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Applying the Fractional Response Model to Survey Research in Accounting

Author

Listed:
  • Susanna Gallani

    (Harvard Business School, Accounting and Management Unit)

  • Ranjani Krishnan

    (Eli Broad School of Management, Michigan State University)

Abstract

Survey research studies make extensive use of rating scales to measure constructs of interest. The bounded nature of such scales presents econometric estimation challenges. Linear estimation methods (e.g. OLS) often produce predicted values that lie outside the rating scales, and fail to account for nonconstant effects of the predictors. Established nonlinear approaches such as logit and probit transformations attenuate many shortcomings of linear methods. However, these nonlinear approaches are challenged by corner solutions, for which they require ad hoc transformations. Censored and truncated regressions alter the composition of the sample, while Tobit methods rely on distributional assumptions that are frequently not reflected in survey data, especially when observations fall at one extreme of the scale owing to surveyor and respondent characteristics. The fractional response model (FRM) (Papke and Wooldridge 1996, 2008) overcomes many limitations of established linear and non-linear econometric solutions in the study of bounded data. In this study, we first review the econometric characteristics of the FRM and discuss its applicability to survey-based studies in accounting. Second, we present results from Monte Carlo simulations to highlight the advantages of using the FRM relative to conventional models. Finally, we use data from a hospital patient satisfaction survey, compare the estimation results from a traditional OLS method and the FRM, and conclude that the FRM provides an improved methodological approach to the study of bounded dependent variables.

Suggested Citation

  • Susanna Gallani & Ranjani Krishnan, 2015. "Applying the Fractional Response Model to Survey Research in Accounting," Harvard Business School Working Papers 16-016, Harvard Business School, revised Jan 2017.
  • Handle: RePEc:hbs:wpaper:16-016
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    Citations

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    Cited by:

    1. Aucejo, Esteban M. & French, Jacob & Zafar, Basit, 2023. "Estimating students’ valuation for college experiences," Journal of Public Economics, Elsevier, vol. 224(C).
    2. Tawanda Chingozha & Dieter von Fintel, 2019. "Property rights, market access and crop cultivation in Southern Rhodesia: evidence from historical satellite data," Working Papers 03/2019, Stellenbosch University, Department of Economics.
    3. Wisdom Akpalu & Michael Adu Okyere, 2023. "Fish Protein Transition in a Coastal Developing Country," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(3), pages 825-843, March.
    4. Ferrando, Annalisa & Preuss, Carsten, 2018. "What finance for what investment? Survey-based evidence for European companies," EIB Working Papers 2018/01, European Investment Bank (EIB).
    5. Annalisa Ferrando & Carsten Preuss, 2018. "What finance for what investment? Survey-based evidence for European companies," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 35(3), pages 1015-1053, December.

    More about this item

    Keywords

    Fractional response model; bounded variables; simulation;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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