Strengths assessment, academic self-efficacy, and learning outcomes in a Christian University sample.
Sutton, Geoffrey W. ; Phillips, Sheri ; Lehnert, Alina B. 等
Associated with the recent surge of interest in positive psychology
is the hypothesis that students are more productive learners when the
focus is on their strengths rather than their weaknesses (Clifton,
Anderson, & Schreiner, 2006). In a recent meta-analysis, Robbins et
al. (2004) reported moderate relationships between psychosocial and
study skill (PSS) factors and two common measures of college outcomes:
cumulative grade point average (GPA) and retention. Of the PSS factors,
the best predictors of GPA were academic self-efficacy and achievement
motivation. In addition, PSS factors yielded incremental contributions
to the prediction of college outcomes beyond the traditional predictors
of standardized tests and high school GPA. In this article, we report
the results of two studies that examine the contribution of strengths to
college outcomes. Specifically, we examined the contribution of
strengths as defined by the StrengthsFinder (SF) instrument in
combination with standardized test scores (American College Test, ACT;
Scholastic Assessment Test, SAT) and academic self-efficacy (ASE). We
concluded with a discussion of the role of strengths-assessment in a
college population.
Strengths
The idea of recognizing a person's talents or strengths
associated with productivity has a long history. There are references to
Chinese examinations used to select people for government service nearly
4,000 years ago (Aiken & Groth-Marnat, 2006). In addition, Hebrew
texts report that Huram's strengths in working with bronze led to
his employment in Israel's first Temple (1 Kings 7:13). In the last
century, Maslow (1943) philosophized about the potential for human
growth, once we transcend our basic needs. By 1990, Csikszentmihalyi
published Flow, which summarized two decades of research on the intense
feelings of enjoyment people experience when they employ their talents
in the pursuit of challenging goals.
In this article, we focused on strengths, defined by Buckingham and
Clifton (2001), as "... consistent near perfect performance in an
activity" (p. 25). Specifically, they operationally defined
strengths using the StrengthsFinder instrument, a 180-item assessment.
The responses are associated with 34 strength themes. The user's
report provides the top five strength themes along with descriptive
information about each theme.
Two characteristics of the SF pose a challenge for those interested
in evaluating the contribution of the SF. First, the SF measures
individual strengths and includes ipsative strategies rather than a
strictly normative approach. Second, the SF authors stress the value of
considering five salient themes from among 34 strengths rather than
other alternatives that might assess contributions from all of the
strengths.
The individual differences approach poses a challenge for those
accustomed to explaining behavior in terms of normative models. Using an
ipsative approach, if two people have empathy as their top strength,
there is no guarantee they possess equal amounts of empathy. Conversely,
if two people obtain the same empathy score on a normative measure, an
observer can assume those individuals perceive themselves to possess
approximately the same amount of empathy. One may reasonably ask the
question, what is the value of knowing that people perceive they have
strengths if, in fact, those self-rated strengths may actually be
weaknesses relative to other members of a particular group such as a
classroom, task force, or committee? Upon reading the technical notes,
it seems the SF is a hybrid measure that combines elements of both
normative and ipsative measures (Lopez, Hodges, & Harter, 2005).
Clearly, if the SF were a completely ipsative measure, it would be
impossible to understand a person based on a list of strengths.
Advisors, educators, and employers would be left in a fog of
subjectivity that would obfuscate reasonable attempts at the usual
activities of advising and guiding learners. However, we argue, in
reality, all notions of personality are derived from experiences in
social groups. That is, we learn about our propensities for empathy,
harmony, and arranging from our experiences with others. We tend to like
the things we do well and dislike the things we do poorly. We learn
notions of doing well and doing poorly from years of feedback from the
adults and peers in our lives. The way people respond to items on the SF
is based on what they have learned about themselves in relationship to
other people.
Recent validity studies indicate SF theme scores do correspond with
subscales on normative measures, including the Big Five, the 16
Personality Factor Questionnaire (16PF), and the California Personality
Inventory(CPI) (Harter & Hodges, 2003; Schreiner, 2006).
Hayes (2001) provided a technical report on the SF in an Appendix
to Buckingham and Clifton's (2001) explanation of the strengths
program. Using a question and answer format, Hayes explained the
conceptual basis for the SF and referred to a four-dimensional model.
"StrengthsFinder is based on a general model of positive
psychology. It captures personal motivation (Striving), interpersonal
skills (Relating), self-presentation (Impacting), and learning style
(Thinking)" (p. 248). Gallup provided a grouping of the 34
strengths into the four dimensions, which we reproduced in Table 1. We
found only one unpublished study that evaluated this four-dimensional
model of strengths. Brashears and Baker (n.d.) examined the utility of
the four SF dimensions along with other admissions data to predict GPA
in a sample of 41 students during 2001. They found a high positive
correlation between Thinking and SAT (r = .744) and moderate negative
correlations between SAT and Relating (r = -.458) and Striving (r =
-.461). However, correlations between SF dimensions and the ACT were
small. In our studies, we explored the stability of the SF themes in two
samples and the intercorrelations between the four SF dimensions,
admissions scores, and GPA. In support of the strengths model, and
indirect support of the SF as a measurement tool, recent studies found
that strengths development activities, based on the SF strength themes,
promote educational outcomes (Cantwell, 2005; Lehnert, 2009; Louis,
2008).
Academic Self-Efficacy
Much of the research on self-efficacy stems from the work of
Bandura (1977). In general, self-efficacy refers to a belief that one
can complete a given task. Bandura found judgments about the degree of
self-efficacy are associated with the activities and settings a person
chooses (Bandura, 1982). Because self-efficacy judgments influence a
person's activities, those judgments can have considerable
functional value. For example, when faced with difficult situations,
those who have doubts about their own capabilities reduce their efforts
or may even give up. However, those who have a strong sense of efficacy
use greater effort to overcome those difficult situations and therefore
accomplish the task (Bandura, 1982). Anticipation of the types of
outcomes largely depends on the beliefs people have about how well they
will be able to perform in certain situations. People who are highly
efficacious will expect favorable outcomes; whereas, those who are not
highly efficacious will expect negative outcomes (Bandura & Locke,
2003).
In our research, we were primarily interested in academic
self-efficacy, beliefs students had about their ability to perform well
in an educational setting. Researchers found that students with higher
self-efficacy tended to be more motivated and earned higher grades
(Lent, Brown, & Larkin, 1984; Schunk, 1983). Self-efficacy was also
a stronger predictor of academic achievement than hope (Hacket, Betz,
Casas, & Rocha Singh, 1992).
More recently, Chemers, Hu, and Garcia (2001) found children with
high efficacy persisted longer and used more efficient problem-solving
strategies than did children with low efficacy. They also found that
students who enter college with confidence in their ability to perform
well academically significantly outperformed those students with less
confidence. In addition, students with higher expectations for academic
success had higher performances. We included a measure of academic
self-efficacy in study two to see if this form of self-efficacy would
make a significant contribution to explaining academic performance
beyond that accounted for by general ability.
Research Overview
We conducted two studies to evaluate the stability of the
StrengthsFinder on a Christian university campus and to explore the
relationships between the SF themes and measures of academic ability and
learning. In addition, we added academic self-efficacy in the second
study.
Study 1
Method
Participants. The sample contained 528 valid records (women = 352,
men = 176). On average, the women were age 18.88 (SD = .49) and the men
were age 19.50 (SD = 1.02). Most students were of European descent
(468). Other ethnic backgrounds were African-American (16), Hispanic/
Latino (26), Asian or Pacific Islander (7), and other (11). Most
students were from an Assemblies of God religious background (391) but a
substantial portion was from various charismatic, nondenominational, and
other backgrounds (127).
The database also contained information about scholastic ability
and progress. On average, student GPA was 3.23 (sd = .57) and they had
earned 39 92 (SD = 19 96) course credits at the time of testing. In
addition, combined SAT (M = 1040.20, SD = 162.95) and ACT (M = 23.23, SD
= 4.39) scores were available.
Measures. We used the ACT and SAT scores as measures of student
learning potential. Most of the students had taken the ACT but a
substantial portion had taken the SAT. In this study, we converted the
SAT scores into ACT scores based on conversion tables provided by
Dorans, Lyu, Pommerich, and Houston (as cited in Schneider & Dorans,
1999). For the small subsample (n = 47) that had taken both tests, the
correlation between SAT and ACT scores was r = .84 (p < .001,
two-tailed). The correlation between the obtained SAT scores and the
scores converted to ACT scores supported the viability of the conversion
procedure (n = 154, r = .94, p < .001, two-tailed).
In a technical report, Lopez, Hodges, and Harter (2005) reported
research documenting the development and validation of the SF. Internal
consistency values were adequate for most strength themes (33 of 34
above coefficient alpha = .65) for a sample of 706 Gallup associates. In
another analysis, test-retest values ranged between .60 and .80 for most
strength themes. Item validity indicated the average item to assigned
strength theme correlations were 6.6 times greater than overall average
item to strength theme correlations. Harter and Hodges (2003) conducted
a validity study and found support for predicted relationships between
select SF strength themes and Big Five personality scales.
Recently, and more relevant to higher education settings, Schreiner
(2006) reported reliability data from a 2005 study of 438 students from
14 colleges and universities that revealed mean test-retest reliability
of .70 across the 34 SF strength themes. The sample produced lower
reliability values for Activator (.52), Consistency (.53), and Maximizer
(.55). The values for the other 31 scales ranged from .60 to .80. The
stability of strength themes was 52% for the top three strength themes
remaining in the top five on retesting. The average internal consistency
was [alpha] = .61. The sample yielded low internal consistency values
(coefficient alpha) for Activator (.42), Arranger (.48), Belief (.57),
Consistency (.47), Context (.51), Ideation (.45), Input (.51), Maximizer
(.56), Relator (.46), Self-Assurance (.49), Significance (.55), and
Strategic (.51). The values for the remaining scales were between .60
and .80.
Schreiner (2006) reported 128 significant correlations between the
SF strength themes and subtests from either the CPI or the 16PF, as
evidence of construct validity. Only four SF strengths were not
significantly related to subscales on other measures (Context,
Individualization, Maximizer, Restorative). Hierarchical cluster
analysis revealed 95% of the SF item pairs met a criterion of 70%
associated with the expected SF strength theme.
Design and procedures. This correlational study is based on an
examination of archival data. Thus, ACT and SAT scores along with GPA
were extracted from a student database. A student's top five
strength themes were obtained from the results of the StrengthsFinder
and entered into a database along with their gender, age, and
accumulated credits. All names were removed from the database we
analyzed.
Because the school only had a list of the top five strength themes
for each student, without descriptive statistics such as means and
standard deviations, we elected to construct an index based on the
aforementioned SF constructs of Relating, Impacting, Thinking, and
Striving. We formed the index by adding one point for each person's
top five strength themes that were a part of one of the four dimensions.
Thus, each person could earn a score ranging from 0 to 5 depending on
how many of the 34 strength themes were associated with a particular
dimension. Following the study, we found a similar approach in an
unpublished manuscript by Brashears and Baker (n.d.).
Results and discussion
Based on the 512 records, the five most frequently occurring
strength themes were Belief (182), Adaptability (159), Developer (150),
Positivity (135), and Empathy (134). Descriptive statistics for the four
strength dimensions of Relating, Impacting, Thinking, and Striving
indicated a normal distribution with measures of skew and kurtosis
within acceptable limits (+/- 1.5) (George & Mallery, 2006). The
intercorrelations for the four dimensions were significantly negatively
correlated. In addition, converted ACT and three strengths dimensions
(Impacting, Striving, Thinking) were significantly correlated with GPA
(see Table 2).
Next, we conducted backwards multiple regression to identify which
of the variables that were significantly correlated with GPA might be
useful predictors. For the overall model, multiple R = .518 ([R.sup.2] =
.268, [R.sup.2.sub.adj] = .262, F(4, 443) = 17.62, p < .001). Only
converted ACT scores and Impacting significantly contributed to the
model. The Fchange values for models that removed Thinking and Striving
were not significant. See Table 3 for the coefficients.
Study 2
In study 2, we attempted to replicate the findings from the
previous year (study 1); however, we added a measure of academic
self-efficacy because of its value in previous research.
Method
Participants. The sample contained 344 valid records (women = 223,
men = 121). On average, the women were age 18.85 (SD = 1.72) and the men
were age 20.03 (SD = 4.39). Most students were of European descent
(304). Other ethnic backgrounds were African-American (15),
Hispanic/Latino (8), Asian or Pacific Islander (8), Native American (2),
and other (5). Most students were from an Assemblies of God religious
background (208) but a substantial portion was from various charismatic,
nondenominational, and other backgrounds (83).
The database also contained information about scholastic ability
and progress. On average, the student GPA was 3.21 (SD = .67) and they
had earned 30.28 (SD = 22.22) course credits at the time of testing. In
addition, SAT (M = 1065.48, SD = 172.83) and ACT (M = 22.83, SD = 4.21)
scores were available. As before, we transformed the SAT scores into
equivalent ACT scores based on the work of Dorans, Lyu, Pommerich and
Houston (as cited in Schneider & Dorans, 1999). Converted ACT scores
averaged 22.59 (SD = 4.32) and were significantly correlated with the
obtained SAT scores (n = 93, r = .97, p < .001, two-tailed).
Materials. We analyzed the same measures as in the first study
except for the addition of academic self-efficacy. We measured academic
efficacy using the Academic Self-Efficacy scale (ASE) (Chemers, Hu,
& Garcia, 2001). The ASE ([alpha] = .83) is an eight-item scale,
which asks participants to rate how well the statements describe them.
Items included statements such as "I know how to take notes"
and "I am very capable of succeeding at the university." All
items were rated on a seven-point Likert-type scale ranging from 1 (very
untrue) to 7 (very true).
Design and procedures. We used the same design as in Study 1;
however, we were able to obtain a measure of academic self-efficacy.
These measures were collected by the professors of the classes in which
students obtained and discussed the results of the StrengthsFinder. All
data were entered into an anonymous database for analysis.
Results and discussion
Based on 344 complete records, the five most frequently occurring
top five strength themes were Belief (112), Adaptability (101),
Restorative (93), Developer (90), and Achiever (85). The first two
strength themes were the same as in Study 1. Three of the top five
strength themes were the same for both samples. When we examined the top
10 most frequent strength themes, all of them were the same for both
samples. Thus, we had reasonable evidence the SF was, on average, a
stable measure of strength themes for the student population. See Table
4 for a comparison of strength themes for the two samples.
Descriptive statistics for the four strength dimensions of
Relating, Impacting, Thinking, and Striving indicated a normal
distribution with measures of skew and kurtosis within acceptable limits
(George & Mallery, 2006). The inter-correlations for the four
dimensions were significantly negatively correlated. In addition,
converted ACT, ASE, and the Impacting strength's dimensions were
significantly correlated with GPA (see Table 5).
Next, we conducted backwards multiple regression to identify which
of the variables that were significantly correlated with GPA might be
useful predictors. For the overall model, multiple R = .548, [R.sup.2] =
.30, [R.sup.2.sub.adj] = .29, F(3, 280) = 40.00, p < .001. Only
converted ACT scores and ASE significantly contributed to the model (p
< .05). The Fchange values for the model that removed Impacting were
not significant. See Table 6 for the coefficients.
General Discussion
The stability of strength themes for two samples, obtained a year
apart, supports the limited previous findings that the SF measure is
relatively stable in a campus population. These findings are interesting
because they lead to several observations. First, students attracted to
this midsize Christian university from year to year may indeed possess
similar strengths; a finding which could have ramifications for
marketing, teaching, and retention. Second, the findings may be
indicative of the general top five strengths of new college students.
Expanded examination of the top strengths of students in colleges
nationwide could provide additional information to ascertain whether
these top five strengths are unique to this campus or whether they may
be indicative of a developmental process.
In addition, the normal distribution of scores and the pattern of
correlations provided initial support for the value of the four SF
dimensions as indexes. For example, we found significant positive
correlations between Thinking and converted ACT scores and significant
negative correlations between Impacting and GPA in both samples. So,
students motivated primarily by intellectual pursuits (learning) were
more likely to have higher ACT scores and students motivated primarily
by active pursuits (self-presentation) were more likely to have lower
grades. Academic self-efficacy provided significant additional
information about GPA beyond college admissions scores alone. In
addition, the Impacting dimension of strengths contributed to predicting
GPA when controlling for college admissions scores and ASE. Not
surprisingly, our findings support previous research that college
admissions test scores are positively correlated with GPA (e.g.,
Garavalia & Gredler, 2002).
What is the context for considering a discussion of strengths? The
broad discussion of positive psychology and the focus on identifying
strengths as represented in our studies of the SF can be considered in
the larger context of identifying and treating symptoms of a disease or
identifying and remediating deficits in insight, knowledge, or skills.
The paradigms in medicine and psychology have yielded measurable results
that can be linked to improvements in health. We would expect most
health care providers would agree that an equally important
consideration is the identification of strengths and strengths-based
activities that can enhance functioning. We suggest several
considerations that might frame a fruitful debate on strengths. First,
research is needed to identify which model of strengths should guide
assessments and interventions. Second, what weight should academic
advisors, educators, health care providers, and employers give to models
that focus on identifying and remediating human weaknesses versus human
strengths. Third, is it possible, or even desirable to develop a
multidimensional model that identifies both strengths and weaknesses and
offers individuals a broader academic, health, or personal improvement
plan based on such a broad assessment?
What is the context for considering our findings? Despite the
widespread use of SF on Christian and secular university and business
campuses, the PsycINFO database contained no published peer-reviewed
studies of the SF. We did find evidence that some researchers recently
investigated the value of strengths interventions (Cantwell, 2005;
Louis, 2008; Lehnert, 2009) in their dissertation research and had
employed the SF to identify the strengths, which were related to the
interventions. Given the data from Gallup researchers, usage levels, and
unpublished research, there is evidence that Gallup's strengths
model has been accorded value as both an assessment of human strengths
and as a tool in developing strengths-based activities that could be
linked to other outcomes valuable to educators and employers. Because
the creators of SF have tied the instrument to positive psychology
(Clifton, Anderson, & Schreiner, 2006) and Christianity (Winseman,
Clifton, & Liesveld, 2003), it is incumbent on users to evaluate
their measures, learning activities, and other strengths-focused
programs on Christian and secular college campuses.
What have we added to the research on strengths and the SF? In this
article, we provided some data and a basis for initiating a public
debate in peer-reviewed publications about the value of strengths in
general, and the SF in particular. We cited the technical reports made
available by the Gallup researchers and found evidence that the top five
themes are relatively stable on a Christian university campus. We also
added a minimal amount of evidence to the construct validity of the four
dimensions as noted in the small correlations we reported. Moreover, the
prominence of the belief strength offers some support for construct
validity on a faith-based campus. However, we note, the SF belief
strength is not associated with the content of one's beliefs.
Nevertheless, further evaluation of belief in other religious
populations could be a promising endeavor.
What are the limitations of our research? Clearly, our studies were
limited by the restrictive characteristics of the Midwestern Christian
university sample. In addition, we did not have a wide array of
instruments in the database that might be considered in a broad-based
evaluation of the construct validity of the SF and the four-dimensional
model.
What recommendations can be offered for users of the SF? Given the
evidence from Gallup scientists about the SF instrument and the recent
dissertation research, there is an emerging basis to support ongoing
research into the value of strengths-based advising and intervention
programs applicable to both secular and Christian university programs.
Because the ASE measure is short and adds predictive value, it would be
relatively easy for schools to include this measure as a part of an
advising process and to construct local norms.
What questions might be addressed by further research? In addition
to addressing the broad questions about strengths and weaknesses, noted
above, we hope additional studies will add to the psychometric
properties of the SF and improvements of future versions of the
instrument.
Additional research is needed to identify effective components of
strengths-based advising and strengths-based teaching that may be linked
to outcomes in education and industry. Further, strengths-based programs
need to be compared to specific components of programs that address both
strengths and weaknesses, especially in the context of academic advising
and career counseling. Moreover, we can conceive of a role for
strengths-based psychotherapy, which has the potential to move beyond
the remediation of impairments in mental status and relationships (based
on traditional models of insight or skill development) to a focus on
enhancing strengths.
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Geoffrey W. Sutton
Sheri Phillips
Alina B. Lehnert
Bradley W. Bartle
Priscilla Yokomizo
Evangel University
Correspondence concerning this article should be addressed to
Geoffrey W. Sutton, Ph.D., Evangel University, 1111 N. Glenstone Ave.,
Springfield, MO 65802 Suttong@evangel.edu
Note
(1.) StrengthsFinder[R] and Clifton StrengthsFinder[TM] and each of
the 34 themes are trademarks of the Gallup Organization and refer to the
measure used in this study. The measure is available from the Gallup
Organization, www.gallup.com.
Authors
Geoffrey W. Sutton, Ph.D. is a professor of psychology at Evangel
University. He is a licensed psychologist who works as a medical
consultant in psychology for the Social Security Administration,
Disability Determinations Division. His research interests focus on
positive psychology and Christian spirituality.
Sheri Philips, Ph.D. is an assistant professor and director of
career development at Evangel University in Springfield MO. Her Ph.D. is
in higher education leadership from Azusa Pacific University. She holds
a B.S. from Evangel College and an M.A. in counseling from Wheaton
College.
Alina B. Lehnert, Ph.D. is an assistant professor of organizational
leadership and associate director of leadership and strength development
at Evangel University. Her Ph.D. is in organizational leadership from
Regent University. She holds an M.S. in counseling from Missouri State
University and a bachelor's degree from Evangel University.
Bradley W. Bartle, B.S. was a psychology senior fellow at Evangel
University when the study was completed. He now lives and works in
Illinois.
Priscilla Yokomizo, B.S. was a psychology senior fellow at Evangel
University when the study was completed. She now lives and works in
Colorado and is pursuing additional education.
Table 1
StrengthsFinder Themes Associated with Dimensions
Relating Impacting Thinking Striving
Communication Command Analytical Achiever
Empathy Competition Arranger Activation
Harmony Developer Connectedness Adaptability
Includer Maximizer Consistency Belief
Individualization Positivity Context Discipline
Relator Woo * Deliberative Focus
Responsibility Futuristic Restorative
Ideation Self-Assurance
Input Significance
Intellection
Learner
Strategic
Note. Four dimensions suggested by Winseman, Clifton, & Liesveld
(2003).
* Woo = Winning Others Over.
Table 2
Means, Standard Deviations, and Correlations for Study 1
Variable 1 2 3
1. Relating --
2. Impacting -.13 ** --
3. Thinking -.48 ** -.46 ** --
4. Striving -.36 ** -.30 ** -.28 **
5. ACT -.17 ** .14 ** .21 **
6. GPA -.04 -.16 ** .10**
N 521 521 521
M 1.32 1.00 1.30
SD .90 .85 1.04
Variable 4 5 6
1. Relating
2. Impacting
3. Thinking
4. Striving --
5. ACT .07 --
6. GPA .08 * .50 ** --
N 521 450 518
M 1.37 22.54 3.05
SD .87 4.07 .75
Note. ACT = Converted American College Test Score,
GPA = Undergraduate Grade Point Average.
* p < .05, ** p < .01 (one-tailed)
Table 3
Multiple Regression Analysis of Variables Explaining GPA in Study 1
Variable B SE Beta t p
Converted ACT .095 .008 .503 12.008 < .001
Impacting -.129 .047 -.146 -2.759 .006
Thinking -.06 .040 -.081 -1.521 .129
Striving -.024 .043 -.028 -.568 .570
Note. The criterion variable was undergraduate GPA and n = 450.
Table 4
Table of Top Ten Strengths
Frequency 2005 Strength Rank Frequency 2006 Strength
182 Belief 1 112 Belief
159 Adaptability 2 101 Adaptability
150 Developer 3 93 Restorative
135 Positivity 4 90 Developer
134 Empathy 5 85 Achiever
121 Includer 6 84 Empathy
109 Input 7 80 Input
109 Responsibility 8 76 Responsibility
109 Restorative 9 75 Positivity
102 Achiever 10 74 Includer
Note. Frequency and ranking of the top 10 strengths
from 2005 (n = 521) and 2006 (n = 344).
Table 5
Means, Standard Deviations, and Correlations for Study 2
Variable 1 2 3 4
1. Relating --
2. Impacting -.16 ** --
3. Thinking -.43 ** -.42 ** --
4. Striving -.33 ** -.32 ** -.33 * --
5. ACT -.08 .07 .21 ** .09
6. ASE -.09 * -.13 ** .12 .07
7. GPA -.04 -.13 ** .07 .06
N 344 344 344 344
M 1.28 .94 1.28 1.50
SD .91 .90 1.08 .94
Variable 5 6 7
1. Relating
2. Impacting
3. Thinking
4. Striving
5. ACT --
6. ASE .24 ** --
7. GPA .49 ** .39 ** --
N 284 344 340
M 22.60 41.01 3.22
SD 4.32 7.16 .67
Note. ACT = Converted American College Test Score,
GPA = Undergraduate Grade Point Average.
* p< .05, ** p< .01 (one-tailed)
Table 6
Multiple Regression Analysis of Variables Explaining GPA in Study 2
Variable B SE Beta t p
Converted ACT .065 .009 .398 7.45 < .001
ASE .023 .005 .241 4.494 <.001
Impacting -.065 .039 -.082 -1.637 .103
Note. Converted ACT = converted American College Test scores.
ASE = Academic Self-Efficacy. N = 284 for this analysis.
