Abstract
Deficit measurement is the sine qua non of neuropsychology. The risk, of course, is that we can be so focused on deficit measurement – and so focused on describing the nature and severity of a person’s cognitive impairment – that we can underappreciate human diversity and overattribute low or unexpected test scores to brain injury or disease. The North American psychometric tradition has long since attempted to minimize possible misattribution of low test scores through a reliance on the normal curve. However, clinicians know that overly formulaic reliance on the normal curve can result in false positive and false negative attributions of cognitive diminishment. Moreover, the normal curve relates to a single test score in relation to a theoretical normal population. Neuropsychologists never rely on single tests. Instead, we administer numerous tests and we interpret performance in combination, not in isolation. Thus, the principles of normative test score interpretation, applied to single test scores, are inherently limited when interpreting performance across a battery of tests.
Cognitive impairment can arise from a single cause or it can have a multifactorial etiology. There are a large number of medical, psychiatric, and neurological diseases, disorders, and conditions that can have an adverse affect on cognition. Clearly, the accurate identification and quantification of cognitive impairment is important in clinical practice, research, and in clinical trials. However, comprehensive, psychometrically-sophisticated guidelines for identifying and quantifying cognitive impairment, across a battery of tests, are not clearly outlined in the neuropsychological literature. The primary exception to this is the work of Reitan and Wolfson for the Halstead Reitan Neuropsychological Battery (Reitan RM, Wolfson D, The Halstead-Reitan neuropsychological test battery: Theory and clinical interpretation, Neuropsychology Press, Tucson, AZ, 1985; Reitan RM, Wolfson D, The Halstead-Reitan neuropsychological test battery: Theory and clinical interpretation, 2nd edn, Neuropsychology Press, Tucson, AZ, 1993) and Golden and colleagues for the Luria–Nebraska Neuropsychological Battery (Golden C, Purish A, Hammeke T, Manual for the Luria-Nebraska Neuropsychological Battery, Western Psychological Services, Los Angeles, 1985; Golden CJ, Freshwater SM, Vayalakkara J, The Luria-Nebraska neuropsychological battery, in Groth-Marnat G (ed), Neuropsychological assessment in clinical practice: A guide to test interpretation and integration, Wiley, New York, 2000, pp 263-289; Moses JA Jr, Golden CJ, Ariel R, Gustavson JL, Interpretation of the Luria-Nebraska neuropsychological battery (Vol 1), Grune and Stratton, New York, 1983). Considerable psychometric work has been done regarding how to interpret combinations of scores derived from these batteries.
The purpose of this chapter is to provide clinicians with psychometrically sophisticated information that is designed to improve their accuracy for identifying cognitive problems in daily practice. This chapter begins by presenting information on current definitions of cognitive impairment (Conceptualizing Cognitive Impairment). In the second section, we describe some of the various classification systems for conceptualizing cognitive impairment (Classifying Cognitive Impairment). Fundamental psychometric principles, derived from analyses on co-normed batteries of tests, are illustrated in the third section (Evaluating Cognitive Impairment: Five Psychometric Principles to Consider). In the final section, we present new psychometric criteria for identifying cognitive impairment across a battery of neuropsychological measures that adhere to the five psychometric rules (Identifying Cognitive Impairment: New Psychometric Criteria for Cognitive Disorder NOS).
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Notes
- 1.
MCI has been divided into several classifications depending on the type of cognitive impairment. Amnestic MCI (aMCI) is the most commonly studied subtype and refers to impairment with memory.
- 2.
We are using the RIST to estimate current intellectual abilities. After determining their current RIST score, we combine this information with clinical judgment to estimate premorbid RIST classification category (e.g., low average, average, high average, or superior). We usually use the obtained RIST as the best estimate of premorbid RIST classification. However, sometimes we might believe that the obtained RIST under-estimates premorbid ability, and thus we might choose one classification higher. An example would be if a person with obvious brain damage obtained a RIST of 109. We might assume that his/her premorbid RIST was more likely to fall in the High Average classification range than in the Average classification range.
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Iverson, G.L., Brooks, B.L. (2011). Improving Accuracy for Identifying Cognitive Impairment. 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_32
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