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Specification curve analysis.

Uri Simonsohn1, Joseph P Simmons2, Leif D Nelson3

  • 1ESADE Business School, Behavioral Science, Universitat Ramon Llull, Barcelona, Spain. urisohn@gmail.com.

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This summary is machine-generated.

Analytical decisions in research can introduce bias. Specification curve analysis, a new method, helps identify robust findings by examining multiple statistical models, revealing which results are reliable and which are not.

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Area of Science:

  • Social Sciences
  • Economics
  • Psychology

Background:

  • Empirical research findings are influenced by analytical decisions, which can introduce bias and variability beyond standard errors.
  • Authors' motivated choices in data analysis can lead to a narrative bias, affecting the objectivity of results.
  • Standard errors do not capture the full extent of uncertainty introduced by analytical choices.

Purpose of the Study:

  • To introduce and illustrate specification curve analysis as a method to address bias and variability in empirical results.
  • To provide a transparent framework for evaluating the robustness of research findings.
  • To assess the reliability of findings related to discrimination and naming conventions.

Main Methods:

  • Specification curve analysis involves identifying all theoretically justified, statistically valid, and non-redundant model specifications.
  • Results from all specifications are displayed graphically to highlight the impact of analytical decisions.
  • Joint inference is conducted across the entire set of specifications to determine overall robustness.

Main Results:

  • Application to two papers revealed varying degrees of robustness for three distinct findings.
  • One finding related to discrimination based on names was robust.
  • One finding regarding hurricane naming conventions was weak, and another was not robust.

Conclusions:

  • Specification curve analysis is a valuable tool for enhancing the transparency and reliability of empirical research.
  • The method effectively distinguishes between robust and non-robust findings, aiding in the interpretation of scientific evidence.
  • This approach helps mitigate narrative bias and quantifies uncertainty stemming from analytical choices.