Treating Noneffortful Responses as Missing.

This study investigates the treatment of rapid-guess (RG) responses as missing data within the context of the effort-moderated model. Through a series of illustrations, this study demonstrates that the effort-moderated model assumes missing at random (MAR) rather than missing completely at random (MCAR), explaining the conditions necessary for MAR. These examples show that RG responses, when treated as missing under the effort-moderated model, do not introduce bias into ability estimates if the missingness mechanism is properly accounted for.

Item Parameter Recovery: Sensitivity to Prior Distribution.

Marginal maximum likelihood, a common estimation method for item response theory 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 marginal maximum estimation.

Comparing Causes of Dependency: Shared Latent Trait or Dependence on Observed Response.

Accurate parameter estimation in the Rasch model involves the assumption of conditional independence, also termed local independence. Conditional on ability, the responses to items A and B should be independent. Two types of conditional dependence are detailed in this pedagogical piece: trait dependency and response dependency.

The Effects of Probability Threshold Choice on an Adjustment for Guessing using the Rasch Model.

This paper investigates a strategy for accounting for correct guessing with the Rasch model that we entitled the Guessing Adjustment. This strategy involves the identification of all person/item encounters where the probability of a correct response is below a specified threshold. These responses are converted to missing data and the calibration is conducted a second time.

Revised Parallel Analysis With Nonnormal Ability and a Guessing Parameter.

Previous work showing that revised parallel analysis can be effective with dichotomous items has used a two-parameter model and normally distributed abilities. In this study, both two- and three-parameter models were used with normally distributed and skewed ability distributions. Relatively minor skew and kurtosis in the underlying ability distribution had almost no effect on Type I error for unidimensional data and reduced power for two-dimensional data slightly with smaller sample sizes of 400.

Examining the Performance of the Metropolis-Hastings Robbins-Monro Algorithm in the Estimation of Multilevel Multidimensional IRT Models.

The purpose of this study was to examine the performance of the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm in the estimation of multilevel multidimensional item response theory (ML-MIRT) models. The accuracy and efficiency of MH-RM in recovering item parameters, latent variances and covariances, as well as ability estimates within and between clusters (e.g.

Infit and Outfit: Interpreting Statistical Significance and Magnitude of Misfit in Conjunction.

In many areas of statistics it is common practice to present both a statistical significance test and an effect size. In contrast, for the Infit and Outfit indices of item misfit, it has historically been common to focus on either the mean square (MS; an index of the magnitude of misfit) or the statistical significance, but not both. If the statistical significance and effect size are to be used together, it is important not only that the Type I error rate matches the nominal alpha level, but also that, for any given magnitude of misfit, the expected value of the MS is independent of sample size.

Partially Compensatory Multidimensional Item Response Theory Models: Two Alternate Model Forms.

Partially compensatory models may capture the cognitive skills needed to answer test items more realistically than compensatory models, but estimating the model parameters may be a challenge. Data were simulated to follow two different partially compensatory models, a model with an interaction term and a product model. The model parameters were then estimated for both models and for the compensatory model.

The Interaction of Ability Differences and Guessing When Modeling Differential Item Functioning With the Rasch Model: Conventional and Tailored Calibration.

In educational testing, differential item functioning (DIF) statistics must be accurately estimated to ensure the appropriate items are flagged for inspection or removal. This study showed how using the Rasch model to estimate DIF may introduce considerable bias in the results when there are large group differences in ability (impact) and the data follow a three-parameter logistic model. With large group ability differences, difficult non-DIF items appeared to favor the focal group and easy non-DIF items appeared to favor the reference group.

A comparison of confirmatory factor analysis and multidimensional Rasch models to investigate the dimensionality of test-taking motivation.

Using a scale of test-taking motivation designed to have multiple factors, results are compared from a confirmatory factor analysis (CFA) using LISREL and a multidimensional Rasch partial credit model using ConQuest. Both types of analyses work with latent factors and allow the comparison of nested models. CFA models most typically model a linear relationship between observed and latent variables, while Rasch models specify a non-linear relationship between observed and latent variables.

Effectiveness of the Challenging Horizons After-School Program for young adolescents with ADHD.

There are no empirically supported psychosocial treatments for adolescents with attention-deficit hyperactivity disorder (ADHD). This study examined the treatment benefits of the Challenging Horizons Program (CHP), a psychosocial treatment program designed to address the impairment and symptoms associated with this disorder in young adolescents. In addition to evaluating social and academic functioning outcomes, two critical questions from previous studies pertaining to the timing, duration, and family involvement in treatment were addressed.

Measuring higher education outcomes with a multidimensional Rasch model.

A multidimensional Rasch model was applied to two instruments measuring abilities in two related areas of a university general education curriculum. Grades from related courses were also calibrated using the Rasch model. Thus, course grades, test items, and persons were all placed on the same metric.