<p>Application of Rasch Analysis for Development and Psychometric Properties of Adolescents’ Quality of Life Instruments: A Systematic Review</p>

Abstract

Background Due to the importance of assessing quality of life (QoL) in healthy and ill adolescents, the evaluation of psychometric properties of these questionnaires is important. Objective To investigate the application of Rasch analysis in psychometric assessment studies on adolescents’ QoL instruments, and to evaluate the quality of reporting Rasch parameters in these studies. Methods This systematic review was conducted by searching for papers in electronic databases PubMed, Web of Science, EMBASE, Cochrane Library and Scopus until December 2018. Results After screening 122 papers, 31 remained in the study. Around 68% of the studies used the Rasch analysis for instrument testing and 32% for the development of new instruments. In 77.4% of studies, both classical and Rasch methods were used parallel to data analysis. In 32.2% of studies, healthy adolescents were the main target group. The most commonly used instrument in Rasch studies was, KIDSCREEN, administered in different countries. Six Rasch parameters were reported with a higher percentage in the studies. Major reported parameters of Rasch analysis were application of the software program (96.7%), test of item fit to the Rasch model (93.5%), unidimensionality (80.6%), type of the identified mathematical Rasch model (74.1%), threshold (58%) and differential item functioning (54.8%). Based on the psychometric evaluation of the QoL instruments, 71% of studies showed acceptable results. Conclusion The application of the Rasch model for psychometric assessment of adolescents’ QoL questionnaires has increased in recent decades. But, there is still no strong and commonly used critical appraisal tool or guideline for the evaluation of these papers.

Evaluation of two weight stigma scales in Malaysian university students: weight self-stigma questionnaire and perceived weight stigma scale

“…This evaluation can be performed using classical test theory methods (e.g., factor analysis and internal consistency) or modern test theory methods (e.g., based on Rasch models). Application of the Rasch model for assessing the psychometric properties of instruments has increased in recent decades [18][19][20]. It is a powerful tool to evaluate the validity and reliability of an instrument, and it can provide different information from psychometric testing in the classical test theory and provide more accurate findings [21].…”

Valid and Reliable? Basic and Expanded Recommendations for Psychometric Reporting and Quality Assessment.

“…Based on the meta-analysis of wellbeing in psychological interventions including 48 measures (van Agteren et al, 2021), a literature search was conducted according to the PRISMA 2020 checklist (Page et al, 2021) using the SCOPUS database. A similar review had been conducted on quality of life measures for adolescents (Dabaghi et al, 2020) and our search terms were based on theirs (see Additional Materials for details). The identified papers were screened by four blinded reviewers using the systematic review software Rayyan (Ouzzani et al, 2016).…”

“…Table 2 shows the final set of checklist items that emerged from the iterative coding process. Since the process of creating a new reporting guideline started with the use of the checklist used by Dabaghi et al (2020), we present these first as numbers 1-10, followed by the items added by our workgroup (criteria 11-16). Some of items 1-10 were modified for clarity.…”

Cross-cultural adaptation and validation of the Italian Spine Youth Quality of Life (ISYQOL) questionnaire's Arabic version

“…Rasch analysis is a valuable and practical statistical model designed to improve the development of valid and high-quality questionnaires. It has several advantages over the classic test theory, which is used in the development of SRS-22r [12,15,29] . Moreover, the ISYQOL questionnaire showed higher validity in assessing HRQOL than the SRS-22r using Spearman's correlation coefficient with better detection of the difference between groups related to curve type, brace wear, trunk appearance, and age [30] …”