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Articles 1 - 7 of 7
Full-Text Articles in Quantitative Psychology
Item Parameter Recovery With And Without The Use Of Priors, Paulius Satkus, Christine E. Demars
Item Parameter Recovery With And Without The Use Of Priors, Paulius Satkus, Christine E. Demars
Department of Graduate Psychology - Faculty Scholarship
Marginal maximum likelihood (MML), a common estimation method for IRT models, is not inherently a Bayesian procedure. However, due to estimation difficulties, Bayesian priors are often applied to the likelihood when estimating 3PL models, especially with small samples. Little focus has been placed on choosing the priors for MML estimation. In this study, using samples sizes of 1000 or smaller, not using priors often led to extreme, implausible parameter estimates. Applying prior distributions to the c-parameters alleviated the estimation problems with samples of 1000; priors on both the a-parameters and c-parameters were needed for the samples of …
Differential Motivation In Remote Educational Assessment: Person-Based Filtering Versus Response-Based Filtering, Sarah Alahmadi, Christine E. Demars
Differential Motivation In Remote Educational Assessment: Person-Based Filtering Versus Response-Based Filtering, Sarah Alahmadi, Christine E. Demars
Department of Graduate Psychology - Faculty Scholarship
Large-scale educational assessments are often considered low-stakes, increasing the possibility of confounding true performance level with low motivation. These concerns are amplified in remote testing conditions. To remove the effects of low effort levels in responses observed in remote low-stakes testing, several motivation filtering methods can be used to purify the data. We estimated scores from assessment data collected remotely in Spring 2021 six ways, applying examinee-based filtering methods (filtering examinees based on total time) and response-based filtering methods (filtering responses using the effort-moderated IRT model), varying the thresholds selected to separate solution behavior (SB) responses from rapid-guessing behavior (RGB). …
Investigating The Self In Self-Report, Samantha L. Boddy
Investigating The Self In Self-Report, Samantha L. Boddy
Masters Theses, 2020-current
Self-report items are ubiquitous in social sciences and services and medical centers. However, there is some concern about whether people are able to accurately report about themselves. One well-known source of concern is social desirability bias (SDB) or socially desirable responding (SDR), which involves people providing overly-positive responses about themselves that better align with social norms than might their actual attitudes or behaviors. However, several researchers (e.g., Brenner & DeLamater, 2016; Hadaway et al., 1998) suggest that a person’s identity in the area of interest may bias their responding. Specifically, that people interpret and respond to items in terms of …
Identifying Rater Effects For Writing And Critical Thinking: Applying The Many-Facets Rasch Model To The Value Institute, Yelisey A. Shapovalov
Identifying Rater Effects For Writing And Critical Thinking: Applying The Many-Facets Rasch Model To The Value Institute, Yelisey A. Shapovalov
Masters Theses, 2020-current
Performance assessments require examinees to carry out a process or produce a product and can be designed to have high fidelity to real-world application of higher-order skills. As such, performance assessments are highly valued in higher education settings. However, performance assessment is vulnerable to psychometric challenges that threaten the validity of scores due to the subjective nature of the scoring process. Specifically, raters must exercise judgement to provide scores to examinee work, which may be impacted by rater effects, or systematic differences in how raters evaluate performance assessment artifacts. Research has indicated that performance assessment may never be fully free …
Does Coding Method Matter? An Examination Of Propensity Score Methods When The Treatment Group Is Larger Than The Comparison Group, Beth A. Perkins
Does Coding Method Matter? An Examination Of Propensity Score Methods When The Treatment Group Is Larger Than The Comparison Group, Beth A. Perkins
Dissertations, 2020-current
In educational contexts, students often self-select into specific interventions (e.g., courses, majors, extracurricular programming). When students self-select into an intervention, systematic group differences may impact the validity of inferences made regarding the effect of the intervention. Propensity score methods are commonly used to reduce selection bias in estimates of treatment effects. In educational contexts, often a larger number of students receive a treatment than not. However, recommendations regarding the application of propensity score methods when the treatment group is larger than the comparison group have not been empirically examined. The current study examined the recommendation to recode the treatment and …
Understanding Motivations To Attend Various Sized Churches: A Study Using Family Communication Patterns, Expectancy Violations, And Anxiety To Predict Church Attendance, Molly Bradshaw
Masters Theses, 2020-current
Two separate studies were conducted to examine whether communication variables impact religious views and church attendance. For the first study, 228 students from a large Southeastern university completed a web survey. The second study was a web survey of 204 adults that was conducted via Amazon Mechanical Turk (MTURK). Both surveys were sent out to determine one’s motivations to attend a small, medium, or large church using family communication, anxiety, expectations, and religion variables as predictors. Family communication, anxiety, and expectancy variables were positively correlated to many aspects of religious views. Hierarchical regression models utilizing demographics, family communication, anxiety, expectancy …
Getting Caught-Up In The Process: Does It Really Matter?, Nikole Gregg
Getting Caught-Up In The Process: Does It Really Matter?, Nikole Gregg
Dissertations, 2020-current
Likert items are the most commonly used item-type for measuring attitudes and beliefs. However, responses from Likert items are often plagued with construct-irrelevant variance due to response style behavior. In other words, variability from Likert-item scores can be parsed into: 1) variance pertinent to the construct or trait of interest, and 2) variance irrelevant to the construct or trait of interest. Multidimensional Item Response Theory (MIRT) is an increasingly common modeling approach to parse out information regarding the response style traits and the trait of interest. These MIRT approaches are categorized into threshold-based approaches and response process approaches. An increasingly …