The Acceptable R-Square in Empirical Modelling for Social Science Research

Datos Bibliográficos

ID23338698
AutoresPeterson K Ozili (0000-0001-6292-1161, Central Bank of Nigeria, Nigeria, autor de correspondencia)
Año2023
Páginas134-143
Fecha de publicación2023-03-10
Peer ReviewedSí
Open AccessNo
TipoCHAPTER
RevistaSocial Research Methodology and Publishing Results (SOURCE_BOOK)
EditorialIGI Global (PUBLISHER • US)
DOI10.4018/978-1-6684-6859-3.ch009
OpenAlexW4323825490
ISBN9781668468593
IdiomaEN
Citas recibidas77
Referencias citadas13

This chapter 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.

Causation · Collinearity · Econometrics · Empirical research · Epistemology · Spurious relationship · Square (algebra) · Statistics · Variable (mathematics) · Variables · Advanced Statistical Methods and Models · Forecasting Techniques and Applications · Mathematics · Philosophy