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Grit, defined as perseverance and passion for long-term goals, has garnered attention in educational research for its potential to predict academic achievement and well-being. Despite this, its psychometric properties and applicability across diverse populations remain underexplored. This study aims to validate the Grit-O scale and explore its relations with various psychosocial and academic outcomes among underrepresented undergraduate STEM scholars. Analyses utilized a mixed-methods approach, including confirmatory and exploratory factor analyses, bivariate correlations, and moderated multiple regression analyses. Results indicate that the Grit-O scale did not demonstrate adequate fit in its original form. However, an amended 11-item scale comprising two correlated factors (perseverance and consistency) showed improved fit and was retained for further analysis. Exploratory analyses revealed consistent factor structures across binary gender groups, suggesting scale robustness across gender identities. Neither perseverance nor consistency significantly predicted entry into a STEM graduate program. The intricate relationship between grit and various academic and psychosocial outcomes offers valuable insights into the influence of perseverance and consistency. Correlations and moderated multiple regression analyses highlight nuanced associations between grit dimensions and academic and mental health outcomes. This study sheds light on Grit-O's psychometric properties, but its utility for measuring academic achievement remains inconclusive. The mixed findings underscore the need for continued psychometric scrutiny and a nuanced understanding of academic achievement dynamics, particularly for underrepresented groups. Future grit research should prioritize rigorous validation of the measure to ensure that the model appropriately fits the population and can be reliably used to draw meaningful conclusions. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

When measuring latent constructs, for example, language ability, we use statistical models to specify appropriate relationships between the latent construct and observe responses to test items. These models rely on theoretical assumptions to ensure accurate parameter estimates for valid inferences based on the test results. This dissertation specifically concerned the local independence (LI) assumption (or presence of local dependence (LD) equivalently) in two specific item response theory (IRT) models -- the partial credit model (PCM) and the generalized partial credit model (GPCM). With a primary focus on several commonly used global fit indices (GFIs), this study investigated how parameter estimates in the studied IRT models were affected by varying degrees of LD, whether the studied GFIs were responsive to model misspecifications due to LD, and any insights regarding making reasonable decisions in terms of good-fit models. The results revealed that, first, parameter estimates in both the PCM and GPCM were severely biased, especially the latent trait and item step parameter estimates. The degree of biases increased with higher levels of LD across different model and data conditions. Other general factors, including the test length and number of item response categories, could also impact biases in parameter estimates. Second, all studied GFIs were responsive to model misfit caused by the presence of LD. Specifically, studied GFIs exhibited moderate to strong associations with biases of the latent trait estimates in expected directions. Lastly, based on the performance of the studied GFIs under baseline and LD-induced conditions, the absolute fit indices (RMSEA and SRMSR) appeared to be more informative than the incremental fit indices (CFI and TLI) in assessing the global fit of the PCM and GPCM. This study provides insights into adopting specific decision criteria of good model fit under various model and data conditions, aiming to reduce potential false positive rates while maintaining reasonable true positive rates. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

This dissertation compares the performance of equivalence test (EQT) and null hypothesis test (NHT) procedures for identifying invariant and noninvariant factor loadings under a range of experimental manipulations. EQT is the statistically appropriate approach when the research goal is to find evidence of group similarity rather than group difference; despite this, the conventional approach to measurement invariance analysis relies upon NHT. EQT has proved effective for invariance detection using global model-data fit statistics in simulated and real-world data (Counsell et al., 2020) but its use in partial measurement invariance (PMI) analysis for evaluation of factor loading differences between groups has not been previously investigated. A Monte Carlo simulation is used to investigate test performance under experimental manipulations of power, model complexity, degree of noninvariance, and choice of EQT bound. Jung and Yoon (2017) use a similar approach to demonstrate that the direct comparison of factor loading differences across groups using NHT confidence intervals effectively detect population patterns of invariant and noninvariant loadings. I compare the performance of NHT to EQT using both conventional benchmarks of factor loading equivalence inferred from the work of Counsell and Cribbie (2015), Nye, Bradburn, Olenick, Bialko, and Drasgow (2019), and Shi et al. (2019) as well as EQT bounds selected post hoc to maximize recovery of the population pattern of invariance or partial invariance. I also investigate the likelihood of false invariance detections for truly noninvariant loadings and false noninvariance detections for truly invariant loadings over a range of EQT bounds, sample sizes, and magnitudes of noninvariance. The results of this simulation indicate that EQT can detect population patterns of invariance or partial invariance, but requires high power (i.e., large sample sizes and strong communalities) as well as optimal selection of the EQT bound. In experimental conditions where these requirements are met, EQT recovers the correct population pattern of invariant and noninvariant factor loadings in more simulation replicates than the NHT approach (Jung & Yoon, 2017). Inspection of individual loading detection rates reveals that false noninvariance errors occur most frequently when power is low, while false invariance errors are common whenever the true magnitude of noninvariance is small and the selected EQT bound is larger than the loading difference. In the EQT framing, false invariance errors in the presence of minor noninvariance might also be interpreted as accurate detections of practically equivalent loadings. These findings support the use of EQT for PMI analysis and reinforce previous findings that EQT is an effective but power-intensive procedure in the multivariate context (Alter & Counsell, 2023). The choice of EQT bound has implications for both the power of the test and the consequences of accepting loading differences falling within the selected EQT bound as invariant. Researchers using EQT to detect PMI should collect large samples in order to maximize the likelihood of detection, and should consider how much misspecification their selected EQT bounds might tolerate. Future development of PMI simulation tools could support the selection and justification of EQT bounds in applied settings. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The landmark theoretical model of Community Cultural Wealth (CCW) was developed almost twenty years ago as a critique of American meritocracy and the deficit views of Students of Color (Yosso, 2005). Most research on CCW since that point has been qualitative in nature, specifically in-depth interviews to understand how Students of Color exact their aspirational, familial, linguistic, navigational, resistant, and social capital as they persist through education. The current study aimed to quantify and validate CCW as a six-factor model and examine the effects of CCW on academic self-efficacy, sense of belonging, grade point average, and mental health in a sample of Black and Latino/a college students. 567 college students from colleges across the United States participated in an online survey with CCW, student outcomes, and demographic items. Results indicated that all six cultural capital confirmatory factor analyses (CFA) exhibited good model fit and converged into a second-order Community Cultural Wealth latent construct, consistent with theory. CFA indicated good model fit for first-order factors for academic self-efficacy, sense of belonging, and mental health. Community Cultural Wealth significantly positively predicted all student outcomes, including explaining 62% of the variance in Academic Self-Efficacy, 25% of the variance in Sense of Belonging, and 14% of the variance in Mental Health. Grade point average was significant but was a weak outcome variable, and participation in college readiness programs did not mediate any relationships as predicted. The quantification of CCW as a six-factor model consistent with theory is a key finding, as it is the first time it has ever been done. Future research should involve buy-in from participants to solidify items used to measure CCW and how CCW affects student outcomes in different student populations. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The purpose of this study was to investigate the effectiveness of four different models (bifactor, CTC(M-1), CTCU and unidimensional) as to optimal model selection when the wording effect associated with negatively worded items was present. A Monte Carlo simulation study was conducted to compare model-data fit and accuracy in parameter estimates of the four models. Design factors include: two true models (CTC(M-1), CTCU models) x three sample sizes (small N=250, moderate N=500, large N=1,000) x two item ratios (positive items:negative items = 2:1 or 1:1) x three distributions (distribution of negative items; symmetric, moderately asymmetric, and extremely asymmetric). The generated data were analyzed with two different estimators (MLMV, WLSMV) under four different models (bifactor, CTC(M-1), CTCU, and unidimensional). Outcomes examined included fit index values (chi-square, RMSEA, SRMR, CFI, TLI, AIC, BIC), power, Type I error, and relative bias in factor loadings on general trait factor estimated from the four tested models. As a second study, the four tested models were applied to empirical data set from the RSE scale to evaluate whether the simulation study results would be observed in practice. Results showed that three tested models (bifactor, CTC(M-1), and CTCU) fit the data equivalently well regardless of study conditions. Misspecified unidimensional models would erroneously be selected as an optimal model when certain study conditions (small sample, asymmetrically distributed negative items, unbalanced number of positive and negative items, WLSMV estimator) were present. Among tested models, the CTC(M-1) tended to produce most accurate factor loadings for both positive and negative items. According to the study's findings, there could be overlaps in the fit index values under several alternative solutions. Researchers should not select one model over the other solutions only because it produced the best fit index values. Instead, a model selection should be determined based on the study's objectives or on substantive considerations. Additionally, researchers should carefully examine the data's features when misspecified unidimensional models yield goof-fit. Unidimensional models ignoring negative wording effects could be recognized as a good-fitting model in some circumstances because of different performance of fit indices. Several practical advice and guidelines are provided for researchers using unidimensional scales with mixed item wording. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

This multiple-manuscript dissertation explored the measurement invariance (MI) testing with multiple-group confirmatory factor analysis (MG-CFA) approach from different perspectives. Study 1 explored MI from a theoretical perspective by conducting a systematic review study on MI practices in education. The findings of this study indicated inconsistency in MI practices and showcased the limitations of the MI practices conducted by researchers in the field of education. Study 2 examined MI from an empirical perspective by implementing a cultural MI test of Strengths and Difficulties Questionnaires for elementary school students in the United States and China. The study provided a step-by-step demonstration of how to conduct an MI test appropriately. Study 3 investigated MI from a methodological perspective with a simulation study. This study examined the impact of model size and group size ratio on the sensitivity of fit measures to detect MI. The study found that model size, in combination with group size ratio, affected the power of CFI, RMSEA, and SRMR for identifying the metric or scalar noninvariance. Study 2 and Study 3 originated from the issues with MI practice identified from Study 1 and served as extensions of Study 1. These three manuscripts contributed to the research on MI testing with the MG-CFA approach theoretically, empirically, and methodologically. Overall, this series of studies help researchers gain a better understanding of the application of MI from different perspectives. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

This dissertation explored whether unnecessary linguistic complexity (LC) in mathematics and biology assessment items changes the direction and significance of differential item functioning (DIF) between subgroups emergent bilinguals (EBs) and English proficient students (EPs). Due to inconsistencies in measuring LC in items, Study One adapted a rubric counting instances of specific grammatical features in items and introduced a method for evaluating lexical features in items. Four raters were asked to count the presence of five grammatical features in assessment items and determine whether each feature contained construct-relevant vocabulary. The items were drawn from four content assessments administered to Massachusetts high school students: two biology assessments and two mathematics assessments. These counts of grammatical and lexical features were modeled in factor analyses to evaluate the multidimensionality of LC and subsequent fit of multidimensional LC models. While there were problems with raters consistently counting construct-irrelevant grammatical features, multidimensional models of LC fit acceptably well. Factor scores obtained from the measurement models for lexical complexity, relative clauses, and complex noun phrases created in Study One were used for Study Two. In Study Two, Rasch hierarchical generalized linear models (HGLMs) were created to evaluate DIF between different subgroups of EBs and EPs on a biology assessment and a mathematics assessment, as including LC as an item covariate may predict item responses differently by comparison group. Seven comparison groups were evaluated across two assessments (mathematics and biology): EPs versus EBs, EPs versus short-term EBs, EPs versus long-term EBs, short-term EBs versus long-term EBs, EPs versus Spanish-speaking EBs, EPs versus non-Spanish-speaking EBs, and non-Spanish-speaking EBs versus Spanish-speaking EBs (reference group versus focal group, respectively). For each comparison group, at least five models were created: a comparison model with all participants in the comparison group with that only accounts for the main effect of focal group status, a "base model" that evaluated DIF for the comparison groups with no LC item covariates, a model including lexical complexity as an item covariate ("LEX predictor"), a model including complex noun phrases as an item covariate ("NP predictor"), and a model including relative clauses as an item covariate ("RC predictor"). If LC predictor models improved model fit, models with multiple LC predictors were created. For the EP versus EB comparison groups on the mathematics assessment, model fit only improved with the NP predictor model, while the LEX, NP, and RC predictor models improved model fit for the EB versus EB comparison groups; a model with all LC predictors improved model fit for the EB versus EB comparison groups. For the biology assessment, the LEX, NP, and RC predictor models improved model fit for all comparison groups; a model with all LC predictors improved model fit for all comparison groups. The main effects of the item covariates (LC factor scores) and their interactions with focal group status were evaluated, as were the number of items within a comparison group that had changes in DIF significance or direction when including a LC predictor. All LC predictors had consistent main effects across comparison groups. For the mathematics assessment, items with higher complex noun phrases factor scores were consistently more difficult for all comparison groups (NP predictor model), and items with higher lexical complexity (LEX predictor model, all predictors model) or relative clauses factor scores (RC predictor model, all predictors model) were consistently more difficult for all EB versus EB comparison groups. For the biology assessment and all comparison groups, items with higher lexical complexity (LEX predictor model, all predictors model) or complex noun phrases factor scores (NP predictor model, all predictors model) were consistently more difficult, and items with lower relative clauses factor scores (RC predictor model, all predictors model) were consistently more difficult, with one exception. In the all predictors models for the EB versus EB comparison groups, only relative clauses had a significant main effect. There were some changes in interactions with LC predictors and focal group status. For the mathematics assessment and EP versus EB comparison groups, complex noun phrases interactions favored EPs. For the mathematics assessment and EB versus EB comparison groups, generally the interactions in the single LC predictor models generally favored STEBs compared to LTEBs and non-Spanish-speaking EBs compared to Spanish-speaking EBs, but when all LC predictors were included, no interactions between LC predictor and focal group status were significant. For the biology assessment and EP versus EB comparison groups, lexical complexity and complex noun phrases factor scores interactions generally favored EPs, and relative clauses factor scores interactions favored EBs and EB subgroups. For the biology assessment and EB versus EB comparison groups, regardless of whether examining the single LC predictor or all predictors models, no interactions between focal group status and LC predictor were significant. Changes in DIF significance and direction were compared between the base model and LC predictor models for all comparison groups. For the mathematics assessment and EP versus EB comparison groups, after conditioning on complex noun phrases, items with complex noun phrases generally exhibited significant DIF favoring EBs, regardless of whether the complex noun phrases factor scores were high (one standard deviation above the mean) or low (due to floor effects, the lowest complex noun phrases factor score). For the biology assessment, all items exhibited significant DIF favoring EBs after accounting for lexical complexity, most items exhibited non-significant DIF after accounting for complex noun phrases or relative clauses, and items were mixed between exhibiting non-significant DIF or significant DIF favoring EBs after accounting for all LC predictors. While items with high relative clauses factor scores exhibited non-significant DIF, some items with low relative clauses factor scores exhibited significant DIF favoring EPs after accounting for relative clauses. Items with two or more high factor scores exhibited non-significant DIF, but items with two or more low factor scores exhibited significant DIF favoring EBs after accounting for all LC predictors. These results were fairly consistent across different EP versus EB comparison groups, although different items were flagged for DIF in initial models not accounting for LC predictors. Items were less difficult for EBs than EPs after accounting for LC features, which suggests the abilities of EBs are underestimated due to LC in items, even if the items have low LC. Considering subgroup differences in these EIRMs, the key takeaway is that while different items are flagged as exhibiting significant DIF for different EP versus EB comparison groups when examining DIF with no LC predictors, there are few subgroup differences in items changing DIF significance or direction after accounting for LC predictors. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The purpose of this study is to evaluate the performance of three commonly used model fit indices when measurement invariance is tested in the context of multiple-group CFA analysis with categorical-ordered data. As applied researchers are increasingly aware of the importance of testing measurement invariance, as well as Likert-type scales are frequently used in social and behavioral sciences, specific guidelines are in need for evaluating model fit in this context. To achieve the study goal, two Monte Carlo simulation studies were conducted. Study 1 investigated the sampling variability of fit indices under different levels of invariance. Based on the sampling variability of fit indices, cutoff values for various levels of invariance proposed. Study 2 investigated the influence of several conditions on the sensitivity of fit indices' changes to two commonly used non-invariance levels: metric invariance and scalar invariance. Then, rejection rates based on cutoff values of fit indices proposed were examined in Study 2.Findings indicated that all three fit indices (CFI, RMSEA, SRMR) appeared to be sensitive to lack of invariance in factor thresholds than factor loadings. Different cutoff values may be applied under various conditions with categorial-ordered data. In addition, using cutoff values should be cautious as factors impacted changes in model fit indices differently. Recommendations for the use of model fit indices in the multiple-group CFA invariance context were provided for applied researchers. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The present cross-sectional study examined the interrelationships between mindfulness, self-compassion, gratitude, and burnout in a sample of Health Service Psychology doctoral trainees (n = 234) using structural equation modeling. Results suggested that mindfulness, self-compassion, and gratitude may each function as important internal resources in mitigating burnout in HSP trainees. A priori models showed poor structural fit and were respecified according to modification indices. The respecified primary model was retained as it showed a good fit (X[superscript 2](df) = 347.497(99), p < 0.001; RMSEA = 0.074; CFI = 0.939; TLI = 0.914; SRMR = 0.054) and significantly better fit than the respecified alternative model ([delta] [X superscript 2](1) = 7.37, p < 0.01). Mindfulness showed direct positive effects on self-compassion (0.643) and gratitude (0.346), and direct negative effects on burnout (-0.344). Self-compassion (-0.271) and gratitude (-0.220) each also showed direct negative effects on burnout. Mindfulness also showed significant specific indirect effects on burnout via self-compassion (-0.174) and gratitude (-0.076). Total indirect effects of mindfulness on burnout were medium negative effects (-0.250). Total effect of mindfulness on burnout was a large negative effect (-0.595). These results can be used to inform changes at the organizational level for doctoral training programs and can be used at an individual level as tools for empowering trainees to increase internal resources that may help them to engage in more sustainable practice. Theoretical, research, and practice implications for counseling psychologists and limitations are discussed. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Over the last two decades, there has been an increasing interest in third language (L3) phonology (Cabrelli Amaro, 2012; Cabrelli Amaro & Wrembel, 2016). However, research so far has been primarily directed at specific stages of learning and has independently explored either production (e.g., Llama, Cardoso, & Collins, 2010; Llama & Cardoso, 2018; Tremblay, 2007; Wrembel, 2011, 2014; Wunder, 2011) or perception (e.g., Onishi, 2016; Wrembel, Marecka, & Kopeckova, 2019). Almost none of the studies has examined the role of individual differences, including language aptitude, in L3 phonological development. To fill in the gap in the literature, the present study examines the role of cross-linguistic influence and language aptitude in both perception and production of L3 Spanish labial stops, /p/ and /b/. Cross-sectional data were collected from 40 Korean-English bilinguals at different proficiency levels of L3 Spanish. The dependent variables consist of VOT and F0 values as well as cross-language discrimination accuracy and identification tendency coupled with goodness-of-fit ratings. Findings demonstrated that L3 learners, regardless of proficiency level, were quite accurate at distinguishing, specifically, the Spanish /p/-English /p/ and the Spanish /b/-Korean lenis /p/ pair; however, this ability did not translate into accurate production among low-proficiency learners. Rather, for VOT, cross-linguistic influence from L2 English affected the production of both target sounds, with effects that faded away with increasing L3 proficiency, albeit slowly in the case of Spanish /b/, possibly the result of markedness effects. For F0, no correlations were found with L3 proficiency, in general. Regarding the relationship between perception and production, significant correlations were observed in some cases, yet more direct links between perception and production were identified among intermediate and advanced learners. Language aptitude appeared to facilitate both the perception and production of the target sounds. Specifically, a moderation analysis revealed a more substantial effect of phonemic coding ability on production at lower levels of L3 proficiency, as predicted in Skehan (2016), and corroborates previous findings (e.g., for L2 grammar, Li, 2015; for segmental learning, Saito, 2019; for feedback noticing, Yilmaz & Koylu, 2016). [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Social and emotional learning (SEL) has become essential in educational settings and human development. Federal policy has begun to incorporate SEL factors into education accountability metrics. Schools, institutes, and scholars have started implementing SEL in K-12 educational contexts. Social interactions and emotions are a daily part of students' lives, both in and out of school, which is why many educators are willing to teach and measure SEL competencies. Moreover, in previous research, it has been found that students' higher SEL competencies are positively associated with higher academic performance and lower risky behaviors. As interest in SEL grows, measurement tools continue to evolve. An interesting finding from the overview of existing SEL measures was that not only a general SEL scale is measured but in practice, multiple dimensions, or subscales of SEL. This finding implies the multidimensional nature of SEL measures. How- ever, researchers have not attempted to use the multidimensional item response theory to identify the multidimensionality of SEL measures and evaluate their psychometric properties. This dissertation aimed to apply MIRT models to the MSS-SEL data, identify the best-fitting model, and conduct a psychometric evaluation of the MSS-SEL items and dimensions. To mediate person-score calibration and evaluation, psychometric properties such as internal structure and item parameter estimates of the MSS-SEL items were evaluated under MIRT framework. The results showed that among the competing correlated models, MGRM fitted better than the MG- PCM to the MSS-SEL data. Additionally, among the competing bifactor models, bifactor-GRM fitted better than the bifactor-GPCM. The results indicated that for both the correlated MIRT model and bifactor model applications, the graded response modeling approach functioned better for the MSS-SEL response data than the generalized partial credit modeling approach. The results of the MGRM application showed that some MSS-SEL factors were relatively more highly correlated than others. One of the bifactor-GRM results showed that the general factor (i.e., SEL) accounted for by 66% of the common variance among items. Each specific factor was accounted almost equally well for the rest of the variance. Overall, the 37 SEL items reflected the general factor reasonably well, and most of the items were moderately/strongly explained by the general factor. This dissertation demonstrated what could have been missed if just a single model (i.e., UIRT, correlated MIRT model, or bifactor model) was fitted to the data. Furthermore, validity evidence was provided for the potential use and interpretation of the MSS-SEL scores. Several implications can be made especially for researchers and practitioners who frequently get involved in all different phases of SEL measurement. First, it is important to plan ahead for an item and scale development and to search for theoretically suitable IRT models. Second, it is crucial to examine the internal structure and use MIRT models when there are multiple SEL constructs to be measured. Third, the types of scores that are meaningful for the report and to be used should be clearly declared by the SEL measurement developers and users. Psychometric evaluation of the existing SEL measures using relevant measurement models will contribute to the validation of the SEL measures in support of the intended interpretation and use of such measures by educators and practitioners. This, of course, is a core component of validity evidence to interpret and use SEL scores at multiple levels. Moreover, this is a part of a larger effort to understand and articulate a set of psychometric principles for SEL measurement. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

This research focuses on a dual mediation model designed to describe the learning process of sixth grade students practicing mathematics to improve Mathematics Operation Proficiency while experiencing different forms of feedback. The variables of interest in this study are the predictor variable feedback type, the mediating variables of Affective and Cognitive Engagement and the outcome variable of Mathematics Operations Proficiency. Mathematics Operations Proficiency was measured via standardized gain scores derived from Mathematics Pre-tests and Posttests. Cognitive Load Theory, Cognitive Information Processing Theory, Engagement Theory, and a model of the effects of feedback were used to develop the dual mediation model used in this research. The results suggest that feedback type significantly affects neither Mathematics Operations Proficiency nor Cognitive Engagement. Furthermore, the model specified does not fit the data, according to the following measures of model fit: [k (superscript 2] test of goodness of fit, the SRMR, the RMSEA, and the PNFI. Factors such as measurement quality, sample size, Common Core Mathematics Standards selected by teacher, the level of cognitive complexity of the items used, the amount time spent preparing for practice, the types of activities spent preparing for the practice, the implementation of the research, and the effects of the COVID19 pandemic on society as a whole and the recruited subjects in particular are all worth consideration in the interpretation of the lack of fit of the model and data collected. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The application of diagnostic classification models (DCMs) in the field of educational measurement is getting more attention in recent years. To make a valid inference from the model, it is important to ensure that the model fits the data. The purpose of the present study was to investigate the performance of the limited information goodness-of-fit statistic Mord in the polytomous response DCMs with the presence of hierarchical attribute structures. The first simulation study investigated the empirical Type I error rates of the Mord statistic under the null hypothesis when the model and data were perfectly fitted. The second simulation study explored the empirical rejection rate of M[subscript ord] under different types of misspecifications: model misspecification, attribute hierarchy misspecification, and Q-matrix misspecification. The impact of test length, item quality, attribute structure, marginal/conditional probability of the mastery of attributes, and the number of response categories were investigated. The results indicated that the M[subscript ord] statistic demonstrated well-calibrated Type I error rates under the null conditions with different types of hierarchical attribute structures. When there were model-data misfits, the M[subscript ord] statistic showed high empirical rejection rates in detecting the misspecified sDINA and sDINO but didn't show enough power to detect the sC-RUM. The M[subscript ord] also exhibited high empirical rejection rates when the generating attribute structure was non-strict hierarchical, but the fitted structure was strict hierarchical. Besides, it was sensitive when the sequence of parent and child attributes was reversed. However, the M[subscript ord] statistic couldn't detect the omission of hierarchical attribute connections. When there were Q-matrix misspecifications, the M[subscript ord] statistic showed extremely high empirical rejection rates in the condition of Q-matrix under-specification but was not sensitive to the Q-matrix over-specification. The higher proportion of Q-matrix misspecification led to higher Mord rejection rates. The item quality exhibited a huge influence on the performance of the M[subscript ord] statistic. The M[subscript ord] statistic demonstrated higher empirical rejection rates with higher item quality, longer test length, fewer item response categories, and lower marginal/conditional probability of the mastery of attributes. The types of attribute structure also demonstrated slight influences on the rejection rates of the M[subscript ord] statistics under some circumstances. The performance of the RMSEA[subscript ord] and SRMSR were explored in the present study to assess the degree of misfit and absolute model fit. The results indicated that the frequently used cut-off value of 0.05 was not appropriate in the framework of polytomous response DCMs. The magnitude of RMSEA[subscript ord] and SRMSR values varied in different situations. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The present study investigated the experience of thriving and burnout among college students with regard to their within-person person resilience resources using a Self-Determination Theory (SDT; Deci & Ryan, 1985; Ryan & Deci, 2000) framework. SDT has proposed and documented that satisfaction of the basic needs for autonomy, competence, and relatedness facilitates indicators of well-being in a variety of contexts. However, few studies have explored the ability of the academically grounded basic psychological needs to mediate relations between students' within-person resilience variables (i.e., dispositional optimism and cognitive reappraisal) and indicators of student well-being (i.e., student thriving and student burnout). Structural equation modeling (SEM) was used to examine a model positing that each basic psychological need would fully mediate these relations among 658 current students at a large, upper-midwestern R1 university. The measurement and structural models were a good fit to the data. Dispositional optimism and cognitive reappraisal each had significant positive relations with all three basic needs. Perceived academic competence and perceived campus relatedness has significant positive relations with student thriving while only perceived academic competence significantly related to student burnout. Academic volitional autonomy did not significantly relate to either student well-being indicator. Perceived academic competence fully mediated relations between both resilience variables and both student well-being outcomes. Perceived campus relatedness also fully mediated relations between both within-person resilience variables and student thriving. Conclusions, implications, limitations, and future directions for investigators are discussed. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Growth mixture modeling (GMM) is a methodological tool used to represent heterogeneity in longitudinal datasets through the identification of unobserved subgroups following qualitatively and quantitatively distinct trajectories in a population. These growth trajectories or functional forms are informed by the underlying developmental theory, are distinct to each subgroup, and form the core assumptions of the model. Therefore, the accuracy of the assumed functional forms of growth strongly influences substantive research and theories of growth. While there is evidence of mis-specified functional forms of growth in GMM literature, the weight of this violation has been largely overlooked. Current solutions to circumvent assumption violations to functional forms of growth are reliant on theory and inferences yielded from previous research. However, the low frequency of systematic replications of study assumptions implies that developmental theories may not always suggest the correct functional form for a given growth phenomenon. The current dissertation is an examination of GMM assumption violation to the functional form of growth. The simulation study quantitatively contrasted the measured differences between the true population model to a series of mis-specified models where the outcome appropriateness was measured using latent classes and model fit indices. Results of the simulation study revealed two key takeaways. Firstly, that the fit indices of the mis-specified models consistently selected the correct number of classes present in the sample. However, closer examination of the mixing proportion of these latent classes revealed that individual's probability of membership to the latent classes was compromised for the mis-specified models. Secondly, the type of functional form mis-specified (i.e., a simple linear or quadratic or complex Gompertz) determined how visible the effects of the mis-specification would be to the researcher. The implications of these findings are further discussed in this paper along with directions for future research. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

This dissertation informed researchers about the performance of different level-specific and target-specific model fit indices in Multilevel Latent Growth Model (MLGM) using unbalanced design and different trajectories. As the use of MLGMs is a relatively new field, this study helped further the field by informing researchers interested in using various specific model fit indices. We evaluated the descriptive information of the various model fit indices under various simulation conditions and the extent to which the fit indices could be influenced by different design factors, based on simulated data with different conditions derived from a correctly specified MLGM. Our simulation design factors included three levels of number of groups (50, 100, and 200), three levels of unbalanced GS (5/15, 10/20, and 25/75), and three trajectories (accelerating, decelerating, and linear). Based on the results, we made recommendations for practical and theoretical research about fit indices. CFI- and TFI-related fit indices performed well in the MLGM could be trustworthy to use to evaluate model fit under similar conditions found in applied settings. However, RMSEA-related fit indices, SRMR-related fit indices, and chi-square-related fit indices varied by the factors included in this study and should be used with caution for evaluating model fit in the MLGM. The use of these fit indices appears to be particularly problematic when dealing with unbalanced design and small sample sizes. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Psychometricians at an organization named the Education and Training Evaluation Commission (ETEC) developed a new test scoring method called the latent D-scoring method (DSM-L) where it is believed that the new method itself is much easier and more efficient to use compared to the Item Response Theory (IRT) method. However, there are no studies that compared the methods directly regarding item fit and person parameter recovery to understand if the new method (DSM-L) provides better item fit and person parameter recovery compared to the IRT. Therefore, the purpose of this study was to compare the use of latent D-scoring method (DSM-L) models (RFM1 and RFM2) and Item Response Theory (IRT) models (1-PL and 2-PL) with respect to item fit and person parameter recovery across different test data from the ETEC. It was found that both the DSM-L and IRT methods show similar results under both Mean Absolute Error (MAE) and Mean Absolute Difference (MAD) item fit statistics methods across the different test data sets from the ETEC. Also, I found that larger sample sizes and test lengths were associated with improved item fit under the MAE item fit statistics across the different methods. I also found that a two-parameter model type (RFM2 and 2-PL models) shows lower mean MAE item fit statistics across the test data sets compared to a one-parameter model type (RFM1 and 1-PL models).Finally, the DSM-L RFM2 model showed a slightly lower RMSE rate compared to the RFM1 model. Also, both RFM1 and RFM2 models showed acceptable rate of bias (bias equal to zero). On the other hand, the IRT 1-PL model showed a lower RMSE rate compared to the 2-PL model, and 1-PL model showed an acceptable rate of bias (bias approximately equal to zero). With the study outcomes in mind, the psychometricians at the ETEC can use either method (DSM-L or IRT) if the goal is to have good item fit under the MAE item fit statistics since both methods showed similar MAE results. I also recommend using the two-parameter model type based on MAE item fit statistics because it shows lower mean MAE compared to using the one parameter model type. On the other hand, if the goal is to have lower RMSE rates and acceptable rates of bias (bias approximately equal to zero) under IRT I recommend using the 1-PL model. Also, if the goal is to have lower RMSE rate under the DSM-L I recommend using the RFM2 model, and if the goal is to have an acceptable rate of bias under the DSM-L I recommend using either the RFM1 or RFM-2 models. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

This study used a convergent, quantitative dominant mixed methods design to investigate the construct of teacher well-being. Three-hundred-forty-five teachers participated in quantitative data collection which included the Teacher Well-being Scale (TWBS; Collie et al., 2016), the Teacher Subjective Well-being Questionnaire (TSWQ; Renshaw et al., 2016), the Workplace PERMA Profiler (Butler & Kern, 2016) and the Brief School Climate Inventory (La Salle et al., 2018). Among these participants, thirteen participated in additional semi-structured interviews. Quantitative data was analyzed using confirmatory factor analysis and item response theory. Qualitative data was analyzed using content analysis. Data were brought together through joint displays and analyzed using a novel process, integrated factor analysis. Quantitative findings suggest that existing teacher well-being scales hold pre-established structural properties and are associated with other, related scales, but item fit varied. Item response theory revealed model-fit challenges using recommended response categories. Model-fit improved for both the TWBS and TSWQ once response categories were reduced. Additionally, some items were flagged for poor fit. Qualitative findings indicated that teacher well-being includes factors at both the individual and environmental level. Specifically, individual dispositions such as having a positive attitude or ability to handle setbacks, supportive leadership, and collaborative school climates emerged as key facilitators of well-being. Job specific stress related to workload and negative relationships emerged as key barriers to well-being. Integration of quantitative and qualitative data was supported by a process of integrated factor analysis. Scale items and qualitative codes were compared for areas of convergence (alignment) and divergence (disagreement). A revised teacher well-being scale is proposed based on integrated findings. Synthesis of quantitative, qualitative, and integrated results suggest the following key findings: (a) teacher well-being is a multidimensional, psycho-social construct that must include both individual and environmental factors; (b) individual dispositions contribute to teachers' experiences and well-being at work; (c) the primary barriers to teacher well-being include job specific stress external to teaching; and (d) revising response categories can lead to stronger use and interpretation of teacher well-being scales. Implications for policy, research, and practice are discussed. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Many popular global model-data fit indices (GFIs), such as Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residuals (SRMSR) are proposed and widely used in the context of structural equation modeling (SEM) with continuous data. The proposed cutoffs of those indices are for continuous data, which are not directly applicable to categorical data (Xu et al., 2017; Xia, 2016; Maydeu-Olivares & Joe, 2014; Yu, 2002). Furthermore, the existing GFIs are developed and have been used with a limited information estimation approach, while a full information estimation approach is the most widely used estimation method in item response theory (IRT). Not many GFIs are available for categorical data when the full information IRT estimation is conducted (Maydeu-Olivares & Joe, 2014). When there is an effect of guessing, as far as previous literature was searched, there is no GFI that demonstrates good performance in detecting the model-data fit for the comparative evaluation of the two-parameter logistic (2PL) and the three-parameter logistic (3PL) models. In addition, there is a lack of studies that conduct a systematic full evaluation of those popular GFIs in the unidimensional IRT (UIRT) and multidimensional IRT (MIRT) modeling (Maydeu-Olivares & Joe, 2014; Xu et al., 2017). This study proposes descriptive GFIs using squared or absolute (raw or standardized) item response residuals from the whole item response data when an IRT model is estimated with the full information estimation. This study also compares the performances of the newly proposed GFIs with the existing GFIs in UIRT and MIRT modeling, especially the sensitivity of the indices in detecting the 2PL and the 3PL model fitting when data are contaminated with examinee guessing. As a secondary interest, Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and M2 test are also included in the comparisons. The newly proposed RI.R2s, residual variation reduction index based on the absolute residual summary for standardized residuals, showed the best performance in distinguishing the 3PL and the 2PL model fitting in unidimensional and multidimensional data, which contained test-taker guessing. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The present study was motivated by the theory-method mismatch between heterotypic continuity (aspects of development that manifest differently across the lifespan thus cannot be measured the same way over time) and longitudinal measurement equivalence (the statistical assumption that the developmental phenomenon studied is measured on the same metric with equal precision across time). To investigate under which conditions measurement non-equivalence might pose threats to the validity of research conclusions from latent growth curve models, this dissertation investigated: (1) Under which conditions violations of longitudinal measurement equivalence impacted the accuracy (bias and stability) of the growth parameters, (2) How well model fit measures (model chi-square, RMSEA, CFI, and TLI) and the likelihood ratio test performed under varied magnitudes, locations, and percentages of non-equivalence, and (3) How stability and bias of the growth parameters and performance of fit measures compared in linear growth, quadratic growth, and latent basis curve models. To examine the performance of the first-order latent growth curve model when longitudinal measurement equivalence was violated, data were generated resembling children's nonlinear growth, with systematically varied magnitudes of non-equivalence (high; medium; low), locations of non-equivalence (item difficulty parameters; item discrimination parameters; both), and percentages of items impacted. Data were generated under a longitudinal IRT 2-PL model with four measurement occasions, then collapsed into composite variables (means). Measurement non-equivalence progressively increased starting with the second measurement occasion. Sample size and number of items were varied to represent conditions in the child development literature. A pattern emerged when examining bias, stability, and fit measure performance for all three models. The outcome variables were hardly influenced by measurement equivalence violations in the item discrimination parameter yet shifted systematically in response to violations involving the item difficulty parameter. Additionally, the quadratic model often fit unreasonably well; fit was similar regardless of the severity of non-equivalence, especially for CFI and TLI. Results suggest that the latent growth curve model is not robust to longitudinal measurement non-equivalence, and that the type and amount of damage varies with proprieties of the violation itself. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Seligman (2011) introduced well-being theory as a multidimensional model to increase and measure well-being. The PERMA model of well-being theory defines well-being in terms of five constructs: Positive Emotion, Engagement, Relationships, Meaning, and Accomplishment. Together, these five constructs are the foundation of individual and community well-being. The end goal of well-being theory is flourishing, which is defined as optimal well-being, where one is in the upper range of all five PERMA elements. The purpose of this study was to test whether all five PERMA elements of well-being could be derived from items in the "2018 Purdue Student Experience at a Research University" (SERU) survey, thus providing support for the multidimensional model in context of undergraduate students at a research-intensive university. Using confirmatory factor analysis, all five PERMA constructs were supported with use of 32 items and demonstrated good model fit statistics. A second order PERMA well-being construct was built and demonstrated adequate model fit with RMSEA = 0.04. In the full PERMA model, all 32 items were significant at p < 0.05. In the full PERMA model, all five constructs were significant at p < 0.001. Accomplishment had the highest factor loading (0.76) and Meaning had the lowest factor loading (0.25). Results from this study provide initial support for use of wellbeing theory in context of undergraduate students. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Development of the Jordan Performance Appraisal System (JPAS) was completed in 1996. This study examined the factor structure of the classroom observation instrument used in the JPAS. Using observed classroom instructional quality ratings of 1220 elementary teachers of Grades 1-6 in the Jordan School District, this study estimated the factor structure of the data and the rater effect on relevant structural parameters. This study also tested for measurement invariance at the within and between levels across teachers of two grade-level groups (a) lower grades: Grades 1-3 and (b) upper grades: Grades 4-6. Factor structure was estimated using complex exploratory factor analysis (EFA) conducted on a subset of the original data. The analysis provided evidence of a three-factor model for the combined groups. The results of multiple confirmatory factor analyses (CFA) conducted using a different subset of the data cross-validated EFA results. Results from multilevel confirmatory factor analysis (MCFA) indicated the three-factor model fit best at both the within and the between levels, and that the intraclass correlation (ICC) was high (0.699), indicating significant rater-level variance. Results from a multilevel multigroup confirmatory factor analysis (MLMG-CFA) indicated that the ICC was not significantly different between groups. Results also indicated configural, metric (weak factorial), and scalar (strong factorial) equivalence between groups. This study provided one of the first examples of how to estimate the impact of cluster-level variables such as rater on grouping variables nested at the within level. It provided an example of how to conduct a multilevel multigroup analysis on count data. It also disproved the assumption that counting classroom teaching behaviors was less subjective than using a categorical rating scale. These results will provide substantial information for future developments made to the classroom observation instrument used in the JPAS. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Current recommended cutoffs for determining measurement invariance have typically derived from simulation studies that have focused on multigroup confirmatory factor analysis, often using continuous data. These cutoffs may be inappropriate for ordered categorical data in a longitudinal setting. This study conducts two Monte Carlo studies that evaluate the performance of four popular model fit indices used to determine measurement invariance. The comparative fit index (CFI), Tucker-Lewis Index (TLI), and root mean square error of approximation (RMSEA) were all found to be inconsistent across various simulation conditions as well as invariance tests, and thus were not recommended for use in longitudinal measurement invariance testing. The standardized root mean square residual (SRMR) was the most consistent and robust fit index across simulation conditions, and thus we recommended using [greater than or equal to] 0.01 as a cutoff for determining longitudinal measurement invariance with ordered categorical indicators. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Second language (L2) phonological acquisition involves learning novel target-language sounds, variable forms of sounds that arise in different phonological contexts, and any phonotactic constraints that govern their appearance. Interlanguage (IL) grammars must adapt to represent sounds and constraints that are novel to the native language (L1) grammar. Research conducted in the perceptual assimilation (PA) paradigm addresses naive pre-learners' and L2 listeners' perception of foreign sounds but typically controls for phonological context rather than manipulating it. This design priority limits phonological insights to position-dependent perceptual knowledge, not addressing high-order phonological knowledge across multiple contexts. Conversely, most investigations of acquisition of L2 phonological distributions neglect to investigate category acquisition. The German so-called dorsal fricatives, voiceless palatal [ç] and voiceless velar [x] (with uvular variant [X]), orthographically represented by the letters , provide a valuable test case to examine development of novel categories and novel distribution patterns simultaneously. Both novel to L1 American English speakers, the dorsal fricatives exist as allophones (i.e, positional variants) of the same phoneme, their distribution determined largely according to the rule of Dorsal Fricative Assimilation (DFA), by which the dorsal fricative must agree in frontness/backness with the segment that precedes it. Furthermore, both languages' phoneme inventories include acoustically similar /h/ but allow it only in simple onsets of syllables, whereas dorsal fricatives most commonly appear in syllable codas. To investigate acquisition of the dorsal fricatives in early L2 exposure, this dissertation undertakes four auditory perception experiments. Chapters 3 and 4 follow the typical PA paradigm, in which participants listen to a particular sound in a static phonological frame, select which category of their L1 (American English) the sound fits best, and then rate its goodness of fit. These two experiments investigate naive pre-learners and early L2 learners' mappings of the German sounds [[esh] ç x k h [voiceless labiodental affricate]], presented auditorily beside either [a] or [[epsilon]] vowels, to L1 English categories denoted orthographically. Innovatively, these experiments investigate three syllable contexts (syllable-initial in a simple onset, penultimate as first consonant in a coda cluster, and syllable-final in a simple coda). These experiments reveal different mappings and perceptual overlaps tat vary according to sound, participant group, and syllable context. Investigation of how learners acquire novel distributions and phonotactic constraints in L2 is undertaken primarily by means of two phoneme detection (PD) experiments, in which participants listen for a specific sound in each trial and respond as fast as possible by keyboard when they perceive it, measuring reaction time (RT) as an indication of phonological processing. These experiments utilize only the penultimate (C)CV_[t] context, which is a common phonological context for the [ç x [esh] k] in German. To avoid the problems inherent with labeling a phonologically unfamiliar listening target for L2 learners, Chapter 5 uses German native speakers (NS) to calibrate a novel version of the PD task that robustly replicates previous findings for PD with regressive nasal assimilation; however, the experiment does not replicate previous results with German DFA, which are mixed and conflicting in the literature. Chapter 6 expands the investigation of DFA and expands to include investigation of the ban again [h] in syllable codas that is common to both German and English. This experiment investigates RT shifts of NSs and L2 learners of German (L1 American English) in response to licit coda sequences (axt][subscript sigma], *[epsilon]çt][subscript sigma]) and illicit sequences (*açt][subscript sigma], *[epsilon]xt][subscript sigma], *aht][subscript sigma], *[epsilon]ht][subscript sigma]). Surprisingly, early L2 learners exhibit greater (albeit variable) sensitivity to violations of DFA and the ban on *Coda-[h] than NSs, who show reliable RT effects only for the *[epsilon]ht][subscript sigma] context. Thus for German NSs, neither PD experiment detects previously reported RT effects associated with violation of DFA in auditory stimuli. In contrast, early L2 learners exhibit a small facilitation effect (faster RT) in response to DFA violations. A second finding is that L2 learners exhibit robust inhibition (slower RT) in response to violation of the phonotactic ban barring [h] in syllable codas, but German NSs mitigate this effect specifically following [a], suggesting that Germans exhibit phonotactic assimilation of [h] in the same context where the acoustically similar axt][subscript sigma] sequence is permitted. Taken together, these results indicate that IL phonological development is context-sensitive and encompasses both novel sound categories and novel phonotactic knowledge. Implications for a unified model of L2 phonological acquisition are discussed, including the representation of early stage phonologization, in the sense of novel phoneme assembly across contexts. To inform the advancement of L2 phonological theory, future research should investigate remaining empirical and theoretical gaps in the research on (German) mid and back continuants in the domains of (socio-)phonetics, dialectology, formal phonology, and the effects of orthographic literacy in L2. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

During the 1990s computers were placed into most educational classrooms; however, they sat underused or not used at all. One reason for this is students lacked the skills to use computers effectively. One set of skills that can help students make use of computers is self-regulated learning. By using think aloud protocol analysis while students complete a task on the computer, a trace of their cognition, metacognition, and behavior can be created. Analyzing these traces, however, has proven difficult due to the high number of variables compared to the typical number of participants. A solution to dealing with this problem is to analyze all possible combinations of variables. In this thesis, I compared the results of two pre-existing variable reduction methods and Best All Subset Regression. It was found that Best All Subset Regression outperformed the existing methods, by fitting better models without diagnostic problems or extensive time demands. Best All Subset Regression also retained more information than the prior methods, so I suggest using it moving forward instead of the aggregation-based methods used previously. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Research has shown that cross-sectional mediation analysis cannot accurately reflect a true longitudinal mediated effect. To investigate longitudinal mediated effects, different longitudinal mediation models have been proposed and these models focus on different research questions related to longitudinal mediation. When fitting mediation models to longitudinal data, the assumption of longitudinal measurement invariance is usually made. However, the consequences of violating this assumption have not been thoroughly studied in mediation analysis. No studies have examined issues of measurement non-invariance in a latent cross-lagged panel mediation (LCPM) model with three or more measurement occasions. The goal of the current study is to investigate the impact of violations of measurement invariance on longitudinal mediation analysis. The focal model in the study is the LCPM model suggested by Cole and Maxwell (2003). This model can be used to examine mediated effects among the latent predictor, mediator, and outcome variables across time. In addition, it can account for measurement error and allow for the evaluation of longitudinal measurement invariance. Simulation methods were used and the investigation was performed using population covariance matrices and sample data generated under various conditions. Eight design factors were considered for data generation: sample size, proportion of non-invariant items, position of latent factors with non-invariant items, type of non-invariant parameters, magnitude of non-invariance, pattern of non-invariance, size of the direct effect, and size of the mediated effect. Results from population investigation were evaluated based on overall model fit and the calculated direct and mediated effects; results from finite sample analysis were evaluated in terms of convergence and inadmissible solutions, overall model fit, bias/relative bias, coverage rates, and statistical power/type I error rates. In general, results obtained from finite sample analysis were consistent with those from the population investigation, with respect to both model fit and parameter estimation. The type I error rate of the mediated effects was inflated under the non-invariant conditions with small sample size (200); power of the direct and mediated effects was excellent (1.0 or close to 1.0) across all investigated conditions. Type I error rates based on the chi-square statistic test were seriously inflated under the invariant conditions, especially when the sample size was relatively small. Power for detecting model misspecifications due to longitudinal non-invariance was excellent across all investigated conditions. Fit indices (CFI, TLI, RMSEA, and SRMR) were not sensitive in detecting misspecifications caused by violations of measurement invariance in the investigated LCPM model. Study results also showed that as the magnitude of non-invariance, the proportion of non-invariant items, and the number of positions of latent variables with non-invariant items increased, estimation of the direct and mediated effects tended to be less accurate. The decreasing pattern of change in item parameters over measurement occasions resulted in the least accurate estimates of the direct and mediated effects. Parameter estimates were fairly accurate under the conditions of the decreasing and then increasing pattern and the mixed pattern of change in item parameters. Findings from this study can help empirical researchers better understand the potential impact of violating measurement invariance on longitudinal mediation analysis using the LCPM model. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The National Student Clearinghouse Research Center (2015) reported that there were 2.4 million student transitions from one institution to another over a six-year period between 2008 and 2014. According to the Student Achievement Measure (SAM, n.d.), which uses National Student Clearinghouse Snapshot 20 data, only 11% of students who transfer from a four-year to another four-year institution completed their bachelor's degree. In addition, Jenkins and Fink's (2016) analysis indicated that of the 33% of community college students that transfer to a four-year institution, only 42% earn a bachelor's degree within six years of entering postsecondary education. Given the low levels of success that transfer students have at four-year institutions, this study aimed to better understand the post-transfer student engagement experience. The NSSE is a widely used survey instrument designed to collect information from four-year college students and their level of engagement with respect to their learning and personal development (NSSE, 2014). For the purposes of this study, it was important to investigate the use of the survey to provide data on student engagement for varying student demographics, particularly transfer students. In 2013, NSSE updated the survey (NSSE 2.0) based on feedback from participating institutions, research on the survey, and much needed terminology updates (Pike, 2013a). The survey transitioned from having five benchmarks to four themes with ten underlying engagement indicators (NSSE, 2014). The purpose of this study was to explore the model fit of the 2013 NSSE (NSSE 2.0) engagement indicator model as applied to persisting transfer students at a single four-year higher education institution using confirmatory factor analysis (CFA). Specifically, the primary research question of the study was: How well does the model of the NSSE engagement indicators organized by content themes fit to the empirical data for persisting transfer students at a public urban research university? Further, the study examined the fit for all transfer students, then for transfer student subgroups including two-year institution transfers (vertical), four-year institution transfers (horizontal), and transfer students that attended both a two-year and another four-year institution (swirling). The findings of this study suggest that none of the subgroups necessarily demonstrated overwhelming good model-fits across the four engagement themes. Based on the CFA results for each of the themes, only one of the four engagement themes showed a good model fit for the all transfer student group and the subgroups. A closer look shows each subgroup did not demonstrate a good fit across two of the four themes. Anecdotally, the vertical transfer subgroup did not meet any of the fit indices for the Campus Environment engagement theme and can be viewed as the subgroup with the worst model fit. However, indication of which subgroup shows a better model fit cannot be determined. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The purpose of the first study was to develop and validate a new self-report survey used to determine the level of agentic, behavioral, cognitive, and emotional engagement of eighth and ninth grade boys and girls in physical education. Much of the development was driven by work completed by Reeve and Tseng (2011), and by Reeve (2013). Two hundred thirty-one surveys were administered to eighth and ninth grade boys and girls, and 220 surveys were included in the analysis. Results from the confirmatory factor analysis (CFA) were used to determine the goodness of fit showed good factor structure and all of the fit indices (X[superscript 2]/df, GFI, CFI, NFI, SRMR, & RMSEA) met a good to an acceptable level. Of the original 21 items in the scale, 18 items were part of the final model. The final model showed to have an expectedly high correlation between agentic and cognitive engagement which have an impact on the discriminant validity of the structure. The purpose of the second study was to design a reliable observation instrument and validate that instrument. The expressed intent of this instrument was to measure student engagement levels in physical education and to create a dependent variable that could be used by teachers and researchers. Semi-Structured interviews with 20 experts in the field of physical education were conducted to create an operational definition of the term engagement. Experts were asked questions about targeted students in 11 physical education lesson video clips that lasted between 15 and 40 seconds and the descriptions given were analyzed for frequency of use. These descriptive words were then used as the basis for the observation instrument and the criteria for proper coding of the behavior of the targeted students. Inter-rater reliability percentages were high for all of the attempts and the determination that the instrument could be used effectively in the field. A training manual with coding conventions was also created to allow new users the opportunity to become efficient and effective observers when using the observation tool in the field. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The fusion of the Internet with instructional design, and curricula delivery methods eliminated transactional distance in online learning. However, distance education (DE) in Nigeria has not aligned its pedagogy to the new reality in technology. The purposes of this non-experimental, predictive, validity study were to determine faculty and administrators' perceived barriers and concerns to online adoption and to validate the behavior engineering model (BEM) instrument. Ninety-six respondents from four public universities in Nigeria completed the questionnaires. Descriptive statistics and structural equation modeling (SEM) were used respectively, to assess barriers and concerns militating against faculty and administrators' online adoption, as well as validate the survey instruments. For faculty and administrators, incentive, motive, knowledge and skills influenced DE adoption. Except for age, all demographic factors influenced faculty's concerns. Gender was observed to influence administrators' concern. "Level of online use" influenced neither faculty nor administrators' concerns. Technographic characteristics influenced faculty, but not administrators. Though the BEM instrument was reliable in measuring faculty and administrator's stages of concern, however, the 6-factor BEM, tested at the 95% significant level, did not give a good fit. The study contributes to positive social change by identifying gaps to effective DE implementation, and recommended the appropriate interventions to transform the DE experience for students and their universities. The study also proposed the framework to fast track Nigeria's vision and mission for DE. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The Next Generation Science Standards propose a multidimensional model of science learning, comprised of Core Disciplinary Ideas, Science and Engineering Practices, and Crosscutting Concepts (NGSS Lead States, 2013). Accordingly, there is a need for student assessment aligned with the new standards. Creating assessments that validly and reliably measure multidimensional science ability is a challenge for the measurement community (Pellegrino, et al., 2014). Multidimensional assessment tasks may need to go beyond typical item designs of standalone multiple-choice and short-answer items. Furthermore, scoring and modeling of student performance should account for the multidimensionality of the construct. This research contributes to knowledge about best practices for multidimensional science assessment by exploring three areas of interest: 1) item design, 2) scoring rubrics, and 3) measurement models. This study investigated multidimensional scaffolding and response format by comparing alternative item designs on an elementary assessment of matter. Item variations had a different number of item prompts and/or response formats. Observations about student cognition and performance were collected during cognitive interviews and a pilot test. Items were scored using a holistic rubric and a multidimensional rubric, and interrater agreement was examined. Assessment data was scaled with multidimensional scores and holistic scores, using unidimensional and multidimensional Rasch models, and model-data fit was compared. Results showed that scaffolding is associated with more thorough responses, especially among low ability students. Students tended to utilize different cognitive processes to respond to selected-response items and constructed-response items, and were more likely to respond to selected-response arguments. Interrater agreement was highest when the structure of the item aligned with the structure of the scoring rubric. Holistic scores provided similar reliability and precision as multidimensional scores, but item and person fit was poorer. Multidimensional subscales had lower reliability, less precise student estimates than the unidimensional model, and interdimensional correlations were high. However, the multidimensional rubric and model provide nuanced information about student performance and better fit to the response data. Recommendations about optimal combinations of scaffolding, rubric, and measurement models are made for teachers, policymakers, and researchers. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Collaborative learning is the current gold standard in pedagogy, yet within collaborative classrooms are many introverted students who are more reflective in nature and may prefer learning on their own or listening to lectures over social learning. Guided by the conception of Jungian introversion and person-environment fit theory, this qualitative phenomenological study investigated the lived experiences of introverted college students to gain a better understanding of how these learners think and feel about various collaborative learning tasks. The primary research question focused on the experience of introverted students participating in collaborative learning. Eleven introverted college freshman at a university in the Southwest participated in semi-structured interviews that focused on their experiences with groups, class discussions, presentations, and various aspects of classrooms and learning. The results of these interviews yielded some expected findings: generally, these introverted participants prefer to work alone, would rather not speak up or present in class, and appreciate time for reflection and thinking. Some of the additional findings were more unexpected--many of these students recalled being reluctant leaders in group projects and described how mandatory social interactions, whether in small groups or by randomly being called on, actually impedes their focus and learning. The results of this study open a window into the introvert's thoughts and preferences in the classroom, allowing educators to gain a deeper understanding of how a large percentage of students learn. Additionally, the results provide educators with a guide to practical pedagogical approaches and further research implications. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

The purpose of this mixed methods study was to find out the ways in which web enhanced language learning (WELL) technologies are learned that make them more likely to be effectively integrated into the K-12 classroom. Specifically, the study investigated characteristics of WELL teacher training that translate to effective classroom implementation. The theoretical and conceptual frameworks used were Mishra and Koehler's (2006) Technological Pedagogical Content Knowledge (TPACK) and the Teachers of English as a Second Language (TESOL) Technology Standards (2008). Both quantitative and qualitative data were gathered from surveys, interviews, and observations of K-12 world language teachers in North Carolina. This study integrated quantitative and qualitative data in a convergent sequential design. Descriptive statistics and Pearson's Chi Square goodness of fit tests were used to analyze the quantifiable responses on the survey and thematic coding was used for the open responses. Thematic coding and categorization were used to analyze the interview and observation data. The findings of the study revealed teachers mostly learned to use WELL technologies by themselves, with colleagues, through teacher education activities and through online sources. Additionally, they stated that effective WELL teacher education activities are interactive, allow for practice time and help teachers preemptively troubleshoot potential problems. Teachers applied their WELL teacher education by using TESOL (2008) defined effective practices in classroom. They used WELL technologies to provide individualized practice, increase student motivation, to engage students into language learning, and provide access to authentic resources. Although teachers learned to use WELL technologies in a variety of ways, they used effective practices to integrate what they had learned into the curriculum. Nonetheless, they still desired increased access to more effective teacher education activities that could aid them in continuing to be knowledgeable practitioners of technology in world language classrooms. This study provided several implications for how teacher educators should conduct training activities, how teacher education programs can support pre-service teachers, and how school districts can support in-service teachers. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Research in education has become increasingly reliant on statistical modeling frameworks to be reflective of the subject matter, to accurately assess what students know and can do, to assist instructors with curriculum design by supplementing informative feedback, and to support policy-makers when making evidence-based decisions. A common feature of these assessment uses is their requirement of valid inferences from the statistical models. From a methodological perspective, this requires the underlying statistical modeling framework to be thoroughly evaluated. Advances in modern technology and learning science bring about unique opportunities for learning and assessment (Mislevy et al., 2014), such as cognitive tutoring systems and simulation-based and game-based assessments. However, the statistical models that are used to analyze the data from such technology assessments must be validated. Due to their representational power to complex learning theories, their capability to model complex assessment data, and their potential to support real-time diagnosis, Bayesian networks (BNs) and the extension--dynamic Bayesian networks (DBNs)--are attracting more and more researchers in educational measurement. However, existing literature in this area has focused dominantly on model evaluations of static BNs and their findings may not necessarily be generalizable to DBNs. In this study I research DBNs as a means to model changes in assessed knowledge and with a major focus on evaluating the performance of selected fit indices to detect misspecifications of temporal dependence of DBNs. Based on a Monte Carlo simulation, seven indices--logarithmic loss, mean absolute error, mean squared error, relative absolute error, root mean square error, root mean squared logarithmic error, and root relative squared error--were compared in terms of their performance to detect two types of specification errors of DBNs: edge inclusion and edge exclusion. In addition, sample size and test length were also manipulated to examine their relationship to the performance of the studied fit indices. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]