Application of a Longitudinal IRTree Model: Response Style Changes Over Time.

Traditional psychometric models focus on studying observed categorical item responses, but these models often oversimplify the respondent cognitive response process, assuming responses are driven by a single substantive trait. A further weakness is that analysis of ordinal responses has been primarily limited to a single substantive trait at one time point. This study applies a significant expansion of this modeling framework to account for complex response processes across multiple waves of data collection using the item response tree (IRTree) framework. This study applies a novel model, the longitudinal IRTree, for response processes in longitudinal studies, and investigates whether the response style changes are proportional to changes in the substantive trait of interest. To do so, we present an empirical example using a six-item sexual knowledge scale from the National Longitudinal Study of Adolescent to Adult Health across two waves of data collection. Results show an increase in sexual knowledge from the first wave to the second wave and a decrease in midpoint and extreme response styles. Model validation revealed failure to account for response style can bias estimation of substantive trait growth. The longitudinal IRTree model captures midpoint and extreme response style, as well as the trait of interest, at both waves.

Measuring Response Style Stability Across Constructs With Item Response Trees.

Individual response style behaviors, unrelated to the latent trait of interest, may influence responses to ordinal survey items. Response style can introduce bias in the total score with respect to the trait of interest, threatening valid interpretation of scores. Despite claims of response style stability across scales, there has been little research into stability across multiple scales from the beneficial perspective of item response trees. This study examines an extension of the IRTree methodology to include mixed item formats, providing an empirical example of responses to three scales measuring perceptions of social media, climate change, and medical marijuana use. Results show extreme and midpoint response styles were not stable across scales within a single administration and 5-point Likert-type items elicited higher levels of extreme response style than the 4-point items. Latent trait of interest estimation varied, particularly at the lower end of the score distribution, across response style models, demonstrating as appropriate response style model is important for adequate trait estimation using Bayesian Markov chain Monte Carlo estimation.

Modeling Changes in Response Style with Longitudinal IRTree Models.

Traditional psychometric modeling focuses on observed categorical item responses, which can over-simplify the respondent cognitive response process. A further weakness is that analysis of ordinal responses has been primarily limited to a single substantive trait at one time point. We propose a significant expansion of this modeling framework to account for complex response processes across multiple waves of data collection using the beneficial item response tree framework. This study proposes a novel model, the longitudinal IRTree, for response processes in longitudinal studies, and investigates whether the response style changes are proportional to changes in the substantive trait of interest. A simulation study demonstrates adequate item parameter recovery in a Bayesian framework, especially with larger sample sizes of 2000. The longitudinal change parameters were recovered similarly well, with improved recovery using informative priors over default priors in Mplus. The empirical application demonstrates that relatively stable observed scores are due to a decrease in response styles offsetting an increase in the latent trait of interest.

Explaining Variability in Response Style Traits: A Covariate-Adjusted IRTree.

Contamination of responses due to extreme and midpoint response style can confound the interpretation of scores, threatening the validity of inferences made from survey responses. This study incorporated person-level covariates in the multidimensional item response tree model to explain heterogeneity in response style. We include an empirical example and two simulation studies to support the use and interpretation of the model: parameter recovery using Markov chain Monte Carlo (MCMC) estimation and performance of the model under conditions with and without response styles present. Item intercepts mean bias and root mean square error were small at all sample sizes. Item discrimination mean bias and root mean square error were also small but tended to be smaller when covariates were unrelated to, or had a weak relationship with, the latent traits. Item and regression parameters are estimated with sufficient accuracy when sample sizes are greater than approximately 1,000 and MCMC estimation with the Gibbs sampler is used. The empirical example uses the National Longitudinal Study of Adolescent to Adult Health's sexual knowledge scale. Meaningful predictors associated with high levels of extreme response latent trait included being non-White, being male, and having high levels of parental support and relationships. Meaningful predictors associated with high levels of the midpoint response latent trait included having low levels of parental support and relationships. Item-level covariates indicate the response style pseudo-items were less easy to endorse for self-oriented items, whereas the trait of interest pseudo-items were easier to endorse for self-oriented items.

Psychometric Validation of the 10-Item USDA Food Security Scale for Use with College Students.

Food insecurity is defined as inadequate access to food due to limited resources. Studies regarding college student food insecurity have shown consistently higher rates than the rest of the nation. Many of these studies measure food insecurity using the United States Department of Agriculture's Adult Food Security Survey Module. Despite its prevalence, the module has not been evaluated for use with the college student population. This study uses Rasch analysis, which underlies the current food insecurity classification approach used by the Department of Agriculture, to investigate the Adult Food Security Survey Module's psychometric properties. A sample of 511 students from a public university in the South was used. Findings indicate that the requirements of the Rasch model do not hold for the module with college students. Specifically, the requirements of equal item discrimination and unidimensionality were violated, along with the presence of moderate to large differential item functioning.

Using SAS PROC MCMC for Item Response Theory Models.

Interest in using Bayesian methods for estimating item response theory models has grown at a remarkable rate in recent years. This attentiveness to Bayesian estimation has also inspired a growth in available software such as WinBUGS, R packages, BMIRT, MPLUS, and SAS PROC MCMC. This article intends to provide an accessible overview of Bayesian methods in the context of item response theory to serve as a useful guide for practitioners in estimating and interpreting item response theory (IRT) models. Included is a description of the estimation procedure used by SAS PROC MCMC. Syntax is provided for estimation of both dichotomous and polytomous IRT models, as well as a discussion on how to extend the syntax to accommodate more complex IRT models.

Multilevel modeling in psychosomatic medicine research.

The primary purpose of this study is to provide an overview of multilevel modeling for Psychosomatic Medicine readers and contributors. The article begins with a general introduction to multilevel modeling. Multilevel regression modeling at two levels is emphasized because of its prevalence in psychosomatic medicine research. Simulated data sets based on some core ideas from the Familias Unidas effectiveness study are used to illustrate key concepts including communication of model specification, parameter interpretation, sample size and power, and missing data. Input and key output files from Mplus and SAS are provided. A cluster randomized trial with repeated measures (i.e., three-level regression model) is then briefly presented with simulated data based on some core ideas from a cognitive-behavioral stress management intervention in prostate cancer.