Reliability and Validity in Neuropsychology

  • Elisabeth M. S. Sherman
  • Brian L. Brooks
  • Grant L. Iverson
  • Daniel J. Slick
  • Esther Strauss


There are now literally hundreds of neuropsychological tests designed for evaluating cognitive abilities in children, adolescents, adults, and older adults. Given this vast library of instruments, how do test users decide which neuropsychological tests to choose? Like most decisions, choosing a test relies on a careful weighing of the relative balance of strengths and weaknesses. Two critical sources of information for making that decision are evidence of a test’s reliability and validity. Carefully examining these will help the user make an informed decision as to whether the test is appropriate for a particular purpose, a particular examinee, and a particular setting. This seems like a straightforward task for most neuropsychologists, who have typically covered basic concepts of reliability and validity during undergraduate or graduate training. Yet, a common mistake is to ask an all-or-none question, such as “is this test reliable?” or “has this test been validated?” Reliability and validity often appear deceptively simple, but continue to be complex topics to master.

The goal of this chapter is to facilitate the process of assessing the reliability and validity of tests for clinical use. We will provide an overview of reliability, including different types of reliability, methods for determining reliability, factors that affect reliability, and limits to reliability. We will also cover basic concepts relating to validity, including specific kinds of evidence contributing to validity, ways of evaluating validity, and basic guidelines for interpreting validity. We will do this while keeping the context focused as much as possible on everyday clinical practice.


Test Score Interrater Reliability Internal Reliability Practice Effect Validity Evidence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Elisabeth M. S. Sherman
    • 1
  • Brian L. Brooks
  • Grant L. Iverson
  • Daniel J. Slick
  • Esther Strauss
  1. 1.Alberta Children’s HospitalUniversity of CalgaryCalgaryCanada

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