Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms "method" and "method bias" and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.

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The rapid growth of research on organizational citizenship behaviors (OCBs) has resulted in some conceptual confusion about the nature of the construct, and made it difficult for all but the most avid readers to keep up with developments in this domain. This paper critically examines the literature on organizational citizenship behavior and other, related constructs. More specifically, it: (a) explores the conceptual similarities and differences between the various forms of “citizenship” behavior constructs identified in the literature; (b) summarizes the empirical findings of both the antecedents and consequences of OCBs; and (c) identifies several interesting directions for future research.

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The goal of this study was to examine the effects of transformational leadership behaviors, within the context of Kerr and Jermier’s (1978) substitutes for leadership. Data were collected from 1539 employees across a wide variety of different industries, organizational settings, and job levels. Hierarchical moderated regression analysis procedures generally showed that few of the substitutes variables moderated the effects of the transformational leader behaviors on followers’ attitudes, role perceptions, and “in-role” and “citizenship” behaviors in a manner consistent with the predictions of Howell, Dorfman and Kerr (1986). However, the results did show that: (a) the transformational leader behaviors and substitutes for leadership each had unique effects on follower criterion variables; (b) the total amount of variance accounted for by the substitutes for leadership and the transformational leader behaviors was substantially greater than that reported in prior leadership research; and (c) several of the transformational behaviors were significantly related to several of the substitutes for leadership variables. Implications of these findings for our understanding of the effects of transformational leader behaviors and substitutes for leadership are then discussed.

The purpose of this study was to review the distinction between formative-and reflective-indicator measurement models, articulate a set of criteria for deciding whether measures are formative or reflective, illustrate some commonly researched constructs that have formative indicators, empirically test the effects of measurement model misspecification using a Monte Carlo simulation, and recommend new scale development procedures for latent constructs with formative indicators. Results of the Monte Carlo simulation indicated that measurement model misspecification can inflate unstandardized structural parameter estimates by as much as 400% or deflate them by as much as 80% and lead to Type I or Type II errors of inference, depending on whether the exogenous or the endogenous latent construct is misspecified. Implications of this research are discussed.