For unidimensional item response theory (UIRT) models, three linking methods, which are the separate, concurrent, and fixed parameter calibration methods, have been developed and widely used in applications such as vertical scaling, differential item functioning, computerized adaptive testing (CAT), and equating. By contrast, even though a few studies have compared the separate and concurrent calibration methods for full multidimensional IRT (MIRT) models or applied the concurrent calibration method to vertical scaling using the bifactor model, no study has yet provided technical descriptions of the concurrent and fixed parameter calibration methods for any MIRT models. Thus, the purpose of this dissertation was to extend the concurrent and fixed parameter calibration methods for UIRT models to the two-tier item factor analysis model. In addition, the relative performance of the separate, concurrent, and fixed parameter calibration methods was compared in terms of the recovery of item parameters and accuracy of IRT observed score equating using both real and simulated datasets. The separate, concurrent, and fixed parameter calibration methods well recovered the item parameters, with the concurrent calibration method performing slightly better than the other two linking methods. Despite the comparable performance of the three linking methods in terms of the recovery of item parameters, however, some discrepancy was observed between the IRT observed score equating results obtained with the three linking methods. In general, the concurrent calibration method provided equating results with the smallest equating error, whereas the separate calibration method provided equating results with the largest equating error due to the largest standard error of equating. The performance of the fixed parameter calibration method depended on the proportion of common items. When the proportion was , the fixed parameter calibration method provided more biased equating results than the concurrent calibration method because of the underestimated specific slope parameters. However, when the proportion of common items was 40%, the fixed parameter calibration method worked as well as the concurrent calibration method.

Apathy, defined as a decrease in purposeful or goal-directed behavior, is common in many neurological disorders. The assessment of apathy in these disorders is important as apathy is associated with differential engagement and response to treatment and future cognitive and functional decline. Although apathy is often described as including three separate symptom dimensions, reflecting diminished interest, action, and emotional expression, investigations of the factor structure of apathy symptoms have been limited by the use of scales which do not comprehensively assess all of three of the proposed dimensions. The current study aimed to develop a novel informant report measure of apathy symptoms, investigate the factor structure of apathy symptoms, and examine the relationship of different types of apathy symptoms to several clinically relevant variables. Participants included 249 informants who reported on an individual with (n=210) or without (n=39) a neurological or psychiatric condition. Results showed the best fitting model of apathy symptoms was a bifactor model in which apathy could be represented as a global dimension with three separate, specific symptom factors reflecting diminished interest and initiative, asociality, and diminished emotional and verbal expression. In general, apathy was associated with poorer cognitive functioning, greater functional impairment, and higher caregiver distress. The specific symptom factors differed somewhat in their association with those same variables, highlighting the utility of measuring different types of symptoms in addition to overall apathy. Future work will refine the apathy measure developed in this study and test the obtained bifactor symptom model in an independent sample.

Given increased evidence related to the importance of fostering life satisfaction in the overall population (Diener & Diener, 1996), as well as recent suggestions regarding the importance of increasing positive academic and social outcomes for children with ADHD (DuPaul, 2007), it is important to gain a clearer understanding of how life satisfaction may be related to symptoms of inattention and hyperactivity/impulsivity. Research on the relationship between life satisfaction and symptoms of inattention, hyperactivity, and impulsivity is currently limited to two studies (Gudjonsson et al., 2009; Ogg et al., 2014). The current study investigated the relationship between symptoms of inattention and hyperactivity/impulsivity and reports of global life satisfaction in 399 high school students. This study used the bifactor model to conceptualize ADHD given that this model provided the best fit when compared to other models of ADHD in the current study and given that there is substantial evidence in the current literature to support the use of this model (Martel, von Eye, & Nigg, 2010). Structural equation modeling results demonstrated that the general factor of ADHD was a significant predictor of life satisfaction when students rated ADHD symptoms, and the inattention factor of ADHD was a significant predictor of life satisfaction when teachers rated ADHD symptoms. In addition, because depressive symptoms have been associated with life satisfaction and inattention, hyperactivity, and impulsivity, the current study examined if life satisfaction moderated or mediated the relationship between inattention, hyperactivity, and impulsivity and depressive symptoms. Results of the present study suggested that life satisfaction serves as a potential but weak moderator in the relationship between general ADHD and depression when symptoms of ADHD were rated by teachers. Results also demonstrated that life satisfaction mediated the relationship between general ADHD symptoms and depressive symptoms when ADHD symptoms were rated by students, and life satisfaction mediated the relationship between inattentive symptoms and depressive symptoms when ADHD symptoms were rated by teachers. The current study contributes to existing literature on life satisfaction given that there are currently only two studies, one which was conducted with an adult population and one of which was conducted with a middle school population, specifically examining levels of life satisfaction in individuals with symptoms of ADHD. The results of this study provide additional confirmation of the negative relationship between ADHD symptoms and life satisfaction. Moreover, this study was the first to examine how life satisfaction may play a role in the relationship between symptoms of ADHD and depressive symptoms. This study supports that life satisfaction primarily plays a mediating role in the relationship between ADHD symptoms and depressive symptoms and provides support for further examination of this role in future studies.

abstract: Statistical mediation analysis allows researchers to identify the most important the mediating constructs in the causal process studied. Information about the mediating processes can be used to make interventions more powerful by enhancing successful program components and by not implementing components that did not significantly change the outcome. Identifying mediators is especially relevant when the hypothesized mediating construct consists of multiple related facets. The general definition of the construct and its facets might relate differently to external criteria. However, current methods do not allow researchers to study the relationships between general and specific aspects of a construct to an external criterion simultaneously. This study proposes a bifactor measurement model for the mediating construct as a way to represent the general aspect and specific facets of a construct simultaneously. Monte Carlo simulation results are presented to help to determine under what conditions researchers can detect the mediated effect when one of the facets of the mediating construct is the true mediator, but the mediator is treated as unidimensional. Results indicate that parameter bias and detection of the mediated effect depends on the facet variance represented in the mediation model. This study contributes to the largely unexplored area of measurement issues in statistical mediation analysis. / Dissertation/Thesis / Masters Thesis Psychology 2016

There is no information in Peru on the prevalence of mental health problems associated with COVID-19 in older adults. In this sense, the aim of the study was to gather evidence on the factor structure, criterion-related validity, and reliability of the Spanish version of the Fear of COVID-19 Scale (FCV-19S) in this population. The participants were 400 older adults (mean age = 68.04, SD = 6.41), who were administered the Fear of COVID-19 Scale, Revised Mental Health Inventory-5, Patient Health Questionnaire-2 items, and Generalized Anxiety Disorder Scale 2 items. Structural equation models were estimated, specifically confirmatory factor analysis (CFA), bifactor CFA, and structural models with latent variables (SEM). Internal consistency was estimated with composite reliability indexes (CRI) and omega coefficients. A bifactor model with both a general factor underlying all items plus a specific factor underlying items 1, 2, 4, and 5 representing the emotional response to COVID better represents the factor structure of the scale. This structure had adequate fit and good reliability, and additionally fear of COVID had a large effect on mental health. In general, women had more fear than men, having more information on COVID was associated to more fear, while having family or friends affected by COVID did not related to fear of the virus. The Spanish version of the Fear of COVID-19 Scale presents evidence of validity and reliability to assess fear of COVID-19 in the Peruvian older adult population.

No description available.

Writing self-efficacy is a vital component to a students’ motivation and will to succeed towards writing. The measurement of writing self-efficacy over the past 40 years, despite its development, continues to largely be represented by Confirmatory Factor Analysis models that are limited due to their restricted item to factor constraints. These constraints, given prior literature and the theoretical understanding of self-efficacy, do not adequately model construct- relevant psychometric multidimensionality as a product of conceptual overlap or a hierarchical or general factor. Given this, the present study’s purpose was to examine the adapted Self-efficacy for Writing Scale (SEWS) for the presence of construct-relevant psychometric multidimensionality through a series of measurement model comparisons and person-centered approaches. Using a sample 1,466 8th, 9th, and 10th graders, a bifactor exploratory structural equation model was found to best represent the data and demonstrate that the SEWS exhibits both construct-relevant multidimensionality as a function of conceptual overlap and the presence of a hierarchical theme. Using factor scores derived from this model, latent profile analysis was conducted to further establish validity of the measurement model and examine how students disaggregate into groups based on their response trends of the SEWS. Three profiles emerged greatly differentiated by global writing self-efficacy, with obvious and substantively varying specific factor differences between profiles. Concurrent, divergent, and discriminant validity evidence was established through a series of analyses that assessed predictors and outcomes of the profiles (e.g. demographics, standardized writing assessments, grades). Theoretical and educator implications and avenues for future researcher were discussed.

The purpose of this study was to build upon the existing MIRT equating literature by introducing a full multidimensional item response theory (MIRT) observed score equating method for mixed-format exams because no such methods currently exist. At this time, the MIRT equating literature is limited to full MIRT observed score equating methods for multiple-choice only exams and Bifactor observed score equating methods for mixed-format exams. Given the high frequency with which mixed-format exams are used and the accumulating evidence that some tests are not purely unidimensional, it was important to present a full MIRT equating method for mixed-format tests. The performance of the full MIRT observed score method was compared with the traditional equipercentile method, and unidimensional IRT (UIRT) observed score method, and Bifactor observed score method. With the Bifactor methods, group-specific factors were defined according to item format or content subdomain. With the full MIRT methods, two- and four-dimensional models were included and correlations between latent abilities were freely estimated or set to zero. All equating procedures were carried out using three end-of-course exams: Chemistry, Spanish Language, and English Language and Composition. For all subjects, two separate datasets were created using pseudo-groups in order to have two separate equating criteria. The specific equating criteria that served as baselines for comparisons with all other methods were the theoretical Identity and the traditional equipercentile procedures. Several important conclusions were made. In general, the multidimensional methods were found to perform better for datasets that evidenced more multidimensionality, whereas unidimensional methods worked better for unidimensional datasets. In addition, the scale on which scores are reported influenced the comparative conclusions made among the studied methods. For performance classifications, which are most important to examinees, there typically were not large discrepancies among the UIRT, Bifactor, and full MIRT methods. However, this study was limited by its sole reliance on real data which was not very multidimensional and for which the true equating relationship was not known. Therefore, plans for improvements, including the addition of a simulation study to introduce a variety of dimensional data structures, are also discussed.

Achievement goal theory is one of the most broadly accepted theoretical paradigms in educational psychology with over 35 years of influencing research and educational practice. The longstanding use of this construct has led to two consequences of importance for this research: 1) many different dimensionality representations have been debated, and 2) methods used to confirm dimensionality of the scales have been supplanted from best practice. A further issue is that goal orientations are used to inform classroom practice, whereas most measurement studies focus on the structure of the personal goal orientation scales rather than the classroom level structure. This study aims to provide an updated understanding of one classroom goal orientation scale using the modern psychometric techniques of multidimensional item response theory and bifactor analysis. The most commonly used scale with K-12 students is the Patterns of Adaptive Learning Scales (PALS); thus, the PALS classroom goal orientation scales will be the subject of this study.

The primary goal of this study was to develop a new procedure for estimating decision consistency and accuracy indices using the bifactor and testlet response theory (TRT) models. This study is the first to investigate decision consistency and accuracy from a multidimensional perspective, and the results have shown that the bifactor model at least behaved in way that met the author's expectations and represents a potential useful procedure. The TRT model, on the other hand, did not meet the author's expectations and generally showed poor model performance. The multidimensional decision consistency and accuracy indices proposed in this study appear to provide good performance, at least for the bifactor model, in the case of a substantial testlet effect. For practitioners examining a test containing testlets for decision consistency and accuracy, a recommended first step is to check for dimensionality. If the testlets show a significant degree of multidimensionality, then the usage of the multidimensional indices proposed can be recommended as the simulation study showed an improved level of performance over unidimensional IRT models. However, if there is a not a significant degree of multidimensionality then the unidimensional IRT models and indices would perform as well, or even better, than the multidimensional models. Another goal of this study was to compare methods for numerical integration used in the calculation of decision consistency and accuracy indices. This study investigated a new method (M method) that sampled ability estimates through a Monte-Carlo approach. In summary, the M method seems to be just as accurate as the other commonly used methods for numerical integration. However, it has some practical advantages over the D and P methods. As previously mentioned, it is not as nearly as computationally intensive as the D method. Also, the P method requires large sample sizes. In addition, the P method has conceptual disadvantage in that the conditioning variable, in theory, should be the true theta, not an estimated theta. The M method avoids both of these issues and seems to provide equally accurate estimates of decision consistency and accuracy indices, which makes it a strong option particularly in multidimensional cases.