A Zero-Inflated Box-Cox Normal Unipolar Item Response Model for Measuring Constructs of Psychopathology

This research introduces a latent class item response theory (IRT) approach for modeling item response data from zero-inflated, positively skewed, and arguably unipolar constructs of psychopathology. As motivating data, the authors use 4,925 responses to the Patient Health Questionnaire (PHQ-9), a nine Likert-type item depression screener that inquires about a variety of depressive symptoms. First, Lucke’s log-logistic unipolar item response model is extended to accommodate polytomous responses. Then, a nontrivial proportion of individuals who do not endorse any of the symptoms are accounted for by including a nonpathological class that represents those who may be absent on or at some floor level of the latent variable that is being measured by the PHQ-9. To enhance flexibility, a Box-Cox normal distribution is used to empirically determine a transformation parameter that can help characterize the degree of skewness in the latent variable density. A model comparison approach is used to test the necessity of the features of the proposed model. Results suggest that (a) the Box-Cox normal transformation provides empirical support for using a log-normal population density, and (b) model fit substantially improves when a nonpathological latent class is included. The parameter estimates from the latent class IRT model are used to interpret the psychometric properties of the PHQ-9, and a method of computing IRT scale scores that reflect unipolar constructs is described, focusing on how these scores may be used in clinical contexts

Book Review of \u3cem\u3eThe Basics of Item Response Theory Using R\u3c/em\u3e

This article reviews the book The Basics of Item Response Theory Using R by Baker and Kim (2017). It describes the structure and goals of the book, provides an overview of each chapter, and concludes with general comments. Both strengths and limitations of the book are discussed

Developmental Delays in Executive Function from 3 to 5 Years of Age Predict Kindergarten Academic Readiness

Substantial evidence has established that individual differences in executive function (EF) in early childhood are uniquely predictive of children’s academic readiness at school entry. The current study tested whether growth trajectories of EF across the early childhood period could be used to identify a subset of children who were at pronounced risk for academic impairment in kindergarten. Using data that were collected at the age 3, 4, and 5 home assessments in the Family Life Project (N = 1,120), growth mixture models were used to identify 9% of children who exhibited impaired EF performance (i.e., persistently low levels of EF that did not show expected improvements across time). Compared to children who exhibited typical trajectories of EF, the delayed group exhibited substantial impairments in multiple indicators of academic readiness in kindergarten (Cohen’s ds = 0.9–2.7; odds ratios = 9.8–23.8). Although reduced in magnitude following control for a range of socioeconomic and cognitive (general intelligence screener, receptive vocabulary) covariates, moderate-sized group differences remained (Cohen’s ds = 0.2–2.4; odds ratios = 3.9–5.4). Results are discussed with respect to the use of repeated measures of EF as a method of early identification, as well as the resulting translational implications of doing so

Integrating Item Accuracy and Reaction Time to Improve the Measurement of Inhibitory Control Abilities in Early Childhood

Efforts to improve children’s executive function are often hampered by the lack of measures that are optimized for use during the transition from preschool to elementary school. Whereas preschool-based measures often emphasize response accuracy, elementary school-based measures emphasize reaction time (RT)—especially for measures inhibitory control (IC) tasks that typically have a speeded component. The primary objective of this study was to test in a preschool-aged sample whether the joint use of item-level accuracy and RT data resulted in improved scoring for three IC tasks relative to scores derived from accuracy data alone. Generally, the joint use of item-level accuracy and RT data resulted in modest improvements in the measurement precision of IC abilities. Moreover, the joint use of item-level accuracy and RT helped eliminate floor and ceiling effects that occurred when accuracy data were considered alone. Results are discussed with respect to the importance of scoring IC tasks in ways that are maximally informative for program evaluation and longitudinal modeling

Item Response Modeling of Multivariate Count Data with Zero-Inflation, Maximum Inflation, and Heaping

Questionnaires that include items eliciting count responses are becoming increasingly common in psychology and health research. Item response data from these types of questionnaires pose analytic challenges, including inflation at zero and the maximum, as well as heaping at preferred digits; such data complexities are not well-suited for conventional IRT modeling approaches and software. This research proposes methodological techniques to overcome those challenges by combining approaches from three related but distinct literatures: IRT models for multivariate count data, latent variable models for heaping and extreme responding, and mixture IRT models. Scales from the Behavioral Risk Factor Surveillance System are used as motivating examples in addressing three questions. First, what are some methods of addressing inflation and heaping in multivariate count item response data? Second, are complex models really needed, or can heaping and inflation in count data be ignored? And finally, what value do count items add to scales? The results suggest that count item response data can be modeled within a latent class IRT framework. The proposed latent class IRT model has a Poisson or negative binomial component for a class of individuals who respond to items according to a strict count process, a nominal response component for a class of individuals who respond to items according to a multiple choice or rounding process, and two degenerate models to describe some of the individuals who always endorse the minimum or maximum counts. A comparison of the full model with more parsimonious models reveals that all four latent classes are needed to describe the empirical item response distributions. Methods of computing scale scores are described. The results also provide evidence that including count items on scales may improve measurement precision, but the degree of improvement is dependent on latent class membership. Count items are likely to be most informative when respondents engage in a true count process. The results also support the idea that if count items are to be used on scales, it is advisable to include more than one. Practical implications are discussed and recommendations are provided for researchers who may wish to use count items on questionnaires.Doctor of Philosoph

Validation of the Vaccination Confidence Scale: A Brief Measure to Identify Parents at Risk for Refusing Adolescent Vaccines

Objective To validate a brief measure of vaccination confidence using a large, nationally representative sample of parents. Methods We analyzed weighted data from 9018 parents who completed the 2010 National Immunization Survey–Teen, an annual, population-based telephone survey. Parents reported on the immunization history of a 13- to 17-year-old child in their households for vaccines including tetanus, diphtheria, and acellular pertussis (Tdap), meningococcal, and human papillomavirus vaccines. For each vaccine, separate logistic regression models assessed associations between parents\u27 mean scores on the 8-item Vaccination Confidence Scale and vaccine refusal, vaccine delay, and vaccination status. We repeated analyses for the scale\u27s 4-item short form. Results One quarter of parents (24%) reported refusal of any vaccine, with refusal of specific vaccines ranging from 21% for human papillomavirus to 2% for Tdap. Using the full 8-item scale, vaccination confidence was negatively associated with measures of vaccine refusal and positively associated with measures of vaccination status. For example, refusal of any vaccine was more common among parents whose scale scores were medium (odds ratio, 2.08; 95% confidence interval, 1.75–2.47) or low (odds ratio, 4.61; 95% confidence interval, 3.51–6.05) versus high. For the 4-item short form, scores were also consistently associated with vaccine refusal and vaccination status. Vaccination confidence was inconsistently associated with vaccine delay. Conclusions The Vaccination Confidence Scale shows promise as a tool for identifying parents at risk for refusing adolescent vaccines. The scale\u27s short form appears to offer comparable performance

Assessing the Psychometric Proprieties of the Attitudes Toward Seeking Professional Psychological Help Scale–Short Form (ATSPPH-SF) Among Latino Adults

The Latino population continues to underutilize mental health services at an alarming rate. The Attitudes Toward Seeking Professional Psychological Help Scale–Short Form (ATSPPH-SF) is one of the most commonly used instruments to assess help-seeking attitudes. The current study sought to evaluate the factor structure and test for the presence of differential item functioning on the ATSPPH-SF with a sample of Latino adult individuals across nativity status (U.S.- vs. foreign-born), language format (English vs. Spanish), and gender. The analyses revealed two relatively independent factors named Openness to Seeking Treatment and Value and Need in Seeking Treatment. Measurement equivalence and practical implications are discussed in the context of use with Latino individuals

The Influence of Parceling on the Implied Factor Structure of Multidimensional Item Response Data

Parceling is a method researchers often use to circumvent issues that arise in handling item-level data; however, the degree to which the true factor structure is preserved after parceling remains ambiguous in the literature. The goal of this thesis was to examine the effects of parceling on the implied factor structure of multidimensional data using both simulation and analytic techniques: does the estimated factor change after parceling? This question was addressed across three studies. Item covariance matrices were computed from bifactor models comprising continuous or dichotomous item responses. The item covariance matrices were then parceled and a one-factor confirmatory factor analysis was fit to the parcel covariance matrices. Additionally, a simulation was carried out in which factor scores from the CFA were compared with the latent variable values from the generating model. Results of both studies suggest that parceling does change the estimated factor. Furthermore, fit statistics overwhelmingly indicate good fit despite a misspecified model. Finally, to illustrate how parceling is used in practice, an application using empirical data is shown. Practical implications are discussed.Master of Art

Item Response Modeling of Multivariate Count Data With Zero Inflation, Maximum Inflation, and Heaping

Questionnaires that include items eliciting count responses are becoming increasingly common in psychology. This study proposes methodological techniques to overcome some of the challenges associated with analyzing multivariate item response data that exhibit zero inflation, maximum inflation, and heaping at preferred digits. The modeling framework combines approaches from three literatures: item response theory (IRT) models for multivariate count data, latent variable models for heaping and extreme responding, and mixture IRT models. Data from the Behavioral Risk Factor Surveillance System are used as a motivating example. Practical implications are discussed, and recommendations are provided for researchers who may wish to use count items on questionnaires

Estimating HIV Medication Adherence and Persistence: Two Instruments for Clinical and Research Use

Antiretroviral therapy (ART) requires lifelong daily oral therapy. While patient characteristics associated with suboptimal ART adherence and persistence have been described in cohorts of HIV-infected persons, these factors are poor predictors of individual medication taking behaviors. We aimed to create and test instruments for the estimation of future ART adherence and persistence for clinical and research applications. Following formative work, a battery of 148 items broadly related to HIV infection and treatment was developed and administered to 181 HIV-infected patients. ART adherence and persistence were assessed using electronic monitoring for 3 months. Perceived confidence in medication taking and self-reported barriers to adherence were strongest in predicting non-adherence over time. Barriers to adherence (e.g., affordability, scheduling) were the strongest predictors of non-adherence, as well as 3- and 7-day non-persistence. A ten-item battery for prediction of these outcomes (www.med.unc.edu/ncaidstraining/adherence/for-providers) and a 30-item battery reflective of underlying psychological constructs can help identify and study individuals at risk for suboptimal ART adherence and persistence