Why inferential statistics are inappropriate for development studies and how the same data can be better used
AbstractThe 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 29780.
Date of creation: 06 Jan 2011
Date of revision:
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;
Find related papers by 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 - Economic Systems - - Capitalist Systems - - - Political Economy of Capitalism
- A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
- O1 - Economic Development, 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-04-16 (All new papers)
- NEP-ECM-2011-04-16 (Econometrics)
- NEP-HPE-2011-04-16 (History & Philosophy of Economics)
- NEP-MIC-2011-04-16 (Microeconomics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Hoover,Kevin D., 2001.
"Causality in Macroeconomics,"
Cambridge University Press, number 9780521452175, December.
- Judea Pearl, 2003. "Statistics and causal inference: A review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 12(2), pages 281-345, December.
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