TY - JOUR

T1 - Exploiting test structure

T2 - Case series, case-control comparison, and dissociation

AU - Smithson, Michael

AU - Davies, Martin

AU - Davies, Anne M Aimola

PY - 2011

Y1 - 2011

N2 - Most neuropsychological tests consist of multiple items, and a subject's test score is the sum of the item scores. The test results for each subject thus comprise multiple data-points, and any data-set with test results from more than one subject has at least a two-level structure, with the test item as the first level and the subject as the second level. This structure may be exploited to yield more nuanced statistical analyses than those that treat each subject's test score as a single data-point. Exploiting this structure allows us to take into account the effect of test length and dispersion on score variance and may enhance statistical power. Focusing on tests for which the score can be regarded as a binomial random variable, and using the binomial general linear model, we describe appropriate statistical methods for exploiting test structure in analysing a case series, comparing a case with a control sample, and testing for dissociation. These methods also allow multiple predictors, both categorical and continuous, to be taken into account, thereby enhancing the capacity of researchers to test hypotheses in a case series and to investigate other explanatory factors, in addition to case-control status.

AB - Most neuropsychological tests consist of multiple items, and a subject's test score is the sum of the item scores. The test results for each subject thus comprise multiple data-points, and any data-set with test results from more than one subject has at least a two-level structure, with the test item as the first level and the subject as the second level. This structure may be exploited to yield more nuanced statistical analyses than those that treat each subject's test score as a single data-point. Exploiting this structure allows us to take into account the effect of test length and dispersion on score variance and may enhance statistical power. Focusing on tests for which the score can be regarded as a binomial random variable, and using the binomial general linear model, we describe appropriate statistical methods for exploiting test structure in analysing a case series, comparing a case with a control sample, and testing for dissociation. These methods also allow multiple predictors, both categorical and continuous, to be taken into account, thereby enhancing the capacity of researchers to test hypotheses in a case series and to investigate other explanatory factors, in addition to case-control status.

KW - Binomial general linear model

KW - Case series

KW - Dissociation

KW - Overdispersion

KW - Test length

UR - http://www.scopus.com/inward/record.url?scp=82755174136&partnerID=8YFLogxK

M3 - Article

C2 - 22114770

AN - SCOPUS:82755174136

VL - 28

SP - 44

EP - 64

JO - Cognitive Neuropsychology

JF - Cognitive Neuropsychology

SN - 0264-3294

IS - 1

ER -