need for closure, achievement goals, and cognitive engagement in high school students

The Journal of Educational Research, 104:110–119, 2011Copyright C© Taylor & Francis Group, LLCISSN: 0022-0671 print / 1940-067 onlineDOI:10.1080/00220670903567406

Need for Closure, Achievement Goals,and Cognitive Engagement in High

School StudentsLEA HARLOWTERESA DEBACKERH. MICHAEL CROWSONUniversity of Oklahoma

ABSTRACT. The authors extend their understanding oflearner motivation by addressing questions around the con-struct of classroom need for closure in a high school sam-ple. First, they established that their classroom need forclosure measure demonstrated adequate psychometric prop-erties when used with high school students. Subsequently,they explored relationships among classroom need for clo-sure, achievement goals, and cognitive engagement using zero-order correlations and path analyses. Findings suggested thatthe 2 facets of classroom need for closure are relatively inde-pendent of each other, and that high levels of preference forcertainty are more likely to be problematic for learners thanhigh levels of preference for structure. Furthermore, the re-lationship between classroom need for closure and cognitiveengagement is partially mediated by mastery goals. Implica-tions for instruction are discussed.

Keywords: achievement goals, motivation, need for closure

T he research literature is rich with studies seekingto understand the relationships among achieve-ment goals, other motivation-related variables,

and academic achievement. Findings from these studieshave led to increasingly sophisticated theoretical explica-tions of achievement goals (Daniels et al., 2008; Elliot &Church, 1997; Grant & Dweck, 2003; McInerny, Marsh, &Yeung, 2003; Senko, Durik, & Harackiewicz, 2008; Thrash& Hurst, 2008) as well as shedding light on how achieve-ment goals are related to interest (Harackiewicz, Durik,Barron, Linnenbrink-Garcia & Tauer, 2008; Hulleman,Durik, Schweigert, & Harackiewicz, 2008), emotions(Pekrun, Elliot, & Maier, 2009; Witkow & Fuligni, 2007),challenge seeking (Jagacinski, Kumar, & Kokkinou, 2008),information exchange (Poortvliet, Janssen, Van Yperen, &Van de Vliert, 2007), and culture (Ho & Hau, 2008). Otherresearchers have investigated how achievement goals are re-lated to cognitive engagement and academic performance(Bandalos, Finney, & Geske, 2003; Greene, Miller, Crow-son, Duke, & Akey, 2004; Kaplan, Lichtinger, & Gorodet-sky, 2009; Vrugt & Oort, 2008; Walker & Greene, 2009) and

how those relations might change over time (Shim, Ryan, &Anderson, 2008). Less is known about possible psychologi-cal antecedents of achievement goals. We propose that needfor closure may be a potent antecedent to achievement goaladoption and may be directly or indirectly (via achievementgoals) related to cognitive engagement as well.

The present study extends our understanding of learnermotivation by addressing both measurement and substantivequestions around the construct of classroom need for closure.First, we investigated correlations among classroom need forclosure, achievement goals, and cognitive engagement, thusshedding further light on sparse and inconsistent findings re-garding the role of need for closure in the classroom. Second,we investigated classroom need for closure as a possible an-tecedent to achievement goals. We hypothesized that highclosure needs may impede adoption of mastery goals dueto the possible association of mastery learning with com-plexity or ambiguity, or promote adoption of performancegoals as the external standards associated with performancegoals may provide relative certainty in regard to learningattainment. Finally, we used path modeling to test whetherclassroom need for closure could predict cognitive engage-ment, and whether achievement goals act as mediators ofthat relationship. Because we conducted this study with ahigh school sample, it also provides an initial indication ofwhether classroom need for closure can be reliably measuredin high school students and whether relationships amongthe variables under study are similar in high school versuscollege learners.

Cognitive Engagement

A common way of describing the cognitive engagementof learners is in terms of their depth of processing (Craik& Lockhart, 1972; Graham & Golan, 1991), such as deep

Address correspondence to Teresa K. DeBacker, Department of Ed-ucational Psychology, University of Oklahoma, 820 Van Vleet Oval,Norman, OK 73019-2041, USA. (E-mail: [email protected])

The Journal of Educational Research 111

versus shallow cognitive processing (Bandalos et al., 2003;Craik & Lockhart, 1972; Greene & Miller, 1996; Ravin-dran, Greene, & DeBacker, 2005). Deep processing refersto efforts on the part of the learner to promote the de-velopment of more elaborate knowledge structures. Whena learner is engaged in deep processing during an academictask, he or she demonstrates high levels of metacognitive en-gagement, which contributes to more strategic approaches toinformation processing. This type of processing is associatedwith deeper comprehension of course material (Entwhisle& Ramsden, 1983; Nolen, 1988) and better overall levels ofacademic performance (Miller, Greene, Montalvo, Ravin-dran, & Nichols, 1996; Pintrich & Garcia, 1991; Schunk,1991). Examples of deep processing strategies include theuse of paraphrasing or summarizing material, using picturesor diagrams to assist in solving problems, checking for un-derstanding, generating analogies, and outlining text, amongothers (Hofer, Yu, & Pintrich, 1998; Miller et al., 1996).

Shallow engagement, on the other hand, refers to low-level cognitive involvement on the part of the learner that isdirected toward reproducing learning material as opposed toelaborating on it. Rather than using metacognitive strategiesdesigned to deepen an individual’s understanding of coursematerial, these strategies are aimed at internalizing infor-mation as is. Examples of shallow processing strategies mayinclude repetitive rehearsal and rote memorization of infor-mation, use of mnemonic techniques, underlining or high-lighting text while reading, and rereading notes, among oth-ers (Hofer et al., 1998; Meece, Blumenfeld, & Hoyle, 1988;Miller et al., 1996; Nolen, 1988; Ravindran, et al., 2005;Weinstein & Mayer, 1986; Zimmerman & Pons, 1986). Inthe present study we were interested in the relationship be-tween indicators of cognitive engagement (i.e., use of deepand shallow processing strategies) and two antecedent con-structs, achievement goals and need for closure.

Achievement Goals

Achievement goals have long been considered importantexplanatory constructs in attempting to understand individ-ual behavior in educational settings (Ames, 1984; Ames &Archer, 1988; Dweck, 1986; Dweck & Elliott, 1983; Ryan,1982). They are defined as the purposes individuals have fortask engagement (Maehr, 1989). Dweck and colleagues sug-gested that selection and pursuit of different achievementgoals influence virtually every aspect of the achievementsituation, often by differentially impacting the manner inwhich individuals structure and process information (Dweck& Elliott, 1983; Dweck & Leggett, 1988; Elliott & Dweck,1988). For example, an individual’s focus of attention andeffort, interpretation of errors and uncertainty, standards forsuccess, type of feedback desired, and views of the instructorcan be influenced in adaptive and/or maladaptive ways de-pending upon the type of achievement goal pursued (Ames,1992; Elliot, 1999; Newman, 1998).

Mastery goals refer to an individual’s orientation towarddeveloping new skills, trying to understand work, improvinglevel of competence, or achieving a sense of mastery basedon self-referenced standards. Goal theory research has shownthat being mastery-oriented leads to better motivationaland academic outcomes than being performance-oriented(Dweck & Leggett, 1988; Elliott & Dweck, 1988; Kaplan &Midgley, 1997; Midgley & Urdan, 2001; Pintrich, Roeser, &DeGroot, 1994; Ryan & Patrick, 2001; Wentzel, 1996). Stu-dents with mastery goals tend to be more concerned with de-veloping skills and mastering a task than with getting a goodgrade or recognition from teachers, peers, or parents. Forthese students developing new skills, understanding, learn-ing, and solving problems are ends in themselves (Ames &Archer, 1988; Nicholls, 1992). Mastery goal orientation ispositively associated with self-efficacy, task value, individualinterest, effort and persistence on tasks, use of deep learningstrategies, and academic performance (Pintrich, 2000; Shih,2005; Wolters, 2004).

Performance goals have been described as goals for whichindividuals seek to gain favorable judgments or avoid nega-tive judgments of their competence (Elliot, 1999; Elliot &Church, 1997; Elliot & Harackiewicz, 1996). Because thegoal is to look competent, students high in performancegoals may use shortcuts designed to facilitate positive com-parisons with others rather than to achieve deep learning(Covington, 1992).

Performance goals have been argued to have various neg-ative learning outcomes. For example, one negative effectmay be a low rate of persistence in the presence of failure,challenge-avoidance of a task, and overall low intrinsic mo-tivation (Ames, 1988; Dweck, 1986; Elliot & Harackiewicz,1996; Shih, 2005). When students with high performancegoals encounter difficulties in finishing a task, they oftenbecome frustrated and are more likely to quit. Moreover,because the focus of learning is on demonstrating compe-tence, performance-oriented students tend to choose tasksin which they are certain they will succeed (Meece et al.,1988).

Elliot and colleagues (Elliot, 1999; Elliot & Church,1997; Elliot & Harackiewicz, 1996; Elliot & McGregor,1999) proposed that performance goals should be differen-tiated into approach (proving relative ability) and avoid-ance (avoiding failure) components. According to these au-thors, performance-avoidance goals are characterized by adesire to avoid unfavorable competence judgments by oth-ers. Performance-approach goals, on the other hand, reflecta desire to attain favorable judgments of an individual’scompetence (Elliot, 1999; Elliot & Church, 1997; Elliot &Harackiewicz, 1996).

An individual who is performance-avoidance orientedis compelled by a fear of failure, especially in the pres-ence of others. This inclination towards failure rather thansuccess generates a focus on avoiding negative outcomes(Elliot & Church, 1997; Elliot & McGregor, 1999), which,in turn, elicits self-protective strategies that result in a host

112 The Journal of Educational Research

of negative processes and outcomes (Elliot & Church, 1997;Midgley & Urdan, 2001). Students who pursue performance-avoidance goals are more likely to think they will performpoorly, so they avoid seeking challenging tasks, do not useeffective study strategies, and are unwilling to seek help(Ames & Archer, 1988; Kaplan & Midgley, 1997; Meeceet al., 1988; Middleton & Midgley, 1997; Wolters, 1998).Additionally, individuals who are performance-avoidanceoriented tend to exhibit high levels of test anxiety, low self-esteem, low levels of intrinsic motivation, use of maladaptivestudy strategies, poor study habits, and a lack of task in-volvement (Elliot & Church, 1997; Elliot & Harackiewicz,1996; Elliot & McGregor, 1999; Harackiewicz, Barron, &Elliot, 1998; Middleton & Midgley, 1997). Overall, indi-viduals who endorse performance-avoidance goals tend toconstrue achievement settings as a threat.

Studies on performance-approach goals have been mixedin the empirical literature in terms of their effects onlearning. Performance-approach goals have been posi-tively linked to better performance outcomes (Elliot &Church, 1997; Elliot & McGregor, 1999; Elliot, Mc-Gregor, & Gable, 1999), effort and persistence whilestudying (Elliot et al., 1999), and intrinsic motiva-tion (Elliot & Harackiewicz, 1996). Students may alsoperform as well as or better than mastery students onachievement-related outcomes such as grades or test perfor-mance (e.g., Harackiewicz et al., 1998; Harackiewicz, Bar-ron, Tauer, Carter, & Elliot, 2000). Moreover, there is ev-idence that a performance-approach orientation relates topositive academic behaviors when it is combined with amastery orientation (Harackiewicz et al., 1998; Midgley &Urdan, 2001; Pintrich, 2000; Wentzel, 1993). On the otherhand, performance-approach goals have also been linked totest anxiety (Elliot & McGregor, 1999), shallow processingof academic information (Elliot et al., 1999; DeBacker &Crowson, 2006), and avoidance of help seeking (Middle-ton & Midgley, 1997). In the present study we measuredthree achievement goals: mastery, performance-approach,and performance-avoidance.

Need for Closure

Variations in knowledge acquisition processes andinformation-seeking can affect the way in which informationis interpreted and retrieved, and the decisions individualsreach. Kruglanski’s lay epistemic theory (LET) assumes that“knowledge is formed in the course of a two-phase sequencein which hypotheses are generated and evaluated” (Kruglan-ski, 1990, p. 335). A key assumption of this model is thatboth epistemic motivation and cognitive capacity affect thehypothesis generation and validation sequence. Individualswho are less motivated or who have less cognitive capac-ity generate fewer hypotheses for consideration during theknowledge construction process (Kruglanski, 1989).

Kruglanski conceptualized the motivational influence onthe hypothesis generation and validation sequence in terms

of a need for closure. Need for closure is defined as the desirefor a firm answer to a question and the eschewal of am-biguity (Kruglanski, 1989, 1990). Kruglanski and Webster(1996) expanded this definition by defining need for closureas the extent to which individuals desire knowledge aboutthe world in which they live that is clear, unambiguous, andessentially unlikely to change. In turn, this desire for cer-tainty and clarity is believed to have important effects onhow individuals use the information available to them toreach conclusions about the social world.

According to Kruglanski and Webster (1996), the need forclosure gives rise to two general processing tendencies. First,individuals may experience the urgency tendency, whichleads them to seize on closure quickly. When this tendencyis activated, an individual will opt for any reasonable an-swer that presents itself, as long as it is immediate. Second,an individual may experience the permanence tendency,resulting in the dual inclination (a) to preserve, or freeze,on past knowledge and (b) to safeguard future knowledge.The freezing tendency, in effect, prevents an individual fromrevising his or her prior knowledge and beliefs in the faceof new information. Numerous research studies have shownthat individuals experiencing high need for closure seize onearly information and then freeze on it, inadequately adjust-ing their opinions even when faced with further information(Heaton & Kruglanski, 1991; Kruglanski & Freund, 1983;Kruglanski & Webster, 1996).

To date, the need for closure has largely been studied incollege students within the realm of social decision mak-ing, leaving potential relationships between closure needsand academic functioning relatively unexplored. For ex-ample, various studies have shown need for closure to belinked to a preference for in-group versus out-of-group mem-bers (Kruglanski, Shah, & Pierro, 2002; Shah, Kruglanski,& Thompson, 1998), resistance to persuasion (Kruglanski,Webster, & Klem, 1993), and both primacy (Webster &Kruglanski, 1994) and recency (Richter & Kruglanski, 1998)effects during impression formation. Three recent studieshave addressed need for closure specifically in the classroom.

DeBacker and Crowson (2006) used Webster andKruglanski’s (1994) survey to explore relationships amongfacets of need for closure and motivation-related variables.They reported that need for closure correlated positively andsignificantly with shallow processing, but failed to accountfor incremental variation in shallow processing after con-trolling for naıve epistemological beliefs. Moreover, need forclosure positively and significantly predicted mastery goalsafter controlling for naive epistemological beliefs, but failedto significantly predict performance goals.

DeBacker and Crowson (2008) introduced a classroom-based measure of need for closure that is better suited to ad-dress relationships among closure needs, achievement goals,and academic outcomes. The classroom closure measure iscomprised of two scales: Preference for Structure and Prefer-ence for Certainty. Items measuring preference for structureaddress students’ needs for a well-ordered and predictable


classroom setting, whereas items measuring preference forcertainty address students’ needs for certain answers andavoidance of ambiguity. In other words, preference for struc-ture relates to the general organizational characteristics of aclass. Preference for certainty relates more to the manner inwhich content of a course is presented to students.

Using the classroom-based measure of need for closure,DeBacker and Crowson (2008) found a negative relation-ship between preference for certainty and mastery goals,and positive relationships between each of the facets ofclassroom need for closure (i.e., preference for structureand preference for certainty) and each of the performancegoals (performance-approach and performance-avoidance).Miranda, DeBacker, and Crowson (2008) reported thesame negative relationship between preference for certaintyand mastery goals, although in their sample preferencefor certainty was positively related to each performancegoal whereas preference for structure was related only toperformance-approach goals. Furthermore, path analysis in-dicated that the relationship between classroom need forclosure and cognitive engagement is largely mediated bymastery goals. We used a newly constructed high school ver-sion of the classroom-specific need for closure measure inthe present study.

Research Questions

The present study was designed to extend our understand-ing of achievement motivation by addressing both mea-surement and substantive questions around the constructof classroom need for closure. Research questions guidingthe study were:

Research Question 1: Can classroom need for closure be reli-ably measured in high school students?

Research Question 2: How are the two facets of classroomneed for closure, the three achievement goals, and indica-tors of cognitive engagement related to each other, and isthe pattern found in high school students similar to thatpreviously found in college learners?

Research Question 3: Do the data support our hypothesis thatclassroom need for closure is an antecedent to achieve-ment goals, and to what extent do achievement goals me-diate the relationship between classroom need for closureand cognitive engagement?

Method

Participants

Participants in the study were 341 high school studentsattending English classes at a large public high school inthe southwestern United States. A total of 52% of studentswere young women. The percentages for ethnicity wereAfrican American (2.6%), Asian American (4.1%), His-panic (5.5%), Native American (4.7%), Caucasian (66.2%),other or multiethnic (13.4%), and missing ethnicity data

(3.5%). The majority of students were juniors (46.1%) orseniors (44%). Students ranged in age from 15 to 19 years,with a mean age of 17.14 years (SD = .819).

Measures

Students completed two Likert-type paper-and-pencil sur-veys: Opinions About Learning and Studying (OLS)–HighSchool (HS) and Approaches to Learning (ATL).

Opinions About Learning and Studying–High School. Theoriginal college version of the 28-item version of the OLSmeasures two facets of need for closure in the classroom:preference for structure (18 items; e.g., “I feel uncomfortablewhen my [name of class] teacher is not well-organized forclass”), which addresses students’ preference for a high de-gree of structure in course organization (e.g., in the syllabusand schedule) and the physical environment; and prefer-ence for certainty (10 items; e.g., “I dislike being asked to do[name of class] projects or class activities that have unpre-dictable outcomes”), which addresses students’ preferencefor certainty in regard to course content. Validity informa-tion can be found in DeBacker and Crowson (2008). In orderto arrive at a high school version of the OLS (i.e., OLS-HS),we removed or altered the wording of those items from theoriginal OLS that we believed may introduce interpretativeproblems for high school students. For the most part, ourchanges impacted the Preference for Certainty subscale.

Approaches to Learning. The ATL Scale is a 41-item sur-vey that has been used in a number of previous studies(DeBacker & Crowson, 2006; DeBacker & Nelson, 2000;Greene & Miller, 1996; Miller, DeBacker, & Greene, 1999)and has accumulated a good body of validity-related ev-idence. Included in the survey are subscales measuringachievement goals (i.e., Mastery, Performance-Approach,and Performance-Avoidance) and cognitive-engagement(i.e., deep and shallow strategy use). Example items fromthe scales are, “I do my work in this class because I wantto understand the ideas” (Mastery); “I do my work in thisclass because I can show other people that I am smart”(Performance-Approach); “I do my work in this class be-cause I don’t want to be the only one who can’t do the workwell” (Performance-Avoidance); “When I study I underlinemain ideas in my notes” (Shallow Strategy Use); and “I cre-ate new examples of my own to check my understanding oftheories and concepts learned in my classes” (Deep StrategyUse).

Procedure

Students were informed of the study in their high schoolEnglish class. Parental consent was obtained for all partici-pants under the age of 18. Subsequently, student volunteersprovided personal assent and completed surveys at schoolduring their English class.


Results

Factor Structure of OLS-HS

We conducted an exploratory factor analysis of the OLS-HS using principal axis factoring. Because our original passat the data was designed merely to explore whether twofactors might account for the variation in survey responses,we chose not to force a two-factor solution at this time.Instead, the number of extracted factors was judged using theKaiser criterion and through examination of the scree plot.Although five factors exhibited eigenvalues greater than 1,the first two clearly explained a substantial proportion ofthe variation in survey responses. Factor 1 (eigenvalue =6.61) and Factor 2 (eigenvalue = 2.64) explained 27.56%and 11.02% of the variance, respectively, prior to rotation.Following varimax rotation, Factor 1 (eigenvalue = 2.53)and Factor 2 (eigenvalue = 2.41) explained 10.54% and10.04% of the variance, respectively. Moreover, the screeplot suggested a clear break in the data after Factor 2. Basedon these results, a two-factor solution appeared most tenable.Of particular note is that, following varimax rotation, allitems designed to measure preference for certainty loaded(with loadings ≥ .44) unambiguously onto only one of theextracted factors.

We reran our factor analysis, forcing our data to fit a two-factor solution. Following varimax rotation, Factor 1 (eigen-value = 5.27) and Factor 2 (eigenvalue = 2.71) explained21.95% and 11.29% of the variation in survey responses,respectively. Moreover, the pattern of factor loadings for therotated data was largely consistent with the two factors pro-posed to underlie the scale. Factor 1 was easily interpretableas Preference for Structure (with only 5 of 18 items loadingbelow |.40|, one of which loaded more highly with Fac-tor 2), whereas Factor 2 was interpretable as Preference forCertainty (with all loadings ≥.52). Two items—items 14and 16—failed to effectively discriminate between the Pref-erence for Structure and Preference for Certainty factors.In general, the results support the use of the OLS-HS as atwo-factor measure of need for closure. The resulting alphas(minus the six problematic items noted above) for Prefer-ence for Structure and Preference for Certainty were .86 and.71, respectively. Means, standard deviations, and internalconsistency estimates for all scales included in the study canbe found in Table 1.

Correlations

Preference for structure and preference for certainty cor-related at a modest level (r = .20), indicating that thesefactors are relatively independent of one another. Prefer-ence for structure correlated positively with mastery goals,performance-approach goals, performance-avoidance goals,shallow processing, and deep processing. Preference forcertainty, on the other hand, correlated negatively withmastery goals and deep processing and positively with

TABLE 1. Descriptive Statistics for All Scales Includedin the Study

Variable M SDCronbach’s

α

Mastery goals 4.11 1.12 .91Performance-approach goals 2.88 1.16 .85Performance-avoidance goals 2.03 0.93 .79Shallow processing 3.07 1.04 .72Deep processing 3.59 0.93 .74Preference for structure 3.65 0.90 .88Preference for certainty 3.41 0.99 .71

performance-avoidance goals. See Table 2 for correlationvalues.

Path Analyses

We used path analysis in order to test whether the re-lationships between preferences for structure and certainty,on the one hand, and deep and shallow processing, on theother, might be mediated through achievement goals withinour high school sample. We utilized LISREL 8.52 (Joreskog& Sorbom, 2002) in order to conduct our analyses, withmissing data treated through pairwise deletion. Moreover,we allowed the disturbance terms between performance-approach and performance-avoidance goals and betweendeep and shallow processing to covary in the path mod-els. We estimated the fit of each of our models by consultingthe following fit indices: comparative fit index (CFI), non-normed fit index (NNFI), standardized root mean squareresidual (SRMR), and the root mean square error of approx-imation (RMSEA), along with its 90% confidence interval(CI). CFI and NNFI values around .95 or greater, SRMRvalues less than .08, and RMSEA values at or less than .05are considered indicative of close fit (Byrne, 2005). SRMRvalues less than .10 and RMSEA values up to .08 can beconsidered favorable (Kline, 2005).

We began by testing a fully unconstrained model with allpossible direct and indirect relationships specified. The re-sulting path model fit the data poorly, NNFI = 0.65, CFI =0.97, SRMR = .042, RMSEA = .19 (90% CIs = .13, .26). Alldirect relationships involving preference for structure werepositive and statistically significant in the model. Preferencefor certainty emerged as a negative and significant predictorof mastery goals and deep processing and a positive and sig-nificant predictor of performance-avoidance goals. Masterygoals was a significant positive predictor of deep and shallowprocessing, whereas performance approach and avoidancegoals failed to predict the study strategy outcome variables.Combined, the predictors accounted for 37%, 6.6%, 13%,37%, and 40% of the variance in mastery goals, performance-approach goals, performance-avoidance goals, shallow pro-cessing, and deep processing, respectively.


TABLE 2. Pearson Product-Moment Correlations Among the Need for Closure, Achievement Goals, and CognitiveEngagement.

Variable 1 2 3 4 5 6

1. Preference for structure –2. Preference for certainty .20∗∗ –3. Mastery goals .48∗∗ −.28∗∗ –4. Performance-approach goals .25∗∗ .03 .32∗∗ –5. Performance-avoidance goals .19∗∗ .35∗∗ −.04 .41∗∗ –6. Shallow processing .51∗∗ −.08 .52∗∗ .19∗∗ .08 –7. Deep processing .41∗∗ −.23∗∗ .61∗∗ .23∗∗ −.02 .52∗∗

∗∗p < .01.

We trimmed the nonsignificant paths from the uncon-strained model and assessed the fit of this new model. Theresulting model yielded substantially better fit to the datathan the original unconstrained model, NNFI = 0.92, CFI= 0.97, SRMR = .05, RMSEA = .09 (90% CIs = .06, .13).In this model, all paths were statistically significant. Basedon an examination of the fit indices, we included a recom-mended covariance between the disturbance terms for mas-tery goals and performance-approach goals. The fit of thisthird, and final, model was very good, NNFI = 1.01, CFI =1.00, SRMR = .01, RMSEA ≤ .01 (90% CIs = .00, .05). Allpaths in this final model were statistically significant, withthe predictors accounting for 37%, 6.5%, 14%, 36%, and

40% of the variance in mastery goals, performance-approachgoals, performance-avoidance goals, shallow processing, anddeep processing, respectively. As reflected in the standard-ized paths coefficients (see Figure 1), preferences for certaintyand structure exerted medium and large effects, respectively,on mastery goals, whereas their effects on performance-avoidance goals were medium and small, respectively. Pref-erence for structure exerted medium effects on performance-approach goals and shallow processing, whereas its effect ondeep processing was somewhat modest. Finally, mastery goalsexerted strong direct effects on deep and shallow processing.

Tests of the indirect effects in the final model werecomputed using Kristopher Preacher’s online, interactive

FIGURE 1. Standardized path coefficients for final trimmed model.


calculator (http://people.ku.edu/∼preacher/sobel/sobel.htm).The indirect effect of preference for structure on shallowand deep processing via mastery goals was significant (ps< .001) in both cases. The standardized indirect effecton deep processing was .26 (medium effect), whereas thestandardized indirect effect on shallow processing was.20 (small effect). The indirect effect of preference forcertainty on deep and shallow processing via masterygoals was also statistically significant (ps < .001) in bothcases. The standardized indirect effects of preference forcertainty on deep and shallow processing were −.18 and−.14, respectively (both small effects). Finally, with respectto total effects, the standardized effect of preference forcertainty on deep processing was −.31 (moderate effect),whereas the effects of preference for structure on deep andshallow processing were .47 (large effect) and .53 (largeeffect), respectively. Clearly the total effects of preferencesfor structure and certainty on depth of processing were nottrivial.

Discussion

The purpose of the present study was to extend our under-standing of achievement motivation by addressing measure-ment questions around the construct of classroom need forclosure as well as substantive questions about how classroomneed for closure may fit into a model of motivation that in-cludes achievement goals and cognitive engagement. First,we tested the effectiveness of a modified version of the OLSto capture classroom need for closure in high school learners.After eliminating six problematic items we were left with twoscales, representing preference for structure and preferencefor certainty, which had clean factor loadings and acceptabledegrees of internal consistency.

Zero-order correlations indicated that preference forstructure and preference for certainty are relatively inde-pendent facets of classroom need for closure. Additionalevidence of the independence of these facets comes fromthe fact that they are related to achievement goals and cog-nitive engagement in distinct ways. Preference for structurewas positively related to each of the achievement goals andboth deep and shallow processing. In other words, studentswho reported high preference for structure also wanted tomaster their class material, perform well in class when com-pared to other students, and avoid looking less academicallyable than their classmates. In addition, they reported usinga variety of study strategies associated with both deep andshallow processing. Arguably, this pattern could suggest anexternal orientation in the learner marked by preference fora well-ordered and predictable educational environment as acontext for engaging in a range of behaviors associated withbeing a good student (learning course material, performingwell in class, employing known study strategies). It also sug-gests a somewhat simplistic or undifferentiated stance towardlearning in which more adaptive and less adaptive goals andstrategies are equally endorsed (all ps < .001).

Preference for certainty, on the other hand, revealed amore concerning pattern of correlations, being negativelyrelated to mastery goals and deep processing, and positivelyrelated to performance-avoidance goals. Students who havea high preference for certainty in regard to course contentare motivated to avoid looking academically incompetent,while failing to endorse adaptive behaviors such as pursuitof mastery goals and use of deep cognitive strategies. Over-all, the pattern of correlations suggests that a high level ofpreference for certainty is more likely to be problematic forlearners than a high level of preference for structure. Thesefindings are similar to those reported in studies of collegestudents (DeBacker & Crowson, 2006, 2008; Miranda et al.,2008).

We used path analysis to test the relationships amongfacets of classroom need for closure, three achievement goals,and two indicators of cognitive engagement in our highschool students. On the basis of previous work (DeBacker& Crowson, 2006, 2008; Greene, DeBacker, Ravindran, &Krows, 1999; Greene, & Miller, 1996; Greene et al., 2004;Walker & Greene, 2009), we expected that at least some ofthe achievement goals would be related to deep or shallowprocessing. We also expected that the two facets of need forclosure would be significant antecedents of at least some ofthe achievement goals; however, no specific hypotheses wereposed as prior findings have been somewhat inconsistent(DeBacker & Crowson, 2006, 2008; Miranda et al., 2008).Furthermore, we anticipated that achievement goals wouldmediate the relationship between classroom need for closureand cognitive engagement, as was found in a recent sampleof college learners (Miranda et al., 2008).

Findings were largely consistent with our expectations.Mastery goals predicted both deep and shallow processing,although the two performance goals did not. It seems that theadoption of mastery goals leads high school students to use avariety of study strategies (both deep and shallow), whereasadoption of performance goals of either valence is not re-lated to cognitive strategy use in a consistent manner. Datasupported modeling of the two facets of need for closure asantecedents of achievement goals. Preference for structurepositively predicted all three achievement goals, whereaspreference for certainty predicted mastery goals (negativerelationship) and performance-avoidance goals (positive re-lationship) mirroring the pattern seen in our zero-order cor-relations.

In regard to possible mediation effects, the final pathmodel indicated that the influence of classroom need forclosure on cognitive engagement is partially mediated bymastery goals. We note that this is the same pattern re-ported in a study of college learners (Miranda et al., 2008).Specifically, preference for structure was related to deep andshallow processing both directly and indirectly via masterygoals. Similarly, preference for certainty was directly relatedto deep processing and indirectly related to both deep andshallow processing via mastery goals. Again, the path modelseems to suggest a greater possibility of deleterious effects


associated with high preference for certainty, compared tohigh preference for structure.

Implications for Instruction

Our findings raise the question of whether and how ex-cessive need for classroom closure (particularly excessivepreference for certainty) might be addressed to ameliorateits negative effects on learning. A rich line of research insocial psychology indicates that environmental conditionscan influence level of need for closure in adults across a vari-ety of experimental tasks. For example, the need for closurecan be heightened by time constraints (Freund, Kruglanski,& Shpitzajzen, 1985; Kruglanski & Freund, 1983; Richter& Kruglanski, 1998), the laboriousness or dullness of a task(Webster, 1993), the presence of noise (Kruglanski et al.,1993; Rubini & Kruglanski, 1997), and fatigue (Webster,Richter, & Kruglanski, 1996). On the other hand, researchalso shows that individuals can voluntarily forestall theirdesire for closure under certain conditions, such as whenfears of making invalid judgmental conclusion are aroused(Kruglanski & Webster, 1996) or when the costs of makinga judgmental mistake are made salient prior to engagementin a decision-making task (Kruglanski, 1990; Kunda, 1990).

Further research is needed to determine whether featuresof the classroom environment may heighten or ease prefer-ence for certainty, which may, in turn, influence whetherstudents adopt more or less adaptive achievement goals orstudy strategies. For example, it is possible that particularclassroom experiences (e.g., heavy reliance on didactic in-struction, assessments containing a strong right or wrongemphasis) may contribute to increased preference for cer-tainty among students, whereas other experiences (e.g., en-couragement to consider multiple perspectives, valuing ofcritical thinking, providing ample time to perform tasks)would help students persist in their learning efforts by reduc-ing the effects of individual differences in need for closureon learning.

Conclusion

Present and previously published findings (DeBacker &Crowson, 2006, 2008; Miranda et al., 2008) suggest thatclassroom need for closure does, in fact, impact achievementgoals and cognitive engagement in high school as well ascollege learners. Furthermore, high levels of preference forcertainty, one facet of classroom need for closure, seem morelikely to impede learning than high levels of preference forstructure, which seem relatively benign. More work is neededto understand the range of implications for learning thatmay be associated with excessive preference for certaintyand whether classroom environments can be created thatwill reduce closure needs or, at the very least, moderate theeffects of individual propensities toward closure during thelearning process.

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AUTHORS NOTE

Lea Harlow is a high school English educator and graduateof the University of Oklahoma Department of EducationalPsychology. Her research interests include achievement mo-tivation in high school students.

Teresa K. DeBacker is a Professor and Chairperson of Edu-cational Psychology at the University of Oklahoma. Her re-search interests include the impact of teachers’ and learners’personal epistemology on children’s and teens’ motivationand achievement.

H. Michael Crowson is an Associate Professor of Educa-tional Psychology at the University of Oklahoma. His re-search interests include the application of need for closuretheory for understanding student and teacher behavior inthe classroom.

need for closure, achievement goals, and cognitive engagement in high school students

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