Continuous norming: Implications for the WAIS-R

Zachary, Robert A.; Gorsuch, Richard L.

doi:10.1002/1097-4679(198501)41:1<86::aid-jclp2270410115>3.0.co;2-w

Cited by 87 publications

(84 citation statements)

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“…If the predicted score is substantially higher than the patient's obtained score, this suggests impaired performance. This is an example of the use of regression to provide continuous norms (Zachary & Gorsuch, 1985) as referred to in the Introduction.…”

Section: Examples Of the Use Of The Methods And Accompanying Programsmentioning

confidence: 99%

Using regression equations built from summary data in the neuropsychological assessment of the individual case.

Crawford

Garthwaite

2007

Neuropsychology

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Regression equations have many useful roles in neuropsychological assessment. This article is based on the premise that there is a large reservoir of published data that could be used to build regression equations; these equations could then be used to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because (a) not all neuropsychologists are aware that equations can be built with only basic summary data for a sample and (b) the computations involved are tedious and prone to error. To overcome these barriers, the authors set out the steps required to build regression equations from sample summary statistics and the further steps required to compute the associated statistics for drawing inferences concerning an individual case. The authors also develop, describe, and make available computer programs that implement the methods. Although caveats attach to the use of the methods, these need to be balanced against pragmatic considerations and against the alternative of either entirely ignoring a pertinent data set or using it informally to provide a clinical "guesstimate."Keywords: neuropsychological assessment, regression equations, single-case methods Regression equations serve a number of useful functions in the neuropsychological assessment of individuals (Chelune, 2003;Crawford, 2004;Crawford & Howell, 1998;Strauss, Sherman, & Spreen, 2006;Temkin, Heaton, Grant, & Dikmen, 1999). For example, they are widely used to estimate premorbid levels of ability in clinical populations by using psychological tests that are relatively resistant to neurological or psychiatric dysfunction (Crawford, 2004;Franzen, Burgess, & Smith-Seemiller, 1997;O'Carroll, 1995).Another common application of regression is in the assessment of change in neuropsychological functioning in the individual case. Here a regression equation can be built (usually by using healthy participants) to predict a patient's level of performance on a cognitive ability measure at retest from their score at initial testing. An obtained retest score that is markedly lower than the predicted score suggests cognitive deterioration (Crawford, 2004;Heaton & Marcotte, 2000;Sherman et al., 2003;Temkin et al., 1999).Clinical samples can also profitably be used to build regression equations for predicting retest scores. For example, Chelune, Naugle, Lüders, Sedlak, and Awad (1993) built an equation to predict memory scores at retest from baseline scores in a sample of temporal lobe epilepsy cases who had not undergone any surgical intervention in the intervening period. The equation was then used to assess the effects of surgery on memory functioning in further individual patients.Regardless of whether the equation was built from a healthy or clinical sample, this approach simultaneously factors in the strength of correlation between scores at test and retest (the higher the correlation the smaller the expected discrepancies), the effects of practice (typically scores will be higher on retest), and reg...

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Section: Examples Of the Use Of The Methods And Accompanying Programsmentioning

confidence: 99%

Using regression equations built from summary data in the neuropsychological assessment of the individual case.

Crawford

Garthwaite

2007

Neuropsychology

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“…Fitted means and standard deviations were derived for 6-month age groupings by regressing age, age squared, and age cubed on the test scores for the comparison group in the manner described by Zachary and Gorsuch (1985). The dysfunctional children's scores were then compared to the fitted mean and SD of the appropriate age group, resulting in an SD score for each dysfunctional child.…”

Section: Methodsmentioning

confidence: 99%

Developmental Dyspraxia: Is it a Unitary Function?

Ayres¹,

Mailloux²,

Wendler³

1987

The Occupational Therapy Journal of Research

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“…For example, tables may be divided by fi ve-year intervals (e.g., norms for individuals between the ages of 20 and 24 years, 25 and 29 years, and so on), ten-year intervals, or any other age range. These norms are appropriate if the mean and standard deviation for the normative sample group approximate the mean and standard deviation for the true population and if the raw scores are normally distributed (Zachary & Gorsuch, 1985 ). One problem with discrete norms is that an individual's apparent performance can shift depending on which age band is used, even though the raw score remains the same.…”

Section: Introductionmentioning

confidence: 99%

“…One problem with discrete norms is that an individual's apparent performance can shift depending on which age band is used, even though the raw score remains the same. Zachary and Gorsuch ( 1985) noted that a person's IQ score on the Wechsler Adult Intelligence Scale-Revised (WAIS-R) could increase up to six points by aging a single day when their raw test scores pass from comparison with the 25-34 to the 35-44 year age group. Because of this instability, Zachary and Gorsuch recommended using continuous norms as an alternative to discrete norms.…”

Section: Introductionmentioning

confidence: 99%

The utility of regression-based norms in interpreting the minimal assessment of cognitive function in multiple sclerosis (MACFIMS)

Parmenter

Testa

Schretlen

et al. 2009

J Int Neuropsychol Soc

172

162

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The Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) is a consensus neuropsychological battery with established reliability and validity. One of the difficulties in implementing the MACFIMS in clinical settings is the reliance on manualized norms from disparate sources. In this study, we derived regression-based norms for the MACFIMS, using a unique data set to control for standard demographic variables (i.e., age, age2, sex, education). Multiple sclerosis (MS) patients (n = 395) and healthy volunteers (n = 100) did not differ in age, level of education, sex, or race. Multiple regression analyses were conducted on the performance of the healthy adults, and the resulting models were used to predict MS performance on the MACFIMS battery. This regression-based approach identified higher rates of impairment than manualized norms for many of the MACFIMS measures. These findings suggest that there are advantages to developing new norms from a single sample using the regression-based approach. We conclude that the regression-based norms presented here provide a valid alternative to identifying cognitive impairment as measured by the MACFIMS.

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Continuous norming: Implications for the WAIS-R

Cited by 87 publications

References 0 publications

Using regression equations built from summary data in the neuropsychological assessment of the individual case.

Using regression equations built from summary data in the neuropsychological assessment of the individual case.

Developmental Dyspraxia: Is it a Unitary Function?

The utility of regression-based norms in interpreting the minimal assessment of cognitive function in multiple sclerosis (MACFIMS)

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