SPSS' for Windows'Advanced StatisticsRelease 6.0

Mariju J. Narusis! SPSS Inc.

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1 Logistic Regression Analysis 1The Logistic Regression Model 2

An Example 3Coefficients for the Logistic Model 4Interpreting the. Regression Coefficients 6Assessing the Goodness of Fit of the Model 7Categorical Variables 11Interaction Terms 14Selecting Predictor Variables 14Diagnostic Methods 19

How to Obtain a Logistic Regression Analysis 23Selecting a Subset of Cases for Analysis 25Defining Categorical Variables 26

.̂ Saving New Variables 27Options 28Additional Features Available with Command Syntax 30

2 General Factorial Analysis of Variance 31A Simple Factorial Design * 31

Where Are We? . 32.Describing the Data 33Equality of Variances 35The Analysis-of-Variance Table 36Full Factorial Model 37Examining Contrasts 37 •*%Simple Contrasts 39The Problem of Multiple Comparisons 40Measuring Effect Size 41Power Computations 41Examining Residuals 42 »

An Analysis-of-Covariance Model 44Testing for Equal Slopes 45

VII

The Analysis-of-Covariance Table 46More Than One Factor 48Types of Sums of Squares 48

How to Obtain a General Factorial Analysis of Variance 48Defining Levels o^Factor Variables 50Specifying Contrasts 50Specifying the MANOVA Model 52Options 55 ;Additional Features Available with Command Syntax 56

Multivariate Analysis of Variance 57Multivariate Analysis of Variance 58

Assumptions 58One-Sample Hotelling's T2 59Descriptive Statistics 60Further Displays for Checking Assumptions 61Testing Hypotheses 67Univariate Tests 69The Two-Sample Multivariate T Test 69

- ~~A Multivariate Factorial Design 79

How to Obtain a Multivariate Analysis of Variance 96Defining Levels of Factor Variables 97Specifying Contrasts 98Specifying the MANOVA Model 100Options 103Additional Features Available with Command Syntax 105

Repeated Measures Analysis of Variance 107Repeated Measures 107 •

Describing the Data 108Analyzing Differences 110Testing for Differences 112Choosing Multivariate or Univariate Results 116Selecting Other Contrasts 117Adding Another Factor 118Testing Hypotheses 120

VIII

Within-Subjects and Between-Subjects Factors 124Analysis of Covariance with a Constant Covariate 129Doubly Multivariate Repeated Measures Designs 132

How to Obtain a Repeated Measures ANOVA 132Doubly Multivariate Repeated Measures 133Within-Subject Variables and Between-Subjects Factors 134Co.variates 135Contrasts 137Specifying the Model 140Options 142Additional Features Available with Command Syntax 144

Hierarchical Loglinear Models 145Loglinear Models 146

A Fully Saturated Model 146The Independence Model 152Hierarchical Models 156Model Selection 157Partitioning the Chi-square Statistic 157Testing Individual Terms in the Model 160Model Selection Using Backward Elimination 162

SPSS Loglinear Analysis Procedures 164

How to Obtain a Hierarchical Loglinear Analysis 164Defining Factor Ranges • 165

, Model Specification 165Options' 167Additional Features Available with Command Syntax 169

Further Topics in Loglinear Models 171. •Loglinear Models 171 **

Frequency Table Models 171Fitting a Logit Model 175Fitting an Unsaturated Logit Model 180Fitting a More Complicated Logit ModelThe Equivalent Loglinear Model 184

Models for Ordinal Data 184The Linear-by-Linear Association Model 186Row^ and Column-Effects Models 187

Incomplete Tables 189Testing Real agaiast Ideal 189•Quasi-lndependerice 190Symmetry Models 191Adjusted Quasj-Symmetry 195

An Ordinal Model for Real versus Ideal 196Parameter Estimates 197The Design Matrix 199

SPSS Loglinear Analysis Procedures 201

How to Obtain a General Loglinear Analysis 201.,Defining Factor Ranges 202Contrast Specification 203Model Specification 204Options 206Additional Features Available with Command Syntax 207

How to Obtain a Logit Loglinear Analysis 207_Defining Ranges of Categorical Variables 209

Contrast Specification 209Model Specification .210Options 212Additional Features Available with Command Syntax 213

Nonlinear Regression 215What Is a Nonlinear Model? 215

Transforming Nonlinear Models 216Intrinsically Nonlinear Models 217Fitting the Logistic Population Growth Model 217Estimating Starting Values 222Computational Problems • 224Additional Nonlinear Regression Options 224

How to Obtain a Nonlinear Regression Analysis 224Defining Model Parameters 226Defining a Loss Function 227Specifying Parameter Constraints 228Saving New Variables 229Options 229Additional Features Available with Command Syntax 232

8 Probit Analysis 233Probit and Logit Response Models 233

An Example 234Comparing Several Groups 237Comparing Relative Potencies of the Agents 239Estimating the Natural Response Rate 241More than One Stimulus Variable 241

How to Obtain a. Probit Analysis 241Defining Factor Range 243

v- Options 244Additional Features Available with Command Syntax 246

9 Life Tables 247The Follow-up Life Table 247

A Personnel Example 247, Assumptions Needed to Use the Life Table 251

Lost to Follow-up 252Plotting Survival Functions 252Comparing Survival Functions 253

How to Obtain Life Tables^ 253Define Event for Status Variable 255Define Range for Factor Variable 255Options 256Additional Features Available with Command Syntax .257

XI

Kaplan-Meier Survival Analysis 259Kaplan-Meier Estimators 259

The SPSS Kaplan-Meier Table 261Plotting the Cumulative Survival 263

Comparing Survival Functions 263Stratified Comparisons of Survival Functions 266

How to Obtain a. Kaplan-Meier Survival Analysis 268Define EvenHor Status Variable 270Comparing Factor Levels -270Saving New Variables 272Options 272Additional Features Available with Command Syntax 273

Cox Regression 275The Cox Regression Model 275

The Hazard Function 277Multiple Covariates .279Testing the Model 281

-Select[ng_Predi.ctor Variables 282Plotting the Estimated Functions 287Checking the. Proportional Hazards Assumption 288Time-Dependent Covariates 289

Graphical Displays 291Looking for Influential Cases 292Examination of Residuals 293

SPSS Cox Regression Procedures 295

How to Obtain a Time-Constant Cox Regression Analysis 295Define Event for Status Variable 298Defining Categorical Predictors - 298Plots 300Saving'New Variables 301Options 302Additional Features Available with Command Syntax 303

XII

How to Obtain a Time-Dependent Cox Regression Analysis 304Defining the Time-Dependent Cox Regression Model 305Define Event for Status Variable 307Defining Categorical Predictors 308Saving the Change in Regression Coefficients 310Options 310Additional Features Available with Command Syntax 312

Writing Your Own Program: The Matrix Language 313Nearest-Neighbor Discriminant Analysis 313

The Matrix Job 314

Repeated Measures Analysis of Categorical Data 318The Matrix Job 320

Using the Matrix Language 322Using Matrix Commands in a Syntax Window 323

Syntax ReferenceIntroduction 327

COXREG 333

HILOGLINEAR 347

KM 356

LOGISTIC REGRESSION 365

L 6 G L I N E " A R " ~ 3 7 8

MANOVA: Overview 391

MANOVA: Univariate 394

MANOVA: Multivariate 420

MANOVA: Repeated Measures 433

MATRIX—END MATRIX 442

NLR 487

PROBIT 503

SURVIVAL 512

XIII

Appendix AMatrix Macros 525Limitations of Macros 525

Using the Macros »526

Canonical Correlation 527Using the CANCORR Macro 530

Discriminant Holdout Classification 531Using the DISCLASS Macro 533

Ridge Regression 534Using the RIDGEREG Macro 536

Appendix BCategorical Variable Coding Schemes 539

Deviation 539Simpje 540Helmert 541Difference 541Polynomial 542RepeafecT 543Special 543Indicator 544

Bibliography 545

Subject Index 551

Syntax Index 567

XIV