A Finite Mixture Item Response Theory Model for Continuous Measurement Outcomes.
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ABSTRACT:
SUBMITTER: Zopluoglu C
PROVIDER: S-EPMC7047264 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Multidimensional item response theory (MIRT) models for response style (e.g., Bolt, Lu, & Kim, 2014, Psychological Methods, 19, 528; Falk & Cai, 2016, Psychological Methods, 21, 328) provide flexibility in accommodating various response styles, but often present difficulty in isolating the effects of response style(s) from the intended substantive trait(s). In the presence of such measurement limitations, we consider several ways in which MIRT models are nevertheless useful in lending insight into how response styles may interfere with measurement for a given test instrument. Such a study can also inform whether alternative design considerations (e.g., anchoring vignettes, self-report items of heterogeneous content) that seek to control for response style effects may be helpful. We illustrate several aspects of an MIRT approach using real and simulated analyses.
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Project description:BACKGROUND:The Self-Administered Food Security Survey Module for Children was developed to assess food insecurity of individual children and has not been used in Portugal. We aimed to apply the mixture item response theory model to the Self-Administered Food Security Survey Module for Children, to assess its reliability and validity, and to estimate the cut-offs of the food security status for Portuguese children. METHODS:The scale was self-administered to 2132 children of the Generation XXI birth cohort. The internal consistency was assessed using Cronbach's alpha. We evaluated dimensionality and/or clustering, and Latent Class Analysis, Latent Trait Analysis and Mixture Latent Trait Analysis were tested. The number of classes and/or traits were defined according to the Akaike Information Criterion, Bayesian Information Criterion, Adjusted Bayesian Information Criterion, Vuong-Lo-Mendell-Rubin Likelihood Ratio Test, Bootstrapped Likelihood Ratio Test and Entropy. Construct validity was explored using socio-demographic characteristics. The classification tree was used to define cut-offs to predict cluster membership. RESULTS:The best model was a Mixture Latent Trait Analysis with 1 factor and 2 classes (food security and food insecurity), assuming class variant item parameters (for items 1 and 3). Based on the estimated posterior probabilities, the food insecurity prevalence was 17.6%. Cronbach's alpha was 0.617. A higher proportion of less-educated mothers and low-income households was observed in the food insecurity class. The classification tree showed an accuracy of 100.0% by identifying the food security and food insecurity groups. CONCLUSION:Our results supported that the Self-Administered Food Security Survey Module for Children provides a valid and reliable measure, which allows the identification of food insecurity among Portuguese children.
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Project description:Advancements in item response theory (IRT) have led to models for dual dependence, which control for cluster and method effects during a psychometric analysis. Currently, however, this class of models does not include one that controls for when the method effects stem from two method sources in which one source functions differently across the aspects of another source (i.e., a nested method-source interaction). For this study, then, a Bayesian IRT model is proposed, one that accounts for such interaction among method sources while controlling for the clustering of individuals within the sample. The proposed model accomplishes these tasks by specifying a multilevel trifactor structure for the latent trait space. Details of simulations are also reported. These simulations demonstrate that this model can identify when item response data represent a multilevel trifactor structure, and it does so in data from samples as small as 250 cases nested within 50 clusters. Additionally, the simulations show that misleading estimates for the item discriminations could arise when the trifactor structure reflected in the data is not correctly accounted for. The utility of the model is also illustrated through the analysis of empirical data.
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Project description:Item response theory "dual" models (DMs) in which both items and individuals are viewed as sources of differential measurement error so far have been proposed only for unidimensional measures. This article proposes two multidimensional extensions of existing DMs: the M-DTCRM (dual Thurstonian continuous response model), intended for (approximately) continuous responses, and the M-DTGRM (dual Thurstonian graded response model), intended for ordered-categorical responses (including binary). A rationale for the extension to the multiple-content-dimensions case, which is based on the concept of the multidimensional location index, is first proposed and discussed. Then, the models are described using both the factor-analytic and the item response theory parameterizations. Procedures for (a) calibrating the items, (b) scoring individuals, (c) assessing model appropriateness, and (d) assessing measurement precision are finally discussed. The simulation results suggest that the proposal is quite feasible, and an illustrative example based on personality data is also provided. The proposals are submitted to be of particular interest for the case of multidimensional questionnaires in which the number of items per scale would not be enough for arriving at stable estimates if the existing unidimensional DMs were fitted on a separate-scale basis.
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Project description:BackgroundComputer and paper examinations in our days are constructed from an item pool which is regularly updated. Given the way that exams are created, one of the major concerns is the security of the items that are being used in order to ensure a good estimation of abilities. The aim of this study is to measure the prevalence of item pre-knowledge in our medical school.MethodsThe Deterministic, Gated Item Response Theory Model (DGM) was applied to estimate the prevalence of students who have had item pre-knowledge from six multiple choice examinations of the Clinical Anatomy course at the Faculty of Medicine of University of Porto. Each examination consisted of 100 items with an average of 200 students and 20% repeated items per examination. The estimation of the sensitivity and specificity was based on a simulation study. The sensitivity and specificity estimates, and apparent prevalence were used to estimate true prevalence of cheating students in the examinations under study.ResultsThe specificity in the DGM for different simulation scenarios was between 68 and 98%, while the sensitivity ranged from 60 to 91%. The apparent prevalence was between 0.0 and 3.4%, while the true prevalence ranged from 1.2 to 3.7%.ConclusionsThe true prevalence was much lower compared to the students self-reported copying of responses from other students; however, it is important to keep monitoring the pre-knowledge prevalence in order to enforce measures in case an increase occurs.
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