A Finite Mixture Item Response Theory Model for Continuous Measurement Outcomes.
Ontology highlight
ABSTRACT:
SUBMITTER: Zopluoglu C
PROVIDER: S-EPMC7047264 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Publications
A Finite Mixture Item Response Theory Model for Continuous Measurement Outcomes.
Educational and psychological measurement 20190627 2
A mixture extension of Samejima's continuous response model for continuous measurement outcomes and its estimation through a heuristic approach based on limited-information factor analysis is introduced. Using an empirical data set, it is shown that two groups of respondents that differ both qualitatively and quantitatively in their response behavior can be revealed. In addition to the real data application, the effectiveness of the heuristic estimation approach under real data analytic conditio ...[more]
Similar Datasets
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.
| S-EPMC6765459 | biostudies-literature
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.
| S-EPMC6977726 | biostudies-literature
Project description:More than 40 questionnaires have been developed to assess functional somatic symptoms (FSS), but there are several methodological issues regarding the measurement of FSS. We aimed to identify which items of the somatization subscale of the Symptom Checklist-90 (SCL-90) are more informative and discriminative between persons at different levels of severity of FSS. To this end, item response theory was applied to the somatization scale of the SCL-90, collected from a sample of 82,740 adult participants without somatic conditions in the Lifelines Cohort Study. Sensitivity analyses were performed with all the participants who completed the somatization scale. Both analyses showed that Items 11 "feeling weak physically" and 12 "heavy feelings in arms or legs" were the most discriminative and informative to measure severity levels of FSS, regardless of somatic conditions. Clinicians and researchers may pay extra attention to these symptoms to augment the assessment of FSS.
| S-EPMC8543564 | biostudies-literature
Project description:IntroductionUp to 20% of older adults in the United States have mild cognitive impairment (MCI), and about one-third of people with MCI are predicted to transition to Alzheimer's disease (AD) within 5 years. Standard cognitive assessments are long and require a trained technician to administer. We developed the first computerized adaptive test (CAT) based on multidimensional item response theory (MIRT) to more precisely, rapidly, and repeatedly assesses cognitive abilities across the adult lifespan. We present results for a prototype CAT (pCAT-COG) for assessment of global cognitive function.MethodsWe sampled items across five cognitive domains central to neuropsychological testing (episodic memory [EM], semantic memory/language [SM], working memory [WM], executive function/flexible thinking, and processing speed [PS]). The item bank consists of 54 items, with 9 items of varying difficulty drawn from six different cognitive tasks. Each of the 54 items has 3 response trials, yielding an ordinal score (0-3 trials correct). We also include three long-term memory items not designed for adaptive administration, for a total bank of 57 items. Calibration data were collected in-person and online, calibrated using a bifactor MIRT model, and pCAT-COG scores validated against a technician-administered neuropsychological battery.ResultsThe bifactor MIRT model improved fit over a unidimensional IRT model (p < 0.0001). The global pCAT-COG scores were inversely correlated with age (r = -0.44, p < 0.0001). Simulated adaptive administration of 11 items maintained a correlation of r = 0.94 with the total item bank scores. Significant differences between mild and no cognitive impairment (NCI) were found (effect size of 1.08 SD units). The pCAT-COG correlated with clinician-based global measure (r = 0.64).DiscussionMIRT-based CAT is feasible and valid for the assessment of global cognitive impairment, laying the foundation for the development of a full CAT-COG that will draw from a much larger item bank with both global and domain specific measures of cognitive impairment.HighlightsAs Americans age, numbers at risk for developing cognitive impairment are increasing.Aging-related declines in cognition begins decades prior to the onset of obvious cognitive impairment.Traditional assessment is burdensome and requires trained clinicians.We developed an adaptive testing framework using multidimensional item response theory.It is comparable to lengthier in-person assessments that require trained psychometrists.
| S-EPMC11694520 | biostudies-literature
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.
| S-EPMC6506989 | biostudies-literature
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.
| S-EPMC8451022 | biostudies-literature
Project description:BackgroundComputerized adaptive testing (CAT) utilizes latent variable measurement model parameters that are typically assumed to be equivalently applicable to all people. Biased latent variable scores may be obtained in samples that are heterogeneous with respect to a specified measurement model. We examined the implications of sample heterogeneity with respect to CAT-predicted patient-reported outcomes (PRO) scores for the measurement of pain.MethodsA latent variable mixture modeling (LVMM) analysis was conducted using data collected from a heterogeneous sample of people in British Columbia, Canada, who were administered the 36 pain domain items of the CAT-5D-QOL. The fitted LVMM was then used to produce data for a simulation analysis. We evaluated bias by comparing the referent PRO scores of the LVMM with PRO scores predicted by a "conventional" CAT (ignoring heterogeneity) and a LVMM-based "mixture" CAT (accommodating heterogeneity).ResultsThe LVMM analysis indicated support for three latent classes with class proportions of 0.25, 0.30 and 0.45, which suggests that the sample was heterogeneous. The simulation analyses revealed differences between the referent PRO scores and the PRO scores produced by the "conventional" CAT. The "mixture" CAT produced PRO scores that were nearly equivalent to the referent scores.ConclusionBias in PRO scores based on latent variable models may result when population heterogeneity is ignored. Improved accuracy could be obtained by using CATs that are parameterized using LVMM.
| S-EPMC4773251 | biostudies-literature
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.
| S-EPMC6659220 | biostudies-literature
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.
| S-EPMC6050112 | biostudies-literature
Project description:Using the bifactor item response theory model to analyze data arising from educational and psychological studies has gained popularity over the years. Unfortunately, using this model in practice comes with challenges. One such challenge is an empirical identification issue that is seldom discussed in the literature, and its impact on the estimates of the bifactor model's parameters has not been demonstrated. This issue occurs when an item's discriminations on the general and specific dimensions are approximately equal (i.e., the within-item discriminations are similar in strength), leading to difficulties in obtaining unique estimates for those discriminations. We conducted three simulation studies to demonstrate that within-item discriminations being similar in strength creates problems in estimation stability. The results suggest that a large sample could alleviate but not resolve the problems, at least when considering sample sizes up to 4,000. When the discriminations within items were made clearly different, the estimates of these discriminations were more consistent across the data replicates than that observed when the discriminations within the items were similar. The results also show that the similarity of an item's discriminatory magnitudes on different dimensions has direct implications on the sample size needed in order to consistently obtain accurate parameter estimates. Although our goal was to provide evidence of the empirical identification issue, the study further reveals that the extent of similarity of within-item discriminations, the magnitude of discriminations, and how well the items are targeted to the respondents also play factors in the estimation of the bifactor model's parameters.
| S-EPMC9574084 | biostudies-literature