Organizational Research Methods
1552-7425
1094-4281
Mỹ
Cơ quản chủ quản: SAGE Publications Inc.
Lĩnh vực:
Decision Sciences (miscellaneous)Management of Technology and InnovationStrategy and Management
Các bài báo tiêu biểu
Bibliometric Methods in Management and Organization We aim to develop a meaningful single-source reference for management and organization scholars interested in using bibliometric methods for mapping research specialties. Such methods introduce a measure of objectivity into the evaluation of scientific literature and hold the potential to increase rigor and mitigate researcher bias in reviews of scientific literature by aggregating the opinions of multiple scholars working in the field. We introduce the bibliometric methods of citation analysis, co-citation analysis, bibliographical coupling, co-author analysis, and co-word analysis and present a workflow for conducting bibliometric studies with guidelines for researchers. We envision that bibliometric methods will complement meta-analysis and qualitative structured literature reviews as a method for reviewing and evaluating scientific literature. To demonstrate bibliometric methods, we performed a citation and co-citation analysis to map the intellectual structure of the Organizational Research Methods journal.
Tập 18 Số 3 - Trang 429-472 - 2015
Testing Mediation and Suppression Effects of Latent Variables Because of the importance of mediation studies, researchers have been continuously searching for the best statistical test for mediation effect. The approaches that have been most commonly employed include those that use zero-order and partial correlation, hierarchical regression models, and structural equation modeling (SEM). This study extends MacKinnon and colleagues (MacKinnon, Lockwood, Hoffmann, West, & Sheets, 2002; MacKinnon, Lockwood, & Williams, 2004, MacKinnon, Warsi, & Dwyer, 1995) works by conducting a simulation that examines the distribution of mediation and suppression effects of latent variables with SEM, and the properties of confidence intervals developed from eight different methods. Results show that SEM provides unbiased estimates of mediation and suppression effects, and that the bias-corrected bootstrap confidence intervals perform best in testing for mediation and suppression effects. Steps to implement the recommended procedures with Amos are presented.
Tập 11 Số 2 - Trang 296-325 - 2008
Common Beliefs and Reality About PLS 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.
Tập 17 Số 2 - Trang 182-209 - 2014
Estimating Effect Sizes From Pretest-Posttest-Control Group Designs Previous research has recommended several measures of effect size for studies with repeated measurements in both treatment and control groups. Three alternate effect size estimates were compared in terms of bias, precision, and robustness to heterogeneity of variance. The results favored an effect size based on the mean pre-post change in the treatment group minus the mean pre-post change in the control group, divided by the pooled pretest standard deviation.
Tập 11 Số 2 - Trang 364-386 - 2008
Refining Lodahl and Kejner’s Job Involvement Scale with a Convergent Evidence Approach: Applying Multiple Methods to Multiple Samples Lodahl and Kejner’s Job Involvement (JI) measure has been and continues to be heavily used despite known measurement deficiencies. Using a convergent evidence approach, the authors examine the psychometric properties of that scale and offer a refined version that accurately taps the JI construct. Based on a combination of five methodologies (qualitative content analysis, classical item analyses, item response theory analyses, partial confirmatory factor analyses, and discriminant validity analysis) applied to five samples, results indicate that numerous items function inadequately as indicators of JI, whereas a core of items have superior item statistics and conceptually match the definition of JI. The advantages of using a convergent evidence approach are discussed.
Tập 4 Số 2 - Trang 91-111 - 2001
A Tale of Three Perspectives Many researchers who use same-source data face concerns about common method variance (CMV). Although post hoc statistical detection and correction techniques for CMV have been proposed, there is a lack of empirical evidence regarding their efficacy. Because of disagreement among scholars regarding the likelihood and nature of CMV in self-report data, the current study evaluates three post hoc strategies and the strategy of doing nothing within three sets of assumptions about CMV: that CMV does not exist, that CMV exists and has equal effects across constructs, and that CMV exists and has unequal effects across constructs. The implications of using each strategy within each of the three assumptions are examined empirically using 691,200 simulated data sets varying factors such as the amount of true variance and the amount and nature of CMV modeled. Based on analyses of these data, potential benefits and likely risks of using the different techniques are detailed.
Tập 12 Số 4 - Trang 762-800 - 2009
How Big Are My Effects? Examining the Magnitude of Effect Sizes in Studies of Measurement Equivalence
Tập 22 Số 3 - Trang 678-709 - 2019
Conceptual Framework and Statistical Procedures for Delineating and Testing Multilevel Theories of Homology Scholars have been interested in the extent to which organizational phenomena generalize across levels of analysis for quite some time. However, theoretical frameworks for developing homologous multilevel theories (i.e., theories involving parallel relationships between parallel constructs at different levels of analysis) have yet to be developed, and current analytical tools for testing such theories and models are limited and inflexible. In this article, the authors first propose a typology of multilevel theories of homology that considers different stages of theory development and different levels of similarity in relationships across levels. Building on cross-validation principles, the authors then delineate and demonstrate a comprehensive and flexible statistical procedure for testing different multilevel theories of homology. Finally, the authors discuss implications for theory, research, and practice, as well as potential caveats of the new statistical tests.
Tập 8 Số 4 - Trang 375-409 - 2005
Experience Sampling Methods: A Discussion of Critical Trends and Considerations for Scholarly Advancement In the organizational sciences, scholars are increasingly using experience sampling methods (ESM) to answer questions tied to intraindividual, dynamic phenomenon. However, employing this method to answer organizational research questions comes with a number of complex—and often difficult—decisions surrounding: (1) how the implementation of ESM can advance or elucidate prior between-person theorizing at the within-person level of analysis, (2) how scholars should effectively and efficiently assess within-person constructs, and (3) analytic concerns regarding the proper modeling of interdependent assessments and trends while controlling for potentially confounding factors. The current paper addresses these challenges via a panel of seven researchers who are familiar not only with implementing this methodology but also related theoretical and analytic challenges in this domain. The current paper provides timely, actionable insights aimed toward addressing several complex issues that scholars often face when implementing ESM in their research.
Tập 22 Số 4 - Trang 969-1006 - 2019
Dispersion-Composition Models in Multilevel Research Multilevel researchers have predominantly applied either direct consensus or referent-shift consensus composition models when aggregating individual-level data to a higher level of analysis. This prevailing focus neglects both theory and empirical evidence, suggesting that the variance of group members' responses may complement the absolute mean level of group members' judgments. The goals of this article are to demonstrate the application of dispersion-composition models for capturing variability among group members' collective judgments and highlight the statistical challenges (and inherent constraints) of using group means and variances as predictors of study criteria. To this end, the authors present and illustrate a six-step sequential framework for applying dispersion-composition models using data from two independent field samples. The authors contend that the application of dispersion-composition models not only will strengthen a study’s conclusions by eliminating potential rival data interpretations but may also shed new light on past findings, potentially opening new doors to a more complete understanding of multilevel phenomena.
Tập 14 Số 4 - Trang 718-734 - 2011