The acceptable R-square in empirical modelling for social science research

Peterson Ozili

MPRA Paper from University Library of Munich, Germany

Abstract: This commentary article examines the acceptable R-square in social science empirical modelling with particular focus on why a low R-square model is acceptable in empirical social science research. The paper shows that a low R-square model is not necessarily bad. This is because the goal of most social science research modelling is not to predict human behaviour. Rather, the goal is often to assess whether specific predictors or explanatory variables have a significant effect on the dependent variable. Therefore, a low R-square of at least 0.1 (or 10 percent) is acceptable on the condition that some or most of the predictors or explanatory variables are statistically significant. If this condition is not met, the low R-square model cannot be accepted. A high R-square model is also acceptable provided that there is no spurious causation in the model and there is no multi-collinearity among the explanatory variables.

Keywords: R-square; low R-square; social science; research; empirical model; modelling; regression. (search for similar items in EconPapers)
JEL-codes: C10 C14 C15 C30 C50 C51 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (4)

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https://mpra.ub.uni-muenchen.de/116496/1/MPRA_paper_116496.pdf revised version (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:115769

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