Influential observations in the estimation of mean vector and covariance matrix

Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites?

Observational data typically contain measurement errors. Covariance-based structural equation modelling (CB-SEM) is capable of modelling measurement errors and yields consistent parameter estimates. In contrast, methods of regression analysis using weighted composites as well as a partial least squares approach to SEM facilitate the prediction and diagnosis of individuals/participants. But regression analysis with weighted composites has been known to yield attenuated regression coefficients when predictors contain errors. Contrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the signal-to-noise ratio (SNR). In particular, the SNR for the regression coefficient via the least squares (LS) method with equally weighted composites is mathematically greater than that by CB-SEM if the items for each factor are parallel, even when the SEM model is correctly specified and estimated by an efficient method. Analytical, numerical and empirical results also show that LS regression using weighted composites performs as well as or better than the normal maximum likelihood method for CB-SEM under many conditions even when the population distribution is multivariate normal. Results also show that the LS regression coefficients become more efficient when considering the sampling errors in the weights of composites than those that are conditional on weights.

An overview of applied robust methods

Data in social sciences are typically non-normally distributed and characterized by heavy tails. However, most widely used methods in social sciences are still based on the analyses of sample means and sample covariances. While these conventional methods continue to be used to address new substantive issues, conclusions reached can be inaccurate or misleading. Although there is no 'best method' in practice, robust methods that consider the distribution of the data can perform substantially better than the conventional methods. This article gives an overview of robust procedures, emphasizing a few that have been repeatedly shown to work well for models that are widely used in social and behavioural sciences. Real data examples show how to use the robust methods for latent variable models and for moderated mediation analysis when a regression model contains categorical covariates and product terms. Results and logical analyses indicate that robust methods yield more efficient parameter estimates, more reliable model evaluation, more reliable model/data diagnostics, and more trustworthy conclusions when conducting replication studies. R and SAS programs are provided for routine applications of the recommended robust method.

Local Influence and Robust Procedures for Mediation Analysis

Existing studies of mediation models have been limited to normal-theory maximum likelihood (ML). Because real data in the social and behavioral sciences are seldom normally distributed and often contain outliers, classical methods generally lead to inefficient or biased parameter estimates. Consequently, the conclusions from a mediation analysis can be misleading. In this article, we propose 2 approaches to alleviate these problems. One is to identify cases that strongly affect testing of mediation using local influence methods or robust methods. The other is to use robust methods for parameter estimation and subsequently test the mediated effect based on the robust estimates. The application of these 2 approaches is illustrated using 1 simulated and 2 real data examples. The interest in 1 real data set is the relationship among marital conflict, children's emotional insecurity, and children's internalizing problems. The other example is concerned with whether ethnic identity mediates the effect of family cohesion on Korean language fluency. Results show that local influence and robust methods rank the influence of cases similarly, but robust methods are more objective. Moreover, when the normality assumption is violated, robust methods give estimates with smaller standard errors and more reliable tests of the mediated effect compared with normal-theory ML. An R program that implements the local influence and robust procedures for mediation analysis is also provided.

Smoking and Cancers: Case-robust Analysis of a Classic Data Set

A typical structural equation model is intended to reproduce the means, variances, and correlations or covariances among a set of variables based on parameter estimates of a highly restricted model. It is not widely appreciated that the sample statistics being modeled can be quite sensitive to outliers and influential observations leading to bias in model parameter estimates. A classic public epidemiological data set on the relation between cigarette purchases and rates of four types of cancer among states in the USA is studied with case-weighting methods that reduce the influence of a few cases on the overall results. The results support and extend the original conclusions; the standardized effect of smoking on a factor underlying deaths from bladder and lung cancer is .79.