{"title":"估计和检验中介效应的信噪比:结构方程模型与加权复合路径分析》。","authors":"Ke-Hai Yuan, Zhiyong Zhang, Lijuan Wang","doi":"10.1007/s11336-024-09975-4","DOIUrl":null,"url":null,"abstract":"<p><p>Mediation analysis plays an important role in understanding causal processes in social and behavioral sciences. While path analysis with composite scores was criticized to yield biased parameter estimates when variables contain measurement errors, recent literature has pointed out that the population values of parameters of latent-variable models are determined by the subjectively assigned scales of the latent variables. Thus, conclusions in existing studies comparing structural equation modeling (SEM) and path analysis with weighted composites (PAWC) on the accuracy and precision of the estimates of the indirect effect in mediation analysis have little validity. Instead of comparing the size on estimates of the indirect effect between SEM and PAWC, this article compares parameter estimates by signal-to-noise ratio (SNR), which does not depend on the metrics of the latent variables once the anchors of the latent variables are determined. Results show that PAWC yields greater SNR than SEM in estimating and testing the indirect effect even when measurement errors exist. In particular, path analysis via factor scores almost always yields greater SNRs than SEM. Mediation analysis with equally weighted composites (EWCs) also more likely yields greater SNRs than SEM. Consequently, PAWC is statistically more efficient and more powerful than SEM in conducting mediation analysis in empirical research. The article also further studies conditions that cause SEM to have smaller SNRs, and results indicate that the advantage of PAWC becomes more obvious when there is a strong relationship between the predictor and the mediator, whereas the size of the prediction error in the mediator adversely affects the performance of the PAWC methodology. Results of a real-data example also support the conclusions.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11458674/pdf/","citationCount":"0","resultStr":"{\"title\":\"Signal-to-Noise Ratio in Estimating and Testing the Mediation Effect: Structural Equation Modeling versus Path Analysis with Weighted Composites.\",\"authors\":\"Ke-Hai Yuan, Zhiyong Zhang, Lijuan Wang\",\"doi\":\"10.1007/s11336-024-09975-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Mediation analysis plays an important role in understanding causal processes in social and behavioral sciences. 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In particular, path analysis via factor scores almost always yields greater SNRs than SEM. Mediation analysis with equally weighted composites (EWCs) also more likely yields greater SNRs than SEM. Consequently, PAWC is statistically more efficient and more powerful than SEM in conducting mediation analysis in empirical research. The article also further studies conditions that cause SEM to have smaller SNRs, and results indicate that the advantage of PAWC becomes more obvious when there is a strong relationship between the predictor and the mediator, whereas the size of the prediction error in the mediator adversely affects the performance of the PAWC methodology. 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引用次数: 0
摘要
中介分析在理解社会和行为科学的因果过程中发挥着重要作用。使用综合得分的路径分析被批评为在变量包含测量误差的情况下会产生有偏差的参数估计,而最近的文献则指出,潜变量模型参数的总体值是由主观分配的潜变量标度决定的。因此,现有研究在比较结构方程模型(SEM)和加权复合路径分析(PAWC)时,就中介分析中间接效应估计值的准确性和精确性得出的结论并不可靠。本文没有比较 SEM 和 PAWC 对间接效应估计值的大小,而是通过信噪比(SNR)对参数估计值进行了比较。结果表明,即使存在测量误差,在估计和检验间接效应时,PAWC 的信噪比也比 SEM 高。特别是,通过因子得分进行的路径分析几乎总能获得比 SEM 更大的信噪比。使用等权重复合材料(EWCs)进行中介分析也比 SEM 更有可能获得更高的信噪比。因此,在实证研究中进行中介分析时,PAWC 在统计上比 SEM 更有效、更强大。文章还进一步研究了导致 SEM SNR 较小的条件,结果表明,当预测因子和中介因子之间存在较强关系时,PAWC 的优势会更加明显,而中介因子预测误差的大小会对 PAWC 方法的性能产生不利影响。一个真实数据实例的结果也支持上述结论。
Signal-to-Noise Ratio in Estimating and Testing the Mediation Effect: Structural Equation Modeling versus Path Analysis with Weighted Composites.
Mediation analysis plays an important role in understanding causal processes in social and behavioral sciences. While path analysis with composite scores was criticized to yield biased parameter estimates when variables contain measurement errors, recent literature has pointed out that the population values of parameters of latent-variable models are determined by the subjectively assigned scales of the latent variables. Thus, conclusions in existing studies comparing structural equation modeling (SEM) and path analysis with weighted composites (PAWC) on the accuracy and precision of the estimates of the indirect effect in mediation analysis have little validity. Instead of comparing the size on estimates of the indirect effect between SEM and PAWC, this article compares parameter estimates by signal-to-noise ratio (SNR), which does not depend on the metrics of the latent variables once the anchors of the latent variables are determined. Results show that PAWC yields greater SNR than SEM in estimating and testing the indirect effect even when measurement errors exist. In particular, path analysis via factor scores almost always yields greater SNRs than SEM. Mediation analysis with equally weighted composites (EWCs) also more likely yields greater SNRs than SEM. Consequently, PAWC is statistically more efficient and more powerful than SEM in conducting mediation analysis in empirical research. The article also further studies conditions that cause SEM to have smaller SNRs, and results indicate that the advantage of PAWC becomes more obvious when there is a strong relationship between the predictor and the mediator, whereas the size of the prediction error in the mediator adversely affects the performance of the PAWC methodology. Results of a real-data example also support the conclusions.
期刊介绍:
The journal Psychometrika is devoted to the advancement of theory and methodology for behavioral data in psychology, education and the social and behavioral sciences generally. Its coverage is offered in two sections: Theory and Methods (T& M), and Application Reviews and Case Studies (ARCS). T&M articles present original research and reviews on the development of quantitative models, statistical methods, and mathematical techniques for evaluating data from psychology, the social and behavioral sciences and related fields. Application Reviews can be integrative, drawing together disparate methodologies for applications, or comparative and evaluative, discussing advantages and disadvantages of one or more methodologies in applications. Case Studies highlight methodology that deepens understanding of substantive phenomena through more informative data analysis, or more elegant data description.