PPT-Factor Analysis and Principal Components

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. 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 . Advanced Psychological Statistics, II. April 7, . 2011. The Plan for Today. Introduce exploratory factor analysis.. Historical applications in psychology.. Basic concepts, extraction, rotation.. Determining number of factors.. Pattern Analysis. Finding patterns among objects on which two or more independent variables have been measured. . Principal Coordinates Analysis . (PCO). Principal . Components Analysis. . (PCA) (. Principal Component Analysis. Chapter 17. Terminology. Measured variables – the real scores from the experiment. Squares on a diagram. Latent variables – the construct the measured variables are supposed to represent. Prof. Andy Field. Slide . 2. Aims. Explore factor . a. nalysis and . p. rincipal . c. omponent . a. nalysis (PCA). What . Are . factors. ?. Representing . factors. Graphs and Equations. Extracting factors. 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 . John.Ohrvik@ki.se. John Öhrvik. . . Göran Nilsson. . . Uppsala University, . Center for Clinical Research . Background. Cardiovascular disease (CVD) is a major cause of morbidity and mortality in the developed world. As risk factors have been identified, more than one risk factor has been observed in many individuals.. 2/36OutlineIAdministrativeIssuesIDecompositionmethodsIFactoranalysisIPrincipalcomponentsanalysisINon-negativematrixfactorization3/36DealingwithmanyvariablesISofarwevelargelyconcentratedoncasesinwhichw 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 . Latent Variables and Factor Analysis. Correlation as the shape of an ellipse of plotted points. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o. o.