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Using Multivariate Statistics

ISBN: 9780321056771 | 0321056779
Edition: 4th
Format: Hardcover
Publisher: Allyn & Bacon
Pub. Date: 1/1/2001

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SummaryTable of Contents
This text takes a practical approach to multivariate data analysis, with an introduction to the most commonly encountered statistical and multivariate techniques. Using Multivariate Statistics provides practical guidelines for conducting numerous types of multivariate statistical analyses. It gives syntax and output for accomplishing many analyses through the most recent releases of SAS, SPSS, and SYSTAT, some not available in software manuals. The book maintains its practical approach, still focusing on the benefits and limitations of applicat... MORE
Prefacexxv
Introduction
1(16)
Multivariate Statistics: Why?
1(4)
... MOREThe Domain of Multivariate Statistics: Numbers of IVs and DVs
1(1)
Experimental and Nonexperimental Research
2(1)
Multivariate Statistics in Nonexperimental Research
3(1)
Multivariate Statistics in Experimental Research
3(1)
Computers and Multivariate Statistics
4(1)
Program Updates
4(1)
Garbage In, Roses Out?
5(1)
Why Not?
5(1)
Some Useful Definitions
5(5)
Continuous, Discrete, and Dichotomous Data
5(2)
Samples and Populations
7(1)
Descriptive and Inferential Statistics
7(1)
Orthogonality
8(1)
Standard and Sequential Analysis
9(1)
Combining Variables
10(1)
Number and Nature of Variables to Include
11(1)
Statistical Power
11(1)
Data Appropriate for Multivariate Statistics
12(4)
The Data Matrix
12(1)
The Corelation Matrix
13(1)
The Variance-Covariance Matrix
14(1)
The Sum-of-Squares and Cross-Products Matrix
14(2)
Residuals
16(1)
Organization of the Book
16(1)
A Guide to Statistical Techniques: Using the Book
17(14)
Research Questions and Associated Techniques
17(9)
Degree of Relationship among Variables
17(1)
Bivariate r
17(1)
Multiple R
18(1)
Sequential R
18(1)
Canonical R
18(1)
Multiway Frequency Analysis
19(1)
Significance of Group Differences
19(1)
One-Way ANOVA and t Test
19(1)
One-Way ANCOVA
19(1)
Factorial ANOVA
20(1)
Factorial ANCOVA
20(1)
Hotelling's T2
20(1)
One-Way MANOVA
21(1)
One-Way MANCOVA
21(1)
Factorial MANOVA
21(1)
Factorial MANCOVA
22(1)
Profile Analysis
22(1)
Prediction of Group Membership
23(1)
One-Way Discriminant Function
23(1)
Sequential One-Way Discriminant Function
23(1)
Multiway Frequency Analysis (Logit)
24(1)
Logistic Regression
24(1)
Sequential Logistic Regression
24(1)
Factorial Discriminant Function
24(1)
Sequential Factorial Discriminant Function
25(1)
Structure
25(1)
Principal Components
25(1)
Factor Analysis
25(1)
Structural Equation Modeling
26(1)
Time Course of Events
26(1)
Survival/Failure Analysis
26(1)
Time-Series Analysis
26(1)
A Decision Tree
26(3)
Technique Chapters
29(1)
Preliminary Check of the Data
30(1)
Review of Univariate and Bivariate Statistics
31(25)
Hypothesis Testing
31(4)
One-Sample z Test as Prototype
31(3)
Power
34(1)
Extensions of the Model
35(1)
Analysis of Variance
35(16)
One-Way Between-Subjects ANOVA
36(4)
Factorial Between-Subjects ANOVA
40(1)
Within-Subjects ANOVA
41(3)
Mixed Between-Within-Subjects ANOVA
44(1)
Design Complexity
45(1)
Nesting
45(1)
Latin-Square Designs
46(1)
Unequal n and Nonorthogonality
46(1)
Fixed and Random Effects
47(1)
Specific Comparisons
47(1)
Weighting Coefficients for Comparisons
48(1)
Orthogonality of Weighting Coefficients
48(1)
Obtained F for Comparisons
49(1)
Critical F for Planned Comparisons
50(1)
Critical F for Post Hoc Comparisons
50(1)
Parameter Estimation
51(1)
Strength of Association
52(1)
Bivariate Statistics: Correlation and Regression
53(2)
Correlation
53(1)
Regression
54(1)
Chi-Square Analysis
55(1)
Cleaning Up Your Act: Screening Data Prior to Analysis
56(55)
Important Issues in Data Screening
57(29)
Accuracy of Data File
57(1)
Honest Correlations
57(1)
Inflated Correlation
57(1)
Deflated Correlation
57(1)
Missing Data
58(1)
Deleting Cases or Variables
59(1)
Estimating Missing Data
60(4)
Using a Missing Data Correlation Matrix
64(1)
Treating Missing Data as Data
65(1)
Repeating Analyses with and without Missing Data
65(1)
Choosing among Methods for Dealing with Missing Data
65(1)
Outliers
66(1)
Detecting Univariate and Multivariate Outliers
67(3)
Describing Outliers
70(1)
Reducing the Influence of Outliers
71(1)
Outliers in a Solution
71(1)
Normality, Linearity, and Homoscedasticity
72(1)
Normality
73(4)
Linearity
77(2)
Homoscedasticity, Homogeneity of Variance, and Homogeneity of Variance-Covariance Matrices
79(1)
Common Data Transformations
80(2)
Multicollinearity and Singularity
82(3)
A Checklist and Some Practical Recommendations
85(1)
Complete Examples of Data Screening
86(25)
Screening Ungrouped Data
86(1)
Accuracy of Input, Missing Data, Distributions, and Univariate Outliers
87(3)
Linearity and Homoscedasticity
90(2)
Transformation
92(1)
Detecting Multivariate Outliers
92(2)
Variables Causing Cases to be Outliers
94(4)
Multicollinearity
98(1)
Screening Grouped Data
99(1)
Accuracy of Input, Missing Data, Distributions, Homogeneity of Variance, and Univariate Outliers
99(3)
Linearity
102(2)
Multivariate Outliers
104(3)
Variables Causing Cases to be Outliers
107(1)
Multicollinearity
108(3)
Multiple Regression
111(66)
General Purpose and Description
111(1)
Kinds of Research Questions
112(3)
Degree of Relationship
113(1)
Importance of IVs
113(1)
Adding IVs
113(1)
Changing IVs
113(1)
Contingencies among IVs
114(1)
Comparing Sets of IVs
114(1)
Predicting DV Scores for Members of a New Sample
114(1)
Parameter Estimates
115(1)
Limitations to Regression Analyses
115(7)
Theoretical Issues
115(1)
Practical Issues
116(1)
Ratio of Cases to IVs
117(1)
Absence of Outliers among the IVs and on the DV
117(1)
Absence of Multicollinearity and Singularity
118(1)
Normality, Linearity, Homoscedasticity of Residuals
119(2)
Independence of Errors
121(1)
Outliers in the Solution
122(1)
Fundamental Equations for Multiple Regression
122(9)
General Linear Equations
123(1)
Matrix Equations
124(4)
Computer Analyses of Small-Sample Example
128(3)
Major Types of Multiple Regression
131(8)
Standard Multiple Regression
131(1)
Sequential Multiple Regression
131(2)
Statistical (Stepwise) Regression
133(5)
Choosing among Regression Strategies
138(1)
Some Important Issues
139(14)
Importance of IVs
139(1)
Standard Multiple Regression
140(2)
Sequential or Statistical Regression
142(1)
Statistical Inference
142(1)
Test for Multiple R
142(1)
Test of Regression Components
143(1)
Test of Added Subset of IVs
144(1)
Confidence Limits around B
145(1)
Comparing Two Sets of Predictors
145(2)
Adjustment of R2
147(1)
Suppressor Variables
148(1)
Regression Approach to ANOVA
149(2)
Centering when Interactions and Powers of IVs Are Included
151(2)
Complete Examples of Regression Analysis
153(17)
Evaluation of Assumptions
154(1)
Ratio of Cases to IVs
154(1)
Normality, Linearity, Homoscedasticity, and Independence of Residuals
154(3)
Outliers
157(1)
Multicollinearity and Singularity
157(2)
Standard Multiple Regression
159(6)
Sequential Regression
165(5)
Comparison of Programs
170(7)
SPSS Package
170(5)
SAS System
175(1)
SYSTAT System
176(1)
Canonical Correlation
177(42)
General Purpose and Description
177(1)
Kinds of Research Questions
178(1)
Number of Canonical Variate Pairs
178(1)
Interpretation of Canonical Variates
178(1)
Importance of Canonical Variates
178(1)
Canonical Variate Scores
178(1)
Limitations
178(4)
Theoretical Limitations
178(2)
Practical Issues
180(1)
Ratio of Cases to IVs
180(1)
Normality, Linearity, and Homoscedasticity
180(1)
Missing Data
181(1)
Absence of Outliers
181(1)
Absence of Multicollinearity and Singularity
181(1)
Fundamental Equations for Canonical Correlation
182(16)
Eigenvalues and Eigenvectors
183(2)
Matrix Equations
185(4)
Proportions of Variance Extracted
189(1)
Computer Analyses of Small-Sample Example
190(8)
Some Important Issues
198(1)
Importance of Canonical Variates
198(1)
Interpretation of Canonical Variates
199(1)
Complete Example of Canonical Correlation
199(17)
Evaluation of Assumptions
200(1)
Missing Data
200(1)
Normality, Linearity, and Homoscedasticity
200(3)
Outliers
203(4)
Multicollinearity and Singularity
207(9)
Canonical Correlation
216(1)
Comparison of Programs
216(3)
SAS System
216(1)
SPSS Package
216(2)
SYSTAT System
218(1)
Multiway Frequency Analysis
219(56)
General Purpose and Description
219(1)
Kinds of Research Questions
220(2)
Associations among Variables
220(1)
Effect on a Dependent Variable
221(1)
Parameter Estimates
221(1)
Importance of Effects
221(1)
Strength of Association
221(1)
Specific Comparisons and Trend Analysis
222(1)
Limitations to Multiway Frequency Analysis
222(2)
Theoretical Issues
222(1)
Practical Issues
222(1)
Independence
222(1)
Ratio of Cases to Variables
223(1)
Adequacy of Expected Frequencies
223(1)
Outliers in the Solution
224(1)
Fundamental Equations for Multiway Frequency Analysis
224(26)
Screening for Effects
225(1)
Total Effect
226(1)
First-Order Effects
227(1)
Second-Order Effects
228(4)
Third-Order Effect
232(1)
Modeling
233(2)
Evaluation and Interpretation
235(1)
Residuals
235(1)
Parameter Estimates
236(5)
Computer Analyses of Small-Sample Example
241(9)
Some Important Issues
250(3)
Hierarchical and Nonhierarchical Models
250(1)
Statistical Criteria
251(1)
Tests of Models
251(1)
Test of Individual Effects
251(1)
Strategies for Choosing a Model
252(1)
SPSS HILOGLINEAR (Hierachical)
252(1)
SPSS GENLOG (General Log-linear)
253(1)
SAS CATMOD, SYSTAT LOGLINEAR, and SYSTAT LOGLIN (General Log-linear)
253(1)
Complete Example of Multiway Frequency Analysis
253(17)
Evaluation of Assumptions: Adequacy of Expected Frequencies
253(1)
Hierarchical Log-linear Analysis
254(1)
Preliminary Model Screening
254(2)
Stepwise Model Selection
256(2)
Adequacy of Fit
258(6)
Interpretation of the Selected Model
264(6)
Comparison of Programs
270(5)
SPSS Package
273(1)
SAS System
274(1)
SYSTAT System
274(1)
Analysis of Covariance
275(47)
General Purpose and Description
275(2)
Kinds of Research Questions
277(2)
Main Effects of IVs
278(1)
Interactions among IVs
278(1)
Specific Comparisons and Trend Analysis
278(1)
Effects of Covariates
278(1)
Strength of Association
279(1)
Parameter Estimates
279(1)
Limitations to Analysis of Covariance
279(4)
Theoretical Issues
279(1)
Practical Issues
280(1)
Unequal Sample Sizes, Missing Data, and Ratio of Cases to IVs
280(1)
Absence of Outliers
281(1)
Absence of Multicollinearity and Singularity
281(1)
Normality of Sampling Distributions
281(1)
Homogeneity of Variance
281(1)
Linearity
282(1)
Homogeneity of Regression
282(1)
Reliability of Covariates
283(1)
Fundamental Equations for Analysis of Covariance
283(8)
Sums of Squares and Cross-Products
284(4)
Significance Test and Strength of Association
288(1)
Computer Analyses of Small-Sample Example
289(2)
Some Important Issues
291(13)
Test for Homogeneity of Regression
291(2)
Design Complexity
293(1)
Within-Subjects and Mixed Within-Between Designs
293(3)
Unequal Sample Sizes
296(2)
Specific Comparisons and Trend Analysis
298(3)
Strength of Association
301(1)
Evaluation of Covariates
302(1)
Choosing Covariates
302(1)
Alternatives to ANCOVA
303(1)
Complete Example of Analysis of Covariance
304(15)
Evaluation of Assumptions
305(1)
Unequal n and Missing Data
305(1)
Normality
305(1)
Linearity
305(1)
Outliers
305(4)
Multicollinearity and Singularity
309(1)
Homogeneity of Variance
309(1)
Homogeneity of Regression
310(1)
Reliability of Covariates
310(1)
Analysis of Covariance
310(1)
Main Analysis
310(3)
Evaluation of Covariates
313(2)
Homogeneity of Regression Run
315(4)
Comparison of Programs
319(3)
SPSS Package
319(1)
SYSTAT System
319(2)
SAS System
321(1)
Multivariate Analysis of Variance and Covariance
322(69)
General Purpose and Description
322(3)
Kinds of Research Questions
325(3)
Main Effects of IVs
325(1)
Interactions among IVs
326(1)
Importance of DVs
326(1)
Parameter Estimates
326(1)
Specific Comparisons and Trend Analysis
327(1)
Strength of Association
327(1)
Effects of Covariates
327(1)
Repeated-Measures Analysis of Variance
327(1)
Limitations to Multivariate Analysis of Variance and Covariance
328(4)
Theoretical Issues
328(1)
Practical Issues
328(1)
Unequal Sample Sizes, Missing Data, and Power
329(1)
Multivariate Normality
329(1)
Absence of Outliers
330(1)
Homogeneity of Variance-Covariance Matrices
330(1)
Linearity
330(1)
Homogeneity of Regression
331(1)
Reliability of Covariates
331(1)
Absence of Multicollinearity and Singularity
331(1)
Fundamental Equations for Multivariate Analysis of Variance and Covariance
332(15)
Multivariate Analysis of Variance
332(7)
Computer Analyses of Small-Sample Example
339(1)
Multivariate Analysis of Covariance
340(7)
Some Important Issues
347(10)
Criteria for Statistical Inference
347(1)
Assessing DVs
348(1)
Univariate F
348(2)
Roy-Bargmann Stepdown Analysis
350(1)
Using Discriminant Function Analysis
351(1)
Choosing among Strategies for Assessing DVs
351(1)
Specific Comparisons and Trend Analysis
352(4)
Design Complexity
356(1)
Within-Subjects and Between-Within Designs
356(1)
Unequal Sample Sizes
356(1)
MANOVA vs. ANOVAs
357(1)
Complete Examples of Multivariate Analysis of Variance and Covariance
357(29)
Evaluation of Assumptions
358(1)
Unequal Sample Sizes and Missing Data
358(2)
Multivariate Normality
360(1)
Linearity
360(1)
Outliers
360(1)
Homogeneity of Variance-Covariance Matrices
361(1)
Homogeneity of Regression
362(3)
Reliability of Covariates
365(1)
Multicollinearity and Singularity
365(1)
Multivariate Analysis of Variance
365(11)
Multivariate Analysis of Covariance
376(1)
Assessing Covariates
377(1)
Assessing DVs
377(9)
Comparison of Programs
386(5)
SPSS package
389(1)
SYSTAT System
389(1)
SAS System
390(1)
Profile Analysis: The Multivariate Approach to Repeated Measures
391(65)
General Purpose and Description
391(1)
Kinds of Research Questions
392(2)
Parallelism of Profiles
392(1)
Overall Difference among Groups
393(1)
Flatness of Profiles
393(1)
Contrasts Following Profile Analysis
393(1)
Parameter Estimates
393(1)
Strength of Association
394(1)
Limitations to Profile Analysis
394(2)
Theoretical Issues
394(1)
Practical Issues
394(1)
Sample Size, Missing Data, and Power
394(1)
Multivariate Normality
395(1)
Absence of Outliers
395(1)
Homogeneity of Variance-Covariance Matrices
395(1)
Linearity
395(1)
Absence of Multicollinearity and Singularity
396(1)
Fundamental Equations for Profile Analysis
396(14)
Differences in Levels
396(2)
Parallelism
398(3)
Flatness
401(2)
Computer Analyses of Small-Sample Example
403(7)
Some Important Issues
410(20)
Contrasts in Profile Analysis
410(3)
Parallelism and Flatness Significant, Levels Not Significant (Simple-Effects Analysis)
413(1)
Parallelism and Levels Significant, Flatness Not Significant (Simple-Effects Analysis)
414(2)
Parallelism, Levels, and Flatness Significant (Interaction Contrasts)
416(5)
Only Parallelism Significant
421(1)
Univariate vs. Multivariate Approach to Repeated Measures
421(2)
Doubly-Multivariate Designs
423(6)
Classifying Profiles
429(1)
Imputation of Missing Values
429(1)
Complete Examples of Profile Analysis
430(23)
Profile Analysis of Subscales of the WISC
430(1)
Evaluation of Assumptions
431(4)
Profile Analysis
435(7)
Doubly-Multivariate Analysis of Reaction Time
442(1)
Evaluation of Assumptions
442(4)
Doubly-Multivariate Analysis of Slope and Intercept
446(7)
Comparison of Programs
453(3)
SPSS Package
453(2)
SAS System
455(1)
SYSTAT System
455(1)
Discriminant Function Analysis
456(61)
General Purpose and Description
456(2)
Kinds of Research Questions
458(3)
Significance of Prediction
458(1)
Number of Significant Discriminant Functions
458(1)
Dimensions of Discrimination
459(1)
Classification Functions
459(1)
Adequacy of Classification
459(1)
Strength of Association
460(1)
Importance of Predictor Variables
460(1)
Significance of Prediction with Covariates
460(1)
Estimation of Group Means
460(1)
Limits to Discriminant Function Analysis
461(2)
Theoretical Issues
461(1)
Practical Issues
461(1)
Unequal Sample Sizes, Missing Data, and Power
461(1)
Multivariate Normality
462(1)
Absence of Outliers
462(1)
Homogeneity of Variance-Covariance Matrices
462(1)
Linearity
463(1)
Absence of Multicollinearity and Singularity
463(1)
Fundamental Equations for Discriminant Function Analysis
463(14)
Derivation and Test of Discriminant Functions
464(3)
Classification
467(2)
Computer Analyses of Small-Sample Example
469(8)
Types of Discriminant Function Analysis
477(4)
Direct Discriminant Function Analysis
478(1)
Sequential Discriminant Function Analysis
478(3)
Stepwise (Statistical) Discriminant Function Analysis
481(1)
Some Important Issues
481(11)
Statistical Inference
481(1)
Criteria for Overall Statistical Significance
481(1)
Stepping Methods
482(1)
Number of Discriminant Functions
482(1)
Interpreting Discriminant Functions
483(1)
Discriminant Function Plots
483(1)
Loading Matrices
484(1)
Evaluating Predictor Variables
485(3)
Design Complexity: Factorial Designs
488(1)
Use of Classification Procedures
489(1)
Cross-Validation and New Cases
489(1)
Jackknifed Classification
490(1)
Evaluating Improvement in Classification
490(2)
Complete Example of Discriminant Function Analysis
492(17)
Evaluation of Assumptions
492(1)
Unequal Sample Sizes and Missing Data
492(1)
Multivariate Normality
492(1)
Linearity
493(1)
Outliers
493(1)
Homogeneity of Variance-Covariance Matrices
493(1)
Multicollinearity and Singularity
493(4)
Direct Discriminant Function Analysis
497(12)
Comparison of Programs
509(8)
SPSS Package
515(1)
SYSTAT System
516(1)
SAS System
516(1)
Logistic Regression
517(65)
General Purpose and Description
517(1)
Kinds of Research Questions
518(3)
Prediction of Group Membership or Outcome
518(1)
Importance of Predictors
518(1)
Interactions among Predictors
518(2)
Parameter Estimates
520(1)
Classification of Cases
520(1)
Significance of Prediction with Covariates
520(1)
Strength of Association
520(1)
Limitations to Logistic Regression Analysis
521(2)
Theoretical Issues
521(1)
Practical Issues
521(1)
Ratio of Cases to Variables
521(1)
Adequacy of Expected Frequencies and Power
522(1)
Linearity in the Logit
522(1)
Absence of Multicollinearity
522(1)
Absence of Outliers in the Solution
523(1)
Independence of Errors
523(1)
Fundamental Equations for Logistic Regression
523(10)
Testing and Interpreting Coefficients
524(1)
Goodness-of-Fit
525(2)
Comparing Models
527(1)
Interpretation and Analysis of Residuals
527(1)
Computer Analyses of Small-Sample Example
527(6)
Types of Logistic Regression
533(3)
Direct Logistic Regression
533(1)
Sequential Logistic Regression
533(2)
Stepwise (Statistical) Logistic Regression
535(1)
Probit and Other Analyses
535(1)
Some Important Issues
536(14)
Statistical Inference
536(1)
Assessing Goodness-of-Fit of Models
537(2)
Tests of Individual Variables
539(1)
Number and Type of Outcome Categories
539(1)
Unordered Response Categories with SYSTAT LOGIT
540(2)
Ordered Response Categories with SAS LOGISTIC
542(3)
Strength of Association for a Model
545(1)
Coding Outcome and Predictor Categories
546(1)
Classification of Cases
547(1)
Hierarchical and Nonhierarchical Analysis
548(1)
Interpretation of Coefficients using Odds
548(1)
Importance of Predictors
549(1)
Logistic Regression for Matched Groups
550(1)
Complete Examples of Logistic Regression
550(25)
Evaluation of Limitations
551(1)
Ratio of Cases to Variables and Missing Data
551(3)
Adequacy of Expected Frequencies
554(4)
Linearity in the Logit
558(1)
Multicollinearity
558(1)
Outliers in the Solution
559(1)
Direct Logistic Regression with Two-Category Outcome
559(4)
Sequential Logistic Regression with Three Categories of Outcome
563(12)
Comparisons of Programs
575(7)
SPSS Package
575(5)
SAS System
580(1)
SYSTAT System
581(1)
Principal Components and Factor Analysis
582(71)
General Purpose and Description
582(3)
Kinds of Research Questions
585(1)
Number of Factors
585(1)
Nature of Factors
586(1)
Importance of Solutions and Factors
586(1)
Testing Theory in FA
586(1)
Estimating Scores on Factors
586(1)
Limitations
586(4)
Theoretical Issues
586(1)
Practical Issues
587(1)
Sample Size and Missing Data
588(1)
Normality
588(1)
Linearity
588(1)
Absence of Outliers among Cases
588(1)
Absence of Multicollinearity and Singularity
589(1)
Factorability of R
589(1)
Absence of Outliers among Variables
589(1)
Fundamental Equations for Factor Analysis
590(19)
Extraction
591(4)
Orthogonal Rotation
595(1)
Communalities, Variance, and Covariance
596(1)
Factor Scores
597(3)
Oblique Rotation
600(3)
Computer Analyses of Small-Sample Example
603(6)
Major Types of Factor Analysis
609(10)
Factor Extraction Techniques
609(1)
PCA vs. FA
610(2)
Principal Components
612(1)
Principal Factors
612(1)
Image Factor Extraction
612(1)
Maximum Likelihood Factor Extraction
613(1)
Unweighted Least Squares Factoring
613(1)
Generalized (Weighted) Least Squares Factoring
613(1)
Alpha Factoring
613(1)
Rotation
614(1)
Orthogonal Rotation
614(2)
Oblique Rotation
616(1)
Geometric Interpretation
616(2)
Some Practical Recommendations
618(1)
Some Important Issues
619(8)
Estimates of Communalities
619(1)
Adequacy of Extraction and Number of Factors
620(2)
Adequacy of Rotation and Simple Structure
622(1)
Importance and Internal Consistency of Factors
623(2)
Interpretation of Factors
625(1)
Factor Scores
626(1)
Comparisons among Solutions and Groups
627(1)
Complete Example of FA
627(21)
Evaluation of Limitations
628(1)
Sample Size and Missing Data
628(1)
Normality
628(1)
Linearity
628(1)
Outliers
628(5)
Multicollinearity and Singularity
633(1)
Factorability of R
633(1)
Outliers among Variables
633(1)
Principal Factors Extraction with Varimax Rotation
633(15)
Comparison of Programs
648(5)
SPSS Package
648(4)
SAS System
652(1)
SYSTAT System
652(1)
Structural Equation Modeling
653(119)
Jodie B. Ullman
General Purpose and Description
653(4)
Kinds of Research Questions
657(2)
Adequacy of the Model
657(1)
Testing Theory
657(1)
Amount of Variance in the Variables Accounted for by the Factors
657(1)
Reliability of the Indicators
657(1)
Parameter Estimates
657(1)
Mediation
658(1)
Group Differences
658(1)
Longitudinal Differences
658(1)
Multilevel Modeling
658(1)
Limitations to Structural Equation Modeling
659(2)
Theoretical Issues
659(1)
Practical Issues
659(1)
Sample Size and Missing Data
659
Multivariate Normality and Absence of Outliers
600(60)
Linearity
660(1)
Absence of Multicollinearity and Singularity
660(1)
Residuals
661(1)
Fundamental Equations for Structural Equations Modeling
661(30)
Covariance Algebra
661(2)
Model Hypotheses
663(2)
Model Specification
665(2)
Model Estimation
667(5)
Model Evaluation
672(2)
Computer Analysis of Small-Sample Example
674(17)
Some Important Issues
691(28)
Model Identification
691(3)
Estimation Techniques
694(2)
Estimation Methods and Sample Size
696(1)
Estimation Methods and Nonnormality
697(1)
Estimation Methods and Dependence
697(1)
Some Recommendations for Choice of Estimation Method
697(1)
Assessing the Fit of the Model
697(1)
Comparative Fit Indices
698(2)
Absolute Fit Index
700(1)
Indices of Proportion of Variance Accounted
700(1)
Degree of Parsimony Fit Indices
701(1)
Residual-Based Fit Indices
702(1)
Choosing among Fit Indices
702(1)
Model Modification
703(1)
Chi-Square Difference Test
703(1)
Lagrange Multiplier Test (LM)
703(10)
Wald Test
713(2)
Some Caveats and Hints on Model Modification
715(1)
Reliability and Proportion of Variance
715(1)
Discrete and Ordinal Data
716(1)
Multiple Group Models
717(1)
Mean and Covariance Structure Models
718(1)
Complete Examples of Structural Equation Modeling Analysis
719(45)
Confirmatory Factor Analysis of the WISC
719(1)
Model Specification for CFA
719(1)
Evaluation of Assumptions for CFA
719(2)
CFA Model Estimation and Preliminary Evaluation
721(9)
Model Modification
730(7)
SEM of Health Data
737(1)
SEM Model Specification
737(1)
Evaluation of Assumptions for SEM
738(4)
Model Estimation and Preliminary Evaluation
742(3)
Model Modification
745(19)
Comparison of Programs
764(8)
EQS
764(1)
LISREL
764(7)
SAS
771(1)
AMOS
771(1)
Survival/Failure Analysis
772(65)
General Purpose and Description
772(1)
Kinds of Research Questions
773(2)
Proportions Surviving at Various Times
773(1)
Group Differences in Survival
774(1)
Survival Time with Covariates
774(1)
Treatment Effects
774(1)
Importance of Covariates
774(1)
Parameter Estimates
774(1)
Contingencies among Covariates
774(1)
Strength of Association and Power
774(1)
Limitations to Survival Analysis
775(1)
Theoretical Issues
775(1)
Practical Issues
775(1)
Sample Size and Missing Data
775(1)
Normality of Sampling Distributions, Linearity, and Homoscedasticity
775(1)
Absence of Outliers
775(1)
Differences between Withdrawn and Remaining Cases
776(1)
Change in Survival Conditions over Time
776(1)
Proportionality of Hazards
776(1)
Absence of Multicollinearity
776(1)
Fundamental Equations for Survival Analysis
776(15)
Life Tables
777(1)
Standard Error of Cumulative Proportion Surviving
778(1)
Hazard and Density Functions
779(1)
Plot of Life Tables
780(1)
Test for Group Differences
781(2)
Computer Analyses of Small-Sample Example
783(8)
Types of Survival Analysis
791(14)
Actuarial and Product-Limit Life Tables and Survivor Functions
791(5)
Prediction of Group Survival Times from Covariates
796(1)
Direct, Sequential, and Statistical Analysis
796(1)
Cox Proportional-Hazards Model
797(1)
Accelerated Failure-Time Model
797(7)
Choosing a Method
804(1)
Some Important Issues
805(8)
Proportionality of Hazards
805(2)
Censored Data
807(1)
Right-Censored Data
807(1)
Other Forms of Censoring
808(1)
Strength of Association and Power
808(1)
Statistical Criteria
809(1)
Test Statistics for Group Differences in Survival Functions
809(1)
Test Statistics for Prediction from Covariates
809(2)
Odds Ratios
811(2)
Complete Example of Survival Analysis
813(16)
Evaluation of Assumptions
814(1)
Accuracy of Input, Adequacy of Sample Size, Missing Data, and Distributions
814(1)
Outliers
814(2)
Differences between Withdrawn and Remaining Cases
816(3)
Change in Survival Experience over Time
819(1)
Proportionality of Hazards
820(1)
Multicollinearity
821(1)
Cox Regression Survival Analysis
822(1)
Effect of Drug Treatment
822(3)
Evaluation of Other Covariates
825(4)
Comparison of Programs
829(8)
SAS System
829(1)
SYSTAT System
829(7)
SPSS Package
836(1)
Time-Series Analysis
837(64)
General Purpose and Description
837(2)
Kinds of Research Questions
839(3)
Pattern of Autocorrelation
841(1)
Seasonal Cycles and Trends
841(1)
Forecasting
841(1)
Effect of an Intervention
841(1)
Comparing Time Series
841(1)
Time Series with Covariates
842(1)
Strength of Association and Power
842(1)
Assumptions of Time-Series Analysis
842(1)
Theoretical Issues
842(1)
Practical Issues
842(1)
Normality of Distributions of Residuals
842(1)
Homogeneity of Variance and Zero Mean of Residuals
843(1)
Independence of Residuals
843(1)
Absence of Outliers
843(1)
Fundamental Equations for Time-Series ARIMA Models
843(22)
Identification of ARIMA (p, d, q) Models
844(1)
Trend Components, d: Making the Process Stationary
844(3)
Auto-Regressive Components
847(1)
Moving Average Components
848(1)
Mixed Models
848(1)
ACFs and PACFs
849(5)
Estimating Model Parameters
854(1)
Diagnosing a Model
855(1)
Computer Analysis of Small-Sample Time-Series Example
855(10)
Types of Time-Series Analysis
865(13)
Models with Seasonal Components
865(4)
Models with Interventions
869(1)
Abrupt, Permanent Effects
870(1)
Abrupt, Temporary Effects
870(2)
Gradual, Permanent Effects
872(5)
Models with Multiple Interventions
877(1)
Adding Continuous Variables
877(1)
Some Important Issues
878(6)
Patterns of ACFs and PACFs
878(3)
Strength of Association
881(1)
Forecasting
882(2)
Statistical Methods for Comparing Two Models
884(1)
Complete Example of a Time-Series Analysis
884(13)
Evaluation of Assumptions
884(1)
Normality of Sampling Distributions
884(1)
Homogeneity of Variance
885(1)
Outliers
885(1)
Baseline Model Identification and Estimation
885(7)
Baseline Model Diagnosis
892(1)
Intervention Analysis
893(1)
Model Diagnosis
893(1)
Model Interpretation
893(4)
Comparison of Programs
897(4)
SPSS Package
897(1)
SAS System
897(3)
SYSTAT System
900(1)
An Overview of the General Linear Model
901(7)
Linearity and the General Linear Model
901(1)
Bivariate to Multivariate Statistics and Overview of Techniques
901(6)
Bivariate Form
901(1)
Simple Multivariate Form
902(2)
Full Multivariate Form
904(3)
Alternative Research Strategies
907(1)
Appendix A A Skimpy Introduction to Matrix Algebra908(10)
A.1 The Trace of a Matrix
909(1)
A.2 Addition or Subtraction of a Constant to a Matrix
909(1)
A.3 Multiplication or Division of a Matrix by a Constant
909(1)
A.4 Addition and Subtraction of Two Matrices
910(1)
A.5 Multiplication, Transposes, and Square Roots of Matrices
911(2)
A.6 Matrix ``Division'' (Inverses and Determinants)
913(1)
A.7 Eigenvalues and Eigenvectors: Procedures for Consolidating Variance from a Matrix
914(4)
Appendix B Research Designs for Complete Examples918(7)
B.1 Women's Health and Drug Study
918(1)
B.2 Sexual Attraction Study
919(3)
B.3 Learning Disabilities Data Bank
922(1)
B.4 Reaction Time to Identify Figures
923(1)
B.5 Clinical Trial for Primary Biliary Cirrhosis
923(1)
B.6 Impact of Seat Belt Law
924(1)
Appendix C Statistical Tables925(12)
C.1 Normal Curve Areas
926(1)
C.2 Critical Values of the t Distribution for α = .05 and .01, Two-Tailed Test
927(1)
C.3 Critical Values of the F Distribution
928(5)
C.4 Critical Values of Chi Square (x2)
933(1)
C.5 Critical Values for Square Multiple Correlation (R2) in Forward Stepwise Selection α = .05
934(2)
C.6 Critical Values for Fmax (S2max/S2min) Distribution for α = .05 and .01
936(1)
References937(8)
Index945

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