IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/115769.html
   My bibliography  Save this paper

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

Author

Listed:
  • Ozili, Peterson K

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.

Suggested Citation

  • Ozili, Peterson K, 2023. "The acceptable R-square in empirical modelling for social science research," MPRA Paper 115769, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:115769
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/115769/1/MPRA_paper_115769.pdf
    File Function: original version
    Download Restriction: no

    File URL: https://mpra.ub.uni-muenchen.de/116496/1/MPRA_paper_116496.pdf
    File Function: revised version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
    2. Curt Hagquist & Magnus Stenbeck, 1998. "Goodness of Fit in Regression Analysis – R 2 and G 2 Reconsidered," Quality & Quantity: International Journal of Methodology, Springer, vol. 32(3), pages 229-245, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guan, Jinping & Du, Xinyu & Zhang, Jiayue & Maymin, Philip & DeSoto, Emma & Langer, Ellen & He, Zhengbing, 2024. "Private vehicle drivers’ acceptance of autonomous vehicles: The role of trait mindfulness," Transport Policy, Elsevier, vol. 149(C), pages 211-221.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Burkey, Mark L. & Obeng, Kofi, 2005. "Crash Risk Reduction at Signalized Intersections Using Longitudinal Data," MPRA Paper 36281, University Library of Munich, Germany.
    2. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 251-299.
    3. Hiau LooiKee & Alessandro Nicita & Marcelo Olarreaga, 2009. "Estimating Trade Restrictiveness Indices," Economic Journal, Royal Economic Society, vol. 119(534), pages 172-199, January.
    4. Dabao Zhang, 2022. "Coefficients of Determination for Mixed-Effects Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 674-689, December.
    5. Wam, Hilde Karine & Pedersen, Hans Chr. & Hjeljord, Olav, 2012. "Balancing hunting regulations and hunter satisfaction: An integrated biosocioeconomic model to aid in sustainable management," Ecological Economics, Elsevier, vol. 79(C), pages 89-96.
    6. Simon Blanchard & Wayne DeSarbo, 2013. "A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification," Psychometrika, Springer;The Psychometric Society, vol. 78(2), pages 322-340, April.
    7. Jaimovich, Dany, 2015. "Missing Links, Missing Markets: Evidence of the Transformation Process in the Economic Networks of Gambian Villages," World Development, Elsevier, vol. 66(C), pages 645-664.
    8. Sanghamitra Bandyopadhyay & Frank A. Cowell & Emmanual Flachaire, 2009. "Goodness-of-Fit: An Economic Approach," Economics Series Working Papers 444, University of Oxford, Department of Economics.
    9. Marco Alfò & Giovanni Trovato, 2004. "Semiparametric Mixture Models for Multivariate Count Data, with Application," CEIS Research Paper 51, Tor Vergata University, CEIS.
    10. Salvatore Ingrassia & Antonio Punzo, 2020. "Cluster Validation for Mixtures of Regressions via the Total Sum of Squares Decomposition," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 526-547, July.
    11. Scuotto, Veronica & Garcia-Perez, Alexeis & Nespoli, Chiara & Messeni Petruzzelli, Antonio, 2020. "A repositioning organizational knowledge dynamics by functional upgrading and downgrading strategy in global value chain," Journal of International Management, Elsevier, vol. 26(4).
    12. Mo'ath ALSHANNAQ & Rana IMAM, 2020. "Evaluating The Safety Performance Of Roundabouts," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 15(1), pages 141-152, March.
    13. Cynthia Wu & Jessica G Woo & Nanhua Zhang, 2017. "Association between urinary manganese and blood pressure: Results from National Health and Nutrition Examination Survey (NHANES), 2011-2014," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-6, November.
    14. Chang, Byeong-Yun & Li, Xu & Kim, Yun Bae, 2014. "Performance comparison of two diffusion models in a saturated mobile phone market," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 41-48.
    15. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha, 2019. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm," Energies, MDPI, vol. 12(6), pages 1-13, March.
    16. Yang Zhang & Bo Guo, 2015. "Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine," Energies, MDPI, vol. 8(11), pages 1-19, November.
    17. Selen CAKMAKYAPAN & Atilla GOKTAS, 2013. "A Comparison Of Binary Logit And Probit Models With A Simulation Study," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 2(1), pages 1-17, JULY.
    18. Sohrabpour, Vahid & Oghazi, Pejvak & Toorajipour, Reza & Nazarpour, Ali, 2021. "Export sales forecasting using artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    19. Jui-Sheng Chou & Chang-Ping Yu & Dinh-Nhat Truong & Billy Susilo & Anyi Hu & Qian Sun, 2019. "Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning," Sustainability, MDPI, vol. 11(24), pages 1-22, December.
    20. Bermeo-Ayerbe, Miguel Angel & Ocampo-Martinez, Carlos & Diaz-Rozo, Javier, 2022. "Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems," Energy, Elsevier, vol. 238(PB).

    More about this item

    Keywords

    R-square; low R-square; social science; research; empirical model; modelling; regression.;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:115769. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.