Top Five Articles from Organizational Research Methods

JPG READINGSummer is just around the corner, bringing with it longer days and warmer weather. To celebrate the season, we present a list of most read articles from Organizational Research Methods to add to your summer reading list.

“Seeking Qualitative Rigor in Inductive Research: Notes on the Gioia Methodology” by Dennis A. Gioia, Kevin G. Corley, and Aimee Hamilton (January 2013)

For all its richness and potential for discovery, qualitative research has been critiqued as too often lacking in scholarly rigor. The authors summarize a systematic approach to new concept development and grounded theory articulation that is designed to
bring “qualitative rigor” to the conduct and presentation of inductive research.

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“Validation of a New General Self-Efficacy Scale” by Gilad Chen, Stanley M. Gully, and Dov Eden (January 2001)

Researchers have suggested that general self-efficacy (GSE)can substantially contribute to organizational theory, research, and practice. Unfortunately, the limited construct validity work conducted on commonly used GSE measures has highlighted such potential problems as low content validity and multidimensionality. The authors developed a new GSE (NGSE) scale and compared its psychometric properties and validity to that of the Sherer et al. General Self-Efficacy Scale (SGSE). Studies in two countries found that the NGSE scale has higher construct validity than the SGSE scale. Although shorter than the SGSE scale, the NGSE scale demonstrated high reliability, predicted specific self-efficacy (SSE) for a variety of tasks in various contexts, and moderated the influence of previous performance on subsequent SSE formation. Implications, limitations, and directions for future organizational research are discussed.

“Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013)” by Jörg Henseler, Theo K. Dijkstra, Marko Sarstedt, Christian M. RingleAdamantios Diamantopoulos, Detmar W. Straub, David J. Ketchen Jr.Joseph F. Hair, G. Tomas M. Hult, and Roger J. Calantone (April 2014)

This article addresses Rönkkö and Evermann’s criticisms of the partial least squares (PLS) approach to structural equation modeling. We contend that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Rönkkö and Evermann’s study: (a) the adherence to the common factor model, (b) a very limited simulation designs, and (c) overstretched generalizations of their findings. Whereas Rönkkö and Evermann claim to be dispelling myths about PLS, they have in reality created new myths that we, in turn, debunk. By examining their claims, our article contributes to reestablishing a constructive discussion of the PLS method and its properties. We show that PLS does offer advantages for exploratory research and that it is a viable estimator for composite factor models. This can pose an interesting alternative if the common factor model does not hold. Therefore, we can conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines.

“Using Generalized Estimating Equations for Longitudinal Data Analysis” by Gary A. Ballinger (April 2004)

The generalized estimating equation (GEE) approach of Zeger and Liang facilitates analysis of data collected in longitudinal, nested, or repeated measures designs. GEEs use the generalized linear model to estimate more efficient and unbiased regression parameters relative to ordinary least squares regression in part because they permit specification of a working correlation matrix that accounts for the form of within-subject correlation of responses on dependent variables of many different distributions, including normal, binomial, and Poisson. The author briefly explains the theory behind GEEs and their beneficial statistical properties and limitations and compares GEEs to suboptimal approaches for analyzing longitudinal data through use of two examples. The first demonstration applies GEEs to the analysis of data from a longitudinal lab study with a counted response variable; the second demonstration applies GEEs to analysis of data with a normally distributed response variable from subjects nested within branch offices ofan organization.

“Answers to 20 Questions About Interrater Reliability and Interrater Agreement” by James M. LeBreton and Jenell L. Senter (October 2008)

The use of interrater reliability (IRR) and interrater agreement (IRA) indices has increased dramatically during the past 20 years. This popularity is, at least in part, because of the increased role of multilevel modeling techniques (e.g., hierarchical linear modeling and multilevel structural equation modeling) in organizational research. IRR and IRA indices are often used to justify aggregating lower-level data used in composition models. The purpose of the current article is to expose researchers to the various issues surrounding the use of IRR and IRA indices often used in conjunction with multilevel models. To achieve this goal, the authors adopt a question-and-answer format and provide a tutorial in the appendices illustrating how these indices may be computed using the SPSS software.

All of the above articles from Organizational Research Methods will be free to access for the next two weeks. Want to know all about the latest research from Organizational Research Methods? Click here to sign up for e-alerts!

*Reading image attributed to Herry Lawford (CC)

Management INK’s Most Popular Articles of 2014

As 2014 draws to a close, we’d like to take a moment to highlight the top three most read posts of the year.

3. Methodological Issues in Strategy and Strategic Management Research: New ORM Virtual Feature Issue

ORM_72ppiRGB_150pixWOrganizational Research Methods has a new Virtual Issue on the topic of Methodological Issues in Strategy and Strategic Management Research… This virtual issue features 17 articles devoted to methodological issues linked to the study of Strategic Management. The articles focus on five different topics: design issues, survey data, innovative approaches to data, qualitative approaches, and construct measurement.

2. Are Academics Too Serious?

As academics, we do work that is both serious and significant. Yet, being too serious can interfere paper-emotions---ease-1158075-mwith our performance and enjoyment of the knowledge creation and dissemination work we do as researchers and educators. In this essay, I call for some reflection on the value of not being too serious. I offer some stories and simple prescriptions in the spirit of pursuing career and life balance, personal effectiveness, and, just as importantly, fun as a not-too-serious academic scholar.

1. Common Beliefs and Reality About PLS

The major shortcoming of Rönkkö and Evermann’s (2013) study is that they neglect that PLS fig1estimates a composite factor model, not a common factor model. Although the composite factor model is often a good approximation to the common factor model, there are important differences. Rönkkö and Evermann (2013) regard PLS simply as a suboptimal estimator of common factor models. But like a hammer is a suboptimal tool to fix screws, PLS is a suboptimal tool to estimate common factor models. In contrast, PLS is a useful tool for estimating composite factor models.

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