Learning and Individual Differences xxx (2014) xxx–xxx

LEAIND-00923; No of Pages 9

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Learning and Individual Differences

j ourna l homepage: www.e lsev ie r .com/ locate / l ind i f

Mediating relationships between academic motivation, academicintegration and academic performance

M.H. Clark a,⁎, Steven C. Middleton b,1, Daniel Nguyen b,2, Lauren K. Zwick b

a Department of Educational and Human Sciences, College of Education and Human Performance, 4000 Central Florida Boulevard, University of Central Florida, Orlando, FL 32816–1250, USAb Department of Psychology, 1125 Lincoln Drive, Southern Illinois University Carbondale, Carbondale, IL 62901–6502, USA

⁎ Corresponding author at: Department of EducationalEducation and Human Performance, 4000 Central FloriFlorida, Orlando, FL 161250–1250, USA. Tel.: +1 407 823

E-mail addresses: M.H.Clark@ucf.edu (M.H. Clark), scm(S.C. Middleton), Daniel.Nguyen@wonderlic.com (D. Nguy

1 Steven Middleton is presently affiliated with Allied Ev2 Daniel Nguyen is presently affiliated with Wonderlic.

http://dx.doi.org/10.1016/j.lindif.2014.04.0071041-6080/© 2014 Elsevier Inc. All rights reserved.

Please cite this article as: Clark, M.H., et aperformance, Learning and Individual Differen

a b s t r a c t

a r t i c l e i n f o

Article history:Received 12 July 2013Received in revised form 1 March 2014Accepted 11 April 2014Available online xxxx

Keywords:Academic motivationAcademic integrationAcademic performanceBootstrap mediationFirst-year college students

Using 81 first-year college students, researchers examined the indirect effects of seven types of academicmotivation on academic performance when mediated by academic integration. When accounting for all othertypes of academic motivation in the statistical model, academic integration only mediated the relationship be-tween intrinsic motivation to accomplish things and first-year grade point average (GPA). Therefore, studentswho attend college to gain a sense of accomplishment believe that college helps them develop intellectuallyand they perform well academically. However, when each motivation type was considered independently ofthe others, intrinsic motivation to know was also indirectly related to GPA, suggesting that students who enjoylearning are likely to perceive the intellectual benefits of college as well.

© 2014 Elsevier Inc. All rights reserved.

1. Introduction

Previous research has demonstrated howwell demographic charac-teristics and cognitive factors predict college performance (Cohn, Cohn,Balch, & Bradley, 2004; Robbins et al., 2004; Sackett, Kuncel, Ameson,Cooper, & Waters, 2009). Although academic performance in highschool and college entrance exam scores are consistently among thebest predictors of college performance and degree attainment (Clark &Cundiff, 2011; Schmitt et al., 2009), other studies have shown that psy-chosocial factors also predict performance (Poropat, 2009; Zajacova,Lynch, & Espenshade, 2005). Of the more commonly studied psychoso-cial factors, motivation to achieve is one of the strongest predictors ofacademic performance (Robbins et al., 2004). While much of the re-search on students’ ability to acclimate to a college setting examinesits relationship with college retention (DaDeppo, 2009; Tinto, 1993),many studies have found that it also predicts academic performance(Pan, Guo, Alikonis, & Bai, 2008; Prospero & Vohra-Gupta, 2007; Ullah& Wilson, 2007). Some researchers have proposed that academic inte-gration mediates the relationship between a variety of social factorsand academic performance (Bean & Eaton, 2001; Cabrera, Nora, &Castaneda, 1993; Rivas, Sauer, Glynn, &Miller, 2007). Althoughprevious

and Human Sciences, College ofda Blvd., University of Central2595; fax: +1 407 823 4880.iddleton@alliedevalgroup.comen).aluation Consulting Group.

l., Mediating relationships bces (2014), http://dx.doi.org/

research has established that both academic motivation and academicintegration are related to academic performance, the present studyfocuses on how academic motivation and academic integration worktogether to predict academic performance. Specifically, we are in-terested in knowing whether or not academic motivation is amongthe psychosocial factors that are mediated by academic integration inits relationship to performance.

1.1. Academic motivation

Academic motivation is the driving factor that influences a personto attend school and obtain a degree. While there have been manytheories of general motivation (Marsh, Craven, Hinkley, & Debus,2003; Middleton & Toluk, 1999; Rotter, 1966), one of the best knowntheories of motivation is Deci and Ryan’s Self-determination theory(SDT) of motivation (1985). Many motivation theories simply makedistinctions between autonomous behavior, that which is done with apersonal intention or choice, and controlled behavior, that which isdone unwillingly or out of compliance (Heider as cited by Deci,Vallerand, Pelletier, & Ryan, 1991; Sheldon & Elliot, 1998). However,SDT is based on a hierarchical model that claims that there are threetypes of behavioral motivation: intrinsic motivation, extrinsic motiva-tion, and amotivation; and four types of behavioral regulation withinextrinsic motivation: external, introjected, identified, and integratedregulation (Deci & Ryan, 1985, 2002, 2008; Deci et al., 1991). Intrinsicmotivation is when behaviors are done out of pleasure or for the sakeof enjoyment, such as when a student studies psychology becauseshe enjoys learning about human thinking and behavior. Extrinsic

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motivation is when behaviors are done to achieve a goal or reward be-yond the activity itself. For instance, a student may attend college withthe expectation of earning a higher salary with a degree, not becausehe enjoys learning. Amotivation is when individuals are not motivatedbecause they do not perceive any reward for their behavior. Therefore,students do not feel responsible for outcomes that affect them. In thiscase, a student may attend college because he feels that he has noother alternative or is coerced to attend by his parents.

Within extrinsic motivation, external regulation is when a personengages in a behavior to obtain an external reward or avoid a punish-ment. For example, a student on academic probation may study severalhours a night for her chemistry exam to avoid academic suspension orexpulsion. Introjected regulation is when one engages in behavior tomaintain personal expectations or avoid guilt. In this sense, motivationis internalized, but individuals are not engaging in activities for the plea-sure of the activity itself. A student may still feel pressured to engage inan activity, but the pressure comes from him- or herself rather than an-other person or goal. For example, a studentmay attend college to proveto himself that he can obtain a college degree. Identified regulation iswhen a person truly values the behavior even though he or she is notdoing it because he or she likes it. For example, a studentmay study sta-tistics because it will help him with his research, but may not enjoy thecomputations. Integrated regulation is when a person engages in a be-havior because the person perceives the activity as part of his or hercharacter or identity. Although a particular activity or behavior is notdone out of enjoyment; it supports other values, needs or behaviorsthat the individual does enjoy. For example, a studentmay study Frenchbecause she likes to travel and find her trips to France are more enjoy-able when she is able to speak French.

Since it was formulated, SDT has been organized using a variety ofdifferent structures as it applies to academic motivation. Some ofthese alternative structures include: (a) a three-factor structure usingonly the three motivation types (Komarraju, Karau, & Schmeck, 2009);(b) a four-factor structure (Sheldon & Elliot, 1998; Smith, Davy, &Rosenberg, 2012); and (c) a hierarchical structure using Deci andRyan’s (1985) three types of motivation as the higher-order factorsand six lower-order factors (Vallerand, Blais, Brière, & Pelletier, 1989;Vallerand et al., 1992). Sheldon and Elliot’s model includes: autonomousintrinsic motivation, autonomous identified motivation, controlledextrinsic motivation, and controlled introjected motivation. Althoughthe construct labels are different from those in Deci and Ryan’s model,the meanings are much the same. Autonomous intrinsic motivation issimilar to Deci and Ryan’s intrinsic motivation; autonomous identi-fied motivation is similar to identified motivation; controlled extrin-sic motivation is similar to extrinsic motivation; and controlledintrojected motivation is similar to introjected motivation. Otherthan using four factors instead of six, the only other clear differencebetween Sheldon and Elliot’s model and Deci and Ryan’s model isthat Sheldon and Elliot classify intrinsic and identified motivationsas autonomous, such that students feel that they are in control of theireducational choices; and extrinsic and introjected motivations arecontrolled by others, such that students feel that they are persuadedto attend college.

When Vallerand et al. (1989) adapted SDT to their academic motiva-tion scales, they found only three distinct regulations of extrinsic motiva-tion emerged from a factor analysis: external, introjected, and identifiedregulations. However, three subfactors for intrinsic motivation werealso identified: intrinsic motivation to know (IM to know), intrinsic mo-tivation toward accomplishments (IM to accomplish things), and intrinsicmotivation to experience stimulation (IM to experience stimulation). IMto know is when a person engages in a behavior for the primary purposeof learning or exploring something new. For example, a studentmay readabout European history because he finds the subject fascinating. IM toaccomplish things is when a behavior is done for the satisfaction ofaccomplishing a task, to feel competent or to create something. For exam-ple, a student may write an optional senior thesis as means of meeting a

Please cite this article as: Clark, M.H., et al., Mediating relationships bperformance, Learning and Individual Differences (2014), http://dx.doi.org/

challenge that is not required. IM to experience stimulation iswhen aper-son engages in a behavior because he or she thinks it is exciting or stim-ulating. For instance, a studentmay attend an acting class because the rollplaying exercises are fun and exciting.

1.2. Academic motivation’s influence on academic performance

Several researchers have found that academic motivation predictsacademic performance among college students (Robbins et al., 2004;Tavani & Losh, 2003). However, many studies are not consistent interms of how each type of motivation relates to performance. Whilesome studies have found that students with higher levels of intrinsicmotivation had higher college GPAs (Cokley, 2003; Davis, Winsler, &Middleton, 2006; Komarraju et al., 2009), others did not find this rela-tionship (Baker, 2003; Prospero & Vohra-Gupta, 2007; Turner, Chan-dler, & Heffer, 2009). Although Komarraju et al. found that intrinsicmotivation was positively related to academic performance using athree-factor model, they found that only IM to accomplish things pre-dicted performance using a seven-factor model. Cokley also found thatamong the three types of intrinsic motivation, only IM to accomplishthings was positively correlated with GPA.

Relationships between external motivation and academicperformance are even less consistent. Among broadly defined sam-ples of college students, researchers found no relationship betweenextrinsic motivation and academic performance (Baker, 2003;Prospero & Vohra-Gupta, 2007; Turner et al., 2009). However,among first-generation college students, Prospero and Vohra-Guptafound that extrinsic motivation predicted lower GPAs. Whereas,Cokley (2003) found that one of the measures of extrinsic motivation,external regulation, was positively related to academic performancewhen using a predominately African-American sample.

In most studies that were reviewed, students who lacked academicmotivation demonstrated poor academic achievement (Cokley, 2003;Turner et al., 2009). However, other studies did not find that amotivationpredicted GPA (Baker, 2003; Komarraju et al., 2009) or that a relationshipwas conditional on other factors. Like for extrinsic motivation,Prospero and Vohra-Gupta (2007) found that amotivation predictedlower GPAs among first-generation college students, but not for non-first-generation students.

1.3. Institutional integration

Institutional integration refers to a student’s ability to adapt to andassimilate into educational environments, such as a high school or col-lege. Tinto (1975), Pascarella and Terenzini (1980), and Astin (1975)propose that there are two main types of institutional integration: aca-demic integration and social integration. Academic integration is a stu-dent’s potential to benefit from academic experiences, which are basedon that student’s academic performance and intellectual development,within an educational setting (Pascarella & Terenzini). This requiresthat the student is able to meet the institution’s educational demandsand that the institution is able to meet the student’s educational desires(Tinto, 1975, 1993). Therefore, academic integration is often based onthe amount of energy put into learning and obtaining good grades andinteractions with faculty. Social integration is a student’s social involve-ment and interactionswith other students (Pascarella & Terenzini). Thiswould include developing friendships, joining clubs and organizations,and informal interactions with faculty and staff to discuss or supportsocial issues (e.g. joining a gay rights support group or protestingagainst sexual assault). While both academic and social integrationmay involve interactions with students and faculty, the distinction isusually between the contexts of those interactions. That is academicintegration focuses on intellectual pursuits and social integrationsupports emotional and psychological well-being.

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1.4. Institutional integration’s influence on academic performance

Pascarella and Terenzini (1980, 1983) suggest that a student’s inte-gration into the university upon starting school is more importantthan prior intentions or other attributions the student brings withthem. Several researchers have found that a student’s academic integra-tion and whether they think they are being academically challenged attheir school will promote a greater amount of intellectual developmentthat will result in better educational outcomes, such as better gradesand successful graduation (Baker, Caison, & Meade, 2007; McKenzie &Schweitzer, 2001; Rienties, Beausaert, Grohnert, Niemantsverdriet, &Kommers, 2012). While researchers have examined the influence thatboth academic and social integration have on academic performanceand retention, they are more likely to find significant relationships be-tween academic integration and these educational outcomes thanwith social integration (Baker et al., 2007; Prospero & Vohra-Gupta,2007; Rienties et al., 2012). There have been a few studies that havefound positive relationships between social integration and academicperformance (Pan et al., 2008; Robbins et al., 2004; Ullah & Wilson,2007), but social integration alonemay not be sufficient in encouragingacademic success. Tinto (1975) postulates that, even if a person becomeshighly integrated into the social community of a university, but does notintegrate into the academic community, the student is not likely tograduate.

Althoughmost of the studies that examined both social and academ-ic integration showed that academic integration was positively relatedto academic performance, some studies have found that academic inte-gration was not a good predictor (Pan et al., 2008) or that specific typesof academic or social integrations differentially affected performance(Fischer, 2007). In one study, institutional integration was negativelyrelated to Australian college students’ GPAs (McKenzie & Schweitzer,2001). However, the measure that they used did not distinguishacademic integration from social integration (Himelstein, 1992).

Pan et al. (2008) compared five types of educational programs thatwere designed to promote success in college students through academicand social integration. Although two types of programs that werespecifically targeted to improve academic integration (the First YearExperience courses and the Academic Help programs) did not improvefirst-year grade point average over the other programs, the Social Integra-tion programs and the General Orientation programs did. The GeneralOrientation programs focused on both academic integration (by describ-ing “program offerings, college expectations, [providing] informationabout assistance and services for examining interests and abilities”) andsocial integration strategies (by encouraging “working relationshipswith faculty [and providing] information about services that help withadjustment to college, and financial aid” p. 93).

Fischer (2007) found that the correlations between social and aca-demic adjustment and GPA varied depending on students’ ethnicityand the types of social and academic relationships they had formed.Asian, Hispanic and African-American students who formed strong,formal, on-campus, social ties (by joining clubs, interest groups orfraternities) had high GPAs. However, Hispanic students with strong,informal, on-campus, social ties (through friendships with other stu-dents) had low GPAs. All students with strong, formal academic tieswith their professor had high GPAs. However, only Asian and Hispanicstudents who sought academic enrichment (i.e., tutoring) saw an in-crease in their GPAs.

1.5. Current study

Although previous studies have found that both academic motiva-tion and academic integration predict academic performance, there islittle evidence to support how motivation and integration are relatedto each other. Among the studies that have examined how both aca-demicmotivation and institutional integration predict academic perfor-mance, they are limited by the way that academic motivation and

Please cite this article as: Clark, M.H., et al., Mediating relationships bperformance, Learning and Individual Differences (2014), http://dx.doi.org/

institutional integration are measured (Allen, 1985; Geiger & Cooper,1995; Prospero & Vohra-Gupta, 2007) or they did not examine therelationship between motivation and integration (Huffman, Sill, &Brokenleg, 1986). Allen (1985) found that achievement drive and socialintegration are not related (r = .04), and Geiger and Cooper (1995)found a small, negative relationship between students’ need for achieve-ment and need for affiliation (r=− .17). However, neither study lookedat how academic motivation was related to academic integration.Prospero and Vohra-Gupta (2007) provide the best evidence for howmotivation and integration concurrently relate to academic performance.However, they used a three-factor model of motivation, rather than theseven-factor model proposed by Vallerand and his colleagues. Not onlydid Prospero andVohra-Guptafind that amotivation, extrinsicmotivationand academic integration predict academic performance for first-generation college students; they found that motivation was relatedto academic integration. Specifically, intrinsic and extrinsic motivationwere positively related to academic integration, while amotivationwas negatively related to academic integration. However, many ofthese relationships were not found among students who were notfirst-generation college students. Because Propsero and Vohra-Guptaincluded academic motivation and integration in a regression modelto predict academic performance, it is not clear whether the null rela-tionships that they found for the non-first-generation students weredue to weak relationships between their predictors and the outcomeor because the predictors correlated with each other. Although therewere fewer strong correlations between the predictors for the non-first-generation students, each non-significant predictorwas significantlycorrelated with at least one other predictor. If academic integration hada direct effect on performance without accounting for motivation, it islikely that amotivation indirectly affected performance.

Given that other researchers have found mediational relationshipsbetween other social factors and academic performance through insti-tutional integration (Cabrera et al., 1993; Rivas et al., 2007) and the cor-relations between academic motivation, integration and performance;we wanted to examine whether or not academic integration mediatedthe relationships between academic motivation and performance. Thepresent study aims to add to and extendprevious research by (a) testingindirect relationships between academic motivation and performancethrough integration, (b) focusing on correlations between academic in-tegration andmotivation, and (c)measuring academicmotivation usingseven different subscales as recommended by Vallerand et al. (1992).Based on the lack of evidence for these indirect relationships in previousresearch, we cannot hypothesize with any certainty which, if any, typeof motivation will indirectly affect performance.

2. Method

2.1. Participants

Participants consisted of 81 first-year undergraduate collegestudents enrolled in an introductory psychology course at a rural Mid-western university. The mean age of students was 19.11 (SD = 4.52)years old. Seventy-three percent identified themselves as women;66.7% identified themselves as White, 21% as African American, 5% asBiracial or Multiracial, 4.9% as Latino, and 2.5% as Asian or Asian-American.

2.2. Measures

2.2.1. The Academic Motivation Scale (AMS-C 28) College VersionThe AMS-C 28 (Vallerand et al., 1992, 2004) is a 28 item measure of

academicmotivation used to determine reasonswhy students attendedcollege. Its seven-subfactor structure is based on Deci and Ryan’s (1985)self-determination theory. The seven subscales are comprised of (a)three measures of intrinsic motivation: intrinsic motivation to know(IM to know), intrinsic motivation toward accomplishments (IM to

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accomplish), and intrinsic motivation to experience stimulation (IMto experience); (b) three measures of extrinsic motivation: externalregulation, introjected regulation, and identified regulation; and(c) amotivation.

Reliability of the AMS-C 28 has been established with measures ofinternal consistency and test-retest reliability (Vallerand et al., 1992).Although each subscale is measured by only four items, Cronbach’salpha indicated that six of the seven subfactors had good internal con-sistency (Nunnally & Bernstein, 1994), ranging from r = .83 to r =.86, for one sample of 745 students. The identified regulation subscalehad an alpha of .62 for the first sample, but r = .72 (pretest) and r =.78 (posttest) for a second sample. Test-retest reliability was good forall seven subscales, ranging from r = .71 to r = .90, in a sample of 57students (Vallerand et al., 1992). For the sample used in this study, allseven subscales had good internal consistency (Chronbach’s alphasranged from .76 to .90). Concurrent and construct validity of the AMS-C 28 has been established with significant correlations between theseven subfactors of the AMS-C 28, existing measures of motivation,motivational antecedents, and motivational consequences (Vallerandet al., 1993).

2.2.2. Institutional Integration Scale (IIS)The IIS (Fox, 1984; Pascarella & Terenzini, 1980) is a 30 item mea-

sure consisting of five subscales that was developed based on Tinto’s(1975) model of a college student’s commitment to school and studentdropout rates. The five subscales are peer group interaction (based onseven items), interactions with faculty (five items), faculty concern forstudent development and teaching (five items), academic and intellec-tual development (seven items), and institutional and goal commit-ments (6 items). The reliability and discriminant validity of Fox’smodified version of the IIS has been established using a sample of 412disadvantaged college students. Cronbach’s alphas, ranging from r =.72 to r= .80, indicated that all five subfactors had good internal consis-tency. Intercorrelations between the subscales confirmed that the fivesubscales were measuring different constructs. Although all five sub-scales were administered, only the academic and intellectual develop-ment subscale was used in the analyses for this study. The internalconsistency for this subscale using this sample was r = .70.

2.2.3. Grade point average (GPA)The participants’ semester GPAswere obtained from theUniversity’s

records and registration office. First-year GPAwas calculated by averag-ing the participants’ GPAs that they earned during the semester inwhich they took themeasures and the following semester. For example,for participants who were recruited in fall 2004, academic motivationwas measured in September 2004, academic integration was measuredin November 2004, and GPA was measured in May 2005.

2.3. Procedure

Participants were recruited from an introductory psychology courseas part of a study examining traits related to academic success and col-lege attrition. Self-reported data were collected at the beginning andend of four semesters in fall 2004, spring 2005, fall 2005 and fall 2008.All participantswere college freshmenwhoprovided researchers accessto their college academic records. During the first four weeks of the se-mester, written self-reported responses were gathered from students ingroups of two to 12 using a battery of questionnaires thatwere intendedto measure personality, academic importance, and demographic infor-mation thought to be related to academic performance. The same stu-dents completed a second battery of measures during the last fourweeks of the semester. The second batterywasmeant to assess changesin demographics, institutional integration, and social adjustment. Of themeasures used in the original data collection only twowere used for thisstudy: the AMS-C 28 from the first battery and the Institutional Integra-tion Measure from the second battery. We obtained participants’ grade

Please cite this article as: Clark, M.H., et al., Mediating relationships bperformance, Learning and Individual Differences (2014), http://dx.doi.org/

point averages (GPA) from their academic records for the semester inwhich they took the measures and the following semester. Whilethere were no foreseeable risks, participants received course credit fortheir participation and ethical standards to protect participants weremaintained throughout the study.

3. Results

3.1. Analytic approach

Althoughmost researchers aremore familiar with the Normal Theo-ry (NT) approach when testing mediational models (Baron & Kenny,1986), a bootstrap approach was more appropriate for these data.For valid results, the NT method requires that a sample size is large(n N 80) and the mediating and dependent variables are normally dis-tributed (Preacher & Hayes, 2008; Shrout & Bolger, 2002). However, inthis study, neither academic integration (KS(81) = .131, p = .001)nor first-year GPA (KS(81) = .123, p = .004) were normally distribut-ed. In the bootstrap approach, samples are randomly drawn from theoriginal data and tend to be less skewed and kurtotic than the originalsample. Indirect effects (ab) are estimated for each of the 1000 drawnsamples and 95% confidence intervals are used to determine whetheror not the indirect effects are statistically significant.

3.2. Statistical Assumptions

Appropriate tests for statistical assumptions for linear regressionwere conducted on the original data set to ensure the validity of the re-sults. First, we tested to see if either gender or the time inwhich sampleswere collected moderated the direct effects. Although gender did notmoderate any of the statistical relationships between academic integra-tion and the motivational types or academic performance, time of datacollection did moderate the relationship between amotivation and aca-demic integration, F(3,73) = 3.288, p = .025. Although it would nor-mally be best to analyze the mediational model separately for eachyear that data were collected, some of the years had so few participantsthat the models could not be run. Therefore, we did not run the modelsseparately for each year. The potential implications for this choice arediscussed in the limitations section at the end of the paper.

Trend analyses indicated that all but one of the relationships be-tween motivation types, academic integration and GPA were linear.There was a significant cubic relationship between IM to accomplishand academic integration, β = 2.862, t(77) = 3.168, p = .002. There-fore,we included the non-linear trends of this variable in themediation-al models. The Shapiro-Wilk’s test indicated that none of the residualerrors from the regressions of each academic motivation type (testedindividually) onto academic integration; all of the academic motivationtypes (tested concurrently) onto academic integration, SW(81) = .959,p= .011; or academic integration onto GPA, SW(81) = .951, p= .004,were normally distributed. Fortunately, the bootstrap method tends toreduce the effects of this statistical violation.

Although tolerance and variance inflation factors (VIF) for the lineartrends in each regression fell within acceptable limits (Tolerance N .2and VIF b 5), several of the correlations between the seven academicmotivation types were high (see Table 1). Because multicollinearitywas a concern when including all of the motivational types in a sin-gle regression model, each mediation model was conducting twice:(1) with the other academic motivational types as covariates and(2) without including the other motivational types in the models.

3.3. Mediational analyses

Using the INDIRECTmacro for SPSS provided by Preacher and Hayes(2008), we computed 1,000 bootstrapped sampleswith a sample size ofN= 81 to estimate the indirect effect of each of the seven types of aca-demicmotivation onGPAwithout accounting for the contribution of the

etween academic motivation, academic integration and academic10.1016/j.lindif.2014.04.007

Table 1Medians, ranges and correlations between academic motivation types, academic integration and first-year grade point average.

Variable Median Range 1 2 3 4 5 6 7 8

1. Intrinsic motivation to know 5.00 1.00–7.002. Intrinsic motivation to accomplish 4.50 1.50–6.75 .693. Intrinsic motivation to experience 3.00 1.00–6.50 .69 .524. Identified regulation 5.75 3.50–7.00 .57 .43 .285. Introjected regulation 5.00 1.00–7.00 .52 .70 .32 .416. External regulation 6.00 2.50–7.00 .31 .36 .12 .51 .397. Amotivation 1.00 1.00–4.25 −.27 −.23 −.16 −.29 .00 −.128. Academic integration 3.71 2.00–5.00 .34 .27 .15 .22 .12 .09 −.159. First-year grade point average 3.30 1.46–4.00 .10 .08 −.12 .15 −.14 .02 −.25 .34

Note: Since not all of the distributions are normal and some of the relationships are curvilinear, medians are reported instead of means and the correlations were computed usingSpearman’s Correlation. Correlations are represented as effect sizes rather than tests of hypotheses; those close to r = .1 are small, r = .3 aremoderate, and r = .5are large (Cohen, 1988).

5M.H. Clark et al. / Learning and Individual Differences xxx (2014) xxx–xxx

other motivation types (see Fig. 1). Using this approach, academicintegration mediated the relationship between two of the seven typesof academic motivation and GPA (see Table 2). The means of the 95%confidence intervals for the indirect effects (ab) suggest that intrinsicmotivation to know and intrinsic motivation to accomplish things areindirectly related to first-year GPA through academic integration.More specifically, the non-linear relationship between intrinsic motiva-tion to accomplish things and first-year GPA are mediated by academicintegration. As illustrated in Fig. 2, for students whose internal motiva-tion to accomplish things is moderate (scores between 3 and 5), there isno relationship between internal motivation to accomplish things andacademic integration. However, for students who had either high(scores above 5) or low scores (below 2), there was a positive relation-ship between internal motivation to accomplish things and academicintegration.

To account for the correlations between the seven types of academicmotivation, we used Hayes and Preacher (2013) MEDIATE macro forSPSS to compute bootstrapped path analyses to estimate the indirect

Fig. 1. Venn diagram of the statistical model that tested each type of academicmotivationindependently of the others.

Please cite this article as: Clark, M.H., et al., Mediating relationships bperformance, Learning and Individual Differences (2014), http://dx.doi.org/

effect of academic motivation on GPA (as illustrated in Fig. 3). Unfortu-nately, after accounting for the other types of academic motivation, ac-ademic integration only mediated the relationship between intrinsicmotivation to accomplish things and GPA (see Table 2). The relationshipsbetween IM to accomplish, academic integration and GPAwere the sameas those found when testing motivation types independently of eachother.

4. Conclusions

4.1. Primary findings

The key findings from this study suggest that some types of intrinsicacademic motivation are mediated by academic integration in their re-lationships with academic performance. Students who attend collegefor the satisfaction of accomplishing academic goals tend to believethat college helps them develop intellectually, which leads them to per-formwell academically.While this relationship is true for thosewho arestronglymotivated to attend college because there are driven to accom-plish things, this is not true for thosewho are lessmotivated by their ac-complishments. However, it does appear that those who derive verylittle internal satisfaction in their accomplishmentswill see few intellec-tual benefits of college and will be unsuccessful there. There is also evi-dence that those attending college because they enjoy learning newthings tend to believe that college will serve as a resource for thisknowledge, which they successfully attain. Although this mediationalrelationship may be stronger than the indirect effect IM to accomplishhad on GPA, it is less consistent andmay not provide a unique contribu-tion towards predicting GPA. Though not statistically significant, theindirect relationship between amotivation and GPA was also notewor-thy. Not surprisingly, many students who did not see the benefits ofattending college did not believe they would intellectually benefitfrom attending college, which was reflected in their poor academicperformance. Although the statistical estimates for this relationshipwere high, the variability of the estimateswas also high. Thismost likelyoccurred because so few people in this sample lacked academicmotivation.

Despite several strong correlations between the motivational types,allowing the seven motivational measures to correlate with each otherhad little effect on how each motivation type influenced academic per-formance through integration. Although the three facets of intrinsicmo-tivation correlated well with each other and the three facets of extrinsicregulation correlated well with each other (see Table 1), themotivationtypes were different enough from each other to support that these aredistinctly different types of academic motivation. In most analyses, wefound the same effects for when each motivational type was tested in-dependently of the others as when we controlled for other motivationtypes in the model. The one exception was the indirect relationship be-tween IM to Know and academic performance. The difference betweenthe two models most likely occurred because IM to Knowwas stronglyrelated to the other types of intrinsic motivation.

etween academic motivation, academic integration and academic10.1016/j.lindif.2014.04.007

Table 2Indirect effects of motivation on GPA.

Independent variable Without controlling for other IVs Controlling for other IVs

Mean Indirect Estimate ab� �

SE 95% C.I Mean Indirect Estimate ab� �

SE 95% C.I

Intrinsic Motivation to Know .054 .026 .018, .126 .050 .041 −.030, .145Intrinsic Motivation to Accomplish1 .009 .005 .002, .021 .006 .004 .001, .016Intrinsic Motivation to Experience .028 .024 −.010, .090 −.026 .024 − .081,− .016Identified Regulation .040 .031 −.001, .124 −.006 .030 −.061, .064Introjected Regulation .017 .018 −.009, .064 −.012 .025 −.080, .022External Regulation .012 .023 −.024, .074 −.006 .023 −.054, .046Amotivation −.073 .060 −.278, .004 −.040 .056 −.163, .058

Notes: 1This models includes linear, quadratic and cubic terms for the independent variable. Estimates in bold are statistically significant.

6 M.H. Clark et al. / Learning and Individual Differences xxx (2014) xxx–xxx

4.2. Implications

Although intrinsicmotivation to accomplish thingswas the onlymo-tivation type that made a unique contribution to academic performancethrough academic integration, intrinsic motivations to know and expe-rience stimulation were also related to academic integration. Therefore,it is reasonable to conclude that intrinsic motivation predicts students’ability to adapt to the intellectual demands of college. Similarly, whileidentified regulation and amotivation may not have been unique pre-dictors of integration, their contributionsmay help academic counselorsidentify those who will have trouble adjusting to college. Hopefully, byknowing how academic motivation influences institutional integration,academic advisors can encourage students whowill likely struggle withcollege adjustment to participate in programs that will help them.

Regardless of the indirect relationships between academic motiva-tion and academic performance, academic integration was positivelycorrelated with first-year grade point average (r = .32, p = .009).Knowing that students are more likely to be successful in college ifthey adapt to their institutions has prompted many universities todevelop programs that teach students to acclimate both socially andacademically (Padgett & Keup, 2011).

4.3. Becoming integrated

While much of academic integration relies on students’ satisfactionwith their academic experiences, their satisfaction may depend ontheir ability to meet academic standards. Among universities that havelow standards of admission, many of the students who are accepted

Fig. 2. Scatterplot showing the cubic relationship between IM to accomplish and academicintegration.

Please cite this article as: Clark, M.H., et al., Mediating relationships bperformance, Learning and Individual Differences (2014), http://dx.doi.org/

into college are not prepared for college-level work (Conley, 2007). Ac-cording to Tinto (1993), students will most likely achieve academic in-tegration when the student is able to meet the institution’s academicexpectations of him or her and when the institution meets the studentseducational needs. If a student is not able to pass their courseworkdespite their best efforts, then intellectual integration will not occur.Likewise, if a student does not have the opportunities to learn skills nec-essary for a certain career path, then intellectual integration will notoccur. If both needs are met, then a student will become committed tothe institution through intellectual integration.

While university staff alone cannot be held accountable for studentadjustment, they can provide and encourage students to strive for aca-demic success through integration. Astin (1975) suggests schools thatoffer mentoring, tutoring, and honors programs help stimulate an indi-vidual’s ambition to learn. Therefore, developing and supporting aca-demic programs that offer assistance to struggling students will helpthem achieve their academic goals andmeet instructors’ standards. Ad-ditionally, Kuh, Schuh, Whitt, and Associates (1991) proposed thatthere is an essential relationship between the interaction with facultythat stimulates the academic integration and quality and amount of ef-fort a student exerts. Students’ intellectual integration and whetherthey think they are being academically challenged at their schooldepend on their view of the instructor’s involvement and educationalfacilitation (Tinto, 1993). Faculty can promote academic integration bycreating engaging, learning activities and classroom environments thatfacilitate discussions and participation.

4.4. Limitations

4.4.1. Generalizability across Student PopulationsThere are several limitations when generalizing these results to other

students and observations of academicmotivation and integration.Whilewe did attempt to resolve some of these limitations through the analysesused to make inferences about the mediating relationships, not all ofthese can be fixed through statistical adjustments. First, we recognizethat using a small sample size is a potential problem that may affectboth the statistical conclusion validity and the external validity of ourresults. While using the bootstrap approach lessened the threats to lowstatistical power and violating statistical assumptions, it cannot ensurethat our sample reflects the behavior of all first-year college studentsand prevented us from examining potential moderating factors.

Because measures of academic motivation and integration weretaken at two different times, only those students who were willingto come to a laboratory for this experiment twice were included inthis study. Not only does this explain the sample size, but the stu-dents in this subsample were different on some characteristicsfrom those who were willing to participate in a similar study thatonly required one attendance session. In particular, Wilcoxon testsindicated that the students in this sample had lower amotivationscores,W= 15157, n=452, z=−3.349, p= .001; higher academicintegration scores, W = 14248, n = 239, z = 8.958, p b .001; andhigher first-year GPAs, W = 21655, n = 452, z = 3.107, p = .002,

etween academic motivation, academic integration and academic10.1016/j.lindif.2014.04.007

Fig. 3. Venn diagram of the statistical model that tested each type of academic motivation after accounting for other types of academic motivation.

7M.H. Clark et al. / Learning and Individual Differences xxx (2014) xxx–xxx

than those who only took the measures on one occasion. Because thestudents used in this sample had a different distribution of scores forsome variables than those from amore general population, it is likelythat the restriction of the range of scores from this sample may haveaffected the results. Therefore, had we included participants from amore diverse sample, we may have been able to see stronger effects.It is also important to point out that we only sampled first-year col-lege students. Upperclassmen who have opportunities to studymore advanced (and perhaps more engaging) topics, such ashuman sexuality, costume design or history of the crusades mayhave also shown different effects.

A second limitation in using a sample size of only 81 students is thatwewerenot able to examinehowdemographic factorsmoderated the in-direct effects.Whilewe could confirm that the year inwhichwe collectedthe data did moderate the relationship between amotivation and aca-demic integration, there were not enough students in some cohorts torun the mediational models separately (i.e., the Spring 2005–Fall 2005cohort had n = 9 and the Fall 2005–Spring 2006 cohort had n = 11).Given that the full models used 11 degrees of freedom, there wouldneed to be several more students in each cohort to even run the models.Results from the regressions used to test formoderation suggested that islikely that academic integration did mediate the relationship betweenamotivation and GPA for students in the 2008–2009 cohort, but therewere not enough students to test this relationship for this cohort alone.Since none of the other cohorts shared this relationship, it was not statis-tically significant when testing students in all cohorts at once.

4.4.2. Generalizability across measured observationsA second major concern is about the generalizability and validity of

the instruments that we used to measure academic motivation and in-stitutional integration. We selected a measure of academic motivationthat we believed was most suitable for our population, since Vallerandet al. (1992, 1993) had demonstrated sufficient psychometric evidencethat it was a valid and reliable measure for college students, and other

Please cite this article as: Clark, M.H., et al., Mediating relationships bperformance, Learning and Individual Differences (2014), http://dx.doi.org/

studies have found that the seven-factor structure holds across a varietyof college samples (Cokley, Bernard, Cunningham, & Motoike, 2001;Fairchild, Horst, Finney, & Barron, 2005). However, as we discussed inour literature review, other researchers have also created and success-fully used other measures of academic motivation, which are based ondifferent factor structures. Therefore, it is possible that the results ofour study may not generalize across observations of academic motiva-tion. Had we used a three-factor model of academic motivation thatonly measured intrinsic motivation, extrinsic motivation, andamotivation (Komarraju et al., 2009) or a four-factor model that onlymeasured intrinsic motivation, identified regulation, external regula-tion, and amotivation (Smith et al., 2012); we may have found strongerindirect effects.

Although Pascarella and Terenzini (1980) Institutional IntegrationScale, is one of themore commonly usedmeasures of academic integra-tion, Baker et al. (2007) suggested that “IRT analyses revealed that anumber of the items did not adequately reflect the construct and shouldbe revised or removed from the measure” (p. 545). A confirmatory fac-tor analysis conducted by French and Oakes (2004) also indicated thatthe “original theoretical model may [have been] problematic” (p. 88).We had actually used Fox’s (1984) modified version of the IIS, whichdemonstrated better internal consistency than Pascarella and Terezini’soriginal scale. However, it could have also benefited from some of themodifications made by French and Oakes, who added four items thatPascarella and Terezini had previously deleted and proposed a differentfactor structure. According to French and Oakes, the distinctions be-tween social and academic integration were not as clear as previous re-searchers had thought and that the ISS measured only two subfactors,Faculty and Student, not five. Therefore, our measure of academic andintellectual development may have measured students’ interactionswith others as much as their perceptions of how college helps themdevelop intellectually. It is possible that had we used the Academic Ad-justment subscale from the Student Adaptation to College Questionnaire(Baker & Siryk, 1999) we would have gotten different results.

etween academic motivation, academic integration and academic10.1016/j.lindif.2014.04.007

8 M.H. Clark et al. / Learning and Individual Differences xxx (2014) xxx–xxx

Acknowledgements

We would like to thank Matthew Herman, Vinetha Belur, NicoleCundiff, Deborah Racey, Alen Avdic, and Blake Hutsell for assistingwith data collection.

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