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

Ozili, Peterson K (2023): The acceptable R-square in empirical modelling for social science research. Forthcoming in:

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: R-square, low R-square, social science, research, empirical model, modelling, regression.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C10 - General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C30 - General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C50 - General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation

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