Larry J. Williams

A Review of Current Practices for Evaluating Causal Models in Organizational Behavior and Human Resources Management Research

This paper reviews the literature on structural model evaluation. It discusses the use of fit indices, the influential work of James, Mulaik, and Brett (19821, with emphasis on their prescriptions for model assessment, and recent developments in model evaluation presented since James et al. It then analyzes current modeling practices to determine how well evaluation techniques are being applied. Although modeling practices have improved since an earlier review (James & James, 19891, several problems remain. Suggestions are made for improving model evaluation.

Exploratory and confirmatory factor analysis: guidelines, issues, and alternatives

AMY E. HURLEY, TERRI A. SCANDURA, CHESTER A. SCHRIESHEIM, MICHAEL T. BRANNICK, ANSON SEERS, ROBERT J. VANDENBERG AND LARRY J. WILLIAMS Department of Professional Studies, Chapman University, U.S.A. Department of Management, University of Miami, U.S.A. Department of Psychology, University of South Florida, U.S.A. Department of Management, Virginia Commonwealth University, U.S.A. Department of Management, The University of Georgia, U.S.A. Department of Management, University of Tennessee, U.S.A.

Method Variance and Marker Variables: A Review and Comprehensive CFA Marker Technique

Lindell and Whitney introduced a partial correlation technique, now referred to as the correlational marker technique, for controlling method variance using a marker variable that is theoretically unrelated to substantive variables in a study. This article (a) first reviews their specific analysis plan, and then (b) reviews empirical studies that have followed all or part of this plan. The authors also (c) describe a structural equation method that has been applied to the analysis of marker variables and (d) review empirical studies using this analytical approach. Next, the authors (e) develop a comprehensive confirmatory factor analysis (CFA) marker technique analysis plan, and (f) demonstrate this plan with an empirical example. Finally, the authors (g) describe how marker variables can be examined along with other method variance processes, (h) discuss the important role of theory in the critical step of selecting marker variables, and (i) discuss assumptions and limitations of the Comprehensive CFA Marker Technique.

Recent Advances in Causal Modeling Methods for Organizational and Management Research

The purpose of this article is to review recent advanced applications of causal modeling methods in organizational and management research. Developments over the past 10 years involving research on measurement and structural components of causal models will be discussed. Specific topics to be addressed include reflective vs. formative measurement, multidimensional construct assessment, method variance, measurement invariance, latent growth modeling (LGM), moderated structural relationships, and analysis of latent variable means. For each of the areas mentioned above an overview of developments will be presented, and examples from organizational and management research will be provided.

12 Structural Equation Modeling in Management Research: A Guide for Improved Analysis

Abstract A large segment of management research in recent years has used structural equation modeling (SEM) as an analytical approach that simultaneously combines factor analysis and linear regression models for theory testing. With this approach, latent variables (factors) represent the concepts of a theory, and data from measures (indicators) are used as input for statistical analyses that provide evidence about the relationships among latent variables. This chapter first provides a brief introduction to SEM and its concepts and terminology. We then discuss four issues related to the measurement component of such models, including how indicators are developed, types of relationships between indicators and latent variables, approaches for multidimensional constructs, and analyses needed when data from multiple time points or multiple groups are examined. In our second major section, we focus on six issues related to the structural component of structural equation models, including how to examine mediatio...

Parsimony‐based fit indices for multiple‐indicator models: Do they work?

A frequently used type of model in applications of covariance structure analysis is one referred to as a multiple‐indicator regression model. This study takes a simulation approach to investigate seven parsimony‐based indices used to evaluate this type of model. Four representative theoretical models were examined, and the number of indicators used to represent latent variables was varied with two of the models. Both correctly and incorrectly specified models were fit to the data. The results show that the Akaike information criteria, the root mean square index, and the Tucker‐Lewis index were the most effective indices. The implications of the findings for the model selection process are discussed.

Modelling relationships between job stressors and injury and near-miss outcomes for construction labourers

Construction work is an inherently dangerous occupation and exposure to additional job stressors is likely to exacerbate the level of danger, increasing workers’ risk for injury. Thus, it is important to identify and then reduce worker exposure to extraneous job stressors. This study examines the relationships between a variety of job stressors and injury or near-miss outcomes among construction workers. Self-reported questionnaire data collected from 408 construction labourers (male and female) via telephone interview were analysed using structural equation modelling. A theoretical model was tested whereby work stressors, classified into three groups, could be related, either directly or indirectly through the mediating effects of physical or psychological symptoms/strain, to self-reported injuries and near misses. Ten of the 12 work-related stressors were found to be directly related to either injury or near misses, including: job demands, job control, job certainty, training, safety climate, skill under-utilization, responsibility for the safety of others, safety compliance, exposure hours, and job tenure. Other stressors (i.e. harassment/discrimination, job certainty, social support, skill under-utilization, safety responsibility, safety compliance, tenure in construction) were indirectly related to injuries through physical symptoms or indirectly related to near misses through psychological strain. There was no support for the modelled gender differences. Implications for health and safety on construction sites are discussed.

Effects of Organizational Formalization on Alienation Among Professionals and Nonprofessionals

In this article the authors replicate a study that was conducted to examine the effects that organizational formalization had on the feelings of alienation of professional employees. The authors of...

A Conditional Reasoning Measure for Aggression

This article describes a new approach for assessing cognitive precursors to aggression. Referred to as the Conditional Reasoning Measurement System, this procedure focuses on how people solve what on the surface appear to be traditional inductive reasoning problems. The true intent of the problems is to determine if solutions based on implicit biases (i.e., biases that operate below the surface of consciousness) are logically attractive to a respondent. The authors focus on the types of implicit biases that underlie aggressive individuals’attempts to justify aggressive behavior. People who consistently select solutions based on these types of biases are scored as being potentially aggressive because they are cognitively prepared to rationalize aggression. Empirical tests of the conditional reasoning system are interpreted in terms of Ozer’s criteria for ideal personality instruments. Noteworthy findings are that the system has acceptable psychometric properties and an average, uncorrected empirical validity of 0.44 against behavioral indicators of aggression (based on 11 studies).

A Confirmatory Factor Analysis Examination of Reverse Coding Effects in Meyer and Allen's Affective and Continuance Commitment Scales

Examination of Meyer and Allens Affective and Continuance Commitment Scales using confirmatory factor analysis with LISREL 7 provided strong support across multiple diagnostics for the existence of a reverse coding method factor defined by the six negatively worded items in the scales.