PPT-Principal Components Analysis with SPSS

Karl L Wuensch Dept of Psychology East Carolina University When to Use PCA You have a set of p continuous variables You want to repackage their variance into m components You will usually want . SPSS. Karl L. Wuensch. Dept of Psychology. East Carolina University. When to Use PCA. You have a set of . p. continuous variables.. You want to repackage their variance into . m. components.. You will usually want . in Regression . Principal Components Analysis. Standing Heights and Physical Stature Attributes Among Female Police Officer Applicants. S.Q. . Lafi. and J.B. . Kaneene. (1992). “An Explanation of the Use of Principal Components Analysis to Detect and Correct for . Removing Redundancies and Finding Hidden Variables. Two Goals. Measurements are not independent of one another . and we . need . a way to reduce the dimensionality and . remove . collinearity. . – Principal components. Pattern Analysis. Finding patterns among objects on which two or more independent variables have been measured. . Principal Coordinates Analysis . (PCO). Principal . Components Analysis. . (PCA) (. Min’-chen. ). muenchen@utk.edu. HelpDesk. : 974-9900. Newsletter: http://listserv.utk.edu/archives/rcnews.html. Research Computing Support. To help UT researchers use information technology and analytic methods to:. OF MULTIVARIATE STATISTICAL . METHOD . IN THE STUDY OF . MORPHOLOGICAL. . FEATURES OF TILAPIA CABREA.  . By.  . Bartholomew A. . Uchendu. (. Ph.D. ).  . Department of . Maths. /Statistics, Federal Polytechnic, . Gavin Band. Why do PCA?. PCA is good at detecting “directions” of major variation in your data. This might be:. Population structure – subpopulations having different allele frequencies.. Unexpected (“cryptic”) relationships.. Gavin Band. Why do PCA?. PCA is good at detecting “directions” of major variation in your data. This might be:. Population structure – subpopulations having different allele frequencies.. Unexpected (“cryptic”) relationships.. Bamshad Mobasher. DePaul University. Principal Component Analysis. PCA is a widely used data . compression and dimensionality reduction technique. PCA takes a data matrix, . A. , of . n. objects by . The basic objective of Factor Analysis is data reduction or structure detection.. The purpose of . data reduction.  is to remove redundant (highly correlated) variables from the data file, perhaps replacing the entire data file with a smaller number of uncorrelated variables.. Dept. of PHS, Division of . Biostats. & . Bioinf. Biostatistics Shares Resource, Hollings Cancer Center. Cancer Control Journal Club. March 3, 2016. Motivating Example. Goals of paper. 1. See if previously defined measurement model of hopelessness in advanced cancer fits this sample. Confirmatory Factor Analysis.. Karl L. Wuensch. Dept of Psychology. East Carolina University. When to Use PCA. You have a set of . p. continuous variables.. You want to repackage their variance into . m. components.. You will usually want . Why do we analyze livelihoods?. Food security . analysis aims at informing geographical . and. socio-economic targeting. Livelihood analysis allows us to answer . one of the key basic questions of food security analysis: “who are the food insecure. 2/36OutlineIAdministrativeIssuesIDecompositionmethodsIFactoranalysisIPrincipalcomponentsanalysisINon-negativematrixfactorization3/36DealingwithmanyvariablesISofarwevelargelyconcentratedoncasesinwhichw