Matching Items (3)
Filtering by

Clear all filters

152057-Thumbnail Image.png
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 develo

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
154498-Thumbnail Image.png
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

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.
ContributorsLiu, Yixing (Author) / Thompson, Marilyn (Thesis advisor) / Green, Samuel (Committee member) / Levy, Roy (Committee member) / Arizona State University (Publisher)
Created2016
154781-Thumbnail Image.png
Description
Researchers who conduct longitudinal studies are inherently interested in studying individual and population changes over time (e.g., mathematics achievement, subjective well-being). To answer such research questions, models of change (e.g., growth models) make the assumption of longitudinal measurement invariance. In many applied situations, key constructs are measured by a collection

Researchers who conduct longitudinal studies are inherently interested in studying individual and population changes over time (e.g., mathematics achievement, subjective well-being). To answer such research questions, models of change (e.g., growth models) make the assumption of longitudinal measurement invariance. In many applied situations, key constructs are measured by a collection of ordered-categorical indicators (e.g., Likert scale items). To evaluate longitudinal measurement invariance with ordered-categorical indicators, a set of hierarchical models can be sequentially tested and compared. If the statistical tests of measurement invariance fail to be supported for one of the models, it is useful to have a method with which to gauge the practical significance of the differences in measurement model parameters over time. Drawing on studies of latent growth models and second-order latent growth models with continuous indicators (e.g., Kim & Willson, 2014a; 2014b; Leite, 2007; Wirth, 2008), this study examined the performance of a potential sensitivity analysis to gauge the practical significance of violations of longitudinal measurement invariance for ordered-categorical indicators using second-order latent growth models. The change in the estimate of the second-order growth parameters following the addition of an incorrect level of measurement invariance constraints at the first-order level was used as an effect size for measurement non-invariance. This study investigated how sensitive the proposed sensitivity analysis was to different locations of non-invariance (i.e., non-invariance in the factor loadings, the thresholds, and the unique factor variances) given a sufficient sample size. This study also examined whether the sensitivity of the proposed sensitivity analysis depended on a number of other factors including the magnitude of non-invariance, the number of non-invariant indicators, the number of non-invariant occasions, and the number of response categories in the indicators.
ContributorsLiu, Yu, Ph.D (Author) / West, Stephen G. (Thesis advisor) / Tein, Jenn-Yun (Thesis advisor) / Green, Samuel (Committee member) / Grimm, Kevin J. (Committee member) / Arizona State University (Publisher)
Created2016