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- All Subjects: Endogenous instrumentality
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Description
Investigation of measurement invariance (MI) commonly assumes correct specification of dimensionality across multiple groups. Although research shows that violation of the dimensionality assumption can cause bias in model parameter estimation for single-group analyses, little research on this issue has been conducted for multiple-group analyses. This study explored the effects of mismatch in dimensionality between data and analysis models with multiple-group analyses at the population and sample levels. Datasets were generated using a bifactor model with different factor structures and were analyzed with bifactor and single-factor models to assess misspecification effects on assessments of MI and latent mean differences. As baseline models, the bifactor models fit data well and had minimal bias in latent mean estimation. However, the low convergence rates of fitting bifactor models to data with complex structures and small sample sizes caused concern. On the other hand, effects of fitting the misspecified single-factor models on the assessments of MI and latent means differed by the bifactor structures underlying data. For data following one general factor and one group factor affecting a small set of indicators, the effects of ignoring the group factor in analysis models on the tests of MI and latent mean differences were mild. In contrast, for data following one general factor and several group factors, oversimplifications of analysis models can lead to inaccurate conclusions regarding MI assessment and latent mean estimation.
ContributorsXu, Yuning (Author) / Green, Samuel (Thesis advisor) / Levy, Roy (Committee member) / Thompson, Marilyn (Committee member) / Arizona State University (Publisher)
Created2018
Description
A simulation study was conducted to explore the robustness of general factor mean difference estimation in bifactor ordered-categorical data. In the No Differential Item Functioning (DIF) conditions, the data generation conditions varied were sample size, the number of categories per item, effect size of the general factor mean difference, and the size of specific factor loadings; in data analysis, misspecification conditions were introduced in which the generated bifactor data were fit using a unidimensional model, and/or ordered-categorical data were treated as continuous data. In the DIF conditions, the data generation conditions varied were sample size, the number of categories per item, effect size of latent mean difference for the general factor, the type of item parameters that had DIF, and the magnitude of DIF; the data analysis conditions varied in whether or not setting equality constraints on the noninvariant item parameters.
Results showed that falsely fitting bifactor data using unidimensional models or failing to account for DIF in item parameters resulted in estimation bias in the general factor mean difference, while treating ordinal data as continuous had little influence on the estimation bias as long as there was no severe model misspecification. The extent of estimation bias produced by misspecification of bifactor datasets with unidimensional models was mainly determined by the degree of unidimensionality (i.e., size of specific factor loadings) and the general factor mean difference size. When the DIF was present, the estimation accuracy of the general factor mean difference was completely robust to ignoring noninvariance in specific factor loadings while it was very sensitive to failing to account for DIF in threshold parameters. With respect to ignoring the DIF in general factor loadings, the estimation bias of the general factor mean difference was substantial when the DIF was -0.15, and it can be negligible for smaller sizes of DIF. Despite the impact of model misspecification on estimation accuracy, the power to detect the general factor mean difference was mainly influenced by the sample size and effect size. Serious Type I error rate inflation only occurred when the DIF was present in threshold parameters.
Results showed that falsely fitting bifactor data using unidimensional models or failing to account for DIF in item parameters resulted in estimation bias in the general factor mean difference, while treating ordinal data as continuous had little influence on the estimation bias as long as there was no severe model misspecification. The extent of estimation bias produced by misspecification of bifactor datasets with unidimensional models was mainly determined by the degree of unidimensionality (i.e., size of specific factor loadings) and the general factor mean difference size. When the DIF was present, the estimation accuracy of the general factor mean difference was completely robust to ignoring noninvariance in specific factor loadings while it was very sensitive to failing to account for DIF in threshold parameters. With respect to ignoring the DIF in general factor loadings, the estimation bias of the general factor mean difference was substantial when the DIF was -0.15, and it can be negligible for smaller sizes of DIF. Despite the impact of model misspecification on estimation accuracy, the power to detect the general factor mean difference was mainly influenced by the sample size and effect size. Serious Type I error rate inflation only occurred when the DIF was present in threshold parameters.
ContributorsLiu, Yixing (Author) / Thompson, Marilyn (Thesis advisor) / Levy, Roy (Committee member) / O’Rourke, Holly (Committee member) / Arizona State University (Publisher)
Created2019
Description
In 2012, there were an estimated 43.7 million adults in the United States that had a diagnosable mental, behavioral, or emotional disorder (US Department of Health and Human Services [HHS], 2013). Given the large frequency of disorders, it is beneficial to learn about what factors influence psychological distress. One construct that has been increasingly examined in association with mental disorders is time perspective. The current study will investigate whether or not time perspective, as measured by the Zimbardo Time Perspective Inventory (ZTPI), has a unique contribution to the prediction of psychological distress. Studies have shown that time perspective has been related to psychological symptomology. Also, previous studies have shown that time perspective has been related to the constructs of neuroticism and negative affect, which have also been shown to be related to psychological distress. I also included the deviation from an optimal time perspective (DOTP) as a predictor separate from the ZTPI scales. So, I investigated whether or not time perspective has a unique influence on psychological distress when controlling for the previously mentioned related constructs. I also controlled for gender and age by including them as covariates in the regression analyses. I found that the past positive sub-scale and DOTP were significant predictors of psychological distress. Implications of these findings are discussed.
ContributorsZoloto, Alexander (Author) / Tracey, Terence (Thesis advisor) / Kemer, Gulsah (Committee member) / Randall, Ashley (Committee member) / Arizona State University (Publisher)
Created2015
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
Description
Instrumentality is an important motivational construct that empathizes the connection between a present task and a future goal. Instrumentality is conceptualized as a task-specific variable. Reflecting context-dependent characteristics, two different types of instrumentality are distinguished: endogenous and exogenous instrumentality. Endogenous instrumentality is the perception that learning in a present task is useful to achieving valued future goals and exogenous instrumentality is the perception that outcome in a present task is instrumental to achieving valued future goals. This study investigated the differential relationships among each instrumentality type, academic achievements, and motivational variables. Three studies were conducted to investigate the relationship between each type of instrumentality and students’ achievement and motivational variables such as achievement goals, situational interests, and pressure and the moderating role of self-efficacy on the relationship. Study 1 investigated how endogenous and exogenous instrumentality was related to students’ achievement respectively. In addition, it was examined whether self-efficacy moderated in the relationship between each instrumentality and achievement. Study 2 was conducted to find that how each instrumentality was related to three different types of achievement goals, which were mastery, performance-approach, and performance-avoidance goals. Interaction between each type of instrumentality and self-efficacy was examined to find a moderating effect by self-efficacy on accounting for the relationship between instrumentality and achievement goals. Study 3 examined the role of each instrumentality on situational interest, pressure and achievement. The results showed that endogenous instrumentality predicted grade positively regardless students’ self-efficacy level, whereas exogenous instrumentality positively predicted grade of students with high self-efficacy and negatively predicted grade of students with low-self-efficacy. In addition, endogenous instrumentality predicted mastery goals positively and performance-avoidance goals negatively, whereas exogenous instrumentality predicted both performance-approach and performance avoidance goals positively. Moreover, students with high self-efficacy were less likely to adopt performance-avoidance goals when they perceived more endogenous instrumentality. It was also found that endogenous instrumentality was a positive predictor of situational interest and a negative predictor of pressure, whereas exogenous instrumentality was a negative predictor of situational interest and as a positive predictor of pressure. There was a mediating effect of pressure on the relationship between each instrumentality and grade.
ContributorsKim, Wonsik (Author) / Husman, Jenefer (Thesis advisor) / Thompson, Marilyn (Committee member) / Bong, Mimi (Committee member) / Arizona State University (Publisher)
Created2016
Description
Possible selves research has focused primarily on academic achievement and student learning, for at-risk, adolescent or college aged students. The research has not examined an occupation possible self, nor the implications of how time is considered by incarcerated populations. This study was designed to expand the Possible Selves Questionaire (PSQ) designed by Oyserman for an occupational achievement code and explore any unique codes present for incarcerated young adult males, aged 18-22. Additionally, this study was designed to compare two distinct time horizons for incarcerated young adults, a more proximal one-year event which would represent continued incarceration and a post-release distal time horizon.
A pilot study was conducted to establish the occupation and population codes, coding system, member checks and review processes that were then applied to interview 126 incarcerated young adult males between the ages of 18 and 22 in Arizona correctional facilities. The study produced not only an occupational achievement code, but also refined codes for interpersonal relationships requiring the addition of a spiritual/social code to account for church activities, religion, and spiritual groups, while narrowing the existing interpersonal relationships code to focus on family, children, a spouse or partner. Analysis demonstrated that incarcerated young adults create fewer identified strategies and have fewer aligned strategies to achieve post-release goals. Time served and expected sentences were determined to be significantly associated with the identification of goals, strategies, and development of aligned strategies. The impact of the different time horizon events of during and post incarceration were significant as well, participants identified five times as many goals one year from now in comparison to post-release, and on average 1.5 more strategies to achieve identified goals.
The study demonstrated that the participants expected sentence was a significantly associated covariate to the number of Future Possible Selves’(FPS) defined, number of strategies defined to achieve those FPS goals, and number of aligned strategies to FPS goals across time horizons of 1 year and post release. However, time served was only found to be a statistically significant covariate for both goal identification and strategy identification, not strategy alignment.
A pilot study was conducted to establish the occupation and population codes, coding system, member checks and review processes that were then applied to interview 126 incarcerated young adult males between the ages of 18 and 22 in Arizona correctional facilities. The study produced not only an occupational achievement code, but also refined codes for interpersonal relationships requiring the addition of a spiritual/social code to account for church activities, religion, and spiritual groups, while narrowing the existing interpersonal relationships code to focus on family, children, a spouse or partner. Analysis demonstrated that incarcerated young adults create fewer identified strategies and have fewer aligned strategies to achieve post-release goals. Time served and expected sentences were determined to be significantly associated with the identification of goals, strategies, and development of aligned strategies. The impact of the different time horizon events of during and post incarceration were significant as well, participants identified five times as many goals one year from now in comparison to post-release, and on average 1.5 more strategies to achieve identified goals.
The study demonstrated that the participants expected sentence was a significantly associated covariate to the number of Future Possible Selves’(FPS) defined, number of strategies defined to achieve those FPS goals, and number of aligned strategies to FPS goals across time horizons of 1 year and post release. However, time served was only found to be a statistically significant covariate for both goal identification and strategy identification, not strategy alignment.
ContributorsO'Neill, Edward (Author) / Husman, Jenefer (Thesis advisor) / Mathur, Sarup (Committee member) / Platt, Derrick (Committee member) / Arizona State University (Publisher)
Created2016
Description
Although models for describing longitudinal data have become increasingly sophisticated, the criticism of even foundational growth curve models remains challenging. The challenge arises from the need to disentangle data-model misfit at multiple and interrelated levels of analysis. Using posterior predictive model checking (PPMC)—a popular Bayesian framework for model criticism—the performance of several discrepancy functions was investigated in a Monte Carlo simulation study. The discrepancy functions of interest included two types of conditional concordance correlation (CCC) functions, two types of R2 functions, two types of standardized generalized dimensionality discrepancy (SGDDM) functions, the likelihood ratio (LR), and the likelihood ratio difference test (LRT). Key outcomes included effect sizes of the design factors on the realized values of discrepancy functions, distributions of posterior predictive p-values (PPP-values), and the proportion of extreme PPP-values.
In terms of the realized values, the behavior of the CCC and R2 functions were generally consistent with prior research. However, as diagnostics, these functions were extremely conservative even when some aspect of the data was unaccounted for. In contrast, the conditional SGDDM (SGDDMC), LR, and LRT were generally sensitive to the underspecifications investigated in this work on all outcomes considered. Although the proportions of extreme PPP-values for these functions tended to increase in null situations for non-normal data, this behavior may have reflected the true misfit that resulted from the specification of normal prior distributions. Importantly, the LR and the SGDDMC to a greater extent exhibited some potential for untangling the sources of data-model misfit. Owing to connections of growth curve models to the more fundamental frameworks of multilevel modeling, structural equation models with a mean structure, and Bayesian hierarchical models, the results of the current work may have broader implications that warrant further research.
In terms of the realized values, the behavior of the CCC and R2 functions were generally consistent with prior research. However, as diagnostics, these functions were extremely conservative even when some aspect of the data was unaccounted for. In contrast, the conditional SGDDM (SGDDMC), LR, and LRT were generally sensitive to the underspecifications investigated in this work on all outcomes considered. Although the proportions of extreme PPP-values for these functions tended to increase in null situations for non-normal data, this behavior may have reflected the true misfit that resulted from the specification of normal prior distributions. Importantly, the LR and the SGDDMC to a greater extent exhibited some potential for untangling the sources of data-model misfit. Owing to connections of growth curve models to the more fundamental frameworks of multilevel modeling, structural equation models with a mean structure, and Bayesian hierarchical models, the results of the current work may have broader implications that warrant further research.
ContributorsFay, Derek (Author) / Levy, Roy (Thesis advisor) / Thompson, Marilyn (Committee member) / Enders, Craig (Committee member) / Arizona State University (Publisher)
Created2015
Description
This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex performance assessment within a digital-simulation educational context grounded in theories of cognition and learning. BN models were manipulated along two factors: latent variable dependency structure and number of latent classes. Distributions of posterior predicted p-values (PPP-values) served as the primary outcome measure and were summarized in graphical presentations, by median values across replications, and by proportions of replications in which the PPP-values were extreme. An effect size measure for PPMC was introduced as a supplemental numerical summary to the PPP-value. Consistent with previous PPMC research, all investigated fit functions tended to perform conservatively, but Standardized Generalized Dimensionality Discrepancy Measure (SGDDM), Yen's Q3, and Hierarchy Consistency Index (HCI) only mildly so. Adequate power to detect at least some types of misfit was demonstrated by SGDDM, Q3, HCI, Item Consistency Index (ICI), and to a lesser extent Deviance, while proportion correct (PC), a chi-square-type item-fit measure, Ranked Probability Score (RPS), and Good's Logarithmic Scale (GLS) were powerless across all investigated factors. Bivariate SGDDM and Q3 were found to provide powerful and detailed feedback for all investigated types of misfit.
ContributorsCrawford, Aaron (Author) / Levy, Roy (Thesis advisor) / Green, Samuel (Committee member) / Thompson, Marilyn (Committee member) / Arizona State University (Publisher)
Created2014
Description
The primary objective of this study was to revise a measure of exogenous instrumentality, part of a larger scale known as the Perceptions of Instrumentality Scale (Husman, Derryberry, Crowson, & Lomax, 2004) used to measure future oriented student value for course content. Study 1 piloted the revised items, explored the factor structure, and provided initial evidence for the reliability and validity of the revised scale. Study 2 provided additional reliability evidence but a factor analysis with the original and revised scale items revealed that the revised scale was measuring a distinct and separate construct that was not exogenous instrumentality. Here this new construct is called extrinsic instrumentality for grade. This study revealed that those that endorse a high utility value for grade report lower levels of connectedness (Husman & Shell, 2008) and significantly less use of knowledge building strategies (Shell, et al., 2005). These findings suggest that there are additional types of future oriented extrinsic motivation that should be considered when constructing interventions for students, specifically non-major students. This study also provided additional evidence that there are types of extrinsic motivation that are adaptive and have positive relationships with knowledge building strategies and connectedness to the future. Implications for the measurement of future time perspective (FTP) and its relationship to these three proximal, future oriented, course specific measures of value are also discussed.
ContributorsPuruhito, Krista (Author) / Husman, Jenefer (Thesis advisor) / Glenberg, Arthur (Committee member) / Lindstron-Johnson, Sarah (Committee member) / Levy, Roy (Committee member) / Arizona State University (Publisher)
Created2017