Running head: RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE

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1 Running head: RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE Optimism, Academic Self-efficacy and Peer Social Support as Indicators of Study Engagement and Academic Performance in First-year University Students Irene Snijder Student number: Supervisor: Dr. F. Rutger Kappe Second assessor: Ph.D. Meta Aurelia Master thesis Work and Organizational Psychology June 6, 2017 Vrije Universiteit Amsterdam

2 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 2 Abstract Whereas study is comparable to work and students are the employees from the future, it is crucial to investigate the predictors and consequences of study engagement among students. Therefore, this present study tried to answer the research question if study engagement mediates the relationship between optimism, academic self-efficacy and social support, and dependent variables academic performance and leave intention. The present cross-sectional study examined this relationship among 140 first-year University students using online- and hardcopy questionnaires. A mediating role for study engagement was found in the relationship between academic self-efficacy and leave intention among Dutch first-year University students. Future research should extend this study by including more participants and various other predictors that are associated with study engagement and academic performance.

3 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 3 Optimism, Self-efficacy and Social Support as Indicators of Study Engagement and Academic Performance in First-year University Students Several studies investigated the wellbeing of students and found that a considerable amount of University students experience high levels of stress (Cotton, Dollard, & Jonge, 2002; Jacobs & Dodd, 2003; Stoeber, Childs, Hayward, & Feast, 2011). Students experience high levels of stress due to academic overload, the burden to succeed, and the strong desire to obtain high grades to differentiate from other students (Tosevski, Milovancevic, & Gajic, 2010). In addition, financial pressures contribute to the stress levels of University students as well (El Ansari & Stock, 2010; Friedman, 2014). More specifically, first-year University students experience a lot of changes and challenges when moving to another city. As they have to deal with financial concerns and pressures, and having to adapt to University, they are easily affected by stress (El Ansari & Stock, 2010; Friedman, 2014; Kotzé & Niemann, 2013; Yorke, 2006). Higher stress levels negatively affect the educational performances of University students (Schaufeli, Martínez, Marques, Salanova, & Bakker, 2002; Stewart, Lam, Betson, Wong, & Wong, 1999). Eventually, higher levels of stress in students are associated with higher dropout rates (Cotton et al., 2002; Kuh, Cruce, Shoup, Kinzie, & Gonyea, 2008). As shown within the organizational psychology literature, high levels of stress increase the risk of exhaustion and even of burnout (Bakker et al., 2005; Demerouti, Bakker, Nachreiner, & Schaufeli, 2001; Maslach & Leiter, 2001). Subsequently, the contrary of burnout is engagement (Maslach & Leiter, 2001; Schaufeli, Bakker, & Salanova, 2006; Schaufeli, Taris, Le Blanc, Peeters, Bakker, & De Jonge, 2001). Work engagement is described as experiencing a state of mind that is positive and fulfilling, and is a measure for wellbeing among employees (Schaufeli & Bakker, 2004). Higher levels of engagement are indicators of lower turnover intentions and better performance in organizational context (Bakker et al., 2004; Bakker, Tims, & Derks, 2006; Schaufeli & Bakker, 2003). Even though students are not employed at the University, the structured activities like attending class and preparing assignments are comparable with work, since they are aimed towards a specific goal (Salanova et al., 2010). When students are involved and committed to their studies, they experience engagement (Salanova et al., 2010; Schaufeli &

4 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 4 Salanova, 2007; Stoeber et al., 2011). Hence, study engagement, comparable to work engagement, can be described by vigorous feelings, dedication to one s studies and the absorption in study-related tasks (Schaufeli, Martínez, et al., 2002). As a consequence of vigor, dedication and absorption, higher levels of engagement in students are associated with better performances (Salanova, Llorens, Cifre, Martinez, & Schaufeli, 2003; Salanova et al., 2010). Since engagement is positively related to performance, and negatively related to leave intention, among both employees as well as students (Salanova, Schaufeli, Martínez, & Bresó, 2010; Schaufeli, Martínez, Pinto, Salanova, & Bakker, 2002; Van Marcke, 2008), indicators of study engagement, academic performance and leave intention are interesting and important to universities (Friedman, 2014). In organizational context, the drivers of work engagement and performance, are defined as job resources (Bakker & Demerouti, 2008; Bakker, Demerouti, de Boer, & Schaufeli, 2003; Bakker, Demerouti, & Verbeke, 2004). Autonomy, feedback, social support, job control and task significance are examples of job resources (Bakker & Demerouti, 2007). Moreover, personal resources such as self-esteem, selfefficacy, hope and optimism, are also contributing to work engagement and performance (Avey, Luthans, Smith, & Palmer, 2010; Xanthopoulou et al., 2007). In study context as well, personal- and study resources are meaningful in predicting study engagement, academic performance and are important for wellbeing (Kotzé & Niemann, 2013; Ouweneel et al., 2011; Ouweneel et al., 2013). This present study investigates the influence of study engagement on academic performance and leave intention among Dutch first-year University students. Also, the effects of optimism, academic selfefficacy and peer social support, treated as student resources, on study engagement are investigated. Eventually, the research question concerning the mediating role of study engagement in de relationship between student resources and academic performance and leave intention, is examined. Resources, engagement and performance among students are already investigated (Bresó, Schaufeli, & Salanova, 2011; Ouweneel et al., 2011; Ouweneel, Schaufeli, & Le Blanc, 2013; Siu et al., 2014; Kuh et al., 2008). However, the relationship between student resources, and academic performance and leave intention as dependent variables, mediated by study engagement, has not been studied so far. Investigating these

5 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 5 relationships provides more knowledge on how to improve study success and prevent dropout among University students. The results of this study can show practical relevance by creating training programs in which student resources are used to improve students engagement and performance. As noted previously, work engagement is a positive and fulfilling state of mind, positively linked to organizational outcomes such as organizational commitment and performance (Bakker & Demerouti, 2008; Demerouti & Bakker, 2007; Hakanen, Bakker, & Schaufeli, 2006). Also, higher levels of work engagement are associated with lower levels of turnover intention (Bakker, Demerouti, & Schaufeli, 2003; Schaufeli & Bakker, 2004). Work engagement is identified by vigor, dedication and absorption. Vigor is explained by having high levels of energy and mental resilience while working (Schaufeli & Bakker, 2004, p. 295), dedication is the way one is strongly involved in their task, and absorption is a state in which someone is concentrated to a certain extent in one s work, that time passes by quickly (Schaufeli & Bakker, 2004; Schaufeli, Salanova et al., 2002). According to Bakker and Schaufeli (2008) the interest of employers in employees with high levels of work engagement rises. Engaged employees are passionate about their work and experience high levels of energy (Schaufeli, Salanova, et al., 2002). Moreover, they are often more creative, more productive and open for special efforts (Bakker & Demerouti, 2008). As a result of positive emotions, better health, and providing their engagement towards others, engaged employees tend to perform better (Bakker & Demerouti, 2007). Bakker et al. (2004) found, in a study amongst 146 employees of different companies, that the ratings of colleagues about each other s performances are higher among engaged employees. A study among 190 Dutch employees from different organizations, in which performance was measured using peer-ratings (Bakker, Tims, & Derks, 2006), confirmed the positive relation between engagement and performance. After the association between engagement and performance is confirmed (Bakker et al., 2004; Bakker et al., 2006; Bakker & Demerouti, 2007), the antecedents of work engagement are evaluated. To predict the level of engagement and performance, it is necessary to investigate the factors that contribute to engagement. According to various studies, job resources and job demands operate as the fundamental predictors of work engagement (i.a. Bakker & Demerouti, 2007; Schaufeli & Salanova, 2007). The

6 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 6 association between job resources, job demands and work engagement is an element of the Job Demands- Resources model of work engagement (Schaufeli & Bakker, 2004). See Figure 1 for an overview of this model. Both engagement as burnout are included in the model in Figure 1, whereas in the original Job Demands-Resources (JD-R) model only burnout was included (Demerouti, Bakker, Nachreiner, & Schaufeli, 2000; Demerouti, Bakker, Nachreiner, et al., 2001). Initially, the field of psychology used to focus merely on mental illness rather than wellbeing. Positive psychology and positive organizational behavior alternated this by focusing on positive states, traits and behaviors of employees in organizations (Bakker & Schaufeli, 2008; Luthans & Youssef, 2007). Therefore, work engagement is included in this model. Figure 1. Expanded Job Demands-Resources model according to Schaufeli and Bakker (2004). Strain equals burnout, and motivation equals engagement. Figure is from the article of Bakker and Demerouti (2006, p. 313). According to the job demands-resources model, job demands are the aspects of the job that are physical, psychological, social, or organizational that require sustained physical and/or psychological (cognitive and emotional) effort and are therefore associated with certain physiological and/or psychological costs (Bakker et al., 2003, p. 344). Examples of job demands are a high workload, a physical environment that is not pleasant, unfavorable working hours and emotional demands. Job

7 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 7 demands are the initiators of the health impairment process. The health impairment process addresses job strain and stress, due to job demands, which are negatively associated with organizational outcomes (Figure 1). Performance, organizational commitment, absenteeism, and turnover are examples of organizational outcomes (Bakker, Demerouti, & Schaufeli, 2003; Luthans, Youssef, & Avolio, 2007; Schaufeli, Salanova, Gonzalez-Romà, & Bakker, 2002). For example, strain is positively associated with exhaustion and health problems (Bakker & Demerouti, 2007; Halbesleben & Buckley, 2004). Job resources on the other hand, refer to those physical, psychological, social, or organizational aspects of the job that are either/or: (1) functional in achieving work goals; (2) reduce job demands and the associated physiological and psychological costs; (3) stimulate personal growth and development (Bakker et al., 2003, p. 344; Bakker et al., 2004). Autonomy, feedback, social support, job control and task significance are examples of job resources (Bakker & Demerouti, 2007). The motivational process assumes the potential of job resources to positively influence work engagement and organizational outcomes. Job resources encourage learning and development, which reinforces intrinsic motivation in employees. Moreover, job resources serves as an extrinsic motivator, by encouraging achieving work goals (Bakker & Demerouti, 2007). In addition to job resources, personal resources are involved in predicting engagement and organizational outcomes (Xanthopoulou, Bakker, Demerouti and Schaufeli, 2007). Personal resources are parts of the self, which are related to resiliency and the ability to control the environment successfully, and are positively related to work engagement. Self-esteem, self-efficacy, optimism, hope and resilience are examples of personal resources (Avey, Luthans, Smith, & Palmer, 2010; Xanthopoulou et al., 2007). Previous studies show that job- and personal resources serve as important specific contributions to engagement, performance and wellbeing (Barbier, Hansez, Chmiel, & Demerouti, 2013; Demerouti & Bakker, 2011). For example, personal resource optimism proves to be beneficial to wellbeing and engagement (Alarcon, Bowling, & Khazon, 2013; Avey et al., 2010; Xanthopoulou et al., 2007). Likewise, high levels of energy and a high amount of self-efficacy are indicators of engaged employees (Schaufeli, Salanova, et al., 2002). Furthermore, social support proves to be a strong predictor of work

8 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 8 engagement (Bakker & Demerouti, 2007; Haines, Hurlbert & Zimmer, 1991). Encouraging social support tend to raise beneficial influence on mental health. High levels of support, from supervisor, non-work or co-worker, are negatively associated with high strain and positively associated with higher levels of work performance (Sargent & Terry, 2000). The above reported literature is based on organizational context. Furthermore, associations between resources, study engagement and performance are also found in study context. From a psychological point of view, students are involved in structured activities that are aimed towards a specific goal. Completing an assignment in order to graduate, or attending class to pass exams are examples of such activities. Considering that these activities are comparable to work, students are similar to employees (Salanova et al., 2010; Schaufeli & Salanova, 2007; Stoeber et al., 2011). Concerning that students devote their physical and psychological energy to the academic experience (Astin, 1984, p. 518), students experience study engagement, similar to work engagement among employees (Salanova et al., 2010). Engagement in students is described as feeling vigorous, being dedicated to one s studies, and being absorbed in study-related tasks (Ouweneel et al., 2011, p. 142; Schaufeli & Bakker, 2004; Schaufeli, Martínez, et al., 2002; Schaufeli, Salanova, et al., 2002). Study engagement is associated with lower levels of perceived stress (Gan, Yang, Zhou, & Zhang, 2007; Stoeber et al., 2011). Because of the challenges students face, motivation due to positivity and study engagement lead to higher-level performances (Kuh et al., 2008; Siu et al., 2014; Tosevski et al., 2010). In other words, the more engaged students are, the better they perform (Salanova, Llorens, Cifre, Martinez, & Schaufeli, 2003). All the three aspects of engagement; vigor, dedication and absorption, show a positive relationship with academic performance (Schaufeli, Martínez, et al., 2002; Schaufeli, Salanova et al., 2002). Eventually, academic performance serves as an indicator of students future success (Chen, 2005). Comparable to job resources in organizational context, are study resources in study context. Personal- and study resources are meaningful in predicting study engagement and are important for wellbeing (Kotzé & Niemann, 2013; Ouweneel et al., 2011; Ouweneel, Schaufeli, & Le Blanc, 2013). In a

9 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 9 study among 391 Dutch University students, personal resources as self-efficacy and optimism are found to be predictors of study engagement (Ouweneel et al., 2011). In line with research of Ouweneel et al. (2011), self-efficacy and optimism show positive associations with study engagement among 103 Hong Kong University students (Siu, Bakker, & Jiang, 2014). Optimism is explained as the way in which someone believes that something good will happen to them (Scheier & Carver, 1985). According to Scheier and Carver (1992), optimists have the ability to cope with difficult and stressful events in which they experience less distress and a positive impact on their wellbeing. Optimism also shows to contribute to higher levels of performance in a study among 1032 (r =.16, p <.01) and 232 (r =.23, p <.01) employees in the United States (Youssef & Luthans, 2007). This contribution occurs, because high levels of optimism can change the interpretation of an undesirable situation, and turn it into an opportunity for learning and development (Youssef & Luthans, 2007). Personal resource self-efficacy is the belief in one s capabilities to organize and execute the course of action required to produce given attainments (Bandura, 1997, p. 3). Self-efficacy has shown to positively influence engagement and wellbeing, because high levels of self-efficacy generate more willingness to spend energy and effort on task- or assignment completing (Llorens, Schaufeli, Bakker & Salanova, 2007; Niemivirta & Tapola, 2007; Ouweneel et al., 2011). Also, self-efficacy causes more task involvement and absorption (Ouweneel et al., 2011). Chemers, Hu and Garcia (2001) showed, in a study among first-year University students from the United States, a positive relation between academic self-efficacy and performance (r =.34, p <.001). They explain this relationship by the increasing use of cognitive activities and strategies that are very specific, and the positive impact that efficacy has on coping abilities. In line with research of Chemers et al. (2001), a study of Elias and Loomis (2002) among 138 University students in the United States, found that academic self-efficacy is a significant predictor of academic grades (r =.52, p <.01). Furthermore, in a research investigating the psychosocial and study skill factors and college outcomes, self-efficacy has been found to be one of the strongest predictors (r =.50) of academic performance (Robbins, Lauver, Le, Davis, & Langley, 2004). A study of Bresó, Schaufeli and Salanova (2011), consisting a self-efficacy intervention among students, presented a positive relationship between self-efficacy, study engagement

10 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 10 and performance (Llorens et al., 2007; Salanova, Agut, & Peiró, 2005). Besides personal resources optimism and self-efficacy as predictors of study engagement, study resource social support is also found to predict study engagement. A study among 149 students from the United States showed that social support, especially from friends, contributes as a buffer against stress (Jacobs & Dodd, 2003). Cotton et al. (2002) confirmed the findings that social support is found to be an important factor in reducing psychological distress among students, and has beneficial consequences for student wellbeing. With the JD-R model as a basis, this present study investigates the influence of optimism, selfefficacy and social support on academic performance, mediated by study engagement. These relationships were investigated among first-year University students in the Netherlands. All the used variables were treated context-specific, since this research operates in a study setting. Self-efficacy was treated as academic self-efficacy, social support as peer social support, engagement as study engagement and performance as academic performance. In the current research, optimism, academic self-efficacy and peer social support together were treated as student resources. To measure the dependent variable academic performance, the current study used self-reported GPA and obtained credits. Leave intention was also measured as a dependent variable. Prior research proved the predictive value of resources on academic performance (Ouweneel et al., 2011), therefore the following hypotheses were conducted: Hypothesis 1: Optimism is positively related to GPA (1a), credits (1b) and negatively related to leave intention (1c) Hypothesis 2: Academic self-efficacy is positively related to GPA (2a), credits (2b) and negatively related to leave intention (2c) Hypothesis 3: Peer social support is positively related to GPA (3a), credits (3b) and negatively related to leave intention (3c) As engagement showed to positively relate to performance and negatively relate to turnover among employees (Bakker & Demerouti, 2007), and study engagement showed to achieve the same

11 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 11 amongst University students (Kuh et al., 2008), the following hypotheses were used to investigate the direct associations between study engagement and academic performance and leave intention: Hypothesis 4: Study engagement is positively related to GPA (4a), credits (4b), and negatively related to leave intention (4c) Inspired by the mediating role of work engagement between job resources and organizational outcomes in the JD-R model (W. B. Schaufeli & Bakker, 2004), the subsequent hypotheses show the expectations of the mediating role of study engagement: Hypothesis 5: Study engagement mediates the relationship between optimism (5a), academic self-efficacy (5b), peer social support (5c), and academic performance and leave intention The conceptual model in Figure 2 supports the previous mentioned hypotheses. Optimism Selfefficacy Social support GPA Credits Student resources Academic performance Study engagement Leave intention Vigor Absorption Dedication Figure 2. Conceptual model of study engagement as a mediator between student resources and academic performance and leave intention, derived from the JD-R model (Gubbels & Kappe, 2017). The dotted lines represent the direct associations from student resources and the dashed lines represent the direct associations from study engagement.

12 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 12 Method Participants and research design A cross-sectional study was conducted among first-year students of different Universities and diverse disciplines, in the Netherlands. The data was collected between 12 April and 5 May 2017 and participants completed an online or hardcopy questionnaire once. Participants were invited through social media canals such as Facebook and LinkedIn to complete the online questionnaire. Additionally, students were asked to complete a hardcopy questionnaire while they had their break during lectures. In total 158 Dutch University students participated. However, from 158 first-year students, 18 participants did not complete the entire questionnaire and were therefore excluded. From the 140 participants, 22.9 % (32) was male and 77.1 % (108) was female. Only 2 of the 140 participants were born in another country, all of the other 138 participants (98.6%) were born in the Netherlands. Most of the participants studied at the Vrije Universiteit Amsterdam (77.1%). Other participants studied, for example, at the Universiteit van Amsterdam (12.9%), Universiteit Utrecht (6.4%), or Rijksuniversiteit Groningen (1.4%). Psychology was the most common study among the participants (45.0%). Other participants studied, for example, Communicatie- en Informatie wetenschappen (16.4%), Aardwetenschappen (12.2%), or Econometrie (7.9%). Of the 140 participants 42.1% (59) considered themselves as an above average student, 54.3% (76) as an average student, and 3.6% (5) as a below average student. Of the 140 students, 56.4% (79) never thought about quitting their studies and 32.1% (45) rarely thought about quitting their studies. If the students thought regularly (7.9%), often (2.9%) or always (0.7%) about quitting their studies, the most common reason was the wrong study choice (13.6%), followed by the doubt of getting through the school year, grade-wise (11.4%). Other reasons were psychical- (2.9%) or physical complaints (0.7%). Procedure An online questionnaire was posted on several social media canals like Facebook and Linked-In to directly connect to first-year University students or through other contact persons. The questionnaire consisted of an informed consent to make sure only first-year University students participated (Appendix

13 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 13 A). The online questionnaire was created using the Qualtrics program, in such manner that the students could complete the questionnaire on their smartphone or laptop. Also, teachers from different universities in the Netherlands were approached to help distributing the questionnaire or give permission to ask their students to fill in the questionnaire. At several lectures the hardcopy questionnaire was distributed at the start of the lecture, and collected after the lecture. Materials Academic performance For measuring academic performance as one of the dependent variables of this research, students were asked to report their Grade Point Average, further in this study reported as GPA. Additional to their GPA, students were asked to report their obtained credits so far and the maximum of credits they can obtain at the moment they filled in the questionnaire, since they not completed their study year yet. A relative amount of credits is calculated from the reported obtained and maximum of credits. A score of 100 indicates that the obtained credits are equal to the maximum credits to obtain. Besides GPA and credits, leave intention was treated as a dependent variable. To measure the intention to leave their study, students were asked if they thought about quitting their studies on a scale from 1 ( Never ) to 5 ( Always ). This statement was followed by a question asking for the reason for their thought about quitting their study. These reasons can be found in the descriptions of the participants or in the questionnaire in Appendix A. Optimism To measure the level of optimism, the Dutch version of the Life Orientation Test-Revised (LOT-R) was used. The LOT-R consists of 10 statements concerning three positive (1, 4, 10) and three negative items (3, 7, 9). Four filler items (2, 5, 6, 8) remain, which were not used in scoring the levels of optimism. These 10 statements were answered on a 5-point Likert scale with a range from 1 ( Strongly disagree ) to 5 ( Strongly agree ). An example item of this questionnaire is: In general I expect that more good than bad things will happen to me ( Over het algemeen verwacht ik dat me meer goede dan slechte dingen zullen overkomen ). The negative statements were recoded before analyzing the data and the scores on the six items were combined, because the filler items were not included in the end score. A

14 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 14 Cronbach s alpha of.78 was noted by Scheier et al. (1994), whereas the current study reported a Cronbach s alpha of.69. Academic Self-efficacy To measure academic self-efficacy, 5 Dutch statements were answered on a 5-point Likert-scale with a range from 1 ( Strongly disagree ) to 5 ( Strongly agree ) (Frese, 2009; Midgley et al., 2000). An example item of this questionnaire was: I am certain I can master the skills taught in class this year ( Ik weet zeker dat ik alle vaardigheden die ik in deze studie moet leren onder de knie krijg. ). The current study reported a Cronbach s alpha of.79, comparable to the study of Midgley et al. (2000), where a Cronbach s alpha of.78 was reported. Peer Social-support Peer social support was measured using the academische en sociale integratie questionnaire (Severiens & Joukes, 2001). This questionnaire consists of 8 statements about formal social integration ( Contacten met medestudenten hielpen mij om goed te presteren ) and 5 statements concerning informal social integration ( Ik had goede sociale contacten met medestudenten ). These statements were answered on a 5-point Likert scale with answer options from 1 ( Strongly disagree ) to 5 ( Strongly agree ). The Cronbach s alpha of this questionnaire in the present study was good (α =.85). Study engagement To measure study engagement, based on the subscales vigor, dedication, and absorption, the shortened Utrechtse Bevlogenheidsschaal studiebelevingslijst (UBES-S-9) was used (Schaufeli & Bakker, 1999; Schaufeli & Bakker, 2003). This Dutch questionnaire exists of 9 items among which 3 items to analyze vigor ( Als ik studeer bruis ik van de energie ), 3 items measuring dedication ( Mijn studie inspireert mij ), and 3 items to measure absorption ( Wanneer ik studeer vliegt de tijd voorbij ). These items were answered on a 7-point Likert-scale in a range from 1 ( Strongly disagree ) to 7 ( Strongly agree ). According to the reliability analysis, the internal consistency of the UBES-S-9 was good (α =.82). The complete questionnaire is shown in appendix A.

15 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 15 Data analysis The data of the online and offline questionnaires were analyzed by using IBM SPSS Statistics 23. In advance of analyzing the data, participants that did not complete the questionnaire or were other than first-year students, were removed from the data set. Preliminary analyses were conducted to ensure no violation of the assumptions of normality, linearity, homoscedasticity, multicollinearity, independence of residuals, and outliers. A score outside 3 standard deviations of the mean is considered an outlier. Overall, an alpha level of.05 was used for defining the significance of all the statistical analyses. To ensure no differences in gender on the different variables, several t-tests were conducted. Pearson product-moment correlations were conducted to investigate the correlations between the predictors and the dependent variables, between the predictors and the mediator, and between the mediator and the dependent variables. To explore the mediating role of study engagement, a couple of Sobel tests were conducted (Baron, & Kenny, 1986). The Sobel tests examined the mediating role of study engagement between the student resources and academic performance and leave intention. For each hypothesis, two regression analyses were conducted before performing the Sobel test. The first regression analysis examined the direct association between the predictor, one of the student resources, and the expected mediator, in this case study engagement. The second regression analysis calculated the direct relationship between the expected mediator, study engagement, and the dependent variable, in this case academic performance or leave intention. Unstandardized regression coefficients and associated standard errors were inserted in the Sobel test calculator, to establish the mediating effect of study engagement ( Additionally, the mediating role of study engagement was examined by a Structural Equation Model (SEM) analysis using AMOS. SEM is known to examine more complex models than SPSS produces. More dependent variables can be included and, therefore, the less variance will occur. SEM allows measuring latent variables, where unreliability in measurement is modeled and controlled for. For this study an example of a latent variable is study engagement, since it is measured by vigor, absorption

16 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 16 and dedication. Also, SEM allows variables to be both an independent as a dependent variable. A structural equation model examines causal and associative relationships among observed and unobserved variables. SEM is adjustable for this model, since GPA, credits and leave intention were treated as different dependent variables. Model fit was evaluated with the Maximum Likelihood-Ratio Chi-square (χ 2, p >.05), the Root Mean Square Error of Approximation (RMSEA <.05), and the Non-Normed Fit Index (NNFI >.95). Results Prior to the analyses investigating the hypotheses of the present study, the descriptive statistics of the different independent and dependent variables are shown in Table 1. Table 1 Descriptive Statistics of Variables (n = 140) Variables M (SD) Range Skewness Kurtosis Age (2.03) Study hours (9.34) Study engagement (7.43) Optimism (3.46) Academic self-efficacy (2.93) Peer social support (6.28) GPA 7.13 (0.79) Credits (relative) (19.86) Note. Standard error of Skewness is.21, Standard error of Kurtosis is.41. Study hours are including lectures and preparations. Credits (relative) are obtained credits divided by maximal to obtain credits, with 100 as a maximum of obtained credits. As shown in Table 1, the distributions of age, study hours and GPA are skewed to the right. The distributions of the other variables are skewed to the left. GPA comes closest to a symmetrical shape. Age and credits are highly skewed, whereas academic self-efficacy is approximately symmetric. The other variables are moderately skewed. According to the kurtosis value, age has a high, central peak. The other

17 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 17 variables all show kurtosis values below 3, this means that, compared to a normal distribution, the tails are shorter and thinner. Also, the central peak is generally lower and broader. Considering an overrepresentation of females in the sample, a couple of independent t-tests were conducted in SPSS to compare the scores on the different variables between males and females. In the scores on hours of study per week, self-rating, optimism, academic self-efficacy, study engagement, credits and leave intention, no significant differences occurred (see Table 2). However, in the scores on GPA, males and females vary significantly (t (138) = -2.23, p =.03, two-tailed). Males obtained less high grades (M = 6.78, SD =.83) than females did (M = 7.21, SD =.76). Although, a small eta squared (.03) was found, describing the size of the differences in the means (mean difference = -.35, 95% CI: -.66 to -.04). As a result of the significant small difference, GPA is treated cautiously when performing subsequent analyses. Table 2 shows the different means, standard deviations and t statistics of males and females, on the variables used in the present research. The descriptive statistics and correlations between gender, age, study hours, self-rating, study engagement, optimism, academic self-efficacy, peer social support, GPA, credits and leave intention, are shown in Table 3. Table 2 Means (M), Standard Deviations (SD) and t statistic of Males (n = 32) and Females (n = 108) on the Variables Variables Males M (SD) Females Optimism (3.85) (3.35) Academic self-efficacy (3.37) (2.81) Peer social support (7.20) (6.01) Study engagement (6.91) (7.61) GPA 6.87 (.83) 7.21 (.76) * Credits (24.23) (18.15) Leave intention 1.66 (.79) 1.57 (.82) Study hours (9.79) (9.19) Self-rating 1.72 (.46) 1.58 (.58) Note. df = 138. *p <.05 t

18 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 18 To examine the associations between optimism and academic performance and leave intention, Hypothesis 1a, 1b and 1c were investigated. In this study, optimism does not show a significant relationship with GPA, credits, nor leave intention, as can be seen in Table 3. Therefore, hypotheses 1a, 1b and 1c were not supported by the current data. To investigate the positive relationship between academic self-efficacy and academic performance, Pearson product-moment correlations were conducted. Considering academic self-efficacy, the correlation matrix (Table 3) showed a significant positive association with GPA, indicating that higher levels of academic self-efficacy are associated with higher GPA. Whereas there are differences in GPA between males and females, a separate analysis for males and females was conducted. There was a non-significant connection between academic self-efficacy and GPA for males (r =.15, p =.416), and a significant correlation among females (r =.30, p =.001). This result indicates that the association between academic self-efficacy and GPA only exists for females in this sample. Hypothesis 2a is hereby partly supported. The connection between academic self-efficacy and credits (H2b) was not supported by the data of the current study. Hypothesis 2c was supported, since academic self-efficacy is significantly negatively related to intention to leave (r = -.31, p <.001). Demonstrating that the higher the scores on academic self-efficacy, the less likely a first-year University student will consider leaving University. Hypotheses 3a and 3b presumed the positive association between peer social support and academic performance. Peer social support was positively associated with (3a) credits (r =.19, p =.028), and not with GPA (3b). Hypothesis 3c is not supported by this study; there was no significant association between peer social support and leave intention. In sum, when investigating the associations between student resources and academic performance and leave intention, only academic self-efficacy and peer social support showed associations with academic performance and intention to leave. Academic self-efficacy was positively related to GPA and negatively related to leave intention. However, the association between academic self-efficacy and GPA only showed a significant positive association among females. Peer social support showed a positive association with credits.

19 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 19 With hypothesis 4, the relationships between study engagement and academic performance and leave intention were investigated. Hypothesis 4a, which assumed a positive relationship between study engagement and GPA, was not supported. Yet, because of the earlier mentioned differences between males and females in the scores on GPA, a separate analysis for males and females was conducted. With the differences between males and females included, this relation was not significant for both males (r =.09, n = 32, p =.617) as females (r =.12, n = 108, p =.220). Hereby, current study lacked support for hypothesis 4a. Hypothesis 4b assumed a positive relationship between study engagement and credits. This hypothesis was not supported by the current study, as showed in Table 3. The assumption that study engagement was negatively related to leave intention was confirmed by the present study (r = -.30, p <.001). Indicating that the higher the score on study engagement, the less likely a student is considering leaving University (see method section for scoring leave intention). In sum, the current study did not find a relationship between study engagement and GPA (H4a) nor credits (H4b), although the negative relationship between study engagement and leave intention (H4c) was supported. Several Sobel tests examined the mediating role of study engagement between student resources and academic performance, and leave intention. A mediating effect of study engagement between academic self-efficacy and leave intention (z = -2.76, p =.010) was demonstrated. Since the association between academic self-efficacy and leave intention was significant (r = -.31, p <.001), study engagement partly mediates the relationship between academic self-efficacy and leave intention. The unstandardized regression coefficients, standard errors of the regression analyses, and Sobel z-statistics, are shown in Table 4. The mediating role of study engagement between academic self-efficacy and leave intention is confirmed by a Structural Equation Model (SEM) analysis using AMOS. First investigating a model, which did not include mediation but analyzes all the variables. This model consists of latent variables as student resources and study engagement. In this model the expected relationship between student resources and study engagement and GPA, credits and leave intention is included (See Figure 3). In this first model, the Chi-square test was significant χ² (22, N = 140) = 38.24, p =.019. This significant result

20 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 20 indicates that the model is a poor fit to the data. The following fit-indices confirmed this finding: CFI =.924, NNFI =.876, and RMSEA =.073, indicating that this is not a very good fitting model. Another model with all the predicted relationships was investigated using SEM. In this model the significant relationships between academic self-efficacy and GPA and peer social support and credits were included (Figure 4). The Chi-square test of this model was not significant, χ² (19, N = 140) = 22.96, p =.240, indicating a good fit with the data. The following fit-indices strengthen this finding: CFI =.982, NNFI =.965, and RMSEA =.039. However this is a good fitting model, as earlier shown in the correlation matrix (Table 3), there were some non-significant correlations, indicating a non-association between those variables. Deleting those non-significant associations introduces the best fitting model (Figure 5). In this model the non-significant associations of optimism to study engagement and study engagement with GPA and credits were deleted. As the non-significant value of the Chi-square test confirmed, χ² (22, N = 140) = 23.20, p =.390, this is a good fitting model. The fit-indices showed the same: CFI =.994, NNFI =.991, and RMSEA =.020. Indicating the mediating role of study engagement between academic self-efficacy and leave intention.

21 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 21 Table 3 Means (M), Standard Deviations (SD) and Correlations between the Variables (n = 140) Variables M SD Gender - 2. Age Study hours Self-rating Study engagement * 0.18* (.82) 6. Optimism * (.69) 7. Academic self-efficacy ** 0.34** 0.20* (.79) 8. Peer social support * ** 0.33** 0.13 (.85) 9. GPA * * -0.52** ** Credits (relative) * -0.48** * 0.46** Leave intention ** -0.30** ** ** -0.20* - Note. Gender: 1 = male, 2 = female. Scores on optimism, academic self-efficacy, peer social support and leave intention ranged from 1 to 5. Scores on study engagement ranged from 1 to 7. Credits are relative since obtained credits are divided by maximal to obtain credits, total of the credits obtained equals 100. Internal consistency (α) of the scales is located between the brackets. ** p <.01, * p <.05 (two-tailed).

22 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 22 Table 4 Results of Regressions and Sobel Test for Study Engagement Serving as a Mediator in the Relationships between Predictors and the Dependent Variables, GPA (1), credits (2), and Intention to Leave (3) Predictor variable b path SE (b) a path SE (a) Sobel (z) SE (z) 1 Optimism Academic self-efficacy.850** Peer social support.299* Optimism Academic self-efficacy.850** Peer social support.299* Optimism -.033** Academic self-efficacy.850** **.01 Peer social support.299* *.00 Note. N = 140. b path = Unstandardized regression coefficient for the direct relationship between the mediator study engagement and the dependent variable (1) GPA, (2) Credits, or (3) Leave intention. a path = Unstandardized regression coefficient for the direct relation between the predictor and the mediator study engagement. ** p <.01, * p <.05 (two-tailed).

23 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 23 GPA Student resources Study Engagement Credits Optimism Academic self-efficacy Peer social support Vigor Absorption Dedication Leave intention Figure 3. First SEM model including latent variables for student resources and study engagement. Optimism GPA Academic self-efficacy Peer social support Study Engagement Vigor Absorption Dedication Credits Leave intention Figure 4. SEM model including latent variable study engagement and associations between academic self-efficacy and GPA, and Peer social support and ECTs.

24 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 24 Optimism GPA.26** Academic self-efficacy -.31**.34** Study Engagement Credits.25** -.30** Peer social support.19* Leave intention Vigor Absorption Dedication Figure 5. The Structural Equation Model (SEM) including the latent variable study engagement. The direct associations of academic self-efficacy are marked by the dotted lines and the direct associations of peer social support, are marked by the dashed lines. Non-significant relations are deleted from the model. ** p <.01, * P <.05 (two-tailed).

25 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 25 Discussion Using Pearson product-moment correlations, regression analyses and SEM, the relationships between student resources, study engagement, academic performance and leave intention were examined. Considering the direct relationships, the association between academic self-efficacy and GPA was confirmed only for female students (H2a). Hypothesis 2c, whereby the association between academic self-efficacy and leave intention was supposed, was confirmed by the current study. Peer social support is found to be associated with credits (H3b) and study engagement is associated with leave intention (H4c). Therefore, in response to the research question, the only mediation this present study supports is the mediating role of study engagement in the relationship between academic self-efficacy and leave intention. In light of the findings of the direct associations between the student resources and academic performance and leave intention, there were no associations with optimism. Optimism was not significantly associated with GPA, credits, nor leave intention. The relationships between optimism, academic performance and leave intention were expected according to a significant correlation between optimism and GPA (r =.21) in previous research among first-year students in the United States (Chemers, Hu, & Garcia, 2001). In the research of Chemers et al. (2001) however, the Life Orientations Test (LOT) of Scheier and Carver (1985) was used. This test measures optimism using 8 items, instead of the 6 items of the LOT-R. Chemers et al. (2001) reported a Cronbach s alpha of.80, which is higher than the alpha of the present study (α =.69). Rand (2009), who found a Cronbach s alpha of.79, and Kotzé and Niemann (2013), who found an alpha of.51, also used the LOT-R in their research and did not find evidence for the relationship between optimism and academic performance. These findings make it reasonable that the LOT is better able to measure optimism among students than the LOT-R is. Another explanation could refer to the sample of the studies. From the studies of Rand (2009), Kotzé and Niemann (2013), and the present study, that did not find an association between optimism and academic performance, (most) participants were students from a Psychology course. Moreover, the study of Chemers et al. (2001) consisted of first-year participants of a University in the United States, not

26 RESOURCES AS INDICATORS OF STUDY ENGAGEMENT AND PERFORMANCE 26 specifically in Psychology courses. This wider sample could have been contributed to the findings of Chemers et al. (2001). Noteworthy are the few associations optimism has with the other variables. In the final SEM model (Figure 5), optimism is only associated with the other resources, academic self-efficacy and peer social support. The relationship between optimism and academic self-efficacy is not very surprising since optimism and self-efficacy both operate as personal resources. In addition, an explanation of this relationship is that the statements regarding optimism and academic self-efficacy are comparable. Optimistic people believe in their own abilities and have an optimistic view towards the future (Scheier & Carver, 1992). The statements measuring academic self-efficacy reflect comparable views with statements as: I'm certain I can figure out how to do the most difficult class work (Midgley et al. 2000, p. 19). Academic self-efficacy is positively related to GPA, indicating that higher levels of academic self-efficacy are associated with a higher GPA. This finding is in line with Chemers et al. (2001), who found that self-efficacy serves as a predictor of academic performance among first-year students (r =.34, p <.001). Elias and MacDonald (2007) even found evidence for academic self-efficacy to be highly important to future academic performance (r =.52, p <.01). However, the relation between academic self-efficacy and GPA found in the current study is small to moderate (r =.26, p <.01). This could be a consequence of the overall less associations of GPA with the other variables. An explanation for this situation could be that a difficulty for the students in reporting GPA occurred. It could have been unclear to the students if they should report all of their grades, including the insufficient grades, or only the sufficient grades, associated with their obtained credits. This example can also explain the non-existent association between study engagement and GPA, although this relation was suspected according to previous studies among students (Kuh et al., 2008, Salanova et al., 2010; Schaufeli, Martínez et al., 2003). The skewness and kurtosis values however, showed that GPA comes closest to a symmetrical shape. This means that GPA is practically normally distributed. Most of the students reported a GPA between 6 and 7 and some of the students had a more extreme GPA such as a 5 or a 9. These extreme values are divided in the tails of the distribution. Scores on academic self-efficacy are approximately symmetric, in