Using Item Response Theory to Compare Irritability Measures in Early Adolescent and Childhood Samples.
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ABSTRACT:
SUBMITTER: Dougherty LR
PROVIDER: S-EPMC8724905 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This article introduces three new variants of entropy to detect person misfit (Ei, EMi , and EMRi ), and provides preliminary evidence that these measures are worthy of further investigation. Previously, entropy has been used as a measure of approximate data-model fit to quantify how well individuals are classified into latent classes, and to quantify the quality of classification and separation between groups in logistic regression models. In the current study, entropy is explored through conceptual examples and Monte Carlo simulation comparing entropy with established measures of person fit in item response theory (IRT) such as lz, lz*, U, and W. Simulation results indicated that EMi and EMRi were successfully able to detect aberrant response patterns when comparing contaminated and uncontaminated subgroups of persons. In addition, EMi and EMRi performed similarly in showing separation between the contaminated and uncontaminated subgroups. However, EMRi may be advantageous over other measures when subtests include a small number of items. EMi and EMRi are recommended for use as approximate person-fit measures for IRT models. These measures of approximate person fit may be useful in making relative judgments about potential persons whose response patterns do not fit the theoretical model.
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Project description:INTRODUCTION:Models from signal detection theory are commonly used to score neuropsychological test data, especially tests of recognition memory. Here we show that certain item response theory models can be formulated as signal detection theory models, thus linking two complementary but distinct methodologies. We then use the approach to evaluate the validity (construct representation) of commonly used research measures, demonstrate the impact of conditional error on neuropsychological outcomes, and evaluate measurement bias. METHOD:Signal detection-item response theory (SD-IRT) models were fitted to recognition memory data for words, faces, and objects. The sample consisted of U.S. Infantry Marines and Navy Corpsmen participating in the Marine Resiliency Study. Data comprised item responses to the Penn Face Memory Test (PFMT; N = 1,338), Penn Word Memory Test (PWMT; N = 1,331), and Visual Object Learning Test (VOLT; N = 1,249), and self-report of past head injury with loss of consciousness. RESULTS:SD-IRT models adequately fitted recognition memory item data across all modalities. Error varied systematically with ability estimates, and distributions of residuals from the regression of memory discrimination onto self-report of past head injury were positively skewed towards regions of larger measurement error. Analyses of differential item functioning revealed little evidence of systematic bias by level of education. CONCLUSIONS:SD-IRT models benefit from the measurement rigor of item response theory-which permits the modeling of item difficulty and examinee ability-and from signal detection theory-which provides an interpretive framework encompassing the experimentally validated constructs of memory discrimination and response bias. We used this approach to validate the construct representation of commonly used research measures and to demonstrate how nonoptimized item parameters can lead to erroneous conclusions when interpreting neuropsychological test data. Future work might include the development of computerized adaptive tests and integration with mixture and random-effects models.
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Project description:The current research reports both latent profile (person-oriented) and item response theory (IRT) analyses of the School Burnout Inventory (SBI) in United States undergraduate samples. Study 1 (n = 1,007) comprises a latent profile analysis (LPA) that identified four mutually exclusive subgroups based on patterns of school burnout responses. Covariate analyses of grade point average and negative affect suggested that school burnout profiles function similarly to variable-oriented approaches. Study 2 (n = 544) explored longitudinal patterns of school burnout among college students via use of a repeated measures LPA. Findings suggested that the profiles identified reflect a relatively stable school burnout trajectory over time. Covariate analysis of sleep quality and academic engagement demonstrated differences across profiles, but the patterns were similar to variable-oriented statistical approaches. Study 3 (n = 2,364) utilized an IRT analysis of the SBI to identify a short, efficient measure. Item information curves and graded response model item discrimination parameters identified a 4-item SBI scale (SBI-4) that offered reasonably high levels of information for assessing school burnout in comparison to the original nine-item SBI. Implications and future research are identified.
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Project description:Although item response theory (IRT) models such as the bifactor, two-tier, and between-item-dimensionality IRT models have been devised to confirm complex dimensional structures in educational and psychological data, they can be challenging to use in practice. The reason is that these models are multidimensional IRT (MIRT) models and thus are highly parameterized, making them only suitable for data provided by large samples. Unfortunately, many educational and psychological studies are conducted on a small scale, leaving the researchers without the necessary MIRT models to confirm the hypothesized structures in their data. To address the lack of modeling options for these researchers, we present a general Bayesian MIRT model based on adaptive informative priors. Simulations demonstrated that our MIRT model could be used to confirm a two-tier structure (with two general and six specific dimensions), a bifactor structure (with one general and six specific dimensions), and a between-item six-dimensional structure in rating scale data representing sample sizes as small as 100. Although our goal was to provide a general MIRT model suitable for smaller samples, the simulations further revealed that our model was applicable to larger samples. We also analyzed real data from 121 individuals to illustrate that the findings of our simulations are relevant to real situations.
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Project description:OBJECTIVE:Advances in developmentally sensitive measurement have enabled differentiation of normative versus clinically salient irritability in early childhood. However, clinical application of these measures is still nascent. The authors developed an optimized model of clinically salient irritable behaviors at preschool age. Based on this model, the authors derived an empirically based cutoff in relation to concurrent DSM-5 irritability-related disorders (i.e., oppositional defiant disorder, disruptive mood dysregulation disorder, other depressive disorders) and used longitudinal models to test the predictive validity of the cutoff for impairment and irritability trajectories and later DSM disorders. METHOD:Preschool children oversampled for irritability were followed over 3 time points into early school age (N = 425; mean age at baseline 4.7 years, mean follow-up 2.9 years). Mothers reported on children's irritability using the developmentally validated Multidimensional Assessment of Profile of Disruptive Behavior (MAP-DB) Temper Loss scale, impairment using the Family Life Impairment Scale, and DSM categories using the Preschool Age Psychiatric Assessment and the Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version. RESULTS:Of 22 MAP-DB Temper Loss behaviors, 2 behaviors-1 normative (easily frustrated) and 1 rare dysregulated (destructive tantrums)-were uniquely related to cross-domain impairment. At baseline, these 2 irritability items identified diagnostic status (oppositional defiant disorder, disruptive mood dysregulation disorder, other depressive disorders) with good sensitivity (70-73%) and specificity (74-83%). Children above the irritability cutoff at baseline also exhibited more persistent irritability and impairment and greater likelihood of DSM disorders in early school age. CONCLUSION:Clinical identification of early-onset irritability can be enhanced using brief, developmentally optimized indicators. Further research to apply these findings to tiered early intervention is important.
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