Abstract
The purpose of this chapter is to illustrate how an understanding of the psychometric properties of tests, normative samples, and test scores are an essential foundation for meaningful and accurate clinical interpretations and reduces the likelihood of misinterpreting test results. Our goal is to present this information in an easy-to-understand format that facilitates clinicians’ knowledge of basic psychometrics in the context of test score interpretation. Clinical examples using commonly used tests will be provided throughout to illustrate the relevance and utility of these concepts in clinical practice.
With regard to sample distributions, we will review concepts relating to non-normality and the influence of score distribution characteristics on derived scores. Floor and ceiling effects, equivalence of normative data sets, and truncated distributions will be discussed with regard to test items and test norms. When comparing scores between tests, we will review the role of test measurement error. We will also discuss normal variability and briefly comment on the prevalence of low test scores in healthy people, and how to use this information for supplementing clinical judgment. Finally, we will provide an overview of various methods for interpreting change in test performance over time.
*The current chapter is based upon the chapter, “Psychometrics in neuropsychological assessment,” from Strauss et al. (2006) and co-written with Daniel J. Slick.
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Notes
- 1.
This example assumes that the scores are normally distributed. This example also assumes that higher raw scores reflect better performance.
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Brooks, B.L., Sherman, E.M.S., Iverson, G.L., Slick, D.J., Strauss, E. (2011). Psychometric Foundations for the Interpretation of Neuropsychological Test Results. In: Schoenberg, M., Scott, J. (eds) The Little Black Book of Neuropsychology. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-76978-3_31
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