Testing for a deficit in single-case studies: Effects of departures from normality
In neuropsychological single-case research inferences concerning a patient's cognitive status are often based on referring the patient's test score to those obtained from a modestly sized control sample. Two methods of testing for a deficit ( z and a method proposed by Crawford and Howell...
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| Veröffentlicht in: | Neuropsychologia 2006, Vol.44 (4), p.666-677 |
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| creator | Crawford, John R. Garthwaite, Paul H. Azzalini, Adelchi Howell, David C. Laws, Keith R. |
| description | In neuropsychological single-case research inferences concerning a patient's cognitive status are often based on referring the patient's test score to those obtained from a modestly sized control sample. Two methods of testing for a deficit (
z and a method proposed by Crawford and Howell [Crawford, J. R. & Howell, D. C. (1998). Comparing an individual's test score against norms derived from small samples. The Clinical Neuropsychologist, 12, 482–486]) both assume the control distribution is normal but this assumption will often be violated in practice. Monte Carlo simulation was employed to study the effects of leptokurtosis and the combination of skew and leptokurtosis on the Type I error rates for these two methods. For Crawford and Howell's method, leptokurtosis produced only a modest inflation of the Type I error rate when the control sample
N was small-to-modest in size and error rates were lower than the specified rates at larger
N. In contrast, the combination of leptokurtosis and skew produced marked inflation of error rates for small
Ns. With a specified error rate of 5%, actual error rates as high as 14.31% and 9.96% were observed for
z and Crawford and Howell's method respectively. Potential solutions to the problem of non-normal data are evaluated. |
| doi_str_mv | 10.1016/j.neuropsychologia.2005.06.001 |
| format | Article |
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z and a method proposed by Crawford and Howell [Crawford, J. R. & Howell, D. C. (1998). Comparing an individual's test score against norms derived from small samples. The Clinical Neuropsychologist, 12, 482–486]) both assume the control distribution is normal but this assumption will often be violated in practice. Monte Carlo simulation was employed to study the effects of leptokurtosis and the combination of skew and leptokurtosis on the Type I error rates for these two methods. For Crawford and Howell's method, leptokurtosis produced only a modest inflation of the Type I error rate when the control sample
N was small-to-modest in size and error rates were lower than the specified rates at larger
N. In contrast, the combination of leptokurtosis and skew produced marked inflation of error rates for small
Ns. With a specified error rate of 5%, actual error rates as high as 14.31% and 9.96% were observed for
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z and a method proposed by Crawford and Howell [Crawford, J. R. & Howell, D. C. (1998). Comparing an individual's test score against norms derived from small samples. The Clinical Neuropsychologist, 12, 482–486]) both assume the control distribution is normal but this assumption will often be violated in practice. Monte Carlo simulation was employed to study the effects of leptokurtosis and the combination of skew and leptokurtosis on the Type I error rates for these two methods. For Crawford and Howell's method, leptokurtosis produced only a modest inflation of the Type I error rate when the control sample
N was small-to-modest in size and error rates were lower than the specified rates at larger
N. In contrast, the combination of leptokurtosis and skew produced marked inflation of error rates for small
Ns. With a specified error rate of 5%, actual error rates as high as 14.31% and 9.96% were observed for
z and Crawford and Howell's method respectively. Potential solutions to the problem of non-normal data are evaluated.</description><subject>Bias</subject><subject>Biological and medical sciences</subject><subject>Brain Damage, Chronic - diagnosis</subject><subject>Computer Graphics</subject><subject>Data Collection - statistics & numerical data</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Humans</subject><subject>Methodology. Experimentation</subject><subject>Monte Carlo Method</subject><subject>Monte Carlo simulation</subject><subject>Neuropsychological Tests - statistics & numerical data</subject><subject>Neuropsychology</subject><subject>Non-normality</subject><subject>Psychology. Psychoanalysis. Psychiatry</subject><subject>Psychology. Psychophysiology</subject><subject>Psychometrics - statistics & numerical data</subject><subject>Psychometrics. Statistics. Methodology</subject><subject>Reference Values</subject><subject>Reproducibility of Results</subject><subject>Robustness</subject><subject>Single-case methods</subject><subject>Statistical methods</subject><issn>0028-3932</issn><issn>1873-3514</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkMFq3DAQhkVpabZpX6Ho0tzsjiRblnsolJA2gUChpGcxK49SLba1lezAvn217EKgp5wGhm_-mfkYuxJQCxD6866eaU1xnw_uTxzjY8BaArQ16BpAvGIbYTpVqVY0r9kGQJpK9UpesHc57wCgaaV5yy6EhkZLaTbs1wPlJcyP3MfEkQ_kgwsLDzPPpTtS5TATz8s6BMpf-I335JbMoy_oHtOyJsrcpzjxOaYJx7Ac3rM3HsdMH871kv3-fvNwfVvd__xxd_3tvnKN0UvVeyc1bQ10ndQ4eInklWkag25rfNu2fjDUIAyDML4HQEeoeoeq7XvZolGX7OqUu0_x71resFPIjsYRZ4prth10QkmlC_j1BLoUc07k7T6FCdPBCrBHq3Zn_7dqj1YtaFusloCP503rdqLhefyssQCfzgBmh6NPOLuQn7muNaKHI3d74qh4eQqUbHaBZkdDSMWrHWJ46U3_AKgMocU</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Crawford, John R.</creator><creator>Garthwaite, Paul H.</creator><creator>Azzalini, Adelchi</creator><creator>Howell, David C.</creator><creator>Laws, Keith R.</creator><general>Elsevier Ltd</general><general>Elsevier Science</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>2006</creationdate><title>Testing for a deficit in single-case studies: Effects of departures from normality</title><author>Crawford, John R. ; Garthwaite, Paul H. ; Azzalini, Adelchi ; Howell, David C. ; Laws, Keith R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c486t-9fc26eb807726adf2aef38448acb8f555fd8e4a0dd18f900acea39ca359925a83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Bias</topic><topic>Biological and medical sciences</topic><topic>Brain Damage, Chronic - diagnosis</topic><topic>Computer Graphics</topic><topic>Data Collection - statistics & numerical data</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Humans</topic><topic>Methodology. Experimentation</topic><topic>Monte Carlo Method</topic><topic>Monte Carlo simulation</topic><topic>Neuropsychological Tests - statistics & numerical data</topic><topic>Neuropsychology</topic><topic>Non-normality</topic><topic>Psychology. Psychoanalysis. Psychiatry</topic><topic>Psychology. Psychophysiology</topic><topic>Psychometrics - statistics & numerical data</topic><topic>Psychometrics. Statistics. Methodology</topic><topic>Reference Values</topic><topic>Reproducibility of Results</topic><topic>Robustness</topic><topic>Single-case methods</topic><topic>Statistical methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Crawford, John R.</creatorcontrib><creatorcontrib>Garthwaite, Paul H.</creatorcontrib><creatorcontrib>Azzalini, Adelchi</creatorcontrib><creatorcontrib>Howell, David C.</creatorcontrib><creatorcontrib>Laws, Keith R.</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Neuropsychologia</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Crawford, John R.</au><au>Garthwaite, Paul H.</au><au>Azzalini, Adelchi</au><au>Howell, David C.</au><au>Laws, Keith R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Testing for a deficit in single-case studies: Effects of departures from normality</atitle><jtitle>Neuropsychologia</jtitle><addtitle>Neuropsychologia</addtitle><date>2006</date><risdate>2006</risdate><volume>44</volume><issue>4</issue><spage>666</spage><epage>677</epage><pages>666-677</pages><issn>0028-3932</issn><eissn>1873-3514</eissn><coden>NUPSA6</coden><abstract>In neuropsychological single-case research inferences concerning a patient's cognitive status are often based on referring the patient's test score to those obtained from a modestly sized control sample. 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z and a method proposed by Crawford and Howell [Crawford, J. R. & Howell, D. C. (1998). Comparing an individual's test score against norms derived from small samples. The Clinical Neuropsychologist, 12, 482–486]) both assume the control distribution is normal but this assumption will often be violated in practice. Monte Carlo simulation was employed to study the effects of leptokurtosis and the combination of skew and leptokurtosis on the Type I error rates for these two methods. For Crawford and Howell's method, leptokurtosis produced only a modest inflation of the Type I error rate when the control sample
N was small-to-modest in size and error rates were lower than the specified rates at larger
N. In contrast, the combination of leptokurtosis and skew produced marked inflation of error rates for small
Ns. With a specified error rate of 5%, actual error rates as high as 14.31% and 9.96% were observed for
z and Crawford and Howell's method respectively. Potential solutions to the problem of non-normal data are evaluated.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><pmid>16046228</pmid><doi>10.1016/j.neuropsychologia.2005.06.001</doi><tpages>12</tpages></addata></record> |
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| subjects | Bias Biological and medical sciences Brain Damage, Chronic - diagnosis Computer Graphics Data Collection - statistics & numerical data Fundamental and applied biological sciences. Psychology Humans Methodology. Experimentation Monte Carlo Method Monte Carlo simulation Neuropsychological Tests - statistics & numerical data Neuropsychology Non-normality Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Psychometrics - statistics & numerical data Psychometrics. Statistics. Methodology Reference Values Reproducibility of Results Robustness Single-case methods Statistical methods |
| title | Testing for a deficit in single-case studies: Effects of departures from normality |
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