Organizational Research Methods

This article seeks to promote the advancement of empirical research on team mental models by (a) highlighting the conceptual work that must precede the selection of any measurement tool, (b) delineating measurement standards for group-level cognitions, and (c) evaluating a set of techniques for measuring team mental models. Pathfinder, multidimensional scaling, interactively elicited cognitive mapping, and text-based cognitive mapping are critiqued and compared according to their treatment of content and structure, as well as their psychometric properties. We conclude that these four techniques hold promise for measuring team mental models and illustrate the variability in measurement options. However, careful attention to the research question and research context must precede the selection of any measurement tool.

When gross deviations from parametric assumptions are observed, conventional data transformations are often applied with little regard for substantive theoretical implications. One such transformation involves using the logarithm of positively skewed dependent variables. Log transformations were shown to severely decrease estimates of true moderator effects using moderated regression procedures in a Monte Carlo simulation. Estimates of moderator effect sizes were substantially better estimates of the true latent moderator effect (i.e., larger by a multiple of 2.6 to 534) when estimated using a simple percentile bootstrap procedure in the original, positively skewed data. Conclusions with regard to the presence or absence of a true moderator effect using a simple bootstrap procedure were unaffected by the violation of parametric assumptions in the original, positively skewed data. In contrast, moderated regression analysis performed on a log-transformed dependent variable severely increased Type-II error. Implications are drawn for applied psychological and management research.

A common practice in applications of structural equation modeling techniques is to create composite measures from individual items. The purpose of this article was to provide an empirical comparison of several composite formation methods on model fit. Data from 1, 177 public school teachers were used to test a model of union commitment in which alternative composite formation methods were used to specify the measurement components of the model. Bootstrapping procedures were used to generate data for two additional sample sizes. Results indicated that the use of composites, in general, resulted in improved overall model fit as compared to treating all items as individual indicators. Lambda values and explained criterion variance indicated that this improved model fit was due to the creation of strong measurement models. Implications of these results for researchers using composites are discussed.