Augmenting visual analysis in single-case research with hierarchical linear modeling.

The purpose of this article is to demonstrate how hierarchical linear modeling (HLM) can be used to enhance visual analysis of single-case research (SCR) designs. First, the authors demonstrated the use of growth modeling via HLM to augment visual analysis of a sophisticated single-case study. Data were used from a delayed multiple baseline design, across groups of participants, with an embedded changing criterion design in a single-case literacy project for students with moderate intellectual disabilities (MoID). Visual analysis revealed a functional relation between instruction and sight-word acquisition for all students. Growth HLM quantified relations at the group level and revealed additional information that included statistically significant variability among students at initial-baseline probe and also among growth trajectories within treatment subphases. Growth HLM showed that receptive vocabulary was a significant predictor of initial knowledge of sight words, and print knowledge significantly predicted growth rates in both treatment subphases. Next, to show the benefits of combining these methodologies to examine a different behavioral topography within a more commonly used SCR design, the authors used repeated-measures HLM and visual analysis to examine simulated data within an ABAB design. Visual analysis revealed a functional relation between a hypothetical intervention (e.g., token reinforcement) and a hypothetical dependent variable (e.g., performance of a target response). HLM supported the existence of a functional relation through tests of statistical significance and detected significant variance among participants' response to the intervention that would be impossible to identify visually. This study highlights the relevance of these procedures to the identification of evidence-based interventions.

When plausibility manipulations work: an examination of their role in the development of false beliefs and memories.

In the current study we examined whether prevalence information and imagery encoding influence participants' general plausibility, personal plausibility, belief, and memory ratings for suggested childhood events. Results showed decreases in general and personal plausibility ratings for low prevalence events when encoding instructions were not elaborate; however, instructions to repeatedly imagine suggested events elicited personal plausibility increases for low-prevalence events, evidence that elaborate imagery negated the effect of our prevalence manipulation. We found no evidence of imagination inflation or false memory construction. We discuss critical differences in researchers' manipulations of plausibility and imagery that may influence results of false memory studies in the literature. In future research investigators should focus on the specific nature of encoding instructions when examining the development of false memories.

Improving DHH students' grammar through an individualized software program.

The purpose of this study was to determine if the frequent use of a targeted, computer software grammar instruction program, used as an individualized classroom activity, would influence the comprehension of morphosyntax structures (determiners, tense, and complementizers) in deaf/hard-of-hearing (DHH) participants who use American Sign Language (ASL). Twenty-six students from an urban day school for the deaf participated in this study. Two hierarchical linear modeling growth curve analyses showed that the influence of LanguageLinks: Syntax Assessment and Intervention (LL) resulted in statistically significant gains in participants' comprehension of morphosyntax structures. Two dependent t tests revealed statistically significant results between the pre- and postintervention assessments on the Diagnostic Evaluation of Language Variation-Norm Referenced. The daily use of LL increased the morphosyntax comprehension of the participants in this study and may be a promising practice for DHH students who use ASL.

A Monte Carlo study of the impact of missing data and differential item functioning on theta estimates from two polytomous Rasch family models.

This paper examines the impact of differential item functioning (DIF), missing item values, and different methods for handling missing item values on theta estimates with data simulated from the partial credit model and Andrich's rating scale model. Both Rasch family models are commonly used when obtaining an estimate of a respondent's attitude. The degree of missing data, DIF magnitude, and the percentage of DIF items were varied in MCAR data conditions in which the focal group was 10% of the total population. Four methods for handling missing data were compared: complete-case analysis, mean substitution, hot-decking, and multiple imputation. Bias, RMSE, means, and standard errors of the theta estimates for the focal group were adversely affected by the amount and magnitude of DIF items. RMSE and fidelity coefficients for both the reference and focal group were adversely impacted by the amount of missing data. While all methods of handling missing data performed fairly similarly, multiple imputation and hot-decking showed slightly better performance.

Measurement Model Quality, Sample Size, and Solution Propriety in Confirmatory Factor Models.

Sample size recommendations in confirmatory factor analysis (CFA) have recently shifted away from observations per variable or per parameter toward consideration of model quality. Extending research by Marsh, Hau, Balla, and Grayson (1998), simulations were conducted to determine the extent to which CFA model convergence and parameter estimation are affected by n as well as by construct reliability, which is a measure of measurement model quality derived from the number of indicators per factor (p/f) and factor loading magnitude. Results indicated that model convergence and accuracy of parameter estimation were affected by n and by construct reliability within levels of n. Sample size recommendations for applied researchers using CFA are presented herein as a function of relevant design characteristics.