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Factors influencing academic success and self-esteem among diverse college students.

INTRODUCTION

Academic success in college is crucial because of the many possibilities that affect the student's present and future. Academic success, sometimes measured with the attainment of an undergraduate degree, means a student is now qualified for his or her first job. For others, it becomes the certification for a higher rank or higher pay. Sometimes academic success is not necessarily determined by "the degree" but by the incremental factors that lead to the degree such as the individual course grade point average. As many scholarship aspirants will attest, this is sometimes the deciding factor in differentiating selected students from their peers. With insight from twenty years of research, Pascarella and Terenzini (1991) along with Astin (1993), these researchers have agreed that the college grade point average is a well-accepted standard for academic success.

For many students there is much at stake for them to perform well in college. They have a lot wagering on their success which includes the time, effort as well as their financial investment. Therefore, there is no surprise if the prediction of scholarly attainment has attracted much discussion and has even been termed a large scale operation (Mouw & Khanna, 1993). Though it may seem like a large scale operation, knowledge about the predictors of academic success may prevent premature withdrawal and other retention-related challenges that can lead to academic failure.

In the areas of general education (Barthelemy & Lounsbury, 2009; Webb, Christian, & Armitage, 2007), psychological and behavioral practices (Dollinger, Matyja, & Huber, 2008) and peer conversations, evidence can be found that self-esteem as a stand-alone topic is considerably researched. In a perpetually growing body of self-esteem literature much focus has been centered on self-esteem in education. However, there are gaps in the research addressing the predictors of academic success and self-esteem in the undergraduate population.

The purpose of this study is, therefore, to fill some of those gaps by focusing on the roles of the big five personality traits, general stress, academic motivation and social support among others, and their impact on academic success and self-esteem.

REVIEW OF THE LITERATURE

A review of the literature on the big five personality traits in education reveals that much research has been done on the subject matter. Dollinger, Matyja, and Huber (2008) for example found significant relationships between students' personalities and exam scores. Other researchers have examined the effect of the big five traits on student's academic accomplishment. Chowdury (2008) in his findings asserted that there were positive and significant relationships between a student's overall score and openness, conscientiousness, agreeableness and neuroticism. Though there was a positive correlation found between student grade and extraversion, it was not significant.

There has been empirical evidence (Chowdury, 2008; Farsides & Woodfield, 2003; Tok & Morali, 2009) to suggest that that openness predicts academic accomplishment. According to Chowdury (2008) openness and neuroticism, were stronger predictors of academic success; however research of O'Connor and Paunonen (2007) as well as Busato et al. (2000) produced some new information. These researchers found that there were indeed strong and consistent associations between conscientiousness and academic success. Negative relationships, based on the finding of O'Connor and Paunonen (2007) were sometimes found between extraversion and scholastic success.

A number of researchers (Chamorro-Premuzic & Furnham, 2004; Kappe & Flier, 2012; Saklofske, Austin, Mastoras, Beaton, & Osborne, 2012; Tok & Morali, 2009; Wagerman & Funder, 2007) seem to agree that the best predictor of academic success is conscientiousness. Kappe and Flier (2012) for instance studied the predictive validity of intelligence and personality factors on a number of different academic measures. Their results indicated that conscientiousness explained five times as much variance in GPA as does intelligence.

Findings have implied that there are correlations between self-esteem and the big five factors. Using a sample size of 628,640 participants, Erdle, Gosling, and Potter (2009) sought to determine if higher order factors of the big five personality traits (plasticity and stability) were associated with self-esteem. Their findings purported that there were associations between self-esteem, plasticity (conscientiousness, agreeableness and neuroticism) and stability (openness to experience and extraversion).

Research findings also imply an inverse relationship with stress and self-esteem. For example, Hudd et al. (2006) claimed that students under stress exhibit lower levels of self-esteem. In light of the discussion the authors of this study advance that stress, academic motivation, the Big Five factors and social support can be used to predict self-esteem and therefore will evaluate the following hypothesis:

H1: Dispositional factors of general stress, academic motivation, self-efficacy, openness, conscientiousness, extraversion, agreeableness, neuroticism and social support will influence self-esteem

A significant predictor of academic success is self-efficacy (Brady-Amoon & Fuertes, 2011; Pajares, 1996; Salanova, Martinez, & Llorens, 2012; Zimmerman, Bandura & MartinezPons, 1992). Academic self-efficacy can determine future success owing to related past success (Salanova, Martinez & Llorens, 2012). From studies conducted by Salanova et al. (2012), past academic success positively impacts academic self-efficacy. Self-efficacy was also found to influence future academic success positively.

If social support is lacking, traditional undergraduates may feel insecure about their academic success (Vedder, Boekaerts, & Seegers, 2005) and research also suggests that students may be less motivated to achieve scholastically if they perceive that their peers, parents and instructors do not endorse their academic efforts (Legault, Green-Demers, & Pelletier, 2006). Social support, particularly family support predicted college GPA (Cheng, Ickes & Verhofstadt, 2012). Furthermore, the study done by Cheng, Ickes and Verhofstadt (2012) also suggested that social support from family members aided the academic success of females compared to their male counterparts. In light of the aforementioned findings, the following hypothesis has been formulated:

H2: Dispositional factors of general stress, academic motivation, self-efficacy, openness, conscientiousness, extraversion, agreeableness, neuroticism and social support will influence academic success.

Saklofske et al. (2012) found differences in the association between stress and academic success. Their findings also implied that stress did not negatively correlate with academic performance: highly stressful incidences were not parallel to poor performance. The following hypothesis will be examined:

H3: Self-esteem will influence academic success.

METHOD

Participants and Procedures

The survey packet was administered during class times to various students across the campus of a comprehensive university located in the southeastern part of the United States. It contained six surveys and took approximately 15 minutes to complete. All research participants were volunteers. Of the 220 questionnaires distributed, 216 responded and were useable for a response rate of 98.2 percent.

The demographics of the included sample are described below:

1. Gender: Male = 47.2%; Female = 52.8%

2. Ethnicity: African-America = 89.4%; Caucasian = 5.1%; Hispanics-Black = 2.3%

3. Age: 18-23 years = 88.4%; 24 years and older = 11.6%

4. Work Status: Not working = 61.6; working = 38.4

5. College Rank: Freshman = 19.9%; Sophomore = 13.9%; Junior = 23.6%; and Senior = 42.6%

6. Attendance Status: Full Time = 94.5%; Part time = 5.5

Measurements of Variables

In the present study, the authors of this study used the following constructs to develop and test the hypothesized model shown in Figure 1. Unless stated otherwise, the authors of this study used a Likert-type response format for the survey items.

Self Esteem- This concept was measured by ten (10) items to assess this construct. The scale was developed by Rosenberg (1965). A sample item is "I take a positive attitude toward myself." The Cronbach alpha was .87.

Academic Motivation--The twenty-eight (28) items developed by Vallerand et al., (1992). Participants responded to short phrased that were preceded by the stem, "Why Do You Go To College ..." An example phrase that followed this stem is "because with only a high school degree I would not find a high-paying job later on." The alpha was .93.

Self- Efficacy--Chen, Gully and Eden (2001) developed an instrument of eight (8) items that assessed self-efficacy. An example item is "I believe I can succeed at most any endeavor." The alpha was .94.

General Stress--Nine (9) items developed by Dzokoto, Hicks and Miller (2007) were used to assess general stress. Participants responded on a scale anchored from Not At All Stressed to Highly Stressed concerning categories such as "Academic Stress, Financial Stress, Problems With Professors" to name a few. The alpha was .77.

Perceived Social Support--This construct was measured by the twelve item instrument developed by Zimet et al. (1988). An example item is "I can talk about my problems with my family." The alpha was .91.

The Big Five Inventory--This concept included five dimensions of personality (openness, extraversion, conscientiousness, agreeableness, and neuroticism). The five dimensions were assessed by using the 44 item instrument developed by John and Srivastava (1999). Participants responded to short phrased that were preceded by the stem, "I see myself as someone who." Some example phrases that followed this stem are "is talkative, gets nervous easily, and is inventive." The alpha was .82.

Academic Success--Academic success was assessed by asking subjects to write their cumulative grade point average on a background information form that also captured demographical information.

Data Analysis

The proposed model presented in Figure 1was tested using structural equation modeling (SEM) to evaluate the research hypotheses by using the Linear Structural Relations (LISREL) computer program (Joreskog & Sorbom, 1993). SEM's major strength is that using latent variables permits estimation of relationships among theoretically interesting constructs that are free of the effects of measurement unreliability. The covariance matrix was used as the input for all path analysis models, and the maximum likelihood estimation procedure was employed to produce the model parameters. To examine model fit, the authors utilized measures of absolute fit and incremental fit to determine how well the data fit the hypothesized model (Hair, Anderson, Tatham, & Black, 1998).

[FIGURE 1 OMITTED]

RESULTS OF THIS STUDY

The means, standard deviations, and zero-order correlations are provided in Table 1. Correlations among the constructs were in the expected direction and consistent with the literature.

Common Method Variance Tests

Because all constructs were measured using self-reports, the authors examined whether common method variance was a serious issue. As recommended by Podsakoff and Organ (1986), Harman's one-factor test was performed. In this test, all survey items were entered together into an un-rotated factor analysis and the results were examined. If substantial common method variance is present, then either a single factor would emerge or one general factor would account for most of the total variance explained in the items (Podsakoff & Organ, 1986). After entering the 111 items into the factor analysis model, 18 factors emerged from the analysis, and the first factor only accounted for 17.193 percent of the total variance. In addition, no general factor emerged from the factor analysis. Thus, common method variance was not deemed a serious issue in this study.

Model Fit Measures

The authors of this current study used the following fit indices to assess the fit of the nomological network developed in Figure 1. The goodness-of-fit index is a measure of absolute fit of the model by comparing the fitted model with the actual data, and ranges from 0-1. Values greater than 0.90 demonstrate that the model fits the data well (Hair et al., 1998; Joreskog & Sorbom, 1996).

The absolute fit measures, maximum likelihood ratio chi-square statistic (X) and goodness-of-fit index (GFI), provide a measure of the extent to which the covariance matrix estimated by the hypothesized model reproduces the observed covariance matrix (James & Brett, 1984). In addition, the root mean square error of approximation (RMSEA) was considered as it provides an estimate of the measurement error. Another fit index, the non-normed fit index (NNFI)), was used to assess model fit; the NNFI assesses a penalty for adding additional parameters to the model.

The authors of this current study also used the normed fit index (NFI) because it provides information about how much better the model fits than a baseline model, rather than as a sole function of the difference between the reproduced and observed covariance matrices (Bentler & Bonett, 1980). The comparative fit index (CFI) has similar attributes to the NFI and compares the predicted covariance matrix to the observed covariance matrix and is least affected by sample size.

Test of the Model

The two-step approach to structural equation modeling was employed (Anderson & Gerbing, 1988). First, the measurement model was inspected for satisfactory fit indices. After establishing satisfactory model fit, the structural coefficients were interpreted.

Measurement Model

As shown in Table 2, the measurement model had acceptable fit indices. That is, the Chisquare statistic was at its minimum, and the p-value was nonsignificant. The GFI was above its recommended threshold level of 0.90 (Hair et al., 1998), and the RMSEA was less than 0.08, indicative of an acceptable model (Steiger & Lind, 1980). The Chi-square divided by the degrees of freedom coefficient was less than three, which indicates acceptable model fit (Arbuckle & Wothke, 1995). The CFI, NFI, and NNFI all indicated an acceptable fit of the model to the data.

Interpretation of Structural Equation Model

Table 3 presents the structural coefficients for the model. Self-esteem was one of two endogenous variables in the study. Hypothesis 1 stated that dispositional factors such as academic motivation, self-efficacy, openness, et cetera would influence self-esteem. Partial support was established for Hypothesis 1 because the path from academic motivation to self-esteem was significant and in a positive direction; the path from self-efficacy to self-esteem was significant and in a positive direction; the path from openness to self-esteem was significant and in a positive direction; in addition, the path from agreeableness to self-esteem was significant and in a positive direction; the path from neuroticism to self-esteem was significant and in a negative direction. Stress, conscientiousness and extraversion were not significant predictors of self-esteem.

Academic success was the second endogenous variable in the model. Partial support was also established for Hypothesis 2, which stated that dispositional factors would influence academic success. The path from agreeableness to academic success was positive and in a positive direction. However, stress, academic motivation, self-efficacy, openness, conscientiousness, extraversion, neuroticism, or social support were not significant predictors of academic success in the model. Hypothesis 3 was not supported because the path from self-esteem to academic success was nonsignificant.

In summary, academic motivation, self-efficacy, openness, agreeableness, and neuroticism predicted self-esteem. Only agreeableness predicted academic success, and self-esteem did not influence academic success in the model. As noted in Table 3 the R-square values for self-esteem and academic success were 32 percent and 10 percent, respectively.

IMPLICATIONS

University counselors and student affairs personnel may use these findings to identify the levels of self-esteem among students and formulate strategies that increase academic motivation. Moreover, the role of personality may be crucial to determining how students feel about themselves. The findings indicate that two dimensions of personality, openness and neuroticism, may contribute to the self-esteem of subjects in an academic setting. For students that are highly neurotic, improvement in self-esteem, self-efficacy and learning may result from positive feedback and constructive criticism. This could engage the student early in the advising session in a manner that is less likely to shut them down from actually hearing and acknowledging any behavioral missteps that decreases their opportunities for higher academic success and performance. Students who may be low on neuroticism may be more open and agreeable which would suggest a different approach to advising for student engagement such that rapport is established quicker and therefore the time is more efficiently utilized.

CONTRIBUTIONS

The findings contribute to the existing body of knowledge because this study identifies some potential antecedents of both self-esteem and academic success of subjects in a university setting. Subjects who are more open to new experiences and believe they can accomplish a given task apparently feel better about themselves and have lower levels of neuroticism. Another contribution of the current research is that the sample contained a large percentage of African Americans, which adds to the richness of the extant literature. This provides academic advisors/counselors, who see a multicultural student body, with a more efficacious research driven information base to more knowledgeably and ostensibly confidently engage a diverse student body.

LIMITATIONS

As is true of most empirical research, the current research has some limitations. First, the cross-sectional design of the study does not allow for causal inferences. Another limitation of the study was that all data were collected via self-report measures, which may lead to the problem of common method bias and inflated the predictive relationships. However, as recommended by Podsakoff and Organ (1986) and detailed in the results section, the authors conducted Harmon's One Factor test, which did not indicate that common method variance was problematic in the structural equation model. Because of the modest sample size from one university these findings are tentative and replication is encouraged.

SUGGESTIONS FOR FUTURE RESEARCH

A future area of inquiry would be to examine additional antecedents of academic success, especially since only a small percentage of the variance was explained. Another interesting research avenue would be to consider the influence of credit hours taken per semester and hours worked per week among students and how these predictors may influence the exogenous variables. The information from those added variables would likely increase the impact on the general stress variable.

Additionally, the authors of this study see an increasing amount of literature which indicates that self-efficacy could be considered as one of the dependable variables and strategically increasing levels of self-efficacy may offer promising results for academic performance and retention (Brady-Amoon & Fuertes, 2011; Beale & Brown, 2010: Jaret and Reitzes, 2009; Zimmerman, Bandura, & Martinez-Pons, 1992; and Pajares, 1996). Longitudinal designs are needed in this area to examine the behavior of these constructs to determine whether they wax or wane over time and under what conditions.

CONCLUSION

The current research investigated the antecedents of self-esteem and academic success of university students. Academic motivation, self-efficacy, openness, agreeableness, and neuroticism predicted self-esteem. However, only agreeableness predicted academic success, and self-esteem did not directly influence academic success in the model. This was somewhat perplexing. When reviewing the bivariate correlational levels of significance, it can be seen that there are high levels of statistical significance between self-esteem and all of the variables in the expected directions.

That is, the authors expected negative relationships between self-esteem and stress and neuroticism and positive relationships with all of the other variables in the model. That is indeed what the results reflect. However this pattern did not bear up in the multivariate SEM which indicates some significant interference or moderation of these effects of self-esteem on academic success.

Perhaps GPA alone may not have been sufficient for measuring or serving as a single viable proxy for Academic Success for this sample or maybe included in the demographic information are the number of credit hours as well as working hours per week. This could have indeed impacted, muted or moderated the impact on academic success as currently defined. Certainly, if one has a high number of credit hours and/or high number of weekly work hours, that leaves much less time for intense study that is consistently needed to maintain a high GPA or academic success. This is likely to be a significant part of the variance.

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Chevanese Samms Brown

Ulysses J. Brown, III

Savannah State University

Ruby L. Beale

Hampton University

Julia K. Gould

Boston Scientific Corporation

Chevanese Samms Brown is an Assistant Professor of Management at Savannah State University. She received her PhD from Louisiana State University.

Ulysses J. Brown, III is an Associate Professor of Management at Savannah State University. He received his PhD from Jackson State University.

Ruby L. Beale is a Professor and Chair of Business Administration at Hampton University. She earned her PhD from the University of Michigan.

Julia K. Gould is a Finance Rotation Associate at Boston Scientific Corporation.
Table 1
Means, Standard Deviations, and Pearson Zero-Order Correlations

Variables    Mean     s.d.     1          2          3

1. SelfEs    35.42    5.44
2. GPA       2.89     0.53     .186 **
5. Stress    16.59    4.78     -.463 **   53 **
4. Amotv     151.12   24       .265 **    -.181 *
5. SelfEf    56.25    5.27     .529 **    0.12       -.169 *
6. Open      34.68    6.1      .198 **    0.095      -0.072
7. Consct    53.84    5.6      .427 **    .208**     -.413 **
S. Extrav    27.06    5.62     .221 **    -0.002     -0.07
9. Agree     55.65    5.63     .300**     0.059      -304 **
10. Neurot   20.07    5.82     -.406**    -.216 **   .393 **
11. SocSpt   49.51    9.69     .254**     0          -.271 **

Variables    4         5          6        7          8

1. SelfEs
2. GPA
5. Stress
4. Amotv
5. SelfEf    .213 **
6. Open      .204 **   .276 **
7. Consct    .350 **   .371 **    .407 **
S. Extrav    .172 *    .233 **             0.122
9. Agree     .306 **   .201 **    .2S6 **  .504 **    0.097
10. Neurot   -0.084    -.219 **   -.157 *  -.405 **   -.258 **
11. SocSpt   .16 7*    .309 **    0.111    .244 **    0.04S

Variables    9          10        11

1. SelfEs
2. GPA
5. Stress
4. Amotv
5. SelfEf
6. Open
7. Consct
S. Extrav
9. Agree
10. Neurot   -.283 **
11. SocSpt   .231 **    -.171 *

* p < .05 ** p < .01

SelfEs = Self Esteem; GPA = Cumulative Grade Point Average;
Stress = General Stress; Amotv = Academic Motivation;
SelfEf = Self Efficacy, Open = Openness; Consct =
Conscientiousness, Extrav = Extraversion; Agree = Agreeableness;
Neurot = Neuroticism; SocSpt = Social Support.

Table 2
Fit Indices for the Hypothesized Model *

Model      [chi square]   p-value   [chi square]   RMSEA
           (df)                     /df

Baseline   11.17(5)       0.473     2.235          .038

Model      GFI    NNFI   NFI      CFI

Baseline   .968   .974   .977     .982

* Statistics are based on a sample of 216 respondents.

RMSEA = Root Mean Square Error of Approximation

GFI = Goodness of Fit Index

NNFI = Non-Normed Fit Index

NFI = Normed Fit index

CFI = Comparative Fit Index

df = Degrees of Freedom

Table 3
Standardized Path Coefficients for the Model *

Parameter              Path Coefficient   T-value   [R.sup.2]

Self Esteem                                         32%

General Stress         -.03               -1.61
Academic Motivation    0.22               2.82 *
Self Efficacy          0.39               4.97 *
Openness               0.20               2.79 *
Conscientiousness      0.04               0.55
Extraversion           0.10               1.26
Agreeableness          0.22               3.72 *
Neuroticism            -0.13              -2.17 *
Social Support         0.07               1.01

Academic Success                                    10%

General Stress         -.01               -0.20
Academic Motivation    0.02               1.37
Self Efficacy          0.01               0.62
Openness               0.01               0.81
Conscientiousness      0.04               0.33
Extraversion           0.10               1.26
Agreeableness          0.21               2.02 *
Neuroticism            -0.03              -0.77
Social Support         0.01               0.44
Self Esteem            0.01               0.82

* Statistics are based on a sample of 216 respondents. * p < .05
** p < .10
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Author:Brown, Chevanese Samms; Brown, Ulysses J., III; Beale, Ruby L.; Gould, Julia K.
Publication:International Journal of Education Research (IJER)
Geographic Code:1USA
Date:Mar 22, 2014
Words:4816
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