CliftonStrengths® Meta-Analysis:
Strengths-Based Interventions
Reduce Student AttritionC O R E Y E . TAT E L , P H . D. , G A L L U P
E M I LY M . LO R E N Z , P H . D. , G A L L U P
J A M E S K . H A R T E R , P H . D. , G A L L U P
S A N G E E TA A G R AW A L , M . S . , G A L L U P
J I M A S P L U N D, M . A . , G A L L U P

C O P Y R I G H T S TA N D A R D SThis document contains proprietary research, copyrighted materials and literary property of
Gallup, Inc. It is for the guidance of your organization only and is not to be copied, quoted, publishedor divulged to others outside your organization. All of Gallup, Inc.’s content is protected by copyright.
Neither the client nor the participants shall copy, modify, resell, reuse or distribute the program
materials beyond the scope of what is agreed upon in writing by Gallup, Inc. Any violation of thisAgreement shall be considered a breach of contract and misuse of Gallup, Inc.’s intellectual property.
This document is of great value to Gallup, Inc. Accordingly, international and domestic laws andpenalties guaranteeing patent, copyright, trademark and trade secret protection safeguard the ideas,
concepts and recommendations related within this document.
No changes may be made to this document without the express written permission of Gallup, Inc.
Gallup® and CliftonStrengths® are trademarks of Gallup, Inc. All other trademarks and copyrights areproperty of their respective owners.

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿

Table of Contents
2

Introduction

3

Method6Meta-Analysis

10

Utility Analysis

12

Publication Bias

15

Discussion

16

References

18

Appendixes

1

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿Introduction
Knowing your strengths early in life is an important aspect of establishing a strong sense of self,
a hopeful outlook for the future and positive self-awareness. Adolescents and young adults who
know and use their strengths report higher levels of wellbeing, greater engagement with school,
more positive perceptions of their leadership development and stronger hope for the future(Cherry et al., 2019; Lane & Schutts, 2014; Soria et al., 2015; Soria & Stubblefield, 2014).
CliftonStrengths (also known as Clifton StrengthsFinder or CSF) is an assessment tool thatmeasures an individual’s strengths — their naturally recurring patterns of thought, feelingor behavior. It was designed to empower individuals to discover and cultivate their natural
strengths. Don Clifton, the creator of the StrengthsFinder assessment, received a Presidential
Commendation from the American Psychological Association (APA) in 2003 as the father ofstrengths-based psychology and the grandfather of positive psychology for his efforts.
Individuals who complete the assessment are provided with a personalized CliftonStrengths reportthat provides insights on how they can make the most of their strengths and manage potential
weaknesses in life, work, school and relationships. Many also receive coaching on how to interpret
their report or leverage their results to create actionable insights. A previous meta-analysis foundthat when organizations implement strengths-based employee development, it leads to desirable
outcomes such as lower employee turnover rates (Asplund et al., 2016).
Many colleges and universities integrate CliftonStrengths and strengths-based
development into student orientation, academic advising, residence life and course curriculum,
among other aspects of the student experience as part of students’ personal and professional
development during postsecondary education. To date, more than 32 million people havetaken the CliftonStrengths assessment, including 6 million students. Over 500,000 students
discovered their CliftonStrengths last year.
Researchers across numerous institutions have studied relationships between CliftonStrengths
and higher education outcomes, such as student engagement, wellbeing, leadership skills,
graduation rates and first-year student retention. A review of this research demonstrates a rangeof findings, with the majority of the research demonstrating significant positive outcomes, andsome studies, primarily those with insubstantial sample sizes, demonstrating lack of power
to identify statistically significant effects. Importantly, there is no evidence in this literature tosuggest that strengths-based interventions have any negative impact on college students.
Among studies, there is variability in sample sizes, type of student or institution
measured, measurement instruments, and, importantly, the nature, quality and fidelityof CliftonStrengths interventions. We identified the variability in research outcomes as
an opportunity to conduct a meta-analysis to estimate the true relationship between
CliftonStrengths interventions and student outcomes, and the generalizability of the relationshipacross situations.
This report includes a meta-analysis of the relationship between strengths-based interventions
and college student retention. While there are other relevant outcomes that may be investigatedwith meta-analytic methods in the future, this analysis serves as a first step and quantitative
proof of concept of the impact that strengths-based interventions have on student outcomes.
2

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿

MethodLiterature Search

We conducted a literature search to identify academic papers published in 2000
(when CSF was first introduced) or later that report studies that involved the influenceof strengths-based interventions on undergraduate student retention. We entered apredefined set of search terms that were expected to yield relevant results into two wellestablished academic databases (ERIC and PsycInfo) to identify relevant studies. The
specific search terms used and the number of results that each term yielded in eachdatabase appear in Appendix A. All searches were conducted in the summer of 2023.
We identified 71 of the results yielded from the literature search as papers involving
CliftonStrengths and higher education. We also included two additional papers authored byscholars in our network, which brought the total pool of potentially relevant papers to 73.

Eligibility Screening

We evaluated the 73 papers with respect to a set of eligibility criteria based on the
study’s scope. These eligibility criteria determined which studies would be included in themeta-analysis. The eligibility criteria were as follows:• Primary Empirical Study: Studies were required to be primary empirical studies ofdegree-seeking college students, meaning that they involved data collected fromstudents enrolled at a degree-granting college or university.

• Data Requirements: Studies were required to report statistics that were needed to
calculate effect sizes for the meta-analysis (i.e., Cohen’s d). This included descriptive
statistics or effect sizes that could be converted to Cohen’s d.
• CliftonStrengths Intervention: Eligible studies involved a strengths-based
intervention. These interventions involved at a minimum taking the CliftonStrengths
assessment. Example interventions include discussing the results withanother individual (e.g., a professor, an academic adviser, peers), participating instrengths‑based programming through residence life or study groups, and taking acourse that incorporated CliftonStrengths-based development into the curriculum.
• Comparison Group: Eligible studies included a comparison group that either did
not participate in a strengths-based intervention or participated in a less involved
strengths-based intervention (e.g., a feedback session with a counselor versus justtaking the assessment).
• Student Retention: All studies were required to report a retention rate for boththe intervention group and comparison group. We defined retention rates as the
proportion of first-year students who remained enrolled the following year.

3

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿

Studies from six of 73 papers met all of the study’s eligibility criteria and were includedin the meta-analysis. Figure 1 provides a summary of the eligibility screening process,including how many studies were excluded based on each of the eligibility requirements.
Three of the eligible studies were published journal articles and three were doctoral
dissertations. All studies involved retention of students from their first year of college totheir second year.
FIGURE 1

Eligibility Screening Literature Search4

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿Study Coding

We coded each of the six eligible studies for data used in the meta‑analysis. Specificinformation coded included the sample size of both the intervention group and thecomparison group and the statistics needed to compute or convert the effect size.
When descriptive statistics (i.e., means and standard deviations) were provided for eachgroup, they were used to calculate Cohen’s d. When descriptive statistics were notprovided but effect sizes that could be converted to Cohen’s d were provided, the effectsizes were converted.*
One study included in the meta-analysis included effect sizes for three strengths-based
interventions and these effect sizes were averaged to obtain a single effect size for the
sample (Soria & Stubblefield, 2015). Another study included in the meta-analysis did notprovide specific sample size information for the students in the intervention versus thecomparison group, but the sample sizes were estimated from other information that wasreported (Gazaway, 2018).
The specific formulas used to convert effect sizes and the inferences used to estimate
sample sizes when relevant appear in Appendix B.

*

Another option that was considered was using Cohen’s h, an effect size used to quantify the magnitude of differences
between proportions. A decision was made to use Cohen’s d rather than Cohen’s h for two reasons. First, theinterpretation of Cohen’s d as a mean difference in standard deviation units is more practical than the interpretation of
Cohen’s h, for which statisticians typically rely on Cohen’s (1988) thresholds for small, medium and large effects. Second,
only half of the studies included in the meta-analysis reported sufficient information to properly calculate Cohen’s h.

5

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿

Meta-Analysis
A meta-analysis is a statistical integration and aggregation of data accumulated acrossdifferent studies. Meta-analysis has the potential to provide uniquely powerful information
because it accounts for sampling error, or idiosyncrasies related to the specific sample
that individual studies used. By accounting for sampling error, meta-analysis provides anestimate of what the true relationship between two variables is in the population.
Meta-analysis typically also accounts for statistical artifacts including measurement error
and allows for the exploration of moderators. However, neither of these are accounted forin the current meta-analysis. Measurement error was not corrected for in the current study
because estimates of the reliability of student retention were not available. Therefore, theeffect sizes reported in this study may be lower than true score correlations. Moderators
were not explored due to limitations of available data, though we hope to explore
moderators of the relationship between strengths-based interventions and student
retention in future studies.
For the present analysis, we used Hunter-Schmidt random effects meta-analysis methods(Schmidt & Hunter, 2014). Cohen’s d effect sizes were converted to correlations for the
purposes of conducting the meta-analysis and converted back to Cohen’s d following
analyses for the purpose of interpretation. When the sample sizes of the two groups
compared by an effect size are asymmetric, the effect size can be attenuated. Basedon Schmidt and Hunter’s (2014, pg. 287) recommendations, we corrected the individual
correlations for asymmetric sample sizes prior to computing the meta-analysis. The
corrected correlations were then weighted based on the variance expected fromsampling error in a “bare bones” meta-analysis to produce a meta-analytic estimate of
the relationship between the two variables in the population.

6

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿

The formulas used to convert Cohen’s d to r, to correct r for asymmetric sample sizes
and to calculate the variance expected from sampling error, are provided below. Table
1 provides the specific effect sizes and sample sizes from each study included in themeta-analysis.

**

TA B L E 1

Study Effect Sizes and Sample Sizes

Paper

d

rc

n intervention

n comparisonn totalGazaway, 2018

0.35

0.17

2,625

2,625

5,250

Soria &
Stubblefield, 2015

0.75

0.35

687

806

1,493

Soria & Taylor, 2016

0.42

0.22

313

642

955

Soria et al., 2018

0.23

0.12

614

614

1,228

Swanson, 2006

3.15

0.84

51

64

115

Williamson, 2002

2.13

0.73

32

40

72

d = mean difference in standard deviation units

rc = correlation corrected for asymmetric sample sizes
n = sample size**

Sample sizes used to weight effect sizes were based on the total sample size (the sum of the sample size for both groups).
This method assumes equivalent variances between the two groups. We would have preferred to use the degrees of
freedom for Welch’s t-test plus one, which would not require the assumption of equal variances between groups. However,we were unable to do so because three of the six effect sizes were converted from odds ratios and SDs required by
the formula for the degrees of freedom for Welch’s t-test were therefore unavailable. It is unlikely that this difference
drastically impacted the results reported in this document.

7

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿

Full meta-analytic results are provided in Table 2. The meta-analysis consists of six effectsizes representing 9,113 students. The mean observed correlation was r = 0.21 with
a 95% confidence interval of [0.12, 0.30] and an 80% credibility interval of [0.08, 0.35].
The mean observed Cohen’s d effect size was 0.43 with a 95% confidence interval of[0.25, 0.63] and an 80% credibility interval of [0.15, 0.74].
Given that a Cohen’s d effect size represents a mean difference on a scale of standard
deviation units, these results indicate that the average retention rate for strengths-based
intervention groups was typically 0.43 standard deviation units higher than the averageretention rate for the comparison groups.
Figure 2 displays a visualization of the meta-analytic results as a forest plot. For each
respective study, the blue circles on the forest plot represent observed correlations,and the error bars extending outward from each circle represent the correlation’s
95% confidence interval. Studies with larger sample sizes have larger circles andnarrower confidence intervals, whereas studies with smaller sample sizes have smaller
circles and wider confidence intervals. The green diamond at the top of the plot represents
the meta‑analytic correlation. The figure clearly visualizes the extent to which individual
studies contributed to the overall meta-analytic results.

Strengths-based interventions increase student
retention rates by 0.43 standard deviation units.
FIGURE 2

Forest Plot

The green diamond in the chart represents the overall meta-analytic correlation and the blue circles represent
correlations from individual studies. The size of the circles are proportional to the sample sizes of the individual
studies, which indicates which studies contributed most to the overall meta-analytic results. The error bars
visualized represent 95% confidence intervals for the individual study correlations.

8

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿
TA B L E 2

Meta-Analytic ResultsMeta-Analysis of the Relationship Between Strengths-Based Interventions and Student Retention
Analysis items

Results

Number of Students

9,113

Number of r’s

6

Mean Effect Size r

0.21

Observed SD r

0.11

True Effect SD r

0.11

Mean Effect Size d

0.43% of Variance Accounted for — Sampling Error

5%

95% Confidence Interval r[0.12, 0.30]

95% Confidence Interval d[0.25, 0.63]

80% Credibility Interval r[0.08, 0.35]

80% Credibility Interval d[0.15, 0.74]

r = correlationd = mean difference in standard deviation units

9

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿

Utility Analysis
Effect sizes such as those reported in the meta-analytic results can be challenging tointerpret. Conventions regarding the utilities of relative effect sizes (Cohen, 1988) may
not be informative because the practical significance of those effects depends on the
improvement on the independent variable and the benefits of changes in the dependent
variable. Research literature includes many examples of large practical benefits shown instudies with numerically moderate effect sizes (Abelson, 1985; Lipsey, 1990; Sechrest& Yeaton, 1982).
A related issue is the fact that many interested parties may be unfamiliar with Cohen’s dand its interpretation. Therefore, we generated estimates of utility based on ourmeta‑analytic results. To estimate the utility of the results, we multiplied the meta-analytic
Cohen’s d (0.43) by the standard deviation yielded by the meta-analysis (0.11). Because
Cohen’s d represents a difference in standard deviation units, the result of this analysis
can be interpreted as the expected amount that retention rates would rise if all students
were to participate in strengths-based interventions. For practical utility, we translated thefindings from increases in retention rates to decreases in attrition rates to illustrate theproportion of students who could be retained through strengths-based interventions that
may have otherwise discontinued enrollment (i.e., a risk ratio).

The utility analysis indicates that strengths‑based
interventions typically decrease student
attrition rates by 4.73 percentage points.
The 80% credibility interval, which represents
the realistic range of attrition rate decreases
that can be expected, is 1.65% to 8.14%.

For illustrative purposes, Table 3 displays the first-year student attrition rate for public,
private nonprofit, private for-profit institutions and all institutions reported by the National
Center for Education Statistics in the Integrated Postsecondary Education Data System, or
IPEDS (National Center for Education Statistics, 2023), as well as the potential impact thatstrengths-based interventions can have on attrition rates based on the utility analysis.
Risk ratios are expressed as an estimate of the current risk of students discontinuing
enrollment compared to the lesser risk if every student were to undergo a strengths‑based
intervention in their first year. For example, based on the results in Table 3, first-year
students attending a public higher education institution are presently 25% less likely toleave school if they participate in a strengths-based intervention.

10

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿

Table 3 also includes estimates of the number of students per school who typicallydiscontinue enrollment that could be retained if strengths-based interventions were
implemented effectively across the institution. To derive this estimate, the average
freshman class size during the 2022 fall semester (defined as the number first-timedegree/certificate seeking undergraduate students) was calculated for each institution
type based on data obtained from IPEDS for U.S. Title IV participating, degree-granting
schools with full-time first-year students.*** These class sizes were then used to calculate
the specific number of students per school who might be prevented from discontinuing
enrollment within their first year based on the current and projected attrition rates. For
example, on average, public higher education institutions with a freshman class of 1,165
could prevent 55 students per year from discontinuing enrollment after their first year if all
first-year students receive strengths-based interventions.
TA B L E 3

Utility Analysis

Institution Type

Current
Attrition Rate

Projected
Attrition Rate

Projected
Attrition
Rate 80% CR
Lower Bound

Projected
Attrition
Rate 80% CR
Upper Bound% Less Likely
Risk Ratio

All Institutions

19.0%

14.3%

10.9%

17.3%

25%

Public

18.8%

14.1%

10.7%

17.1%

25%

Private Nonprofit

18.6%

13.9%

10.5%

16.9%

25%

Private For‑Profit

37.5%

32.8%

29.4%

35.9%

13%

CR = Credibility interval

TA B L E 4

Utility Analysis

Institution Type

Avg. First-Year Fall
Enrollment

n Current
Attritionn Projected
Attrition

All Institutions

896

170

128

42

Public

1,165

219

164

55

Private Nonprofit

445

83

62

21

Private For-Profit

218

82

71

11

*** Class sizes of 10 or fewer students were excluded from these calculations as outliers.

11

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

n Students Retained via
Strengths Interventions

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿

Publication Bias
In a meta-analysis of effect sizes from publicly available research, it is necessary toconsider and investigate the possibility of publication bias. The research publication
process favors larger effect sizes and results that are statistically significant (Rosenthal,1979). Publication bias can subsequently lead to misleading estimations of the relationshipbetween two variables in the population because they neglect effect sizes that are small
and/or not statistically significant (Ferguson & Brannick, 2012).
The first line of defense against publication bias in the current study was the inclusionof doctoral dissertations in addition to published research. Half of the studies (three outof six) included in the meta-analysis were doctoral dissertations. However, the inclusionof unpublished research is not a silver bullet, and it is necessary to further evaluate thepossibility of publication bias. Given that half of the studies in this meta-analysis were
unpublished, publication bias in this instance may more accurately reflect availability bias.
A funnel plot, which is a common method for evaluating publication bias, is presented in
Figure 3. A funnel plot consists of the effect size strength on the x-axis and the effect size
standard error on the y-axis (Sterne & Harbord, 2004). Studies with higher standard errors
consist of smaller sample sizes, and vice versa. The vertical line in Figure 3 represents themean r, and diagonal lines represent 95% confidence intervals.
In circumstances where there is little evidence of publication bias, funnel plots are
symmetrical. In other words, with little evidence of publication bias, effect sizes would be
similarly distributed on both sides of the mean effect size. However, as Figure 3 displays,
there are two large correlations with larger standard errors than the rest of the effect sizes
in the meta-analysis in the bottom right-hand corner of the plot. These two effect sizes
drive an asymmetry that suggests the possibility of publication bias.**** The correlation
between effect size strength and the standard error is 0.81, which is further evidence of
publication bias.

**** The two large effect sizes were both converted from proportions (or retention rates). The formula for the standard
deviation of a proportion is constructed such that large proportions lead to small standard deviations. Given
that Cohen’s d statistic is a mean difference in standard deviation units, it is logical that effect sizes derived fromsmall standard deviations would be large in magnitude. It is possible that the need to convert the effect sizes fromproportions therefore inflated these correlations.

12

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿In a meta-analysis of only six studies, it is not surprising to detect the possibility of
publication bias because the effects of outliers in a small meta-analysis are more
pronounced. To provide an estimate of what the relationship between strengths-based
interventions and student retention would have been without the presence of publicationbias, the two large effect sizes were removed, and the meta-analysis was re-run using only
the effect sizes from the other four studies.
This meta-analysis produced a mean r of 0.20 with a 95% confidence interval of[0.13, 0.27] and an 80% credibility interval of [0.11, 0.29], and a mean d of 0.41 with a
95% confidence interval of [0.26, 0.56] and an 80% credibility interval of [0.22, 0.60]. As
Table 5 displays, the degree of similarity between these results and the results presented
previously suggest, despite the possibility of publication bias, the potential bias likely had
little effect on the primary results or takeaways.
FIGURE 3

Funnel Plot

Vertical line = mean r from meta-analytic results
Diagonal lines = 95% confidence intervals

13

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿
TA B L E 5

Comparison of Meta-Analytic Results With Results Accounting for
Publication Bias
Results

Results Accounting for Publication Bias

0.21

0.20

95% CI for mean r[0.12, 0.30][0.13, 0.27]

80% CR for mean r[0.08, 0.35][0.11, 0.29]

0.43

0.41

95% CI for mean d[0.25, 0.63][0.26, 0.56]

80% CR for mean d[0.15, 0.74][0.22, 0.60]

Mean rMean dCI = Confidence interval
CR = Credibility interval

14

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿

Discussion
The present research demonstrates that strengths-based interventions, when usedeffectively, offer a clear opportunity for higher education institutions to positively impact
students’ likelihood to remain enrolled and subsequently reap the multitude of benefits
associated with postsecondary education.
Specifically, colleges and universities that integrate CliftonStrengths can significantly
reduce the rate of first-year student attrition by approximately 4.73% [80% CR: 1.65%
to 8.14%]. For the average public university with 1,165 students in the freshman class,this translates to 55 fewer students discontinuing their enrollment between their freshmanand sophomore year.
The implications for students — and their futures — are far-reaching when considering
the damaging effects of stopping out. Students who start college but do not finish often
find themselves worse off in life than their counterparts who never enrolled at all. Thisis because they frequently carry a significant student loan burden without a degree toshow for it.
In contrast, Americans who complete any level of postsecondary education experiencegreater life satisfaction, earn higher incomes, have better physical and mental health andare more likely to be civically engaged (Jones, 2023).
Despite the clear benefits of earning a college degree, many students face myriad
personal and financial challenges to remain enrolled. In 2022, 41% of college students
indicated they have considered stopping out in the prior six months, a significant rise from
37% who reported the same in 2021 (Gallup-Lumina Foundation, 2023).
When asked about reasons for considering discontinuing their enrollment in college, morethan half of students cited emotional stress (55%), followed by personal mental healthreasons (47%) and the cost of the degree or program (29%). These findings indicate thatwellbeing-related factors remain a primary tipping point for students’ likelihood to stayin college.
The CliftonStrengths assessment, when effectively implemented among college anduniversity students, has the potential to create life-altering effects on retention for all
students, especially those who find themselves at this critical juncture.

Acknowledgements
The authors of this report would like to thank Kate Den Houter and Declan Gilmer
for their contributions to the preliminary literature search and coding that served
as an important precursor to this analysis and report.

15

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿

References
Abelson, R. P. (1985). A variance explanation paradox: Where little is a lot. Psychological Bulletin,
97(1), 129-133. https://doi.org/10.1037/0033-2909.97.1.129
Asplund, J., Harter, J. K., Agrawal, S., & Plowman, S. K. (2016). The relationship between strengths-based
employee development and organizational outcomes 2015 strengths meta-analysis. Gallup 2015
Strengths Meta-Analysis Report.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. New York, NY:
Routledge Academic.
Cherry, K. M., McArthur, B. A., & Lumley, M. N. (2019). A multi-informant study of strengths, positive
self-schemas and subjective well-being from childhood to adolescence. Journal of HappinessStudies, 21, 2169–2191. https://doi.org/10.1007/s10902-019-00171-1
Ferguson, C. J., & Brannick, M. T. (2012). Publication bias in psychological science: Prevalence,
methods for identifying and controlling, and implications for the use of meta-analyses. Psychologicalmethods, 17(1), 120-128. https://doi.org/10.1037/a0024445
Gallup-Lumina Foundation, 2023. The State of Higher Education 2023 Report (luminafoundation.
org). https://www.luminafoundation.org/resource/the-state-of-higher-education-2023-report/*Gazaway, A. R. (2018). Talent theme dimensions and academic success among undergraduate
agriculture and natural resources students. [Doctoral Dissertation, Oklahoma State University].
Jones, J. M. (2023). Postsecondary Education Linked to Volunteerism, Better Health (gallup.com).
https://news.gallup.com/poll/510254/postsecondary-education-linked-volunteerism-betterhealth.aspx
Lane, F., & Schutts, J. (2014). Predicting the Presence of Purpose Through the Self-Efficacy Beliefsof One’s Talents. Journal of College and Character, 15(1), 15-24. https://doi.org/10.1515/jcc2014-0003
Lipsey, M. W. (1990). Design sensitivity: Statistical power for experimental research. Newbury Park,
CA: SAGE Publications, Inc.
National Center for Education Statistics. (2023). Table 2. Number of first-time degree/certificateseeking undergraduate students in the fall 2021 cohort, number still enrolled in fall 2022, andretention rates at Title IV institutions, by enrollment status, level of institution, degree-granting status,and control of institution: United States, fall 2021 and fall 2022 [Data table]. IPEDS Data Explorer.
Retrieved from https://nces.ed.gov/ipeds/search/viewtable?tableId=36097
Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin,
86(3), 638–641. https://doi.org/10.1037/0033-2909.86.3.638
Schmidt, F. L., & Hunter, J. E. (2014). Methods of meta-analysis: Correcting error and bias in researchfindings (3rd ed.). Thousand Oaks, CA: SAGE Publications, Inc.

16

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿
Sechrest, L., & Yeaton, W. H. (1982). Magnitudes of experimental effects in social science research.
Evaluation Review, 6(5), 579–600. https://doi.org/10.1177/0193841X8200600501*Soria, K. M., Laumer, N. L., Morrow, D. J., & Marttinen, G. (2017). Strengths-based advising
approaches: Benefits for first-year undergraduates. NACADA Journal, 37(2), 55-65.
https://doi.org/10.12930/NACADA-16-010
Soria, K. M., & Stubblefield, R. (2014). First-year college students’ strengths awareness: Building afoundation for student engagement and academic excellence. Journal of the First-Year Experience& Students in Transition, 26(2), 69-88.
Soria, K. M., Roberts, J. E., & Reinhard, A. P. (2015). First-year college students’ strengths awarenessand perceived leadership development. Journal of Student Affairs Research and Practice, 52(1),
89‑103. https://doi.org/http://dx.doi.org/10.1080/19496591.2015.996057*Soria, K. M., & Stubblefield, R. (2015a). Building a strengths-based campus to support student
retention. Journal of College Student Development, 56(6), 626-631. https://doi.org/10.1353/
csd.2015.0056*Soria, K. M., & Taylor, L. (2016). Strengths-based approaches in college and university student
housing: Implications for first-year students’ retention and engagement. The Journal of College andUniversity Student Housing, 42(2), 60-75.
Sterne, J. A., & Harbord, R. M. (2004). Funnel plots in meta-analysis. The Stata Journal, 4(2), 127-141.
https://doi.org/10.1177/1536867X0400400204*Swanson, J. E. (2006). Success in the first year: Impact of alternative advising on students at a liberalarts college. [Doctoral Dissertation, Western Michigan University].
*Williamson, J. S. (2002). Assessing student strengths: Academic performance and persistence offirst-time college students at a private church-affiliated college. [Doctoral Dissertation, Universityof Sarasota].
*Included in meta-analysis

17

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿

Appendixes
Appendix A. Literature Search Key Terms and Results
Across Databases
A P P E N D I X TA B L E 1

PsycInfo Searches
Date

Search Term

Limiters/Expanders

Results

6/2/2023

CliftonStrengths OR CliftonStrengths OR Strengthsfinder
OR Strengths Finder OR StrengthsQuest OR Strengths
Quest AND student# OR college student OR university
student OR higher ed# OR post-secondary education OR
post secondary education OR postsecondary education
OR college# OR university#

Publication Year: 2000-;
Language: English; AgeGroups: Adulthood (18 yrs& older), Young Adulthood(18-29 yrs), Thirties (30-39yrs), Middle Age (40-64yrs), Aged (65 yrs & older),Very Old (85 yrs & older);Population Group: Human49

6/29/2023

CliftonStrengths OR Clifton Strengths OR Strengthsfinder
OR Strengths Finder OR StrengthsQuest OR Strengths
Quest AND student# OR education# OR high school# OR
secondary education OR ninth grade# OR ninth-grade# OR
9th grade# OR grade 9 OR tenth grade# OR tenth-grade#OR 10th grade# OR grade 10 OR eleventh grade# OR
eleventh-grade# OR 11th grade# OR grade 11 OR twelfth
grade# OR twelfth-grade# OR 12th grade# OR grade 12

Publication Year: 2000-;
Language: English; AgeGroups: Adolescence(13‑17 yrs); Population
Group: Human

12

7/2/2023

CliftonStrengths OR Clifton Strengths OR Strengthsfinder
OR Strengths Finder OR StrengthsQuest OR Strengths
Quest AND student# OR education# OR elementary# OR
early childhood# OR preschool# OR primary education OR
kindergarten# OR intermediate# OR middle school# OR
junior high# OR grade 1 OR first-grade# OR first grade#OR 1st grade# OR grade 2 OR second-grade# OR second
grade# OR 2nd grade# OR grade 3 OR third-grade# OR
third grade# OR 3rd grade# OR grade 4 OR fourth-grade#
OR fourth grade# OR 4th grade# OR grade 5 OR fifthgrade# OR fifth grade# OR 5th grade# OR grade 6 OR
sixth-grade# OR sixth grade OR 6th grade# OR grade 7
OR seventh-grade# OR seventh grade# OR 7th grade#
OR grade 8 OR eighth-grade# OR eighth grade# OR
8th grade#

Publication Year: 2000‑;
Language: English; AgeGroups: Childhood(birth-12 yrs), Neonatal(birth-1 mo), Infancy (2-23mo), Preschool Age (2-5yrs), School Age (6-12 yrs);
Population Group: Human

918

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿
A P P E N D I X TA B L E 2

ERIC SearchesDate

6/2/2023

6/29/2023

7/2/2023

Search Term

Limiters/Expanders

CliftonStrengths OR Clifton Strengths OR
Strengthsfinder OR Strengths Finder OR
StrengthsQuest OR Strengths Quest

Date Published: 2000-01-01;
Education Level: Adult Education,
Higher Education, Postsecondary
Education, Two-Year Colleges;
Publication Type: Journal Articles;
Language: English; Expanders Apply equivalent subjects; Searchmodes - Boolean/Phrase

17

CliftonStrengths OR Clifton Strengths OR
Strengthsfinder OR Strengths Finder OR
StrengthsQuest OR Strengths Quest

Date Published: 2000-01-01;
Education Level: Grade 9, Grade10, Grade 11, Grade 12, HighSchool Equivalency Programs, HighSchools, Secondary Education;
Language: English

1

CliftonStrengths OR Clifton Strengths OR
Strengthsfinder OR Strengths Finder OR
StrengthsQuest OR Strengths Quest

Date Published: 2000-01-01;
Education Level: Early Childhood
Education, Elementary Education,
Elementary Secondary Education,
Grade 1, Grade 2, Grade 3, Grade 4,
Grade 5, Grade 6, Grade 7, Grade8, Intermediate Grades, JuniorHigh Schools, Kindergarten, Middle
Schools, Preschool Education,
Primary Education; Language:
English3

19

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

Results

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿

Appendix B. Formulas to Calculate and Convert EffectSizes, Sample Size Estimations
Formulas to calculate and convert effect sizes

When descriptive statistics (i.e., means and standard deviations) were available for boththe intervention group and the comparison group, Cohen’s d was calculated based on theformula below:

In some cases, it was necessary to convert other effect sizes reported by authors toCohen’s d statistics.
Three of the studies included in the meta-analysis reported odds ratios (Soria et al., 2017;
Soria & Stubblefield, 2015; Soria & Taylor, 2016), which are commonly reported for logistic
regression equations. Odds ratios were converted to Cohen’s d statistics using the formulaprovided. However, because the logistic regression equations reported by authors of the
primary studies included other predictors (e.g., demographic characteristics, educational
background, previous academic performance), the Cohen’s d statistics converted fromodds ratios represent the influence of strengths-based interventions on student retention
while holding the other variables constant. While this is a limitation based on the data
available, it still provides a useful estimate of the effect size while controlling for theother variables.

Effect sizes from two of the studies included in the meta-analysis were derived fromstudent retention rates corresponding with the intervention and comparison groups(Swanson, 2006; Williamson, 2002). For these studies, the formula above was used to
calculate Cohen’s d, but it was necessary to calculate the standard deviation of the
proportion to use for the calculation of the pooled standard deviation. The standard
deviations for each of the proportions were calculated using the formula:

20

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn

CliftonStrengths® Meta-Analysis: Strengths-Based Interventions Reduce Student Attrition﻿﻿Sample size estimations

There was one study for which the sample size used in the meta-analysis was estimated.
In this study, the author reported means and standard deviations of student retention
rates for the seven years before and seven years after a strengths-based intervention wasincorporated into the curriculum of an entry-level course (Gazaway, 2018). The numberof students that comprised each seven-year period was not reported. However, theauthor did provide a number for student enrollment over a three-year period, which was
1,124 students. Therefore, we used the calculations below to derive an estimate of thetotal number of students enrolled over the entire 14-year period.

21

Copyright © 2024 Gallup, Inc. All rights reserved.
Retention_MetaAnalysis_Ext_Rpt_040524v4_hs_kn