TY - JOUR
T1 - Fixed effects models versus mixed effects models for clustered data
T2 - Reviewing the approaches, disentangling the differences, and making recommendations
AU - McNeish, Daniel
AU - Kelley, Ken
N1 - Publisher Copyright:
© 2018 American Psychological Association.
PY - 2019/2/1
Y1 - 2019/2/1
N2 - Clustered data are common in many fields. Some prominent examples of clustering are employees clustered within supervisors, students within classrooms, and clients within therapists. Many methods exist that explicitly consider the dependency introduced by a clustered data structure, but the multitude of available options has resulted in rigid disciplinary preferences. For example, those working in the psychological, organizational behavior, medical, and educational fields generally prefer mixed effects models, whereas those working in economics, behavioral finance, and strategic management generally prefer fixed effects models. However, increasingly interdisciplinary research has caused lines that separate the fields grounded in psychology and those grounded in economics to blur, leading to researchers encountering unfamiliar statistical methods commonly found in other disciplines. Persistent discipline-specific preferences can be particularly problematic because (a) each approach has certain limitations that can restrict the types of research questions that can be appropriately addressed, and (b) analyses based on the statistical modeling decisions common in one discipline can be difficult to understand for researchers trained in alternative disciplines. This can impede cross-disciplinary collaboration and limit the ability of scientists to make appropriate use of research from adjacent fields. This article discusses the differences between mixed effects and fixed effects models for clustered data, reviews each approach, and helps to identify when each approach is optimal. We then discuss the within-between specification, which blends advantageous properties of each framework into a single model.
AB - Clustered data are common in many fields. Some prominent examples of clustering are employees clustered within supervisors, students within classrooms, and clients within therapists. Many methods exist that explicitly consider the dependency introduced by a clustered data structure, but the multitude of available options has resulted in rigid disciplinary preferences. For example, those working in the psychological, organizational behavior, medical, and educational fields generally prefer mixed effects models, whereas those working in economics, behavioral finance, and strategic management generally prefer fixed effects models. However, increasingly interdisciplinary research has caused lines that separate the fields grounded in psychology and those grounded in economics to blur, leading to researchers encountering unfamiliar statistical methods commonly found in other disciplines. Persistent discipline-specific preferences can be particularly problematic because (a) each approach has certain limitations that can restrict the types of research questions that can be appropriately addressed, and (b) analyses based on the statistical modeling decisions common in one discipline can be difficult to understand for researchers trained in alternative disciplines. This can impede cross-disciplinary collaboration and limit the ability of scientists to make appropriate use of research from adjacent fields. This article discusses the differences between mixed effects and fixed effects models for clustered data, reviews each approach, and helps to identify when each approach is optimal. We then discuss the within-between specification, which blends advantageous properties of each framework into a single model.
KW - Fixed effect model
KW - HLM
KW - Multilevel model
KW - Panel data
KW - Random coefficients model
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U2 - 10.1037/met0000182
DO - 10.1037/met0000182
M3 - Article
C2 - 29863377
AN - SCOPUS:85047863095
SN - 1082-989X
VL - 24
SP - 20
EP - 35
JO - Psychological Methods
JF - Psychological Methods
IS - 1
ER -