OpenMx: A Modular Research Environment for Item Response Theory Method Development.
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ABSTRACT:
SUBMITTER: Pritikin JN
PROVIDER: S-EPMC7495793 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Brief screening instruments focusing on autism spectrum disorder (ASD) that can be administered in primary care are scarce; there is a need for shorter and more precise instruments. The Autism-Tics, AD/HD and other Comorbidities inventory (A-TAC) has previously been validated for ASD reporting excellent validity. This study aims to determine the psychometric properties of each item in the ASD domain (17 items) in the A-TAC using item response theory (IRT), and thereby construct and validate a short form that could be used as a screening instrument in the general population. METHODS:Since 2004, parents of all 9-year-old Swedish twins have been invited to participate in a telephone interview in the Child and Adolescent Twin Study in Sweden (CATSS). The CATSS is linked to the National Patient Register (NPR), which includes data from in- and outpatient care. Data on ASD (A-TAC) collected in CATSS were compared with diagnoses from the NPR. Diagnoses that had been made both before (previous validity) and after (predictive validity) the interviews were included. The sample was divided into a developmental sample and a validation sample. An IRT model was fitted to the developmental sample and item parameters were used to select a subset of items for the short form. The performance of the proposed short form was examined in the validation sample by the use of receiver operation characteristic curves. RESULTS:Four items which were able to discriminate among individuals with more autism traits were deemed sufficient for use in the short form. The values of the area under the receiver operating characteristic curve for a clinical diagnosis of ASD was .95 (previous validity) and .72 (predictive validity). CONCLUSIONS:The proposed short form with 4 out of the original 17 items from A-TAC, showed excellent previous validity while the predictive validity was fair. The validity of the short form was in agreement with previous validations of the full ASD domain. The short form can be a valuable screening instrument in primary care settings in order to identify individuals in need for further assessment and for use in epidemiological studies.
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Project description:Testing item-level fit is important in scale development to guide item revision/deletion. Many item-level fit indices have been proposed in literature, yet none of them were directly applicable to an important family of models, namely, the higher order item response theory (HO-IRT) models. In this study, chi-square-based fit indices (i.e., Yen's Q 1, McKinley and Mill's G 2, Orlando and Thissen's S-X 2, and S-G 2) were extended to HO-IRT models. Their performances are evaluated via simulation studies in terms of false positive rates and correct detection rates. The manipulated factors include test structure (i.e., test length and number of dimensions), sample size, level of correlations among dimensions, and the proportion of misfitting items. For misfitting items, the sources of misfit, including the misfitting item response functions, and misspecifying factor structures were also manipulated. The results from simulation studies demonstrate that the S-G 2 is promising for higher order items.
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Project description:PurposeMeaningful thresholds are needed to interpret patient-reported outcome measure (PROM) results. This paper introduces a new method, based on item response theory (IRT), to estimate such thresholds. The performance of the method is examined in simulated datasets and two real datasets, and compared with other methods.MethodsThe IRT method involves fitting an IRT model to the PROM items and an anchor item indicating the criterion state of interest. The difficulty parameter of the anchor item represents the meaningful threshold on the latent trait. The latent threshold is then linked to the corresponding expected PROM score. We simulated 4500 item response datasets to a 10-item PROM, and an anchor item. The datasets varied with respect to the mean and standard deviation of the latent trait, and the reliability of the anchor item. The real datasets consisted of a depression scale with a clinical depression diagnosis as anchor variable and a pain scale with a patient acceptable symptom state (PASS) question as anchor variable.ResultsThe new IRT method recovered the true thresholds accurately across the simulated datasets. The other methods, except one, produced biased threshold estimates if the state prevalence was smaller or greater than 0.5. The adjusted predictive modeling method matched the new IRT method (also in the real datasets) but showed some residual bias if the prevalence was smaller than 0.3 or greater than 0.7.ConclusionsThe new IRT method perfectly recovers meaningful (interpretational) thresholds for multi-item questionnaires, provided that the data satisfy the assumptions for IRT analysis.
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Project description:Allostatic load is commonly operationalized using a sum-score of high-risk biomarkers. However, this method implies that biomarkers contribute equally to allostatic load, as each is given equal weight. Our goal in this methodological paper is to evaluate this, and complementarily, to identify biomarkers that are most informative and least informative for developing an allostatic load index. Item response theory models provide an alternate approach to calculating the allostatic load score, by treating individual biomarkers (e.g. “items”) as indicators of a latent allostatic load construct. Item response theory scores account for the data-driven discriminating power of each biomarker, and an individual’s pattern of biomarker responses. To demonstrate feasibility of this approach, we used data from the 2015–2016 National Health Examination and Nutrition Survey (NHANES; N = 3751), with twelve allostatic load biomarkers representing immune response, metabolic function and cardiovascular health. Item response theory models revealed that body-mass-index and C-reactive protein were the most informative biomarkers for allostatic load. Both higher allostatic load sum-score and allostatic load item response theory score were associated with lower socio-economic status (p = 0.008; p<0.001, respectively). Further, both formulations of allostatic load were positively associated with a nine-item depression screener (p<0.001 for both), but only the item response theory score was also positively associated with the impact of depressive symptoms on daily life (p = 0.045). Item response theory scores may be more finely tuned to tease out effects, compared to sum-scores, and also provide more flexibility when there are missing biomarker measurements. Supplemental R code for our approach are included. Highlights • Methodological paper to introduce item response theory for calculating allostatic load.• Biomarker data from NHANES 2015–2016 representative of United States adults.• Body-mass-index and C-reactive protein most informative for allostatic load.• Item response theory captures more variability in allostatic load compared to sum-scores.• Future work - item response theory can standardize allostatic load across datasets.
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Project description:BackgroundModern psychometric methods based on item response theory (IRT) can be used to develop adaptive measures of health-related quality of life (HRQL). Adaptive assessment requires an item bank for each domain of HRQL. The purpose of this study was to develop item banks for five domains of HRQL relevant to arthritis.MethodsAbout 1,400 items were drawn from published questionnaires or developed from focus groups and individual interviews and classified into 19 domains of HRQL. We selected the following 5 domains relevant to arthritis and related conditions: Daily Activities, Walking, Handling Objects, Pain or Discomfort, and Feelings. Based on conceptual criteria and pilot testing, 219 items were selected for further testing. A questionnaire was mailed to patients from two hospital-based clinics and a stratified random community sample. Dimensionality of the domains was assessed through factor analysis. Items were analyzed with the Generalized Partial Credit Model as implemented in Parscale. We used graphical methods and a chi-square test to assess item fit. Differential item functioning was investigated using logistic regression.ResultsData were obtained from 888 individuals with arthritis. The five domains were sufficiently unidimensional for an IRT-based analysis. Thirty-one items were deleted due to lack of fit or differential item functioning. Daily Activities had the narrowest range for the item location parameter (-2.24 to 0.55) and Handling Objects had the widest range (-1.70 to 2.27). The mean (median) slope parameter for the items ranged from 1.15 (1.07) in Feelings to 1.73 (1.75) in Walking. The final item banks are comprised of 31-45 items each.ConclusionWe have developed IRT-based item banks to measure HRQL in 5 domains relevant to arthritis. The items in the final item banks provide adequate psychometric information for a wide range of functional levels in each domain.
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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:Composite assessments aim to combine different aspects of a disease in a single score and are utilized in a variety of therapeutic areas. The data arising from these evaluations are inherently discrete with distinct statistical properties. This tutorial presents the framework of the item response theory (IRT) for the analysis of this data type in a pharmacometric context. The article considers both conceptual (terms and assumptions) and practical questions (modeling software, data requirements, and model building).
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Project description:Matrix reasoning tasks are among the most widely used measures of cognitive ability in the behavioral sciences, but the lack of matrix reasoning tests in the public domain complicates their use. Here, we present an extensive investigation and psychometric validation of the matrix reasoning item bank (MaRs-IB), an open-access set of matrix reasoning items. In a first study, we calibrate the psychometric functioning of the items in the MaRs-IB in a large sample of adult participants (N = 1501). Using additive multilevel item structure models, we establish that the MaRs-IB has many desirable psychometric properties: its items span a wide range of difficulty, possess medium-to-large levels of discrimination, and exhibit robust associations between item complexity and difficulty. However, we also find that item clones are not always psychometrically equivalent and cannot be assumed to be exchangeable. In a second study, we demonstrate how experimenters can use the estimated item parameters to design new matrix reasoning tests using optimal item assembly. Specifically, we design and validate two new sets of test forms in an independent sample of adults (N = 600). We find these new tests possess good reliability and convergent validity with an established measure of matrix reasoning. We hope that the materials and results made available here will encourage experimenters to use the MaRs-IB in their research.
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Project description:OBJECTIVE:External shame reflects a person's anxiety that he or she might be rejected by others. The Other as Shamer Scale (OAS) is a scale for assessing external shame. The Japanese version of the OAS was developed, and its reliability and validity were examined using Item Response Theory (IRT). RESULTS:A survey was conducted with university students (N?=?199). Exploratory factor analysis of the results indicated a significantly high factor loading on the first factor, which was identical to the original version of the scale as well as high internal consistency. Moreover, the results confirmed that each item had adequate discrimination and information levels, suggesting that external shame could be discriminated against with high accuracy for a wide range of relatively low and relatively high external shame groups. These results suggest that the OAS could be used to screen external shame as a stress factor and to assess intervention effects.
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Project description:Item response theory (IRT) is a psychometric method that provides probabilistic model-based measurements. Its use is relatively recent in the assessment of food consumption, especially through dietary assessment tools. This study aims (1) to develop a food-based diet quality scale for Brazilian schoolchildren using IRT, and (2) to apply the scale to a representative sample of schoolchildren from a Southern Brazilian city. The scale was developed with daily consumption frequency of foods from 835 students who completed the Food Intake and Physical Activity of Schoolchildren questionnaire. Questionnaire foods were grouped into 10 items according to their nutritional similarities and were evaluated by full-information factor analysis that indicated a dominant factor explaining 28% of the variance. Psychometric item analysis was performed using Samejima's model. The scale covered all levels of diet quality, from "very poor" (scores < 95) to "very good" (scores ≥ 130). Children who had higher diet quality scores consumed beans, meat, fish, eggs, fruits, vegetables, dairy products, and water more frequently, while reducing the consumption of ultraprocessed sugary foods, ultraprocessed savoury snacks and sausages, and sugary drinks. Of 6323 children, an average of less than 10% consumed the highest diet quality scores (good or very good diet quality) and about 60% of children consumed low diet quality scores. The scale can be applied to other schoolchildren with the same measure precision.
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