Performance of the Grade of Membership Model Under a Variety of Sample Sizes, Group Size Ratios, and Differential Group Response Probabilities for Dichotomous Indicators

Holmes Finch W.

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Performance of the Grade of Membership Model Under a Variety of Sample Sizes, Group Size Ratios, and Differential Group Response Probabilities for Dichotomous Indicators

机译：各种样本尺寸下的成员模型等级的性能，组尺寸比例和二分法指标的差分组响应概率

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Social scientists are frequently interested in identifying latent subgroups within the population, based on a set of observed variables. One of the more common tools for this purpose is latent class analysis (LCA), which models a scenario involvingkfinite and mutually exclusive classes within the population. An alternative approach to this problem is presented by the grade of membership (GoM) model, in which individuals are assumed to have partial membership in multiple population subgroups. In this respect, it differs from the hard groupings associated with LCA. The current Monte Carlo simulation study extended on prior work on the GoM by investigating its ability to recover underlying subgroups in the population for a variety of sample sizes, latent group size ratios, and differing group response profiles. In addition, this study compared the performance of GoM with that of LCA. Results demonstrated that when the underlying process conforms to the GoM model form, the GoM approach yielded more accurate classification results than did LCA. In addition, it was found that the GoM modeling paradigm yielded accurate results for samples as small as 200, even when latent subgroups were very unequal in size. Implications for practice were discussed.

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《Educational and Psychological Measurement》 |2021年第3期|共26页
作者
Holmes Finch W.;
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作者单位

Ball State Univ Muncie IN 47306 USA;

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正文语种 eng
中图分类教育心理学;
关键词
grade of membership model; latent class analysis; clustering;

机译：成员模型等级;潜在课程分析;聚类;

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Performance of the Grade of Membership Model Under a Variety of Sample Sizes, Group Size Ratios, and Differential Group Response Probabilities for Dichotomous Indicators

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