Journal
ORGANIZATIONAL RESEARCH METHODS
Volume 23, Issue 2, Pages 211-237Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/1094428118773455
Keywords
multiple regression; quantitative research; measurement design; research design; computer simulation procedures; Monte Carlo; bootstrapping; quantitative research
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Despite scholars' admonitions regarding the use of ratios in statistical analyses, the practice is common in management research. This is particularly true in the area of strategic management, where important variables of interest are operationalized as ratios. In this study, we employ simulations to demonstrate the implications of using ratios in statistical analyses. Our simulations illustrate that ratio variables produce inaccurate parameter estimates and can result in lower levels of statistical power (i.e., the ability to uncover hypothesized relationships). We also find that when an independent or a dependent variable is a ratio, the relationship between the independent and dependent variable fluctuates as the dispersion of the denominator changes. These fluctuations occur even when the correlations between the unscaled variables remain exactly the same. We also find that including ratios in models as control variables influences estimates of relationships between focal independent and dependent variables. This is true even when neither the independent or dependent variable is a ratio. We provide several recommendations for researchers who may be interested in avoiding the pitfalls of ratio variables.
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