The International Journal of Management Education 19 (2021) 100430
Available online 3 November 2020
1472-8117/© 2020 Elsevier Ltd. All rights reserved.
The interplay between institutional integration and self-efficacy in the academic performance of first-year university students: A multigroup approach
Tugrul Cabir Hakyemez
a,*, Sona Mardikyan
baSakarya University, Management Information Systems Department, Sakarya, Turkey
bBogazici University, Management Information Systems Department, Istanbul, Turkey
A R T I C L E I N F O Keywords:
Institutional integration Self-efficacy Academic performance Structural equation modelling Multigroup analysis
A B S T R A C T
This study aims to reveal how the interplay between institutional integration and self-efficacy affects academic performance. We obtained the data from 520 first-year students in the busi- ness school of a public university in Turkey. The results suggest that academic self-efficacy and academic and intellectual development are positively linked to academic performance. Institu- tional commitment, however, showed no positive association with academic performance in any of the models we developed. A multigroup analysis indicated that the proposed relationships are moderated by financial aid. Theoretical and policy implications are discussed.
1. Introduction
The question of what determines academic performance is a critical one for both researchers, practitioners, and students. The answer is of vital importance, particularly in the case of first-year university students, who often find the transition from secondary school to a university environment a challenging one. The transition period is viewed as a basis for eventual academic performance (Olani, 2009) and following persistence after first year (Herzog, 2005). A recent report reveals that 26.1% of first-year students in U.S.
universities did not continue their studies the next year (NSC Research Center, 2018). Slightly lower dropout rates have been reported across Europe (e.g., 12.2% in Austria, 9.7% in the U.K. and 16% in Ireland) (European Commission, 2015). According to the Turkish Ministry of Education, 1.1 million university students dropped out between 2013 and 2019 (Süzer, 2019), indicating a yearly average dropout rate of a 13% (Gündo˘gdu, 2020). Considering these figures, higher education institutions need to recognize the importance of a positive university environment to foster retention and lower the drop-out rate (Ma et al., 2019; Stinebrickner & Stinebrickner, 2014).
In his interactionalist theory, Tinto (1975, 1988, 1993) underlines the critical role played by institutional integration in voluntary dropout and academic dismissal. He maintains that failure to meet the institution’s academic performance expectations induces ac- ademic dismissal and that voluntary dropout is much more related to a lack of perceived integration of students into their institutions.
In Tinto’s theory, integration refers to the degree to which students share the attitudes and beliefs of their peers and the faculty members and the degree to which they adhere to the rules and requirements of their institutional culture (Pascarella & Terenzini, 1991). Tinto therefore argued that integration ensures institutional and goal commitment, academic and intellectual development, and
* Corresponding author.
E-mail address: [email protected] (T. Cabir Hakyemez).
Contents lists available at ScienceDirect
The International Journal of Management Education
journal homepage: www.elsevier.com/locate/ijme
https://doi.org/10.1016/j.ijme.2020.100430
Received 4 September 2019; Received in revised form 5 July 2020; Accepted 10 October 2020
persistence. Such a direct causality, however, does not always hold true for academic performance during the transition because institutional integration is related primarily to a student’s ability to adapt. For instance, establishing close relationships with peers and participating in extra-curricular activities are essential indicators of institutional integration, yet they are irrelevant to academic performance (Liu & Liu, 2000). To help first-year students avoid academic dismissal, the role of institutional integration needs to be examined in order to gain a firm understanding of how the transition influences academic performance.
Tinto’s theory focuses on explaining voluntary dropout, but it does not delve into the factors that underlie academic dismissal, which significantly limits its applicability to academic performance. Motivational factors that affect students’ progress towards their career and educational goals need to be accounted for. To this end, global and domain-specific self-efficacy beliefs were used in the present study to explain the variability in the level of academic performance. These self-beliefs in one’s own capabilities (Bandura, 1977) shape an individual’s choice of activities, influence the degree of persistence in pursuing their goals, and potentially have a tremendous impact on eventual academic performance. We complement Tinto’s interactionalist theory by adding self-efficacy to explain the unexplored areas in academic dismissals. We analyze the relationship between institutional integration and self-efficacy and their combined effect on academic performance as measured by GPA. We selected second-term GPA as an indicator of aca- demic performance so as to capture the institutional effects on both academic self-efficacy and academic performance.
Whether the proposed predictors of academic performance are valid for the various observed student groups complicates the academic performance phenomenon. A firm understanding of the differential impacts of diverse student characteristics on academic performance is crucial. This understanding should also be internalized by higher education managers so that they are able to establish an academic and social environment that accommodates the needs of diverse student groups. This can be achieved to a great extent through group-specific policies and practices that are informed by insights gained from monitoring the academic performance of these groups. We therefore took student diversity into account in developing our model. First, we tested the homogeneity of the sample based on personal factors (age, gender, financial aid, hometown size) and department size. We then identified specific characteristics of the observed groups and determined the degree to which the effects of the motivational and environmental factors on academic perfor- mance differ based on certain group characteristics, which provided useful insights about the group-varying dynamics of academic performance.
This paper contributes to the literature in two main areas:
•To the best of our knowledge, this is the first study that complements Tinto’s interactionalist theory by using both general self- efficacy and academic self-efficacy to explain academic performance.
•This is the first multigroup study to use such a wide range of student characteristics and department size to ensure the validity of the models across different student groups and to use several evaluation metrics.
2. Literature review
The primary focus of this study is how student–institution interactions affect academic performance. The theoretical boundaries for the literature review are the perceived degree of student integration into the institution and how students’ self-efficacy influences this perception and their academic performance. The literature review will lay the foundation for these hypothesized relationships, focusing first on studies that examine the role of institutions in creating academic and social integration, and academic performance, and then on studies that confirm either direct or indirect effects (mediated by institutional integration) of general and academic self- efficacy on academic performance. This is followed by studies that provide empirical support for the moderating role of certain characteristics in the relationships established in the conceptual model.
2.1. The institution’s role in academic performance
Tinto (1975, 1988, 1993) stated that each first-year university student completes their institutional integration in three stages:
separation, transition and incorporation, referring to the “rites of passage” in Van Gennep’s (1960) seminal anthropological work.
Interaction with the environment is crucial if students are to develop a sense of institutional integration during their transition to university life. Tinto stated that integration occurs in academic and social domains. Social integration is defined as the perceived congruency between an individual and their environment, and it generally encapsulates experiences with peer students. Academic integration, on the other hand, involves experiences with faculty and staff (Nora & Cabrera, 1996) and is best reflected by grades and academic and intellectual development (Tinto, 1975).
Students who receive guidance and support from faculty and staff (academic integration) may exhibit more controlled and robust progress in their academic work. This substantial influence of academic integration on academic and intellectual development is also evidenced in previous research (e.g., Sax et al., 2005; Strayhorn, 2008; Tarazona & Rosenbusch, 2019; Volkwein et al., 1986). We therefore assert a positive relationship between those factors. Academic and intellectual development require stimulation and continuous extra effort in coursework (Spady, 1971) and is thus viewed as an important pillar of academic performance. Empirical support for the positive relationship between academic and intellectual development and academic performance also exists (e.g., Nora
& Cabrera, 1996; Williams et al., 2017, pp. 1–15; Wentzel et al., 2018). We thus hypothesize:
H1. Academic integration positively affects academic and intellectual development.
H2. Academic and intellectual development positively affect academic performance.
Astin (1984) suggested that the degree of students’ institutional commitment might be the result of the type of integra- tion—academic or social—on which students spend most of their physical and psychological energy. Those who immerse themselves in extracurricular activities and social relationships (social integration) inevitably do so at the expense of their course-related tasks and educational goals (i.e., their academic integration), likely resulting in academic failure. Students need to maintain a balance between these integration domains to attain a high level of academic performance, as shown by Strauss and Volkwein (2004) and Tarazona and Rosenbusch (2019), who report that both academic and social integration have a significant impact on students’ institutional commitment. This suggests that social integration primarily supports institutional commitment in that it helps students develop a sense of belonging in a social environment. Therefore, we posit that both social and academic integration positively affect institutional commitment.
H3. Academic integration positively affects institutional commitment.
H4. Social integration positively affects institutional commitment.
Institutional commitment and academic performance are usually thought to coexist along the same continuum, i.e., there need not be a temporal sequence between the measuring times of these concepts. However, Bean and Bradley (1986) investigated the reciprocal relationship between satisfaction (corresponding to institutional commitment in Tinto’s interactional model) and GPA (used as an indicator of academic performance). They reported that satisfaction has a greater impact on GPA than GPA has on satisfaction, a finding which provides insights into how to establish a temporal sequence between these elements. From this point, we can hy- pothesize that institutional commitment may be positively linked to academic performance, as evidenced in many studies (e.g., Strahan, 2003; Wilkins et al., 2016; Woosley & Miller, 2009).
H5. Institutional commitment positively affects academic performance.
2.2. The role of self-efficacy in academic performance
Tinto’s interactionist dropout theory provides institutions with invaluable insights about actions they can take to reduce the number of dropouts, but an excessive focus on student–environment interactions fails to capture other crucial aspects of factors that can lead to dropping out such as economic, social, technological and global forces that impede institutional integration (Melguizo, 2011). Tinto (2017) himself declared self-efficacy as “the foundation upon which student success is built” (p. 3). Motivated by this premise, we decided to supplement Tinto’s integration model with self-efficacy, since research has repeatedly shown that it has a significant impact on academic performance.
Self-efficacy, a central component of social cognitive theory (Bandura, 1977) which views humans as proactive and self-regulating agents, refers to one’s beliefs in their own capabilities to achieve a specified task in a general context; it is characterized by persistence, goal commitment and self-regulating abilities. These “self-referent” beliefs mainly influence aspirations, the strength of goal com- mitments, and perseverance in unfavorable situations (Bandura et al., 1996). Building upon this social-cognitive framework, Lent et al.
(1994) developed their social cognitive career theory (SCCT), which describes how academic and career-related behaviors are shaped by interactions between individuals and their environments, emphasizing the critical role of self-efficacy. Similarly, receiving edu- cation in a selected environment as part of their career decision undoubtedly facilitates institutional integration (Peterson, 1993).
From another perspective, general self-efficacy substantially influences the amount of effort students expend social and academic integration to reach their career goals (Lin, 2002).
Self-efficacy beliefs are heavily reliant upon the domains of functioning; they do not demonstrate a static disposition in every context (Bandura, 1997). As an example of these context-specific self-efficacy beliefs, academic self-efficacy refers to an individual’s confidence in their ability to successfully perform academic tasks at designated levels in the academic domain (Schunk, 1991). To examine the differential effects of general and academic self-efficacy beliefs on academic performance, Pajares (1996) compared the explanatory power of these beliefs and found a more pronounced effect for academic self-efficacy. Yet we cannot ignore the funda- mental role of general self-efficacy, as it serves as a basis for the further evolution of academic self-efficacy during the first year.
Bres´o et al. (2011) reported that self-efficacy is positively correlated with first-semester GPA, but this positive relationship does not exist with second-term GPA. Similarly, Feldman and Kubota (2015) noted that general self-efficacy is not a significant predictor of overall first-year academic performance. This vanishing effect may be explained by the fact that students’ pre-university character- istics lose their impact on academic performance during their first year (Kuh et al., 2008). We can thus argue that general self-efficacy facilitates academic and social integration by promoting prosocial behavior during the transition period (Bandura et al., 1996). It is important to note that, once a student has integrated, self-efficacy can be replaced by other course-related factors (e.g., academic self-efficacy) that affect academic performance. Academic self-efficacy can be improved through positive academic experiences (Tinto, 2017) and is mainly promoted by faculty members who provide support, advice and feedback (faculty involvement). Students with higher academic self-efficacy are likely to know when and how to ask for assistance from faculty in the face of difficulties, which is central to academic integration. We thus assert that a high degree of academic integration helps these students regulate their study strategies and habits in the pursuit of their academic goals. Supportive evidence is provided by Weng et al. (2010a), who found a positive relationship between academic self-efficacy and academic integration for Taiwanese students. Thus, we hypothesize:
H6. General self-efficacy positively affects academic self-efficacy.
H7. General self-efficacy positively affects (a) social integration (b) academic integration
H8. Academic integration positively affects academic self-efficacy.
A clear academic growth pattern has been observed in students with high academic self-efficacy: they set higher academic goals (Jayanthi et al., 2014), spend more time on studying (Torres & Solberg, 2001), and enjoy more of the learning process than other students. More specifically, these students view academic challenges as opportunities to improve their academic skills, which fuels their motivation and concentration as they strive to maximize their intellectual growth (Zander et al., 2018).
Academic self-efficacy helps students persevere in challenging course-related tasks by keeping them motivated towards academic goals through self-regulating academic behaviors. This relationship has been examined extensively (e.g., Zimmerman et al., 1992;
Chemers et al., 2001; Bandura et al., 1996; Gore, 2006; Feldman & Kubota, 2015; Grigg et al., 2018). In a systematic review of 59 papers published between 2003 and 2015, Honicke and Broadbent (2016) identified an established positive relationship between academic self-efficacy and academic performance. Similarly, Richardson et al. (2012) noted a medium-sized correlation in their meta-analysis, where they extracted 50 significantly influential variables from 242 papers published between 1997 and 2010. Another meta-analytic path analysis also revealed a significant impact of academic self-efficacy on academic performance (Brown et al., 2008).
Drawing upon these findings, we hypothesize the following:
H9. Academic self-efficacy positively affects academic and intellectual development.
H10. Academic self-efficacy positively affects academic performance.
Fig. 1 presents the conceptual model for this study.
2.3. Moderating effects of student characteristics on student outcomes
The degree of variation in the hypothesized relationships across diverse student groups must be identified to ensure the validity of the conceptual model of the present study. To this end, it examines the moderating effects of a wide range of student and department characteristics: gender, age, financial aid, hometown size, department size.
Gender is reported as one of the most distinguishing characteristics amongst university students. Ahmed et al. (2019) conducted an in-depth investigation of the differential influences of gender on the students’ academic and social behaviors. They reported that males scored significantly higher on the well-being and self-control dimensions of emotional intelligence than females, who scored higher on emotionality. Using correlational and group comparison methods, they also found that well-being and self-control were positively correlated with academic performance, whereas emotionality shows a negative correlation. Pomerantz et al. (2002), on the other
Fig. 1.The research model.
hand, found that female students outperform male students in academic tasks, but they are much more vulnerable to internal distress.
A similar finding was reported by Pajares (2002), who suggested that female students are much more modest in terms of their self-efficacy beliefs than their male peers, but they attain a higher level of academic performance through their superior self-regulated learning abilities. Dayio˘glu and Türüt-As¸ik (2007) reported similar findings for Turkish students after controlling for the field of study and individual attributes. A differential impact of gender on institutional integration status is also reported (e.g., Baker et al., 2007).
We, therefore, argue that the hypothesized relationships in our research model may be moderated by gender.
University students of different age groups may exhibit different attitudes and behaviors, as they often have different lifestyles.
Tinto (1987) indicated that older students tend to have more family and work responsibilities that hinder their involvement with university life—a difference that often manifests itself in their level of academic performance. In a study that used a multigroup analysis, McKenzie and Gow (2004) concluded that the rank of importance for influential factors on academic performance is considerably different for mature and young students. Another study found that mature students have a significantly lower level of course-specific self-efficacy beliefs than their younger peers (Jameson & Fusco, 2014). In short, age may also be a moderating factor in the relationships between self-efficacy, institutional integration and academic performance.
The contribution of financial aid (in the form of a grant or a loan) to academic performance is another area that has received a great deal of academic and professional attention (Jones-White et al., 2014). Financial aid, a common source of stress that has a negative effect on attitudes and behaviors, may amplify the anxiety levels of university students, which may impair academic performance.
Boatman and Long (2016) reported a positive relationship between receiving a grant and academic performance. Flynn (2014) also uncovered a positive influence of financial aid on both academic attainment and retention. We thus assert a moderating role for financial aid in academic performance.
Hometown size denotes the population of a city where a student was raised and can be viewed as an important factor that has a differential impact on academic performance. It is an indicator of social advantage in that students from metropolitan cities are more likely than their peers from rural towns to develop strong skills to access and benefit from educational resources and facilities. This skillset may serve as an initial orientation for students from metropolitan areas about how to reach out to academic support pro- fessionals. Bean (1980) and Childs et al. (2017) reported significant effects of hometown size on academic performance, as it yields invaluable background information about past academic experiences. In sum, hometown size may have a moderating effect on the hypothesized relationships in the model.
Lastly, department size, indicated by student-faculty ratio, articulates the level of facilities and opportunities that a department can provide to their students. In departments with a low student-faculty ratio, the probability of face-to-face contact with faculty members is significantly reduced, which therefore contributes to social integration by virtue of the fact that students rely more on interactions with peers. The same ratio, however, may harm academic integration, academic and intellectual development and academic per- formance. The empirical evidence for the positive association between faculty contact and various student outcomes (satisfaction with education, the perceived impact of college on skill development, academic achievement etc.) exist in previous research (e.g., Umbach
& Porter, 2002; Kim, 2017). We therefore hypothesize that department size has a differential impact on the academic and social
experiences of first-year students.
3. Research methodology 3.1. Data and procedure
Data were collected through a survey distributed to first-year students in all departments of the business faculty of a public uni- versity in Turkey during the 5th week of the spring semester of the 2014–2015 academic year. Participation was voluntary, and the surveys were completed in class. A total of 520 students responded, 77 of which were excluded because they did not satisfy the basic criteria (e.g., they were not first-year students or they failed to answer all the questions), yielding a sample size of 443. A student ID was required of each participant; we guaranteed and strictly guarded the confidentiality of their personal information. The participants’
second-term GPAs were provided by the registrar’s office.
3.2. Measures
We used a French and Oakes (2004) revised version of Pascarella and Terenzini’s (1980) Institutional Integration scale to capture the perceived integration of students in five areas: peer-group interactions (PGI), interactions with faculty (IWF), faculty concern for student development and teaching (FCSDT), academic and intellectual development (AID), and institutional and goal commitment (IGC). Academic integration was measured by both IWF and FCSDT, and social integration was captured by PGI. The IGC and AID subscales were used to measure institutional commitment and academic and intellectual development, respectively. These scales have been used by various researchers to empirically validate Tinto’s interactional theory of dropout (e.g., Bers & Smith, 1991; Chrysikos et al., 2017; Pascarella & Chapman, 1983; Pascarella & Terenzini, 1983; Weng et al., 2010b). For general and domain-specific self-efficacy beliefs, we utilized two separate scales: the eight-item General Self-Efficacy Scale (GSES) developed by Chen et al.
(2001) and Yılmaz et al.’s (2007) seven-item Academic Self-Efficacy Scale (ASES), an adaptation of Jerusalem and Schwarzer (1981).
All items were answered on a 5-point Likert scale.
Student characteristics were also collected through the survey. Age was obtained by the question What is your age? The answer was then discretized into two suggested categories: traditional and non-traditional students. A traditional student is described as having started university between the ages of 18 and 21 years (Peltier et al., 2000). For gender, female was coded as 1 and male as 2. Financial
aid status was measured by the question Do you receive any form of financial aid? The coding was 0 (none), 1 (grant), 2 (loan), or 3 (both loan and grant.) We collapsed these categories into a binary variable − 1 (yes) and 0 (no)—because the perceived level of debt was unlikely to directly influence academic performance (Ross et al., 2006), especially in the first year of university, where debt is at a minimum. Hometown size was measured by a binary variable. A metropolis with a population of 1,000,000 or more was coded 1; all others were coded 0. Departments were initially coded as follows: 1- business administration, 2- human resources management, 3- health management, 4- tourism and hotel management 5- international trade, 6- management information systems. Department size was also measured by a binary variable. Large departments, where faculty-to-student ratio (FSR) was below 0.1, were coded as 1, and the smaller departments were coded as 2. The large departments were business administration, human resources management and tourism and hotel management, each with nearly 2000 students. The small departments were management information systems, health management, and international trade, each with approximately 50 students.1
3.3. Analysis
The intertwined relationships between self-efficacy, institutional integration, and academic performance for the various groups were examined using the statistical packages SPSS 20 and SPSS AMOS 21. First, we conducted explanatory and confirmatory factor analyses to estimate the main measurement model before testing the final structural model, as suggested by (J¨oreskog, 1993). We then tested the structural model to reveal the main effects. The final step was to conduct a multigroup analysis for the various observed groups to test whether the suggested relationships were invariant across groups in terms of selected student and departmental characteristics.
Prior to conducting a multigroup analysis, measurement invariance must be tested and established, since it is necessary to ensure that same attributes are related to the same set of observations in the same way in each group (Borsboom, 2006), allowing for meaningful comparisons. Vandenberg and Lance (2000), in an exhaustive literature review, identified a number of hierarchical measurement invariance tests that are often conducted in an increasingly restrictive manner. Teo (2014) suggested that configural, metric and scalar invariance tests are sufficient to establish measurement invariance, since further measurement invariance tests are excessively rigorous (Byrne, 2010). We therefore selected these three metrics for the present study. Configural invariance was tested to check that the basic model structure (i.e., the pattern of fixed and non-fixed parameters) was invariant across groups (in line with Hong et al., 2003). It acts as a base model for comparison with all subsequent models, and if it is not satisfied, the following metric and scalar invariance tests will not be supported (Bollen, 1989). Metric invariance is concerned with whether individuals in different groups respond to items in the same way; it is tested by constraining all the factor loadings to be equal across groups (Steenkamp &
Baumgartner, 1998). Scalar invariance requires that the observed differences in the means of items be induced by differences of means in underlying constructs (Meredith, 1993). To test scalar invariance, the intercepts of the indicators are constrained to be equal across groups (Teo & Noyes, 2010).
Our methodology combined the procedures of Teo (2014) for testing measurement invariance and those of Byrne (1998) and McKenzie and Gow (2004) for conducting multigroup analysis on structural paths:
1. To test the measurement invariance:
a. Create base models to test configural invariance for each selected characteristic. If any of the base models fails, drop that characteristic from the analysis.
b. Conduct chi-square (Δχ2) difference tests between the base model and the full metric invariance model. If the difference is not significant (p-value >.05), the latter is considered to fully satisfy metric invariance across groups. Otherwise, identify and remove non-invariant items and test partial metric invariance across groups.
c. Conduct Δχ2 tests between the metric invariance models and scalar invariance models. Again, if the difference is not significant (p-value > .05) between these constrained models, the latter is assumed to fully satisfy scalar invariance across groups.
Otherwise, identify and remove non-invariant items and test partial scalar invariance across groups.
2. Conduct a Δχ2 difference test to determine whether imposing equal paths aggravates the performance of the structural model or not. If there is no significant difference between the unconstrained (parameters are free to vary across groups) and the fully constrained model (all parameters are constrained to be invariant across groups), accept the latter as the model, as it is more parsimonious.
3. If there is a significant difference between these models, iteratively free all the parameters to detect which parameters cause the major differences between groups. Evaluate the calculated Δχ2 after each iteration. After freeing all the paths which caused sig- nificant improvement on the fully constrained model, test the overall Δχ2 between the unconstrained and the resulting partially constrained models. If the difference is still significant, the unconstrained model is the most appropriate. Otherwise, the partially constrained model is selected as the ultimate structural model.
After conducting the procedure for each group, we interpreted the path estimates of the selected models.
1 The student-faculty ratio was calculated based on the numbers on the official website of Sakarya University in the second semester of the 2014–2015 academic year (Sakarya University, 2014).
4. Results
4.1. Descriptive results
The descriptive statistics for student characteristics and academic performance are summarized in Table 1. In general, the groups exhibited a balanced distribution, except for department size, where the number of large-department students is almost four times greater than the number of small-department students. For academic performance, the average GPA for the current sample remained under 2.0, the average (mean =1.894, SD =0.768).
4.2. Exploratory factor analysis (EFA)
The Institutional Integration Scale (IIS) of French and Oakes (2004)(see Appendix A) comprises 34 items on five subscales:
Peer-Group Interactions (PGI), Interactions with Faculty (IWF), Faculty Concern for Student Development and Teaching (FCSDT), Academic and Intellectual Development (AID), and Institutional and Goal Commitments (IGC). A preliminary exploratory factor analysis minimizes the possible differential effects of the institution (Titus, 2004) or country (Booth & Gerard, 2011) in the model. A principal component analysis (PCA) with rotation varimax was used to discover the underlying dimensions of institutional integration.
We first removed five items (IIS4, IIS5, IIS14, IIS19, IIS31) because they had loadings below the specified threshold of 0.45, as sug- gested by Tabachnick and Fidell (2007). Noting that McIver and Carmines (1981) suggest that a construct should consist of at least three items, we removed the single-itemed (IIS25) and the two-itemed (IIS15, IIS29) slack factors that were unlikely to fully represent a construct. The remaining items converged under four factors. In our sample, we found several differences in the dimensions of institutional integration. One was that most of the items under FCSDT and IWF subscales loaded onto the same factor. Our inter- pretation of this situation was that Turkish university students view any interaction with faculty as a single experience. We therefore merged the FCSDT and IWF subscales with the Academic Integration subscale. Another difference was that the items of the IGC subscale were found to be related only to institutional commitment. Unlike in the original scale, IIS2 (I am satisfied with my academic experience at this university) loaded onto the IGC subscale instead of AID. This is understandable, however, given that this item asks about an experience associated with the specific institution. A third difference was that IIS8 (Getting good grades is important to me) loaded onto the AID subscale rather than onto IGC.
The calculated KMO value (a measure of the suitability of a dataset for factor analysis) of the revised IIS is “marvellous” (Kaiser, 1974) with a value of 0.930. All the remaining loadings in the revised scales exceeded the threshold. We then conducted a reliability analysis for each subscale. Cronbach’s α values for the revised subscales are presented in Table 2. They all exceed the minimum acceptable level of 0.7 established by Nunnally (1978).
We used the same procedure for General Self-Efficacy Scale (GSES) and the Academic Self-Efficacy Scale (ASES). The respective KMO values for GSES and ASES (0.926 and 0.837, respectively) were satisfactory (Kaiser, 1974). All the item loadings to the related factors were above 0.45. Cronbach’s α values for these scales were also at an acceptable level (GSES α =0.903, ASES α =0.816). The results of the EFA are displayed in Table 2.
4.3. Confirmatory factor analysis
A confirmatory factor analysis (CFA) is a prerequisite for structural equation modelling (SEM) and is particularly concerned with validating measurement models. Standardized loading estimates of items to constructs were used to validate the measurement model.
The acceptable range for these values is between 0.5 and 1 (Hair et al., 2009). We set 0.6 as the threshold for including an item in the Table 1
Summary statistics.
Group
Characteristic N Percentage (%) Mean SD
Gender
Female 267 62,3
Male 176 37,7
Age Traditional 252 56,8
Non-traditional 191 43,2
Department
Large 365 82,3
Small 78 17,7
Hometown Size
Metropolis 158 35,6
Non-metropolis 285 64,4
Financial Aid
Yes 266 60
None 177 40
Second Term GPA 1.894 0.768
measurement model. Four items (IIS8, IIS12, IIS23, IIS24) were below the threshold. The initial measurement model was therefore not verified by the confirmatory factor analysis (CFA), so we removed all the failing items and reran the CFA. The results of the revised model are displayed in Table 3.
All items loaded to the constructs adequately in the revised measurement model. The goodness of fit of the measurement model was evaluated by the following measures: the Normed χ2 (calculated χ2/degree of freedom), the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the parsimonious comparative fit index (PCFI). The calculated values for these indices are shown in Table 4.
The validity of the constructs in the measurement model were evaluated using the indicators of convergent, discriminant and face validity. Convergent validity, which is concerned with the extent to which items represent the related construct, was tested by factor loadings, average variance extracted (AVE), reliability, and composite reliability (CR). The results are given in Table 5. All the con- structs in the measurement model satisfied the minimum reliability (α ≥0.7). For the AVE of the constructs, general self-efficacy, social integration, and academic integration exceeded the minimum acceptable threshold of 0.5, as proposed by Hair et al. (2009), but the AVE values of academic self-efficacy, institutional and goal commitment, and academic and intellectual development remained below the suggested level. However, the convergent validity for these constructs could still be asserted, as they had AVE values below 0.5 and their composite reliabilities were above 0.6 (Fornell & Larcker, 1981). Discriminant validity can be maintained only when “items correlate higher within a construct than they correlate with other items from other constructs that are theoretically not correlated”
Table 2
Explanatory factor analysis results.
Measures Factor Loadings
Institutional Integration Scale (IIS) Academic Integration (α =0.917)
IIS18 0.780
IIS16 0.752
IIS27 0.750
IIS13 0.728
IIS22 0.721
IIS28 0.706
IIS32 0.703
IIS10 0.709
IIS30 0.679
IIS24 0.658
Social Integration (α =0.842)
IIS20 0.711
IIS26 0.806
IIS17 0.751
IIS1 0.660
IIS34 0.618
IIS23 0.507
Institutional Commitment (α =0.826)
IIS21 0.733
IIS11 0.698
IIS33 0.641
IIS3 0.728
IIS2 0.608
Academic and Intellectual Development (α =0.751)
IIS6 0.707
IIS9 0.720
IIS8 0.681
IIS7 0.535
IIS12 0.546
General Self Efficacy Scale(GSES) (α ¼0.903)
SE1 0.829
SE2 0.811
SE3 0.783
SE4 0.782
SE5 0.761
SE6 0.747
SE7 0.745
SE8 0.727
Academic Self Efficacy Scale(ASES) (α ¼0.816)
AE1 0.789
AE2 0.772
AE3 0.708
AE4 0.700
AE5 0.656
AE6 0.639
AE7 0.581
(Zait¸ & Bertea, 2011, p. 216). We selected AVE analysis for evaluating the discriminant validity of the constructs. The AVE estimates for two constructs should exceed the squared correlation between these constructs in this analysis (Hair et al., 2009). The results in Table 6 indicated that discriminant validity had been established for all the constructs, with the exception of general self-efficacy and academic self-efficacy. This situation has several possible explanations. One is that these constructs share a common “self-referent” foundation, as suggested by Bandura (1977). Another possibility is that items under domain-specific self-efficacy scales represent a redundant measure of the general domain (Pajares, 1996). It may also be the case that the difference is not obvious from the current sample. Face validity refers to the degree to which a scale measures what it is meant to measure (Mosier, 1947). All the scales employed in this study were retrieved by an extensive literature review; thus, we can maintain face validity for these constructs.
Table 3
Confirmatory factor analysis results of the revised model.
Construct Standardized Loading Estimates Squared Standardized Loading Estimates Significant at α =0.001 General Self Efficacy
SE1 0.707 0.499 0.000***
SE2 0.702 0.492 0.000***
SE3 0.753 0.567 0.000***
SE4 0.803 0.644 0.000***
SE5 0.778 0.605 0.000***
SE6 0.721 0.519 0.000***
SE7 0.744 0.553 0.000***
SE8 0.676 0.456 0.000***
Academic Self Efficacy
AE1 0.684 0.467 0.000***
AE2 0.641 0.410 0.000***
AE3 0.608 0.369 0.000***
AE4 0.695 0.483 0.000***
AE7 0.714 0.509 0.000***
Social Integration
II17 0.711 0.505 0.000***
II26 0.804 0.646 0.000***
II1 0.660 0.435 0.000***
II34 0.683 0.466 0.000***
II20 0.699 0.488 0.000***
Academic Integration
II32 0.719 0.516 0.000***
II30 0.712 0.506 0.000***
II13 0.748 0.559 0.000***
II22 0.787 0.619 0.000***
II28 0.746 0.556 0.000***
II27 0.761 0.579 0.000***
II16 0.801 0.641 0.000***
II18 0.799 0.638 0.000***
II10 0.679 0.461 0.000***
Institutional Commitment
II11 0.761 0.579 0.000***
II33 0.744 0.553 0.000***
II3 0.629 0.395 0.000***
II21 0.756 0.571 0.000***
II2 0.612 0.374 0.000***
Academic and Intellectual Development
II6 0.685 0.469 0.000***
II7 0.618 0.381 0.000***
II9 0.720 0.518 0.000***
Note: ** significant at α =0.05 level, *** significant at α =0.01 level.
Table 4
Goodness of Fit statistics of the revised measurement model.
Goodness of Fit Indices Calculated Value Acceptable Interval Reference
χ2 value 1293.57
Degree of Freedom (d.f.) 574
Absolute Fit Indices
Normed χ2 2.25 χ2/d.f. ≤3 Ros et al. (2015)
RMSEA 0.053 RMSEA≤0.08 Hair et al. (2009)
Incremental Fit Indices
Comparative Fit Index (CFI) 0.91 0,9 ≤CFI≤1 Hair et al. (2009)
Parsimony Fit Indices
Parsimony Comparative Fit Index (PCFI) 0.83 The higher the better between [0,1] Hooper et al. (2008)
4.4. Main structural model
We selected Normed χ2, RMSEA (absolute fit indices) and CFI (comparative fit indices) to evaluate the goodness of fit of the model.
Table 7 shows that the model achieved a satisfactory fit.
The structural model revealed that all the proposed relationships were supported, with the exception of the one between insti- tutional commitment and academic performance. The standard path coefficients (SPC) between the constructs are displayed in Table 8.
We found that institutional factors were strong predictors of academic performance. Surprisingly, the institutional commitment did not have a significant impact (SPC = − 0.06, p-value >.05) on academic performance (H5). The relationship between academic and intellectual development and academic performance (H2) was positive (SPC =0.195, p-value <.01). The effect of academic self- efficacy also positively affected academic performance (SPC =.158, p-value <.05) (H10).
The effects of academic self-efficacy (SPC =0.424, p-value <.01) and academic integration (SPC =0.421, p-value <.01) on academic and intellectual development were nearly equal (H1 and H9).
Institutional commitment was influenced mainly by social integration (H3) (SPC =0.482, p-value <.01). Academic integration was also found to have a positive impact on institutional commitment (H4) (SPC =0.409, p-value <.01). Academic integration had a slightly positive impact on academic self-efficacy (H8) (SPC = 0.092, p-value <.05). Finally, general self-efficacy significantly influenced academic self-efficacy, social integration and academic integration with SPCs of 0.771 (p-value <.01), 0.473 (p-value <
.01) and 0.455 (p-value <.01), respectively. These results indicate that H6, H7(a) and H7(b) were supported by the structural model.
Table 5
Convergent validity results.
Construct Reliability (Cronbach’s α) Average Variance Extracted (AVE) Composite Reliability
General Self Efficacy 0.903 0.541 0.904
Academic Self Efficacy 0.816 0.447 0.802
Social Integration 0.835 0.508 0.837
Academic Integration 0.911 0.563 0.921
Institutional Commitment 0.826 0.494 0.829
Academic and Intellectual Development 0.708 0.456 0.715
Table 6
Discriminant validity results.
Construct1 Construct2 Correlation Squared Correlation AVE of Construct1 AVE of Construct2
SE ASE 0.802 0.643 0.542 0.447
SE SI 0.443 0.196 0.542 0.508
SE AI 0.435 0.189 0.542 0.563
SE IGC 0.415 0.172 0.542 0.494
SE AID 0.586 0.343 0.542 0.455
ASE SI 0.443 0.196 0.447 0.508
ASE FI 0.441 0.194 0.447 0.563
ASE IC 0.425 0.180 0.447 0.494
ASE AID 0.610 0.372 0.447 0.455
SI AI 0.517 0.267 0.508 0.563
SI IGC 0.652 0.425 0.508 0.494
SI AID 0.563 0.316 0.508 0.455
AI IGC 0.593 0.351 0.563 0.494
AI AID 0.636 0.404 0.563 0.455
IC AID 0.654 0.427 0.494 0.455
Notes: SE =Self-efficacy, ASE ¼Academic Self-efficacy, SI =Social Integration, AI ¼Academic Integration, AID ¼Academic and Intellectual Development, IC =Institutional Commitment.
Table 7
Goodness of fit statistics of the structural model.
Goodness of Fit Indices Calculated Value Acceptable Interval Reference
χ2 value 1392.89
Degree of Freedom (d.f.) 584
Absolute Fit Indices
Normed χ2 2.38 χ2/d.f. ≤3 Ros et al. (2015)
RMSEA 0.056 RMSEA≤.08 Hair et al. (2009)
Comparative Fit Indices
CFI 0.90 0.9 ≤CFI≤1 Hair et al. (2009)
TheInternationalJournalofManagementEducation19(2021)100430
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Table 8
Goodness of Fit Indices for Measurement Invariance models.
Configural Invariance Full Metric Invariance Full Scalar Invariance
χ2 χ2/d.f. CFI PCFI RMSEA χ2 χ2/d.f. CFI PCFI RMSEA χ2 χ2/d.f. CFI PCFI RMSEA
Age 2070.61 1.80 0.89 0.81 0.043 – – – – – – – – – –
Gender 2091.80 1.82 0.89 0.81 0.043 – – – – – – – – – –
HS 2089.91 1.82 0.89 0.81 0.043 – – – – – – – – – –
FA 2087.94 1.82 0.89 0.81 0.043 2137.48 1.81 0.88 0.83 0.043 2166.72 1.77 0.89 0.86 0.042
DS 2137.12 1.86 0.88 0.80 0.044 2179.65 1.84 0.88 0.83 0.044 2225.50 1.83 0.88 0.85 0.043
Note: HS=Hometown Size, FA=Financial Aid, DS =Departmental Size Note2: The full invariance models are not calculated for the observed groups where full metric and scalar invariance are not supported.
Cabir Hakyemez and S. Mardikyan
4.5. Multigroup analysis 4.5.1. Measurement invariance
The results of the measurement invariance tests for each observed characteristic are reported below.
4.5.1.1. Age. Constraining factor loadings to be equal across age groups significantly aggravated the model fit of the base model (Δχ2
=51.31, Δd.f. =35, p-value <.05); full metric invariance was therefore not supported. We then identified three items that were non- invariant across age groups: SE3 (In general, I think that I can obtain outcomes that are important to me), IIS20 (I have developed close personal relationships with other students), and IIS30 (Many faculty members I have had contact with are genuinely interested in teaching), so we excluded these items from the analysis. The resulting model produced an insignificant drop in the goodness of fit (Δχ2 =27.18, Δd.
f. =32, p-value >.05), meaning that metric invariance was partially supported. Next we tested our scalar invariance model, which we based on the previous partially metric invariance model, and found an insignificant decrease in the model fit (Δχ2 =27.42, Δd.f. =32, p-value >.05). This indicates that scalar invariance was fully supported, based on the partially metric model.
4.5.1.2. Gender. Constraining factor loadings to be equal across gender groups did not significantly reduce the fit of the base model (Δχ2 =31.62, Δd.f. =35, p-value >.05); therefore, full metric invariance was supported. We then tested the full scalar invariance model, which was not confirmed by the initial results (Δχ2 =51.5, Δd.f. =35, p-value <.05). The revision of items revealed that five items that were non-invariant between genders: SE3 (I can obtain outcomes that are important to me), SE8 (Even when things are tough, I can perform quite well), AE3(I know what to do to get good grades very well), AE7 (I generally know how to handle the issues that I am supposed to learn while preparing the exam), and IIS30 (Many faculty members I have had contact with are genuinely interested in teaching). We excluded these items from the model and eventually obtained an insignificant drop in the model fit (Δχ2 =26.36, Δd.f. =29, p-value >
.05), indicating that scalar invariance was partially supported for gender groups.
4.5.1.3. Financial Aid.Constraining factor loadings to be equal across financial aid groups did not significantly drop the model fit of the base model (Δχ2 =49.54, Δd.f. =35, p-value >.05). Full metric invariance was thus supported. Next, based on the previous full metric invariance model, we tested full scalar invariance by further constraining intercepts of the indicators to be equal across groups and found an insignificant decrease in the model fit (Δχ2 =29.24, Δd.f. =35, p-value >.05). This indicates that full scalar invariance was also supported.
4.5.1.4. Hometown Size.Constraining factor loadings to be equal hometown size groups significantly aggravated the model fit of the base model (Δχ2 =80.52, Δd.f. =35, p-value <.01); full metric invariance was thus not supported. We identified nine items that were non-invariant across age groups and excluded these items from the analysis: SE2 (When facing difficult tasks, I am certain that I will accomplish them), SE4 (I believe I can succeed at most any endeavor I set my mind to), SE8 (Even when things are tough, I can perform quite well), IIS22 (I am satisfied with my opportunities to meet and interact informally with faculty members), IIS10 (My interpersonal relationships with students have positively influenced my intellectual growth and interest in ideas), IIS34 (Many faculty members I have had contact with are interested in helping students grow in more than just academic areas), IIS20 (I have developed close personal relationships with other students), IIS21 (I will most likely register at this university next fall) and IIS33 (It is important for me to graduate from this university). We then achieved an insignificant drop in the model fit (Δχ2 =30.60, Δd.f. =26, p-value >.05), which means that measurement invariance was partially supported. Next, we tested the scalar invariance model based on the previous partially metric invariance model and found an insig- nificant decrease in the model fit (Δχ2 =27.25, Δd.f. =26, p-value >.05). This indicates that scalar invariance was also partially supported.
4.5.1.5. Department Size. Constraining factor loadings to be equal across departmental size groups did not significantly deteriorate the model fit of the base model (Δχ2 =42.52, Δd.f. =35, p-value >.05); full metric invariance was therefore supported. Based on the full metric invariance model, we tested the full scalar invariance model by constraining intercepts of the indicators to be equal across groups and also found an insignificant decrease in the model fit (Δχ2 =45.84, Δd.f. =35, p-value >.05). This means that scalar invariance was also fully supported. The results of the goodness of fit indices for measurement invariance models and the relevant chi- square tests are summarized in Tables 8 and 9.
Table 9
Results of chi-square difference tests for measurement invariance.
Model Comparison Test of metric invariance (Model 1 and Model 2) Test of scalar invariance (Model 2 and Model 3)
Δd.f. Δχ2 p-value Satisfied? Δd.f. Δχ2 p-value Satisfied?
Age 32 27.18 0.709 Partial 32 27.42 0.698 Partial
Gender 35 31.62 0.632 Yes 29 26.36 0.606 Partial
HS 26 30.60 0.244 Partial 26 27.25 0.396 Partial
FA 35 49.54 0.053 Yes 35 29.24 0.742 Yes
DS 35 42,52 0.179 Yes 35 45,84 0.09 Yes
Note: HS=Hometown Size, FA=Financial Aid, DS =Department Size.
4.5.2. Multigroup analysis of structural paths
We first imposed equality constraints on the paths in the structural model and performed χ2 difference tests for financial aid and department size groups. In doing so, we tested the null hypothesis to identify the moderating effects of selected variables on the hypothesized relationships, as per Floh and Treiblmaier (2006).
These results indicated that the parameters on the structural models differed significantly only for financial aid (Δχ2 =61.66, Δd.f.
=40, p-value <.05). For department size, putting equality constraints on structural paths did not significantly drop the model fit (Δχ2
=32.42, Δd.f. =40, p-value >.05), so the fully constrained model was selected as the most appropriate model. This indicated that financial aid moderated the proposed relationships in the research model, but no similar effect was found for department size. We then identified the parameters leading to major differences across financial aid groups by iteratively freeing the paths between constructs in the revised research model (see Fig. 2) starting from general self-efficacy and onwards. That is, we first freed the paths between general self-efficacy and social integration and between academic self-efficacy and academic integration. For the next level, we freed the paths between academic self-efficacy and academic and intellectual development, academic integration and academic and intellectual development; between social integration and institutional commitment; between academic integration and institutional commitment;
and between academic integration and academic self-efficacy. Finally, the paths from academic self-efficacy, institutional commit- ment, academic and intellectual development to academic performance were freed. After freeing all the parameters that significantly improved the fully constrained model, we obtained a partially constrained model. The χ2 difference between unconstrained and this partially constrained model was evaluated to determine the most appropriate and parsimonious model.
For financial aid, we observed no improvement in the fully constrained model upon freeing the parameters. This means that the unconstrained model was the most appropriate, regardless of the paths being freed. As a result, we accepted the unconstrained model as the most appropriate model. The resulting absolute and comparative fit indices are summarized in Table 10.
The absolute fit indices suggested that all the selected models satisfied the minimum acceptable levels for both normed χ2 (χ2/d.f.
≤3) and RMSEA (≤0.08), but the CFI remained slightly below the recommended threshold. The individual structural models for each financial aid group are displayed in Figs. 3 and 4.
The most striking contrast between financial aid groups existed in the factors related to academic performance. Academic self- efficacy was the only influential predictor of academic performance for students receiving financial aid (SPC =0.249, p-value <
.05), whereas academic and intellectual development was the only predictor for students who received no financial aid (SPC =0.445, p-value <.01). The hypothesized relationships between institutional commitment and academic performance were not supported for either group. Another important finding was that academic integration had a significant impact on academic self-efficacy for students who received no financial aid (SPC =0.170, p-value <.05) while such an influence was not observed for financial aid recipients (SPC
= 0.035, p-value > .05). The difference across financial aid groups occurred between academic integration and institutional commitment. Academic integration led to much higher institutional commitment for students who received no financial aid (SPC = 0.497, p-value <.01) than for financial aid recipients (SPC =0.351, p-value <.01). The last contrast indicated that general self- efficacy was a more influential factor in social integration for financial aid recipients, as academic integration was better explained by general self-efficacy for non-recipients of financial aid. The remaining significant effects were similar across financial aid groups.
Fig. 2. Main structural model with Standardized Path Coefficients (SPC) (* significant at α =0.05 level; ** significant at α =0.01 level).
Table 10
Goodness of Fit Statistics of the selected models.
Grouping Characteristic Normed χ2 RMSEA CFI
Financial Aid-UM 1.88 0.045 0.88
Department Size-FCM 1.93 0.045 0.87
Note: UM=Unconstrained Model FCM=Fully Constrained Model.
Fig. 3. Financial Aid UM-Yes model with Standardized Path Coefficients (SPC) (* significant at α =0.05 level; ** significant at α =0.01 level).
Fig. 4.Financial Aid UM-None model with Standardized Path Coefficients (SPC) (* significant at α =0.05 level; ** significant at α =0.01 level).
