PPT-Lecture 17 Factor Analysis

Syllabus Lecture 01 Describing Inverse Problems Lecture 02 Probability and Measurement Error Part 1 Lecture 03 Probability and Measurement Error Part 2 Lecture 04 The L 2 Norm and Simple Least Squares. 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:. Factor Analysis. Syllabus. Lecture 01 Describing Inverse Problems. Lecture 02 Probability and Measurement Error, Part 1. Lecture 03 Probability and Measurement Error, Part 2 . Lecture 04 The L. 2. 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 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. 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.. MatLab. Lecture 16:. Orthogonal Functions. . Lecture 01. . Using . MatLab. Lecture 02 Looking At Data. Lecture 03. . Probability and Measurement Error. . Lecture 04 Multivariate Distributions. 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.