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Quantitative analysis in social sciences: An brief introduction for non-economists

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  • Niño-Zarazúa, Miguel

Abstract

In this paper, I present an introduction to quantitative research methods in social sciences. The paper is intended for non-Economics undergraduate students, development researchers and practitioners who although unfamiliar with statistical techniques, are interested in quantitative methods to study social phenomena. The paper discusses conventional methods to assess the direction, strength and statistical significance of the correlation between two or more variables, and examines regression techniques and experimental and quasi-experimental research designs to establish causality in the analysis of public interventions.

Suggested Citation

  • Niño-Zarazúa, Miguel, 2012. "Quantitative analysis in social sciences: An brief introduction for non-economists," MPRA Paper 39216, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:39216
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    File URL: https://mpra.ub.uni-muenchen.de/39216/1/MPRA_paper_39216.pdf
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    References listed on IDEAS

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    1. Esther Duflo & Michael Kremer & Jonathan Robinson, 2011. "Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya," American Economic Review, American Economic Association, vol. 101(6), pages 2350-2390, October.
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    4. Hoddinott, John & Skoufias, Emmanuel, 2004. "The Impact of PROGRESA on Food Consumption," Economic Development and Cultural Change, University of Chicago Press, vol. 53(1), pages 37-61, October.
    5. Dean Karlan & Jonathan Zinman, 2010. "Expanding Credit Access: Using Randomized Supply Decisions to Estimate the Impacts," Review of Financial Studies, Society for Financial Studies, vol. 23(1), pages 433-464, January.
    6. Glenn W. Harrison & John A. List, 2004. "Field Experiments," Journal of Economic Literature, American Economic Association, vol. 42(4), pages 1009-1055, December.
    7. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    8. Michael Bamberger & Vijayendra Rao & Michael Woolcock, 2009. "Using Mixed Methods in Monitoring and Evaluation: Experiences from International Development’," Brooks World Poverty Institute Working Paper Series 10709, BWPI, The University of Manchester.
    9. Glewwe, Paul & Kremer, Michael & Moulin, Sylvie & Zitzewitz, Eric, 2004. "Retrospective vs. prospective analyses of school inputs: the case of flip charts in Kenya," Journal of Development Economics, Elsevier, vol. 74(1), pages 251-268, June.
    10. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, Oxford University Press, vol. 119(1), pages 249-275.
    11. Jessica Cohen & Pascaline Dupas, 2010. "Free Distribution or Cost-Sharing? Evidence from a Randomized Malaria Prevention Experiment," The Quarterly Journal of Economics, Oxford University Press, vol. 125(1), pages 1-45.
    12. Paul Schultz, T., 2004. "School subsidies for the poor: evaluating the Mexican Progresa poverty program," Journal of Development Economics, Elsevier, vol. 74(1), pages 199-250, June.
    13. repec:feb:artefa:0090 is not listed on IDEAS
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    More about this item

    Keywords

    Quantitative methods; Statistics; Social Sciences; Research Design; Development;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C00 - Mathematical and Quantitative Methods - - General - - - General

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