Bridging Methodological Divides Between Macro- and Microresearch: Endogeneity and Methods for Panel Data

Abstract

Both macro- and micro-oriented researchers frequently use panel data where the outcome of interest is measured repeated times. Panel data support at least five different modeling frameworks (within, between, incremental/emergent, cross-level, and growth). Researchers from macro- and micro-oriented domains tend to differentially use the frameworks and also use different analytic tools and terminology when using the same modeling framework. These differences have the potential to inhibit cross-discipline communication. In this review, we explore how macro- and microresearchers approach panel data with a specific emphasis on the theoretical implications of choosing one framework versus another. We illustrate how fixed-effects and random-effects models differ and how they are similar, and we conduct a thorough review of 142 articles that used panel data in leading management journals in 2017. Ultimately, our review identifies ways that researchers can better employ fixed- and random-effects models, model time as a meaningful predictor or ensure unobserved time heterogeneity is controlled, and align hypotheses to analytic choice. In the end, our goal is to help facilitate communication and theory development between macro- and micro-oriented management researchers.