Comparison of methods for constructing confidence intervals of standardized indirect effects

Cheung, Mike W.‐L.

doi:10.3758/brm.41.2.425

Cited by 219 publications

(165 citation statements)

References 58 publications

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“…To obtain the standardized estimates, it is procedurally tempting for one to standardize the variables first and analyze the data on the basis of the correlation matrix, because this can save lots of effort. However, as is shown in Table 3 and 5 (under the heading of "Correlation"), an analysis based on the correlation matrix in general gives us correct parameter estimates but incorrect SEs (e.g., Bentler, 2007;Cheung, 2009a;Cudeck, 1989). When we analyze the correlation matrix, the variances of the dependent variables depend on other random parameters and are, therefore, subject to sampling variability (i.e., they are not fixed at 1.0 nonstochastically), which eventually affects the accuracy of the standard error estimates.…”

Section: Discussionmentioning

confidence: 99%

Comparing standardized coefficients in structural equation modeling: a model reparameterization approach

Kwan

Chan

2011

Behav Res

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We propose a two-stage method for comparing standardized coefficients in structural equation modeling (SEM). At stage 1, we transform the original model of interest into the standardized model by model reparameterization, so that the model parameters appearing in the standardized model are equivalent to the standardized parameters of the original model. At stage 2, we impose appropriate linear equality constraints on the standardized model and use a likelihood ratio test to make statistical inferences about the equality of standardized coefficients. Unlike other existing methods for comparing standardized coefficients, the proposed method does not require specific modeling features (e.g., specification of nonlinear constraints), which are available only in certain SEM software programs. Moreover, this method allows researchers to compare two or more standardized coefficients simultaneously in a standard and convenient way. Three real examples are given to illustrate the proposed method, using EQS, a popular SEM software program. Results show that the proposed method performs satisfactorily for testing the equality of standardized coefficients.

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Section: Discussionmentioning

confidence: 99%

Comparing standardized coefficients in structural equation modeling: a model reparameterization approach

Kwan

Chan

2011

Behav Res

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“…Standardised regression weights were used to interpret the direct effects. The bias-corrected bootstrap confidence intervals technique was used to test the significance of standardized indirect effects (Cheung, 2009;MacKinnon, Lockwood, &Williams, 2004). The RMediation programme (previously "PRODCLIN"; MacKinnon, Fritz, Williams & Lockwood, 2007) was used to confirm the mediational effect; this method uses the distribution-of-product method to build confidence intervals for the mediational effects and has been shown to produce more accurate Type I error rates and good statistical power (Tofighi & MacKinnon, 2011).…”

Section: Data Analytic Strategymentioning

confidence: 99%

Borderline Personality Traits, Rumination, and Self-Injurious Behavior: An Empirical Test of the Emotional Cascades Model in Adult Male Offenders

Gardner

Dodsworth²,

Selby

2014

Journal of Forensic Psychology Practice

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“…Moreover, as we showed in the manuscript, the method is already available for use also with the standardized regression coefficients (b). Even though the use of standardized regression coefficients in epidemiology has been the subject of debate [9] [51] [52], they are routinely used in the social sciences [53] and they become popular in genetics with genome-wide association studies [54]- [56]. Thus, we believe that the method can be useful also in this respect.…”

Section: Discussionmentioning

confidence: 99%

On the Covariance of Regression Coefficients

Bagos¹,

Αδάμ²

2015

OJS

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In many applications, such as in multivariate meta-analysis or in the construction of multivariate models from summary statistics, the covariance of regression coefficients needs to be calculated without having access to individual patients' data. In this work, we derive an alternative analytic expression for the covariance matrix of the regression coefficients in a multiple linear regression model. In contrast to the well-known expressions which make use of the cross-product matrix and hence require access to individual data, we express the covariance matrix of the regression coefficients directly in terms of covariance matrix of the explanatory variables. In particular, we show that the covariance matrix of the regression coefficients can be calculated using the matrix of the partial correlation coefficients of the explanatory variables, which in turn can be calculated easily from the correlation matrix of the explanatory variables. This is very important since the covariance matrix of the explanatory variables can be easily obtained or imputed using data from the literature, without requiring access to individual data. Two important applications of the method are discussed, namely the multivariate meta-analysis of regression coefficients and the so-called synthesis analysis, and the aim of which is to combine in a single predictive model, information from different variables. The estimator proposed in this work can increase the usefulness of these methods providing better results, as seen by application in a publicly available dataset. Source code is provided in the Appendix and in http://www.compgen.org/tools/regression.

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Comparison of methods for constructing confidence intervals of standardized indirect effects

Cited by 219 publications

References 58 publications

Comparing standardized coefficients in structural equation modeling: a model reparameterization approach

Comparing standardized coefficients in structural equation modeling: a model reparameterization approach

Borderline Personality Traits, Rumination, and Self-Injurious Behavior: An Empirical Test of the Emotional Cascades Model in Adult Male Offenders

On the Covariance of Regression Coefficients

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