Principles and Applications of Multilevel Modeling in Human Resource Management Research

• Published date: 01 November 2016
• Date: 01 November 2016
• DOI: http://doi.org/10.1002/hrm.21666

Human Resource Management, November–December 2016, Vol. 55, No. 6. Pp. 951–965

© 2015 Wiley Periodicals, Inc.

Published online in Wiley Online Library (wileyonlinelibrary.com).

DOI:10.1002/hrm.21666

Correspondence to: Jie Shen, School of Management, University of South Australia Business School, City West

Campus, Adelaide, South Australia, SA5001, Phone: +61 430355248, E-mail: jie.shen@unisa.edu.au

(Ostroff & Bowen, 2000). Employee attitudinal and

behavioral responses to HRM policies and prac-

tices may be similar in the same organization and

different in others due to contextual effects (Bliese

& Hanges, 2004). Ignoring the inherent depen-

dence of hierarchal data would result in deflated

standard errors and inflated values of model fit

or correlations (Rowe & Hill, 1998). This type of

dependence in data structure is likely to lead to

gross errors of prediction if using nonmultilevel

modeling statistical approaches such as ordinary

least squares (OLS) regression, designed to analyze

the same level of data (Snijders & Bosker, 2012).

Human resource management (HRM)

research often involves hierarchal data

from more than one level of analysis.

Individual employees are nested in teams

or departments that are entrenched

within organizations. In turn, organizations are

nested in industries embedded in larger environ-

ments, such as geographic regions, nations or

economic or political blocks. HRM, as a subset of

organizational policies, is a higher-level variable,

as individuals within the same organization/unit

share the same HRM policies and practices, but

individuals in different organizations/units do not

PRINCIPLES AND APPLICATIONS

OF MULTILEVEL MODELING

INHUMAN RESOURCE

MANAGEMENT RESEARCH

JIE SHEN

Multilevel modeling is important for human resource management (HRM)

research in that it often analyzes and interprets hierarchal data residing at more

than one level of analysis. However, HRM research in general lags behind other

disciplines, such as education, health, marketing, and psychology in the use of

a multilevel analytical strategy. This article integrates the most recent literature

into the theoretical and applied basics of multilevel modeling applicable to HRM

research. A range of multilevel modeling issues have been discussed and they

include statistical logic underpinning multilevel modeling, level conceptualiza-

tion of variables, data aggregation, hypothesis tests, reporting mediation paths,

and cross-level interactions. An empirical example concerning complex cross-

level mediated moderation is presented that will suffi ce to illustrate the princi-

ples and the procedures for implementing a multilevel analytical strategy in HRM

research. © 2015 Wiley Periodicals, Inc.

Keywords: human resource management (HRM), multilevel modeling (MLM),

multilevel structural equation modeling (MSEM), research methods