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Why inferential statistics are inappropriate for development studies and how the same data can be better used

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  • Ballinger, Clint

Abstract

The purpose of this paper is twofold: 1) to highlight the widely ignored but fundamental problem of ‘superpopulations’ for the use of inferential statistics in development studies. We do not to dwell on this problem however as it has been sufficiently discussed in older papers by statisticians that social scientists have nevertheless long chosen to ignore; the interested reader can turn to those for greater detail. 2) to show that descriptive statistics both avoid the problem of superpopulations and can be a powerful tool when used correctly. A few examples are provided. The paper ends with considerations of some reasons we think are behind the adherence to methods that are known to be inapplicable to many of the types of questions asked in development studies yet still widely practiced.

Suggested Citation

  • Ballinger, Clint, 2011. "Why inferential statistics are inappropriate for development studies and how the same data can be better used," MPRA Paper 29780, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:29780
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    File URL: https://mpra.ub.uni-muenchen.de/29780/1/MPRA_paper_29780.pdf
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    References listed on IDEAS

    as
    1. Hoover,Kevin D., 2001. "Causality in Macroeconomics," Cambridge Books, Cambridge University Press, number 9780521002882, November.
    2. Judea Pearl, 2003. "Statistics and causal inference: A review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(2), pages 281-345, December.
    3. Western, Bruce & Jackman, Simon, 1994. "Bayesian Inference for Comparative Research," American Political Science Review, Cambridge University Press, vol. 88(2), pages 412-423, June.
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    5. Ann Markusen, 2003. "Fuzzy Concepts, Scanty Evidence, Policy Distance: The Case for Rigour and Policy Relevance in Critical Regional Studies," Regional Studies, Taylor & Francis Journals, vol. 37(6-7), pages 701-717.
    6. Hall, Peter A. & Franzese, Robert J., 1998. "Mixed Signals: Central Bank Independence, Coordinated Wage Bargaining, and European Monetary Union," International Organization, Cambridge University Press, vol. 52(3), pages 505-535, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    frequentist statistics; Bayesian statistics; causation; determinism; explanation; spatial autocorrelation; mulitple regression; international development; econometrics; comparative method; datasets; descriptive statistics; tabular analysis; visual analysis; maps; regession modeling; quantitative; qualitative; macrosociology; superpopulations; apparent populations; indeterminism; statistical assumptions;
    All these keywords.

    JEL classification:

    • B0 - Schools of Economic Thought and Methodology - - General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • P16 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - Capitalist Institutions; Welfare State
    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • F5 - International Economics - - International Relations, National Security, and International Political Economy
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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