Advancing human resource management scholarship through multilevel modeling

Abstract HRM systems are an organization-level construct that affect outcomes at the firm, unit, and individual levels of analysis. The multilevel nature of the field creates a need for both theoretical and empirical modeling that cuts across levels to effectively understand the linkages between HRM systems and various operational and financial performance outcomes. Ordinary least squares (OLS) regression which is designed to analyze the same level of data is not suited for analyzing such hierarchal data. Multilevel modeling accounts for variance among variables at different levels; dealing with sources of errors more rigorously than OLS. Multilevel structural equation modeling separately estimates between and within effects, takes into account measurement errors and allows for criterion variables that are situated at higher levels. Thus, multilevel modeling significantly advances HRM research by more accurately predicting HRM effects and estimating complex HRM models. The articles included in this collection demonstrate the value and application of multilevel modeling, both theoretically and empirically, to HRM research.

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