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Instrumental variable estimation of reinforcer effects.

Greg Jensen1

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|January 25, 2019
PubMed
Summary
This summary is machine-generated.

Reinforcement, a common behavior analysis predictor, creates biased regression results due to feedback loops. Instrumental variable estimation (IVE) offers a method for unbiased parameter estimation using independent instruments.

Keywords:
concurrent schedulesgeneralized matchinginstrumental variable estimationrats

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Area of Science:

  • Behavioral science
  • Econometrics
  • Psychology

Background:

  • Reinforcement is often used as a predictor in behavioral regression analyses.
  • This predictor violates the independence assumption required for regression analysis.
  • This violation creates a causal feedback loop, leading to biased regression parameter estimation.

Purpose of the Study:

  • To introduce and describe the technique of instrumental variable estimation (IVE).
  • To demonstrate how IVE can address bias in regression analyses of behavior.
  • To provide examples of IVE application in generalized matching models.

Main Methods:

  • Instrumental variable estimation (IVE) was employed.
  • Instruments are variables independently associated with predictors and outcomes.
  • The technique was applied to real data and simulation models of generalized matching.

Main Results:

  • Instrumental variable estimation (IVE) provides unbiased regression parameter estimates.
  • Instruments allow for the assessment of bias strength.
  • The study demonstrated unbiased parameter estimation in generalized matching models.

Conclusions:

  • Instrumental variable estimation (IVE) is a valid technique for analyzing behavioral data with reinforcement predictors.
  • IVE overcomes the limitations of traditional regression when dealing with feedback loops.
  • This method enhances the accuracy of behavioral regression models.