Applied Statistics Using SAS and SPSS

Slides:

Advertisements

Similar presentations

Aaker, Kumar, Day Seventh Edition Instructor’s Presentation Slides

Advertisements

Factor Analysis and Principal Components Removing Redundancies and Finding Hidden Variables.

Factor Analysis Continued

Chapter Nineteen Factor Analysis.

© LOUIS COHEN, LAWRENCE MANION & KEITH MORRISON

Lecture 7: Principal component analysis (PCA)

Overview of Factor Analysis Construct combinations of quantitative variablesConstruct combinations of quantitative variables Reduce a large set of variables.

Data mining and statistical learning, lecture 4 Outline Regression on a large number of correlated inputs  A few comments about shrinkage methods, such.

Dr. Michael R. Hyman Factor Analysis. 2 Grouping Variables into Constructs.

Goals of Factor Analysis (1) (1)to reduce the number of variables and (2) to detect structure in the relationships between variables, that is to classify.

Education 795 Class Notes Factor Analysis II Note set 7.

Social Science Research Design and Statistics, 2/e Alfred P. Rovai, Jason D. Baker, and Michael K. Ponton Factor Analysis PowerPoint Prepared by Alfred.

© 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

Factor Analysis Istijanto MM, MCom. Definition Factor analysis  Data reduction technique and summarization  Identifying the underlying factors/ dimensions.

Psy 427 Cal State Northridge Andrew Ainsworth PhD.

Factor Analysis in Individual Differences Research: The Basics Psych 437.

1 Applied Statistics Using SAS and SPSS Topic: Factor Analysis By Prof Kelly Fan, Cal State Univ, East Bay.

Advanced Correlational Analyses D/RS 1013 Factor Analysis.

Applied Quantitative Analysis and Practices

Factor Analysis Psy 524 Ainsworth. Assumptions Assumes reliable correlations Highly affected by missing data, outlying cases and truncated data Data screening.

Be humble in our attribute, be loving and varying in our attitude, that is the way to live in heaven.

Thursday AM  Presentation of yesterday’s results  Factor analysis  A conceptual introduction to: Structural equation models Structural equation models.

Principal Component vs. Common Factor. Varimax Rotation Principal Component vs. Maximum Likelihood.

© 2007 Prentice Hall19-1 Chapter Nineteen Factor Analysis © 2007 Prentice Hall.

Introduction to Multivariate Analysis of Variance, Factor Analysis, and Logistic Regression Rubab G. ARIM, MA University of British Columbia December 2006.

Marketing Research Aaker, Kumar, Day and Leone Tenth Edition Instructor’s Presentation Slides 1.

Lecture 12 Factor Analysis.

Education 795 Class Notes Factor Analysis Note set 6.

Chapter 13.  Both Principle components analysis (PCA) and Exploratory factor analysis (EFA) are used to understand the underlying patterns in the data.

Feature Selection and Extraction Michael J. Watts

Factor Analysis I Principle Components Analysis. “Data Reduction” Purpose of factor analysis is to determine a minimum number of “factors” or components.

FACTOR ANALYSIS 1. What is Factor Analysis (FA)? Method of data reduction o take many variables and explain them with a few “factors” or “components”

Factor Analysis Basics. Why Factor? Combine similar variables into more meaningful factors. Reduce the number of variables dramatically while retaining.

Principal Component Analysis

Multivariate statistical methods. Multivariate methods multivariate dataset – group of n objects, m variables (as a rule n>m, if possible). confirmation.

Chapter 14 EXPLORATORY FACTOR ANALYSIS. Exploratory Factor Analysis  Statistical technique for dealing with multiple variables  Many variables are reduced.

FACTOR ANALYSIS & SPSS. First, let’s check the reliability of the scale Go to Analyze, Scale and Reliability analysis.

Chapter 12 REGRESSION DIAGNOSTICS AND CANONICAL CORRELATION.

Lecture 2 Survey Data Analysis Principal Component Analysis Factor Analysis Exemplified by SPSS Taylan Mavruk.

Factor and Principle Component Analysis

FACTOR ANALYSIS & SPSS.

Exploratory Factor Analysis

EXPLORATORY FACTOR ANALYSIS (EFA)

Analysis of Survey Results

Dimension Reduction in Workers Compensation

Factor analysis Advanced Quantitative Research Methods

Dimension Reduction via PCA (Principal Component Analysis)

Happiness comes not from material wealth but less desire.

Measuring latent variables

Analysing data from a questionnaire:

Happiness comes not from material wealth but less desire.

Treat everyone with sincerity,

Measuring latent variables

Descriptive Statistics vs. Factor Analysis

Happiness comes not from material wealth but less desire.

Measuring latent variables

The greatest blessing in life is

EPSY 5245 EPSY 5245 Michael C. Rodriguez

Principal Components Analysis

Principal Component Analysis

Chapter_19 Factor Analysis

Factor Analysis (Principal Components) Output

Applied Statistics Using SPSS

Applied Statistics Using SPSS

Lecture 8: Factor analysis (FA)

Applied Statistics Using SPSS

Measuring latent variables

Presentation transcript:

Applied Statistics Using SAS and SPSS Topic: Factor Analysis By Prof Kelly Fan, Cal State Univ, East Bay

Outline Introduction Principal component analysis Rotations Using communalities other than one

Introduction Reduce data Summarize many ordinal categorical factors by a few combinations of them (new factors)

Example. 6 Questions Goal: a measure of depression and a measure of happiness (how pleasant) 6 questions with response using number 1 to 7. The smaller the number is, the stronger the subject agrees. 4: no opinion

Example. 6 Questions I usually feel blue. People often stare at me. I think that people are following me. I am usually happy. Someone is trying to hurt me. I enjoy going to parties. Q. Which questions will a depressed person likely agree with? A happy person?

Data Set: Subj 1 2 3 4 5 6 7 8 9 Q u e s t i o n

Data Set: Subj 10 11 12 13 14 15 Q u e s t i o n 1 6 3 5 2 7 4

Principal Component Analysis Analyze >> Data Reduction >> Factor… The bigger the eigenvalue is, the more information this factor (component) carries.

A Visual Tool: Scree Plot

Communalities Communalities represent how much variance in the original variables is explained by all of the factors kept in the analysis.

SPSS Output

SPSS Output

A Visual Tool: Component Plot (Loading plots in SPSS)

Discussion Q4 & Q6 are highly and positively correlated and so should be at the same direction of any factor (here component 1 & 2) Similarly, the other questions should be at the same direction of factor 1 & 2 (component 1 & 2) Need a rotation!!

Rotation: Varimax Method

Varimax rotation

Before and After Rotation

Rotation: Promax Method (optional) Used when factors (depression/happiness) are allowed to be correlated (non-orthogonal)

Using Communalities Other Than One When the original questions are not equally important Different methods of “extraction”

Un-weighted Least Squares Initial communality of a question is the R^2 (squared multiple correlation) of regressing all others against this question

Before and After Varimax Rotation

Varimax rotation