Measuring Situational Interest in Academic Domains

Transcription

1 Educational and Psychological Measurement OnlineFirst, published on March 3, 2010 as doi: / Measuring Situational Interest in Academic Domains Educational and Psychological Measurement XX(X) SAGE Publications DOI: / Lisa Linnenbrink-Garcia, 1 Amanda M. Durik, 2 AnneMarie M. Conley, 3 Kenneth E. Barron, 4 John M. Tauer, 5 Stuart A. Karabenick, 6 and Judith M. Harackiewicz 7 Abstract Three studies were conducted to develop and validate scores on a new measure appropriate for assessing adolescents situational interest (SI) across various academic settings. In Study 1 (n 858), a self-report questionnaire was administered to undergraduates in introductory psychology. Confirmatory factor analyses (CFA) supported a three-factor model that differentiated between interest generated by (a) the presentation of course material that grabbed students attention (triggered-si), (b) the extent to which the material itself was enjoyable and engaging (maintained-si-feeling), and (c) whether the material was viewed as important and valuable (maintained-si-value). CFA analyses in Study 2 (n 284) and Study 3 (n 246) also supported the three-factor situational interest model for middle and high school students in mathematics. Moreover, situational interest was shown to be distinct from individual interest and was a statistically significant predictor of change in individual interest across the school year. Keywords situational interest, interest development, academic motivation, classroom context 1 Duke University, Durham, NC, USA 2 Northern Illinois University, DeKalb, IL, USA 3 University of California, Irvine, CA, USA 4 James Madison University, Harrisonburg, VA, USA 5 University of St. Thomas, Saint Paul, MN, USA 6 University of Michigan, Ann Arbor, MI, USA 7 University of Wisconsin Madison, Madison, WI, USA Corresponding Author: Lisa Linnenbrink-Garcia, Department of Psychology & Neuroscience, Duke University, PO Box 90086, Durham, NC , USA llinnen@duke.edu

2 2 Educational and Psychological Measurement XX(X) Early research on the effects of situational factors on student interest focused on textbased interest, with an examination of how specific features of text predicted students interest and engagement while reading (see Schraw & Lehman, 2001 for a review). More recently, there has been an increased focus on how elements of the situation more globally affect interest (e.g., Ainley, Hidi, & Berndorff, 2002; Alexander, Fives, Buehl, & Mulhern, 2002; Bergin, 1999; Del Favero, Boscolo, Vidotto, & Vicentini, 2007; Durik & Harackiewicz, 2007; Hidi & Harackiewicz, 2000; Hidi & Renninger, 2006; Hoffmann, 2002; Krapp, 2002; Renninger & Hidi, 2002). Relatively speaking, however, there is little research on how general contextual factors, such as the classroom environment or the form of instruction, can promote interest in a particular domain. A reason for this lack of research may be that the field lacks a tool for measuring contextual (e.g., situational) interest in a variety of content domains and across a variety of schooling levels. As such, our goal was to develop such a scale (Situational Interest Scale) that would be applicable to a broad range of academic contexts and appropriate for adolescents across three levels of schooling (middle school, high school, and college). Conceptualizing Student Interest Interest theorists typically differentiate between two main kinds of interest: individual interest and situational interest. Individual interest has a dispositional quality, residing in the person across situations (Rathunde, 1993; Renninger, 2000; Renninger, Hidi, & Krapp, 1992; Schiefele, 1991). In contrast, situational interest emerges in response to features in the environment (Hidi & Anderson, 1992; Hidi & Baird, 1986; Hidi & Renninger, 2006; Krapp, 2002). Theoretical models of individual interest identify situational factors as critical in the development of individual interest (e.g., Hidi & Baird, 1986; Hidi & Harackiewicz, 2000; Hidi & Renninger, 2006; Krapp, 2002; Schiefele, 2001). Situational interest (SI) consists of both an attentional and affective reaction to the situation, which can be differentiated into two forms triggered-si and maintained-si (Dewey, 1913; Hidi & Baird, 1986; Hidi & Harackiewicz, 2000; Hidi & Renninger, 2006; Krapp, 2002; Mitchell, 1993). Triggered-SI involves heightening the affective experiences individuals associate with the environment. Triggered-SI is similar to Mitchell s (1993) conceptualization of catch in that both constructs involve arousing or grabbing an individual s interest; however, triggered-si refers specifically to initiating interest (Hidi, 2001; Hidi & Harackiewicz, 2000; Hidi & Renninger, 2006). In contrast, maintained-si, which is also referred to as hold, is a more involved, deeper form of situational interest in which individuals begin to forge a meaningful connection with the content of the material and realize its deeper significance (Dewey, 1913; Hidi, 2001; Mitchell, 1993). Although situational interest can refer to interest that emerges in a variety of settings, we focus specifically on situational interest that emerges in educational settings. Within these settings, features that promote triggered-si influence students ongoing affective experiences so that students actively attend to the material (Deci & Ryan,

3 Linnenbrink-Garcia et al ; Mitchell, 1993; Schiefele & Rheinberg, 1997). For example, secondary school students reported that games and puzzles stimulated their interest in math (Mitchell, 1993). Instructors might also trigger students interest by weaving colorful examples into lectures and using attention-grabbing technology. These strategies have the potential to help students be engaged and focused on the material while the material is presented. However, triggered-si can be short-lived if the environment does not continue to support it. Learning contexts can promote maintained-si if they cause individuals to feel empowered by the knowledge presented to them in the situation (Dewey, 1913; Hidi & Harackiewicz, 2000; Hidi & Renninger, 2006; Krapp, 2002; Mitchell, 1993; Rathunde, 1993). For example, certain classroom activities might encourage students to think about the importance of course material, which can maintain and increase situational interest (Deci, Eghrari, Patrick, & Leone, 1994). Other instructional strategies might enliven students enjoyment of and connection to the course material. Maintained-SI is distinct from triggered-si because the enjoyment of working with the material is based in the domain content rather than peripheral aspects of the material. In contrast to situational interest, individual interest is relatively stable across situations. Our conceptualization of individual interest is based largely on the work of both Renninger (2000) and Schiefele (1991, 2001; see Krapp, 1999, 2002; Renninger et al., 1992; Schiefele, 1999 for further discussions). Renninger (2000) and Hidi and Renninger (2006) conceptualize individual interest as an individual s long-term, deepening personal connection to a domain. In this view, interest deepens as individuals acquire knowledge of a domain and come to value it, which in turn inspires curiosity and further exploration of the domain. Schiefele (1991, 2001) further decomposed stored value into two components: feeling-related components, which characterize individuals affective experiences while engaging with domain content (e.g., enjoyment, excitement), and value-related components, which emerge as individuals come to believe a domain is important and meaningful. Although individual interest and situational interest are conceptually distinct, some models reveal that situational interest can grow into individual interest (Hidi & Renninger, 2006; Krapp, 2002). Triggered-SI can initiate this process by attracting students attention to the material, but this experience will not necessarily translate into individual interest. Maintained-SI provides the link between triggered-si and individual interest. Once students hone in on the course material (triggered-si), those who view it as enjoyable and meaningful (maintained-si) are likely to value the material beyond the context of that particular course and may seek out new opportunities to have contact with the domain and expand their knowledge. It is through this transformation of maintained-si that individual interest is thought to develop. If maintained-si can develop into individual interest, it is possible that maintained- SI is structured similarly to individual interest. We aim to extend prior research on maintained-si to test whether maintained-si consists of feeling and value components. Thus, one goal of this research is to determine whether there is empirical justification for differentiating between maintained-si-feeling and maintained-si-value. Despite the

4 4 Educational and Psychological Measurement XX(X) proposed similarity in underlying structure, there are also important differences between maintained-si and individual interest. For instance, maintained-si develops in response to exposure to material in a particular context, whereas individual interest results from repeated exposure to material in many contexts. Moreover, maintained-si will not necessarily carry over to other contexts. Existing Measures of Situational Interest Although there are measures of text-based situational interest (Schraw, 1997), there are very few measurement tools available to assess situational interest with respect to the broader classroom environment. Two notable measures of classroom-based situational interest currently exist. First, Mitchell s (1993) scale to measure situational interest in secondary school math classrooms separates catch (similar to triggered-si) from hold (similar to maintained-si). However, embedded in this instrument are questions relating to specific techniques an instructor might use to engage students in math (e.g., puzzles, computers, and group work). This is problematic because these items are not relevant to students in classrooms that do not use those techniques. Instead, we felt a measure would have more utility if it focused on students overall experience of situational interest, rather than on particular sources of situational interest. Only then will it be possible to test the relative effectiveness of various interest-enhancing strategies, especially across a wide range of classroom contexts. Chen, Darst, and Pangrazi (2001) developed a scale designed to measure situational interest that does not refer to particular instructional techniques. However, it focuses solely on the feeling-related beliefs that accompany a particular learning situation and does not tap into the importance of the material; therefore, this scale does not fully capture the proposed value-related aspects of maintained-si. Finally, there are a number of scales that have been developed for intervention studies (e.g., Hoffmann, 2002); however, these scales were specifically designed to assess the effectiveness of interventions and thus cannot be readily used to study situational interest in a broad range of classroom contexts. Overview of Current Research We conducted three studies to investigate the underlying structure of situational interest and to develop a new measurement tool for use in varying domains and developmental levels. In Study 1, we describe our initial attempt to assess situational interest in introductory psychology courses at the college level using both exploratory (EFA) and confirmatory factor analysis (CFA). Study 2 extended our findings from Study 1 by examining situational interest in seventh through twelfth grade mathematics classes. The items used in Study 2 were revised to more closely match our theoretical conceptions, and CFA was used to test the fit of the models derived from Study 1. In Study 2, we also assessed individual interest to evaluate whether the situational interest scales were distinct from individual interest and whether situational interest could be used to

5 Linnenbrink-Garcia et al. 5 predict individual interest. In Study 3, which also targeted adolescents in middle school and high school mathematics classes, the scales were further refined, and CFA was conducted on the final model derived from Study 2. Study 1 As part of a larger study (Harackiewicz, Durik, Barron, Linnenbrink-Garcia, & Tauer, 2008) examining the relation of motivation to college students engagement and learning in introductory psychology, we assessed situational interest in psychology. The course was taught almost exclusively in lecture format; therefore, students experience of the class was largely defined by the lecture. Our measure of triggered-si focused on students affective reactions to class lectures. To assess maintained-si, we developed items that measured feeling-related and value-related beliefs associated with the course material. Data were collected from a sufficient number of students to conduct independent exploratory and confirmatory factor analyses on randomly divided halves of the sample. In the confirmatory analyses, we compared the degree of fit for four nested models (A D) derived from theory (see Figure 1). Model A tests a two-factor model consisting of triggered-si and maintained-si. Model A is rooted in initial theorizing about situational interest (e.g., Mitchell, 1993) and does not distinguish between the feeling and value components of maintained-si. Model B tests our proposed three-factor model, which differentiates triggered-si, maintained-si-feeling, and maintained-si-value. Model C tests a two-factor model consisting of feeling and value, based solely in theorizing about individual interest (Schiefele, 1991, 2001). Model C does not differentiate between triggered and maintained-si, as the feeling component consists of the items used to assess triggered-si and maintained-si-feeling. Finally, Model D tests a onefactor model based on the possibility that situational interest should not be divided into subcomponents but instead represents a single, general factor. Method Participants. The total sample included 858 undergraduate students from five introductory psychology classes (taught by four instructors) at a large university. The sample consisted primarily of Caucasian students; the majority of the students (69%) were female. The sample was divided randomly into two halves yielding a group of 429 participants for each factor analysis (exploratory and confirmatory). Analyses revealed no reliable differences between the groups on gender, year in college, class, or course grades. A nultivariate analysis of variance (MANOVA) revealed no statistically significant multivariate difference between the groups on the item scores. Materials and procedure. Thirteen weeks into the semester, trained research assistants administered a 17-item, self-report questionnaire to participants (see Table 1). Six items measured students affective reactions to the lecture presentation as enjoyable and

6 6 Educational and Psychological Measurement XX(X) Model A Triggered-SI Maintained-SI Model B Triggered-SI Maintained- SI-Feeling Maintained- SI-Value Model C SI-Value SI-Feeling Model D Situational Interest Model E Triggered-SI Individual Interest Situational Interest Maintained- SI-Feeling Maintained- SI-Value Figure 1. Models used in the current study overview of situational interest models Model A: Two-factor model with triggered-si and maintained-si-hold. Model B: Three-factor model with triggered-si, maintained-si-feeling, and maintained-si-value. Model C: Two-factor model with SI-feeling and SI-value. Model D: One-factor situational interest model. Model E: Situational and individual interest model. Error variance terms and observed variable names are omitted from the model for simplicity of presentation. The number of indicators for the latent variables is based on the final models tested in Study 2. SI situational interest. engaging (triggered-si). Maintained-SI was assessed with 11 items. These items focused on how much the course material itself (rather than the presentation style) was enjoyable (maintained-si-feeling) and meaningful (maintained-si-value). Participants rated all items from 1 (strongly disagree) to 7 (strongly agree).

7 Linnenbrink-Garcia et al. 7 Table 1. Pattern and Structure Coefficients for Exploratory Factor Analysis for Study 1 (n 429) Maintained-SI Factor I Triggered-SI Factor II Item Pattern Structure Pattern Structure h 2 M1 I think the field of psychology is very interesting M2 Psychology fascinates me M3 I m excited about psychology M4 I think what we are learning in this course is important M5 I think what we are studying in Introductory Psychology is useful for me to know M6 I think the field of psychology is an important discipline M7 To be honest, I just don t find psychology interesting M8 I find the content of this course personally meaningful M9 a I think this class is interesting M10 I see how I can apply what we are learning in Introductory Psychology to real life M11 a This class has been a waste of my time T1 I don t like the lectures very much T2 The lectures in this class aren t very interesting T3 I enjoy coming to lecture T4 The lectures in this class really seem to drag on forever T5 I like my instructor T6 a I am enjoying this psychology class very much Note: SI situational interest; T triggered-si; M maintained-si; h 2 communality coefficient after extraction. a. Items dropped from the final exploratory factor analysis. Results Exploratory factor analyses. We conducted EFA using principal axis factoring with oblimin rotation ( 0) to analyze the correlation matrix of the 17 SI items. Bivariate correlations and descriptive statistics for all items are available from the authors on request. This approach to EFA was taken to account for the possibility of measurement error and because we expected the underlying extracted factors to be correlated.

8 8 Educational and Psychological Measurement XX(X) Multiple criteria were used to determine the number of factors to extract. First, parallel analysis was conducted following the guidelines put forth by Thompson and Daniel (1996). A comparison of the eigenvalues extracted from a random data matrix to the values extracted from the actual data suggested a two-factor solution. Specifically, the eigenvalues for the first two factors in the actual data (10.23, 1.78) were larger than those for the random data (1.44, 1.28); however, the eigenvalue for the third factor in the actual data (0.76) was lower than that observed for the random data (1.24). Moreover, the pattern of eigenvalues in the scree plot indicated a two-factor solution, and only the first two factors had eigenvalues that exceeded 1.0. The two extracted factors accounted for 70.6% of the variance and were highly correlated (r.65), suggesting that our decision to obliquely rotate the results was appropriate. The pattern coefficients associated with each item were consistent with Model A, suggesting that there were two overarching dimensions of situational interest. We labeled the two factors maintained-si (Factor I) and triggered-si (Factor II). In general, the pattern and structure coefficients mapped on to the expected underlying dimensions. No pattern coefficients were below.50 on their intended factor; however, for three items, the pattern coefficients on the opposite factor were above.25 (see Table 1). These items (M9, M11, and T6) all contained the word class as the object of interest. This word is ambiguous in terms of referring to class instruction or class material, which likely contributed to the cross-loading. Although oblique rotations allow for relatedness between factors, we dropped these three items in an attempt to reduce the ambiguity and to create scales that, while related, were more conceptually distinct. An EFA with the remaining 14 items again suggested a two-factor solution. The maintained-si (Factor I) and triggered-si (Factor II) factors had eigenvalues of 8.13 and 1.74, respectively; these values were higher than those extracted from a random data set (1.28, 1.20). The eigenvalue for the third factor (0.73) was less than 1.0 and lower than that observed for the third factor extracted from the random data (1.19). These results suggest that students distinguish their evaluations of the classroom instruction (triggered-si) from their evaluations of the course material (maintained- SI) and are consistent with earlier research on situational interest. Next we tested the replicability of this two-factor model as well as the previously described alternative models using CFA on the second half of our sample. Confirmatory factor analyses. We used Mplus, version 4.2 (Muthén & Muthén, 2006), to test four models based on the covariance matrix of 14 observed variables, resulting in a total of 105 input coefficients. The models were nested, as they contained the same parameters and differed only in terms of which parameters were free in each model (Hoyle & Panter, 1995). Initial analyses showed that multivariate skewness (sample value 23.42, p.01) and kurtosis (sample value , p.01) were statistically significant. To address this multivariate nonnormality, we tested these models using robust maximum likelihood (ML) estimation and report the scaled 2 statistics (Satorra & Bentler, 1994). We assessed the fit of each model with the scaled 2 statistic, the standardized root mean square residual (SRMR), and the comparative fit index (CFI; Hu & Bentler, 1999). We chose CFI because it is robust to violations of multivariate normality in

9 Linnenbrink-Garcia et al. 9 Table 2. Statistics From Models in Study 1 (n 429) and Study 2 (n 278) Using Robust Maximum Likelihood Estimation (n 429) Model df Scaled 2 CFI SRMR Study 1 A ** B ** C ** D ** Study 2 A ** B ** C ** D ** Note: df degrees of freedom; CFI comparative fit index; SRMR standardized root mean squared residual. **p.01. samples of this size (West, Finch, & Curran, 1995). We used the recommended twoindex strategy to assess fit, with values greater than.95 for CFI and less than.07 for SRMR indicating good fit (Hu & Bentler, 1999). Given that the 2 statistic is quite sensitive to sample size (Hu & Bentler, 1995), we focus more on the CFI and SRMR. Table 2 contains the fit indexes for each model. We first tested Model A, the two-factor model (triggered-si, maintained-si) supported above using EFA. The 2 statistic was statistically significant indicating lack of fit. However, SRMR was acceptable, and CFI was slightly below the preferred fit. Thus, the model that was backed by EFA was promising but fell just short of the twoindex criterion for CFI. The first alternative model was a three-factor model (Model B) consisting of triggered- SI, maintained-si-feeling, and maintained-si-value. Model B is the closest alternative to Model A, as it keeps the triggered-si factor intact but separates maintained-si into two subcomponents of feeling and value. Although the 2 statistic was statistically significant, the CFI and SRMR fell within acceptable ranges suggesting that Model B is a plausible model for the data. The second alternative model (Model C) had two factors designed to test whether situational interest should be differentiated into SI-feeling and SI-value rather than triggered-si and maintained-si. SI-feeling consisted of all of the items focused on emotional responses and thus included triggered-si and maintained-si-feeling from Model B. In Model C, SI-value was identical to maintained-si-value from Model B. Both the 2 statistic and fit indexes suggested that the model was not plausible. The final alternative model had one factor designed to test a simple, one-dimensional model (Model D). This model also fit the data poorly. To examine the relative fit of each model, we began by examining the change in the scaled 2, accounting for differences in degrees of freedom which reflect model

10 10 Educational and Psychological Measurement XX(X) complexity (Hoyle & Panter, 1995). This was especially relevant in the case of Model B, which appeared to provide the best fit, but was also the most complex. We compared the two most plausible models, Models A and B, using a scaled 2 difference test (Satorra & Bentler, 1994). There was a statistically significant difference in the fit of the two models, 2 (2) , p.01, with Model B providing a better fit than Model A. Not surprisingly, the fit for Model B was also better than the fit for Model C, 2 (2) , p.01, and Model D, 2 (3) , p.01. Thus, the CFA analyses indicated that Model B provided the best fit of the four models tested for the data. Next, we examined the measurement properties of Model B (see Table 3). For maintained-si-feeling, all four observed variables had similarly large standardized parameter estimates (considering absolute value in cases of reversed items). The parameter estimates for maintained-si-value items and triggered-si were more varied, ranging from.74 to.90. A comparison of pattern and structure coefficients revealed similar results and did not raise any additional concerns regarding the underlying factor structure (see Graham, Guthrie, & Thompson, 2003). Following the guidelines put forth by Henson (2001), the item scores for each scale evidenced good reliability, assessed in terms of Cronbach s alpha, construct reliability (Fornell & Larker, 1981), and coefficient H (Hancock & Mueller, 2001). The standardized factor covariances for the latent factors were quite high. Triggered-SI was associated with both maintained-si-feeling (r.61, p.001) and maintained-si-value (r.60, p.001), and maintained-sifeeling was related to maintained-si-value (r.90, p.001). These high correlations between the latent factors are consistent with the results from the EFA, and will be considered further in the discussion. Discussion We found evidence to support a two-factor model and a three-factor model, both of which conformed to theoretical conceptualizations of situational interest (Hidi & Renninger, 2006; Krapp, 2002; Schiefele, 2001). The two-factor model (supported by the EFAs) separated triggered-si and maintained-si, whereas the three-factor model (supported by the CFAs) further separated maintained-si into maintained-si-feeling and maintained-si-value. The separation of triggered-si and maintained-si indicates that students reactions to the presentation of course material can be distinguished from their reactions to the material itself. Our assumption underlying triggered-si is that it is a purely affective experience that arises from students positive emotional reactions to lecture presentations. This is consistent with theoretical descriptions of triggered-si (Schraw & Lehman, 2001). Importantly, triggered-si was empirically distinct from maintained-si-feeling. Whereas triggered-si refers to affective reactions to lectures in general, the maintained-si-feeling refers to affective reactions to domain content experienced in the classroom. In this way, triggered-si taps individuals responses to the presentation of material, and not necessarily from the material itself.

11 Table 3. Factor Coefficients and Scale Characteristics for Model B for Study 1 (n 429) Maintained-SI-Feeling Maintained-SI-Value Triggered-SI Unstd SE Std Unstd SE Std Unstd SE Std M1 I think the field of psychology is very interesting (0.80) (0.54) M2 Psychology fascinates me (0.77) (0.52) M3 I m excited about psychology (0.79) (0.54) M4 I think what we are learning in this course is important (0.80) (0.53) M5 I think what we are studying in Introductory (0.81) (0.54) Psychology is useful for me to know M6 I think the field of psychology is an important discipline (0.67) (0.44) M7 To be honest, I just don t find psychology interesting ( 0.75) ( 0.51) M8 I find the content of this course personally meaningful (0.73) (0.49) M10 I see how I can apply what we are learning in (0.71) (0.47) Introductory Psychology to real life T1 I don t like the lectures very much ( 0.54) ( 0.53) T2 The lectures in this class aren t very interesting ( 0.52) ( 0.51) T3 I enjoy coming to lecture (0.51) (0.50) T4 The lectures in this class really seem to drag on forever ( 0.51) ( 0.50) T5 I like my instructor (0.45) (0.44) Scale variance Scale standard error Cronbach s % confidence interval for Cronbach s Coefficient H Construct reliability Percentage variance attributed to construct Note: Structure coefficients are indicated in parentheses in the standardized column. Three of the unstandardized regression weights were set to 1 in order to scale the latent variables. Standard errors and significance level were not calculated for these variables. All other coefficients were determined to be statistically significant at p <.05. The reliabilities for the item scores in the combined maintained-si scale were also good (.95,.95, 68% variance attributed to the construct). 11

12 12 Educational and Psychological Measurement XX(X) The CFA model with the best fit split the maintained-si items into two factors, maintained-si-feeling and maintained-si-value. Evidently, students were able to develop an affinity for the material they were learning, which could be both emotionally engaging and meaningful. The maintained-si-feeling and maintained-si-value factors parallel distinctions made in theoretical accounts of individual interest (Schiefele, 1991; 2001), although maintained-si is conceptually distinct from individual interest because maintained-si is constrained to experiences in a particular context. Maintained-SI might develop into individual interest (Krapp, 2002; Hidi & Renninger, 2006), but we were not able to test that in this particular data set. Two limitations in Study 1 are critical to address. First, some of the maintained- SI items tested in Study 1 referred to the general domain of psychology rather than content that explicitly came from the course. This may be admissible in an introductory college course in which students likely had little contact with the domain prior to the course. However, we were concerned that this wording could cloud the distinction between situational interest and individual interest and we wanted to construct scales that would be useful for domains in which students had prior experience (e.g., mathematics). Therefore, we refined the items in Study 2 to more cleanly tap situational interest that emerged specifically from a given course. Second, although the data from Study 1 revealed a distinction between different situational interest factors, they did not address whether these factors were separable from individual interest. Study 2 Study 2 was designed to refine the items used in Study 1, to extend the use of these scales to middle school and high school contexts, and to directly test the distinction between situational interest and individual interest. To do this, we made three important changes. First, we altered the maintained-si items from Study 1 to explicitly reflect interest derived from a specific course rather than interest in a domain more generally. The aim was to design the situational interest scales to be useful within contexts where students arrive with extensive prior experience. For example, middle school students who arrive in a mathematics class have experienced mathematics in many contexts, which likely have already shaped their individual interest (or lack of) in the domain. Second, we revised the triggered-si items to focus on the course instructor rather than on lecture, so that the measures would be appropriate for classes using a variety of instructional techniques. We tested these revised scales within middle and high school mathematics classes, thus extending their generalizability in terms of content domain and target population. Third, we extended the study design to test whether the situational interest scales were distinct from individual interest. To do this, we assessed both situational interest and individual interest in mathematics classes so that we could model the underlying factor structure for both forms of interest. We also included a longitudinal component so that we could test the unique contribution of situational interest in the development of individual interest over time.

13 Linnenbrink-Garcia et al. 13 Method The initial sample included 284 adolescents in Grades 7 through 12 from a large, urban area in the western United States. This was a subsample drawn from a larger research study examining a large mathematics reform project (Conley, 2007; Karabenick et al., 2009). Surveys assessing situational and individual interest were administered to this initial sample approximately 1 month into the fall semester (October). The sample included middle school (42%) and high school (58%) students. Approximately half of the students were female (52%). The sample, which was more ethnically diverse than in Study 1, included primarily Latino students (80%), but also students of Asian (15%), Caucasian (4%), or African American (1%) descent. Six participants were excluded from the CFAs because they did not have complete data on all items. Individual interest was assessed again in a second wave of data (Wave 2) collected during the subsequent spring semester (May). A number of students in the initial sample were not available to participate in Wave 2 because they had moved (36) or were on a field trip (29). Of the remaining 219 students, 38 were absent on the day of data collection. The final sample for both waves consisted of 181 students. The situational interest scales were revised from Study 1 to address the limitations noted previously (see Table 4). Individual interest was assessed using an adapted version of the task-value scale from the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich, Smith, Garcia, & McKeachie, 1993). The scale consisted of 8 items (.90) designed to tap both the feeling and value students associated with math (see the appendix). Students rated all items on a scale ranging from 1 (not at all true) to 5 (very true). Results The analyses for Study 2 addressed two separate questions. First, we sought to replicate the CFAs conducted in Study 1 in order to examine the underlying factor structure for the new items. Second, we investigated the distinction between individual interest and situational interest. We conducted preliminary analyses to examine whether the students with complete data (n 181) differed from those who were absent from class for Wave 2 (n 38). The t tests revealed no statistically significant differences in mean levels of individual interest, triggered-si, or maintained-si-feeling. However, students who were absent for Wave 2 (M 3.47, SD 1.16) reported statistically significantly lower maintained- SI-value at Wave 1 than those who were present during Wave 2 (M 3.89, SD 0.98), t(217) 2.29, p.05). Chi-square tests revealed no reliable differences in the distribution of gender, ethnicity, or grade level for these groups. Confirmatory factor analyses: Situational interest models A D. Tests for both multivariate skewness (sample value 11.72, p.001) and kurtosis (sample value , p.001) indicated that the data were not multivariate normal. Therefore, we tested these models using robust ML estimation and report scaled 2 statistics (Satorra & Bentler, 1994).

14 Table 4. Factor Coefficients and Scale Characteristics for Model B for Study 2 (Fall; n 278) Triggered-SI Maintained-SI-Feeling Maintained-SI-Value Unstd SE Std Unstd SE Std Unstd SE Std T1 My math teacher is exciting (0.58) (0.48) T2 When we do math, my teacher does things (0.57) (0.47) that grab my attention T3 This year, my math class is often entertaining (0.67) (0.56) T4 My math class seems to drag on forever (reversed) MF1 What we are learning in math class this year (0.70) (0.64) is fascinating to me MF2 I am excited about what we are learning (0.71) (0.65) in math class this year MF3 This year, I really enjoy the math we do in class (0.66) (0.61) MF4 To be honest, I don t find the math we do in class interesting (reversed) MV1 What we are studying in math class is useful (0.55) (0.62) for me to know MV2 The things we are studying in math this year are (0.54) (0.61) important to me MV3 What we are learning in math this year is important (0.54) (0.60) for my future goals MV4 What we are learning in math this year (0.54) (0.60) can be applied to real life Scale variance Scale standard error Cronbach s % confidence interval for Cronbach s Coefficient H Construct reliability Percentage variance attributed to construct Note: Structure coefficients are indicated in parentheses in the standardized column. Coefficients are not provided for T4 and MF4, as these two variables were dropped from the final model. Three of the unstandardized regression weights were set to 1 in order to scale the latent variables. Standard errors and significance level were not calculated for these variables. All other coefficients were determined to be statistically significant at p <

15 Linnenbrink-Garcia et al. 15 Using Mplus 4.2 (Muthén & Muthén, 2006), we constructed four nested models (A D), mirroring those tested in Study 1. The preliminary versions of these models included all of the items; however, two reversed items used to assess triggered-si (T4) and maintained-si-feeling (MF4) had standardized parameter estimates less than.45 on the relevant latent factors (.39 and.41, respectively). Therefore, these two items were removed from the final models. As in Study 1, we used the two-index strategy (Hu & Bentler, 1999) to assess the fit of each model (see Table 2). Model B, the three-factor model consisting of triggered-si, maintained-si-feeling, maintained-si-value, was the only model with fit indexes within the acceptable ranges (above.95 for CFI and less than.07 for SRMR) suggesting that the three-factor model with the revised scales was also a plausible model. The fit for Model B was statistically significantly better than Model A, 2 (2) 98.98, p.01; Model C, 2 (2) 54.39, p.01; and Model D, 2 (3) , p.01 (Satorra & Bentler, 1994). As such, our findings from Study 2 support our contention that situational interest is best represented by a three-factor model. The standardized parameter estimates for Model B were all at or greater than.70 (see Table 4). Following the guidelines put forth by Graham et al. (2003), an examination of the structure coefficients revealed no additional concerns and confirmed the pattern coefficient results. The standardized factor covariance indicates that the three latent factors were highly correlated. Triggered-SI was most strongly related to maintained-sifeeling (r.81, p.001) but was also positively associated with maintained-si-value (r.67, p.001). Maintained-SI-feeling and maintained-si-value were also highly correlated (r.75, p.001). Although these three latent sub-factors are highly correlated, the results from the CFA suggest that there is utility in differentiating among these three subcomponents. Finally, we assessed the reliability of the scores in each subscale (see Table 4). The reliability of the scores was good, with all values exceeding.80 (Henson, 2001). Differentiating individual and situational interest. Recall that triggered and maintained situational interests are theorized to be part of a developmental model whereby situational interest can develop into individual interest over time (Krapp, 2002; Hidi & Renninger, 2006). Therefore, it is critical to show that the measures of situational interest and individual interest are distinct. We tested a fifth model (Model E, see Figure 1) that contained two correlated primary latent variables for individual interest and situational interest, as well as a second-order latent variable for situational interest consisting of three latent variables to reflect triggered-si, maintained-si-feeling, and maintained-si-value. The second-order latent variable for situational interest is mathematically equivalent to Model B (where the three subfactors are correlated). To test this model, it was necessary to fix the error variance for maintained-si-feeling to be equal to its error variance (.12) obtained in Model B because a negative variance for this factor emerged in initial analyses. Chen, Bollen, Paxton, Curren, and Kirby (2001) recommend this solution provided both the original and constrained models converge, which they did. The data were not multivariate normal in terms of skew (sample value 49.39, p.001) and kurtosis (sample value , p.001); therefore, we

16 16 Educational and Psychological Measurement XX(X) Table 5. Zero-Order Correlations Among Calculated Scale Scores for Study 2 (Fall, Spring, n 181) Individual Interest: Fall 2. Individual Interest: Spring Situational Interest: Fall Triggered-SI: Fall Maintained-SI-feeling: Fall Maintained SI-value: Fall Note: All correlations are statistically significant at p.001. used robust ML estimation to test Model E and report the scaled 2 statistic (Satorra & Bentler, 1994). Overall, the model fit was reasonable, scaled 2 (132) , p.001; CFI.92, SRMR 0.08, but did not quite meet the stricter cut-off values of the two-index strategy. Nevertheless, given the greater complexity of this model, it seems reasonable to suggest that situational interest can be differentiated from individual interest. However, the latent factors assessing individual interest and situational interest were highly correlated (r.79, p.001, see Table 5 for bivariate correlations between observed scales). To further explore whether situational interest uniquely contributes to the development of individual interest, we conducted a series of hierarchical multiple regression analyses. In particular, we tested whether situational interest during Wave 1 predicted individual interest during Wave 2, after controlling for Wave 1 levels of individual interest. In addition to reporting the standardized beta coefficient and significance level, we also report the squared semipartial correlation to evaluate the percentage of variability in the outcome variable that is uniquely accounted for by each predictor. For each regression, we entered Wave 1 individual interest on the first step predicting Wave 2 individual interest. In the second step we added a measure of situational interest. We conducted four regressions, using different measures of situational interest in the second step. The different measures of situational interest included the composite measure of situational interest (akin to the latent variable of situational interest tested in Model E), as well as the three separate subfactors of situational interest (triggered- SI, maintained-si-feeling, and maintained-si-value). Composite situational interest. In the first step, Wave 1 individual interest was a strong predictor of Wave 2 individual interest, F(1, 181) 96.65, p.001, adjusted R Adding the composite measure in the second step resulted in a statistically significant increase in the portion of variance explained by 4%, F change (1, 180) 11.06, p.001. Both Wave 1 situational interest, t(180) 6.13, p.001,.24, sr 2.04, and individual interest, t(180) 3.33, p.001,.45, sr 2.12, were statistically significant predictors of Wave 2 individual interest. Thus, even after controlling for initial individual interest, students with higher situational interest in math at the beginning of the school year had higher levels of individual interest in math by the

17 Linnenbrink-Garcia et al. 17 end of the year, suggesting that situational interest may uniquely contribute to individual interest. Subfactors of situational interest. We also examined how triggered-si, maintained- SI-feeling, and maintained-si-value predicted changes in individual interest. Because these three subfactors were highly correlated, each Wave 1 subfactor was entered individually along with Wave 1 individual interest in its own regression analysis predicting Wave 2 individual interest. The results of only the second step of the three regressions are reported here because the first step was identical to that of the previous analysis. In these analyses, Wave 1 triggered-si, t(179) 3.56, p.001,.23, sr 2.04) and Wave 1 maintained-si-feeling, t(180) 3.25, p.001,.25, sr 2.04, were unique predictors of Wave 2 individual interest. However, Wave 1 maintained- SI-value was not a statistically significant predictor of Wave 2 individual interest, t(179) 1.30, p.05,.09, sr Discussion Overall, our findings from Study 2 help to extend the assessment of situational interest to other domains and other contexts. In particular, we found that the same three-factor situational interest model, consisting of triggered-si, maintained-si-feeling, and maintained-si-value, provided the best fit for our sample of ethnically diverse adolescents in the mathematics domain. Interestingly, triggered-si and maintained-si-feeling were more strongly correlated in this sample than they were for the college student sample. These differences in the relations among factors across samples may be attributable to developmental changes (secondary vs. undergraduate samples) or to domain differences (psychology vs. mathematics). Before speculating further, we felt it important to replicate the effects found in Study 2 in a new sample. Study 2 also helps support our contention that individual interest and situational interest, although related, represent two separate constructs. This was reflected in both the factor analysis and in the longitudinal regression analyses. Specifically, situational interest measured early in the school year predicted the change in individual interest during the school year. Thus, these measures can be helpful for examining how situational interest might develop into individual interest. The results of the regression analyses using the subfactors of situational interest revealed that triggered-si and maintained-sifeeling may be more important in predicting the change in individual interest than maintained-si-value, at least for mathematics. This variability in the predictive utility of the three situational interest factors suggests that these factors might play different roles in the development of individual interest in different domains or contexts. It is also possible that maintained-si-value develops into individual interest more slowly than does maintained-si-feeling because perceived value may evolve over time. Study 3 We conducted a third study to make additional modifications to the measure. We added items to both the triggered-si and maintained-si-feeling scales to replace the

18 18 Educational and Psychological Measurement XX(X) two reversed items that were omitted in Study 2. We also wanted to drop two additional items on the maintained-si-feeling and maintained-si-value scales that seemed only marginally acceptable based on modification indices. Therefore, we developed and tested two additional items, one for each of the maintained-si scales, to optimize the fit of the data to the model. Method The sample used for Study 3 (n 246) was a second, and independent, subsample of the larger research project on school reform (Conley, 2007; Karabenick et al., 2009). Data for this sample were collected only during the spring semester. Ten participants from this sample were excluded from analyses because they had incomplete data. The sample included middle school (46%) and high school (54%) students. Approximately half of the students were female (46%). The students were of Latino (72%), Asian (16%), Caucasian (10%), or American Indian (1%) descent. Data collection procedures were the same as those described in Study 2. As discussed previously, several changes were made to the situational interest survey to address minor concerns with the measures used in Study 2 (see Table 6). As in Study 2, all items were rated on a scale ranging from 1 (not at all true) to 5 (very true). Bivariate correlations and descriptive statistics for the items are available from the authors on request. Results and Discussion To assess model fit, we tested the three-factor triggered-si, maintained-si-feeling, maintained-si-value model (Model B from Study 2) with the newly revised items. As in Studies 1 and 2, the assumption of multivariate normality was violated both in terms of skewness (sample value 19.90, p.001) and kurtosis (sample value , p.001); therefore, we used robust ML estimation to test the model (Satorra & Bentler, 1994). The fit indexes suggest that Model B was quite plausible, scaled 2 (51) , p.001; CFI.97, SRMR.04. The standardized parameter estimates were all high (at or greater than.75, see Table 6). The correlations among the latent factors were similar to those reported for Studies 1 and 2. Triggered-SI was more strongly associated with maintained-si-feeling (r.88, p.001) than with maintained-si-value (r.61, p.001); the two maintained-si factors were also strongly correlated with each other (r.76, p.001). Finally, all indexes of reliability of item scores were good (Henson, 2001; see Table 6). Overall, the changes made in Study 3 corrected concerns from Study 2 and led to improved fit of the model. Moreover, the scales in Study 3 have an equal number of items to assess each dimension of situational interest. We recommend using these revised scales, which we refer to collectively as the Situational Interest Survey.