Dataset Information

Reporting Valid and Reliable Overall Scores and Domain Scores Using Bi-Factor Model.

ABSTRACT: Recently, large-scale testing programs have an increasing interest in providing examinees with more accurate diagnostic information by reporting overall and domain scores simultaneously. However, there are few studies focusing on how to report and interpret reliable total scores and domain scores based on bi-factor models. In this study, the authors introduced six methods of reporting overall and domain scores as weighted composite scores of the general and specific factors in a bi-factor model, and compared their performance with Yao's MIRT (multidimensional item response theory) method using both simulated and empirical data. In the simulation study, four factors were considered: test length, number of dimensions, correlation between dimensions, and sample size. Major findings are that Bifactor-M4 and Bifactor-M6, the methods utilizing discrimination parameters of the specific dimensions to compute the weights, provided the most accurate and reliable overall and domain scores in most conditions, especially when the test was long, the correlation between dimensions was high and the number of dimensions was large; additionally, Bifactor-M4 recovered the relationship of true ability parameters the best of all the proposed methods; On the contrary, Bifactor-M2, the method with equal weights, performed poor on the overall score estimation; Bifactor-M3 and Bifactor-M5, the methods where weights were computed using the discrimination parameters of all the dimensions, performed poor on the domain score estimation; Bifactor-M1, the original factor method, obtained the worst estimations.

SUBMITTER: Liu Y

PROVIDER: S-EPMC6739746 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Reporting Valid and Reliable Overall Scores and Domain Scores Using Bi-Factor Model.

Liu Yue Y Li Zhen Z Liu Hongyun H

Applied psychological measurement 20181210 7

Recently, large-scale testing programs have an increasing interest in providing examinees with more accurate diagnostic information by reporting overall and domain scores simultaneously. However, there are few studies focusing on how to report and interpret reliable total scores and domain scores based on bi-factor models. In this study, the authors introduced six methods of reporting overall and domain scores as weighted composite scores of the general and specific factors in a bi-factor model, ...[more]

PMID: 31534290

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Project description:ContextThe Landing Error Scoring System (LESS) is a clinical tool often used in research and practice to identify athletes presenting high injury-risk biomechanical patterns during a jump-landing task.ObjectiveTo systematically review the literature addressing the psychometric properties of the LESS.Data sourcesThree electronic databases (PubMed, Web of Science, and Scopus) were searched on March 28, 2018, using the term "Landing Error Scoring System."Study selectionAll studies using the LESS as main outcome measure and addressing its reliability, validity against motion capture system, and predictive validity were included. Original English-language studies published in peer-reviewed journals were reviewed. Studies using modified versions of the LESS were excluded.Study designSystematic literature review.Level of evidenceLevel 4.Data extractionStudy design, population, LESS testing procedures, LESS scores, statistical analysis, and main results were extracted from studies using a standardized template.ResultsTen studies met inclusion criteria and were appraised using Newcastle-Ottawa Quality Assessment Scale adapted for cross-sectional studies. The overall LESS score demonstrated good-to-excellent intrarater (intraclass correlation coefficient [ICC], 0.82-0.99), interrater (ICC, 0.83-0.92), and intersession reliability (ICC, 0.81). The validity of the overall LESS score against 3-dimensional jump-landing biomechanics was good when individuals were divided into 4 quartiles based on LESS scores. The validity of individual LESS items versus 3-dimensional motion capture data was moderate-to-excellent for most of the items addressing key risk factors for anterior cruciate ligament (ACL) injury. The predictive value of the LESS for ACL and other noncontact lower-extremity injuries remains uncertain based on the current scientific evidence.ConclusionThe LESS is a reliable screening tool. However, further work is needed to improve the LESS validity against motion capture system and confirm its predictive validity for ACL and other noncontact lower-extremity injuries.

Dataset Information

Reporting Valid and Reliable Overall Scores and Domain Scores Using Bi-Factor Model.

Publications

Reporting Valid and Reliable Overall Scores and Domain Scores Using Bi-Factor Model.

Similar Datasets

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