The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addit...

Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

This article presents the rationale and procedures for conducting a process analysis in evaluation research. Such an analysis attempts to identify the process that mediates the effects of some treatment, by estimating the parameters of a causal chain between the treatment and some outcome variable. Two different procedures for estimating mediation are discussed. In addition we present procedures for examining whether a treatment exerts its effects, in part, by altering the mediating process that produces the outcome. Finally, the benefits of process analysis in evaluation research are underlined.

Process Analysis: Estimating Mediation in Treatment Evaluations

INTRODUCTION 419 BASIC CONCEPTS 421 Structural Equations and Path Diagrams 421 Model Specification and Research Design 425 Model Testing 427 Model Modification and Reconfirmation 429 Example: Models of Attitude-Behavior Relations 429

Multivariate analysis with latent variables: causal modeling

Prior research has provided useful insights into the structure of compensation plans and their incentive effects.' However, one important limitation of these studies is the virtual absence of any cross-sectional analyses of the attributes of compensation contracts.2 This absence is related, in part, to the problems associated with controlling for "other factors" that affect compensation. That is, compensation contracts are

http://disciplinas.stoa.usp.br/pluginfile.php/159922/mod_resource/content/1/LL.pdf

An Analysis of the Use of Accounting and Market Measures of Performance in Executive Compensation Contracts

The empirical study of the causes and consequences of political democracy has been the subject of considerable research. Yet cumulative development of this research is hampered by the controversial aspects and limitations of the existing indices of political democracy. These issues concern the validity of the indicators, the unknown reliability, and the limited sample and temporal coverage of these indices. After a discussion of these issues, a revised index of political democracy that overcomes some of these limitations is presented. The indicators of the revised index are analyzed by means of confirmatory factor analysis and the reliability of the measure is discussed. The index is generally better than existing measures in reliability, sample size, and temporal coverage; but the remaining limitations of the index are reviewed. The moderate to high correlations of this index with other democracy indices support its external validity. Yet, the differences in empirical results possible by using different indices are demonstrated. Finally, there are two appendices. The first provides the technical details of the confirmatory factor analysis. The second appendix lists the values of the political democracy index.

Issues in the Comparative Measurement of Political Democracy

This paper reviews Joreskog's model for the analysis of covariance structures by first introducing the simpler case of confirmatory factor analysis. The mathematical results necessary for estimation and hypothesis testing are presented in a way which should be more accessible to sociologists than the original sources. The usefulness of Joreskog's techniques is indicated by reformulating a series of models which have been estimated by sociologists using techniques without statistical justification in the format of covariance structures. Identification is considered in this context. The argument is made that these methods can greatly extend our ability to construct structural equation models containing measurement error.

J. Scott Long

Papers

Estimation and Hypothesis Testing in Linear Models Containing Measurement Error A Review of Joreskog's Model for the Analysis of Covariance Structures