PPT-FACTOR ANALYSIS FACTOR ANALYSIS

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. SPSS. Karl L. Wuensch. Dept. . of Psychology. East Carolina University. What is a Common Factor?. It is an abstraction, a hypothetical construct . that . relates . to at least two of our measurement variables.. STA431: Spring 2013. See last slide for copyright information. Factor Analysis: The Measurement Model. D. 1. D. 8. D. 7. D. 6. D. 5. D. 4. D. 3. D. 2. F. 1. F. 2. Example with 2 factors and 8 observed variables. AnnMaria De Mars, PhD.. The Julia Group & 7 Generation Games . WHY?. Imagine this. What exactly were you planning on doing with that?. Let’s say you have a massive pile of data …. . You Could:. Prepared by. Ferry . Dzulkifli. Tita. . Borshalina. FACTOR ANALYSIS. Factor Analysis Defined. Factor analysis . . .. is an interdependence technique whose primary purpose is to define the underlying structure among the variables in the analysis.. Lua Augustin, Savannah Guo, and Blair Marquardt. Learning Outcomes. To understand. :. What . is factor analysis.. What is its model.. Latent vs. observable variables; examples of each. .. Potential applications of factor analysis. The purpose of factor analysis is to discover patterns in the relationships among the . variables. Factor Analysis. Form of multiple correlations. Checking for construct validity. Do questions measure the same dimension?. http://dss.princeton.edu/training/ Factor analysis: introFactor analysis is used mostly for data reduction purposes:To get a small set of variables (preferably uncorrelated) from a large set of variab BMTRY 726. 7/19/16. Factor Rotation. Recall, can conduct orthogonal transformations of the factors and still reconstruct the covariance of . X. . Means can use orthogonal transformations of the factor loading matrix to “simplify” the interpretation of the factors. First, . let’s. . check. . the. . reliability. of . the. . scale. Go. . to. . Analyze. , . Scale. . and. . Reliability. . analysis. Select . the. . items. . and. transfer . them. . into. STA431: Spring . 2015. See last slide for copyright information. Factor Analysis: The Measurement Model. D. 1. D. 8. D. 7. D. 6. D. 5. D. 4. D. 3. D. 2. F. 1. F. 2. Example with 2 factors and 8 observed variables. 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. 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.. 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.. variables. Factor Analysis. Form of multiple correlations. Checking for construct validity. Do questions measure the same dimension?. For use between or within a test. Identify items which cluster together.