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1.
Expectancy-value researchers have described the components of subjective task value in multiple ways, leading to multiple competing structural representations of subjective task value data. The purpose of this study was to examine these competing multidimensional factor structures by comparing correlated factor, hierarchical, and bifactor representations of both confirmatory factor analysis and exploratory structural equation modeling (ESEM) models across three theoretical conceptualizations of subjective task value. Results indicate that, in an undergraduate life science learning context (n = 334), the best representation for subjective task value data was a bifactor ESEM model that allowed for the disentangling of general and specific variance of general subjective task value, specific value beliefs, and specific costs. Full measurement invariance of the retained structure across continuing generation and first-generation students was found, and no differential item functioning was found across gender. General subjective task value and specific opportunity cost significantly and positively predicted achievement and specific utility value significantly and negatively predicted achievement, providing support for the criterion-related validity of the general and specific factors for predicting achievement outcomes.  相似文献   

2.
The article employs exploratory structural equation modeling (ESEM) to evaluate constructs of economic, cultural, and social capital in international large-scale assessment (LSA) data from the Progress in International Reading Literacy Study (PIRLS) 2006 and the Programme for International Student Assessment (PISA) 2009. ESEM integrates the theory-generating approach of exploratory factor analysis (EFA) and theory-testing approach of confirmatory factor analysis (CFA). It relaxes the zero-loading restriction in CFA, allowing items to load on different factors simultaneously, and it provides measurement invariance tests across countries not available in EFA. A main criticism of international LSA studies is the extended use of indicators poorly grounded in theory, like socioeconomic status, that prevent the study of mechanisms underlying associations with student outcomes. This article contributes to addressing this criticism by providing statistical criteria to evaluate the fit of well-defined sociological constructs with the empirical data.  相似文献   

3.
Exploratory structural equation modeling (ESEM) is an approach for analysis of latent variables using exploratory factor analysis to evaluate the measurement model. This study compared ESEM with two dominant approaches for multiple regression with latent variables, structural equation modeling (SEM) and manifest regression analysis (MRA). Main findings included: (1) ESEM in general provided the least biased estimation of the regression coefficients; SEM was more biased than MRA given large cross-factor loadings. (2) MRA produced the most precise estimation, followed by ESEM and then SEM. (3) SEM was the least powerful in the significance tests; statistical power was lower for ESEM than MRA with relatively small target-factor loadings, but higher for ESEM than MRA with relatively large target-factor loadings. (4) ESEM showed difficulties in convergence and occasionally created an inflated type I error rate under some conditions. ESEM is recommended when non-ignorable cross-factor loadings exist.  相似文献   

4.
The purposes of this study were to (a) test the hypothesized factor structure of the Student-Teacher Relationship Scale (STRS; Pianta, 2001) for 308 African American (AA) and European American (EA) children using confirmatory factor analysis (CFA) and (b) examine the measurement invariance of the factor structure across AA and EA children. CFA of the hypothesized three-factor model with correlated latent factors did not yield an optimal model fit. Parameter estimates obtained from CFA identified items with low factor loadings and R2 values, suggesting that content revision is required for those items on the STRS. Deletion of two items from the scale yielded a good model fit, suggesting that the remaining 26 items reliably and validly measure the constructs for the whole sample. Tests for configural invariance, however, revealed that the underlying constructs may differ for AA and EA groups. Subsequent exploratory factor analyses (EFAs) for AA and EA children were carried out to investigate the comparability of the measurement model of the STRS across the groups. The results of EFAs provided evidence suggesting differential factor models of the STRS across AA and EA groups. This study provides implications for construct validity research and substantive research using the STRS given that the STRS is extensively used in intervention and research in early childhood education.  相似文献   

5.
Minor cross-loadings on non-targeted factors are often found in psychological or other instruments. Forcing them to zero in confirmatory factor analyses (CFA) leads to biased estimates and distorted structures. Alternatively, exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM) have been proposed. In this research, we compared the performance of the traditional independent-clusters-confirmatory-factor-analysis (ICM-CFA), the nonstandard CFA, ESEM with the Geomin- or Target-rotations, and BSEMs with different cross-loading priors (correct; small- or large-variance priors with zero mean) using simulated data with cross-loadings. Four factors were considered: the number of factors, the size of factor correlations, the cross-loading mean, and the loading variance. Results indicated that ICM-CFA performed the worst. ESEMs were generally superior to CFAs but inferior to BSEM with correct priors that provided the precise estimation. BSEM with large- or small-variance priors performed similarly while the prior mean for cross-loadings was more important than the prior variance.  相似文献   

6.
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7.
A directly applicable latent variable modeling procedure for classical item analysis is outlined. The method allows one to point and interval estimate item difficulty, item correlations, and item-total correlations for composites consisting of categorical items. The approach is readily employed in empirical research and as a by-product permits examining the latent structure of tentative versions of multiple-component measuring instruments. The discussed procedure is straightforwardly utilized with the increasingly popular latent variable modeling software Mplus, and is illustrated on a numerical example.  相似文献   

8.
In this article we evaluate the psychometric properties of a scale for a perceptual measure of the extent to which manufacturing organizations develop proprietary equipment. We use a confirmatory factor analysis (CFA) approach to assess unidimensionality and reliability as well as convergent, discriminant and concurrent validity. Convergent and discriminant validity is assessed using CFA of the multitrait-multimethod (MTMM) matrix. In addition, we assess the scale's factorial invariance across industries. Results suggest that although method effects are present, the scale demonstrates internal consistency and validity. Implications of this study in the field of operations strategy and general strategy are discussed.  相似文献   

9.
Student evaluation of teaching (SET) is now common practice across higher education, with the results used for both course improvement and quality assurance purposes. While much research has examined the validity of SETs for measuring teaching quality, few studies have investigated the factors that influence student participation in the SET process. This study aimed to address this deficit through the analysis of an SET respondent pool at a large Canadian research-intensive university. The findings were largely consistent with available research (showing influence of student gender, age, specialisation area and final grade on SET completion). However, the study also identified additional influential course-specific factors such as term of study, course year level and course type as statistically significant. Collectively, such findings point to substantively significant patterns of bias in the characteristics of the respondent pool. Further research is needed to specify and quantify the impact (if any) on SET scores. We conclude, however, by recommending that such bias does not invalidate SET implementation, but instead should be embraced and reported within standard institutional practice, allowing better understanding of feedback received, and driving future efforts at recruiting student respondents.  相似文献   

10.
This study examined the extent of measurement invariance of the Basic Psychological Needs in Exercise Scale responses (BPNES; Vlachopoulos & Michailidou, 2006) across male (n = 716) and female (n = 1,147) exercise participants. BPNES responses from exercise participants attending private fitness centers (n = 1,012) and community exercise programs (n = 851) were used. The 3-factor BPNES confirmatory factor analysis model, discriminant validity, and scale reliability were supported for both male and female participants separately. The multisample models supported the configural invariance, partial metric invariance, partial measurement error invariance, and partial scalar invariance of the BPNES responses across gender. Both male and female participants attached the same meaning to the constructs assessed by the BPNES items. The BPNES score invariance properties support tests of the needs universality hypothesis offered by self-determination theory across gender in exercise and meaningful comparison of the autonomy, competence, and relatedness construct latent means across gender.  相似文献   

11.
This simulation study examines the efficacy of multilevel factor mixture modeling (ML FMM) for measurement invariance testing across unobserved groups when the groups are at the between level of multilevel data. To this end, latent classes are generated with class-specific item parameters (i.e., factor loading and intercept) across the between-level classes. The efficacy of ML FMM is evaluated in terms of class enumeration, class assignment, and the detection of noninvariance. Various classification criteria such as Akaike’s information criterion, Bayesian information criterion, and bootstrap likelihood ratio tests are examined for the correct enumeration of between-level latent classes. For the detection of measurement noninvariance, free and constrained baseline approaches are compared with respect to true positive and false positive rates. This study evidences the adequacy of ML FMM. However, its performance heavily depends on the simulation factors such as the classification criteria, sample size, and the magnitude of noninvariance. Practical guidelines for applied researchers are provided.  相似文献   

12.
When time-intensive longitudinal data are used to study daily-life dynamics of psychological constructs (e.g., well-being) within persons over time (e.g., by means of experience sampling methodology), the measurement model (MM)—indicating which constructs are measured by which items—can be affected by time- or situation-specific artifacts (e.g., response styles and altered item interpretation). If not captured, these changes might lead to invalid inferences about the constructs. Existing methodology can only test for a priori hypotheses on MM changes, which are often absent or incomplete. Therefore, we present the exploratory method “latent Markov factor analysis” (LMFA), wherein a latent Markov chain captures MM changes by clustering observations per subject into a few states. Specifically, each state gathers validly comparable observations, and state-specific factor analyses reveal what the MMs look like. LMFA performs well in recovering parameters under a wide range of simulated conditions, and its empirical value is illustrated with an example.  相似文献   

13.
This paper examines the stability and validity of a student evaluations of teaching (SET) instrument used by the administration at a university in the PR China. The SET scores for two semesters of courses taught by 435 teachers were collected. Total 388 teachers (170 males and 218 females) were also invited to fill out the 60‐item NEO Five‐Factor Inventory together with a demographic information questionnaire. The SET responses were found to have very high internal consistency and confirmatory factor analysis supported a one‐factor solution. The SET re‐test correlations were .62 for both the teachers who taught the same course (n = 234) and those who taught a different course in the second semester (n = 201). Linguistics teachers received higher SET scores than either social science or humanities or science and technology teachers. Student ratings were significantly related to Neuroticism and Extraversion. Regression results showed that the Big‐Five personality traits as a group explained only 2.6% of the total variance of student ratings and academic discipline explained 12.7% of the total variance of student ratings. Overall the stability and validity of SET was supported and future uses of SET scores in the PR China are discussed.  相似文献   

14.
This article presents a new method for multiple-group confirmatory factor analysis (CFA), referred to as the alignment method. The alignment method can be used to estimate group-specific factor means and variances without requiring exact measurement invariance. A strength of the method is the ability to conveniently estimate models for many groups. The method is a valuable alternative to the currently used multiple-group CFA methods for studying measurement invariance that require multiple manual model adjustments guided by modification indexes. Multiple-group CFA is not practical with many groups due to poor model fit of the scalar model and too many large modification indexes. In contrast, the alignment method is based on the configural model and essentially automates and greatly simplifies measurement invariance analysis. The method also provides a detailed account of parameter invariance for every model parameter in every group.  相似文献   

15.
Chinese University of Hong Kong students (N = 844) selected a “good” and a “poor” teacher, and rated each using a Chinese translation of the Students' Evaluations of Educational Quality (SEEQ) instrument. Multigroup confirmatory factor analysis (CFA) models, based on a 3 × 2 design, were constructed to test the invariance of the SEEQ factor structure across 3 discipline groups (a between‐group comparison of ratings by students in arts, social sciences, and education; in business administration; and in engineering, medicine, and science) and across ratings of good and poor teachers (via within‐subjects comparison). The selected model imposed between‐group invariance constraints on factor loadings, factor correlations, and factor variances across the 3 discipline groups and within‐subjects invariance constraints on factor loadings across ratings of good and poor teachers. The results support the use of SEEQ in this Chinese setting, demonstrating the generality of North American research findings and the usefulness of CFA in this research area.  相似文献   

16.
This study examined a 13-item instrument measuring approaches to learning (AtL) as a component of school readiness in the context of early childhood socio-emotional development. Few instruments, limited to preschool teacher ratings, measure AtL among kindergarteners with short easy-to-use questionnaires. We investigated psychometric properties of the instrument designed to provide practical measures of AtL behaviours identified in the Arizona Early Learning Standards with teacher (n?=?205) and guardian (n?=?1025) samples. We found a one-factor structure via exploratory factor analysis and confirmatory factor analysis (CFA). The multi-group CFA for combined teacher and guardian models indicated a good fit, which demonstrated the structure validity of the AtL instrument. This finding, combined with evidence of reliability of the instrument, supported the educational utility of the AtL as a new tool for measuring school readiness among kindergarteners in Arizona.  相似文献   

17.
Multigroup exploratory factor analysis (EFA) has gained popularity to address measurement invariance for two reasons. Firstly, repeatedly respecifying confirmatory factor analysis (CFA) models strongly capitalizes on chance and using EFA as a precursor works better. Secondly, the fixed zero loadings of CFA are often too restrictive. In multigroup EFA, factor loading invariance is rejected if the fit decreases significantly when fixing the loadings to be equal across groups. To locate the precise factor loading non-invariances by means of hypothesis testing, the factors’ rotational freedom needs to be resolved per group. In the literature, a solution exists for identifying optimal rotations for one group or invariant loadings across groups. Building on this, we present multigroup factor rotation (MGFR) for identifying loading non-invariances. Specifically, MGFR rotates group-specific loadings both to simple structure and between-group agreement, while disentangling loading differences from differences in the structural model (i.e., factor (co)variances).  相似文献   

18.
Developments concerning report cards have led to a potential shift from reporting traditional grades to reporting multiple competencies within and across subjects. In this study, we analyzed the dimensional structure of the teacher judgments on a competency-based report card on fourth-grade elementary school students (N = 469). With a methodologically innovative approach of combining exploratory structural equation modeling (ESEM) and confirmatory factor analysis (CFA), we found one learning-oriented and one social-oriented generic subject-unspecific factor of competency judgments and single factors for each included subject. All subject factors showed relatively high correlations with the respective traditional grades. Second-order commonalities further indicated a general factor represented almost perfectly by the learning-oriented generic judgments. Our analyses generally justified the use of competency-based report cards in terms of the dimensional structure and the association with traditional grades. Further, generic subject-unspecific competency judgments contribute to disentangling the multidimensionality of teacher judgments.  相似文献   

19.
Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration was investigated via Monte Carlo simulations. Data were generated based on FMM models with (1) a zero covariate effect, (2) a covariate effect on the latent class variable, and (3) covariate effects on both the latent class variable and the factor. For each population model, different analysis models that excluded or misspecified covariate effects were fitted. Results highlighted the importance of including proper covariates in measurement invariance testing and evidenced the utility of a model comparison approach in searching for the correct specification of covariate effects and the level of measurement invariance. This approach was demonstrated using an empirical data set. Implications for methodological and applied research are discussed.  相似文献   

20.
An extension of two confirmatory factor models for multitrait-multimethod measurement designs with structurally different methods to the analysis of latent interaction effects is presented: the nonlinear latent difference (NL-LD) model and the nonlinear correlated trait–correlated method-minus-one (NL-CTC[M – 1]) model. Both models are compared with regard to (a) the psychometric definition of the latent variables, (b) the capabilities of explaining latent method effects, and (c) the analysis of latent interaction effects. Using the latent moderated structural equation approach, we show how moderated method effects can be examined in the NL-CTC(M – 1) model. This fine-grained analysis of method effects is not feasible using the classical NL-LD model. We propose an extended version of the NL-LD model, which recovers the results of the NL-CTC(M – 1) model. The different versions of the nonlinear multimethod models are illustrated using real data from a multirater study. Finally, the advantages and challenges of incorporating latent interaction effects in complex CFA–MTMM models are discussed.  相似文献   

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