Rank-order versus mean based statistics for neuroimaging. Export

@article{statistics:npm, abstract = {Traditional analysis of neuroimaging data uses parametric statistics, such as the t-test. These tests are designed to detect mean differences. In fact, even nonparametric techniques such as Statistical non-Parametric Mapping (SnPM) use the mean-based t statistic to measure effect size. We note that these measures may not be particularly sensitive for detecting differences when the mean is not an accurate measure of central tendency - for example if one of the groups is experiencing a ceiling or floor effect (causing a skewed data distribution). Here we introduce a nonparametric approach for neuroimaging data analysis that is based on the rank-order of data (and is therefore less influenced by outliers than the t-test). We suggest that this approach may offer a small benefit for datasets where the assumptions of the t-test have been violated, for example datasets where data from one of the groups exhibits a skewed distribution due to floor or ceiling effects.}, address = {Department of Communication Sciences and Disorders, University of South Carolina, SC 29208, USA.}, author = {Rorden, Chris and Bonilha, Leonardo and Nichols, Thomas E E.}, citeulike-article-id = {1204057}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.neuroimage.2006.12.043}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S1053811906012201}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/17391987}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=17391987}, citeulike-linkout-4 = {http://www.sciencedirect.com/science/article/B6WNP-4MWXPVJ-2/2/c41629b6ff698a1738a9b3b60a548b30}, day = {25}, doi = {10.1016/j.neuroimage.2006.12.043}, issn = {1053-8119}, journal = {Neuroimage}, keywords = {statistics, vbm}, month = {January}, posted-at = {2008-08-31 02:37:50}, priority = {2}, title = {Rank-order versus mean based statistics for neuroimaging.}, url = {http://dx.doi.org/10.1016/j.neuroimage.2006.12.043}, year = {2007} }

TY - JOUR ID - statistics:npm L3 - citeulike-article-id:1204057 N2 - Traditional analysis of neuroimaging data uses parametric statistics, such as the t-test. These tests are designed to detect mean differences. In fact, even nonparametric techniques such as Statistical non-Parametric Mapping (SnPM) use the mean-based t statistic to measure effect size. We note that these measures may not be particularly sensitive for detecting differences when the mean is not an accurate measure of central tendency - for example if one of the groups is experiencing a ceiling or floor effect (causing a skewed data distribution). Here we introduce a nonparametric approach for neuroimaging data analysis that is based on the rank-order of data (and is therefore less influenced by outliers than the t-test). We suggest that this approach may offer a small benefit for datasets where the assumptions of the t-test have been violated, for example datasets where data from one of the groups exhibits a skewed distribution due to floor or ceiling effects. JF - Neuroimage SN - 1053-8119 AD - Department of Communication Sciences and Disorders, University of South Carolina, SC 29208, USA. TI - Rank-order versus mean based statistics for neuroimaging. KW - statistics KW - vbm AU - Rorden, Chris AU - Bonilha, Leonardo AU - Nichols, Thomas E PY - 2007/01/25/ UR - http://dx.doi.org/10.1016/j.neuroimage.2006.12.043 ER -