Are we really measuring what we say we're measuring? Using video techniques to supplement traditional construct validation procedures.

Several researchers have persuasively argued that the most important evidence to consider when assessing construct validity is whether variations in the construct of interest cause corresponding variations in the measures of the focal construct. Unfortunately, the literature provides little practical guidance on how researchers can go about testing this. Therefore, the purpose of this article is to describe how researchers can use video techniques to test whether their scales measure what they purport to measure.

Sources of method bias in social science research and recommendations on how to control it.

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.

The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions.

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.

Common method biases in behavioral research: a critical review of the literature and recommended remedies.

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.