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- Genre: Academic theses
Description
Possible selves researchers have uncovered many issues associated with the current possible selves measures. For instance, one of the most famous possible selves measures, Oyserman (2004)'s open-ended possible selves, has proven to be difficult to score reliably and also involves laborious scoring procedures. Therefore, this study was initiated to develop a close-ended measure, called the Persistent Academic Possible Selves Scale for Adolescents (PAPSS), that meets these challenges. The PAPSS integrates possible selves theories (personal and social identities) and educational psychology (self-regulation in social cognitive theory). Four hundred and ninety five junior high and high school students participated in the validation study of the PAPSS. I conducted confirmatory factor analyses (CFA) to compare fit for a baseline model to the hypothesized models using Mplus version 7 (Muthén & Muthén, 2012). A weighted least square means and a variance adjusted (WLSMV) estimation method was used for handling multivariate nonnormality of ordered categorical data. The final PAPSS has validity evidence based on the internal structure. The factor structure is composed of three goal-driven factors, one self-regulated factor that focuses on peers, and four self-regulated factors that emphasize the self. Oyserman (2004)'s open-ended questionnaire was used for exploring the evidence of convergent validity. Many issues regarding Oyserman (2003)'s instructions were found during the coding process of academic plausibility. It was complicated to detect hidden academic possible selves and strategies from non-academic possible selves and strategies. Also, interpersonal related strategies were over weighted in the scoring process compared to interpersonal related academic possible selves. The study results uncovered that all of the academic goal-related factors in the PAPSS are significantly related to academic plausibility in a positive direction. However, self-regulated factors in the PAPSS are not. The correlation results between the self-regulated factors and academic plausibility do not provide the evidence of convergent validity. Theoretical and methodological explanations for the test results are discussed.
ContributorsLee, Ji Eun (Author) / Husman, Jenefer (Thesis advisor) / Green, Samuel (Committee member) / Millsap, Roger (Committee member) / Brem, Sarah (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study investigated the internal factor structure of the English language development Assessment (ELDA) using confirmatory factor analysis. ELDA is an English language proficiency test developed by a consortium of multiple states and is used to identify and reclassify English language learners in kindergarten to grade 12. Scores on item parcels based on the standards tested from the four domains of reading, writing, listening, and speaking were used for the analyses. Five different factor models were tested: a single factor model, a correlated two-factor model, a correlated four-factor model, a second-order factor model and a bifactor model. The results indicate that the four-factor model, second-order model, and bifactor model fit the data well. The four-factor model hypothesized constructs for reading, writing, listening and speaking. The second-order model hypothesized a second-order English language proficiency factor as well as the four lower-order factors of reading, writing, listening and speaking. The bifactor model hypothesized a general English language proficiency factor as well as the four domain specific factors of reading, writing, listening, and speaking. The Chi-square difference tests indicated that the bifactor model best explains the factor structure of the ELDA. The results from this study are consistent with the findings in the literature about the multifactorial nature of language but differ from the conclusion about the factor structures reported in previous studies. The overall proficiency levels on the ELDA gives more weight to the reading and writing sections of the test than the speaking and listening sections. This study has implications on the rules used for determining proficiency levels and recommends the use of conjunctive scoring where all constructs are weighted equally contrary to current practice.
ContributorsKuriakose, Anju Susan (Author) / Macswan, Jeff (Thesis advisor) / Haladyna, Thomas (Thesis advisor) / Thompson, Marilyn (Committee member) / Arizona State University (Publisher)
Created2011
Description
Although the issue of factorial invariance has received increasing attention in the literature, the focus is typically on differences in factor structure across groups that are directly observed, such as those denoted by sex or ethnicity. While establishing factorial invariance across observed groups is a requisite step in making meaningful cross-group comparisons, failure to attend to possible sources of latent class heterogeneity in the form of class-based differences in factor structure has the potential to compromise conclusions with respect to observed groups and may result in misguided attempts at instrument development and theory refinement. The present studies examined the sensitivity of two widely used confirmatory factor analytic model fit indices, the chi-square test of model fit and RMSEA, to latent class differences in factor structure. Two primary questions were addressed. The first of these concerned the impact of latent class differences in factor loadings with respect to model fit in a single sample reflecting a mixture of classes. The second question concerned the impact of latent class differences in configural structure on tests of factorial invariance across observed groups. The results suggest that both indices are highly insensitive to class-based differences in factor loadings. Across sample size conditions, models with medium (0.2) sized loading differences were rejected by the chi-square test of model fit at rates just slightly higher than the nominal .05 rate of rejection that would be expected under a true null hypothesis. While rates of rejection increased somewhat when the magnitude of loading difference increased, even the largest sample size with equal class representation and the most extreme violations of loading invariance only had rejection rates of approximately 60%. RMSEA was also insensitive to class-based differences in factor loadings, with mean values across conditions suggesting a degree of fit that would generally be regarded as exceptionally good in practice. In contrast, both indices were sensitive to class-based differences in configural structure in the context of a multiple group analysis in which each observed group was a mixture of classes. However, preliminary evidence suggests that this sensitivity may contingent on the form of the cross-group model misspecification.
ContributorsBlackwell, Kimberly Carol (Author) / Millsap, Roger E (Thesis advisor) / Aiken, Leona S. (Committee member) / Enders, Craig K. (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2011
Description
The tendency for psychopathology to aggregate within families is well-documented, though little is known regarding the level of specificity at which familial transmission of symptomology occurs. The current study first tested competing higher-order structures of psychopathology in adolescence, indexing general and more specific latent factors. Second, parent-offspring transmission was tested for broadband domain specificity versus transmission of a general liability for psychopathology. Lastly, genetic and environmental mechanisms underlying the familial aggregation of psychopathology were examined using nuclear twin-family models. The sample was comprised of five hundred adolescent twin pairs (mean age 13.24 years) and their parents drawn from the Wisconsin Twin Project. Twins and parents completed independent diagnostic interviews. For aim 1, correlated factors, bifactor, and general-factor models were tested using adolescent symptom count data. For aim 2, structural equation modeling was used to determine whether broadband domain-specific transmission effects were necessary to capture parent-offspring resemblance in psychopathology above and beyond a general transmission effect indexed by the latent correlation between a parental internalizing factor and offspring P-factor. For aim 3, general factor models were fitted in both generations, and factor scores were subsequently extracted and used in nuclear twin-family model testing. Results indicated that the bifactor model exhibited the best fit to the adolescent data. Familial aggregation of psychopathology was sufficiently accounted for by the transmission of a general liability. Lastly, the best fitting reduced nuclear twin-family model indicated that additive genetic, sibling-specific shared environmental, and nonshared environmental influences contributed to general psychopathology. Parent-offspring transmission was accounted for by shared genetics only, whereas co-twin aggregation was additionally explained by sibling-specific shared environmental factors. Results provide novel insight into the specificity and etiology of the familial aggregation of psychopathology.
ContributorsOro, Veronica (Author) / Lemery-Chalfant, Kathryn (Thesis advisor) / Chassin, Laurie (Committee member) / Doane, Leah D (Committee member) / Arizona State University (Publisher)
Created2019
Description
A simulation study was conducted to explore the influence of partial loading invariance and partial intercept invariance on the latent mean comparison of the second-order factor within a higher-order confirmatory factor analysis (CFA) model. Noninvariant loadings or intercepts were generated to be at one of the two levels or both levels for a second-order CFA model. The numbers and directions of differences in noninvariant loadings or intercepts were also manipulated, along with total sample size and effect size of the second-order factor mean difference. Data were analyzed using correct and incorrect specifications of noninvariant loadings and intercepts. Results summarized across the 5,000 replications in each condition included Type I error rates and powers for the chi-square difference test and the Wald test of the second-order factor mean difference, estimation bias and efficiency for this latent mean difference, and means of the standardized root mean square residual (SRMR) and the root mean square error of approximation (RMSEA).
When the model was correctly specified, no obvious estimation bias was observed; when the model was misspecified by constraining noninvariant loadings or intercepts to be equal, the latent mean difference was overestimated if the direction of the difference in loadings or intercepts of was consistent with the direction of the latent mean difference, and vice versa. Increasing the number of noninvariant loadings or intercepts resulted in larger estimation bias if these noninvariant loadings or intercepts were constrained to be equal. Power to detect the latent mean difference was influenced by estimation bias and the estimated variance of the difference in the second-order factor mean, in addition to sample size and effect size. Constraining more parameters to be equal between groups—even when unequal in the population—led to a decrease in the variance of the estimated latent mean difference, which increased power somewhat. Finally, RMSEA was very sensitive for detecting misspecification due to improper equality constraints in all conditions in the current scenario, including the nonzero latent mean difference, but SRMR did not increase as expected when noninvariant parameters were constrained.
When the model was correctly specified, no obvious estimation bias was observed; when the model was misspecified by constraining noninvariant loadings or intercepts to be equal, the latent mean difference was overestimated if the direction of the difference in loadings or intercepts of was consistent with the direction of the latent mean difference, and vice versa. Increasing the number of noninvariant loadings or intercepts resulted in larger estimation bias if these noninvariant loadings or intercepts were constrained to be equal. Power to detect the latent mean difference was influenced by estimation bias and the estimated variance of the difference in the second-order factor mean, in addition to sample size and effect size. Constraining more parameters to be equal between groups—even when unequal in the population—led to a decrease in the variance of the estimated latent mean difference, which increased power somewhat. Finally, RMSEA was very sensitive for detecting misspecification due to improper equality constraints in all conditions in the current scenario, including the nonzero latent mean difference, but SRMR did not increase as expected when noninvariant parameters were constrained.
ContributorsLiu, Yixing (Author) / Thompson, Marilyn (Thesis advisor) / Green, Samuel (Committee member) / Levy, Roy (Committee member) / Arizona State University (Publisher)
Created2016