IAP BOOK SERIES
Research in Human Resource Management
Call for PapersSpecial Issue of Research in Human Resource Management
Research Methods in Human Resource Management
Research in Human Resource Management will be publishing a Special Issue (SI) on research methods (methods hereinafter) in human resource management (HRM). Empirical research in HRM has focused on such issues as recruiting, testing, selection, training, motivation, compensation, and employee well-being. A review of the literature on these and other issues suggests that less than optimal methods have often been used in HRM research. Among the methods-related problems are using (a) measures or manipulations that have little or no construct validity, (b) samples of units (e.g., participants, organizations) that bear little or no correspondence to target populations, (c) research designs that have little or no potential for generating sound causal inferences, (d) samples that are too small to provide for adequate statistical power, and (e) data analytic strategies that are inappropriate for the issues addressed by a study. As a result, our understanding of various HRM phenomena has suffered and improved methods may serve to enhance both the science and practice of HRM.
Purpose of the Special Issue
In view of the above, the purpose of the SI is to provide researchers with resources that will enable them to improve the internal validity, external validity, construct validity, and statistical conclusion validity (Shadish, Cook & Campbell, 2002) of research in HRM and the related fields of industrial and organizational psychology, and organizational behavior. Sound research in these fields should serve to improve both the science and practice of HRM.
We solicit contributions to the SI that are consistent with the just-noted purpose. They can be of several types, including (a) reviews of the literature (including meta-analyses) that specify research methods-related problems in HRM and offer recommendations for addressing them, (b) descriptions of new or seldom used methods that have the potential to address important HRM issues, and (c) critiques of frequently used methods in HRM. Contributions should focus on quantitative as opposed to qualitative research (e.g., case studies, ethnography).
Ideally, the contributions should have a general as opposed to a narrow focus. Thus, a paper that deals with research on the construct validity of predictors used in selection would be preferable to one that focuses on the criterion-related validity of a single operational definition of a predictor variable. In addition, a review of the literature on the relation between job satisfaction and job performance that considers methodological issues would be superior to one that does not deal with such issues.
Co-Editors of the Special Issue
The SI is being co-edited by Eugene F. Stone-Romero of the University of New Mexico and Patrick J. Rosopa of Clemson University.
Authors who are interested in having their work considered for the SI should submit an Abstract (APA format) on their proposed contribution by 1 August 2018. It should have the following components:
• A cover page with (a) the names of the author and all co-authors, and (b) contact information for the first author, including telephone numbers and email addresses.
• A description of the proposed article that is no more than five, double-spaced pages in length. It should provide a clear indication of its methodological contribution.
• A references section that is no more than 5 double-spaced pages long.
Abstracts will be reviewed by the Co-Editors and the authors of the Abstracts that appear to have the greatest potential to make a contribution to the SI will be asked to prepare a full manuscript (APA format). It has a length limit of 50 double-spaced pages (including references, tables, and figures) and will be due on 1 May 2019. Feedback on it will be provided by 30 July 2019 and authors will have until 1 October 2019 to submit revisions.
Abstracts and any other correspondence should be sent electronically to both Eugene F. Stone-Romero (email address: email@example.com) and Patrick J. Rosopa (email address: firstname.lastname@example.org).
Shadish, W.R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.
Editorial Advisory Board
Herman Aguinis, George Washington University.
Derek Avery, Wake Forest University.
David Balkin, University of Colorado.
Donna Blancero, Bentley University.
John Boudreau, University of Southern California.
James Breaugh, University of Missouri-Saint Louis.
Julio Canedo, University of Houston-Downtown. Jeanette Cleveland, Colorado State University.
Cary Cooper, University of Manchester.
Petru Curşeu, Babeş-Bolyai University.
Diana Deadrick, Old Dominion University.
Rodger Griffeth, Ohio University.
Julia Hoch, California State University-Northridge.
Linda Isenhour, Eastern Michigan University.
Richard Johnson, University at Albany.
Gary Latham, University of Toronto.
Robert Liden, University of Illinois at Chicago.
Kim Lukaszewski, Wright State University.
Kevin Murphy, University of Limerick.
Stella Nkomo, University of Pretoria.
Miguel Olivas-Lujan, Clarion University and Monterrey Tech.
Mark Roehling, Michigan State University.
Patrick Rosopa, Clemson University.
Alan Saks, University of Toronto at Scarborough.
Terri Scandura, University of Miami.
Rene Schalk, Tilburg University.
John Schaubroeck, Michigan State University.
Lynn Shore, Colorado State University.
Eugene Stone-Romero, University of New Mexico.
Shay Tzafrir, University of Haifa.
Sandra Wayne, University of Illinois at Chicago.