SAS macros for longitudinal IRT models

Olsbjerg, Maja

doi:10.7287/peerj.preprints.26740

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…The psychometric properties of the PHQ-9 were assessed by investigating the fit of the scale to the PCM and to the GPCM [17,[36][37][38]. Analyses were run using RUMM2030 [36], Mplus [37,38], R [39], and SAS [40,41]. Among the evaluation criteria applied were ordered response thresholds, unidimensionality, item fit, local independence, measurement invariance (DIF), and the standard error of measurement as evaluated by test information functions.…”

Section: Discussionmentioning

confidence: 99%

Psychometric analysis of the Patient Health Questionnaire in Danish patients with an implantable cardioverter defibrillator (The DEFIB-WOMEN study)

Pedersen

Mathiasen

Makransky

2016

Journal of Psychosomatic Research

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Section: Discussionmentioning

confidence: 99%

Psychometric analysis of the Patient Health Questionnaire in Danish patients with an implantable cardioverter defibrillator (The DEFIB-WOMEN study)

Pedersen

Mathiasen

Makransky

2016

Journal of Psychosomatic Research

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“…The IRT model-based evaluation of item fit used a unidimensional IRT model-the generalized partial credit model (GPCM) [25]. For each scale, we compared the observed item scores with the scores expected under the model using an implementation in SAS statistical software version 9.4 (SAS Institute, Cary, NC, USA) [26]. This provides a graphical test of item fit where the item's mean score is plotted against the group's scale mean and evaluated against values.…”

Section: Discussionmentioning

confidence: 99%

A Multidimensional Tool Based on the eHealth Literacy Framework: Development and Initial Validity Testing of the eHealth Literacy Questionnaire (eHLQ)

Kayser¹,

Karnoe²,

Furstrand³

et al. 2017

Preprint

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Background: For people to be able to access, understand, and benefit from the increasing digitalization of health services, it is critical that services are provided in a way that meets the user's needs, resources, and competence. Objective:The objective of the study was to develop a questionnaire that captures the 7-dimensional eHealth Literacy Framework (eHLF).Methods: Draft items were created in parallel in English and Danish. The items were generated from 450 statements collected during the conceptual development of eHLF. In all, 57 items (7 to 9 items per scale) were generated and adjusted after cognitive testing. Items were tested in 475 people recruited from settings in which the scale was intended to be used (community and health care settings) and including people with a range of chronic conditions. Measurement properties were assessed using approaches from item response theory (IRT) and classical test theory (CTT) such as confirmatory factor analysis (CFA) and reliability using composite scale reliability (CSR); potential bias due to age and sex was evaluated using differential item functioning (DIF).Results: CFA confirmed the presence of the 7 a priori dimensions of eHLF. Following item analysis, a 35-item 7-scale questionnaire was constructed, covering (1) using technology to process health information (5 items, CSR=.84), (2) understanding of health concepts and language (5 items, CSR=.75), (3) ability to actively engage with digital services (5 items, CSR=.86), (4) feel safe and in control (5 items, CSR=.87), (5) motivated to engage with digital services (5 items, CSR=.84), (6) access to digital services that work (6 items, CSR=.77), and (7) digital services that suit individual needs (4 items, CSR=.85). A 7-factor CFA model, using small-variance priors for cross-loadings and residual correlations, had a satisfactory fit (posterior productive P value: .27, 95% CI for the difference between the observed and replicated chi-square values: −63.7 to 133.8). The CFA showed that all items loaded strongly on their respective factors. The IRT analysis showed that no items were found to have disordered thresholds. For most scales, discriminant validity was acceptable; however, 2 pairs of dimensions were highly correlated; dimensions 1 and 5 (r=.95), and dimensions 6 and 7 (r=.96). All dimensions were retained because of strong content differentiation and potential causal relationships between these dimensions. There is no evidence of DIF. Conclusions:The eHealth Literacy Questionnaire (eHLQ) is a multidimensional tool based on a well-defined a priori eHLF framework with robust properties. It has satisfactory evidence of construct validity and reliable measurement across a broad range

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“…Analysis of IRT models can be done in R [37][38][39][40] in SAS, [41][42][43] or using the proprietary software package IRTPRO. 44 An important feature of IRT models is that they specify the conditional probabilities of each response option, given the value of the latent health characteristics.…”

Section: Item Response Theory (Irt)mentioning

confidence: 99%

Psychometric validation of PROM instruments

Comins

Krogsgaard

Brodersen

et al. 2021

Scandinavian Med Sci Sports

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The aim was to provide an overview of the different statistical methods for validation of patient-reported outcome measures, ranging from simple statistical methods available in all software packages to advanced statistical models that require specialized software. A non-technical summary of classical test theory (CTT) and modern test theory (MTT) is provided. Specifically, confirmatory factor analysis, item response theory, and Rasch analysis is outlined. One CTT and three MTT methods were used to validate the two subscales (Symptoms and Quality of Life) from the Knee Injury and Osteoarthritis Outcome Score (KOOS). For each methodology, two analyses were considered: (i) a unidimensional analysis ignoring the pre-specified dimensionality, and (ii) a two-dimensional analysis using the pre-specified dimensionality.While CTT did not adequately address central issues regarding the validity of the KOOS subscales, the three MTT methods yielded very similar results. In conclusion, MTT methods offer analysis of all relevant properties related to the validity of patient-reported outcome measures, while this is not the case for CTT. Claims about sufficient validity based on CTT methods are inadequate and should not be trusted. K E Y W O R D Sclassical test theory, confirmatory factor analyses, construct validity, differential item functioning, modern test theory, patient-reported outcome measures, psychometric validation, Rasch analyses | 1227Since E is assumed to be identically distributed for all persons and independent of TS and other error scores, it follows that SEM is a general measure of the accuracy of measurement that does not depend on the population and sampling of persons measured by the PROM.

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SAS macros for longitudinal IRT models

Cited by 3 publications

References 18 publications

Psychometric analysis of the Patient Health Questionnaire in Danish patients with an implantable cardioverter defibrillator (The DEFIB-WOMEN study)

Psychometric analysis of the Patient Health Questionnaire in Danish patients with an implantable cardioverter defibrillator (The DEFIB-WOMEN study)

A Multidimensional Tool Based on the eHealth Literacy Framework: Development and Initial Validity Testing of the eHealth Literacy Questionnaire (eHLQ)

Psychometric validation of PROM instruments

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