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Empirical selection of referent variables: Comparing multiple-indicator multiple-cause-interaction modeling and moderated nonlinear factor analysis.
Psychological Methods ( IF 7.6 ) Pub Date : 2023-11-13 , DOI: 10.1037/met0000613
Cheng-Hsien Li 1
Psychological Methods ( IF 7.6 ) Pub Date : 2023-11-13 , DOI: 10.1037/met0000613
Cheng-Hsien Li 1
Affiliation
The fulfillment of measurement invariance/equivalence is considered a prerequisite for meaningfully proceeding with substantive cross-group comparisons. In the multiple-group confirmatory factor analysis approach, one model identification issue has unfortunately received little attention: the specification of a referent variable in the test of measurement invariance. A multiple-indicator multiple-cause (MIMIC) model with moderated effects (i.e., MIMIC-interaction modeling; Woods & Grimm, 2011) and a moderated nonlinear factor analysis (MNLFA; Bauer, 2017) model for detecting uniform and nonuniform measurement inequivalences in tandem were proposed to identify credible referent variables. The performance of two search strategies, constrained and free baseline models, and MIMIC-interaction and MNLFA methodologies were evaluated in a Monte Carlo simulation. Effects of different configurations of the number of inequivalent variables, type and magnitude of inequivalence, magnitude of group differences in factor means and variances, and sample size in combination with each search strategy were determined. Results showed that the constrained baseline model strategy generally outperformed the free baseline model strategy for identifying credible referent variables, functioning well when up to one-third of the observed variables were noninvariant. Moreover, MNLFA performed better than MIMIC-interaction modeling for the selection of referent variables across nearly all conditions investigated in the study. The superiority of MNLFA over MIMIC-interaction modeling was specifically evident in the models with relatively small samples, large between-group latent variance differences, or a combination of both. An empirical example was presented to demonstrate the applicability of MNLFA with the constrained baseline model strategy for referent variable selection. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
中文翻译:
参考变量的经验选择:比较多指标多因相互作用模型和有调节的非线性因子分析。
满足测量不变性/等效性被认为是有意义地进行实质性跨组比较的先决条件。不幸的是,在多组验证性因素分析方法中,一个模型识别问题很少受到关注:测量不变性检验中参考变量的指定。具有调节效应的多指标多因 (MIMIC) 模型(即 MIMIC 交互模型;Woods & Grimm,2011)和调节非线性因子分析(MNLFA;Bauer,2017)模型,用于检测均匀和非均匀测量不等式提出串联来确定可信的参考变量。在蒙特卡罗模拟中评估了两种搜索策略、约束和自由基线模型以及 MIMIC 交互和 MNLFA 方法的性能。确定了不同配置的不等变量数量、不等性的类型和大小、因子均值和方差的组差异大小以及样本大小与每种搜索策略的组合的影响。结果表明,在识别可信参考变量方面,约束基线模型策略通常优于自由基线模型策略,当多达三分之一的观察变量是非不变时,该策略运行良好。此外,在研究中调查的几乎所有条件下,MNLFA 在选择参考变量方面都比 MIMIC 交互模型表现得更好。MNLFA 相对 MIMIC 交互模型的优越性在样本相对较小、组间潜在方差差异较大或两者兼而有之的模型中尤其明显。提出了一个实证例子来证明 MNLFA 与约束基线模型策略对于参考变量选择的适用性。(PsycInfo 数据库记录 (c) 2023 APA,保留所有权利)。
更新日期:2023-11-13
中文翻译:
参考变量的经验选择:比较多指标多因相互作用模型和有调节的非线性因子分析。
满足测量不变性/等效性被认为是有意义地进行实质性跨组比较的先决条件。不幸的是,在多组验证性因素分析方法中,一个模型识别问题很少受到关注:测量不变性检验中参考变量的指定。具有调节效应的多指标多因 (MIMIC) 模型(即 MIMIC 交互模型;Woods & Grimm,2011)和调节非线性因子分析(MNLFA;Bauer,2017)模型,用于检测均匀和非均匀测量不等式提出串联来确定可信的参考变量。在蒙特卡罗模拟中评估了两种搜索策略、约束和自由基线模型以及 MIMIC 交互和 MNLFA 方法的性能。确定了不同配置的不等变量数量、不等性的类型和大小、因子均值和方差的组差异大小以及样本大小与每种搜索策略的组合的影响。结果表明,在识别可信参考变量方面,约束基线模型策略通常优于自由基线模型策略,当多达三分之一的观察变量是非不变时,该策略运行良好。此外,在研究中调查的几乎所有条件下,MNLFA 在选择参考变量方面都比 MIMIC 交互模型表现得更好。MNLFA 相对 MIMIC 交互模型的优越性在样本相对较小、组间潜在方差差异较大或两者兼而有之的模型中尤其明显。提出了一个实证例子来证明 MNLFA 与约束基线模型策略对于参考变量选择的适用性。(PsycInfo 数据库记录 (c) 2023 APA,保留所有权利)。