Indirect Effects in Sequential Mediation Models
Evaluating Methods for Hypothesis Testing and Confidence Interval Formation
Datos Bibliográficos
| ID | 19289701 |
|---|---|
| Autores | Davood Tofighi (0000-0001-8523-7776, University of New Mexico, autor de correspondencia), Ken Kelley (0000-0002-4756-8360, University of Notre Dame) |
| Año | 2020 |
| Volumen | 55 |
| Número | 2 |
| Páginas | 188-210 |
| Fecha de publicación | 2020-03-03 |
| Open Access | No |
| Tipo | ARTICLE |
| Revista | Multivariate Behavioral Research (JOURNAL) |
| Identificadores de la revista | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Editorial | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2019.1618545 |
| PMID | 31179751 |
| OpenAlex | W2949680066 |
| Idioma | EN |
| Citas recibidas | 11 |
| Referencias citadas | 87 |
Complex mediation models, such as a two-mediator sequential model, have become more prevalent in the literature. To test an indirect effect in a two-mediator model, we conducted a large-scale Monte Carlo simulation study of the Type I error, statistical power, and confidence interval coverage rates of 10 frequentist and Bayesian confidence/credible intervals (CIs) for normally and nonnormally distributed data. The simulation included never-studied methods and conditions (e.g., Bayesian CI with flat and weakly informative prior methods, two model-based bootstrap methods, and two nonnormality conditions) as well as understudied methods (e.g., profile-likelihood, Monte Carlo with maximum likelihood standard error [MC-ML] and robust standard error [MC-Robust]). The popular BC bootstrap showed inflated Type I error rates and CI under-coverage. We recommend different methods depending on the purpose of the analysis. For testing the null hypothesis of no mediation, we recommend MC-ML, profile-likelihood, and two Bayesian methods. To report a CI, if data has a multivariate normal distribution, we recommend MC-ML, profile-likelihood, and the two Bayesian methods; otherwise, for multivariate nonnormal data we recommend the percentile bootstrap. We argue that the best method for testing hypotheses is not necessarily the best method for CI construction, which is consistent with the findings we present
Bayesian inference · Bayesian probability · Confidence interval · Coverage probability · Credible interval · Econometrics · Frequentist inference · Monte Carlo method · Multivariate statistics · Null hypothesis · Statistical hypothesis testing · Statistics · Type I and type II errors · Computer Science · Decision-Making and Behavioral Economics · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
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An Introduction to the Bootstrap
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences
Bootstrap Methods and their Application
An Introduction to the Bootstrap
Statistical Rethinking
Introduction to Statistical Mediation Analysis
Power Analysis for Complex Mediational Designs Using Monte Carlo Methods
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Robustness?
Resampling and Distribution of the Product Methods for Testing Indirect Effects in Complex Models
Monte Carlo sampling methods using Markov chains and their applications
Tests of the Three-Path Mediated Effect
Keeping (future) rivals down
Bayesian structural equation modeling
Interpretation and identification of causal mediation.
Better Bootstrap Confidence Intervals
Applications of Structural Equation Modeling in Psychological Research
The Performance of ML, GLS, and WLS Estimation in Structural Equation Modeling Under Conditions of Misspecification and Nonnormality
Bayesian mediation analysis.
Simulating Multivariate Nonnormal Distributions
Measures of multivariate skewness and kurtosis with applications
On The So-Called “Huber Sandwich Estimator” and “Robust Standard Errors”
Performance of Bootstrapping Approaches to Model Test Statistics and Parameter Standard Error Estimation in Structural Equation Modeling
Ensuring Positiveness of the Scaled Difference Chi-square Test Statistic
Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models
A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
A general approach to causal mediation analysis.
The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis.
A comparison of methods to test mediation and other intervening variable effects.
Bias in cross-sectional analyses of longitudinal mediation.
Mediation in experimental and nonexperimental studies
A Cautionary Note on the Use of the Vale and Maurelli Method to Generate Multivariate, Nonnormal Data for Simulation Purposes
Prejudice, Discrimination and Racism
The Relative Trustworthiness of Inferential Tests of the Indirect Effect in Statistical Mediation Analysis
Explanation of Two Anomalous Results in Statistical Mediation Analysis
Confidence Limits for the Indirect Effect
Assessing Mediational Models
A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis
Assessing Omitted Confounder Bias in Multilevel Mediation Models
Univariate and multivariate skewness and kurtosis for measuring nonnormality
Advantages of Monte Carlo Confidence Intervals for Indirect Effects
Causal Inference, Path Analysis, and Recursive Structural Equations Models
Direct and Indirect Effects
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Social dominance orientation
Measurement error in the analysis of interaction effects between continuous predictors using multiple regression
The unicorn, the normal curve, and other improbable creatures
Bootstrapping Goodness-of-Fit Measures in Structural Equation Models
| Citas por año | 1,83 |
|---|---|
| Intervalo de citas | 2020 - 2026 (7) |
| Altamente citado | No |