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Correcting for Primary Study Misspecifications in Meta-Analysis

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Author Info

  • Mark J. Koetse

    ()
    (Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam)

  • Raymond J.G.M. Florax

    ()
    (Vrije Universiteit, and Purdue University)

  • Henri L.F. de Groot

    ()
    (Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam)

Abstract

In non-experimental sciences the errors associated with model misspecifications in primarystudies carry over to meta-analysis. We use Monte Carlo simulations to analyse the effects ofthese misspecifications on results of a meta-analysis using a meta-estimator that calculates asimple average effect and a meta-estimator that includes dummy variables to control forprimary study misspecification. The results show that using the dummy variable model goes along way in mitigating the negative effects of error propagation on the bias and mean squarederror of the meta-estimator, and the size and the power of statistical tests. Although primarystudy misspecifications can usually be observed and controlled for in a meta-analysis, themore complex interactions between these observed characteristics are far more difficult tocontrol for in practice. Our results show that these interactions may, however, substantiallyaffect the outcomes of a meta-analysis. When meta-analysis is used to look for a ‘true’ effectrather than for analysing variation in outcomes, our results provide an argument for studyselection on model quality to avoid the impact of error propagation in meta-analysis.

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File URL: http://papers.tinbergen.nl/05029.pdf
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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 05-029/3.

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Date of creation: 16 Mar 2005
Date of revision: 31 Jan 2013
Handle: RePEc:dgr:uvatin:20050029

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Web page: http://www.tinbergen.nl

Related research

Keywords: Meta-analysis; Monte Carlo simulation; Omitted variable bias; Elasticities; Model Misspecification;

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References

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  1. Ian J. Bateman & Andrew P. Jones, 2003. "Contrasting Conventional with Multi-Level Modeling Approaches to Meta-Analysis: Expectation Consistency in U.K. Woodland Recreation Values," Land Economics, University of Wisconsin Press, vol. 79(2), pages 235-258.
  2. Weichselbaumer, Doris & Winter-Ebmer, Rudolf, 2003. "A Meta-Analysis of the International Gender Wage Gap," CEPR Discussion Papers, C.E.P.R. Discussion Papers 4127, C.E.P.R. Discussion Papers.
  3. Robert Goldfarb, 1995. "The economist-as-audience needs a methodology of plausible inference," Journal of Economic Methodology, Taylor & Francis Journals, Taylor & Francis Journals, vol. 2(2), pages 201-222.
  4. Jasper M. Dalhuisen & Raymond J. G. M. Florax & JHenri L. F. de Groot & Peter Nijkamp, 2003. "Price and Income Elasticities of Residential Water Demand: A Meta-Analysis," Land Economics, University of Wisconsin Press, vol. 79(2), pages 292-308.
  5. Florax, R.J.G.M., 2002. "Accounting for dependence among study results in Meta-Analysis: methodology and applications to the valuation and use of natural resources," Serie Research Memoranda, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics 0005, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  6. Card, David & Krueger, Alan B, 1995. "Time-Series Minimum-Wage Studies: A Meta-analysis," American Economic Review, American Economic Association, American Economic Association, vol. 85(2), pages 238-43, May.
  7. Smith, V Kerry & Huang, Ju-Chin, 1995. "Can Markets Value Air Quality? A Meta-analysis of Hedonic Property Value Models," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 103(1), pages 209-27, February.
  8. Gorg, Holger & Strobl, Eric, 2001. "Multinational Companies and Productivity Spillovers: A Meta-analysis," Economic Journal, Royal Economic Society, Royal Economic Society, vol. 111(475), pages F723-39, November.
  9. V. Smith & Subhrendu Pattanayak, 2002. "Is Meta-Analysis a Noah's Ark for Non-Market Valuation?," Environmental & Resource Economics, European Association of Environmental and Resource Economists, European Association of Environmental and Resource Economists, vol. 22(1), pages 271-296, June.
  10. T. D. Stanley, 2001. "Wheat from Chaff: Meta-analysis as Quantitative Literature Review," Journal of Economic Perspectives, American Economic Association, American Economic Association, vol. 15(3), pages 131-150, Summer.
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Cited by:
  1. Jon Nelson & Peter Kennedy, 2009. "The Use (and Abuse) of Meta-Analysis in Environmental and Natural Resource Economics: An Assessment," Environmental & Resource Economics, European Association of Environmental and Resource Economists, European Association of Environmental and Resource Economists, vol. 42(3), pages 345-377, March.
  2. Mark Koetse & Raymond Florax & Henri Groot, 2010. "Consequences of effect size heterogeneity for meta-analysis: a Monte Carlo study," Statistical Methods and Applications, Springer, Springer, vol. 19(2), pages 217-236, June.
  3. Mark J. Koetse & Raymond J.G.M. Florax & Henri L.F. de Groot, 2007. "The Impact of Effect Size Heterogeneity on Meta-Analysis: A Monte Carlo Experiment," Tinbergen Institute Discussion Papers, Tinbergen Institute 07-052/3, Tinbergen Institute.
  4. G. Cornelis van Kooten & Susanna Laaksonen-Craig & Yichuan Wang, 2007. "Costs of Creating Carbon Offset Credits via Forestry Activities: A Meta-Regression Analysis," Working Papers, University of Victoria, Department of Economics, Resource Economics and Policy Analysis Research Group 2007-03, University of Victoria, Department of Economics, Resource Economics and Policy Analysis Research Group.

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