Bayesian Interpretation of Test Reliability

Jones, W. Paul

doi:10.1177/0013164491513009

Cited by 4 publications

(2 citation statements)

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“…In addition to making dichotomous decisions about the presence or absence of a COI, clinical neuropsychologists use interval estimates to represent the uncertainty about the observed test scores of patients. One typically construes such an interval estimate by calculating the standard error of measurement based on the standard deviation of the test scores (Sx) and the reliability of the test (rxx) and adding or subtracting the standard error from the observed test score (Crawford, 2003;Jones, 1991). One may also correct the observed test score for the test's reliability (Crawford, 2003;Jones, 1991).…”

Section: Bayesian Interval Estimates Are Easy To Interpretmentioning

confidence: 99%

“…The value of Bayesian methods an interval estimate by calculating the standard error of measurement based on the standard deviation of the test scores (S x ) and the reliability of the test (r xx ) and adding or subtracting the standard error from the observed test score (Crawford, 2003;Jones, 1991). One may also correct the observed test score for the test's reliability (Crawford, 2003;Jones, 1991). To obtain a 95% confidence interval, one can multiply the standard error with the z-score 1.96 (Equation 4).…”

Section: Bayesian Interval Estimates Are Easy To Interpretmentioning

confidence: 99%

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The Value of Bayesian Methods for Accurate and Efficient Neuropsychological Assessment

Huygelier

Gillebert

Moors

2021

J Int Neuropsychol Soc

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Objective: Clinical neuropsychology has been slow in adopting novelties in psychometrics, statistics, and technology. Researchers have indicated that the stationary nature of clinical neuropsychology endangers its evidence-based character. In addition to a technological crisis, there may be a statistical crisis affecting clinical neuropsychology. That is, the frequentist null hypothesis significance testing framework remains the dominant approach in clinical practice, despite a recent surge in critique on this framework. While the Bayesian framework has been put forward as a viable alternative in psychology in general, the possibilities it offers to clinical neuropsychology have not received much attention. Method: In the current position paper, we discuss and reflect on the value of Bayesian methods for the advancement of evidence-based clinical neuropsychology. Results: We aim to familiarize clinical neuropsychologists and neuropsychological researchers to Bayesian methods of inference and provide a clear rationale for why these methods are valuable for clinical neuropsychology. Conclusion: We argue that Bayesian methods allow for a more intuitive answer to our diagnostic questions and form a more solid foundation for sequential and adaptive diagnostic testing, representing uncertainty about patients’ observed test scores and cognitive modeling of test results.

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