistent in reporting information 

on the participants served and maintain records electronically. 

The data files compiled in Florida, Indiana, and Texas contained a wealth of information on 

students in Talent Search. This included important demographic information such as age, race, 

and gender; the school the student was enrolled in for ninth grade; and postsecondary outcomes, 

such as first-time application for financial aid and postsecondary enrollment. Because we 

compiled a large amount of data in each state, both in terms of the number of data elements 

available and the size of the student samples, we were able to use complex propensity score 

matching models to identify nonparticipating students who were most similar to Talent Search 

participants. We were also able to obtain these data at a far lower cost and in a shorter time 

frame than if we had collected data directly from students and their families over a five- to 

seven-year period. 

However, not all data on student characteristics and on secondary or postsecondary 

outcomes of interest were available in any one state, and the type of information that was 

missing differed across states. However, the variations in the data, as well as our strategies for 

drawing comparison students in each state, provided a good test of the sensitivity of the findings 

to using different data sources and comparison students. Findings that were consistent across all 

three states suggest greater robustness. 

Findings Across States 

Talent Search participants were more likely than comparison students to apply for federal 

financial aid and enroll in public postsecondary institutions in Florida, Indiana, and Texas. 

• Financial Aid Applications. Talent Search participants were more likely than 

nonparticipants from similar backgrounds to be first-time applicants for financial aid 

in the 1999–2000 school year. The difference in financial aid application for Talent 

Search participants and nonparticipants was 17, 14, and 28 percentage points, 

respectively, for Florida, Indiana, and Texas (Figure 1). The difference was smallest 

in Indiana, where we had the strongest measures of educational aspirations. Even in 

Indiana, however, the gap represents application levels for financial aid that are onethird higher for Talent Search participants. 
xviii

• Postsecondary Enrollment. Talent Search participants were more likely than 

nonparticipants to enroll in a public college or university in their state by the 1999–

2000 school year. Initial enrollment in a postsecondary institution was higher by 14, 

6, and 18 percentage points, respectively, for Florida, Indiana, and Texas (Figure 2). 

• Enrollment in Two- Versus Four-Year Institutions. Talent Search participants were 

more likely to enroll in two-year and four-year institutions and the gains were larger 

and more statistically robust for two-year enrollment (Figure 3). Enrollment by type 

of institution (two- or four-year) was linked to the type of institution hosting the 

Talent Search project. In general, projects increased two- or four-year enrollment, 

but not both. Talent Search projects may have increased enrollment by exposure to 

their type of institution, or to their specific institution. 

FIGURE 1

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS WHO WERE

FIRST-TIME APPLICANTS FOR FEDERAL FINANCIAL AID, 1999–2000,

BY STATE

42

56 59

25

42

31

0

20

40

60

80

Florida*** Indiana*** Texas***

Percent of Students

Talent Search Participants Comparison Students

Note: Talent Search participants were students in ninth grade in 1995–96 served by a Talent Search project

that supplied data for this study. Comparison students are nonparticipants from the same target high 

schools as Talent Search participants who matched to a Talent Search participant.

***Difference is statistically significant at the 1 percent level.
xix

We also examined differences in high school completion in Florida and Texas, the states 

where this information was available in administrative records. There were large differences in 

Florida and Texas—9 and 14 percentage points, respectively—between Talent Search 

participants and nonparticipants. However, we are less confident this finding can be attributed to 

participation in Talent Search than the findings on application for financial aid and initial college 

enrollment. Estimates of the differences in Florida and Texas could be biased upward if Talent 

Search project staff chose to serve students who were most likely to complete high school and 

most Talent Search project services do not directly target high school retention, so there is no 

program-based explanation for generating a difference of this magnitude. Also, we cannot verify 

that Talent Search participants also had higher rates of high school completion in Indiana. The 

magnitude of the differences in high school completion suggests that the data available in Florida 

and Texas may not be adequate to control for characteristics, such as educational aspirations and 

motivation, of students who choose to participate in Talent Search. It is possible that some of the 

difference in high school completion is due to participation in Talent Search, but the evidence is 

not as strong as the findings on application for financial aid and postsecondary enrollment. 

FIGURE 2

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS WHO

ENROLLED IN A PUBLIC POSTSECONDARY INSTITUTION, 1999–2000,

BY STATE

51 51 51

37

45

33

0

20

40

60

80

Florida*** Indiana*** Texas***

Percent of Students

Talent Search Participants Comparison Students

Note: Talent Search participants were students in ninth grade in 1995–96 served by a Talent Search project

that supplied data for this study. Comparison students are nonparticipants from the same target high 

schools as Talent Search participants who matched to a Talent Search participant.

***Difference is statistically significant at the 1 percent level.
xx

CONCLUSION 

The study was not a randomized experiment and we cannot attribute differences in outcomes 

solely to participation in Talent Search. However, despite the limitations in the data and the 

design, we are more confident that some of the differences in first-time application for financial 

aid and initial postsecondary enrollment can be attributed to participation in Talent Search, for 

several reasons. First, the magnitude of the differences in first-time application for financial aid 

and postsecondary enrollment were larger than the difference in high school completion and 

remained even when examining these outcomes among high school graduates only. This 

suggests that even if unmeasured characteristics accounted for all the differences in high school 

completion, some of the differences in postsecondary outcomes could still be attributed to 

participation in Talent Search. Second, the higher rates of application for financial aid and 

postsecondary enrollment were consistent across all three states (although the differences were 

smallest in Indiana, where the two groups had similar educational aspirations). 

39

16

30 29

13

18

0

10

20

30

40

Florida*** Indiana*** Texas***

Percent of Students

FIGURE 3

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS WHO ENROLLED 

IN A PUBLIC TWO-YEAR OR FOUR-YEAR INSTITUTION, 1999–2000,

BY STATE

14

35

21

9

32

14

0

10

20

30

40

Florida*** Indiana Texas***

Percent of Students

Note: Talent Search participants were students in ninth grade in 1995–96 served by a Talent Search project

that supplied data for this study. Comparison students are nonparticipants from the same target high 

schools as Talent Search participants who matched to a Talent Search participant.

***Difference is statistically significant at the 1 percent level.

Two-Year Institution Four-Year Institution

Talent Search Participants Comparison Students
xxi

A third and important reason is consistency with the program’s objectives and services it 

delivers. Talent Search projects report directly targeting services toward activities to increase 

application for financial aid and postsecondary enrollment. From the research conducted for 

Phase I of this study, we found that help with application for financial aid is “hands on”—it 

involves sitting with students to complete their paper-and-pencil applications or at a computer to 

complete the Web-based Free Application for Federal Student Aid (FAFSA). Talent Search 

project staff also reported providing college orientation activities, such as help completing 

college applications, visits to college campuses, and arranging for participants to shadow 

students—often Talent Search alumni—at the project’s host institution. Looking at the findings 

across projects, the potential relationship between enrollment in a two- or four-year institution 

and the type of institution hosting the Talent Search project suggests that orientation to the host 

institution itself may increase postsecondary enrollment for participants. 

The findings we present in this report suggest that assisting low-income students who have 

college aspirations to overcome information barriers—an important objective of the Talent 

Search program—may be effective in helping these students achieve their aspirations. Practical 

information—direct guidance on how to complete applications for financial aid and admission to 

college and what a college campus looks and feels like—may have been one of the key services 

that Talent Search projects delivered. 

1

I. INTRODUCTION 

A. PROGRAM DESCRIPTION

One of the original federal TRIO programs, Talent Search was designed to help low-income, 

potentially first-generation college students prepare for and gain access to college. The program 

helps students achieve these goals by:

• Providing information on the types of high school courses students should take to 

prepare for college 

• Providing information on financial aid available to pay for college 

• Helping students access financial aid through applications for grants, loans, 

and scholarships 

• Orienting students to different types of colleges and the college application process 

Talent Search has consistently been the largest of the TRIO programs in the number of 

students served. Talent Search funding in fiscal year (FY) 2004 was approximately $144 million 

and it served 382,500 students in 470 projects across the country. The program has grown since 

FY 2000, the period relevant to this study, when Talent Search served approximately 320,000 

students in 360 projects across the country.1

 Talent Search, which spent approximately $313 per 

student in FY 2000, is a relatively low-intensity program.2

 It targets students who have 

academic potential for college but who may need extra information and help in navigating the 

financial aid and college application process. The low-intensity structure of Talent Search is 

reflected in the limited number of services participants received. According to a survey of 

 1

 Other original TRIO programs were much smaller in the same fiscal year. In 2000, Student Support Services 

(SSS) served approximately 180,000 students. In 1999, Upward Bound served fewer than 60,000 students 

combined. For a profile of TRIO programs, see U.S. Department of Education 2003.

2

 For example, in FY 2001, the average cost per student served by Upward Bound was $4,800 per year. 
2

project directors in 2000, while almost three of ten Talent Search participants received 20 or 

more hours per year of services, nearly half received 10 hours per year of services or fewer.3

1. Context for Studying Talent Search 

The importance for success in the labor market of completing high school and earning a 

postsecondary degree is well documented. The difference in earnings between high school and 

college graduates is great, even among young workers. For full-time workers ages 25 to 34, 

median earnings are 65 to 70 percent higher for workers with a bachelor’s degree than for 

workers with a high school diploma only. Workers who do not complete high school fare much 

worse, earning only 75 percent of what high school graduates earn and less than half of what 

workers with a bachelor’s degree earn (U.S. Department of Education 2004). These differences 

increase with workers’ ages, as earnings also rise more rapidly among college-educated workers 

with experience (Murphy and Welch 1992). In addition to the economic benefits of a college 

education, evidence suggests positive externalities are generated by individuals who complete 

college, such as having a greater civic orientation, being more likely to vote, and assuming 

leadership roles in their communities (Astin 1993; Bowen and Bok 1998). 

Low-income students are less likely than middle- and upper-income ones to complete high 

school and attend college (and thus less likely to reap the benefits of attending college) for 

several reasons. First, the barriers low-income students face are due, in part, to the 

socioeconomic status of their families—it is more difficult for low-income families to allocate 

time and money toward schooling and away from other necessities. Second, the environment 

outside the household compounds these issues. Recent studies of high school dropout rates 

across the country indicate that not only do low-income students have higher dropout rates than 

 3

 For details on the program as it was operating in the late 1990s, see Cahalan et al. 2004. 
3

other students, but middle-income students at high-poverty high schools have higher dropout 

rates than middle-income students at other high schools. Attending a high-poverty high school is 

particularly deleterious for high school completion among African Americans (Swanson 2004; 

Balfanz and Legters 2004). Third, most low-income students do not have a parent who has a 

college degree, which presents both financial and informational barriers for students accessing 

financial aid and navigating the college admissions process. In 1999, 82 percent of students with 

parents with a bachelor’s degree enrolled in college immediately after high school, compared to 

54 percent of students with parents with high school diplomas only. Differences in high school 

curricula, parents’ education, and test scores can partly explain the gap in enrollment rates 

between low- and high-income students (U.S. Department of Education 2001; Jacobson 

et al. 2001; McPherson and Schapiro 1998). However, evidence indicates that, even though 

financial aid programs such as Pell Grants, college tuition tax credits, and student loan programs 

are available, differences in financial resources also play an important role (Kane 1999). Talent 

Search tries to help students overcome these obstacles by primarily targeting low-income, 

potentially first-generation college students.4

Additionally, low-income students are not taking full advantage of financial aid programs. 

A study by the American Council on Education showed that 20 to 30 percent of college-going 

students with family incomes below $40,000 (and thus likely to be eligible for federal financial 

aid) did not file a Free Application for Federal Student Aid (FAFSA) in 2000. This includes 

approximately 850,000 students who may have been eligible for a Pell Grant (King 2004). Other 

studies have also shown that while low-income students may have high educational aspirations, 

 4

 In each Talent Search project, up to one-third of participants need not meet either of these criteria. Most 

projects, however, have considerably fewer than one-third of participants who do not meet either of the criteria.
4

they find the college admissions test-taking, financial aid application, and college application 

process difficult to navigate (St. John et al. 2002; Avery and Kane 2004). 

2. Services Offered by Talent Search 

Talent Search helps students overcome the informational barriers that stem from attending 

high-poverty high schools and being the first generation in their families to attend college. The 

services and goals of the Talent Search program are specifically intended to address some of the 

barriers described above. Talent Search projects reported providing a wide range of services in 

1999–2000, but the services most commonly provided fall into three categories: (1) academic 

support, (2) career development, and (3) financial aid. Table I.1 shows the percent of projects 

providing the services most commonly reported in those categories. The services listed include 

only those that more than 90 percent of the projects provided. In addition, project directors listed 

financial aid services and visiting college campuses as the top two services that contributed to 

achieving the projects’ objectives. 

3. Study Description and Report Overview 

Assessing the effectiveness of Talent Search in achieving its program goals is challenging, 

due to the large number of students it serves, the diffusion of projects across all 50 states and 

many schools, and the low intensity of services. Because many services focus on providing 

information to students, conducting an evaluation using a randomized design or nonrandomized 

study based on a longitudinal survey of students could be difficult to implement and 

prohibitively expensive. Based on three designs proposed by Mathematica Policy Research Inc. 

(MPR) in 2000 (Maxfield et al. 2000), the U.S. Department of Education (ED) chose to study the 

effect of participation in Talent Search on secondary and postsecondary outcomes in selected 

states, relying on administrative records gathered from several sources and quasi-experimental 
5

TABLE I.1 

SERVICES MOST COMMONLY OFFERED BY 

TALENT SEARCH PROJECTS IN 2000 

Type of Service 

Projects Providing Service 

(Percent) 

Academic Support 

Test taking and study skills 98 

Academic advising and course selection 94 

Career Development 

College orientation activities 98 

Visits to college campuses 96 

Referrals and counseling 91 

Financial Aid 

Individual financial aid counseling 98 

Financial aid workshop 97 

Assistance with pencil-and-paper FAFSA 97 

Scholarship searches 94 

Source: Tables 6.1, 6.3, and 6.4 from Cahalan et al. 2004, based on MPR’s Survey of Talent Search 

Project Directors in 1999–2000. 

statistical techniques, which allow us to identify students who did not participate in the program 

but who were similar to Talent Search participants. 

In Chapter II, we describe the methods used in the analysis. In chapters III, IV, and V, we 

describe the data sources used to compile records on students, the strengths and weaknesses of 

the data, the specific analytic approach for identifying nonparticipating students who are similar 

to Talent Search participants in each state, and estimates of the effects of Talent Search on 

completion of high school, financial aid, and postsecondary enrollment in Texas, Indiana, and 

Florida.5

 In the final chapter, we discuss our findings across the states and assess the success of 

our analytic approach.

 5

 We present findings in Texas first since the data collected in Texas were compiled entirely by MPR staff. We 

follow with the findings in Indiana and Florida to provide contrast in the type of information available across the 

three states. The order of the presentation of the state findings do not reflect the importance of the findings in one 

state over another. 
6
7

II. STUDY DESIGN AND METHODOLOGY 

A. STUDY DESIGN 

1. Research Questions 

The two main research questions posed in this design and addressed in this report are: (1) Is 

it possible to rely on administrative records to compile a complete, retrospective record of 

participation in Talent Search, characteristics of students in secondary school, and secondary and 

postsecondary outcomes? and (2) Can administrative data sources and quasi-experimental 

techniques be used to identify students who are not participating in the program but have 

characteristics similar to the Talent Search students in order to estimate the relationships between 

participation in Talent Search and secondary and postsecondary outcomes? 

2. State and Project Selection 

We chose Florida, Indiana, and Texas for our analysis based on the findings of the feasibility 

study (Maxfield et al. 2000). We also conducted additional research and negotiations in other 

states. The requirements for the administrative data were high: states had to provide secondary 

and postsecondary data that would allow us to track the experiences of one cohort of ninth-grade 

students over several years. In addition, we needed detailed identifying information on students 

so that we could merge the data from Talent Search projects and federal financial aid 

applications with the state data. A brief description of the data sources in each state is provided 

in the chapter on that state; Appendix B contains a more detailed explanation of the data sources 

and process of merging information across those sources. 

The cohort we chose for this study consisted of students in the ninth grade in the fall of the 

1995–96 school year. We chose this cohort to allow collection of retrospective data that enable 

us to assess outcomes related to the Talent Search program goals: high school completion, 
8

application for financial aid, and college enrollment. Students from this cohort who graduated 

from high school “on time” in spring 1999 were first-time applicants for financial aid and new 

entrants in college in fall 1999. We obtained data on first-time applicants for financial aid and 

college enrollment for 1999, 2000, and (in some cases) 2001, thus allowing extra time for 

students who take longer to complete high school or wait after completing high school before 

applying for aid and enrolling in college.

After we identified the states for the analysis, we attempted to collect student participation 

and identification data from all Talent Search projects in each state; thus, we tried to obtain 

information on a complete census of Talent Search participants from 1993–2000.6

 We focused 

on 1993–2000 because the cohort chosen for the study, students who were in ninth grade in 

1995–96, progressed from middle school through high school during this time period. Many 

projects did not have records of participants as far back as 1993, but all projects that provided 

data for the study had records back to 1995. We received data from at least 60 percent of 

projects in Florida, Indiana, and Texas, but we did not receive data from all projects in any state. 

Therefore, our findings do not reflect the experience of the entire population of participants, even 

for the cohort of students in ninth grade in 1995–96, although they do reflect the experiences of 

most of the participants in the cohort in these three states.7

 6

 Some Talent Search participants may have first participated in the program in years prior to 1995–96, when 

they were in middle school. In order to capture services received in middle school, we extended the window of 

Talent Search program participation back to 1993. 

7

 Appendix B contains a detailed description of the data collected from Talent Search projects. 
9

B. ANALYTIC APPROACH 

1. The Preferred Comparison Group 

Our goal in assessing the effect of Talent Search on participants was to determine what 

would have happened to Talent Search students if the program had not been available to them. A 

randomized study would have provided the most valid framework for such an analysis, splitting 

students who wished to participate in Talent Search into groups of those assigned to participate 

and those not assigned to participate. Because a randomized study was determined to be 

infeasible, however, we attempted to identify a group of students who were similar to Talent 

Search participants but who had not participated in the program. Within the quasi-experimental 

framework, the preferred set of comparison students would (1) be drawn from the same districts 

as the Talent Search participants (for each project) but not from the same high schools; (2) have 

persisted to the same point in high school; and (3) be similar on all observable characteristics. 

Drawing comparison students from other high schools is useful because students in target 

high schools who chose to participate in Talent Search when it was offered may be 

fundamentally different in important characteristics from those who chose not to participate. 

Some of these characteristics—such as motivation and aspirations—were not consistently 

available in the data sources we compiled. If Talent Search participants are fundamentally 

different from nonparticipants, the estimated effects of participation based on comparisons 

between these groups may, in part, be attributable to differences in motivation that predate 

students’ participation in Talent Search. If Talent Search staff targeted students with higher 

college aspirations than otherwise similar students, the analysis will overestimate the effects of 

participation on outcomes.8

 We believe that selecting students from the same districts as Talent 

 8

 For information on the types of students Talent Search targeted, see Cahalan et al. 2004. 
10

Search participants, but not from the same target high schools, may help to minimize bias from 

self-selection into Talent Search. 

The second characteristic of our preferred comparison group is that they persist to the same 

point in high school as Talent Search participants did before the program served them.9 Students 

who persisted through high school may differ from those who did not persist for reasons that are 

difficult to measure, such as motivation or a supportive home environment. Because we believe 

these unobservable characteristics to be important in determining educational outcomes, we 

sought to control for them by forcing an exact match on persistence through high school. The 

pool of potential comparison students shrinks as students drop out of high school or are unable to 

be tracked, but we are confident that the remaining pool of students is more likely to resemble 

Talent Search participants than all students who were in ninth grade in the fall of 1995. 

Finally, as with all matching analyses, we want to use a matching strategy to identify a 

comparison group that is as similar as possible to the participant group, particularly in observable 

characteristics correlated with educational outcomes, such as gender, economic status, and 

academic performance in high school. Therefore, comparison students should be as similar as 

possible to Talent Search participants on these characteristics, as well as on other related, 

observable characteristics. Without a similar distribution, or “balance,” on these key 

characteristics, it is difficult to ascertain whether differences in outcomes are due to participation 

in the program or to differences between the two samples. Furthermore, Rubin (2001) argues 

that regression analysis alone cannot adjust for substantial differences in the distribution of 

covariates between the two groups. Therefore, for the propensity score matching approach to be 

 9

 Differences in persistence after Talent Search serves students could be attributable to Talent Search and 

should be included in the estimate of the effect of the program on participants. 
11

statistically valid, participants and the comparison groups chosen through the matching 

procedure must have similar distributions on the observable characteristics. 

We defined the participant population as students in the ninth-grade cohort in 1995–96 

served by Talent Search projects between 1993 and 2000.10 The initial pool of potential 

comparison students was the population of all nonparticipants in the state who also were in the 

ninth grade in fall 1995 and for whom we were able to collect data. We stratified, by geography 

and year, samples of participants and nonparticipants, into several groups per state. 

2. Using Propensity Score Models to Identify the Comparison Group 

Within each group, we estimated propensity scores on which the two samples were to be 

matched (Rosenbaum and Rubin 1983 and 1985). An unweighted logit model was estimated to 

distinguish between members of the two samples; the dependent variable (participation in Talent 

Search) was coded as 1 for participants and 0 for nonparticipants. The explanatory variables 

varied by state; in each state, however, they included demographic, socioeconomic, and some 

academic characteristics. We used the model to estimate the log odds (the output of the 

propensity score model) of participating in Talent Search, conditional on the explanatory 

variables, for each participant and nonparticipant in the group.11

Within each group, we matched Talent Search participants to similar nonparticipating 

comparison students. Two students were deemed comparable when their log odds were 

sufficiently close. We matched each participant to all comparison students deemed 

comparable—a technique called “matching with replacement.” Talent Search participants for 

 10 Participants were defined as students served between 1993 and 2000 by the Talent Search projects that 

provided data for the study. Some projects included information on the number of years students were served and 

the specific services students received, but this information was not consistently available. 

11 The log odds of being in the participant sample equals the natural log of P(1) / P(0), where P(1) equals the 

probability of being in the participant sample, and P(0) equals the probability of being in the comparison sample. 

We predicted these probabilities using the estimated coefficients from the logit model. 
12

whom we could not find a similar comparison student were excluded from the study because no 

comparison student could provide credible information about the outcomes we would have 

observed without the student’s participation in Talent Search. In each state, however, we were 

able to match 95 percent or more of Talent Search participants to at least one comparison 

student. All comparison students who were matched to one or more participant sample members 

were included in the matched comparison group. 

Another component of the algorithm for matching participant and comparison students was 

to select the appropriate “caliper range” that defines how close the log odds of a participant and a 

comparison student must be to be deemed a suitable match. Narrow caliper ranges can leave 

many participants unmatched when similar students might be found in the comparison sample; 

wide caliper ranges generate matches between students who may be less similar. We tested 

several caliper ranges in matching participants to the comparison students, based on fixed 

fractions of the standard deviation of the log odds within the matching group. Each caliper range 

generated different matched participant and comparison samples. Therefore, selecting a caliper 

range was equivalent to selecting a pair of matched participant and comparison samples. 

Ultimately, we selected a narrow caliper range which led to matched participant and comparison 

samples with a smaller number of significant differences in variables that describe baseline 

characteristics and academic achievement, while still finding matches for a high percentage 

of participants.12

 12 We used a caliper range of 1/20th of the standard deviation of the log odds from the propensity score model, 

a small caliper to ensure that we matched only those who had propensity scores very close to the participant being 

matched. Due to the large size of the nonparticipant population in the administrative data available, we were able to 

obtain well-matched comparison groups using even smaller calipers. Using smaller calipers, however, led to more 

participants dropping out of the analysis because we could find no suitable comparison students. We were able to 

identify matches for 95 percent or more of all participants in each state. 
13

As part of the matching procedure, students in the comparison sample who matched to 

participants were assigned weights based on their matches. The total of the weights assigned to 

the matched comparison sample is the same as the total number of Talent Search participants—

that is, when using the weights, the participant and comparison samples are the same size. For 

all comparison group members who were within the caliper range for a participant, each 

comparison group member received an equal share of the participant’s weight. For example, if 

10 comparison students fell within the caliper range of participant A, each comparison student 

would receive a weight of 1/10 from participant A. Furthermore, because matching is done with 

replacement, a comparison group member could fall within the caliper range of several 

participants. If one of the comparison group members from the example above also matched 

participant B, who had five comparison students within the caliper range, that comparison group 

member would receive an additional weight of 1/5 from matching participant B. Each matched 

comparison student was assigned a weight equal to the sum of the weight contributions from 

each participant to whom he or she was matched. In the example above, the comparison student 

would receive a weight of 1/10 from participant A, plus 1/5 from participant B, for a total 

of 3/10. 

The tables in each chapter show the results of matching. In general, we were unable to draw 

comparison students with all three characteristics of the preferred comparison group described 

above: in Texas and Florida, we were able to control for persistence and match on observable 

characteristics but only within the target high schools. In Indiana, we matched well on 

observable characteristics within the target high schools but lacked data on persistence. 

Therefore, in each state we drew two different comparison groups to Talent Search participants, 

to test whether our findings were sensitive to the comparison group drawn. In each state our 

findings were consistent compared to the comparison group chosen. That is, the results were 
14

similar regardless of the comparison group chosen. Therefore, in presenting our findings for 

each state in chapters III–V, we present results using only the comparison group drawn from 

students within the same target high schools who had the same balance of observable 

characteristics as Talent Search participants, allowing us to compare results across states. 

C. ESTIMATING THE EFFECT OF TALENT SEARCH ON SECONDARY AND 

POSTSECONDARY OUTCOMES 

1. Empirical Specification 

We used a regression-adjusted approach to estimate the effect of participation in Talent 

Search on high school completion, first-time application for federal financial aid, and college 

enrollment. To compute the average effect of participation in Talent Search, we estimate a 

statistical model that predicts the outcome of interest as a function of participation status and 

background characteristics (for example, sex, race, ethnicity, and academic-risk status). This 

approach allowed us to (1) adjust for the small remaining differences in observable 

characteristics between Talent Search participants and the matched comparison group and 

(2) increase the precision of our estimates. The basic form of the model is:

i i ii 01 2 y PX =+ + + ββ β ε , 

where yi is the outcome of interest; Pi equals 1 if the student participated in Talent Search and 

0 otherwise; Xi is a vector that includes the student’s characteristics; εi is a random error term 

that captures the effects of unobserved factors that influence the outcome; and β0, β1, and β2 are 

parameters or vectors of parameters to be estimated. The parameter of greatest interest is β1,

which shows the impact of participating in Talent Search on student outcomes. We estimate the 

parameters in the above equation using ordinary least squares for both categorical and 

continuous outcomes. All regression models are weighted to account for matching, as described 
15

in the preceding section, and allow for potential heteroskedasticity of standard errors due to 

clustering at the project level. 

2. Standard Errors 

By using a caliper range to determine potential comparison students who are similar to any 

given participating student, rather than a one-to-one match, we increase the size of our matched 

comparison student sample. The weighting algorithm accounts for the number of matches per 

treatment student, and the standard error calculations from the outcomes equation estimation 

account for the variance in the weights.13

3. Reliability of Propensity Score Matching Methods 

In using a matched comparison group based on propensity score design, we adhered to the 

criteria developed by Rosenbaum and Rubin in a series of articles (Rosenbaum and Rubin 1983 

and 1985; Rubin 2001) for replicating a randomized design as well as possible. That is, we used 

as many covariates as possible in estimating our propensity models, we report findings only for 

matched comparison groups with a distribution of characteristics similar to those of Talent 

Search participants, and we test the sensitivity of our findings to alternative comparison groups 

that also meet these criteria. One study showed that using propensity score matching can 

replicate program evaluation findings based on experimental designs (Dehejia and Wahba 1999); 

however, other analyses of the same data did not support this finding (Smith and Todd 2004). In 

addition, other studies have found that (1) nonexperimental methods in general, and propensity 

score methods specifically, do not replicate findings from randomized studies well; and (2) there 

 13 We estimated robust standard errors to account for clustering and used the “svyreg” procedure in Stata to 

account for the variance in the weights. 
16

is no consistent pattern of nonexperimental studies over- or underestimating the impacts of 

programs found using randomized designs (Agodini and Dynarski 2004; Glazerman et al. 2003). 

The limitations in the matched comparison group design have implications for the findings 

presented in the following chapters. Because the data available and our strategy for drawing 

comparison groups differed across each state, we have the most confidence in findings that are 

consistent across states. We report project-level impacts to assist in the interpretation of our 

state-level findings, but we do not rely on findings on individual projects in our assessment of 

the overall success of Talent Search in the three states. 
17 

III. TEXAS 

A. INTRODUCTION 

In 1999, there were 17 Talent Search projects operating in Texas, serving more than 

15,000 students (Cahalan et al. 2004). Sixteen of these projects were operating in 1995. This is 

many more projects, serving many more students, than were operating in Florida or Indiana at 

the time.14 The analysis in this chapter is based on more than 4,000 Talent Search participants in 

10 Talent Search projects throughout Texas who were in ninth grade in the fall of 1995, along 

with similar nonparticipants who were in the same school districts. The data we obtained from 

state agencies included secondary school records on the entire 1995–96 ninth-grade cohort and 

postsecondary school records on students in the cohort who had enrolled in public postsecondary 

institutions in Texas. Compared to the nonparticipants, the students participating in Talent 

Search had higher rates of high school completion, first-time application for federal financial aid, 

and enrollment in public postsecondary institutions in Texas. The magnitudes of these 

differences in rates varied, but the differences in application for financial aid were the largest. 

This chapter describes the data sources that were used to compile records on students. It 

also discusses the strengths and weaknesses of the data and the methods we used to identify 

nonparticipating students who were similar to Talent Search participants. We draw comparisons 

between Talent Search participants and nonparticipants to estimate the relationships between 

Talent Search participation and high school completion, application for financial aid, and 

 14 While Texas served the largest number of students in absolute terms, Indiana served the highest proportion 

of the low-income secondary school students in the state, followed by Texas and Florida (see Table 3.21 in Cahalan 

et al. 2004). 
18 

postsecondary enrollment. The chapter concludes with a discussion of the findings and an 

assessment of the success of using these data to examine the effect of Talent Search in Texas. 

B. DATA 

We used four main data sources (Table III.1): 

1. Secondary School Records. The Texas Education Agency (TEA) provided these 

records, which included demographic, socioeconomic, and academic characteristics 

of the cohort of students in ninth grade in fall 1995. These data come from several 

TEA data collections. We obtained demographic and academic characteristics from 

the “fall snapshot” of ninth-graders in 1995. We took enrollment status, grade level, 

and high school exit status from data collected each summer following the school 

years from 1996 to 2000. We collected eighth-grade test scores, based on 

administration of the Texas Assessment of Academic Skills (TAAS) in spring 1994 

and spring 1995.15

2. Talent Search Project Records. We obtained records from 13 of the 17 Talent 

Search projects operating in 1999; 11 projects provided information on all students 

served during the period of this study (1995–2000).16 Although the various Talent 

Search projects provided different types of data, all projects provided the name, 

social security number (SSN), date of birth, and gender for the students they served. 

Project records also indicated the years a student was served. 

3. Federal Financial Aid Application Records. We obtained these records from the 

Office of Postsecondary Education (OPE) in ED. They contained the names of 

nearly all first-time applicants for federal financial aid who lived in Texas in 1999 

and 2000. 

4. Postsecondary School Records. The Texas Higher Education Coordinating Board 

(THECB) supplied these records. They included information on enrollment, parttime or full-time enrollment status, credits earned, and enrollment in a two- or fouryear institution for all students in the cohort who enrolled in a public college or 

university in Texas for the school years 1999–2002. 

As discussed in Chapter II, we selected the cohort in this study to allow the collection of 

retrospective data in order to assess several outcomes related to the goals of the Talent Search 

 15 In 2003, the Texas Assessment of Knowledge and Skills (TAKS) replaced the TAAS. 

16 One additional project began operating in 1998–99 and did not serve enough students in the target cohort to 

be included in the analysis, thus all analyses are based on participants served by 10 projects. 
19 

TABLE III.1 

TEXAS DATA SOURCES 

Source Variables Years 

Number of Students from 

Fall 1995 Ninth-Grade 

Cohort 

Texas Education Agency 

(TEA) Public Education 

Information Management 

System (PEIMS) 

Name, SSN, and date of 

birth for all students in the 

ninth grade 

School year 1995–96 335,563 

Texas Education Agency 

(TEA) Public Education 

Information Management 

System (PEIMS) 

Enrollment status, grade 

level, and high school exit 

status 

Summer 1996 

Summer 1997 

Summer 1998 

Summer 1999 

329,019a

297,020 

265,759 

218,372 

Texas Education Agency 

Student Assessment 

Division 

Eighth-grade scores on the 

Texas Assessment of 

Academic Skills (TAAS) 

Spring 1994 

Spring 1995 

273,866 

U.S. Department of 

Education, Office of 

Postsecondary Education 

(OPE) 

First-time applicants for 

federal financial aid who 

were living in Texas 

School year 1999–2000 

School year 2000–01 

86,145b

Texas Higher Education 

Coordinating Board 

(THECB) 

Postsecondary enrollment, 

credits, and type of 

institution 

School year 1999–2000 

School year 2000–01 

School year 2001–02 

122,055 

Talent Search Projects Lists of students and years 

served 

1993–2000 4,177c

Note: Figures indicate the number of students in the ninth-grade cohort who were identified with records in the 

other data sources. 

a

The number of cohort students remaining declines each year as students leave Texas public schools. 

b

There were approximately 180,000 first-time applicants for federal aid from Texas in 1999–2000 and 2000–01. c

Based on a total of 31,000 students served by 10 Talent Search projects in the years 1993–2000. 
20 

program, including high school completion, application for financial aid, and college enrollment. 

Students in this cohort who graduated from high school “on time” in spring 1999 were first-time 

applicants for financial aid and new entrants in college in fall 1999. We obtained data on firsttime applicants for financial aid for both 1999 and 2000, to expand the window for students who 

take an additional year to complete high school or wait a year after completing high school 

before applying for aid. Similarly, we collected data on postsecondary enrollment for 1999, 

2000, and 2001, to allow students two extra years to complete high school and enroll in college 

and to assess initial persistence in college for as many in the cohort as possible. Altogether, 

these data sources yielded records on 335,563 students who were in ninth grade in a Texas public 

school in fall 1995.17 Talent Search projects that provided data served 4,177 of these students at 

some point during secondary school. 

1. Strengths and Weaknesses of the Data 

The use of administrative records resulted in a database with several strengths for studying 

the effect of Talent Search on participants: 

• The data are comprehensive and comprise the full sample of ninth-graders in 

Texas public schools in the fall of 1995. Because Talent Search projects are spread 

throughout the state, a critical requirement was to be able to draw comparisons for 

students from across the state. MPR staff took care to merge the records from several 

data sources using procedures that were thorough and accurate. 

• The data contain important predictors of high school completion, application for 

financial aid, and college enrollment. These characteristics include race, ethnicity, 

gender, economic status (defined as eligibility for free or reduced-price lunches), a 

state indicator for being at-risk of dropping out of school, high school curriculum, 

participation in gifted and talented programs, and test scores. Identifying comparison 

students using statistical modeling is more feasible when we can determine whether 

students are similar in characteristics related to the outcomes studied. 

 17 We restricted analysis to students in the ninth grade who were between 13.7 and 17 years of age in fall 1995. 

This reduced the analysis sample to 321,000. 
21 

• The data include persistence through high school, which allows us to control for 

important observable and related unobservable characteristics in selecting 

comparison students. It is essential that the many Talent Search students who are not 

served until they are juniors or seniors in high school be compared only to students 

who have also progressed to a similar point in school. 

• The data provide reliable information on key outcomes, such as high school 

completion status, application for federal financial aid, enrollment in college, and 

initial persistence in college. These outcomes, because they are based on data that 

come from administrative sources, are likely to be more accurate and comprehensive 

than results based on self-reported information. 

The data also have limitations for determining differences between Talent Search 

participants and similar nonparticipants: 

• The data do not contain some characteristics that are important predictors of key 

outcomes. These include the parents’ education level, family structure, and education 

and career aspirations and plans. It would be useful to control for these variables in 

the analysis, especially if Talent Search staff target students who already aspire to 

attend college; without them, we are likely to overstate the effect of participation in 

Talent Search. 

• Students drop out of the data as the state loses track of them. Although grade level 

and high school completion status are recorded for each student in each year, the state 

loses track of some students. In the target cohort in this study, Texas did not record 

an exit status for 22 percent of the sample. Many of those students are also missing 

grade level enrollment information at some point. Therefore, the sample suffers from 

attrition even though initially it was a complete census of the cohort. 

• Postsecondary enrollment data are limited to attendance at public colleges and 

universities in Texas. This limitation implies that overall postsecondary enrollment 

rates of students in this cohort are understated. However, this affects our estimates of 

the difference in postsecondary enrollment between Talent Search participants and 

comparison students only if one group is more likely to enroll in private or out-ofstate institutions. 

• Information on participation in Talent Search, application for federal financial 

aid, and postsecondary enrollment is not integrated with information on secondary 

schooling. The data that Talent Search projects, OPE, and THECB provided do not 

share a common identification system with the TEA. Although all data sources 

included SSNs for students, 10 percent of students in the TEA data have no valid 

SSNs. Some of these cases could be matched to SSNs using names and dates of 

birth; however, some of the students in the cohort who participated in Talent Search, 

applied for financial aid, or enrolled in college probably remain unidentified. 
22 

• We did not gather data for all Talent Search participants in the ninth-grade cohort, 

as some projects did not supply data. If projects not supplying data are 

systematically different from those that did provide data, the study findings will not 

apply to all the students served by Talent Search in Texas. 

The compilation of the data in Texas demonstrates the feasibility of extracting information 

from administrative records to study the effect of Talent Search on participants. The 

comprehensiveness and accuracy of the data are the greatest advantages of this approach. On the 

other hand, given that educational aspirations are an important factor affecting the outcomes of 

Talent Search participants, the lack of information on aspirations is a serious limitation of 

the data. 

2. Characteristics of Talent Search Participants and Potential Comparison Students 

The characteristics of Talent Search participants differed significantly from those of 

nonparticipants across the state as a whole, as well as within the high schools that individual 

projects targeted. We identified target high schools through the projects’ 1999 Annual 

Performance Reports (APRs) and verified them through MPR’s survey of projects in 1999–2000. 

Table III.2 shows that Talent Search students were more likely than all other students in the state 

to be black or Hispanic, economically disadvantaged, and taking a vocational or technical 

course—all characteristics associated with lower rates of high school completion and college 

enrollment.18 On the other hand, they also had characteristics associated with higher rates of 

high school completion and college enrollment: they were more likely than nonparticipants to be 

female, to be the traditional age for entering ninth grade, and to be participating in a gifted and 

talented program. They also had slightly higher eighth-grade test scores. 

 18 Appendix Table A.III.1 defines the variables used in the analysis.
23 

TABLE III.2 

BASELINE CHARACTERISTICS OF TALENT SEARCH PARTICIPANTS AND ALL OTHER STUDENTS, 

ALL OF TEXAS 

(Percents) 

Participants All Other Students 

Demographic Characteristics 

Male 38.3 52.9 *** 

White 26.7 44.9 *** 

Black 25.4 14.9 *** 

Hispanic 47.2 37.7 *** 

Home language is Spanish 14.4 14.2 *** 

Age in ninth grade (mean)a 14.8 15.0 *** 

Overage in ninth grade 16.1 28.5 *** 

Academic Characteristics 

Enrolled in a gifted and talented program 11.0 8.0 *** 

At risk for dropping out of school 46.8 47.8 ** 

Economically disadvantaged 50.9 37.7 *** 

Limited English proficiency 6.5 8.9 *** 

Special education services 5.4 12.3 *** 

Enrolled in a vocational or technical course 44.7 33.1 *** 

Enrolled in a vocational or technical education program 8.1 9.1 ** 

Number of Students 4,169 331,386 

 

Eighth-Grade Test Scoresb 

Raw math score (number of questions correct)c 39.4 38.3 *** 

Percent scored in top 25 percent in state for math 25.9 26.7 

Percent scored in bottom 25 percent in state for math 21.5 26.7 *** 

Raw reading score (number of questions correct)d 36.2 34.4 *** 

Percent scored in top 25 percent in state for reading 26.7 25.6 

Percent scored in bottom 25 percent in state for reading 20.1 26.9 *** 

Score on essay teste 2.5 2.4 *** 

Missing test scores 7.6 18.5 *** 

Number of Students 3,852 270,008 

Note: Participants were all students in the ninth grade in the fall of 1995 who were served by 10 Talent Search 

projects in 2000. 

a

Average age in years. 

b

Based on sample with nonmissing test scores only. 

c

Range on raw math test score is 0 to 60. 

d

Range on raw reading test score is 0 to 48. 

e

Range on essay score is 0 to 4. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
24 

Appendix Table A.III.2 shows the characteristics of Talent Search participants and 

nonparticipants served by each of the 10 projects in this study.19 In the target high schools the 

projects served, the participants and nonparticipants differed significantly in racial composition, 

economic and at-risk status, and test scores. Talent Search students also were more likely than 

nonparticipants to be female and black or Hispanic, as well as economically disadvantaged. 

Typically, however, participants had much higher test scores and were much more likely to be in 

programs for the gifted, and they also were more likely than nonparticipants to be the traditional 

age in the ninth grade.20

Table III.3 shows that Talent Search students, compared to all other students across the state, 

persisted from year to year at far higher rates than other students. The differences were larger 

within the high schools that Talent Search projects targeted (Appendix Table A.III.3). Although 

persistence through high school can be considered a result of participation in Talent Search, 

nearly 70 percent of the Talent Search participants identified were not served until 1998, the 

junior year in high school for students who persisted at grade level. Thus, in addition to differing 

from other students in the observable characteristics, Talent Search participants may have 

differed in other unobserved characteristics related to their persistence through high school. 

Based on data the TEA provided, Talent Search projects in Texas appear to have served the 

population the program targeted: low-income students with average academic ability who were 

likely to complete high school. While students at the schools Talent Search targeted were 

 19 Talent Search participants were identified by the project that served them. Characteristics of nonparticipants 

were reported in their respective high schools when they were in the ninth grade. Although it is the ninth-grade 

characteristics of participants that are reported, these students may not have been at the high school where they 

eventually participated in Talent Search when they were in the ninth grade. 

20 We classify students between 13 years and 9 months and 15 years and 3 months as “traditional age.” 

Students older than 15 years and 3 months are considered overage in ninth grade. 
25 

TABLE III.3 

BELOW GRADE AND PERSISTENCE OF TALENT SEARCH PARTICIPANTS 

AND ALL OTHER STUDENTS IN TEXAS 

(Percents) 

 Below Grade Persistence 

 Participants All Other Students Participants All Other Students 

1996–97 8.4 18.0 *** 97.5 90.5 *** 

1997–98 7.3 18.1 *** 96.7 89.1 *** 

1998–99 5.3 14.3 *** 89.8 82.1 *** 

1996–99 84.7 66.1 *** 

Note: Participants were all students in the ninth grade in the fall of 1995 who were served by 10 

Talent Search projects by 2000. Nonparticipants include all students in Texas in the ninth grade 

in fall 1995. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level 

***Difference is statistically significant at the 1 percent level.

generally more disadvantaged than students throughout Texas, Talent Search participants were 

stronger-than-average students at their own high schools. 

C. COMPARISON GROUPS 

The study’s objective was to assess what would have happened to Talent Search students 

had the program not been available to them. As discussed in the methodology section of 

Chapter II, we drew a comparison group of students who were similar to Talent Search 

participants but who had not participated in the program. In the quasi-experimental framework, 

we wanted to draw the preferred set of comparison students from the same districts as the Talent 

Search students for each project, although not from the same high schools, and those students 

would have persisted to the same point in high school and would be similar on all 

observable characteristics. 
26 

The data file contains slightly more than 4,000 Talent Search participants distributed over 

10 projects in Texas, thus preventing all these issues from being analyzed with one estimation 

strategy. For example, if one were to try to exactly match participants and comparison students 

on specific characteristicssuch as race, gender, and students within the same districts and at the 

same point in time in schoolthe result would have been as many as 120 “groups” of 

participants, each with a unique comparison group.21 Given that we used nearly two-dozen 

variables in the propensity score model, a model could not have been estimated for all groups.22 

Because the data were not comprehensive enough to follow the preferred approach, we created 

two comparison groups: one based on students within the target high schools, the other based on 

students in nontarget high schools within the district; however, we were only able to identify 

comparison students similar to Talent Search participants on observable characteristics within 

the target high schools. We describe our analytic approach for drawing comparison groups and 

the resulting samples in the following section. 

Comparison Group One: Students in Target High Schools Within the District 

Our initial attempt to draw nonparticipants students for comparison with Talent Search 

participants focused on observable characteristics. Using students from the same high schools as 

Talent Search participants provided a pool of potential comparison students who were in the 

same educational environment and had backgrounds more similar to participants than those from 

other schools in the district. While all students were in ninth grade in 1995, the time at which 

students first participated in Talent Search varied widely, with some entering the program for the 

 21 This example assumes six combinations of race (black, white, and Hispanic) and gender, 10 projects, and 

two time periods (early and late) for persistence: 6 * 10 * 2 = 120. 

22 For example, with 4,000 participants spread over 120 matching groups, the average cell would have 

33 participants, and many would have fewer. With a large number of explanatory variables, the likelihood of 

finding a combination of variables that perfectly predict participation or nonparticipation is high, leading to 

meaningless coefficients from the propensity score model. 
27 

first time early in high school and some much later. Therefore, to deal with issues related to 

persistence through high school, we divided the sample of students participating for the first time 

usually into two time periods per project. For example, if Talent Search participants entered the 

program for the first time fairly evenly over the high school years, this led to the creation of one 

early cohort (1996 and 1997) and one late cohort (1998 and later) of participants. The early 

group required potential comparison group members still to have been in school in 1996. The 

later group of participants required potential comparison group members to still be enrolled in 

Texas schools in 1998. 

Table III.4 shows the aggregate results of drawing the comparison students by project and 

the years Talent Search participants were first served. The first set of columns, titled “Full 

Sample,” provides mean values for the baseline characteristics of the participants and all 

potential comparison students, along with an indicator of the level at which the difference in 

means is statistically significant. The second set of columns, titled “Matched Sample,” provides 

mean values for the baseline characteristics of the participant and comparison students matched 

using the propensity score model, also followed by an indicator of the level of statistical 

significance for the difference. The means for the full samples and matched participant sample 

are unweighted, whereas the matched comparison sample means are weighted to account for the 

results from matching. The table illustrates that even though the unmatched participant and 

comparison samples differed from each other, the matched samples resembled each 

other closely. 

Comparison Group Two: Students in Nontarget High Schools Within the District 

Our second attempt to draw nonparticipant students for comparison with Talent Search 

participants attempted to deal with selection bias. For each project, the pool of potential 

nonparticipant comparison students was limited to students who attended any high school in the 
28 

TABLE III.4 

ASSESSING BALANCE BETWEEN TALENT SEARCH PARTICIPANTS AND NONPARTICIPANTS 

IN THE SAME HIGH SCHOOLS IN TEXAS 

(Percents) 

 Full Samples Matched Samples 

 Participants Nonparticipants Participants Nonparticipants 

Demographic Characteristics 

Male 38.2 54.0 *** 38.7 38.6 

White 27.1 30.2 *** 27.4 27.3 

Black 25.4 17.8 *** 25.1 24.3 

Hispanic 46.8 51.0 *** 46.9 46.9 

Home language is Spanish 14.2 15.9 *** 14.2 14.1 

Age in ninth grade (mean)a 14.8 15.0 *** 14.8 14.8 

Overage in ninth grade 15.3 28.3 *** 15.4 15.3 

Academic Characteristics 

Enrolled in a gifted and talented program 11.1 6.3 *** 10.6 10.3 

At risk for dropping out of school 46.6 54.4 *** 46.9 46.9 

Economically disadvantaged 50.9 50.2 50.6 50.6 

Limited English proficiency 6.3 11.2 *** 6.3 6.2 

Special education services 5.5 13.3 *** 5.6 5.5 

Enrolled in a vocational or technical course 45.0 39.1 *** 45.0 45.4 

Enrolled in a vocational or technical 

education program 8.1 9.5 *** 8.1 7.8 

Eighth-Grade Test Scores 

Raw math score (number of questions correct) 36.7 30.5 *** 36.6 36.6 

Percent scored in top 25 percent in state 

for math 24.2 16.8 *** 23.9 23.7 

Percent scored in bottom 25 percent in state 

for math 26.9 42.8 *** 27.1 26.9 

Raw reading score (number of 

questions correct) 33.8 28.0 *** 33.7 33.7 

Percent scored in top 25 percent in state 

for reading 24.9 17.4 *** 24.6 24.6 

Percent scored in bottom 25 percent in state 

for reading 25.5 42.4 *** 25.6 25.4 

Score on essay test 2.4 1.9 *** 2.3 2.4 

Missing test scores 7.0 15.7 *** 7.0 6.9 

Number of Students 4,112 46,810 4,027 30,842 

Note: We matched nonparticipant comparison students to Talent Search students using a propensity score 

model as described in Chapter II. The participants’ means for the matched sample differed slightly from 

the means for the full sample, as some participants had no suitable comparison students; these unmatched 

participants were dropped from the participant sample. The sample includes all 10 projects that provided 

data. The number of nonparticipants in the matched sample were those who matched to a participant; the 

sample was then weighted to equal the number of participants in the analysis. 

a

Average age in years. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
29 

district that Talent Search did not target. This allowed for the selection of a comparison group 

comprised of students who did not have the opportunity to participate in Talent Search, and had 

therefore not made a decision not to participate. Table III.5 shows the results of matching Talent 

Search participants to nonparticipants who attended nontarget high schools within the district. 

This method was applicable to 8 of the 10 projects; in the remaining projects, all students in the 

district attended target high schools. 

Although this approach may be appropriate for reducing self-selection bias among 

participants, the resultant samples of participants and nonparticipants remained significantly 

different in several key observable characteristics. Through matching, differences in means were 

eliminated for some variables, such as male, limited English proficiency, and raw reading test 

score, but this did not remove differences in racial composition or in economic or gifted status. 

Overall, the table reveals our inability to draw a sample of nonparticipants that was completely 

similar to the Talent Search participant group. Rubin (2001) states that regression analysis alone 

should not be relied on to adjust for substantial differences in the distribution of covariates 

between the two groups. Therefore, the remaining disparity in the matched samples suggests that 

we cannot be confident that the comparison group represents what would have happened to 

Talent Search participants without the program.23

D. RESULTS 

There were moderate to large differences in secondary and postsecondary outcomes between 

Talent Search participants and comparison students drawn from the same target high schools. In 

 23 Although comparison students drawn from nontarget high schools were not similar to Talent Search students 

based on characteristics available in the data, we analyzed the difference between Talent Search participants and 

students from nontarget high schools to test whether our findings were sensitive to the comparison group chosen. 

Our findings were similar using either comparison group. 
30 

TABLE III.5 

ASSESSING BALANCE BETWEEN TALENT SEARCH PARTICIPANTS AND NONPARTICIPANTS 

AT DIFFERENT HIGH SCHOOLS IN THE SAME DISTRICTS IN TEXAS 

(Percents) 

 Full Samples Matched Samples 

 Participants Nonparticipants Participants Nonparticipants 

Demographic Characteristics 

Male 37.8 52.7 *** 38.0 38.5 

White 27.7 33.3 *** 27.6 34.0 ***

Black 26.5 20.7 *** 26.3 20.9 ***

Hispanic 45.2 43.0 *** 45.4 40.4 ***

Home language is Spanish 14.0 15.1 *** 14.0 14.2 

Age in ninth grade (mean)a 14.8 14.9 *** 14.8 14.7 ***

Overage in ninth grade 14.7 24.9 *** 14.7 14.5 

Academic Characteristics 

Enrolled in a gifted and talented program 11.3 7.9 *** 11.1 15.0 ***

At risk for dropping out of school 45.9 44.8 45.8 42.0 ***

Economically disadvantaged 49.3 37.3 *** 49.0 41.8 ***

Limited English proficiency 5.9 8.4 *** 5.9 5.9 

Special education services 5.5 11.8 *** 5.5 4.7 

Enrolled in a vocational or technical course 45.7 38.1 *** 45.7 40.9 ***

Enrolled in a vocational or technical 

education program 7.9 5.2 *** 7.5 2.4 ***

Eighth-Grade Test Scores 

Raw math score (number of questions correct) 36.9 31.6 *** 36.9 37.9 ***

Percent scored in top 25 percent in state 

for math 24.6 20.1 *** 24.4 30.0 ***

Percent scored in bottom 25 percent in state 

for math 26.5 40.1 *** 26.4 25.4 

Raw reading score (number of 

questions correct) 33.9 28.9 *** 33.9 34.0 

Percent scored in top 25 percent in state 

for reading 25.5 20.4 *** 25.3 28.6 ***

Percent scored in bottom 25 percent in state 

for reading 25.3 39.5 *** 25.3 24.7 

Score on essay test 2.4 2.0 *** 2.4 2.4 ***

Missing test scores 6.7 15.6 *** 6.6 7.6 ***

Number of Students 3,853 85,110 3,789 30,557 

Note: We matched nonparticipant comparison students to Talent Search students using a propensity score 

model as described in Chapter II. The participants’ means for the matched sample differed slightly from 

the means for the full sample, as some participants had no suitable comparison students; these unmatched 

participants were dropped from the participant sample. The sample includes all 10 projects that provided 

data. The number of nonparticipants in the matched sample were those who matched to a participant; the 

sample was then weighted to equal the number of participants in the analysis. 

a

Average age in years. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
31 

this section we present the differences and follow with a discussion of the findings. We used a 

regression-adjusted approach to estimate the relationship of participation in Talent Search to high 

school completion, first-time application for federal financial aid, and postsecondary 

enrollment.24 This approach allowed us to make an adjustment for the remaining differences in 

observable characteristics between the participant sample and the matched comparison group 

and, thus, increased the precision of the estimates. 

1. High School Completion 

Talent Search participants were more likely to complete high school than comparison 

students drawn from target high schools; 86 percent of Talent Search students graduated from 

high school by 2000, which was 9 percentage points higher than the 77 percent graduation rate of 

the comparison students drawn from the same target high schools (Figure III.1). High school 

graduation rates for Talent Search participants and comparison students were much higher than 

the 60 percent overall graduation rate for the state. These rates were also much higher than the 

rate at any single project’s target high schools or districts where the high schools were located, 

which ranged from 43 to 68 percent. 

The difference in high school completion shown in Figure III.1 masks substantial variation 

by project and year of Talent Search participation, but nearly all the estimates are positive. Table 

III.6 shows differences by project and year of entry into Talent Search; as noted above, 

separating the participants in each project by the time at which they first entered Talent Search 

allows us to examine whether the entry year is related to the effect of participation. We found 

differences in high school completion rates between Talent Search participants and comparison 

 24 See Chapter II for estimation details; control variables included the observable characteristics listed in the 

rows of Table III.1. 
32 

FIGURE III.1 

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS WHO COMPLETED HIGH 

SCHOOL AND WERE FIRST-TIME APPLICANTS FOR FINANCIAL AID FROM TEXAS IN 1999 

OR 2000 

students at most projects, and nearly all of the estimates are positive and statistically significant. 

Variation across projects is to be expected, given differences in comparison group activities 

across sites; however, the project-level analysis allows us to rule out the possibility that large 

impacts at isolated projects are driving the aggregate results. 

2. Application for Financial Aid 

There were large differences in first-time application for financial aid between Talent Search 

participants and comparison students (Figure III.1). Nearly twice as many participants 

(62 percent) as comparison students (35 percent) applied for aid in either the 1999–2000 or the

86

77

62

35

0

20

40

60

80

100

Completed High School*** First-Time Applicants for

Financial Aid***

Percent of Students

Talent Search Participants Comparison Students

***Difference in percentages is statistically significant at the 1 percent level.
33 

TABLE III.6 

PERCENTAGE OF TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS 

WHO COMPLETED HIGH SCHOOL IN TEXAS, BY PROJECT 

Project and Year of Entry 

into Talent Search Participants Comparison Students Difference 

Project 1 

1993–97 74.2 56.4 17.7 *** 

1998–2000 93.2 82.6 10.6 *** 

Project 2 

1998 87.1 68.2 18.9 *** 

1999–2000 94.2 78.7 15.5 *** 

Project 3 

1993–97 79.3 71.4 7.9 ** 

1998 88.5 78.2 10.3 *** 

1999–2000 97.1 92.2 4.9 *** 

Project 4 

1993–96 61.2 55.9 5.3 ** 

1997–2000 65.5 66.6 -1.1 

Project 5 

1994–98 72.7 61.2 11.5 *** 

1999–2000 91.5 88.4 3.2 

Project 6 

1997–2000 90.4 81.4 9.1 ** 

Project 7 

1997 77.7 75.5 2.2 

1998–2000 76.8 86.0 -9.2 ** 

Project 8 

1996–97 77.4 63.5 13.9 *** 

1998–2000 77.8 72.5 5.3 

Project 9 

1993–98 70.4 53.6 16.7 *** 

1999–2000 98.0 91.2 6.8 *** 

Project 10 

1995–96 76.3 66.2 10.1 *** 

1997–2000 59.7 64.4 -4.7 

Note: Means are regression-adjusted and indicate the percent of Talent Search participants and comparison 

students who achieved each outcome. The comparison student sample was weighted to equal the number 

of participants in the analysis. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
34 

2000–01 school year. The rate of first-time application for federal financial aid in these two 

years was 26 percent for the entire state and 21 to 38 percent at target high schools and districts. 

In terms of application for financial aid, the comparison students did not differ as dramatically 

from all students in the target high schools and districts. As Table III.7 shows, there were 

substantial differences across projects, though the difference is not concentrated in any one or 

two projects. 

3. Postsecondary Enrollment 

Talent Search participants were more likely than comparison students to enroll in a public 

postsecondary institution in Texas. Enrollment in a public postsecondary institution in 1999, 

2000, or 2001 was 18 percentage points higher for participants (58 percent) than comparison 

students (40 percent), as shown in Figure III.2.25 Talent Search participants also were more 

likely to be enrolled as full-time students; 38 percent of participants were enrolled full-time in 

1999, 2000, or 2001, versus 25 percent of comparison students.26

The higher rates of overall postsecondary enrollment were the result of higher rates of 

enrollment at both two- and four-year institutions (Figure III.3) 38 percent of Talent Search 

participants enrolled in two-year institutions compared to 26 percent of comparison students and 

27 percent of Talent Search students enrolled in four-year institutions compared to 19 percent of 

comparison students. 

 25 The results reported were based on enrollment from 1999 to 2001. We also examined differences in 

enrollment in 1999, 2000, and 2001. Nearly all the differences in enrollment over the three years are the result of 

differences in enrollment in 1999. The differences after 1999, which indicate delayed entry into college after 

completing high school, are small and are not always statistically significant. 

26 We consulted with THECB staff, and defined a student as full-time if he or she earned at least 12 credits 

a semester. 
35 

TABLE III.7 

PERCENTAGE OF TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS 

WHO WERE FIRST-TIME APPLICANTS FOR FEDERAL FINANCIAL AID 

IN 1999 OR 2000 IN TEXAS, BY PROJECT 

Project and Year of Entry 

into Talent Search Participants Comparison Students Difference 

Project 1 

1993–97 49.2 33.5 15.7 *** 

1998–2000 73.0 48.7 24.3 *** 

Project 2 

1998 59.2 38.4 20.8 *** 

1999–2000 75.9 45.2 30.6 *** 

Project 3 

1993–97 45.3 33.1 12.2 * 

1998 65.5 38.2 27.3 *** 

1999–2000 73.8 40.7 33.2 *** 

Project 4 

1993–96 31.6 27.4 4.3 

1997–2000 43.0 33.8 9.1 ** 

Project 5 

1994–98 46.2 37.5 8.7 ** 

1999–2000 80.5 50.6 30.0 *** 

Project 6 

1997–2000 42.5 34.0 8.5 

Project 7 

1997 42.2 31.5 10.7 ** 

1998–2000 42.9 32.2 10.6 * 

Project 8 

1996–97 39.2 21.9 17.4 *** 

1998–2000 38.9 27.3 11.6 

Project 9 

1993–98 20.9 18.7 2.2 

1999–2000 81.1 32.4 48.8 *** 

Project 10 

1995–96 57.9 42.3 15.6 *** 

1997–2000 49.3 38.6 10.7 ** 

Note: Means are regression-adjusted and indicate the percent of Talent Search participants and comparison 

students who achieved each outcome. The comparison student sample was weighted to equal the number 

of participants in the analysis. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
36 

FIGURE III.2 

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS WHO ENROLLED 

IN A PUBLIC POSTSECONDARY INSTITUTION IN TEXAS IN 1999, 2000, OR 2001 

Statistically significant differences in enrollment across projects were more varied than 

differences in high school completion or first-time application for financial aid, with very small 

or no differences at 4 of the 10 projects (Table III.8). Notably, some differences in enrollment 

depended on the type of institution that served as the Talent Search host. For enrollment at fouryear institutions, the projects with statistically significant differences were primarily those hosted 

by four-year institutions (Table III.9). For two-year institutions, the projects with statistically 

significant enrollment differences across the various years of entry into Talent Search were a 

mixture of two-year and community-based projects (Table III.10). 

58

38 40

25

0

20

40

60

80

 Enrolled in a Public 

Postsecondary Institution***

 Enrolled Full-Time in a Public 

Postsecondary Institution***

Percent of Students

Talent Search Participants Comparison Students

Note: Enrollment includes all Talent Search participants and comparison students who enrolled 

in a public college or university in Texas in 1999, 2000, or 2001.

*** Difference in percentages is statistically significant at the 1 percent level.
37 

FIGURE III.3 

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS WHO ENROLLED IN A PUBLIC 

POSTSECONDARY INSTITUTION IN TEXAS IN 1999, 2000, OR 2001, BY INSTITUTION TYPE 

4. Postsecondary Persistence 

Although no data were available on postsecondary degree completion, we examined several 

outcomes indicating persistence at a public college or university in Texas: (1) enrollment at a 

four-year institution for two consecutive years (1999 and 2000); (2) continuous full-time 

enrollment in a four-year institution for three consecutive years (1999, 2000, and 2001); 

(3) credits earned in either four- or two-year institutions by year (1999, 2000, and 2001); and 

(4) total credits earned over the three years.27

 27 Total credits earned over the three-year period reflected the overall intensity of enrollment, which may be a 

function of both continuous and full-time enrollment. 

38

27 26

19

0

10

20

30

40

Enrolled in a Two-Year Public Institution in

Texas***

Enrolled in a Four-Year Public Institution in

Texas***

Percent of Students

Talent Search Participants Comparison Students

Note: Enrollment includes all Talent Search participants and comparison students who enrolled 

in a public college or university in Texas in 1999, 2000, or 2001. 

*** Difference in percentages is statistically significant at the 1 percent level.
38

TABLE III.8 

PERCENTAGE OF TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS 

WHO ENROLLED IN ANY PUBLIC POSTSECONDARY INSTITUTION 

IN TEXAS, BY PROJECT 

Project and Year of Entry 

into Talent Search Participants Comparison Students Difference 

Project 1 

1993–97 48.3 34.4 14.0 *** 

1998–2000 70.3 51.0 19.2 *** 

Project 2 

1998 49.8 32.7 17.0 *** 

1999–2000 56.3 36.1 20.1 *** 

Project 3 

1993–97 52.8 38.4 14.5 *** 

1998 52.9 38.5 14.4 *** 

1999–2000 66.9 47.3 19.6 *** 

Project 4 

1993–96 33.7 30.5 3.1 

1997–2000 50.0 38.7 11.3 *** 

Project 5 

1994–98 44.1 38.5 5.6 

1999–2000 68.6 55.0 13.7 *** 

Project 6 

1997–2000 49.3 42.1 7.2 

Project 7 

1997 56.2 49.6 6.6 

1998–2000 57.1 54.0 3.2 

Project 8 

1996–97 49.2 42.4 6.8 * 

1998–2000 44.4 45.2 -0.7 

Project 9 

1993–98 35.5 20.5 15.0 *** 

1999–2000 72.9 43.2 29.7 *** 

Project 10 

1995–96 50.9 44.4 6.5 

1997–2000 50.8 43.3 7.5 * 

Note: Means are regression-adjusted and indicate the percent of Talent Search participants and comparison 

students who achieved each outcome. The comparison student sample was weighted to equal the number 

of participants in the analysis. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
39

TABLE III.9 

PERCENTAGE OF TALENT SEARCH PARTICIPANTS AND COMPARISON 

STUDENTS WHO ENROLLED IN A FOUR-YEAR PUBLIC 

POSTSECONDARY INSTITUTION IN TEXAS, BY PROJECT 

Project and Year of Entry 

into Talent Search Participants Comparison Students Difference 

Project 1 

1993–97 10.0 7.4 2.7 

1998–2000 18.9 11.8 7.2 

Project 2 

1998 33.3 19.8 13.6 *** 

1999–2000 38.5 21.3 17.2 *** 

Project 3 

1993–97 15.1 13.6 1.5 

1998 27.6 14.9 12.7 *** 

1999–2000 28.0 18.1 9.9 *** 

Project 4 

1993–96 17.0 10.7 6.3 *** 

1997–2000 29.6 16.6 13.0 *** 

Project 5 

1994–98 13.3 10.4 2.9 

1999–2000 32.2 24.6 7.6 * 

Project 6 

1997–2000 15.1 12.5 2.6 

Project 7 

1997 27.3 21.3 5.9 

1998–2000 25.0 22.5 2.5 

Project 8 

1996–97 22.7 21.4 1.2 

1998–2000 19.4 22.9 -3.5 

Project 9 

1993–98 9.9 8.2 1.7 

1999–2000 32.8 24.5 8.4 *** 

Project 10 

1995–96 16.7 7.6 9.1 *** 

1997–2000 14.9 8.8 6.2 

Note: Means are regression-adjusted and indicate the percent of Talent Search participants and comparison 

students who achieved each outcome. The comparison student sample was weighted to equal the number 

of participants in the analysis. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
40

TABLE III.10 

PERCENTAGE OF TALENT SEARCH PARTICIPANTS AND COMPARISON 

STUDENTS WHO ENROLLED IN A TWO-YEAR PUBLIC 

POSTSECONDARY INSTITUTION IN TEXAS, BY PROJECT 

Project and Year of Entry 

into Talent Search Participants Comparison Students Difference 

Project 1 

1993–97 41.7 30.5 11.2 ** 

1998–2000 59.5 46.4 13.1 ** 

Project 2 

1998 17.4 16.9 0.6 

1999–2000 23.8 18.9 4.9 ** 

Project 3 

1993–97 41.5 29.0 12.5 ** 

1998 34.5 27.8 6.6 

1999–2000 50.9 35.9 15.0 *** 

Project 4 

1993–96 21.1 23.9 -2.9 

1997–2000 28.2 27.7 0.5 

Project 5 

1994–98 36.4 31.5 0.5 

1999–2000 47.5 38.7 8.7 * 

Project 6 

1997–2000 35.6 31.3 4.3 

Project 7 

1997 39.7 37.9 1.8 

1998–2000 41.1 41.8 -0.7 

Project 8 

1996–97 35.4 28.0 7.4 * 

1998–2000 30.6 31.9 -1.3 

Project 9 

1993–98 27.3 13.6 13.7 *** 

1999–2000 50.6 23.4 27.2 *** 

Project 10 

1995–96 48.3 41.5 6.8 

1997–2000 41.8 39.8 2.0 

Note: Means are regression-adjusted and indicate the percent of Talent Search participants and comparison 

students who achieved each outcome. The comparison student sample was weighted to equal the number 

of participants in the analysis. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
41

As the first row of Table III.11 indicates, a higher percentage of Talent Search students than 

comparison students enrolled in a four-year institution in both 1999 and 200016 percent, 

compared to 11 percent. The differences in continuous full-time enrollment in a public four-year 

institution between Talent Search students and nonparticipants were very small: 9 versus 

7 percentage points. Given the differences in initial enrollment, full-time enrollment, and 

continuous enrollment, Talent Search participants also earned more credits in public colleges in 

Texas than comparison students earned. Talent Search students earned, on average, 4.7 more 

credits than comparison students in the 1999 school year, and 9.7 more credits over the three 

schools years 1999, 2000, and 2001.28 Over three years, Talent Search participants completed 

approximately three more courses than comparison students. 

When we examined differences in college enrollment and persistence, we were concerned 

that some of the differences could be the result of differences that we observed in high school 

completion rates. Students who did not complete high school in Texas were, not surprisingly, 

less likely to enroll in a public postsecondary institution in Texas. When we examined 

differences in college enrollment and persistence among Talent Search participants and 

comparison students who did graduate from high school, we found, as expected, that high school 

graduates had higher college enrollment rates than students in the full cohort.29 However, the 

differences in overall enrollment rates reported above were similar. Differences in continuous 

enrollment in a four-year institution were still very smallonly two percentage points—and 

differences in total credits earned in 1999, 2000, and 2001 were evident. The latter group 

 28 Credits earned include credits earned during the summer. One course at a public postsecondary institution in 

Texas usually is three credits. 

29 In studying high school graduates between the two groups, we used the matching process described earlier. 

However, we did not verify that the subset of high school graduates was similar based on baseline characteristics. 
42

TABLE III.11 

CONTINUOUS ENROLLMENT IN FOUR-YEAR INSTITUTIONS AND TOTAL CREDITS EARNED 

BY TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS IN TEXAS 

Participants

Comparison 

Students Difference 

All Students in the Cohort 

Enrollment in a Four-Year Institution (Percent) 

Enrolled in 1999 and 2000 16.1 11.3 4.8 ***

Enrolled full-time in 1999, 2000, and 2001 8.6 6.7 2.2 ***

Total Credits (Average) 

Two- and four-year institutions in 1999 12.6 7.9 4.7 ***

Two- and four-year institutions from 1999 to 2001 30.2 20.4 9.7 ***

All High School Graduates in the Cohort 

Enrollment in a Four-Year Institution (Percent) 

Enrolled in 1999 and 2000 18.5 13.7 4.8 ***

Enrolled full-time in 1999, 2000, and 2001 9.9 7.7 2.2 ***

Total Credits (Average) 

Two- and four-year institutions in 1999 14.3 9.6 4.7 ***

Two- and four-year institutions from 1999 to 2001 34.3 24.8 9.5 ***

All Students in the Cohort Who Earned Some College Credits 

Total Credits (Average) 

Two- and four-year institutions in 1999 24.5 23.3 1.2 ***

Two- and four-year institutions from 1999 to 2001 52.3 48.2 4.1 ***

Note: Means are regression-adjusted and indicate the percent of Talent Search participants and comparison 

students who achieved each outcome or the number of credits earned. Enrollment and credits earned 

were at public colleges and universities in Texas. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
43

earned, on average, 9.5 credits more than comparison students who completed high school in 

Texas. Thus, the large 18 percentage point difference in initial enrollment is complemented by 

Talent Search participants completing an average of 1.5 more courses in the first year and 

approximately three more courses over three years. 

As a final test of the differences in both intensity and persistence of enrollment, we 

examined the total credits earned among students who had earned at least some credits.30 In this 

case, the differences between Talent Search participants and nonparticipants were very small. 

Talent Search students earned one or two more credits in 1999 alone and three to four more 

credits (approximately one additional course) over the years 1999, 2000, and 2001. Most of the 

difference in total credits observed for the full cohort did not result from higher rates of 

persistence of Talent Search students conditional on earning some college credits. Rather, it 

followed from differences in initial enrollment, including differences in full-time enrollment and 

earning a few more credits in the first year of college. 

E. DISCUSSION OF RESULTS 

There were moderate to large differences between Talent Search participants and 

comparison students in high school completion, first-time application for financial aid, and 

postsecondary enrollment. Eighty-six percent of Talent Search participants completed high 

school in Texas, compared to 77 percent of comparison students; a difference of this magnitude 

is considered large in studies of effective dropout prevention programs (U.S. Department of 

Education 1998). Sixty-two percent of Talent Search participants were first-time applicants for 

federal financial aid, almost twice the percentage rate for comparison students. Differences in 

 30 Only students with some college credit were studied between the two groups, but the propensity model was 

not reestimated to verify that the groups were similar in baseline characteristics. 
44

postsecondary enrollment in public institutions in Texas were not as dramatic as differences in 

application for financial aid—58 versus 40 percent—but this difference is also large, compared 

to findings from studies of other programs designed to promote college enrollment (Myers et al. 

2004; Perna and Swail 2002). The differences in secondary and postsecondary outcomes were 

consistent and statistically robust; they were generally positive and statistically significant at 

most Talent Search projects. 

The difference in high school graduation rates occurred at nearly all projects. Without 

random assignment, however, our strategy for selecting comparison students had limitations. 

Students drawn from within the target high schools matched well on observable characteristics, 

but the students who chose to participate in Talent Search may have been fundamentally 

different in unmeasured characteristics, such as motivation and aspirations, from students who 

chose not to. If so, the estimated effect of participation based on comparisons between these two 

groups may be attributable, in part, to differences in these unobservable characteristics, 

potentially overstating the effect of Talent Search. 

The magnitude of the difference in high school completion rates suggests that we could not 

account for some important unmeasured characteristics. For example, students who persisted 

through high school may be different from students who did not persist for reasons that are 

difficult to measure, such as motivation or a supportive home environment.31 Although Talent 

Search projects report that persistence and completion of high school by participants is a goal of 

the program, services typically are not intensive and do not directly target retention. In Texas, 

 31 In initial specifications that did not control for persistence by forcing a match on still being enrolled in high 

school at the point at which the participant entered Talent Search, the effect of Talent Search on high school 

graduation was found to be 15 percentage points, compared to 9 percentage points when persistence was included. 

Explicit controlling for persistence reduced the magnitude of the difference in high school completion by more than 

one-third. 
45

Talent Search projects served nearly half the participants in this cohort for the first time during 

their senior year of high school. Therefore, when project staff offered assistance with college 

and financial aid, they may have targeted students most likely to complete the final high school 

year. The pattern of differences across projects and years indicates that Talent Search 

participants were more likely to graduate than similar comparison students, but the 9 percentage 

point difference in high school completion may be an upper bound with upward bias resulting 

from unmeasured characteristics. 

The magnitude of the differences in first-time application for financial aid was also large. In 

this case, there are reasons to attribute this difference more to participation in Talent Search than 

to unmeasured characteristics. Three lines of evidence support this conclusion:

1. Talent Search staff often helped students complete applications for financial aid.

32 

Assistance was sometimes described as “hands-on,” as staff members sat with 

students and completed their applications with them. 

2. When controlled for persistence through high school, the magnitude of the 

differences in applications for financial aid was only slightly smaller than the 

specifications where no control was imposed. This suggests that unobservable 

characteristics may not play as strong a role in financial aid application as they did 

for high school graduation. 

3. The magnitude of the differences in applications for financial aid were largest for 

students first served by Talent Search during their last year in high school. 

Services that focused on the financial aid and college application process for students 

in their last year of high school probably would generate a large difference 

associated with these particular students. 

The 28 to 29 percentage point differences in first-time application for financial aid may be an 

upper bound because of unmeasured characteristics. It seems plausible, however, that Talent 

 32 More than 95 percent of Talent search projects reported that they provided this service. For a discussion of 

the most commonly provided services, see Chapter I. 
46

Search increased the likelihood of completing an application for financial aid for participants in 

this cohort, perhaps by less than this amount. 

Differences in postsecondary enrollment also were large—the enrollment rate of Talent 

Search participants in public postsecondary institutions in Texas was 18 percentage points higher 

than for comparison students.33 A random assignment study on the higher-intensity Upward 

Bound program found smaller differences in overall postsecondary enrollment than the Talent 

Search findings (Myers et al. 2004). However, the differences we found here were smaller than 

the differences found in enrollment in four-year institutions among students with low educational 

expectations in the Upward Bound study. 

The difference in college enrollment between Talent Search participants and comparison 

students was not as large as the difference in first-time application for financial aid. Talent 

Search staff can work directly with students to help them complete applications for financial aid 

and for admission to a two- or four-year institution. However, staff may be able to do less to 

ensure that a student actually enrolls in, and attends, college. This also might explain why we 

found no differences in enrollment rates at four of the ten Talent Search projects and no 

difference in application for financial aid at one of the ten Talent Search projects.34

Finally, the pattern of differences in enrollment in two- and four-year institutions across 

projects suggest that Talent Search participants are more likely to enroll in the type of institution 

which is serving as the host for the Talent Search project. Talent Search participants may 

 33 Talent Search participants also earned more credits than comparison students earned. These differences 

were smaller in magnitude than the differences in enrollment and were probably caused by differences in initial 

enrollment and, perhaps, early persistence, rather than by persistence through the first few years of college. 

34 If Talent Search participants were more likely to enroll in private or out-of-state institutions, the differences 

found between the two groups would be lower than if data were available on all types of enrollment. In addition, 

there may be students who enrolled in college but never applied for financial aid because they would not qualify 

based on family income. Given the characteristics of the students Talent Search projects targeted, however, this 

probably was uncommon among Talent Search participants. 
47

become familiar with the host institution of their project or other institutions like it, which may 

make it more likely that they will enroll in the same type of institution.35

Although our analysis in Texas demonstrated the feasibility of compiling a comprehensive 

set of administrative records to estimate the effect of Talent Search on participants, the type of 

data available in these records lacked important characteristics that may contribute to a more 

rigorous analysis. One such missing characteristic is the students’ college aspirations. Because 

the Talent Search staff may largely target students with college aspirations, it is difficult to know 

precisely how much this missing information generated a bias in the estimates. One way to 

assess this potential bias would be to conduct a similar analysis using data with information on 

aspirations and educational plans and to compare the magnitude of the differences in outcomes. 

Fortunately, analysis performed in other states allows for such a comparison. We present results 

of the analyses of data for Indiana in the next chapter. 

 35 Information was available on the type of institution students enrolled in but not on the specific institution, so 

we could not test the hypothesis of Talent Search participants enrolling in their host institutions. We test this 

hypothesis in the chapter describing the results in Florida. 

49

IV. INDIANA 

A. INTRODUCTION 

In this chapter, we present our findings for Talent Search programs in Indiana. We describe 

the data sources we used to compile records on students, the strengths and weaknesses of the 

data, and our analytic approach to identifying nonparticipants who are similar to Talent Search 

participants. We also describe our analyses of outcomes, including application for financial aid 

and postsecondary enrollment and persistence. Compared to similar nonparticipants, the 

students participating in Talent Search had higher rates of first-time application for federal 

financial aid and enrollment in public postsecondary institutions in Indiana. The magnitudes of 

these differences varied across projects in the state, but the largest and most consistent were the 

differences in application for financial aid. 

The sources and type of data available for analyzing the effect of Talent Search on 

participants in Indiana differed from the data available for Florida and Texas. This limited our 

analysis in some ways, such as not allowing us to account for high school persistence and 

completion. However, it also allowed us to account for important factors we could not account 

for in the other states, such as education and career aspirations and self-reported concerns about 

the barriers students might face in obtaining education in the future. The data available in 

Indiana are a good test of the sensitivity of our findings on participation in Talent Search to 

different sources of data, types of information available, and samples of students included in 

the analysis. 

B. DATA 

Unlike Florida and Texas, Indiana does not maintain a system of records on secondary 

school students. As described below, data on the experiences, attitudes, and characteristics of 
50

students when they were in secondary school were generated from a survey of ninth-graders 

administered by a state-supported center affiliated with Indiana University. No information was 

available on students’ progression through high school, such as grade level each year and high 

school completion. Data on postsecondary outcomes, such as application for financial aid and 

enrollment in college in Indiana, were based on state and federal data sources and were similar to 

the data available in the other states. 

Following are the main sources of data for our study in Indiana (Table IV.1): 

• Secondary School Experiences and Educational and Career Expectations. We 

obtained these through a survey of students in ninth grade in Indiana in fall 1995, 

administered by the Indiana Career and Postsecondary Advancement Center 

(ICPAC).36 This survey included information on demographic characteristics, such as 

age, race, gender, education level of parents, and family structure. In addition, the 

survey contained information on grades obtained before ninth grade, participation in 

several academic programs, educational expectations, career interests, and avenues of 

training students were considering, along with students’ views on potential barriers 

for achieving education and career goals. 

• Postsecondary Enrollment Information and Financial Aid Records. We obtained 

these from the Indiana Commission for Higher Education’s Student Information 

System (SIS) and the State Student Assistance Commission of Indiana (SSACI). The 

SIS provided financial aid and postsecondary enrollment information for all students 

who attended public colleges and universities in Indiana. The SSACI contained 

similar information on students who received state grants but included students who 

attended private or out-of-state institutions. We obtained data for the 1999–2000, 

2000–2001, and 2001–2002 school years. 

• Talent Search Project Records. We obtained these from seven of the eight projects 

operating in Indiana in 1999.37 Talent Search projects provided different types of 

data, but we obtained information on names and dates of birth of nearly all 

participants and social security numbers (SSNs) for some participants. In addition, 

projects indicated each year that Talent Search served a student. 

 36 Appendix Table A.IV.1 describes the variables from the ninth-grade survey that we used in our analysis.

37 One project that provided us with data began operating only in the 1998–99 school year, and only one 

student this project served matched the cohort. Therefore, the findings reported in this chapter apply to only six of 

the eight Talent Search projects in Indiana. 
51

TABLE IV.1 

INDIANA DATA SOURCES 

Source Variables Years 

Number of Students 

from the Fall 1995 

Ninth-Grade Cohort

Indiana Career and 

Postsecondary Advancement 

Center 

Data from survey 

completed by student 

includes basic demographic 

information and extensive 

information on academic 

and career plans 

School year 1995–96 

School year 1997–98 

65,979 

Indiana Commission for 

Higher Education's Student 

Information System 

Postsecondary enrollment, 

application and receipt of 

federal and state financial 

aid 

1999–2001 29,245 

U.S. Department of 

Education, Office of 

Postsecondary Education 

(OPE) 

First-time applicants for 

federal financial aid who 

were living in Indiana 

School year 1999–2000 

School year 2000–01 

29,069 

Talent Search Projects Lists of students and years 

served 

1993–2000 1,166 

Note: Figures indicate the number of students in the ninth-grade cohort who were identified with 

records in the other data sources. 

• Federal Financial Aid Application Records. We obtained these from the Office of 

Postsecondary Education (OPE) of the U.S. Department of Education (ED) for nearly 

all Indiana residents who were first-time applicants for federal financial aid in 

1999–2000 and 2000–01. 

We chose to study Indiana students who were in the ninth grade during the 1995–96 school 

year because of the extensive survey data available on this cohort as it began high school. This 

was the only cohort of students where this type of information was available, and it is the same 

cohort we studied in Florida and Texas. Students who graduated from this cohort “on time” in 

spring 1999 were first-time applicants for federal aid and enrolled in college in 1999. We 

collected information on application for financial aid and postsecondary enrollment for 1999 and 
52

2000, to allow an additional year for some students to complete high school or wait a year after 

completing high school before applying for aid and enrolling in college. The postsecondary 

information also allows us to assess persistence in college for students who initially enrolled in 

1999 but persisted through the first two years of college. 

Compilation of the data sources yields 65,979 records on students who completed an ICPAC 

survey when they were in ninth grade in fall 1995. Talent Search projects providing us with data 

served 1,166 of these students, making it possible for us to identify enough Talent Search 

participants and potential comparison students to analyze outcomes for Talent Search 

participants and other, similar students who did not participate in Talent Search. 

1. Strengths and Weaknesses of the Data 

The compilation of survey data and administrative records in Indiana generated data with 

several strengths for studying the effect of Talent Search on participants:

• The survey data include several measures that are important predictors of 

application for financial aid and postsecondary enrollment. These predictors 

include students’ educational expectations, career plans, and potential barriers for 

obtaining postsecondary education, as well as academic performance and interest in 

academic programs in high school. The aspiration measures, in particular, are 

important for assessing whether Talent Search students have high educational 

aspirations before entering the program and identifying other students who have 

similar aspirations. 

• The sample consists of students who were willing to complete the ICPAC survey 

and who may already have been considering postsecondary options when they were 

in ninth grade. This self-selected sample may be more homogenous than the 

complete cohort of ninth-grade students in unobservable ways that affected the 

likelihood of their completing high school and enrolling in a postsecondary 

institution. This homogeneity may make it easier to identify comparison students 

suitable to match to Talent Search participants. 

• Information on postsecondary enrollment and application for financial aid is based 

on administrative records and is more likely to be reliable than self-reported 

information. In addition, because we have complete postsecondary data for two 

years, we can examine initial persistence in postsecondary education. 
53

The data also have limitations in assessing the relationship of participation in Talent Search 

to postsecondary outcomes: 

• Information on ninth-grade characteristics is self-reported and does not include 

data on some important predictors of application for financial aid and 

postsecondary enrollment. For example, these data do not include information on 

family income or on test scores. Nor do they include administrative information on 

high school curriculum and persistence through high school. 

• The sample does not include the full cohort of students in ninth grade in the fall of 

1995. We cannot generalize our findings to all students in the cohort, as the missing 

students may include students who may have been served by Talent Search but who 

did not complete a questionnaire. In addition, we did not receive data from one 

Talent Search project; to the extent this project differs from the others, our findings 

cannot be generalized to all Talent Search projects. 

• Information on postsecondary enrollment is not comprehensive. Students who did 

not apply for state aid and who attended a private or an out-of-state institution did not 

appear as being enrolled in a postsecondary institution in these data; therefore, we 

understate postsecondary enrollment. This will affect our estimates of the difference 

between postsecondary enrollment between Talent Search participants and the 

comparison students if they differ in this respect. 

• Due to confidentiality restrictions on the ICPAC, SIS, and SSACI data, MPR staff 

were not allowed to merge the data across the various sources. Data provided to us 

contained no information that could be used to identify individuals, and we could not 

verify that the procedures used to match the data across the sources were accurate. 

Because SSNs were not available for most of the sample, matching the data across the 

sources was particularly challenging; however, the ICPAC, SIS, and SSACI data have 

been merged and used in other studies (see, for example, St. John et al. 2002). 

Compilation of the records in Indiana demonstrates that it is feasible to compile information 

from administrative data with which to study the effect of Talent Search on participants. For this 

study, the Indiana data are an excellent complement to the data available in the other states, 

because they contain important details on the educational expectations that were not available in 

Florida or Texas. Because of the relative strengths and weaknesses of the data available in 

Indiana, we had to take a slightly different analytic approach to our analysis than we did in 

Florida and Texas. 
54

2. Characteristics of Talent Search Participants and Potential Comparison Students 

The characteristics of Talent Search participants and all other students differed for students 

in Indiana. Table IV.2 shows that, across the state, Talent Search participants were more likely 

than other students to be black, have a parent without a bachelor’s degree, and live with only one 

parent. Participants were also more likely to be female, expect to attend a two-year college, and 

have participated in Indiana’s 21st Century Scholar program (a program focused on low-income 

eighth-graders, designed to increase their commitment to taking the steps to prepare for college). 

While most of the demographic characteristics of participants are associated with lower average 

educational outcomes, the academic and educational expectations characteristics are correlated 

with higher educational outcomes. Given the goals of the Talent Search program, it is notable 

that Talent Search participants were slightly more likely than nonparticipants to believe they 

could not afford college and to have no one to advise them about their future. The characteristics 

of Talent Search students presented in Table IV.2 indicate that the program is serving the 

students the program was intended to reach: disadvantaged, potentially first-generation college 

students, with good academic performance in high school and with college aspirations. 

In addition to differences between participants and other students across the state, 

participants often were quite different from nonparticipants even within the same high school. 

Frequently, these differences paralleled those found at the state level, although, in general, Talent 

Search participants were more likely to be interested in academic programs than nonparticipants 

within target high schools. Appendix Table A.IV.2 shows the characteristics of Talent Search 

participants and nonparticipants served by each of the groups of projects in this study. 
 55 Continued on next page

TABLE IV.2 

BASELINE CHARACTERISTICS OF TALENT SEARCH PARTICIPANTS 

AND ALL OTHER STUDENTS IN NINTH GRADE IN FALL 1995 

ALL OF INDIANA 

(Percents) 

 Participants All Other Students 

Demographic Characteristics 

Age in ninth grade (mean)a 14.2 14.0 

Overage in ninth grade 16.4 14.0 ** 

Male 43.6 48.5 *** 

Race 

White 51.4 70.8 *** 

Black 21.2 6.3 *** 

All other races 15.0 12.6 ** 

Race is missing 12.4 10.3 ** 

Primary language spoken at home 

English 83.5 85.4 * 

Other languages 1.5 1.3 

Language spoken is missing 15.0 13.3 

Parents' education 

Bachelor's degree 22.4 31.8 *** 

No bachelor's degree 48.4 43.1 *** 

Student does not know parents' education 18.6 16.4 * 

Parents' education is missing 10.6 8.7 ** 

Student's living arrangement 

Lives with mother and father 39.4 55.2 *** 

Lives with parent and stepparent 16.6 15.9 

Lives with one parent 30.1 18.1 *** 

Lives with other guardian 4.9 2.9 *** 

Living arrangement is missing 9.1 7.8 

Academic Characteristics 

Educational expectations 

Will not complete high school 0.2 0.5 *** 

Complete high school 6.6 7.4 

Complete some college 5.9 5.6 

Complete two years of college 11.2 7.8 *** 

Complete four years of college 59.2 61.0 

Education plans are undecided 11.2 11.8 

Education plans are missing 5.8 5.8 

Grades in schoolb 

Mostly As 6.2 10.1 *** 

Mostly As and Bs 32.2 31.5 

Mostly Bs 7.3 8.1 

Mostly Bs and Cs 27.3 26.7 

Mostly Cs and below 21.4 17.6 *** 

Grades are missing 5.7 5.9 
TABLE IV.2 (continued) 

56

 Participants All Other Students 

Academic programs of interestc 

21st Century Scholar Program 22.8 6.8 *** 

Indiana Core 40 Program 26.9 29.0 

Advanced Placement Program 33.7 34.5 

Indiana Academic Honors Diploma 12.7 14.3 

Indiana Academy for Science, Math, and Humanities 12.0 11.4 

Earning college credit in high school 46.8 42.5 *** 

Tech Prep program 17.8 17.1 

Consulted with career counselor 21.0 26.2 *** 

Interest in additional training after high schoolc 

Apprenticeship 49.1 45.4 *** 

Military training 16.0 15.9 

Employer training 26.6 24.7 

Job with no additional training 11.9 11.8 

Barriers to obtaining postsecondary educationc 

Not sure I can afford it 35.1 32.3 ** 

Not sure I can succeed 13.6 13.0 

Not sure how to prepare 27.9 26.6 

Not sure I can get into schools I want 27.4 25.8 

No one to advise about future 5.3 3.2 *** 

Not sure what I want to do with my life 22.6 24.0 

No barriers 19.6 23.9 *** 

Future career interestsc 

Agriculture and natural resources 5.4 6.5 

Arts and entertainment 29.8 29.4 

Building and construction trades 14.7 17.2 ** 

Business, management, and finance 28.0 25.7 

Communications 15.9 14.9 

Education 21.3 20.4 

Forestry, conservation, and environment 9.6 12.5 *** 

Industrial and manufacturing 3.2 4.2 * 

Medical / Health services 33.9 33.0 

Office and clerical 9.3 7.4 ** 

Science, math, computer, or engineering 15.0 15.9 

Service 20.8 18.3 ** 

Technical 12.0 11.6 

Transportation 6.1 6.4 

Number of Students 1,166 64,813 

Note: Participants were all students in the ninth grade in the fall of 1995 who were served by seven Talent 

Search projects in 2000. 

a

Average age in years. 

b

Categories of grades reflects the language on the ICPAC survey, and students’ self-reporting of grades. c

Totals in these categories do not sum to 100 since students could record more than one response. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
57

C. COMPARISON GROUPS 

As noted in Chapter II, it would be desirable for us to draw comparison students from 

nontarget high schools in the same districts as Talent Search participants, because doing so 

allows us to control for geographic differences while also limiting the self-selection of students 

who chose to participate in Talent Search when it was offered. Ultimately, we drew two 

comparison groups, one from within Talent Search projects’ target high schools, the other from 

across the state. Below, we describe the characteristics of Talent Search projects and school 

districts that dictated our strategy for identifying comparison students in Indiana. 

Due to the structure of Talent Search projects and districts in Indiana, we could not draw 

students from nontarget high schools within the districts for three main reasons. First, many 

districts in Indiana are relatively small, often consisting of only one high school and its feeder 

school. In these cases, there are no students within the same district who do not attend the target 

high school, forcing us to either drop a project from the analysis or widen the pool of potential 

comparison group members. Second, there were instances in which different Talent Search 

projects served target high schools in the same district, so we could not isolate potential 

comparison students by project.38 Third, we only knew the school a student attended when the 

student was in ninth grade, and at that time, many students were not in the target school where 

Talent Search eventually served them.39

 38 We determined target schools in Indiana the same way we determined them in the other states, through 

MPR’s survey of all Talent Search projects in 1999–2000 and through the projects’ 1999 APRs.

39 For example, a student may have been in a middle or high school not served by Talent Search when he or 

she was in ninth grade but may have moved to a target high school at some point after ninth grade. Approximately 

one-third of Talent Search participants did not appear to be in any target school served by Talent Search projects 

when they were in ninth grade. 
58

To address some of the limitations discussed above, we created three project groupings 

based on the geographic location of the projects and the extent to which the projects served either 

the same target schools or different target schools within the same districts. These groupings are: 

1. Project Group 1—only one project, which was not located near any other projects 

included in our analysis and did not serve any target high schools that other projects 

served 

2. Project Group 2—two projects with overlapping high schools and districts 

3. Project Group 3—four projects with overlapping target schools and districts40

Students who persisted longer in high school may have been fundamentally different than 

students who did not. To the extent that these differences are also associated with education 

outcomes and are not captured by the data we have available, this could limit our ability to 

generate valid estimates of the relationship between participation in Talent Search and 

postsecondary outcomes. The Indiana data are based on experiences and expectations in the 

ninth grade only, and there is no information that allows us to control for persistence in high 

school. This is a concern, because controlling for persistence in high school in Texas reduced by 

one-third the estimates of the effect of Talent Search on high school completion. This may be 

less of a problem in Indiana than it was in Texas, however, for two reasons: (1) a higher 

percentage of Talent Search participants in Indiana were served relatively early in their high 

schools careers—more than half the Talent Search participants were first served by 1997, the 

sophomore year in high school for students who were at grade level in high school; and (2) the 

data include a much richer set of background characteristics on educational aspirations, which 

 40 One project in Group 3 is the project that served only one student in our cohort. We included this project in 

Group 3 because some students eventually served by other projects were in the target schools of this project before 

the project began operating in 1998–99. 
59

may capture some of the underlying differences associated with high school persistence and 

postsecondary outcomes. 

Because the pool of students within the same districts—but outside target high schools—

often was small, we tested whether we could find a similar comparison group by using all 

nonparticipants in the entire state, while excluding those who were in the target high school of 

any Talent Search project. However, even when we used more than 60,000 nonparticipants 

across the state as potential comparison group members, we could not find a comparison group 

that was similar to the participants, due to the relative importance of various characteristics in the 

propensity score model. 

Comparison Group One: Students Within the Target Schools of Talent Search Project 

Groups 

One approach we used to identify comparison students similar to Talent Search participants 

was to draw from the narrowly defined population of students within the target high schools, as 

in Texas. This approach may introduce bias due to selection issues (although the problem can be 

reduced if the extensive set of background characteristics are correlated with those choosing to 

participate in Talent Search), but it also provides the opportunity to match with students who are 

in environments much more similar to those of participants. Table IV.3 displays the aggregate 

results of drawing the comparison students from within target high schools by group of projects, 

showing that, while the unmatched participant and comparison samples differ considerably, the 

matched participant and comparison samples had no remaining significant differences. 

Comparison Group Two: Strata by Race and 21st Century Scholar Program Participation 

Because of the difficulty in finding comparison students similar to Talent Search 

participants outside target schools, we refined our approach. First, we split the combined 

participant sample and the nonparticipants into three racial groups: “black,” “white,” and 
 60 Continued on next page

TABLE IV.3 

ASSESSING BALANCE BETWEEN TALENT SEARCH PARTICIPANTS AND NONPARTICIPANTS 

IN THE SAME HIGH SCHOOLS IN INDIANA 

(Percents) 

 Full Sample Matched Samples 

 Participants Nonparticipants Participants Nonparticipants 

Demographic Characteristics 

Age in ninth grade (mean)a 14.8 14.8 14.8 14.8 

Overage in ninth grade 17.1 15.8 17.1 17.2 

Male 42.6 47.5 *** 42.5 42.0 

Race 

White 51.6 56.7 *** 52.4 51.8 

Black 21.6 17.1 *** 20.7 21.1 

All other races 14.6 16.6 * 14.7 15.5 

Race is missing 12.2 9.6 *** 12.3 11.6 

Parents' education 

Bachelor's degree 22.2 28.3 *** 22.5 22.3 

No bachelor's degree 48.6 43.9 *** 48.2 47.7 

Student does not know parents' education 19.1 19.8 19.1 20.0 

Parents' education is missing 10.1 8.0 *** 10.2 9.9 

Student's living arrangement 

Lives with mother and father 39.3 50.3 *** 39.8 39.5 

Lives with parent and stepparent 16.1 15.4 16.1 16.7 

Lives with one parent 30.9 23.2 *** 30.5 30.8 

Lives with other guardian 5.0 4.2 4.9 4.7 

Living arrangement is missing 8.7 6.9 *** 8.8 8.4 

Academic Characteristics 

Educational expectations 

Will not complete high school 0.1 0.7 *** 0.1 0.6 ***

Complete high school 6.4 8.2 *** 6.3 6.5 

Complete some college 5.8 5.7 5.8 6.3 

Complete two years of college 11.1 9.1 *** 11.0 10.7 

Complete four years of college 60.8 59.1 60.7 60.5 

Education plans are undecided 10.5 11.9 10.7 10.2 

Education plans are missing 5.4 5.4 5.5 5.4 

Grades in schoolb 

Mostly As 6.3 8.8 *** 6.4 6.0 

Mostly As and Bs 33.1 28.6 *** 33.0 32.6 

Mostly Bs 7.4 8.0 7.4 7.8 

Mostly Bs and Cs 27.1 29.0 27.3 27.2 

Mostly Cs and below 21.1 20.4 20.9 21.2 

Grades are missing 5.0 5.2 5.1 5.2 

Academic programs of interest 

21st Century Scholar Program 23.1 12.2 *** 22.4 22.6 

Indiana Core 40 Program 27.1 22.6 *** 26.8 27.0 

Advanced Placement Program 34.9 34.5 34.8 35.3 
TABLE IV.3 (continued) 

61

 Full Sample Matched Samples 

 Participants Nonparticipants Participants Nonparticipants 

Indiana Academic Honors Diploma 12.9 13.8 12.8 13.0 

Indiana Academy for Science, Math, 

and Humanities 12.4 12.9 12.6 12.5 

Earning college credit in high school 48.5 48.4 48.3 48.3 

Tech Prep program 17.8 18.7 18.0 17.7 

Consulted with career counselor 20.9 20.5 20.5 20.4 

Interest in additional training after high school 

Apprenticeship 49.8 49.4 49.8 50.0 

Military training 16.0 15.1 15.9 16.1 

Employer training 27.2 25.2 27.2 27.3 

Job with no additional training 11.7 12.9 11.8 11.4 

Barriers to obtaining postsecondary education 

Not sure I can afford it 36.1 31.6 *** 35.9 36.0 

Not sure I can succeed 13.4 12.3 13.5 14.0 

Not sure how to prepare 28.6 27.2 28.4 28.4 

Not sure I can get into schools I want 27.6 25.7 27.3 27.3 

No one to advise about future 5.4 3.9 *** 5.1 5.3 

Not sure what I want to do with my life 22.5 22.8 22.4 21.9 

No barriers 19.6 23.5 *** 20.0 19.7 

Future career interests 

Arts and entertainment 29.5 29.8 29.5 29.3 

Building and construction trades 14.4 18.0 *** 14.2 14.3 

Business, management, and finance 29.0 27.5 28.5 29.0 

Communications 16.0 14.8 15.9 15.8 

Education 21.7 21.2 21.6 21.3 

Industrial and manufacturing 3.3 4.4 *** 3.2 3.4 

Medical / Health services 34.9 33.0 34.9 35.0 

Office and clerical 9.6 7.3 *** 9.4 9.2 

Science, math, computer, or engineering 15.4 15.7 15.3 15.7 

Service 20.9 19.0 20.7 21.1 

Technical 12.0 12.8 12.0 12.0 

Number of Students 1,102 10,700 1,083 9,844 

Note: We matched nonparticipant comparison students to Talent Search students using a propensity score 

model as described in Chapter II. The participants’ means for the matched sample differed slightly from 

the means for the full sample, as some participants had no suitable comparison students; these unmatched 

participants were dropped from the participant sample. The number of nonparticipants in the matched 

sample were those who matched to a participant; the sample was then weighted to equal the number of 

participants in the analysis. 

a

Average ages in years. 

b

Categories of grades reflects the language on the ICPAC survey, and students’ self-reporting of grades. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
62

“other.” Next, we estimated the propensity score models and found the comparison group using 

only black participants and black nonparticipants; then we repeated the process for “white” and 

“other.” By restricting the comparison groups to students of the same race, we eliminated many 

of the other differences in observable characteristics. However, there was still a higher level of 

participation in the 21st Century Scholar program for Talent Search participants. The 21st 

Century Scholar program focuses on low-income eighth-graders and is designed to increase their 

commitment to taking the steps to prepare for college. It includes support services, such as 

workshops, mentoring, and campus visits, to students and parents throughout the students’ 

secondary schooling and supplements to state financial aid grants for postsecondary schooling. 

Because many Talent Search participants were also enrolled in the 21st Century Scholar 

program, and the two programs share some goals, the effects of participation in Talent Search 

could be confounded by participation in the 21st Century Scholar program. Thus, we felt it was 

important to match precisely on participation in the 21st Century Scholar program so that same 

percentage of Talent Search participants and comparison students participated in the 21st 

Century Scholar program. 

To achieve the appropriate mix of 21st Century Scholar program participants among Talent 

Search participants and comparison students, we divided each of the three groups of Talent 

Search participants and nonparticipants into 21st Century Scholar program participants and 

nonparticipants. This resulted in six groups for matching, based on race (black, white, and other) 

and 21st Century Scholar program participation (participant or not). Overall, most of the 

differences in observable characteristics have been eliminated, with a few remaining differences, 

depending on the group. Looking at all six groups, however, we find few significant differences 

remaining between the matched participant and nonparticipant groups. 
63

D. RESULTS 

The two approaches to finding a comparison group yielded groups of nonparticipants who 

appear to be similar to Talent Search participants on observable characteristics, although the 

groups were generated using very different strategies and are distinct groups with no overlap. 

We estimated the relationship of participation in Talent Search on the outcomes of interest, using 

both comparison groups, to see whether the findings were sensitive to these differences. As the 

findings were quite similar, we present the findings for the comparison group made up of 

students from target high schools. Using a regression-adjusted approach, we estimated the 

relationship of participation in Talent Search to first-time application for federal financial aid, 

application for aid in Indiana, and postsecondary enrollment and persistence.41

1. Application for Financial Aid 

Figure IV.1 illustrates the difference in the percent of Talent Search participants and 

comparison students who applied for financial aid, based on federal data on first-time application 

for federal financial aid and based on state data on all applicants for financial aid in 1999–2000 

or 2000–01. As the figure shows, 59 percent of Talent Search participants were first-time 

applicants for federal financial aid in 1999 or 2000, compared to 45 percent of the comparison 

students. The percentage of students who applied for aid based on state data is even higher, 

although the difference between Talent Search participants and comparison students is nearly the 

same: 69 percent, compared to 56 percent. Using two sources of financial aid application data, 

we estimate that the difference in rates of application for financial aid between Talent Search 

participants and comparison students was 13 to 14 percentage points. 

 41 See Chapter II for estimation details; control variables included the observable characteristics listed in the 

rows of Table IV.1. 
64

FIGURE IV.1 

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS 

WHO APPLIED FOR FINANCIAL AID IN INDIANA 

2. Postsecondary Enrollment 

Figure IV.2 shows the difference in enrollment rates between Talent Search participants and 

comparison students in public colleges or universities in Indiana.42 The left panel displays 

enrollment in either 1999–2000 or 2000–01, and the right panel displays continuous enrollment 

in both years. We find a difference of 4 percentage points for enrollment rates in either year—

56 percent of Talent Search participants enrolled versus 52 percent of comparison students;

 42 Enrollment rates may include a small number of students who enrolled in private or out-of-state institutions 

who received a grant from the state. 

59

69

45

56

0

20

40

60

80

First-Time Applicant for Federal Financial Aid

in 1999 or 2000***

Applied for Any Financial Aid in Indiana in

1999 or 2000***

Percent of Students

Talent Search Participants Comparison Students

Note: First-time applicants for federal aid include all students who applied for federal aid for the 

first time in 1999 or 2000. Applicants for financial aid from the state include all students 

who applied for federal or state aid in 1999 or 2000. 

***Difference is statistically significant at the 1 percent level.
65

FIGURE IV.2 

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS 

WHO ENROLLED IN COLLEGE IN INDIANA 

however, there was no significant difference in postsecondary persistence across the two years. 

Although Talent Search participants were more likely than comparison students to enroll in 

college, the most robust difference between the groups was in two-year college enrollment. 

Figure IV.3 shows the differences in enrollment in two- and four-year institutions: the difference 

in enrollment rates at both two- and four-year institutions is three percentage points, although 

only the difference for two-year institutions is statistically significant. 

To further explore differences in enrollment in two- and four-year institutions, we present 

the differences in application for aid and postsecondary enrollment by project group. Table IV.4 

shows the differences between Talent Search participants and comparison students, by project

56

31

52

30

0

20

40

60

Enrolled in an Indiana Postsecondary

Institution in 1999 or 2000***

Enrolled Continuously in an Indiana

Postsecondary Institution, 1999 to 2000

Percent of Students

Talent Search Participants Comparison Students

Note: Enrollment includes all students in research sample who enrolled in a public college or 

university in Indiana in 1999 or 2000. Enrollment also includes students who received 

state financial aid but enrolled in a private or out-of-state institution.

***Difference is statistically significant at the 1 percent level.
66

FIGURE IV.3 

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS 

ENROLLED IN COLLEGE IN INDIANA, BY DEGREE PROGRAM 

group, for the four enrollment variables: (1) enrollment in either 1999 or 2000, (2) enrollment in 

both 1999 and 2000, (3) enrollment in a two-year institution, and (4) enrollment in a four-year 

institution. The findings clearly differ by project group. The only project with a statistically 

significant difference in overall postsecondary enrollment is Project Group 3. The difference in 

enrollment in two-year institutions, however, is driven entirely by Project Group 2, and the 

difference in four-year enrollment by Project Group 3. There is no difference in postsecondary 

enrollment between Talent Search participants and comparison students in Project Group 1. 

16

35

13

32

0

10

20

30

40

Enrolled in Associate's Program in Indiana 

in 1999 or 2000***

Enrolled in Bachelor's Program in Indiana 

in 1999 or 2000

Percent of Students

Talent Search Participants Comparison Students

Note: Enrollment includes all students in research sample who enrolled in a public college or 

university in Indiana in 1999 or 2000. Enrollment also includes students who received 

state financial aid but enrolled in a private or out-of-state institution.

***Difference is statistically significant at the 1 percent level.
67

TABLE IV.4 

PERCENTAGE OF TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS WHO APPLIED 

FOR FINANCIAL AID OR ENROLLED IN A POSTSECONDARY INSTITUTION IN INDIANA, 

BY PROJECT GROUP 

 Participants 

Comparison 

Students Difference 

Applied for Federal Aid in 1999 or 2000 

Project Group 1 50.0 41.4 8.6 *** 

Project Group 2 60.6 45.8 14.8 *** 

Project Group 3 63.3 48.1 15.2 *** 

Applied for Aid from Indiana 

Project Group 1 52.7 52.7 0.0 

Project Group 2 72.9 57.5 15.4 *** 

Project Group 3 77.8 59.8 18.0 *** 

Enrolled in a Postsecondary Institution in Indiana 

in 1999–2000 

Project Group 1 44.4 47.2 -2.8 

Project Group 2 59.3 55.1 4.2 

Project Group 3 61.0 52.9 8.1 *** 

Enrolled in a Two-Year Institution in Indiana 

in 1999–2000 

Project Group 1 8.5 10.2 -1.8 

Project Group 2 28.1 17.1 11.1 *** 

Project Group 3 9.4 11.8 -2.5 

Enrolled in a Four-Year Institution in Indiana 

in 1999–2000 

Project Group 1 28.8 29.8 -0.9 

Project Group 2 28.5 32.1 -3.6 

Project Group 3 47.3 34.4 12.9 *** 

Note: Participants are all students in ninth grade in the fall of 1995 who were served by seven Talent Search 

projects by 2000. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
68

E. DISCUSSION OF RESULTS 

Talent Search participants had higher rates of application for financial aid and postsecondary 

enrollment than comparison students from target high schools, but except for the higher rate of 

application for financial aid, there were substantial differences across project groups, suggesting 

that though financial aid services may be similar across projects, other services related to helping 

students in their college decisions may vary. 

• Talent Search participants were 13 percentage points more likely than comparison 

students to be first-time applicants for federal financial aid and applicants for aid 

from the state. The differences in application for federal aid range from 11 to 

15 percentage points across project groups, while the differences in application for 

aid from Indiana range from 0 to 15 percentage points. 

• Talent Search participants were 4 percentage points more likely to enroll in a 

postsecondary institution in Indiana than comparison students in 1999 or 2000. 

Across project groups, the differences varied from 0 to 8 percentage points.

• There was no difference in persistence in college from 1999 to 2000 between Talent 

Search participants and comparison students. 

• Talent Search participants were 3 percentage points more likely than comparison 

students to enroll in a two-year institution in Indiana. However, there was no 

statistically significant difference in enrollment in a four-year institution. Talent 

Search had no statistically effect on any type of postsecondary enrollment at Project 

Group 1, a positive statistically significant effect on enrollment in a two-year 

institution at Project Group 2, and a positive statistically significant effect on 

enrollment in a four-year institution at Project Group 3. 

The differences in postsecondary enrollment varied by the type of host institution for the 

Talent Search project. The host institution in Project Group 1 was not a postsecondary 

institution, one of the host institutions in Project Group 2 was a four-year institution and one was 

a two-year institution, and all the host institutions in Project Group 3 were four-year institutions. 

Thus, the difference in two-year enrollment was generated by the only project group that 

included a two-year host institution, and the difference in four-year enrollment was generated by 

the project group made up entirely of four-year host institutions. Our data do not allow us to 
69

determine whether Talent Search participants are simply more likely to apply to their host 

institution or to other two- and four-year institutions as well. Regardless of where Talent Search 

students enroll, they are enrolling at a higher rate than comparison students, and this probably is 

beneficial for their educational achievement. Because there were no differences in persistence in 

college, it is difficult to determine whether Talent Search participants in Indiana also have higher 

rates of degree completion. 

We cannot rule out the possibility that some of these findings are due to other important 

characteristics that influence application for financial aid and enrollment in college but that are 

not available—for example, indicators of family income and test scores of participants. 

However, data available in Indiana allow us to capture educational expectations and plans before 

most students participate in Talent Search. Controlling for initial educational expectations 

makes it more likely that some of the differences can be attributed to participation in Talent 

Search itself. Finally, the data in Indiana also did not include information on one key outcome of 

interest: high school completion. In the next chapter, we present our findings in Florida, where 

we analyzed the broadest range of secondary and postsecondary outcomes. 

71 

V. FLORIDA 

A. INTRODUCTION 

In 1999, eight Talent Search projects, serving a total of nearly 6,000 students, were 

operating in Florida (Cahalan et al. 2004). There were fewer projects and fewer students than in 

Texas, but the number of projects and students served was similar to Indiana.43 School districts 

in Florida are large, and no Talent Search project served all high schools within a school district. 

Our analysis in this chapter is based on 908 Talent Search participants in five Talent Search 

projects from across Florida who were in ninth grade in the 1995–96 school year, along with 

similar nonparticipants who were in the same school districts. 

The data available in Florida included secondary school records on the entire 1995–96 ninthgrade cohort and postsecondary school records on students in the cohort who enrolled in public 

postsecondary institutions in Florida. Estimates indicate that Talent Search participants had 

higher rates than similar nonparticipants of high school completion, of exam-taking for college 

entrance, of first-time application for federal financial aid, and of postsecondary enrollment, 

persistence and completion in public two-year institutions in Florida. As in other states, the 

magnitudes of these differences varied across projects, but differences in application for financial 

aid were largest and most consistent. 

In the following sections of this chapter, we describe the data sources used to compile 

records on students, list the strengths and weaknesses of the data, and describe our analytic 

approach to identifying nonparticipating students who were similar to Talent Search participants. 

 43 Though similar to Indiana in the absolute number of students served by Talent Search, Florida’s programs 

serve a much lower percentage of low-income secondary students than either Indiana’s or Texas’s programs. 
72 

We also provide estimates of the relationship of participation in Talent Search to high school 

completion, financial aid application, and postsecondary enrollment. 

B. DATA 

We used three main sources of data for our study in Florida (Table V.1): 

1. Secondary and Postsecondary School Records. We obtained these from the Florida 

Department of Education’s (FLDOE’s) K-20 Data Warehouse. The warehouse is a 

repository for records on all primary, secondary, and postsecondary public school 

students in Florida. The secondary school records include demographic, socioeconomic, and academic characteristics, as well as school, grade level, and grade 

promotion status in each year. Secondary school records also include high school 

degree completion and college entrance exam test taking. Postsecondary records 

include information on enrollment, including name of institution.44 We obtained 

data for the 1995–99 through 2002–03 school years. 

2. Talent Search Project Records. We obtained these from seven of the eight Talent 

Search projects operating in 1999. However, only five projects provided information 

on, and served enough, students during the time period of interest for this study 

(1995–2000) to be included in the analysis.45 Talent Search projects provided 

individual identifying information for most students and indicated each year that a 

student was served. 

3. Federal Financial Aid Application Records. We obtained these for nearly all firsttime applicants for federal financial aid who lived in Florida in 1999 and 2000 from 

the Office of Postsecondary Education (OPE), U.S. Department of Education (ED). 

As noted in the other chapters, the cohort chosen for this study consisted of all students in 

ninth grade in the 1995–96 school year, to allow collection of retrospective data that enabled us 

to assess several outcomes related to the goals of the Talent Search program. The data available 

in Florida allowed us to examine the effect of Talent Search on more steps along the educational 

pathway: high school completion, test taking before college, application for financial aid, and 

 44 We discerned the institution type (two- or four-year) from the institution name. 

45 One of the projects began operating in 1998–99 and did not serve enough students in the target cohort to be 

included in the analysis; another project did not send us data on all the students it served. 
73 

TABLE V.1 

FLORIDA DATA SOURCES 

Source Variables Years 

Number of Students 

from Fall 1995 

Ninth-Grade Cohort

Florida K-20 Education 

Data Warehouse 

Demographic and academic 

information on secondary 

and postsecondary 

enrollment 

1995–2002 233,188 

U.S. Department of 

Education, Office of 

Postsecondary Education 

(OPE) 

First-time applicants for 

federal financial aid who 

were living in Florida 

School year 1999–2000 

School year 2000–01 

49,672 

Talent Search Projects Lists of students and years 

served 

1993–2000 908 

Note: Figures indicate the number of students in the ninth-grade cohort who were identified with 

records in the other data sources. 

college enrollment and persistence.46 Compilation of these data sources yielded records on 

233,188 students who were in ninth grade in Florida public schools during the 1995–96 school 

year.47 Talent Search projects that provided us with data served 908 of these students at some 

point during secondary school. 

1. Strengths and Weaknesses of the Data 

Compilation of administrative records generated a database with several strengths for 

studying the effect of Talent Search on participants: 

 46 We also examined completion of two-year degrees, because students in the cohort who enrolled in a twoyear institution soon after completing high school may have completed a degree by 2003. 

47 We restricted our analysis to students who were between 13.3 and 17.3 years old in ninth grade in the 

1995–96 school year. This reduced our analysis sample to 223,903. 
74 

• The data are comprehensive and contain the full sample of ninth-graders in 

Florida public schools in 1995–96. Talent Search projects were spread throughout 

the state and differed in terms of the students served, so it is critical to be able to draw 

comparison students from similar environments. 

• The data contain information on observable characteristics that are important 

predictors of educational outcomes. These characteristics include race, ethnicity, 

gender, economic status (defined as eligible for free or reduced-price lunches), 

disabilities, and participation in dropout prevention and gifted and talented programs. 

• The data contain information on persistence through high school, which allows us 

to control for important characteristics in selecting comparison students. In 

addition, because we know which schools students attended each year, we are able to 

compare Talent Search participants to students who were in the same high schools as 

the participants at the precise time Talent Search served them. 

• The data contain reliable information on key outcomes, such as high school 

completion status, application for federal financial aid, enrollment in college, and 

initial persistence in college. Because these data come from administrative sources, 

the outcomes are more likely to be accurate and may be more comprehensive than 

self-reported information. 

However, these data also have limitations in terms of assessing the differences between 

Talent Search participants and similar nonparticipants: 

• The data do not contain information on some characteristics that are important 

predictors of the outcomes we are studying. These include test scores (an indicator 

of academic ability), education level of parents, family structure, and student 

educational and career aspirations and plans. 

• Information on postsecondary enrollment is limited to attendance at public colleges 

and universities in Florida. Thus, we understate the overall postsecondary 

enrollment rates of students in this cohort. However, this only affects our estimates 

of the difference in postsecondary enrollment between Talent Search participants and 

comparison students if one group is more likely to enroll in private or out-of-state 

institutions. 

• Because some projects did not send us data, we did not obtain data on all Talent 

Search participants in the ninth-grade cohort. If these projects are systematically 

different from those that provided data, our findings will not apply to all the students 

Talent Search served in Florida. 

Compilation of the records in Florida again demonstrates that it is feasible to compile 

comprehensive and accurate information from administrative data with which to study the effect 
75 

of Talent Search on participants. However, the lack of information on test scores and 

educational aspirations may be a serious limitation of the data. 

2. Characteristics of Talent Search Participants and Potential Comparison Students 

The characteristics of Talent Search participants differed substantially from students across 

the state as a whole, as well as within the high schools that individual projects targeted. Across 

the state as a whole, Talent Search participants differed from other students on several 

characteristics, many of which are correlated with high school completion and college 

enrollment.48 Compared with all other students in the state, Talent Search students were more 

likely to be black and economically disadvantaged (as measured by eligibility for free or 

reduced-price lunches) than other students. However, they were also more likely than other 

students to be female and the traditional age when entering ninth grade, less likely to have 

participated in a dropout prevention program, and slightly less likely to be classified as 

physically or learning disabled (Table V.2).49 As students in our cohort progressed through high 

school, participants were much more likely than other students to persist and be at grade level in 

each year (Table V.3).50 Therefore, Talent Search projects in Florida seem to have served the 

population the program sought to target: participants were from low-income families, but less 

at-risk academically than low-income students in general, and were likely to complete 

high school. 

 48 Appendix Table A.V.1 describes the variables we used in the analysis.

49 At all projects, Talent Search participants were more likely than nonparticipants to be female, economically 

disadvantaged, nonwhite, and a traditional age in ninth grade; however, there was variation across projects in 

participation in gifted and dropout prevention programs (Appendix Table A.V.2). 

50 At all projects, Talent Search participants were more likely than nonparticipants to persist through high 

school (Appendix Table A.V.2). 
76 

TABLE V.2 

BASELINE CHARACTERISTICS OF TALENT SEARCH PARTICIPANTS AND ALL OTHER STUDENTS, 

ALL OF FLORIDA 

(Percents) 

 Participants 

All Other 

Students 

Demographic Characteristics 

Male 33.9 52.9 *** 

Race 

White 44.6 55.3 *** 

Black 45.7 25.4 *** 

Hispanic 4.5 16.0 *** 

All other races 5.3 3.3 *** 

Primary language spoken at home 

English 96.9 86.4 *** 

Spanish 1.9 10.3 *** 

Other languages 1.2 3.4 *** 

United States citizen 94.7 86.4 *** 

Economically disadvantaged 63.1 37.2 *** 

Age in ninth grade (mean)a 14.6 14.9 *** 

Overage in ninth grade 9.7 26.3 *** 

Academic Characteristics 

Gifted 4.1 3.9 *** 

Any dropout prevention program 17.3 28.7 *** 

Dropout prevention for disruptive students 2.6 8.1 *** 

Dropout prevention for alternative education 13.2 18.9 *** 

Dropout prevention for Department of Juvenile Justice 5.1 7.9 *** 

Emotionally or physically disabled 5.8 8.2 *** 

Learning disabled 3.6 8.2 *** 

Number of Students 909 222,995 

Note: Participants were all students in the ninth grade in the fall of 1995 who were served by five Talent Search 

projects in 2000. 

a

Average age in years. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
77 

TABLE V.3 

BELOW GRADE AND PERSISTENCE OF TALENT SEARCH PARTICIPANTS 

AND ALL OTHER STUDENTS IN FLORIDA 

(Percents) 

 Below Grade Persistence 

 Participants 

All Other 

Students Participants

All Other 

Students 

1996–97 18.9 22.7 *** 98 88 *** 

1997–98 20.2 28.5 *** 96 87 *** 

1998–99 20.7 25.3 ** 92 81 *** 

1996–99 86 62 *** 

Note: Participants were all students in the ninth grade in the fall of 1995 who were served by five 

Talent Search projects by 2000. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 

C. COMPARISON GROUPS 

We drew two different samples of students who were similar to Talent Search participants 

but did not participate in the program: one sample from the same high schools as Talent Search 

participants, the other from the different high schools within the same districts.51 Drawing 

comparison students from nonparticipants within target high schools yielded a sample of students 

most similar to Talent Search participants based on the characteristics available in the data. The 

samples were statistically equivalent on all characteristics, except for a very small difference in 

native language spoken at home (Table V.4). The school districts in Florida are larger than in 

Indiana or Texas, and we were more successful in matching comparison students with similar 

 51 For a discussion of the relative merits of drawing comparison groups from students within and outside target 

high schools, see Chapter II. 
78 

TABLE V.4 

ASSESSING BALANCE BETWEEN TALENT SEARCH PARTICIPANTS AND NONPARTICIPANTS 

IN THE SAME HIGH SCHOOLS IN FLORIDA 

(Percents) 

 Full Samples Matched Samples 

 Participants Nonparticipants Participants Nonparticipants

Demographic Characteristics 

Male 33.9 52.3 *** 34.2 34.0 

Race 

White 44.6 60.3 *** 45.0 44.2 

Black 45.6 24.8 *** 45.3 45.6 

Hispanic 4.5 11.1 *** 4.3 5.1 

All other races 5.3 3.9 * 5.3 5.2 

Primary language spoken at home 

English 96.9 91.9 *** 97.0 96.2 

Spanish 1.9 5.9 *** 1.8 1.9 

Other languages 1.2 2.2 *** 1.2 1.9 *

United States citizen 94.7 88.2 *** 94.8 94.7 

Economically disadvantaged 63.1 36.3 *** 62.9 63.6 

Age in ninth grade (mean)a 14.6 14.8 *** 14.6 14.6 

Overage in ninth grade 9.7 19.1 *** 9.8 10.7 

Academic Characteristics 

Gifted 4.1 4.5 3.7 4.2 

Any dropout prevention program 17.2 23.7 *** 17.2 19.7 *

Dropout prevention for disruptive students 2.5 4.2 *** 2.6 2.8 

Dropout prevention for alternative education 13.1 15.1 13.1 13.3 

Dropout prevention for Department of 

Juvenile Justice 5.0 8.2 *** 5.0 6.1 

Emotionally or physically disabled 5.8 8.3 *** 5.8 5.8 

Learning disabled 3.6 8.8 *** 3.7 4.2 

Number of Students 908 67,049 900 42,514 

Note: We matched nonparticipant comparison students to Talent Search students using a propensity score 

model as described in Chapter II. The participants' means for the matched sample differed slightly from 

the means for the full sample, as some participants had no suitable comparison students; these unmatched 

participants were dropped from the participants sample. The number of nonparticipants in the matched 

sample were those who matched to a participant; the sample was then weighted to equal the number of 

participants in the analysis. 

a

Average age in years. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
79 

characteristics to Talent Search participants within the districts where Talent Search project were 

operating but not at the same high schools. The samples were similar on all characteristics 

except for race, participation in gifted programs, and participation in a dropout prevention 

program sponsored by the Department of Juvenile Justice (Table V.5). 

Because the Florida data included information on persistence through high school, we used a 

strategy for identifying comparison students to Talent Search participants similar to the one we 

used in Texas. We drew comparison students by Talent Search project, and controlled for 

persistence through high school by dividing the participant sample into students first served early 

or later in their high school careers, requiring potential comparison group members to still have 

been in the same schools as Talent Search participants when the participants first participated in 

Talent Search.52 We analyzed the differences in secondary and postsecondary outcomes between 

Talent Search participants and comparisons students using both comparison groups, and our 

findings were not sensitive to the comparison group chosen. Therefore, we report the findings 

based on comparison students drawn from the same target high schools, because those 

comparison students were most similar to Talent Search participants. 

D. RESULTS 

Talent Search participants were more likely than comparison students to complete each step 

on the path to higher educational achievement, including high school completion, college 

entrance test taking, application for financial aid, and college enrollment and persistence. We 

used a regression-adjusted approach to estimate the relationship of participation in Talent Search 

to secondary and postsecondary outcomes to (1) adjust for the remaining differences in 

 52 When sample sizes permitted, we broke the sample into two time periods per project. However, as the 

Talent Search projects in Florida did not serve as many students from this cohort as those in Texas, we could not 

divide the sample of participants at two of the projects. 
80 

TABLE V.5 

ASSESSING BALANCE BETWEEN TALENT SEARCH PARTICIPANTS AND NONPARTICIPANTS 

AT DIFFERENT HIGH SCHOOLS IN THE SAME DISTRICTS IN FLORIDA 

(Percents) 

 Full Samples Matched Samples 

 Participants Nonparticipants Participants Nonparticipants 

Demographic Characteristics 

Male 33.9 52.7 *** 34.6 34.3 

Race

White 44.6 62.7 *** 46.1 49.1 

Black 45.6 22.9 *** 45.0 39.6 ** 

Hispanic 4.5 10.7 *** 4.4 6.2 ** 

All other races 5.3 3.8 ** 4.4 5.1 

Primary language spoken at home 

English 96.9 92.7 *** 97.3 97.7 

Spanish 1.9 5.4 *** 1.8 1.6 

Other languages 1.2 1.9 * 0.9 0.7 

United States citizen 94.7 89.4 *** 94.9 95.7 

Economically disadvantaged 63.1 33.8 *** 62.3 60.5 

Age in ninth grade (mean)a 14.6 14.8 *** 14.6 14.6 

Overage in ninth grade 9.7 19.8 *** 9.8 10.1 

Academic Characteristics 

Gifted 4.1 5.0 3.6 5.9 ***

Any dropout prevention program 17.2 24.0 *** 17.0 19.0 

Dropout prevention for disruptive students 2.5 4.5 *** 2.6 3.1 

Dropout prevention for alternative education 13.1 14.8 12.8 10.7 

Dropout prevention for Department of 

Juvenile Justice 5.0 8.6 *** 5.1 7.4 ** 

Emotionally or physically disabled 5.8 8.3 *** 5.8 5.0 

Learning disabled 3.6 8.6 *** 3.8 3.7 

Number of Students 908 88,156 878 13,843 

Note: We matched nonparticipant comparison students to Talent Search students using a propensity score 

model as described in Chapter II. The participants' means for the matched sample differed slightly from 

the means for the full sample, as some participants had no suitable comparison students; these unmatched 

participants were dropped from the participants sample. The number of nonparticipants in the matched 

sample were those who matched to a participant; the sample was then weighted to equal the number of 

participants in the analysis. 

a

Average age in years. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
81 

observable characteristics between the Talent Search participant sample and the matched 

comparison group and (2) increase the precision of our estimates.53 In this section, we present 

the differences in outcomes for participants and comparison students across Florida and by 

project; in the next section, we discuss our interpretation of these findings. 

1. High School Completion, Application for Financial Aid, and College Entrance Test 

Taking 

Talent Search participants were more likely than comparison students to take the steps 

necessary to enroll in college—that is, complete high school (through obtaining a high school 

diploma or GED), apply for financial aid, and take standardized college entrance and admissions 

exams. Eighty-four percent of Talent Search students completed high school by 2000—

14 percentage points higher than the 70 percent rate of the comparison students. The difference 

in first-time application for financial aid was even larger—52 percent, compared to 33 percent of 

comparison students—a difference of 19 percentage points (Figure V.1). The differences also 

were evident in taking standardized college entrance and admission exams: Talent Search 

participants (45 percent) were more likely than comparison students (32 percent) to take the CPT 

or CLAST exams, with an even larger gap in test taking between participants (44 percent) and 

comparison students (27 percent) for the SAT or ACT exams.54

There were substantial differences in pre-college outcomes across projects. The difference 

in application for financial aid was most consistent, occurring at all five projects (Table V.6). 

 53 See Chapter II for estimation details; control variables included the observable characteristics listed in the 

rows of Table V.1. 

54 The CPT is used as a college placement exam by public community colleges in Florida. The CLAST is a 

test of basic college communications and math skills; it is required for obtaining an associate’s degree and entering a 

four-year institution from a two-year institution. The categories of these tests are not mutually exclusive; some 

students took more than one of these tests. 
82 

FIGURE V.1 

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS WHO COMPLETED HIGH SCHOOL, 

APPLIED FOR FINANCIAL AID, AND TOOK COLLEGE ENTRANCE EXAMS IN FLORIDA 

The differences in high school completion and college entrance test taking were also fairly 

consistent, occurring in four of the five projects (Tables V.6 and V.7). 

2. Postsecondary Enrollment 

Talent Search participants were more likely than comparison students to enroll in public 

postsecondary institutions in Florida. We found that 1999–2000 enrollment in public 

postsecondary institutions in Florida was 15 percentage points higher for participants 

(51 percent) than comparison students (36 percent) overall, as well as at both two-year (39 versus 

29 percent) and four-year (14 versus 9 percent) institutions (Figure V.2, left panel). When we 

expanded the enrollment window to 2003, the differences in enrollment rates were larger for 

both types of institutions (Figure V.2, right panel). Expanding the window of enrollment to 2003 

84

52

45 44

70

33 32

27

0

20

40

60

80

100

Completed High

School***

Applied for Financial

Aid***

CPT/CLAST*** SAT/ACT***

Percent of Students

Talent Search Participants Comparison Students

aIndicates students who were first-time applicants for federal financial aid in Florida in 1999 or 2000.

***Difference in percentages is statistically significant at the 1 percent level.

a
83 

TABLE V.6 

PERCENTAGE OF TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS 

WHO COMPLETED HIGH SCHOOL AND APPLIED FOR FINANCIAL AID 

IN FLORIDA, BY PROJECT 

Project and Year of Entry into 

Talent Search Participants 

Comparison 

Students Difference 

High School Completion 

Project 1 

1993–96 76.9 68.9 8.0 *** 

1997–99 93.1 76.4 16.7 *** 

Project 2 

1996–2000 71.1 59.8 11.2 *** 

Project 3 

1993–98 76.5 63.5 13.0 *** 

1999–2000 79.7 85.6 -5.9 

Project 4 

1993–95 84.5 65.6 18.9 *** 

1996–99 96.7 71.9 24.8 *** 

Project 5 

1995–99 96.7 69.4 27.3 *** 

Application for Financial Aid 

Project 1 

1993–96 42.7 32.1 10.6 *** 

1997–99 47.5 39.7 7.8 *** 

Project 2 

1996–2000 46.3 33.9 12.4 *** 

Project 3 

1993–98 38.8 27.3 11.5 ** 

1999–2000 51.2 42.4 8.9 ** 

Project 4 

1993–95 54.3 26.2 28.1 *** 

1996–99 79.1 32.3 46.8 *** 

Project 5 

1995–99 61.7 28.2 33.5 *** 

Note: Means are regression-adjusted and indicate the percent of Talent Search participants and comparison 

students who achieved each outcome. The comparison student sample was weighted to equal the number 

of participants in the analysis. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
84 

TABLE V.7 

PERCENTAGE OF TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS 

WHO TOOK COLLEGE ENTRANCE EXAMS IN FLORIDA, BY PROJECT 

Project and Year of Entry into 

Talent Search Participants 

Comparison 

Students Difference 

CLP / CLAST 

Project 1 

1993–96 45.5 36.4 9.1 ** 

1997–99 57.4 38.8 18.6 *** 

Project 2 

1996–2000 36.4 24.7 11.7 *** 

Project 3 

1993–98 30.6 26.9 3.7 

1999–2000 31.7 35.0 -3.3 

Project 4 

1993–95 54.3 29.8 24.5 *** 

1996–99 48.4 33.7 14.7 *** 

Project 5 

1995–99 58.3 29.4 29.0 *** 

SAT / ACT 

Project 1 

1993–96 39.2 27.9 11.3 *** 

1997–99 58.4 35.1 23.3 *** 

Project 2 

1996–2000 30.6 22.8 7.8 ** 

Project 3 

1993–98 32.9 25.6 7.3 * 

1999–2000 31.7 30.8 0.9 

Project 4 

1993–95 49.1 23.8 25.3 *** 

1996–99 74.7 31.1 43.7 *** 

Project 5 

1995–99 45.0 22.2 22.8 *** 

Note: Means are regression-adjusted and indicate the percent of Talent Search participants and comparison 

students who achieved each outcome. The comparison student sample was weighted to equal the number 

of participants in the analysis. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
85 

FIGURE V.2 

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS WHO ENROLLED IN A PUBLIC 

POSTSECONDARY INSTITUTION IN FLORIDA 

substantially increased enrollment at two-year institutions for participants and comparison 

students, suggesting that initial enrollment in two-year institutions occurs well after completion 

of high school. 

There was substantial variation in overall enrollment and in two- and four-year enrollment 

by project. Although the magnitude of the differences in overall enrollment varied, there were 

positive and statistically significant differences at four of the five projects (Table V.8). There 

were also differences in two- and four-year enrollment that appear to be related to the type of 

host institution. At Projects 1 and 5, both hosted by two-year institutions, Talent Search 

51

39

14

36

29

9

0

20

40

60

80

Overall** TwoYear**FourYear**

Percent of Students

73

67

24

57

52

16

0

20

40

60

80

Overall** TwoYear**FourYear***

Percent of Students

Enrolled in 1999–2000 Enrolled by 2003

**Difference in percentages is statistically significant at the 5 percent level.

***Difference in percentages is statistically significant at the 1 percent level.

Talent Search Participants Comparison Students
86 

TABLE V.8 

PERCENTAGE OF TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS 

WHO ENROLLED IN ANY PUBLIC POSTSECONDARY INSTITUTION 

IN FLORIDA, BY PROJECT 

Project and Year of Entry into 

Talent Search Participants 

Comparison 

Students Difference 

All Postsecondary Institutions, 

Enrollment by 1999–2000 

Project 1 

1993–96 40.6 37.4 3.2 

1997–99 48.5 38.8 9.7 ** 

Project 2 

1996–2000 42.2 28.0 14.2 *** 

Project 3 

1993–98 49.4 40.6 8.8 * 

1999–2000 50.4 52.6 -2.2 

Project 4 

1993–95 51.7 31.8 19.9 *** 

1996–99 63.7 36.0 27.8 *** 

Project 5 

1995–99 64.2 29.2 35.0 *** 

All Postsecondary Institutions, 

Enrollment by 2003 

Project 1 

1993–96 71.3 61.1 10.3 *** 

1997–99 81.2 64.8 16.4 *** 

Project 2 

1996–2000 57.9 46.9 11.0 ** 

Project 3 

1993–98 70.6 65.2 5.4 

1999–2000 66.7 73.4 -6.7 

Project 4 

1993–95 67.2 46.0 21.3 *** 

1996–99 87.9 52.4 35.5 *** 

Project 5 

1995–99 83.3 49.2 34.2 *** 

Note: Means are regression-adjusted and indicate the percent of Talent Search participants and comparison 

students who achieved each outcome. The comparison student sample was weighted to equal the number 

of participants in the analysis. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
87

participants were significantly more likely than comparison students to attend a two-year 

institution in 1999–2000, immediately after high school, with no significant difference at fouryear institutions (Tables V.9 and V.10). Expanding the window for enrollment to 2003 reveals 

that Talent Search participants at these projects were more likely to eventually attend a four-year 

institution. Students from these two projects may have been more likely to attend a two-year 

school initially and transfer to a four-year school a few years later. On the other hand, at Projects 

2 and 4, both hosted by four-year institutions, Talent Search participants were more likely than 

comparison students to enroll in a four-year college immediately following high school. 

3. Postsecondary Persistence and Completion 

The differences in postsecondary persistence between Talent Search participants and 

comparison students were smaller than the differences in initial enrollment but were positive and 

statistically significant. Talent Search participants were more likely than comparison students to 

stay in a two-year college for two consecutive years (23 versus 15 percent). Similarly, they were 

more likely to have attended a four-year institution for two consecutive years (14 versus 

10 percent) (Figure V.3).55 The persistence in two-year colleges appeared to translate into 

degrees: 13 percent of Talent Search participants received a two-year degree by 2003, compared 

with 8 percent of nonparticipants (Figure V.3).56

Across individual projects, there was a positive and statistically significant difference in the 

rate of persistence at two-year institutions at four of the five projects, while the difference in the 

rate of persistence at four-year institutions occurred at only one project (Table V.11). Notably 

 55 We defined persistence in a two- or four-year institution as two consecutive years of enrollment between 

1999–2003 at that type of institution. We did not verify that the two consecutive years of enrollment occurred at the 

same institution. 

56 We did not examine four-year degree completion, because our data do not extend far enough in time for 

most students to complete a four-year degree. 
88

TABLE V.9 

PERCENTAGE OF TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS 

WHO ENROLLED IN A FOUR-YEAR PUBLIC POSTSECONDARY INSTITUTION 

IN FLORIDA, BY PROJECT 

Project and Year of Entry into 

Talent Search Participants 

Comparison 

Students Difference 

Four-Year Institutions, Enrollment 

by 1999–2000 

Project 1 

1993–96 8.4 7.8 0.6 

1997–99 11.9 11.5 0.4 

Project 2 

1996–2000 14.1 8.0 6.1 ** 

Project 3 

1993–98 16.5 9.6 6.9 * 

1999–2000 14.6 10.5 4.1 

Project 4 

1993–95 17.2 9.7 7.6 ** 

1996–99 29.7 11.8 17.9 *** 

Project 5 

1995–99 6.7 6.1 0.6 

Four-Year Institutions, Enrollment 

by 2003 

Project 1 

1993–96 18.9 15.2 3.7 *** 

1997–99 31.7 20.9 10.8 ** 

Project 2 

1996–2000 15.7 12.6 3.1 

Project 3 

1993–98 21.2 16.0 5.2 

1999–2000 19.5 16.2 3.4 

Project 4 

1993–95 26.7 14.7 12.0 *** 

1996–99 45.1 19.6 25.5 *** 

Project 5 

1995–99 18.3 11.0 7.3 ** 

Note: Means are regression-adjusted and indicate the percent of Talent Search participants and comparison 

students who achieved each outcome. The comparison student sample was weighted to equal the number 

of participants in the analysis. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
89

TABLE V.10 

PERCENTAGE OF TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS 

WHO ENROLLED IN A TWO-YEAR PUBLIC POSTSECONDARY INSTITUTION 

IN FLORIDA, BY PROJECT 

Project and Year of Entry into 

Talent Search Participants 

Comparison 

Students Difference 

Two-Year Institutions, Enrollment 

by 1999–2000 

Project 1 

1993–96 34.3 30.8 3.5 

1997–99 38.6 28.4 10.2 ** 

Project 2 

1996–2000 30.6 21.0 9.6 ** 

Project 3 

1993–98 35.3 32.0 3.3 

1999–2000 38.2 43.1 -4.9 

Project 4 

1993–95 37.9 23.2 14.8 *** 

1996–99 39.6 25.6 14.0 *** 

Project 5 

1995–99 59.2 24.9 34.3 *** 

Two-Year Institutions, Enrollment 

by 2003 

Project 1 

1993–96 65.0 56.7 8.3 ** 

1997–99 72.3 59.1 13.2 *** 

Project 2 

1996–2000 51.2 40.4 10.9 ** 

Project 3 

1993–98 62.4 59.9 2.5 

1999–2000 61.8 68.2 -6.4 

Project 4 

1993–95 63.8 42.3 21.5 *** 

1996–99 80.2 47.4 32.8 *** 

Project 5 

1995–99 80.8 46.7 34.1 *** 

Note: Means are regression-adjusted and indicate the percent of Talent Search participants and comparison 

students who achieved each outcome. The comparison student sample was weighted to equal the number 

of participants in the analysis. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
90

FIGURE V.3 

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS WHO PERSISTED IN PUBLIC 

POSTSECONDARY INSTITUTIONS IN FLORIDAa

similar to the enrollment patterns, the positive and significant difference in two-year degree 

attainment was found only at Projects 1 and 5, projects hosted by two-year institutions 

(Table V.12). 

E. DISCUSSION OF RESULTS 

The breadth of data available in Florida allowed us to examine several steps along the 

educational career of the ninth-grade cohort of 1995–96. In general, the magnitudes of the 

differences between Talent Search participants and comparison students for these outcomes 

ranged from moderate to large. There were substantial differences in magnitudes across the 

projects. Talent Search participants, however, had higher rates of first-time application for 

23

14 13 15

10 8

0

20

40

60

Persist at Two-Year

Institution***

Persist at Four-Year

Institution**

Completed Two-Year

Degree***

Percent of Students

Talent Search Participants Comparison Students

aPersistence is defined as two consecutive years of enrollment in the type of institution specified 

between 1999–2003.

**Difference in percentages is statistically significant at the 5 percent level.

***Difference in percentages is statistically significant at the 1 percent level.
91

TABLE V.11 

PERCENTAGE OF TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS 

WHO PERSISTED IN PUBLIC POSTSECONDARY INSTITUTIONS 

IN FLORIDA, BY PROJECT 

Project and Year of Entry into 

Talent Search Participants 

Comparison 

Students Difference 

Four-Year College Persistence 

Project 1 

1993–96 10.5 9.0 1.5 

1997–99 20.8 13.6 7.2 * 

Project 2 

1996–2000 9.9 7.8 2.1 

Project 3 

1993–98 11.8 10.4 1.4 

1999–2000 10.6 9.3 1.3 

Project 4 

1993–95 16.4 9.6 6.7 ** 

1996–99 33.0 12.5 20.5 *** 

Project 5 

1995–99 20.0 8.3 11.7 *** 

Two-Year College Persistence 

Project 1 

1993–96 21.7 16.7 5.0 

1997–99 25.7 16.4 9.3 ** 

Project 2 

1996–2000 19.0 9.9 9.1 *** 

Project 3 

1993–98 16.5 15.3 1.2 

1999–2000 22.0 21.0 1.0 

Project 4 

1993–95 22.4 13.4 9.0 ** 

1996–99 25.3 15.8 9.5 ** 

Project 5 

1995–99 30.0 14.1 15.9 *** 

Note: Means are regression-adjusted and indicate the percent of Talent Search participants and comparison 

students who achieved each outcome. The comparison student sample was weighted to equal the number 

of participants in the analysis. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
92

TABLE V.12 

PERCENTAGE OF TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS 

WHO COMPLETED A TWO-YEAR DEGREE IN FLORIDA, BY PROJECT 

Project and Year of Entry into 

Talent Search Participants 

Comparison 

Students Difference 

Two-Year Degree 

Project 1 

1993–96 9.8 6.9 2.9 

1997–99 17.8 7.3 10.6 *** 

Project 2 

1996–2000 4.1 4.5 -0.3 

Project 3 

1993–98 11.8 9.7 2.0 

1999–2000 11.4 10.7 0.7 

Project 4 

1993–95 12.9 7.9 5.0 

1996–99 16.5 9.7 6.8 * 

Project 5 

1995–99 20.0 8.3 11.7 *** 

Note: Means are regression-adjusted and indicate the percent of Talent Search participants and comparison 

students who achieved each outcome. The comparison student sample was weighted to equal the number 

of participants in the analysis. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 

financial aid than comparison students at all five projects in the analysis. They also had higher 

rates of high school completion, college entrance test taking and enrollment in public 

postsecondary institutions at all but one of the five projects. 

The type of institution hosting the Talent Search project appeared to be related to many of 

the postsecondary enrollment and persistence outcomes. Talent Search participants had higher 

rates of initial enrollment and were more likely to persist in two-year institutions at four of the 

five projects. The persistence was more likely to lead to completion of a two-year degree for 

participants at projects hosted by a two-year institution. On the other hand, Talent Search 
93

participants had higher rates of initial enrollment in four-year institutions only at projects hosted 

by four-year institutions. The host institution may have played a direct role in some outcomes, 

as Talent Search participants were more likely than comparison students to enroll at the host 

institution for some of the projects. 

The structure and type of the data available in Florida were similar to what was available in 

Texas, and the analysis has the same limitations. We cannot be confident that we have 

controlled for all unobserved differences in Talent Search participants and comparison students. 

Therefore, some of the differences in secondary and postsecondary outcomes could be the result 

of the type of students who chose to participate in Talent Search and not participation in the 

program itself. The differences in high school completion, in particular, were large relative to 

other interventions explicitly designed to increase high school completion and may reflect an 

estimation bias if Talent Search project staff chose to serve students who were most likely to 

complete high school based on characteristics beyond those included in our data. Differences in 

application in financial aid and college admissions test taking were also large and likely to suffer 

from the same estimation bias. However, it is reasonable to attribute these differences to 

participation in Talent Search, as helping students with application for financial aid and 

preparing for college entrance tests are services nearly all Talent Search projects offer.57

With regard to postsecondary outcomes, we are most confident that participation in Talent 

Search increased enrollment and persistence at two-year institutions. The differences were 

observed at four of the five projects, were largest at projects hosted by two-year institutions, and 

were driven in part by direct enrollment at the host institution. Thus, direct exposure and 

preparation for attending two-year institutions may be not only feasible given the relatively 

 57 In addition, the magnitude of the differences for financial aid and college admission test taking persisted 

when restricting the sample to only students who completed high school. 
94

modest intervention provided by Talent Search, it also appears to be effective for increasing 

attendance and completion at those institutions. Given the limited resources, improving 

persistence at four-year institutions may be beyond the scope of what most Talent Search 

projects can do. 
95

VI. COMPARING FINDINGS ACROSS THE STATES 

A. IMPLICATIONS OF THE DATA FOR STUDYING TALENT SEARCH 

The compilation of data from administrative data sources to study the effect of Talent 

Search on participants succeeded in Florida, Indiana, and Texas. We assembled information on 

the characteristics and outcomes of the cohort of students who were in ninth grade during the 

1995–96 school year, along with participation by any of the cohort members in Talent Search. 

We could not compile a suitable data file for analysis in Minnesota (due to a lack of access to 

state secondary school records) and Washington (due to missing or poor-quality Talent Search 

project data).58 Obtaining student level data which included information identifying students to 

facilitate merging records across data sources was challenging to obtain for the years of interest, 

1995–2000. Data from recent years should be easier to attain as more states develop systems for 

compiling secondary and postsecondary school records, and federal programs are more 

consistent in reporting information on the participants served and maintain records electronically. 

The data files compiled in Florida, Indiana, and Texas contained a wealth of information on 

students before participation in Talent Search. This included important demographic information 

such as age, race, and gender; the school the student was enrolled in for ninth grade; and 

postsecondary outcomes, such as first-time application for financial aid and postsecondary 

enrollment. Because we compiled a large amount of data in each state, both in terms of the 

number of data elements available and the size of the student samples, we were able to use 

complex propensity score matching models to identify nonparticipating students who were most 

similar to Talent Search participants. However, comprehensive data on student characteristics 

 58 For more detail on data compilation in each state, see Appendix B. 
96

before participation in Talent Search and on secondary or postsecondary outcomes of interest 

were not available in all states, with the type of information missing differing across states. The 

most important differences were information on (1) special classifications in high school, 

including eligibility for free or reduced-price lunches, indicators for at-risk of dropping out of 

high school, and participation in gifted programs—these were available in Florida and Texas but 

not in Indiana; (2) persistence through high school and high school completion—these were 

available in Florida and Texas but not in Indiana; and (3) secondary and postsecondary 

aspirations—these were available in Indiana but not in Florida or Texas. Thus, analyzing the 

effect of Talent Search on participants in any one state had limitations. However, the variations 

in the data, as well as our strategies for drawing comparison students in each state, enabled us to 

test the sensitivity of the findings to using different data sources and comparison students. While 

we have reservations about attributing participation in Talent Search to improved outcomes in 

any one state, we are more confident of findings that are consistent across all three states. 

B. FINDINGS 

A few differences were consistently positive and statistically significant across states and 

projects. The largest of these was in application for federal financial aid: Talent Search 

participants were much more likely than comparison students to be first-time applicants for 

financial aid in the 1999–2000 school year. Figure VI.1 illustrates this result, presenting the 

overall finding for each of the three states using similar comparison groups—students drawn 

from the same target high schools as Talent Search participants. The gap in financial aid 

application for Talent Search participants and comparison students was 17, 14, and 

28 percentage points, respectively, for Florida, Indiana, and Texas. The difference was smallest 

in Indiana, where we had the strongest measures of educational aspirations; even in Indiana, 
97

FIGURE VI.1 

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS WHO WERE FIRST-TIME 

APPLICANTS FOR FEDERAL FINANCIAL AID, 1999–2000, BY STATE 

however, the gap represents application levels for financial aid that are one-third higher for 

Talent Search participants. 

The next step, after the financial hurdle, is for the student to enroll in college. Here again, 

we found that Talent Search participants were more likely than comparison students to enroll in a 

public college or university in their state by the 1999–2000 school year. Figure VI.2 shows that 

initial enrollment in a postsecondary institution was higher by 14, 6, and 18 percentage points, 

respectively, for Florida, Indiana, and Texas. The gaps are smaller than for financial aid, but 

they follow the same pattern in magnitude across the states. 

42

56 59

25

42

31

0

20

40

60

80

Florida*** Indiana*** Texas***

Percent of Students'

Talent Search Participants Comparison Students

Note: Talent Search participants were students in ninth grade in 1995–96 served by a Talent Search 

project that supplied data for this study. Comparison students are nonparticipants from the same 

target high schools as Talent Search participants who matched to a Talent Search participant.

***Difference is statistically significant at the 1 percent level.
98

FIGURE VI.2 

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS WHO ENROLLED 

IN A PUBLIC POSTSECONDARY INSTITUTION, 1999–2000, BY STATE 

While we find differences in enrollment rates for both two- and four-year institutions, the 

differences are larger and more statistically robust for two-year enrollment, shown in Figure 

VI.3.59 Our analyses also show some evidence that enrollment by type of institution was linked 

to type of institution hosting the Talent Search project. In general, projects did not increase 

enrollment in both types of institutions. Instead, Talent Search projects may increase enrollment 

by exposure to their type of institution, either specifically or in general. 

 59 The level of enrollment in public two-year institutions was much lower and the level of enrollment in public 

four-year institutions much higher in Indiana than in Florida and Texas. We believe this is because Florida and 

Texas have many more public two-year institutions than Indiana does and, possibly, because of the self-selection of 

the students who chose to complete the ninth-grade survey in Indiana. 

51 51 51

37

45

33

0

20

40

60

80

Florida*** Indiana*** Texas***

Percent of Students

Talent Search Participants Comparison Students

Note: Talent Search participants were students in ninth grade in 1995–96 served by a Talent Search 

project that supplied data for this study. Comparison students are nonparticipants from the same 

target high schools as Talent Search participants who matched to a Talent Search participant.

***Difference is statistically significant at the 1 percent level.
99

FIGURE VI.3 

TALENT SEARCH PARTICIPANTS AND COMPARISON STUDENTS WHO ENROLLED 

IN A PUBLIC TWO-YEAR OR FOUR-YEAR INSTITUTION, 1999–2000, BY STATE 

We also examined differences in high school completion in Florida and Texas, the only two 

states where this information was available. There were large differences in Florida and Texas—

9 and 14 percentage points, respectively—between Talent Search participants and comparison 

students. However, we are less confident this finding can be attributed to participation in Talent 

Search than the findings on application for financial aid and initial college enrollment. Estimates 

of the differences in Florida and Texas could be biased upward if Talent Search project staff 

chose to serve students who were most likely to complete high school and most Talent Search 

project services do not directly target high school retention, so there is no program-based 

39

16

30 29

13

18

0

10

20

30

40

Florida*** Indiana*** Texas***

Percent of Students

14

35

21

9

32

14

0

10

20

30

40

Florida*** Indiana Texas***

Percent of Students

Two-Year Institution Four-Year Institution

Talent Search Participants Comparison Students

Note: Talent Search participants were students in ninth grade in 1995–96 served by a Talent Search 

project that supplied data for this study. Comparison students are nonparticipants from the same 

target high schools as Talent Search participants who matched to a Talent Search participant.

***Difference is statistically significant at the 1 percent level.
100

explanation for generating a difference of this magnitude. Also, we cannot verify that Talent 

Search participants also had higher rates of high school completion in Indiana. The magnitude of 

the differences in high school completion suggests that the data available in Florida and Texas do 

not allow us to completely control for characteristics such as educational aspirations and 

motivation of students who participate in Talent Search when assessing the effect of the program 

on high school completion. 

C. IMPLICATIONS 

Because a randomized experiment was not used in the study, we cannot attribute differences 

in outcomes solely to participation in Talent Search. Other, unmeasured characteristics may 

explain some of the differences. However, despite limitations in the data, we are more confident 

that some of the differences in first-time application for financial aid and initial postsecondary 

enrollment can be attributed to participation in Talent Search, for several reasons. First, the 

magnitudes of the differences in first-time application for financial aid and postsecondary 

enrollment were larger than the difference in high school completion and persisted even when 

examining these outcomes among high school graduates only. This suggests that, even if 

unmeasured characteristics accounted for all the differences in high school completion, some of 

the differences in postsecondary outcomes could still arise from participation in Talent Search. 

Second, the higher rates of application for financial aid and postsecondary enrollment were 

consistent across all three states (although the differences were smallest in Indiana, where the 

two groups had similar educational aspirations). Finally, Talent Search projects report directly 

targeting services toward activities to increase application for financial aid and 

postsecondary enrollment. 

From the research conducted for Phase I of this study, researchers found that help with 

application for financial aid is “hands on”—it involves sitting with students to complete their 
101

paper-and-pencil applications or at a computer to complete the Web-based Free Application for 

Federal Student Aid (FAFSA). Talent Search project staff also reported providing college 

orientation activities, such as help completing college applications, visits to college campuses, 

and arranging for participants to shadow students—often Talent Search alumni—at the project’s 

host institution. Looking at the findings across projects, the potential relationship between 

enrollment in a two- or four-year institution and the type of institution hosting the Talent Search 

project suggests that orientation to the host institution itself may increase postsecondary 

enrollment for participants. 

The findings we present in this report suggest that assisting low-income students who have 

college aspirations to overcome information barriers—an important objective of the Talent 

Search program—may be effective in helping those students achieve their aspirations. Practical 

information—direct guidance on how to complete applications for financial aid and admission to 

college and what a college campus looks and feels like—may have been one of the key services 

that Talent Search projects delivered. 

103

REFERENCES 

Agodini, Roberto and Mark Dynarski. “Are Experiments the Only Option? A Look at Dropout 

Prevention Programs.” Review of Economics and Statistics, vol. 86, no. 1, 2004, 

pp. 180–194. 

Astin, Alexander. What Matters in College? Four Critical Years Revisited. San Francisco, 

Calif.: Jossey-Bass, 1993. 

Avery, Christopher and Thomas J. Kane. “Student Perceptions of College Opportunities: The 

Boston COACH Program.” In College Choices, edited by Caroline M. Hoxby. Cambridge, 

Mass.: National Bureau of Economic Research, 2004. 

Balfanz, Robert and Nettie Legters. “Locating the Dropout Crisis: Which High Schools Produce 

the Nation’s Dropouts? Where Are They Located? Who Attends Them?” Technical report. 

Center for Research on the Education of Children Placed at Risk. Baltimore, Md.: Johns 

Hopkins University, June 2004. 

Bowen, William G. and Derek Bok. The Shape of the River. Princeton, N.J.: Princeton 

University Press, 1998. 

Cahalan, Maggie, Tim Silva, Justin Humphrey, Melissa Thomas, and Kusima Cunningham. 

“Implementation of the Talent Search Program, Past and Present.” Washington, D.C.: U.S. 

Department of Education, Policy and Program Studies Service, January 2004. 

Dehejia, Rajeev H. and Sadek Wahba. “Causal Effects in Nonexperimental Studies: 

Reevaluating the Evaluation of Training Programs.” Journal of the American Statistical 

Association, vol. 94, no. 448, 1999, pp. 1,053–1,062. 

Dynarski, Mark, Philip Gleason, Anu Rangarajan, and Robert Wood. “Impacts of Dropout 

Prevention Programs.” Washington, D.C.: U.S. Department of Education, Planning and 

Evaluation Service, June 1998.

Glazerman, Steven, Dan Levy, and David Myers. “Nonexperimental Versus Experimental 

Estimates of Earnings Impacts.” Annals of the American Academy of Political and Social 

Science, vol. 589, 2003, pp. 63–91. 

Jacobson, Jonathan, Cara Olsen, Jennifer King Rice, Stephen Sweetland, and John Ralph. 

“Educational Achievement and Black-White Inequality.” Statistical Analysis Report 2001-

061, released by the U.S. Department of Education, National Center for Education Statistics. 

Washington, D.C.: July 2001. 

Kane, Thomas J. The Price of Admission: Rethinking How Americans Pay for College. 

Washington, D.C.: Brookings Institution Press, 1999. 

King, Jennifer. “Missed Opportunities: Students Who Do Not Apply for Financial Aid.” 

Washington, D.C.: American Council on Education, 2004. 
104

Maxfield, Myles, Maggie Cahalan, Tim Silva, Justin Humphrey, and Melissa Thomas. 

“Evaluation of the Federal Talent Search Program: Phase II and Phase III Feasibility 

Report.” Final report submitted to the U.S. Department of Education, Planning and 

Evaluation Service. Washington, D.C.: Mathematica Policy Research Inc., October 2000. 

McPherson, Michael S. and Morton Owen Schapiro. The Student Aid Game: Meeting Need and 

Rewarding Talent in American Higher Education. Princeton, N.J.: Princeton University 

Press, 1998. 

Murphy, Kevin and Finis Welch. “The Structure of Wages.” Quarterly Journal of Economics, 

vol. 107, 1992, pp. 285–326. 

Myers, David, Robert Olsen, Neil Seftor, Julie Young, and Christina Tuttle. “The Impacts of 

Upward Bound: Results from the Third Follow-Up Data Collection.” Washington, D.C.: 

Mathematica Policy Research Inc., 2004. 

Perna, Laura W. and Scott W. Swail. “Pre-College Outreach and Early Intervention Programs.” 

In Condition of Access: Higher Education for Low-Income Students, edited by Donald E. 

Heller. Westport, Conn.: Praeger Publishers, 2002. 

Rosenbaum, Paul R. and Donald B. Rubin. “Constructing a Control Group Using Multivariate 

Matched Sampling Methods That Incorporate Propensity Score.” American Statistician, vol. 

39, 1985, pp. 33–38. 

Rosenbaum, Paul R. and Donald B. Rubin. “The Central Role of the Propensity Score in 

Observational Studies for Casual Effects.” Biometrika, vol. 70, 1983, pp. 41–45. 

Rubin, D.B. “Using Propensity Scores to Help Design Observational Studies: Application to the 

Tobacco Litigation.” Health Services and Outcomes Research Methodology, vol. 2, 2001, 

pp. 169–188. 

St. John, Edward, P., Glenda Droogsma Musoba, Ada B. Simmons, and Choong-Geun Chung. 

“Meeting the Access Challenge: Indiana’s Twenty-First Century Scholar Program.” 

Indianapolis, Ind.: Lumina Foundation, 2002. 

Smith, Jeffrey and Petra Todd. “Does Matching Overcome LaLonde’s Critique of 

Nonexperimental Estimators?” Journal of Econometrics, vol. 125, nos. 1–2, 2004, 

pp. 305–353. 

Swanson, Christopher B. “Who Graduates? Who Doesn’t? A Statistical Portrait of Public High 

School Graduation, Class of 2001.” Research report. Washington, D.C.: Urban Institute 

Education Policy Center, February 2004. 

U.S. Department of Education. The Condition of Education 2004. Washington, D.C.: National 

Center for Education Statistics, 2004. 

U.S. Department of Education. A Profile of the Federal TRIO Programs: 2002. Washington, 

D.C.: Office of Postsecondary Education, 2003. 

U.S. Department of Education. Digest of Education Statistics 2000. Washington, D.C.: 

National Center for Education Statistics, 2001. 
APPENDIX A 

CHAPTER TABLES 

 107 Continued on next page 

TABLE A.III.1 

VARIABLE DESCRIPTIONS—TEXAS 

Ninth-Grade Characteristics 

Male—Student is a male. 

White—Student is white not Hispanic. 

Black—Student is black not Hispanic. 

Hispanic—Student’s ethnic origin is Hispanic. 

Home Language Is Spanish—Student identified Spanish as the language spoken at home. 

Age in Ninth Grade—Age of the students, in years, as of Sept. 1, 1995. The analyses were 

restricted to students between 13.7 and 17 years of age. 

Overage in Ninth Grade—The student was 15.3 years of age or older on Sept. 1, 1995. 

Gifted—The student participated in a state-approved gifted and talented program. 

At Risk—The student was identified as at risk of dropping out of school based on state-defined 

criteria. 

Economically Disadvantaged—The student was eligible for free or reduced-price meals or had 

some other economic disadvantage. 

Limited English Proficiency—The student has been identified as limited in English proficiency 

by the Language Proficiency Assessment Committee. 

Special Education—The student participated in a special education instructional and related 

services program. 

Enrolled in a Vocational or Technical Course—The student was enrolled in a state-approved 

career and technology education course as an elective. 

Enrolled in a Vocational or Technical Education Program—The student participated in the 

district’s career and technology coherent sequence of courses program, or was a participant in 

the district’s Tech Prep program. 

Eighth-Grade Test Scores 

Raw Math Score—Total number of multiple-choice math items answered correctly on the 

eighth-grade TAAS. 

Percent Scored in Top 25 Percent in State for Reading—The student’s indexed math score 

was in the top 25 percent of scores for the state. 

Percent Scored in Bottom 25 Percent in State for Reading—The student’s indexed reading 

score was in the bottom 25 percent of scores for the state. 
TABLE A.III.1 (continued) 

108

Score on Essay Test—Final score on written composition in eighth-grade TAAS. 

Missing Test Score—The student did not have a test score from the spring 1994 or spring 1995 

administration of the TAAS. 

Grade-Level Status 

Below Grade—The student was below grade in a given year based on enrollment in ninth grade 

in fall 1995. 

TAAS = Texas Assessment of Academic Skills.
 109 Continued on next page 

TABLE A.III.2 

BASELINE CHARACTERISTICS OF TALENT SEARCH PARTICIPANTS 

AND NONPARTICIPANTS IN TEXAS, BY PROJECT 

(Percents) 

 Project 1 Project 2 

 Participants Nonparticipants Participants Nonparticipants 

Demographic Characteristics 

Male 29.3 56.3 *** 34.9 55.7 ***

White 17.2 9.3 *** 0.8 1.6 

Black 2.3 2.3 77.8 64.0 ***

Hispanic 80.5 88.0 *** 20.9 33.8 ***

Home language is Spanish 16.3 25.0 ** 13.9 19.4 ***

Age in ninth grade (mean)a 14.8 15.3 *** 14.7 15.6 ***

Overage in ninth grade 16.3 40.7 *** 15.0 54.5 ***

Academic Characteristics 

Enrolled in a gifted and talented program 21.9 2.9 *** 11.9 2.0 ***

At risk for dropping out of school 56.7 72.6 *** 42.2 53.3 ***

Economically disadvantaged 60.9 75.4 *** 37.0 39.3 

Limited English proficiency 12.1 23.1 *** 5.4 11.2 ***

Special education services 2.8 13.8 *** 5.0 13.0 ***

Enrolled in a vocational or technical course 39.1 34.0 48.6 56.4 ***

Enrolled in a vocational or technical 

education program 3.3 5.0 0.5 0.8 

Number of Students 215 1,626 765 5,760 

Eighth-Grade Test Scoresb 

Raw math score (number of questions 

correct)c 36.0 30.1 *** 36.7 30.1 ***

Percent scored in top 25 percent in state 

for math 19.4 7.2 *** 17.6 7.5 ***

Percent scored in bottom 25 percent in state 

for math 30.8 47.5 *** 28.0 52.9 ***

Raw reading score (number of 

questions correct)d 34.0 28.4 *** 35.8 28.9 ***

Percent scored in top 25 percent in state 

for reading 19.9 9.7 *** 21.4 8.5 ***

Percent scored in bottom 25 percent in state 

for reading 26.9 47.2 *** 21.1 47.8 ***

Score on essay teste 2.5 2.1 *** 2.5 2.0 ***

Missing test scores 6.5 16.6 *** 8.5 30.5 ***

Number of Students 201 1,356 700 4,005 
TABLE A.III.2 (continued) 

 110 Continued on next page 

 Project 3 Project 4 

 Participants Nonparticipants Participants Nonparticipants 

Demographic Characteristics 

Male 36.6 55.5 *** 42.2 54.4 ***

White 21.8 39.1 *** 12.1 16.7 

Black 4.5 2.9 46.1 35.5 ***

Hispanic 73.2 57.8 *** 40.5 43.2 

Home language is Spanish 13.8 8.6 *** 16.7 19.1 

Age in ninth grade (mean)a 14.7 15.0 *** 15.2 15.4 ** 

Overage in ninth grade 11.5 27.5 *** 39.6 44.0 * 

Academic Characteristics 

Enrolled in a gifted and talented program 9.6 7.0 * 2.8 2.5 

At risk for dropping out of school 48.6 51.6 45.9 43.8 

Economically disadvantaged 65.5 46.9 *** 52.4 47.3 ** 

Limited English proficiency 10.1 6.0 *** 4.1 9.1 ***

Special education services 5.4 13.5 *** 4.5 13.0 ***

Enrolled in a vocational or technical course 41.3 39.1 33.3 32.5 

Enrolled in a vocational or technical 

education program 4.7 11.0 *** 9.3 7.5 

Number of Students 426 2,732 462 3,010 

Eighth-Grade Test Scoresb 

Raw math score (number of questions 

correct)c 39.0 37.2 37.6 35.8 ***

Percent scored in top 25 percent in state 

for math 22.2 23.7 19.4 15.4 ** 

Percent scored in bottom 25 percent in state 

for math 21.3 29.4 *** 30.5 32.4 

Raw reading score (number of 

questions correct)d 36.1 33.9 *** 34.5 33.3 ** 

Percent scored in top 25 percent in state 

for reading 26.3 24.7 19.2 15.5 * 

Percent scored in bottom 25 percent in state 

for reading 22.9 30.2 *** 25.1 30.7 ** 

Score on essay teste 2.4 2.3 *** 2.4 2.3 ** 

Missing test scores 2.8 12.8 *** 11.9 22.3 ** 

Number of Students 414 2,381 407 2,338 
TABLE A.III.2 (continued) 

 111 Continued on next page 

 Project 5 Project 6 

 Participants Nonparticipants Participants Nonparticipants 

Demographic Characteristics 

Male 33.6 55.0 *** 33.3 51.6 ***

White 1.8 13.4 *** 60.0 68.1 

Black 0.7 0.5 33.3 29.9 

Hispanic 97.4 85.7 *** 5.3 1.3 

Home language is Spanish 36.1 21.2 *** 0.0 0.5 

Age in ninth grade (mean)a 14.8 15.0 *** 14.6 14.9 ***

Overage in ninth grade 15.3 27.4 *** 2.7 21.6 ***

Academic Characteristics 

Enrolled in a gifted and talented program 10.2 6.5 ** 10.7 9.1 

At risk for dropping out of school 65.0 70.0 * 42.7 48.4 

Economically disadvantaged 81.0 68.4 *** 54.7 37.3 ***

Limited English proficiency 23.7 19.2 * 0.0 0.4 

Special education services 3.3 11.6 *** 2.7 15.0 ***

Enrolled in a vocational or technical course 42.3 33.8 *** 73.3 63.2 * 

Enrolled in a vocational or technical 

education program 3.6 6.1 4.0 11.2 ** 

Number of Students 274 2,367 75 1,703 

Eighth-Grade Test Scoresb 

Raw math score (number of questions 

correct)c 36.9 33.7 *** 43.3 39.7 ** 

Percent scored in top 25 percent in state 

for math 22.1 14.8 *** 41.4 28.8 ** 

Percent scored in bottom 25 percent in state 

for math 27.3 39.4 *** 14.3 21.7 

Raw reading score (number of 

questions correct)d 33.4 31.3 *** 39.8 36.1 ***

Percent scored in top 25 percent in state 

for reading 18.6 15.9 31.4 29.9 

Percent scored in bottom 25 percent in state 

for reading 31.6 37.2 * 8.6 20.1 ** 

Score on essay teste 2.4 2.2 *** 2.8 2.5 ***

Missing test scores 7.7 21.4 *** 6.7 17.0 ** 

Number of Students 253 1,850 70 1,413 
TABLE A.III.2 (continued) 

 112 Continued on next page 

 Project 7 Project 8 

 Participants Nonparticipants Participants Nonparticipants 

Demographic Characteristics 

Male 35.4 52.1 *** 32.2 54.6 ***

White 81.2 70.3 *** 62.2 81.8 ***

Black 14.4 17.4 16.7 5.2 ***

Hispanic 2.8 11.4 *** 19.7 12.3 ***

Home language is Spanish 1.1 2.7 6.4 4.1 

Age in ninth grade (mean)a 14.8 14.9 * 14.7 15.0 ***

Overage in ninth grade 16.6 18.2 12.9 23.0 ***

Academic Characteristics 

Enrolled in a gifted and talented program 17.7 10.3 *** 6.4 9.1 

At risk for dropping out of school 42.0 44.4 44.6 44.3 

Economically disadvantaged 35.9 35.5 38.2 22.0 ***

Limited English proficiency 1.1 2.2 4.7 3.2 

Special education services 5.0 15.0 *** 6.4 16.2 ***

Enrolled in a vocational or technical course 35.9 44.9 ** 46.8 43.2 

Enrolled in a vocational or technical 

education program 33.7 16.2 *** 18.5 17.2 

Number of Students 181 2,505 233 1,941 

Eighth-Grade Test Scoresb 

Raw math score (number of questions 

correct)c 43.7 41.3 *** 41.0 40.9 

Percent scored in top 25 percent in state 

for math 40.7 34.2 * 27.1 33.5 * 

Percent scored in bottom 25 percent in state 

for math 13.4 18.0 15.7 19.7 

Raw reading score (number of 

questions correct)d 38.8 36.3 *** 37.5 36.1 

Percent scored in top 25 percent in state 

for reading 36.6 30.6 32.4 33.7 

Percent scored in bottom 25 percent in state 

for reading 11.0 20.7 *** 15.2 21.7 

Score on essay teste 2.7 2.5 ** 2.6 2.4 ***

Missing test scores 5.0 13.8 *** 9.9 16.7 ***

Number of Students 172 2,160 210 1,616 
TABLE A.III.2 (continued) 

 113

 Project 9 Project 10 

 Participants Nonparticipants Participants Nonparticipants 

Demographic Characteristics 

Male 42.1 54.9 *** 48.0 53.4 

White 43.1 46.4 ** 1.0 2.7 

Black 10.2 6.8 *** 0.0 0.0 

Hispanic 46.1 46.1 99.0 97.0 

Home language is Spanish 11.5 10.5 26.0 34.2 ** 

Age in ninth grade (mean)a 14.7 14.9 *** 15.2 15.6 ***

Overage in ninth grade 8.7 23.3 *** 35.7 51.1 ***

Academic Characteristics 

Enrolled in a gifted and talented program 12.7 6.3 *** 5.6 4.1 

At risk for dropping out of school 42.5 51.3 *** 63.8 71.9 ***

Economically disadvantaged 45.4 45.0 82.7 79.1 

Limited English proficiency 2.0 4.1 *** 19.0 29.9 ***

Special education services 6.9 17.2 *** 6.1 12.6 ***

Enrolled in a vocational or technical course 52.2 52.6 16.8 16.3 

Enrolled in a vocational or technical 

education program 8.8 11.9 *** 13.8 10.7 

Number of Students 1,342 2,705 196 4,542 

Eighth-Grade Test Scoresb 

Raw math score (number of questions 

correct)c 42.1 37.9 *** 36.0 31.9 ***

Percent scored in top 25 percent in state 

for math 34.4 24.0 *** 11.0 9.2 

Percent scored in bottom 25 percent in state 

for math 14.3 26.4 *** 27.6 44.6 ***

Raw reading score (number of 

questions correct)d 37.5 34.2 *** 34.3 29.6 ***

Percent scored in top 25 percent in state 

for reading 34.0 24.5 *** 12.9 11.5 

Percent scored in bottom 25 percent in state 

for reading 16.1 29.6 *** 22.1 43.4 ***

Score on essay teste 2.7 2.4 *** 2.2 2.1 * 

Missing test scores 6.0 14.6 *** 16.8 29.2 ***

Number of Students 1,262 2,311 163 3,218 

Note: Participants were all students in the ninth grade in the fall of 1995 who were served by 10 Talent Search 

projects in 2000. 

a

Average age in years. 

b

Based on sample with nonmissing test scores only. 

c

Range on raw math test score is 0 to 60. 

d

Range on raw reading test score is 0 to 48. 

e

Range on essay score is 0 to 4. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level.

 115 Continued on next page 

TABLE A.III.3 

BELOW GRADE AND PERSISTENCE OF TALENT SEARCH PARTICIPANTS 

AND NONPARTICIPANTS IN TEXAS, BY PROJECT 

(Percents) 

Below Grade Persistence 

Participants Nonparticipants Participants Nonparticipants 

Project 1 

1996–97 7.2 30.4 *** 96.3 87.8 *** 

1997–98 7.0 29.7 *** 97.1 85.4 *** 

1998–99 5.1 25.8 *** 87.6 73.4 *** 

1996–99 81.9 55.0 *** 

Project 2 

1996–97 11.7 42.8 *** 99.3 87.5 *** 

1997–98 7.8 33.9 *** 97.6 80.2 *** 

1998–99 5.6 30.6 *** 93.2 72.6 *** 

1996–99 90.3 50.9 *** 

Project 3 

1996–97 4.8 16.1 *** 99.1 91.8 *** 

1997–98 3.6 15.1 *** 99.3 90.0 *** 

1998–99 2.5 12.4 *** 94.7 82.3 *** 

1996–99 93.1 68.1 *** 

Project 4 

1996–97 21.6 24.5 93.0 91.8 

1997–98 19.9 28.6 *** 88.5 86.7 

1998–99 17.0 22.3 ** 76.7 75.2 

1996–99 63.1 59.8 

Project 5 

1996–97 7.2 20.8 *** 97.0 90.0 *** 

1997–98 8.7 26.5 *** 96.6 87.2 *** 

1998–99 7.4 21.9 *** 90.6 80.1 *** 

1996–99 84.9 62.9 *** 
TABLE A.III.3 (continued) 

 116

Below Grade Persistence 

Participants Nonparticipants Participants Nonparticipants 

Project 6 

1996–97 0.0 11.7 *** 98.7 93.0 *** 

1997–98 0.0 10.4 *** 97.3 89.6 *** 

1998–99 0.0 6.0 ** 95.8 84.5 *** 

1996–99 92.0 70.4 *** 

Project 7 

1996–97 5.2 11.3 *** 95.6 94.3 

1997–98 4.9 10.8 ** 93.6 91.0 

1998–99 1.4 7.1 *** 89.5 85.8 

1996–99 80.1 73.6 

Project 8 

1996–97 9.3 14.3 ** 98.7 91.6 *** 

1997–98 9.0 12.7 98.7 88.4 *** 

1998–99 8.0 9.2 89.6 83.8 *** 

1996–99 87.3 67.9 *** 

Project 9 

1996–97 2.0 12.7 *** 98.7 92.2 *** 

1997–98 3.1 13.2 *** 99.5 89.5 *** 

1998–99 2.3 12.4 *** 91.3 81.8 *** 

1996–99 89.7 67.5 *** 

Project 10 

1996–97 26.7 46.9 *** 90.7 84.1 *** 

1997–98 20.5 39.8 *** 85.8 84.1 *** 

1998–99 15.7 37.2 *** 80.1 72.1 *** 

1996–99 62.4 51.0 *** 

Note: Participants were all students in the ninth grade in the fall of 1995 who were served by 10 Talent Search 

projects by 2000. Nonparticipants include all students in Texas in the ninth grade in the fall of 1995. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 
 117 Continued on next page 

TABLE A.IV.1 

VARIABLE DESCRIPTIONS—INDIANA

Age—Student’s age as of Sept. 1, 1995, in years 

Overage indicator—Indicates a student was older than 15.3 years old on Sept. 1, 1995 

Male—Student is male 

Race and Ethnicity 

Black—Student is black not Hispanic 

White—Student is white not Hispanic 

All other races—Student is another race or Hispanic 

Race is missing—No information on a student’s race 

Primary Language Spoken at Home 

English—Primary language is English 

Other languages—Primary language is not English 

Language is missing—No information on primary language spoken at home 

Parents’ Education 

Bachelor’s degree—One or both parents have a college degree 

No Bachelor’s degree—Neither parent has a college degree 

Not known—Student does not know parents’ level of education 

No information on parents’ education 

Student’s Living Arrangement 

Student lived with mother and father 

Student lived with a parent and a stepparent 

Student lived with one parent only 

Student lived with a guardian who is not his or her parent 

No information on the student’s living arrangement 

Highest Level of Education the Student Plans to Achieve 

Will not complete high school 

High school diploma 

Two or fewer years of postsecondary education 

Two years of college 

Four years of college 

Undecided 

No information on student’s educational expectations 
TABLE A.IV.1 (continued) 

118 

Grades in School up to Ninth Gradea

Mostly As 

Mostly As and Bs 

Mostly Bs 

Mostly Bs and Cs 

Mostly Cs and below 

No information on student’s grades 

Participation in Academic Activities by Ninth Grade 

21st Century Scholar program—Student enrolled in college scholarship program for lowincome students in eighth grade 

Student consulted with a career or education counselor 

Academic Programs the Student May Pursue in High School 

Indiana Core 40 Program—Minimum academic course work for public four-year college 

admission 

Indiana Academic Honors Diploma—Core 40 plus more rigorous academic course work 

Advanced Placement program 

Indiana Academy for Science, Math, and Humanities Program 

Earning college credit while in high school 

Core 40 plus Tech Prep courses 

Training Options the Student May Pursue After High School 

Apprenticeship leading to a license in a skilled occupation after high school graduation 

Military training 

Employer training 

No additional training after high school 

Potential Barriers to Postsecondary Education or Training (listed on Table IV.1) 

Career Areas of Interest to the Student (listed on Table IV.1) 

a

Categories of grades reflects the language on the ICPAC survey, and students’ self-reporting of 

grades. 
 119 Continued on next page 

TABLE A.IV.2 

BASELINE CHARACTERISTICS OF TALENT SEARCH PARTICIPANTS AND NONPARTICIPANTS 

IN NINTH GRADE IN FALL 1995, IN INDIANA, BY PROJECT GROUP 

(Percents) 

 Project Group 1 Project Group 2 

 Participants Nonparticipants Participants Nonparticipants 

Demographic Characteristics 

Age in ninth grade (mean)a 14.2 13.6 *** 14.3 13.9 ***

Overage in ninth grade 18.1 16.4 15.0 11.9 ***

Male 55.7 46.0 *** 39.9 48.4 ***

Race 

White 42.2 56.2 *** 67.7 72.1 * 

Black 30.0 16.3 *** 7.2 3.1 ***

All other races 14.7 10.4 ** 13.9 14.0 

Race is missing 13.1 17.1 * 11.3 10.7 

Primary language spoken at home 

English 81.3 81.8 87.1 83.3 ** 

Other languages 2.2 2.2 0.2 0.9 ** 

Language spoken is missing 16.6 15.9 12.7 15.8 * 

Parents' education 

Bachelor's degree 24.1 32.8 *** 19.4 28.7 ***

No bachelor's degree 43.8 40.7 52.4 40.1 ***

Student does not know parents' education 18.8 17.8 17.3 18.0 

Parents' education is missing 13.4 8.7 ** 10.9 13.2 

Student's living arrangement 

Lives with mother and father 34.1 48.5 *** 45.0 53.7 ***

Lives with parent and stepparent 17.8 15.5 15.7 13.9 

Lives with one parent 32.2 24.4 *** 25.4 17.1 ***

Lives with other guardian 5.0 4.5 3.9 3.3 

Living arrangement is missing 10.9 7.2 * 9.9 12.0 

Academic Characteristics 

Educational expectations 

Will not complete high school 0.0 1.1 *** 0.2 0.7 

Complete high school 10.3 8.8 5.3 6.6 

Complete some college 7.5 5.9 6.5 4.8 

Complete two years of college 11.3 9.6 10.6 9.7 

Complete four years of college 52.5 59.2 ** 57.7 55.1 

Education plans are undecided 10.9 10.6 12.7 12.1 

Education plans are missing 7.5 4.8 * 6.9 11.1 ***

Grades in schoolb 

Mostly As 5.0 10.4 *** 7.6 11.4 ***

Mostly As and Bs 24.4 29.1 * 32.3 28.0 * 

Mostly Bs 7.8 8.3 6.5 8.0 

Mostly Bs and Cs 28.1 25.0 26.1 24.2 

Mostly Cs and below 28.8 22.1 ** 19.6 17.4 

Grades are missing 5.9 5.1 7.9 11.1 ** 
TABLE A.IV.2 (continued) 

 120 Continued on next page 

 Project Group 1 Project Group 2 

 Participants Nonparticipants Participants Nonparticipants 

Academic programs of interestc 

21st Century Scholar Program 19.4 10.1 *** 21.2 9.4 ***

Indiana Core 40 Program 28.4 27.7 27.0 20.0 ***

Advanced Placement Program 27.5 32.0 * 35.3 35.8 

Indiana Academic Honors Diploma 10.3 14.2 ** 9.0 11.1 

Indiana Academy for Science, Math, 

and Humanities 13.1 15.0 11.3 13.9 

Earning college credit in high school 42.2 48.6 ** 44.8 38.7 ** 

Tech Prep program 17.5 17.3 20.6 22.4 

Consulted with career counselor 20.0 10.7 *** 18.5 13.7 ** 

Interest in additional training after high schoolc 

Apprenticeship 46.0 50.3 45.7 43.8 

Military training 19.7 16.2 15.0 15.5 

Employer training 27.2 28.1 27.5 21.5 ***

Job with no additional training 14.1 13.6 11.3 10.3 

Barriers to obtaining postsecondary educationc 

Not sure I can afford it 30.0 38.0 *** 37.9 28.2 ***

Not sure I can succeed 15.6 15.3 15.5 12.3 * 

Not sure how to prepare 25.0 30.7 ** 28.2 24.6 

Not sure I can get into schools I want 26.3 28.1 27.5 23.0 ** 

No one to advise about future 6.3 4.4 5.8 3.7 * 

Not sure what I want to do with my life 24.7 25.4 23.6 21.7 

No barriers 17.2 20.7 19.6 22.8 

Future career interests 

Agriculture and natural resources 4.7 2.0 * 7.6 8.5 

Arts and entertainment 30.6 33.9 26.3 24.2 

Building and construction trades 17.8 15.3 15.7 15.3 

Business, management, and finance 26.6 26.0 24.2 23.9 

Communications 13.4 16.1 15.2 12.7 

Education 18.1 21.1 24.5 21.9 

Forestry, conservation, and environment 8.4 9.1 13.2 14.2 

Industrial and manufacturing 4.7 4.7 2.5 3.5 

Medical / Health services 30.9 31.7 37.2 32.4 * 

Office and clerical 10.0 6.8 * 8.5 6.9 

Science, math, computer, or engineering 17.2 15.7 13.6 14.5 

Service 20.3 21.3 17.1 16.2 

Technical 12.8 11.5 9.9 12.2 

Transportation 4.7 5.1 8.1 6.4 

Number of Students 320 1,889 433 2,968 
TABLE A.IV.2 (continued) 

 121 Continued on next page 

Project Group 3 

 Participants Nonparticipants 

Demographic Characteristics 

Age in ninth grade (mean)a 13.9 13.9 

Overage in ninth grade 16.5 15.7 

Male 38.0 48.3 *** 

Race 

White 41.4 47.7 ** 

Black 29.1 23.8 ** 

All other races 9.2 9.2 

Race is missing 20.3 19.3 

Primary language spoken at home 

English 81.6 81.7 

Other languages 2.2 3.1 

Language spoken is missing 16.2 15.3 

Parents' education 

Bachelor's degree 24.2 25.9 

No bachelor's degree 47.7 45.8 

Student does not know parents' education 19.9 21.0 

Parents' education is missing 8.2 7.2 

Student's living arrangement 

Lives with mother and father 37.5 47.3 *** 

Lives with parent and stepparent 16.5 15.5 

Lives with one parent 33.4 25.8 *** 

Lives with other guardian 5.8 4.9 

Living arrangement is missing 6.8 6.5 

Academic Characteristics 

Educational expectations 

Will not complete high school 0.2 0.6 

Complete high school 5.1 9.5 *** 

Complete some college 4.1 6.4 ** 

Complete two years of college 11.6 8.6 * 

Complete four years of college 65.9 58.2 *** 

Education plans are undecided 9.7 12.0 

Education plans are missing 3.4 4.7 

Grades in schoolb 

Mostly As 5.6 6.6 

Mostly As and Bs 38.0 27.2 *** 

Mostly Bs 7.7 7.9 

Mostly Bs and Cs 27.8 31.6 

Mostly Cs and below 17.4 22.4 ** 

Grades are missing 3.4 4.3 
TABLE A.IV.2 (continued) 

 122

Project Group 3 

 Participants Nonparticipants 

Academic programs of interestc 

21st Century Scholar Program 27.1 13.9 *** 

Indiana Core 40 Program 25.7 21.0 ** 

Advanced Placement Program 36.8 32.8 

Indiana Academic Honors Diploma 18.4 14.4 ** 

Indiana Academy for Science, Math, and Humanities 11.9 11.7 

Earning college credit in high school 52.5 51.1 

Tech Prep program 15.0 17.1 

Consulted with career counselor 24.5 25.8 

Interest in additional training after high schoolc 

Apprenticeship 55.2 50.5 * 

Military training 14.0 14.8 

Employer training 25.2 25.2 

Job with no additional training 10.9 13.9 * 

Barriers to obtaining postsecondary educationc 

Not sure I can afford it 36.1 30.4 ** 

Not sure I can succeed 9.9 11.6 

Not sure how to prepare 29.8 26.9 

Not sure I can get into schools I want 28.1 25.7 

No one to advise about future 4.1 4.0 

Not sure what I want to do with my life 19.9 22.0 

No barriers 21.5 23.4 

Future career interests 

Agriculture and natural resources 3.6 3.4 

Arts and entertainment 32.7 30.4 

Building and construction trades 11.1 19.9 *** 

Business, management, and finance 32.9 29.2 

Communications 18.4 14.8 * 

Education 20.3 20.0 

Forestry, conservation, and environment 6.8 8.8 

Industrial and manufacturing 2.7 4.8 ** 

Medical / Health services 32.7 32.1 

Office and clerical 9.4 7.2 

Science, math, computer, or engineering 14.8 15.9 

Service 24.9 19.3 *** 

Technical 13.6 13.3 

Transportation 5.1 5.7 

Number of Students 413 6,699 

Note: Participants were all students in the ninth grade in the fall of 1995 who were served by seven Talent Search 

projects in 2000. Nonparticipants include all other students in Indiana in the ninth grade in the fall of 1995. 

a

Average age in years. 

b

Categories of grades reflects the language on the ICPAC survey, and students’ self-reporting of grades. c

Totals in these categories do not sum to 100 because students could record more than one response. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level.
 123 Continued on next page 

TABLE A.V.1 

VARIABLE DESCRIPTIONS—FLORIDA 

Ninth-Grade Characteristics 

Male—Student is a male. 

White—Student is white not Hispanic. 

Black—Student is black not Hispanic. 

Hispanic—Student’s ethnic origin is Hispanic. 

Other Race—Student is not black, white, or Hispanic. 

U.S. Citizen—Indicates that student is a United States citizen. 

Home Language Is English—Student identified English as their native language. 

Home Language Is Spanish—Student identified Spanish as their native language. 

Other Language—Student’s home language is neither Spanish nor English. 

Age in Ninth Grade—Age of the students, in years, as of Sept. 1, 1995. The analyses were 

restricted to students between 13.3 and 17.3 years of age. 

Overage in Ninth Grade—The student was 15.3 years of age or older on Sept. 1, 1995. 

Gifted—Indicates that the student is identified as gifted on state educational records, using statedefined criteria. 

Any Dropout Program—Indicates that the student was ever placed in any dropout prevention 

program, using state-defined criteria. There were three main categories of dropout prevention 

programs: 

 Programs for disruptive students 

 Programs for alternative education 

 Programs for juvenile offenders and other programs 

Economically Disadvantaged—The student was eligible for free or reduced-price meals in 

secondary school. 

Emotionally or Physically Disabled—Indicates that the student was ever classified as 

emotionally or physically disabled. 

Learning Disabled—Indicates that the student was ever classified as learning disabled. 
TABLE A.V.1 (continued) 

124 

Grade Level Status 

Grade—Student’s grade level in a given academic year. 

Below Grade—The student was below grade in a given academic year based on enrollment in 

ninth grade in the fall of 1995. 

Outcome Measures 

High School Diploma or GED—Indicates that a student received a high school diploma or 

some other high school equivalency degree. 

SAT/ACT—Admission exam, generally only required at four-year institutions. 

CPT—Florida college entry level placement test for community colleges. 

CLAST—Test of basic college level communications and math skills. The CLAST is required 

for obtaining an Associated degree and proceeding to a four-year institution. 

Two-Year Degree—Indicates that the student received a two-year (AA, AS, or other) degree. 
 125 Continued on next page 

TABLE A.V.2 

BASELINE CHARACTERISTICS OF TALENT SEARCH PARTICIPANTS 

AND NONPARTICIPANTS IN FLORIDA, BY PROJECT 

(Percents) 

 Project 1 Project 2 

 Participants Nonparticipants Participants Nonparticipants 

Demographic Characteristics 

Male 31.0 52.5 *** 32.2 54.5 ***

Race

White 47.8 66.0 *** 3.3 49.0 ***

Black 41.6 27.9 *** 95.9 46.5 ***

Hispanic 0.4 2.0 *** 0.0 2.4 ***

All other races 10.2 4.2 *** 0.8 2.1 

Primary language spoken at home 

English 96.7 99.0 * 100.0 99.0 ***

Spanish 0.4 0.1 0.0 0.7 ***

Other languages 2.9 0.9 * 0.0 0.3 ***

United States citizen 96.3 91.0 *** 95.0 81.1 ***

Economically disadvantaged 76.3 44.9 *** 81.0 34.7 ***

Age in ninth grade (mean)a 14.3 14.5 *** 14.9 15.0 

Overage in ninth grade 3.7 9.0 *** 24.0 26.8 

Academic Characteristics 

Gifted 4.1 4.8 0.0 6.9 ***

Any dropout prevention program 15.5 30.8 *** 28.9 28.9 

Dropout prevention for disruptive students 4.1 10.6 *** 3.3 4.1 

Dropout prevention for alternative education 9.8 20.5 *** 24.8 21.8 

Dropout prevention for Department of 

Juvenile Justice 4.1 7.1 ** 5.8 7.3 

Emotionally or physically disabled 4.9 6.1 3.3 7.0 ** 

Learning disabled 1.6 8.6 *** 2.5 8.8 ***

Number of Students 245 11,318 121 4,982 
TABLE A.V.2 (continued) 

 126 Continued on next page 

 Project 3 Project 4 

 Participants Nonparticipants Participants Nonparticipants 

Demographic Characteristics 

Male 39.5 51.9 *** 33.8 51.9 ***

Race

White 55.2 74.5 *** 48.1 54.8 * 

Black 39.0 18.0 *** 29.5 23.1 ** 

Hispanic 2.4 4.4 * 15.7 17.6 

All other races 3.3 3.1 6.7 4.5 

Primary language spoken at home 

English 99.5 97.3 *** 91.4 86.7 ** 

Spanish 0.5 1.9 *** 6.7 9.9 * 

Other languages 0.0 0.9 *** 1.9 3.4 

United States citizen 96.7 93.7 ** 88.1 86.0 

Economically disadvantaged 49.0 32.6 *** 46.7 32.7 ***

Age in ninth grade (mean)a 14.7 14.8 *** 14.6 14.8 ***

Overage in ninth grade 13.3 20.0 *** 7.1 19.8 ***

Academic Characteristics 

Gifted 3.3 5.0 7.1 4.5 

Any dropout prevention program 25.7 22.9 8.1 20.8 ***

Dropout prevention for disruptive students 3.3 3.0 1.0 2.7 ***

Dropout prevention for alternative education 19.5 13.3 ** 6.2 12.5 ***

Dropout prevention for Department of 

Juvenile Justice 9.0 10.1 2.9 8.6 ***

Emotionally or physically disabled 6.7 10.5 ** 5.7 8.2 

Learning disabled 7.1 8.9 1.9 8.7 ***

Number of Students 210 7,358 210 36,853 
TABLE A.V.2 (continued) 

127 

Project 5 

 Participants Nonparticipants 

Demographic Characteristics 

Male 32.0 53.4 *** 

Race 

White 54.9 74.6 *** 

Black 42.6 20.0 *** 

Hispanic 1.6 3.7 * 

All other races 0.8 1.6 

Primary language spoken at home 

English 99.2 98.5 

Spanish 0.8 1.3 

Other languages 0.0 0.2 *** 

United States citizen 99.2 95.8 *** 

Economically disadvantaged 71.3 48.4 *** 

Age in ninth grade (mean)a 14.5 14.9 *** 

Overage in ninth grade 5.7 25.7 *** 

Academic Characteristics 

Gifted 4.1 1.7 

Any dropout prevention program 9.8 25.1 *** 

Dropout prevention for disruptive students 0.0 3.1 *** 

Dropout prevention for alternative education 9.0 18.0 *** 

Dropout prevention for Department of 

Juvenile Justice 2.5 7.7 *** 

Emotionally or physically disabled 9.0 11.7 

Learning disabled 5.7 10.2 ** 

Number of Students 122 6,330 

Note: Participants were all students in the ninth grade in the fall of 1995 who were served by five Talent Search 

projects by 2000. Nonparticipants include all other students in Florida in the ninth grade in the fall of 

1995. 

a

Average age in years. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 

 129

TABLE A.V.3 

BELOW GRADE AND PERSISTENCE OF TALENT SEARCH PARTICIPANTS 

AND NONPARTICIPANTS IN FLORIDA, BY PROJECT 

(Percents) 

 Below Grade Persistence 

 Participants Nonparticipants Participants Nonparticipants 

Project 1 

1996–97 28.0 26.4 97.6 96.4 *** 

1997–98 30.9 36.7 * 97.5 90.7 *** 

1998–99 33.0 37.1 92.3 83.9 *** 

1996–99 87.8 73.3 *** 

Project 2 

1996–97 25.0 26.0 99.2 89.8 *** 

1997–98 22.5 28.9 92.5 87.7 *** 

1998–99 22.7 26.2 87.4 84.0 *** 

1996–99 80.2 66.1 *** 

Project 3 

1996–97 13.3 21.6 *** 96.7 92.4 *** 

1997–98 13.8 23.5 *** 93.1 91.3 *** 

1998–99 11.7 23.7 *** 99.5 89.0 *** 

1996–99 89.5 75.1 *** 

Project 4 

1996–97 16.1 17.1 *** 98.6 91.2 *** 

1997–98 12.7 29.2 *** 95.2 88.0 *** 

1998–99 12.4 24.8 *** 94.4 83.4 *** 

1996–99 88.6 67.0 *** 

Project 5 

1996–97 19.0 25.7 * 99.2 88.3 *** 

1997–98 19.8 30.9 *** 100.0 86.7 *** 

1998–99 19.5 27.0 97.5 81.8 *** 

1996–99 96.7 62.6 *** 

Note: Participants were all students in the ninth grade in the fall of 1995 who were served by five Talent Search 

projects by 2000. Nonparticipants include all other students in Florida in the ninth grade in the fall of 

1995. 

 *Difference is statistically significant at the 10 percent level. 

 **Difference is statistically significant at the 5 percent level. 

***Difference is statistically significant at the 1 percent level. 

APPENDIX B 

COMPILATION OF DATA SOURCES AND FEASIBILITY OF EVALUATIONS 

BASED ON ADMINISTRATIVE RECORDS 

133

A. INTRODUCTION 

Throughout this report, we addressed the first research question posed in Chapter II: Is it 

possible to rely on administrative records to compile a complete, retrospective record of 

participation in Talent Search, characteristics of students while in secondary school, and 

secondary and postsecondary outcomes? In each state included in the final analysis for this 

study—Florida, Indiana, and Texas—we were able to compile retrospective records on 

participation in Talent Search, as well as on secondary and postsecondary outcomes. We 

described the strengths and weakness of the data as they relate specifically to the evaluation of 

Talent Search in each state in each chapter. In this appendix, we describe in greater detail our 

process for obtaining and compiling data from each source described in the report. We also 

describe our experiences in two states—Minnesota and Washington—where we were not 

successful in compiling a retrospective record of Talent Search participation and secondary and 

postsecondary outcomes. 

The main advantages of using administrative records to compile information on Talent 

Search participation and secondary and postsecondary outcomes were: 

• We compiled data on secondary and postsecondary outcomes for one cohort of 

ninth-grade students in Florida, Indiana, and Texas. Our sample included more 

than 600,000 students in these three states, who attended thousands of different 

secondary and postsecondary institutions. We identified more than 4,000 students 

as Talent Search participants. Collecting longitudinal information on this many 

students using a survey or directly from schools would have required many 

more resources. 

• In Florida and Texas, the sample was a complete census of ninth-grade students in 

1995–96. In Indiana, it was nearly 80 percent of the cohort. No state could track 

all students through high school, but all states had information on 70 to 80 percent of 

the students four years later, in 1999. This rate is comparable to what a survey 

administered at the same two time periods might have achieved. 

• Except for the baseline information in Indiana, the data are based on 

administrative information. This information is more likely to be accurate than 

self-reported information, particularly retrospective self-reported information. 
134

The main limitations of this approach were: 

• We were not successful in compiling records of Talent Search participation and 

secondary and postsecondary outcomes in Minnesota or Washington. In 

Minnesota, we could not negotiate access to state secondary data. In Washington, we 

did not receive complete information from enough Talent Search projects to continue 

with the analysis. 

• We could not obtain a comprehensive measure of postsecondary enrollment in any 

state. We also could not examine some important outcomes, such as high school 

completion and college admission test taking, in every state. 

• The data available for estimating propensity score models to identify comparison 

students to Talent Search participants varied substantially across the states. 

Information on students’ educational aspirations and postsecondary plans were only 

available in Indiana. 

• In Florida and Indiana, we were not given access to individual identifying 

information and relied on consultants or state agency staff to properly identify 

students and compile the data. Although we were able to check the data to 

determine whether characteristics and outcomes for the students seemed reasonable, 

we cannot document that the data were merged accurately across all data sources in 

every state. 

In the rest of this appendix, we describe our process for collecting Talent Search project 

records, data on first-time application for federal financial aid, and secondary and postsecondary 

outcomes from state agencies. We describe research on additional data sources to compile more 

complete information on postsecondary outcomes. We conclude with a discussion on using 

administrative records for future program evaluations. 

B. TALENT SEARCH PROJECT DATA 

Based on research conducted as part of the feasibility study for this evaluation (Maxfield 

et al. 2000), we collected Talent Search project records in five states: Florida, Indiana, 

Minnesota, Texas, and Washington. Our goal was to collect data from every project operating in 
135

the states in 1999–2000 and to collect records from 1993–2000.60 The data we hoped to collect 

included (1) student-identifying information, such as name, Social Security number (SSN), and 

date of birth, so we could match Talent Search records with other data sources; (2) specific years 

of participation in Talent Search, including the year first served by Talent Search, to determine 

the point in high school during which students participated in Talent Search; (3) intensity of 

participation in Talent Search, such as number of years or number of hours of participation per 

year, to determine whether more intensive participation is related to improved secondary and 

postsecondary outcomes; and (4) participation in specific activities, to determine if specific 

activities are related to improved secondary and postsecondary outcomes. 

The main barrier to collecting data from every project in every state was that some projects 

provided no data, incomplete data, or data that appeared to be inaccurate. We contacted all 

projects several times about providing data. Some never responded, and some refused to provide 

data. Others responded that they would provide data, but they never did. In addition, some 

projects provided data for only one year of participants or for far fewer participants than they 

reported that they had served in their Annual Performance Reports, and we were unable to 

resolve the discrepancy. Finally, in each state except Minnesota, at least one project began 

operating in 1998 and served too few of the students in our target cohort to be included in the 

analysis. Table B.1 summarizes our Talent Search project data collection efforts in each state. 

We could not obtain Talent Search project records at most of the projects in Washington, so 

we did not proceed with any further analysis of outcomes in that state. Although the one 

remaining project in Washington served a moderate number of students, our findings may not 

have been representative of the experiences of most Talent Search participants in the state. In

 60 We collected data as early as 1993, when available, to capture students in our target cohort who may have 

been served in middle school. Many projects could not provide any data before 1995. 
136

TABLE B.1 

SUMMARY OF TALENT SEARCH PROJECT RECORD DATA COLLECTION 

State 

Total Number of 

Projects 

Operating in 

1999–2000 

Number of 

Projects 

Providing Data 

Number of 

Projects with 

Partial or 

Inaccurate Data 

Number of 

Projects That 

Began Operating 

in 1998 

Number of 

Projects 

Providing Data 

Florida 8 7 1 1 5 

Indiana 8 7 0 1 7 

Minnesota 7 6 1 0 5 

Texas 17 13 2 1 10 

Washington 4 3 1 1 1 

addition, if relatively few students served by the project matched to our target cohort, we may 

have been unable to estimate propensity score models to identify similar nonparticipants. 

Conducting this type of analysis in a state with few projects is risky, because the loss of one or 

two projects results in a lower proportion of participants represented in the analysis than in states 

with several projects. 

The other major challenge in collecting and processing the Talent Search project data was 

the variability of data quality. Project data were inconsistent in the format and type of 

information provided. We received lists of participants on paper, which had to be scanned or 

key-entered into electronic files, and data on tapes, which we had to be sent out to a contractor 

who specialized in reading old data formats. We also received data entered using the Blumen 

software, which was relatively easy to process after we obtained the software. The type of 

information provided ranged from names, SSNs, and the year a student was served by a project, 

to extensive information on the types and frequencies of services that students received. 

The variability in the quality of the Talent Search project data posed three additional 

difficulties: (1) processing project data required far more resources than any other task in this 
137

phase of the study; (2) collecting and processing the project data took more than one year, longer 

than any other task in this phase of the study; and (3) we could not conduct any analysis on the 

relative effectiveness of more intense participation in services or on different types of services. 

In general, data from early program years were more difficult to process than data from later 

years. Processing of data may become less costly as computerized data entry becomes universal. 

However, the variability in the type of information projects recorded did not change over the 

years. This may not change unless projects are required to record specific data elements at the 

student level. 

C. FEDERAL FINANCIAL AID DATA 

Our effort to collect and compile information on first-time applications for federal financial 

aid was very successful. The Office of Postsecondary Education (OPE) in the U.S. Department 

of Education (ED) provided consistent, comprehensive data that were relatively easy to use and 

did not require a great deal of resources to process. Although we focused only on first-time 

applications for federal financial aid, all applications for federal financial aid are available 

through this data source. 

The data source for all applications for federal financial aid is the Free Application for 

Federal Student Aid (FAFSA). The main outcome of interest was first-time application for 

financial aid.61 However, we also collected applicants’ names, SSNs, and dates of birth, so we 

could merge this information into other data sources. As discussed in Chapter II, we collected 

this information for the 1999–2000 and 2000–01 school years to allow an extra year for 

completing high school and enrolling in college. Other information on the applicant data files 

 61 We requested data on first-time applicants only, to assess whether participation in Talent Search influences 

initial application for financial aid. Application for financial aid over several years could be influenced by 

participation in Talent Search, but is partly a function of persisting through college. 
138

included demographic information, estimated family contribution, and Pell Grant 

eligibility status. 

We provided OPE with a data request, and an OPE contractor processed the data and 

provided them in the format we requested. It took two to three months to negotiate access to the 

data and prepare the data request, and another two months to receive the data. The data were 

easy to use and consistent across all states, because the source of the data did not vary by state. 

The data had two limitations: (1) due to confidentiality restrictions, we could obtain data only on 

participants living in a study state when they applied to an institution in one of the study states; 

and (2) some applicants did not have valid SSNs. The confidentiality restrictions did not limit 

the sample substantially, because most applicants apply to at least one institution in their home 

state. The OPE contractor checked the number of applicants from the study states who did not 

apply to any institution in one of the study states and reported that this number was very small. 

Approximately 10 percent of students had invalid SSNs. In all cases with missing or invalid 

SSNs, MPR staff or staff in the state agencies used names, dates of birth, and gender to match 

records across data sources, although there were some students we could not match in each state. 

D. STATE SECONDARY AND POSTSECONDARY DATA 

The structure and the type of student-level secondary and postsecondary data available 

differed in each state, as did our process for obtaining the data. In Chapters III through V, we 

discussed the differences in the type of data available. Here, we discuss the structure of the data 

in each state, mention the agency responsible for collecting and maintaining the data, and 

describe our negotiations with the state to obtain the data. The four main differences in the 

structure of the data, and our access to them, were: 

1. A centralized system for student-level information on public secondary school 

students, available in Florida and Texas 
139

2. Information on public, secondary and postsecondary students integrated into a data 

system with common IDs, available in Florida 

3. Reliable identifying information on students to allow matching across data sources, 

available in all three states but most reliable in Florida and Texas 

4. Access by MPR staff to student-identifying information to allow merging across data 

sources, available in Texas 

1. Texas 

The Texas Education Agency (TEA), through the Public Education Management 

Information System (PEIMS), provided the data on students in public secondary schools. The 

TEA assigns an ID to all students as part of PEIMS. PEIMS also includes student-identifying 

information, such as first and last name, SSN, and date of birth. If a student had no SSN, the 

state created an additional ID similar to an SSN. As discussed in Chapter III, PEIMS also 

includes basic demographic and socioeconomic information, as well as enrollment information, 

including grade level and courses taken each year. In addition, the TEA records a high school 

exit status for students who complete or leave high school. 

The TEA also provided 8th- and 10th-grade test scores for most of the target cohort. Test 

score data came from the Student Assessment Division, a division separate from PEIMS. 

However, the test score data are linked to students by the same PEIMS ID. 

A separate agency—the Texas Higher Education Coordinating Board (THECB)—

maintained data on students in public postsecondary institutions. This data included studentidentifying information—first and last name, SSN, and date of birth, but not the PEIMS ID—as 

well as information on enrollment in public postsecondary institutions in Texas, including 

institution type (two- or four-year), and total credits earned. THECB data are recorded for three 

semesters each year: fall, spring, and summer. 
140

We made formal data requests and signed confidentiality agreements with the TEA and 

THECB. The same confidentiality agreements covered collecting both PEIMS and test score 

data from the TEA, although we had to prepare separate data requests. The TEA processed our 

request and provided the PEIMS data within two months of our initial request. We identified the 

target cohort from the PEIMS data, then prepared the data request for test scores from the 

Student Assessment Division. That division took a few additional months to process and prepare 

our request.62 All the data provided by the TEA were clean and well-documented. Access to 

postsecondary data from the THECB required a separate confidentiality agreement and data 

request. The THECB took five to six months after our initial data request to provide the data. 

The reasons for this delay were (1) a change in the commissioner in spring 2004 and (2) a more 

cumbersome process for reviewing the confidentiality agreement than at the TEA. The data 

provided were also clean and well-documented. 

MPR staff merged data across all data sources: (1) student secondary information and test 

scores for 1995–2000; (2) postsecondary information for 1999, 2000, and 2001; (3) Talent 

Search project data for 1993–2000; and (4) federal financial aid data for 1999 and 2000. Using 

the PEIMS ID, we easily merged all data the TEA provided. We merged the TEA data to all 

other sources by SSN, name, and date of birth. Merging was difficult when no SSNs were 

available, but we were able to merge many records by name, date of birth, and demographic 

information, such as gender. However, we could not conclusively match some cases. Therefore, 

we might not have identified some Talent Search participants in the cohort, and our financial aid 

and postsecondary outcomes might be slightly understated. 

 62 The delay was mostly due to our request falling during the spring test administration of the Texas 

Assessment of Academic Skills (TAAS). After our contact in the division was able to turn to the request, we 

received the data within a few weeks. 
141

2. Indiana 

When we began our discussions to obtain secondary and postsecondary data in Indiana, the 

state had no system for collecting and accessing student-level data for public secondary school 

students. Through our research for this phase of the study, we learned that the Indiana Career 

and Postsecondary Advancement Center (ICPAC), a state-supported center at Indiana University, 

administered a survey to ninth-grade students in 1995–96 and a follow-up survey to students in 

11th grade two years later. ICPAC staff were involved in a project to merge the ICPAC survey 

data with data from the Indiana Commission for Higher Education’s Student Information System 

(SIS). One goal of the data merge project was to determine the effectiveness of Indiana’s 21st 

Century Scholars program, a college preparation program with target students and goals similar 

to those of Talent Search. 

The data we obtained from the ninth-grade survey include basic demographic and academic 

information, extensive information on academic plans and interests for high school, and 

postsecondary plans and aspirations. There is also information on what students view as 

potential barriers to achieving their aspirations. The data from the SIS contain information on 

enrollment, including part- and full-time status along with institution type (two- or four-year). 

The SIS also includes information about receipt of both federal and state financial aid. 

MPR staff did not have direct access to any of these data and were not allowed access to any 

student-level identifying information. We subcontracted the entire data merge to staff at ICPAC, 

which included faculty and graduate students at Indiana University. ICPAC faculty and graduate 

students associated with Indiana University’s Education Policy Center merged information from 

the SIS to information from the ICPAC survey, using identifying information such as name, date 

of birth, gender, ethnicity, home ZIP Code, and SSN when available. MPR turned over Talent 

Search project data and application for federal financial aid data to ICPAC staff. Those staff 
142

members merged records from these data sources to determine which students in the 1995–96 

ninth-grade cohort were Talent Search participants and which were first-time applicants for 

federal financial aid. SSNs were not available for most of the students in the ICPAC sample, so 

merging across all data sources was complex and relied heavily on name, date of birth, and 

gender. ICPAC staff returned a data file with records from all data sources merged together, but 

stripped of all individual identifying information. 

The data merge required much more ICPAC staff time than needed by staff in Florida or 

Texas agencies. MPR could not verify the accuracy of the data merge, because it did not have 

access to identifying information. However, ICPAC staff were experienced researchers and had 

conducted this type of data merge for other analyses. While we were conducting our research, 

ICPAC was dissolved, which presented some challenges in obtaining data.63 However, we were 

able to continue working with faculty and staff at Indiana University to obtain the data. 

3. Florida 

Florida was the only state in the study that had a fully integrated data system with studentlevel information on students in public primary, secondary, and postsecondary schools in the 

state. The Florida K-20 Education Data Warehouse, maintained by the Florida Department of 

Education (FLDOE), is a single repository of data from many sources. It contains longitudinal 

information on students, beginning in 1995. The data requirements for this study overlap with 

the earliest period for which the warehouse maintains data. The warehouse contains studentidentifying information such as SSN, first and last name, and date of birth, as well as a 

warehouse-assigned ID. In addition to basic demographic information, the warehouse contains 

 63 Recently, the Learn More Resource Center replaced ICPAC. The new center is designed to integrate 

education resources in Indiana from Pre-K through college. 
143

information on secondary school enrollment, including school attended, grade level, courses 

taken, and promotion status each year. It also includes information on secondary degrees earned, 

such as a high school diploma, GED, or other certificate.64 In addition, the warehouse data 

included precollege outcomes, such as taking a college admission test or community college 

entrance exam. Finally, the warehouse data contained information on postsecondary outcomes, 

such as receipt of financial aid and scholarships and enrollment, including the name of the 

institution where the students enrolled and postsecondary degrees earned. 

The warehouse does not allow access to individual identifying information. Warehouse staff 

provided MPR with data files containing secondary and postsecondary information on students 

in ninth grade in 1995–96. All files were stripped of individual identifying information but 

included the warehouse ID. MPR staff submitted data files with individual identifying 

information from Talent Search project records and federal financial aid application files. 

Warehouse staff identified those individuals in the warehouse data and added the warehouse ID 

to students in those files. Warehouse staff returned the Talent Search and federal financial aid 

data files to MPR, stripped of identifying information except the warehouse ID, so MPR staff 

were able to merge records across all data sources. While we cannot verify the accuracy of the 

assignment of warehouse IDs to the students from the Talent Search and federal financial aid 

data files, MPR staff can verify the accuracy of the merge across all the data sources. 

Obtaining the data took longer in Florida than in Texas. It took six to seven months to 

receive data after our initial request. This was due, in part, to the length of time it took to receive 

approval from the FLDOE. It was also partly due to delays at the end of summer 2004 because 

of four hurricanes hitting the state, which placed tremendous strain on the FLDOE. The 

 64 The data do not include information on secondary school test scores in 1995. Test scores have been 

available since 1997–98. 
144

documentation for the data is fairly detailed but not quite as easy to use as the documentation 

Texas provided. Warehouse staff were very responsive in answering questions and making 

corrections to data files when needed. The warehouse database is comprehensive: its staff were 

able to identify more than 85 percent of the students who appeared in the Talent Search project 

records and the application for federal financial aid records. 

4. Minnesota 

We also pursued obtaining student-level secondary and postsecondary data in Minnesota. 

During the time frame for this study, Minnesota undertook a major reorganization of state 

agencies. The Minnesota Department of Children, Families, and Learning had provided all 

services related to child welfare, including education. Therefore, we conducted our initial 

research and conversations about the feasibility of obtaining the data with a staff member at the 

department. During the time of our study, Minnesota created a state department of education. 

We were not able to establish a new contact or determine whether student-level data would be 

available during the reorganization. 

A separate agency—the Minnesota Higher Education Services Office (HESO)—was to 

provide postsecondary data; however, our contact at HESO could not help us obtain secondary 

data. Given the design of the study, obtaining postsecondary data alone would not have been 

valuable. We determined that we did not have enough time to begin the data research and 

acquisition process with the new state department of education, process the data, and conduct the 

analysis within the time frame for this study. 
145

E. OTHER DATA SOURCES 

We were able to analyze postsecondary enrollment only in public, postsecondary institutions 

in each state.65 As discussed in each chapter, this may not affect our findings on the difference in 

postsecondary enrollment between Talent Search participants and comparison students if they 

enroll in private or out-of-state institutions at the same rate. We understate overall 

postsecondary enrollment, however, it is possible that the difference between Talent Search 

participants and nonparticipants is larger or smaller than what we report due to missing private 

and out-of-state enrollment. To obtain information on private and out-of-state enrollment, we 

pursued two additional data sources: (1) the Pell Grant recipient files and (2) the National 

Student Clearinghouse. 

1. Pell Grant Recipient Files 

We obtained information on applications for financial aid and an indicator of eligibility for a 

Pell Grant from the financial aid applicant files, which are based entirely on the FAFSA. The 

recipient files maintain information on the receipt of a Pell Grant, including the institution where 

the grant was awarded. Because most Talent Search participants who applied for aid were 

eligible for a Pell Grant, the Pell Grant recipient files could have provided additional 

postsecondary enrollment information on some students. 

Using Pell Grant recipient files would still have resulted in a problematic measure of 

postsecondary enrollment, for two reasons: 

1. Not all Talent Search participants and comparison students were eligible for Pell 

Grants, so we would still have been missing information for some students. 

 65 Indiana’s SIS contained some information on private and out-of-state enrollment but only for the small 

number of students who received state grants or scholarships. 
146

2. We would have been using information based on an outcome, applying for federal 

financial aid, as a potential source to determine another outcome, postsecondary 

enrollment. 

In addition, after initial promising negotiations with OPE to obtain Pell Grant recipient data, 

OPE staff decided confidentiality restrictions prevented them from providing individual 

identifying information on Pell Grant recipients. Thus, it would not have been possible to merge 

this information with the data files compiled in all three states. 

2. National Student Clearinghouse 

The National Student Clearinghouse is a repository of information on enrollment, loans, and 

degrees awarded for postsecondary institutions that join the clearinghouse as members. The 

clearinghouse is a nonprofit organization that began with support from ED to verify enrollment 

for student loan recipients. Currently, the clearinghouse is an enrollment and degree verification 

resource for ED, colleges and universities, and employers. It is supported by fees paid by the 

member institutions, as well as fees for enrollment and degree searches. Currently, the 

clearinghouse has 2,800 member institutions, which represent more than 90 percent of all 

students enrolled in college.66 The clearinghouse can provide information on enrollment if 

provided with name, SSN, and date of birth of the students. 

To test the comprehensiveness of the clearinghouse, we submitted names of 200 students we 

obtained from Talent Search project files in Florida, Indiana, Minnesota, and Texas. We 

submitted names of students Talent Search projects indicated had enrolled in college. We 

submitted some students with name, SSN, and date of birth information, and others with some of 

this information missing. The clearinghouse provided enrollment information on 110 of these 

 66 Figures are based on clearinghouse profile found on their Web site [www.studentclearinghouse.org/ 

about/pdfs/Clearinghouse_profile.pdf]. Cited April 2006. 
147

students—just over half of the sample. Most of the institutions represented among the 

110 students were public institutions in the study states, but a few private institutions were 

represented. The clearinghouse could not locate records on students unless they had all three 

pieces of identifying information, so we did not receive any enrollment information on students 

missing SSN, name, or date of birth. The clearinghouse provided the data quickly—within a 

week of our request—and the data were clean and easy to use. 

We did not pursue using the clearinghouse further because (1) based on our test, we did not 

think the clearinghouse provided postsecondary information on enough of the students in the 

sample and (2) Florida and Indiana would not provide us with identifying information to turn 

over to the clearinghouse.67 The target cohort for this study began entering college in fall 1999. 

The clearinghouse did not have as many member institutions in 1999 and 2000 as it does now, 

and it probably is a much more comprehensive source of postsecondary enrollment information 

now than it was five years ago. 

3. Summary 

We had mixed success using administrative data to assess the effect of Talent Search on 

participants. We conducted an informative analysis in three states, but we were not able to 

conduct the analysis in two states. The resources required to conduct the analysis in three states 

were far less than if we had relied on a longitudinal survey or even one survey with retrospective 

questions. Some specific baseline and outcome information, such as participation in programs 

and special classification in secondary school, and postsecondary enrollment, may be more 

 67 Staff at the Florida K-20 Education Data Warehouse had worked with the clearinghouse in the past and 

indicated they might be willing to submit names and obtain the data if MPR provided the resources. 
148

reliable than self-reported information. Other information, such as test scores and applications 

for financial aid, are probably much more reliable than self-reported information. 

The two main barriers to using administrative records for this project were (1) the 

availability and quality of the Talent Search project records and (2) restructuring of state 

agencies during the project. The quality of the Talent Search project data was certainly 

compromised by the time frame for this study. Maintaining electronic records was not as routine 

in 1995 as it is now, so this should be less of a barrier for future studies. However, refusal to 

provide data or providing incomplete data always may be a barrier to obtaining comprehensive 

project data as long as projects are not required to comply with participation in studies or to 

record specific data elements. While turnover of staff in state agencies always will present a 

challenge for data collection that relies heavily on contacts with specific staff, two states in this 

study—Indiana and Minnesota—went through major restructuring of state agencies during the 

study.68 A review of the Web sites for the state departments of education in Indiana and 

Minnesota suggests the states have restructured and more fully integrated services and data 

collected on primary through postsecondary public education. Although restructuring of state 

agencies presented major challenges for this particular study, our experience working with 

Florida and Texas demonstrates that, if more states develop accessible, comprehensive, and 

relatively easy-to-use information on secondary and postsecondary education, evaluations of 

interventions designed to improve education outcomes should become more comprehensive and 

far less costly to execute. 

 68 The restructuring and creation of state departments of education during this study may have been caused by 

the requirements for testing and documenting progress required under the No Child Left Behind Act. 
The Department of Education’s mission is to provide equal access to education 

and promote educational excellence throughout the nation.

The Department of Education’s mission is to provide equal access to education 

and promote educational excellence throughout the nation.