Control Variables in Marketing Research

Martin Klarmann; Sven Feurer

doi:10.15358/0344-1369-2018-2-26

Contents
- Chapter Expand | CollapseAuthorsPage
- 1–1 Titelei/Inhaltsverzeichnis 1–1
- 2–2 Editorial Bernhard Swoboda Bernhard Swoboda 2–2
- 3–94 Research Articles 3–94
  - 3–25 A User's Guide to the Galaxy of Conjoint Analysis and Compositional Preference Measurement Michael Steiner, Martin Meißner Michael Steiner, Martin Meißner 3–25
  - 26–40 Control Variables in Marketing Research Martin Klarmann, Sven Feurer Martin Klarmann, Sven Feurer 26–40
  - 41–57 Causal Inference Using Mediation Analysis or Instrumental Variables - Full Mediation in the Absence of Conditional Independence Thomas Otter, Max J. Pachali, Stefan Mayer, Jan R. Landwehr Thomas Otter, Max J. Pachali, Stefan Mayer, Jan R. Landwehr 41–57
  - 58–94 Avoiding Pitfalls in Experimental Research in Marketing Anja Spilski, Andrea Gröppel-Klein, Heribert Gierl Anja Spilski, Andrea Gröppel-Klein, Heribert Gierl 58–94
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Marketing ZFP , page 26 - 40

Control Variables in Marketing Research

Autoren

Martin Klarmann Sven Feurer

DOI
- doi.org/10.15358/0344-1369-2018-2-26
- ISSN: 0344-1369
- ISSN online: 0344-1369
Zusammenfassung

In empirical marketing research that does not rely on fully randomized experiments, control variables are an important tool to rule out rival alternative explanations for the observed relationships. Despite their importance for causal inference, control variables often receive little attention from either applied researchers or methodologists. At the same time, overviews of control variable practices in neighboring disciplines demonstrate that researchers struggle with selecting, analyzing, and interpreting control variable results. In response, this article combines a synthesis of the theoretical knowledge on control variables with a review of control variable practices. Against this background, we develop and discuss sixteen recommendations for control variable use in marketing research.

Abstract

In empirical marketing research that does not rely on fully randomized experiments, control variables are an important tool to rule out rival alternative explanations for the observed relationships. Despite their importance for causal inference, control variables often receive little attention from either applied researchers or methodologists. At the same time, overviews of control variable practices in neighboring disciplines demonstrate that researchers struggle with selecting, analyzing, and interpreting control variable results. In response, this article combines a synthesis of the theoretical knowledge on control variables with a review of control variable practices. Against this background, we develop and discuss sixteen recommendations for control variable use in marketing research.
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