Dataset Information

Investigating the Behaviors of M2 and RMSEA2 in Fitting a Unidimensional Model to Multidimensional Data.

ABSTRACT: It has been widely known that the Type I error rates of goodness-of-fit tests using full information test statistics, such as Pearson's test statistic ?2 and the likelihood ratio test statistic G2, are problematic when data are sparse. Under such conditions, the limited information goodness-of-fit test statistic M2 is recommended in model fit assessment for models with binary response data. A simulation study was conducted to investigate the power and Type I error rate of M2 in fitting unidimensional models to many different types of multidimensional data. As an additional interest, the behavior of RMSEA2 was also examined, which is the root mean square error approximation (RMSEA) based on M2. Findings from the current study showed that M2 and RMSEA2 are sensitive in detecting the misfits due to varying slope parameters, the bifactor structure, and the partially (or completely) simple structure for multidimensional data, but not the misfits due to the within-item multidimensional structures.

SUBMITTER: Xu J

PROVIDER: S-EPMC5978478 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Publications

Investigating the Behaviors of M2 and RMSEA2 in Fitting a Unidimensional Model to Multidimensional Data.

Xu Jie J Paek Insu I Xia Yan Y

Applied psychological measurement 20170530 8

It has been widely known that the Type I error rates of goodness-of-fit tests using full information test statistics, such as Pearson's test statistic χ2 and the likelihood ratio test statistic G2, are problematic when data are sparse. Under such conditions, the limited information goodness-of-fit test statistic M2 is recommended in model fit assessment for models with binary response data. A simulation study was conducted to investigate the power a ...[more]

PMID: 29881108

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Project description:Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species' range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists.

Project description:BACKGROUND:Many instruments to identify frail older people have been developed. One of the consequences is that the prevalence rates of frailty vary widely dependent on the instrument selected. The aims of this study were 1) to examine the concordances and differences between a unidimensional and multidimensional assessment of frailty, 2) to assess to what extent the characteristics of a 'frail sample' differ depending on the selected frailty measurement because 'being frail' is used in many studies as an inclusion criterion. METHOD:A cross-sectional study was conducted among 196 community-dwelling older adults (?60?years), which were selected from the census records. Unidimensional frailty was operationalized according to the Fried Phenotype (FP) and multidimensional frailty was measured with the Comprehensive Frailty Assessment Instrument (CFAI). The concordances and differences were examined by prevalence, correlations, observed agreement and Kappa values. Differences between sample characteristics (e.g., age, physical activity, life satisfaction) were investigated with ANOVA and Kruskall-Wallis test. RESULTS:The mean age was 72.74 (SD 8.04) and 48.98% was male. According to the FP 23.59% was not-frail, 56.92% pre-frail and 19.49% frail. According to the CFAI, 44.33% was no-to-low frail, 37.63% was mild frail and 18.04% was high frail. The correlation between FP and the CFAI was r?=?0.46 and the observed agreement was 52.85%. The Kappa value was ??=?0.35 (quadratic ??=?0.45). In total, 11.92% of the participants were frail according to both measurements, 7.77% was solely frail according to the FP and 6.21% was solely frail according to the CFAI. The 'frail sample respondents' according to the FP had higher levels of life satisfaction and net income, but performed less physical activities in comparison to high frail people according to the CFAI. CONCLUSION:The present study shows that the FP and CFAI partly measure the same 'frailty-construct', although differences were found for instance in the prevalence of frailty and the composition of the 'frail participants'. Since 'being frail' is an inclusion criterion in many studies, researchers must be aware that the choice of the frailty measurement has an impact on both the estimates of frailty prevalence and the characteristics of the selected sample.

Dataset Information

Investigating the Behaviors of M2 and RMSEA2 in Fitting a Unidimensional Model to Multidimensional Data.

Publications

Investigating the Behaviors of <i>M</i><sub>2</sub> and RMSEA<sub>2</sub> in Fitting a Unidimensional Model to Multidimensional Data.

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