A Manipulationist View of Causality in Cross-Sectional Survey Research

This paper discusses issues related to establishing causal relationships in empirical survey research. I adopt a manipulationist view of causality because it matches the context of (management) accounting research where we are commonly interested in studying the effects of changes. Strictly speaking, causal relationships cannot be unequivocally proven when the researcher employs cross-sectional surveys — that is, correlation is not causation. Notwithstanding, survey research can be fruitfully engaged to inform pertinent management accounting topics. I discuss four “markers” of causality — theoretical coherence, empirical covariation, temporal/physical separation, and internal validity — and how the researcher can lever these to suggest compelling survey-based inferences. Of these four markers, I particularly emphasize the first as I believe that one piece of any reasonable observer's considerations will be whether the proffered causal relationships are theoretically plausible. Moreover, a stronger theoretical foundation also helps causal inference by suggesting a reasonably complete set of control variables that are useful to eliminate alternative explanations. Overall, I focus rather pragmatically on the limitations of causal inference when using the survey method and what may be done to try and alleviate, although not eliminate, them