Advanced Statistical Analysis
Advanced Statistical Analysis Using SPSS
INTRODUCTION AND OVERVIEW
INTRODUCTION
COURSE GOALS
TAXONOMY OF METHODS
GENERAL APPROACH
DISCRIMINANT ANALYSIS
HOW DOES DISCRIMINANT ANALYSIS WORK?
THE ELEMENTS OF A DISCRIMINANT ANALYSIS
THE DISCRIMINANT MODEL
HOW CASES ARE CLASSIFIED
ASSUMPTIONS OF DISCRIMINANT ANALYSIS
ANALYSIS TIPS
A TWO-GROUP DISCRIMINANT EXAMPLE
CHECKING VARIANCE ASSUMPTION
RUNNING A DISCRIMINANT ANALYSIS
DISCRIMINANT COEFFICIENTS
CLASSIFICATION STATISTICS
PREDICTION
ASSUMPTION OF EQUAL COVARIANCE
MODIFYING THE LIST OF PREDICTORS
CASEWISE STATISTICS AND OUTLIERS
ADJUSTING PRIOR PROBABILITIES
VALIDATING THE DISCRIMINANT MODEL
STEPWISE MODEL SELECTION
THREE–GROUP DISCRIMINANT ANALYSIS
BINARY LOGISTIC REGRESSION
HOW LOGISTIC REGRESSION WORKS
THE LOGISTIC EQUATION
ELEMENTS OF LOGISTIC REGRESSION ANALYSIS
ASSUMPTIONS OF LOGISTIC REGRESSION
LOGISTIC REGRESSION EXAMPLE: LOW BIRTH WEIGHT
ACCURACY OF PREDICTION
INTERPRETING LOGISTIC REGRESSION COEFFICIENTS
MAKING PREDICTIONS
ESTIMATED PROBABILITIES
CHECKING CLASSIFICATIONS
RESIDUAL ANALYSIS
STEPWISE LOGISTIC REGRESSION
ROC CURVES
APPENDIX: COMPARISON TO DISCRIMINANT ANALYSIS
MULTINOMIAL LOGISTIC REGRESSION
MULTINOMIAL LOGISTIC MODEL
A MULTINOMIAL LOGISTIC ANALYSIS: PREDICTING CREDIT RISK
INTERPRETING COEFFICIENTS
CLASSIFICATION TABLE
MAKING PREDICTIONS
APPENDIX: MULTINOMIAL LOGISTIC WITH A TWO-CATEGORY OUTCOME
SURVIVAL ANALYSIS
WHAT IS SURVIVAL ANALYSIS?
CONCEPTS
CENSORING
WHAT TO LOOK FOR IN SURVIVAL ANALYSIS
SURVIVAL PROCEDURES IN SPSS
AN EXAMPLE: KAPLAN-MEIER
COX REGRESSION
AN EXAMPLE: COX REGRESSION
CHECKING THE PROPORTIONAL HAZARDS ASSUMPTION
APPENDIX: BRIEF EXAMPLE OF COX REGRESSION WITH A TIME-VARYING COVARIATE
CLUSTER ANALYSIS
HOW DOES CLUSTER ANALYSIS WORK?
DATA TYPES IN CLUSTERING
WHAT TO LOOK FOR WHEN CLUSTERING
METHODS
HIERARCHICAL METHODS
NON-HIERARCHICAL METHOD: K-MEANS CLUSTERING
NON-HIERARCHICAL METHOD: TWOSTEP CLUSTERING
DISTANCE AND STANDARDIZATION
OVERALL RECOMMENDATIONS
EXAMPLE I: HIERARCHICAL CLUSTERING OF PRODUCT DATA
CLUSTER RESULTS
EXAMPLE II: K-MEANS CLUSTERING OF USAGE DATA
K-MEANS RESULTS
EXAMPLE III: TWOSTEP CLUSTERING OF TELECOM DATA
FACTOR ANALYSIS
USES OF FACTOR ANALYSIS
WHAT TO LOOK FOR WHEN RUNNING FACTOR ANALYSIS
PRINCIPLES
THE IDEA OF A PRINCIPAL COMPONENT
FACTOR ANALYSIS VERSUS PRINCIPAL COMPONENTS
NUMBER OF FACTORS
ROTATION
FACTOR SCORES AND SAMPLE SIZE
METHODS
OVERALL RECOMMENDATIONS
AN EXAMPLE: 1988 OLYMPIC DECATHLON PERFORMANCES
PRINCIPAL COMPONENTS WITH ORTHOGONAL ROTATION
PRINCIPAL AXIS FACTORING WITH AN OBLIQUE ROTATION
ADDITIONAL CONSIDERATIONS
LOGLINEAR MODELS
WHAT ARE LOGLINEAR MODELS?
RELATIONS AMONG LOGLINEAR, LOGIT MODELS, AND LOGISTIC REGRESSION
WHAT TO LOOK FOR IN LOGLINEAR AND LOGIT ANALYSIS
ASSUMPTIONS
PROCEDURES IN SPSS THAT RUN LOGLINEAR OR LOGIT ANALYSIS
MODEL SELECTION EXAMPLE: LOCATION PREFERENCE
APPENDIX: LOGIT ANALYSIS WITH SPECIFIC MODEL (GENLOG)
MULTIVARIATE ANALYSIS OF VARIANCE
WHY PERFORM MANOVA?
MANOVA ASSUMPTIONS
WHAT TO LOOK FOR IN MANOVA
EXAMPLE: MEMORY INFLUENCES
POST HOC TESTS
REPEATED MEASURES ANALYSIS OF VARIANCE
WHY DO A REPEATED MEASURES STUDY?
ASSUMPTIONS
EXAMPLE: ONE-FACTOR DRUG STUDY
EXAMINING RESULTS
FURTHER ANALYSES
PLANNED COMPARISON
REPEATED MEASURES WITH MISSING DATA
APPENDIX: AD VIEWING WITH PRE-POST BRAND RATINGS
