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Using Multivariate Statistics 7th edition [Loose-leaf]

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  • Formāts: Loose-leaf, 848 pages
  • Izdošanas datums: 09-Jul-2018
  • Izdevniecība: Pearson
  • ISBN-10: 0134790545
  • ISBN-13: 9780134790541
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Using Multivariate Statistics 7th editionPalielināt
  • Formāts: Loose-leaf, 848 pages
  • Izdošanas datums: 09-Jul-2018
  • Izdevniecība: Pearson
  • ISBN-10: 0134790545
  • ISBN-13: 9780134790541
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780134790541.html
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For advanced undergraduate and graduate courses in Social Statistics. 

An in-depth introduction to today’s most commonly used statistical and multivariate techniques 

Using Multivariate Statistics, 7th Edition presents complex statistical procedures in a way that is maximally useful and accessible to researchers who may not be statisticians. The authors focus on the benefits and limitations of applying  a technique to a data set – when, why, and how to do it. Only a limited knowledge of higher-level mathematics is assumed. 


Students using this text will learn to conduct numerous types of multivariate statistical analyses; find the best technique to use; understand limitations to applications; and learn how to use SPSS and SAS syntax and output.


0134790545 / 9780134790541 Using Multivariate Statistics, 7/e

Preface xiv
1 Introduction 1(14)
1.1 Multivariate Statistics: Why?
1(4)
1.1.1 The Domain of Multivariate Statistics: Numbers of IVs and DVs
2(1)
1.1.2 Experimental and Nonexperimental Research
2(1)
1.1.3 Computers and Multivariate Statistics
3(1)
1.1.4 Garbage In, Roses Out?
4(1)
1.2 Some Useful Definitions
5(4)
1.2.1 Continuous, Discrete, and Dichotomous Data
5(1)
1.2.2 Samples and Populations
6(1)
1.2.3 Descriptive and Inferential Statistics
7(1)
1.2.4 Orthogonality: Standard and Sequential Analyses
7(2)
1.3 Linear Combinations of Variables
9(1)
1.4 Number and Nature of Variables to Include
10(1)
1.5 Statistical Power
10(1)
1.6 Data Appropriate for Multivariate Statistics
11(3)
1.6.1 The Data Matrix
11(1)
1.6.2 The Correlation Matrix
12(1)
1.6.3 The Variance-Covariance Matrix
12(1)
1.6.4 The Sum-of-Squares and Cross-Products Matrix
13(1)
1.6.5 Residuals
14(1)
1.7 Organization of the Book
14(1)
2 A Guide to Statistical Techniques: Using the Book 15(14)
2.1 Research Questions and Associated Techniques
15(8)
2.1.1 Degree of Relationship Among Variables
15(2)
2.1.1.1 Bivariate r
16(1)
2.1.1.2 Multiple R
16(1)
2.1.1.3 Sequential R
16(1)
2.1.1.4 Canonical R
16(1)
2.1.1.5 Multiway Frequency Analysis
17(1)
2.1.1.6 Multilevel Modeling
17(1)
2.1.2 Significance of Group Differences
17(3)
2.1.2.1 One-Way ANOVA and t Test
17(1)
2.1.2.2 One-Way ANCOVA
17(1)
2.1.2.3 Factorial ANOVA
18(1)
2.1.2.4 Factorial ANCOVA
18(1)
2.1.2.5 Hotelling's T2
18(1)
2.1.2.6 One-Way MANOVA
18(1)
2.1.2.7 One-Way MANCOVA
19(1)
2.1.2.8 Factorial MANOVA
19(1)
2.1.2.9 Factorial MANCOVA
19(1)
2.1.2.10 Profile Analysis of Repeated Measures
19(1)
2.1.3 Prediction of Group Membership
20(2)
2.1.3.1 One-Way Discriminant Analysis
20(1)
2.1.3.2 Sequential One-Way Discriminant Analysis
20(1)
2.1.3.3 Multiway Frequency Analysis (Logit)
21(1)
2.1.3.4 Logistic Regression
21(1)
2.1.3.5 Sequential Logistic Regression
21(1)
2.1.3.6 Factorial Discriminant Analysis
21(1)
2.1.3.7 Sequential Factorial Discriminant Analysis
22(1)
2.1.4 Structure
22(1)
2.1.4.1 Principal Components
22(1)
2.1.4.2 Factor Analysis
22(1)
2.1.4.3 Structural Equation Modeling
22(1)
2.1.5 Time Course of Events
22(7)
2.1.5.1 Survival/Failure Analysis
23(1)
2.1.5.2 Time-Series Analysis
23(1)
2.2 Some Further Comparisons
23(1)
2.3 A Decision Tree
24(3)
2.4 Technique
Chapters
27(1)
2.5 Preliminary Check of the Data
28(1)
3 Review of Univariate and Bivariate Statistics 29(23)
3.1 Hypothesis Testing
29(4)
3.1.1 One-Sample z Test as Prototype
30(2)
3.1.2 Power
32(1)
3.1.3 Extensions of the Model
32(1)
3.1.4 Controversy Surrounding Significance Testing
33(1)
3.2 Analysis of Variance
33(13)
3.2.1 One-Way Between-Subjects ANOVA
34(2)
3.2.2 Factorial Between-Subjects ANOVA
36(2)
3.2.3 Within-Subjects ANOVA
38(2)
3.2.4 Mixed Between-Within-Subjects ANOVA
40(1)
3.2.5 Design Complexity
41(2)
3.2.5.1 Nesting
41(1)
3.2.5.2 Latin-Square Designs
42(1)
3.2.5.3 Unequal n and Nonorthogonality
42(1)
3.2.5.4 Fixed and Random Effects
43(1)
3.2.6 Specific Comparisons
43(5)
3.2.6.1 Weighting Coefficients for Comparisons
43(1)
3.2.6.2 Orthogonality of Weighting Coefficients
44(1)
3.2.6.3 Obtained F for Comparisons
44(1)
3.2.6.4 Critical F for Planned Comparisons
45(1)
3.2.6.5 Critical F for Post Hoc Comparisons
45(1)
3.3 Parameter Estimation
46(1)
3.4 Effect Size
47(1)
3.5 Bivariate Statistics: Correlation and Regression
48(2)
3.5.1 Correlation
48(1)
3.5.2 Regression
49(1)
3.6 Chi-Square Analysis
50(2)
4 Cleaning Up Your Act: Screening Data Prior to Analysis 52(47)
4.1 Important Issues in Data Screening
53(26)
4.1.1 Accuracy of Data File
53(1)
4.1.2 Honest Correlations
53(1)
4.1.2.1 Inflated Correlation
53(1)
4.1.2.2 Deflated Correlation
53(1)
4.1.3 Missing Data
54(8)
4.1.3.1 Deleting Cases or Variables
57(1)
4.1.3.2 Estimating Missing Data
57(4)
4.1.3.3 Using a Missing Data Correlation Matrix
61(1)
4.1.3.4 Treating Missing Data as Data
61(1)
4.1.3.5 Repeating Analyses with and without Missing Data
61(1)
4.1.3.6 Choosing Among Methods for Dealing with Missing Data
62(1)
4.1.4 Outliers
62(5)
4.1.4.1 Detecting Univariate and Multivariate Outliers
63(3)
4.1.4.2 Describing Outliers
66(1)
4.1.4.3 Reducing the Influence of Outliers
66(1)
4.1.4.4 Outliers in a Solution
67(1)
4.1.5 Normality, Linearity, and Homoscedasticity
67(8)
4.1.5.1 Normality
68(4)
4.1.5.2 Linearity
72(1)
4.1.5.3 Homoscedasticity, Homogeneity of Variance, and Homogeneity of Variance-Covariance Matrices
73(2)
4.1.6 Common Data Transformations
75(1)
4.1.7 Multicollinearity and Singularity
76(3)
4.1.8 A Checklist and Some Practical Recommendations
79(1)
4.2 Complete Examples of Data Screening
79(20)
4.2.1 Screening Ungrouped Data
80(8)
4.2.1.1 Accuracy of Input, Missing Data, Distributions, and Univariate Outliers
81(3)
4.2.1.2 Linearity and Homoscedasticity
84(1)
4.2.1.3 Transformation
84(1)
4.2.1.4 Detecting Multivariate Outliers
84(2)
4.2.1.5 Variables Causing Cases to Be Outliers
86(2)
4.2.1.6 Multicollinearity
88(1)
4.2.2 Screening Grouped Data
88(13)
4.2.2.1 Accuracy of Input, Missing Data, Distributions, Homogeneity of Variance, and Univariate Outliers
89(4)
4.2.2.2 Linearity
93(1)
4.2.2.3 Multivariate Outliers
93(1)
4.2.2.4 Variables Causing Cases to Be Outliers
94(3)
4.2.2.5 Multicollinearity
97(2)
5 Multiple Regression 99(68)
5.1 General Purpose and Description
99(2)
5.2 Kinds of Research Questions
101(2)
5.2.1 Degree of Relationship
101(1)
5.2.2 Importance of IVs
102(1)
5.2.3 Adding IVs
102(1)
5.2.4 Changing IVs
102(1)
5.2.5 Contingencies Among IVs
102(1)
5.2.6 Comparing Sets of IVs
102(1)
5.2.7 Predicting DV Scores for Members of a New Sample
103(1)
5.2.8 Parameter Estimates
103(1)
5.3 Limitations to Regression Analyses
103(6)
5.3.1 Theoretical Issues
103(1)
5.3.2 Practical Issues
104(5)
5.3.2.1 Ratio of Cases to IVs
105(1)
5.3.2.2 Absence of Outliers Among the IVs and on the DV
105(1)
5.3.2.3 Absence of Multicollinearity and Singularity
106(1)
5.3.2.4 Normality, Linearity, and Homoscedasticity of Residuals
106(2)
5.3.2.5 Independence of Errors
108(1)
5.3.2.6 Absence of Outliers in the Solution
109(1)
5.4 Fundamental Equations for Multiple Regression
109(6)
5.4.1 General Linear Equations
110(1)
5.4.2 Matrix Equations
111(2)
5.4.3 Computer Analyses of Small-Sample Example
113(2)
5.5 Major Types of Multiple Regression
115(6)
5.5.1 Standard Multiple Regression
115(1)
5.5.2 Sequential Multiple Regression
116(1)
5.5.3 Statistical (Stepwise) Regression
117(4)
5.5.4 Choosing Among Regression Strategies
121(1)
5.6 Some Important Issues
121(17)
5.6.1 Importance of IVs
121(7)
5.6.1.1 Standard Multiple Regression
122(1)
5.6.1.2 Sequential or Statistical Regression
123(1)
5.6.1.3 Commonality Analysis
123(2)
5.6.1.4 Relative Importance Analysis
125(3)
5.6.2 Statistical Inference
128(4)
5.6.2.1 Test for Multiple R
128(1)
5.6.2.2 Test of Regression Components
129(1)
5.6.2.3 Test of Added Subset of IVs
130(1)
5.6.2.4 Confidence Limits
130(1)
5.6.2.5 Comparing Two Sets of Predictors
131(1)
5.6.3 Adjustment of R2
132(1)
5.6.4 Suppressor Variables
133(1)
5.6.5 Regression Approach to ANOVA
134(1)
5.6.6 Centering When Interactions and Powers of IVs Are Included
135(2)
5.6.7 Mediation in Causal Sequence
137(1)
5.7 Complete Examples of Regression Analysis
138(24)
5.7.1 Evaluation of Assumptions
139(5)
5.7.1.1 Ratio of Cases to Ns
139(1)
5.7.1.2 Normality, Linearity, Homoscedastidty, and Independence of Residuals
139(3)
5.7.1.3 Outliers
142(2)
5.7.1.4 Multicollinearity and Singularity
144(1)
5.7.2 Standard Multiple Regression
144(6)
5.7.3 Sequential Regression
150(4)
5.7.4 Example of Standard Multiple Regression with Missing Values Multiply Imputed
154(8)
5.8 Comparison of Programs
162(5)
5.8.1 IBM SPSS Package
163(2)
5.8.2 SAS System
165(1)
5.8.3 SYSTAT System
166(1)
6 Analysis of Covariance 167(36)
6.1 General Purpose and Description
167(3)
6.2 Kinds of Research Questions
170(1)
6.2.1 Main Effects of IVs
170(1)
6.2.2 Interactions Among IVs
170(1)
6.2.3 Specific Comparisons and Trend Analysis
170(1)
6.2.4 Effects of Covariates
170(1)
6.2.5 Effect Size
171(1)
6.2.6 Parameter Estimates
171(1)
6.3 Limitations to Analysis of Covariance
171(3)
6.3.1 Theoretical Issues
171(1)
6.3.2 Practical Issues
172(2)
6.3.2.1 Unequal Sample Sizes, Missing Data, and Ratio of Cases to IVs
172(1)
6.3.2.2 Absence of Outliers
172(1)
6.3.2.3 Absence of Multicollinearity and Singularity
172(1)
6.3.2.4 Normality of Sampling Distributions
173(1)
6.3.2.5 Homogeneity of Variance
173(1)
6.3.2.6 Linearity
173(1)
6.3.2.7 Homogeneity of Regression
173(1)
6.3.2.8 Reliability of Covariates
174(1)
6.4 Fundamental Equations for Analysis of Covariance
174(5)
6.4.1 Sums of Squares and Cross-Products
175(2)
6.4.2 Significance Test and Effect Size
177(1)
6.4.3 Computer Analyses of Small-Sample Example
178(1)
6.5 Some Important Issues
179(10)
6.5.1 Choosing Covariates
179(1)
6.5.2 Evaluation of Covariates
180(1)
6.5.3 Test for Homogeneity of Regression
180(1)
6.5.4 Design Complexity
181(6)
6.5.4.1 Within-Subjects and Mixed Within-Between Designs
181(1)
6.5.4.2 Unequal Sample Sizes
182(3)
6.5.4.3 Specific Comparisons and Trend Analysis
185(2)
6.5.4.4 Effect Size
187(1)
6.5.5 Alternatives to ANCOVA
187(2)
6.6 Complete Example of Analysis of Covariance
189(11)
6.6.1 Evaluation of Assumptions
189(4)
6.6.1.1 Unequal n and Missing Data
189(1)
6.6.1.2 Normality
189(2)
6.6.1.3 Linearity
191(1)
6.6.1.4 Outliers
191(1)
6.6.1.5 Multicollinearity and Singularity
192(1)
6.6.1.6 Homogeneity of Variance
192(1)
6.6.1.7 Homogeneity of Regression
193(1)
6.6.1.8 Reliability of Covariates
193(1)
6.6.2 Analysis of Covariance
193(7)
6.6.2.1 Main Analysis
193(3)
6.6.2.2 Evaluation of Covariates
196(1)
6.6.2.3 Homogeneity of Regression Run
196(4)
6.7 Comparison of Programs
200(3)
6.7.1 IBM SPSS Package
200(1)
6.7.2 SAS System
200(1)
6.7.3 SYSTAT System
200(3)
7 Multivariate Analysis of Variance and Covariance 203(53)
7.1 General Purpose and Description
203(3)
7.2 Kinds of Research Questions
206(2)
7.2.1 Main Effects of IVs
206(1)
7.2.2 Interactions Among IVs
207(1)
7.2.3 Importance of DVs
207(1)
7.2.4 Parameter Estimates
207(1)
7.2.5 Specific Comparisons and Trend Analysis
207(1)
7.2.6 Effect Size
208(1)
7.2.7 Effects of Covariates
208(1)
7.2.8 Repeated-Measures Analysis of Variance
208(1)
7.3 Limitations to Multivariate Analysis of Variance and Covariance
208(4)
7.3.1 Theoretical Issues
208(1)
7.3.2 Practical Issues
209(3)
7.3.2.1 Unequal Sample Sizes, Missing Data, and Power
209(1)
7.3.2.2 Multivariate Normality
210(1)
7.3.2.3 Absence of Outliers
210(1)
7.3.2.4 Homogeneity of Variance- Covariance Matrices
210(1)
7.3.2.5 Linearity
211(1)
7.3.2.6 Homogeneity of Regression
211(1)
7.3.2.7 Reliability of Covariates
211(1)
7.3.2.8 Absence of Multicollinearity and Singularity
211(1)
7.4 Fundamental Equations for Multivariate Analysis of Variance and Covariance
212(11)
7.4.1 Multivariate Analysis of Variance
212(6)
7.4.2 Computer Analyses of Small-Sample Example
218(3)
7.4.3 Multivariate Analysis of Covariance
221(2)
7.5 Some Important Issues
223(7)
7.5.1 MANOVA Versus ANOVAs
223(1)
7.5.2 Criteria for Statistical Inference
223(1)
7.5.3 Assessing DVs
224(3)
7.5.3.1 Univariate F
224(2)
7.5.3.2 Roy-Bargmann Stepdown Analysis
226(1)
7.5.3.3 Using Discriminant Analysis
226(1)
7.5.3.4 Choosing Among Strategies for Assessing DVs
227(1)
7.5.4 Specific Comparisons and Trend Analysis
227(1)
7.5.5 Design Complexity
228(2)
7.5.5.1 Within-Subjects and Between- Within Designs
228(1)
7.5.5.2 Unequal Sample Sizes
228(2)
7.6 Complete Examples of Multivariate Analysis of Variance and Covariance
230(22)
7.6.1 Evaluation of Assumptions
230(5)
7.6.1.1 Unequal Sample Sizes and Missing Data
230(1)
7.6.1.2 Multivariate Normality
231(1)
7.6.1.3 Linearity
231(1)
7.6.1.4 Outliers
232(1)
7.6.1.5 Homogeneity of Variance-Covariance Matrices
233(1)
7.6.1.6 Homogeneity of Regression
233(2)
7.6.1.7 Reliability of Covariates
235(1)
7.6.1.8 Multicollinearity and Singularity
235(1)
7.6.2 Multivariate Analysis of Variance
235(9)
7.6.3 Multivariate Analysis of Covariance
244(8)
7.6.3.1 Assessing Covariates
244(1)
7.6.3.2 Assessing DVs
245(7)
7.7 Comparison of Programs
252(4)
7.7.1 IBM SPSS Package
252(3)
7.7.2 SAS System
255(1)
7.7.3 SYSTAT System
255(1)
8 Profile Analysis: The Multivariate Approach to Repeated Measures 256(43)
8.1 General Purpose and Description
256(1)
8.2 Kinds of Research Questions
257(2)
8.2.1 Parallelism of Profiles
258(1)
8.2.2 Overall Difference Among Groups
258(1)
8.2.3 Flatness of Profiles
258(1)
8.2.4 Contrasts Following Profile Analysis
258(1)
8.2.5 Parameter Estimates
258(1)
8.2.6 Effect Size
259(1)
8.3 Limitations to Profile Analysis
259(1)
8.3.1 Theoretical Issues
259(1)
8.3.2 Practical Issues
259(1)
8.3.2.1 Sample Size, Missing Data, and Power
259(1)
8.3.2.2 Multivariate Normality
260(1)
8.3.2.3 Absence of Outliers
260(1)
8.3.2.4 Homogeneity of Variance-Covariance Matrices
260(1)
8.3.2.5 Linearity
260(1)
8.3.2.6 Absence of Multicollinearity and Singularity
260(1)
8.4 Fundamental Equations for Profile Analysis
260(9)
8.4.1 Differences in Levels
262(1)
8.4.2 Parallelism
262(3)
8.4.3 Flatness
265(1)
8.4.4 Computer Analyses of Small-Sample Example
266(3)
8.5 Some Important Issues
269(11)
8.5.1 Univariate Versus Multivariate Approach to Repeated Measures
269(1)
8.5.2 Contrasts in Profile Analysis
270(7)
8.5.2.1 Parallelism and Flatness Significant, Levels Not Significant (Simple-Effects Analysis)
272(2)
8.5.2.2 Parallelism and Levels Significant, Flatness Not Significant (Simple-Effects Analysis)
274(1)
8.5.2.3 Parallelism, Levels, and Flatness Significant (Interaction Contrasts)
275(1)
8.5.2.4 Only Parallelism Significant
276(1)
8.5.3 Doubly Multivariate Designs
277(2)
8.5.4 Classifying Profiles
279(1)
8.5.5 Imputation of Missing Values
279(1)
8.6 Complete Examples of Profile Analysis
280(17)
8.6.1 Profile Analysis of Subscales of the WISC
280(8)
8.6.1.1 Evaluation of Assumptions
280(2)
8.6.1.2 Profile Analysis
282(6)
8.6.2 Doubly Multivariate Analysis of Reaction Time
288(9)
8.6.2.1 Evaluation of Assumptions
288(2)
8.6.2.2 Doubly Multivariate Analysis of Slope and Intercept
290(7)
8.7 Comparison of Programs
297(2)
8.7.1 IBM SPSS Package
297(1)
8.7.2 SAS System
298(1)
8.7.3 SYSTAT System
298(1)
9 Discriminant Analysis 299(47)
9.1 General Purpose and Description
299(3)
9.2 Kinds of Research Questions
302(2)
9.2.1 Significance of Prediction
302(1)
9.2.2 Number of Significant Discriminant Functions
302(1)
9.2.3 Dimensions of Discrimination
302(1)
9.2.4 Classification Functions
303(1)
9.2.5 Adequacy of Classification
303(1)
9.2.6 Effect Size
303(1)
9.2.7 Importance of Predictor Variables
303(1)
9.2.8 Significance of Prediction with Covariates
304(1)
9.2.9 Estimation of Group Means
304(1)
9.3 Limitations to Discriminant Analysis
304(2)
9.3.1 Theoretical Issues
304(1)
9.3.2 Practical Issues
304(2)
9.3.2.1 Unequal Sample Sizes, Missing Data, and Power
304(1)
9.3.2.2 Multivariate Normality
305(1)
9.3.2.3 Absence of Outliers
305(1)
9.3.2.4 Homogeneity of Variance-Covariance Matrices
305(1)
9.3.2.5 Linearity
306(1)
9.3.2.6 Absence of Multicollinearity and Singularity
306(1)
9.4 Fundamental Equations for Discriminant Analysis
306(9)
9.4.1 Derivation and Test of Discriminant Functions
307(2)
9.4.2 Classification
309(2)
9.4.3 Computer Analyses of Small-Sample Example
311(4)
9.5 Types of Discriminant Analyses
315(1)
9.5.1 Direct Discriminant Analysis
315(1)
9.5.2 Sequential Discriminant Analysis
315(1)
9.5.3 Stepwise (Statistical) Discriminant Analysis
316(1)
9.6 Some Important Issues
316(8)
9.6.1 Statistical Inference
316(1)
9.6.1.1 Criteria for Overall Statistical Significance
317(1)
9.6.1.2 Stepping Methods
317(1)
9.6.2 Number of Discriminant Functions
317(1)
9.6.3 Interpreting Discriminant Functions
318(2)
9.6.3.1 Discriminant Function Plots
318(1)
9.6.3.2 Structure Matrix of Loadings
318(2)
9.6.4 Evaluating Predictor Variables
320(1)
9.6.5 Effect Size
321(1)
9.6.6 Design Complexity: Factorial Designs
321(1)
9.6.7 Use of Classification Procedures
322(2)
9.6.7.1 Cross-Validation and New Cases
322(1)
9.6.7.2 Jackknifed Classification
323(1)
9.6.7.3 Evaluating Improvement in Classification
323(1)
9.7 Complete Example of Discriminant Analysis
324(16)
9.7.1 Evaluation of Assumptions
325(2)
9.7.1.1 Unequal Sample Sizes and Missing Data
325(1)
9.7.1.2 Multivariate Normality
325(1)
9.7.1.3 Linearity
325(1)
9.7.1.4 Outliers
325(1)
9.7.1.5 Homogeneity of Variance- Covariance Matrices
326(1)
9.7.1.6 Multicollinearity and Singularity
327(1)
9.7.2 Direct Discriminant Analysis
327(13)
9.8 Comparison of Programs
340(6)
9.8.1 IBM SPSS Package
344(1)
9.8.2 SAS System
344(1)
9.8.3 SYSTAT System
345(1)
10 Logistic Regression 346(55)
10.1 General Purpose and Description
346(2)
10.2 Kinds of Research Questions
348(2)
10.2.1 Prediction of Group Membership or Outcome
348(1)
10.2.2 Importance of Predictors
348(1)
10.2.3 Interactions Among Predictors
349(1)
10.2.4 Parameter Estimates
349(1)
10.2.5 Classification of Cases
349(1)
10.2.6 Significance of Prediction with Covariates
349(1)
10.2.7 Effect Size
349(1)
10.3 Limitations to Logistic Regression Analysis
350(2)
10.3.1 Theoretical Issues
350(1)
10.3.2 Practical Issues
350(2)
10.3.2.1 Ratio of Cases to Variables
350(1)
10.3.2.2 Adequacy of Expected Frequencies and Power
351(1)
10.3.2.3 Linearity in the Logit
351(1)
10.3.2.4 Absence of Multicollinearity
351(1)
10.3.2.5 Absence of Outliers in the Solution
351(1)
10.3.2.6 Independence of Errors
352(1)
10.4 Fundamental Equations for Logistic Regression
352(8)
10.4.1 Testing and Interpreting Coefficients
353(1)
10.4.2 Goodness of Fit
354(1)
10.4.3 Comparing Models
355(1)
10.4.4 Interpretation and Analysis of Residuals
355(1)
10.4.5 Computer Analyses of Small-Sample Example
356(4)
10.5 Types of Logistic Regression
360(3)
10.5.1 Direct Logistic Regression
360(1)
10.5.2 Sequential Logistic Regression
360(2)
10.5.3 Statistical (Stepwise) Logistic Regression
362(1)
10.5.4 Probit and Other Analyses
362(1)
10.6 Some Important Issues
363(11)
10.6.1 Statistical Inference
363(2)
10.6.1.1 Assessing Goodness of Fit of Models
363(2)
10.6.1.2 Tests of Individual Predictors
365(1)
10.6.2 Effect Sizes
365(2)
10.6.2.1 Effect Size for a Model
365(1)
10.6.2.2 Effect Sizes for Predictors
366(1)
10.6.3 Interpretation of Coefficients Using Odds
367(1)
10.6.4 Coding Outcome and Predictor Categories
368(1)
10.6.5 Number and Type of Outcome Categories
369(3)
10.6.6 Classification of Cases
372(1)
10.6.7 Hierarchical and Nonhierarchical Analysis
372(1)
10.6.8 Importance of Predictors
373(1)
10.6.9 Logistic Regression for Matched Groups
374(1)
10.7 Complete Examples of Logistic Regression
374(22)
10.7.1 Evaluation of Limitations
374(3)
10.7.1.1 Ratio of Cases to Variables and Missing Data
374(2)
10.7.1.2 Multicollinearity
376(1)
10.7.1.3 Outliers in the Solution
376(1)
10.7.2 Direct Logistic Regression with Two-Category Outcome and Continuous Predictors
377(7)
10.7.2.1 Limitation: Linearity in the Logit
377(1)
10.7.2.2 Direct Logistic Regression with Two-Category Outcome
377(7)
10.7.3 Sequential Logistic Regression with Three Categories of Outcome
384(12)
10.7.3.1 Limitations of Multinomial Logistic Regression
384(3)
10.7.3.2 Sequential Multinomial Logistic Regression
387(9)
10.8 Comparison of Programs
396(5)
10.8.1 IBM SPSS Package
396(3)
10.8.2 SAS System
399(1)
10.8.3 SYSTAT System
400(1)
11 Survival/Failure Analysis 401(45)
11.1 General Purpose and Description
401(2)
11.2 Kinds of Research Questions
403(1)
11.2.1 Proportions Surviving at Various Times
403(1)
11.2.2 Group Differences in Survival
403(1)
11.2.3 Survival Time with Covariates
403(1)
11.2.3.1 Treatment Effects
403(1)
11.2.3.2 Importance of Covariates
403(1)
11.2.3.3 Parameter Estimates
404(1)
11.2.3.4 Contingencies Among Covariates
404(1)
11.2.3.5 Effect Size and Power
404(1)
11.3 Limitations to Survival Analysis
404(1)
11.3.1 Theoretical Issues
404(1)
11.3.2 Practical Issues
404(1)
11.3.2.1 Sample Size and Missing Data
404(1)
11.3.2.2 Normality of Sampling Distributions, Linearity, and Homoscedasticity
405(1)
11.3.2.3 Absence of Outliers
405(1)
11.3.2.4 Differences Between Withdrawn and Remaining Cases
405(1)
11.3.2.5 Change in Survival Conditions over Time
405(1)
11.3.2.6 Proportionality of Hazards
405(1)
11.3.2.7 Absence of Multicollinearity
405(1)
11.4 Fundamental Equations for Survival Analysis
405(10)
11.4.1 Life Tables
406(2)
11.4.2 Standard Error of Cumulative Proportion Surviving
408(1)
11.4.3 Hazard and Density Functions
408(1)
11.4.4 Plot of Life Tables
409(1)
11.4.5 Test for Group Differences
410(1)
11.4.6 Computer Analyses of Small-Sample Example
411(4)
11.5 Types of Survival Analyses
415(8)
11.5.1 Actuarial and Product-Limit Life Tables and Survivor Functions
415(2)
11.5.2 Prediction of Group Survival Times from Covariates
417(6)
11.5.2.1 Direct, Sequential, and Statistical Analysis
417(1)
11.5.2.2 Cox Proportional-Hazards Model
417(2)
11.5.2.3 Accelerated Failure-Time Models
419(4)
11.5.2.4 Choosing a Method
423(1)
11.6 Some Important Issues
423(6)
11.6.1 Proportionality of Hazards
423(1)
11.6.2 Censored Data
424(1)
11.6.2.1 Right-Censored Data
425(1)
11.6.2.2 Other Forms of Censoring
425(1)
11.6.3 Effect Size and Power
425(1)
11.6.4 Statistical Criteria
426(1)
11.6.4.1 Test Statistics for Group Differences in Survival Functions
426(1)
11.6.4.2 Test Statistics for Prediction from Covariates
427(1)
11.6.5 Predicting Survival Rate
427(2)
11.6.5.1 Regression Coefficients (Parameter Estimates)
427(1)
11.6.5.2 Hazard Ratios
427(1)
11.6.5.3 Expected Survival Rates
428(1)
11.7 Complete Example of Survival Analysis
429(11)
11.7.1 Evaluation of Assumptions
430(6)
11.7.1.1 Accuracy of Input, Adequacy of Sample Size, Missing Data, and Distributions
430(1)
11.7.1.2 Outliers
430(3)
11.7.1.3 Differences Between Withdrawn and Remaining Cases
433(1)
11.7.1.4 Change in Survival Experience over Time
433(1)
11.7.1.5 Proportionality of Hazards
433(1)
11.7.1.6 Multicollinearity
434(2)
11.7.2 Cox Regression Survival Analysis
436(4)
11.7.2.1 Effect of Drug Treatment
436(1)
11.7.2.2 Evaluation of Other Covariates
436(4)
11.8 Comparison of Programs
440(6)
11.8.1 SAS System
444(1)
11.8.2 IBM SPSS Package
445(1)
11.8.3 SYSTAT System
445(1)
12 Canonical Correlation 446(30)
12.1 General Purpose and Description
446(2)
12.2 Kinds of Research Questions
448(1)
12.2.1 Number of Canonical Variate Pairs
448(1)
12.2.2 Interpretation of Canonical Variates
448(1)
12.2.3 Importance of Canonical Variates and Predictors
448(1)
12.2.4 Canonical Variate Scores
449(1)
12.3 Limitations
449(2)
12.3.1 Theoretical Limitations
449(1)
12.3.2 Practical Issues
450(1)
12.3.2.1 Ratio of Cases to IVs
450(1)
12.3.2.2 Normality, Linearity, and Homoscedasticity
450(1)
12.3.2.3 Missing Data
451(1)
12.3.2.4 Absence of Outliers
451(1)
12.3.2.5 Absence of Multicollinearity and Singularity
451(1)
12.4 Fundamental Equations for Canonical Correlation
451(11)
12.4.1 Eigenvalues and Eigenvectors
452(2)
12.4.2 Matrix Equations
454(3)
12.4.3 Proportions of Variance Extracted
457(1)
12.4.4 Computer Analyses of Small-Sample Example
458(4)
12.5 Some Important Issues
462(1)
12.5.1 Importance of Canonical Variates
462(1)
12.5.2 Interpretation of Canonical Variates
463(1)
12.6 Complete Example of Canonical Correlation
463(10)
12.6.1 Evaluation of Assumptions
463(4)
12.6.1.1 Missing Data
463(1)
12.6.1.2 Normality, Linearity, and Homoscedasticity
463(3)
12.6.1.3 Outliers
466(1)
12.6.1.4 Multicollinearity and Singularity
467(1)
12.6.2 Canonical Correlation
467(6)
12.7 Comparison of Programs
473(3)
12.7.1 SAS System
473(1)
12.7.2 IBM SPSS Package
474(1)
12.7.3 SYSTAT System
475(1)
13 Principal Components and Factor Analysis 476(52)
13.1 General Purpose and Description
476(3)
13.2 Kinds of Research Questions
479(1)
13.2.1 Number of Factors
479(1)
13.2.2 Nature of Factors
479(1)
13.2.3 Importance of Solutions and Factors
480(1)
13.2.4 Testing Theory in FA
480(1)
13.2.5 Estimating Scores on Factors
480(1)
13.3 Limitations
480(3)
13.3.1 Theoretical Issues
480(1)
13.3.2 Practical Issues
481(2)
13.3.2.1 Sample Size and Missing Data
481(1)
13.3.2.2 Normality
482(1)
13.3.2.3 Linearity
482(1)
13.3.2.4 Absence of Outliers Among Cases
482(1)
13.3.2.5 Absence of Multicollinearity and Singularity
482(1)
13.3.2.6 Factorability of R
482(1)
13.3.2.7 Absence of Outliers Among Variables
483(1)
13.4 Fundamental Equations for Factor Analysis
483(13)
13.4.1 Extraction
485(2)
13.4.2 Orthogonal Rotation
487(1)
13.4.3 Communalities, Variance, and Covariance
488(1)
13.4.4 Factor Scores
489(2)
13.4.5 Oblique Rotation
491(2)
13.4.6 Computer Analyses of Small-Sample Example
493(3)
13.5 Major Types of Factor Analyses
496(8)
13.5.1 Factor Extraction Techniques
496(4)
13.5.1.1 PCA Versus FA
496(2)
13.5.1.2 Principal Components
498(1)
13.5.1.3 Principal Factors
498(1)
13.5.1.4 Image Factor Extraction
498(1)
13.5.1.5 Maximum Likelihood Factor Extraction
499(1)
13.5.1.6 Unweighted Least Squares Factoring
499(1)
13.5.1.7 Generalized (Weighted) Least Squares Factoring
499(1)
13.5.1.8 Alpha Factoring
499(1)
13.5.2 Rotation
500(3)
13.5.2.1 Orthogonal Rotation
500(1)
13.5.2.2 Oblique Rotation
501(1)
13.5.2.3 Geometric Interpretation
502(1)
13.5.3 Some Practical Recommendations
503(1)
13.6 Some Important Issues
504(7)
13.6.1 Estimates of Communalities
504(1)
13.6.2 Adequacy of Extraction and Number of Factors
504(3)
13.6.3 Adequacy of Rotation and Simple Structure
507(1)
13.6.4 Importance and Internal Consistency of Factors
508(1)
13.6.5 Interpretation of Factors
509(1)
13.6.6 Factor Scores
510(1)
13.6.7 Comparisons Among Solutions and Groups
511(1)
13.7 Complete Example of FA
511(14)
13.7.1 Evaluation of Limitations
511(4)
13.7.1.1 Sample Size and Missing Data
512(1)
13.7.1.2 Normality
512(1)
13.7.1.3 Linearity
512(1)
13.7.1.4 Outliers
513(1)
13.7.1.5 Multicollinearity and Singularity
514(1)
13.7.1.6 Factorability of R
514(1)
13.7.1.7 Outliers Among Variables
515(1)
13.7.2 Principal Factors Extraction with Varimax Rotation
515(10)
13.8 Comparison of Programs
525(3)
13.8.1 IBM SPSS Package
527(1)
13.8.2 SAS System
527(1)
13.8.3 SYSTAT System
527(1)
14 Structural Equation Modeling by Jodie B. Ullman 528(85)
14.1 General Purpose and Description
528(3)
14.2 Kinds of Research Questions
531(2)
14.2.1 Adequacy of the Model
531(1)
14.2.2 Testing Theory
532(1)
14.2.3 Amount of Variance in the Variables Accounted for by the Factors
532(1)
14.2.4 Reliability of the Indicators
532(1)
14.2.5 Parameter Estimates
532(1)
14.2.6 Intervening Variables
532(1)
14.2.7 Group Differences
532(1)
14.2.8 Longitudinal Differences
533(1)
14.2.9 Multilevel Modeling
533(1)
14.2.10 Latent Class Analysis
533(1)
14.3 Limitations to Structural Equation Modeling
533(2)
14.3.1 Theoretical Issues
533(1)
14.3.2 Practical Issues
534(1)
14.3.2.1 Sample Size and Missing Data
534(1)
14.3.2.2 Multivariate Normality and Outliers
534(1)
14.3.2.3 Linearity
535(1)
14.3.2.4 Absence of Multicollinearity and Singularity
535(1)
14.3.2.5 Residuals
535(1)
14.4 Fundamental Equations for Structural Equations Modeling
535(20)
14.4.1 Covariance Algebra
535(2)
14.4.2 Model Hypotheses
537(1)
14.4.3 Model Specification
538(2)
14.4.4 Model Estimation
540(3)
14.4.5 Model Evaluation
543(2)
14.4.6 Computer Analysis of Small-Sample Example
545(10)
14.5 Some Important Issues
555(19)
14.5.1 Model Identification
555(2)
14.5.2 Estimation Techniques
557(3)
14.5.2.1 Estimation Methods and Sample Size
559(1)
14.5.2.2 Estimation Methods and Nonnormality
559(1)
14.5.2.3 Estimation Methods and Dependence
559(1)
14.5.2.4 Some Recommendations for Choice of Estimation Method
560(1)
14.5.3 Assessing the Fit of the Model
560(4)
14.5.3.1 Comparative Fit Indices
560(2)
14.5.3.2 Absolute Fit Index
562(1)
14.5.3.3 Indices of Proportion of Variance Accounted
562(1)
14.5.3.4 Degree of Parsimony Fit Indices
563(1)
14.5.3.5 Residual-Based Fit Indices
563(1)
14.5.3.6 Choosing Among Fit Indices
564(1)
14.5.4 Model Modification
564(6)
14.5.4.1 Chi-Square Difference Test
564(1)
14.5.4.2 Lagrange Multiplier (LM) Test
565(4)
14.5.4.3 Wald Test
569(1)
14.5.4.4 Some Caveats and Hints on Model Modification
570(1)
14.5.5 Reliability and Proportion of Variance
570(1)
14.5.6 Discrete and Ordinal Data
571(1)
14.5.7 Multiple Group Models
572(1)
14.5.8 Mean and Covariance Structure Models
573(1)
14.6 Complete Examples of Structural Equation Modeling Analysis
574(33)
14.6.1 Confirmatory Factor Analysis of the WISC
574(15)
14.6.1.1 Model Specification for CFA
574(1)
14.6.1.2 Evaluation of Assumptions for CFA
574(2)
14.6.1.3 CFA Model Estimation and Preliminary Evaluation
576(7)
14.6.1.4 Model Modification
583(6)
14.6.2 SEM of Health Data
589(18)
14.6.2.1 SEM Model Specification
589(2)
14.6.2.2 Evaluation of Assumptions for SEM
591(2)
14.6.2.3 SEM Model Estimation and Preliminary Evaluation
593(3)
14.6.2.4 Model Modification
596(11)
14.7 Comparison of Programs
607(6)
14.7.1 EQS
607(1)
14.7.2 LISREL
607(5)
14.7.3 AMOS
612(1)
14.7.4 SAS System
612(1)
15 Multilevel Linear Modeling 613(59)
15.1 General Purpose and Description
613(3)
15.2 Kinds of Research Questions
616(2)
15.2.1 Group Differences in Means
616(1)
15.2.2 Group Differences in Slopes
616(1)
15.2.3 Cross-Level Interactions
616(1)
15.2.4 Meta-Analysis
616(1)
15.2.5 Relative Strength of Predictors at Various Levels
617(1)
15.2.6 Individual and Group Structure
617(1)
15.2.7 Effect Size
617(1)
15.2.8 Path Analysis at Individual and Group Levels
617(1)
15.2.9 Analysis of Longitudinal Data
617(1)
15.2.10 Multilevel Logistic Regression
618(1)
15.2.11 Multiple Response Analysis
618(1)
15.3 Limitations to Multilevel Linear Modeling
618(2)
15.3.1 Theoretical Issues
618(1)
15.3.2 Practical Issues
618(2)
15.3.2.1 Sample Size, Unequal-n, and Missing Data
619(1)
15.3.2.2 Independence of Errors
619(1)
15.3.2.3 Absence of Multicollinearity and Singularity
620(1)
15.4 Fundamental Equations
620(18)
15.4.1 Intercepts-Only Model
623(4)
15.4.1.1 The Intercepts-Only Model: Level-1 Equation
623(1)
15.4.1.2 The Intercepts-Only Model: Level-2 Equation
623(1)
15.4.1.3 Computer Analyses of Intercepts-Only Model
624(3)
15.4.2 Model with a First-Level Predictor
627(6)
15.4.2.1 Level-1 Equation for a Model with a Level-1 Predictor
627(1)
15.4.2.2 Level-2 Equations for a Model with a Level-1 Predictor
628(2)
15.4.2.3 Computer Analysis of a Model with a Level-1 Predictor
630(3)
15.4.3 Model with Predictors at First and Second Levels
633(5)
15.4.3.1 Level-1 Equation for Model with Predictors at Both Levels
633(1)
15.4.3.2 Level-2 Equations for Model with Predictors at Both Levels
633(1)
15.4.3.3 Computer Analyses of Model with Predictors at First and Second Levels
634(4)
15.5 Types of MLM
638(6)
15.5.1 Repeated Measures
638(4)
15.5.2 Higher-Order MLM
642(1)
15.5.3 Latent Variables
642(1)
15.5.4 Nonnormal Outcome Variables
643(1)
15.5.5 Multiple Response Models
644(1)
15.6 Some Important Issues
644(11)
15.6.1 Intraclass Correlation
644(2)
15.6.2 Centering Predictors and Changes in Their Interpretations
646(2)
15.6.3 Interactions
648(1)
15.6.4 Random and Fixed Intercepts and Slopes
648(3)
15.6.5 Statistical Inference
651(2)
15.6.5.1 Assessing Models
651(1)
15.6.5.2 Tests of Individual Effects
652(1)
15.6.6 Effect Size
653(1)
15.6.7 Estimation Techniques and Convergence Problems
653(1)
15.6.8 Exploratory Model Building
654(1)
15.7 Complete Example of MLM
655(13)
15.7.1 Evaluation of Assumptions
656(5)
15.7.1.1 Sample Sizes, Missing Data, and Distributions
656(3)
15.7.1.2 Outliers
659(1)
15.7.1.3 Multicollinearity and Singularity
659(1)
15.7.1.4 Independence of Errors: Intraclass Correlations
659(2)
15.7.2 Multilevel Modeling
661(7)
15.8 Comparison of Programs
668(4)
15.8.1 SAS System
668(2)
15.8.2 IBM SPSS Package
670(1)
15.8.3 HLM Program
671(1)
15.8.4 MLwiN Program
671(1)
15.8.5 SYSTAT System
671(1)
16 Multiway Frequency Analysis 672(42)
16.1 General Purpose and Description
672(1)
16.2 Kinds of Research Questions
673(2)
16.2.1 Associations Among Variables
673(1)
16.2.2 Effect on a Dependent Variable
674(1)
16.2.3 Parameter Estimates
674(1)
16.2.4 Importance of Effects
674(1)
16.2.5 Effect Size
674(1)
16.2.6 Specific Comparisons and Trend Analysis
674(1)
16.3 Limitations to Multiway Frequency Analysis
675(1)
16.3.1 Theoretical Issues
675(1)
16.3.2 Practical Issues
675(1)
16.3.2.1 Independence
675(1)
16.3.2.2 Ratio of Cases to Variables
675(1)
16.3.2.3 Adequacy of Expected Frequencies
675(1)
16.3.2.4 Absence of Outliers in the Solution
676(1)
16.4 Fundamental Equations for Multiway Frequency Analysis
676(19)
16.4.1 Screening for Effects
678(5)
16.4.1.1 Total Effect
678(1)
16.4.1.2 First-Order Effects
679(1)
16.4.1.3 Second-Order Effects
679(4)
16.4.1.4 Third-Order Effect
683(1)
16.4.2 Modeling
683(2)
16.4.3 Evaluation and Interpretation
685(5)
16.4.3.1 Residuals
685(1)
16.4.3.2 Parameter Estimates
686(4)
16.4.4 Computer Analyses of Small-Sample Example
690(5)
16.5 Some Important Issues
695(3)
16.5.1 Hierarchical and Nonhierarchical Models
695(1)
16.5.2 Statistical Criteria
696(1)
16.5.2.1 Tests of Models
696(1)
16.5.2.2 Tests of Individual Effects
696(1)
16.5.3 Strategies for Choosing a Model
696(2)
16.5.3.1 IBM SPSS HILOGLINEAR (Hierarchical)
697(1)
16.5.3.2 IBM SPSS GENLOG (General Log-Linear)
697(1)
16.5.3.3 SAS CATMOD and IBM SPSS LOGLINEAR (General Log-Linear)
697(1)
16.6 Complete Example of Multiway Frequency Analysis
698(12)
16.6.1 Evaluation of Assumptions: Adequacy of Expected Frequencies
698(2)
16.6.2 Hierarchical Log-Linear Analysis
700(10)
16.6.2.1 Preliminary Model Screening
700(2)
16.6.2.2 Stepwise Model Selection
702(1)
16.6.2.3 Adequacy of Fit
702(3)
16.6.2.4 Interpretation of the Selected Model
705(5)
16.7 Comparison of Programs
710(4)
16.7.1 IBM SPSS Package
710(2)
16.7.2 SAS System
712(1)
16.7.3 SYSTAT System
713(1)
17 Time-Series Analysis 714(61)
17.1 General Purpose and Description
714(2)
17.2 Kinds of Research Questions
716(2)
17.2.1 Pattern of Autocorrelation
717(1)
17.2.2 Seasonal Cycles and Trends
717(1)
17.2.3 Forecasting
717(1)
17.2.4 Effect of an Intervention
718(1)
17.2.5 Comparing Time Series
718(1)
17.2.6 Time Series with Covariates
718(1)
17.2.7 Effect Size and Power
718(1)
17.3 Assumptions of Time-Series Analysis
718(2)
17.3.1 Theoretical Issues
718(1)
17.3.2 Practical Issues
718(2)
17.3.2.1 Normality of Distributions of Residuals
719(1)
17.3.2.2 Homogeneity of Variance and Zero Mean of Residuals
719(1)
17.3.2.3 Independence of Residuals
719(1)
17.3.2.4 Absence of Outliers
719(1)
17.3.2.5 Sample Size and Missing Data
719(1)
17.4 Fundamental Equations for Time-Series ARIMA Models
720(17)
17.4.1 Identification of ARIMA (p, d, q) Models
720(9)
17.4.1.1 Trend Components, d: Making the Process Stationary
721(1)
17.4.1.2 Auto-Regressive Components
722(2)
17.4.1.3 Moving Average Components
724(1)
17.4.1.4 Mixed Models
724(1)
17.4.1.5 ACFs and PACFs
724(5)
17.4.2 Estimating Model Parameters
729(1)
17.4.3 Diagnosing a Model
729(5)
17.4.4 Computer Analysis of Small-Sample Time-Series Example
734(3)
17.5 Types of Time-Series Analyses
737(11)
17.5.1 Models with Seasonal Components
737(1)
17.5.2 Models with Interventions
738(9)
17.5.2.1 Abrupt, Permanent Effects
741(1)
17.5.2.2 Abrupt, Temporary Effects
742(3)
17.5.2.3 Gradual, Permanent Effects
745(1)
17.5.2.4 Models with Multiple Interventions
746(1)
17.5.3 Adding Continuous Variables
747(1)
17.6 Some Important Issues
748(5)
17.6.1 Patterns of ACFs and PACFs
748(3)
17.6.2 Effect Size
751(1)
17.6.3 Forecasting
752(1)
17.6.4 Statistical Methods for Comparing Two Models
752(1)
17.7 Complete Examples of Time-Series Analysis
753(18)
17.7.1 Time-Series Analysis of Introduction of Seat Belt Law
753(9)
17.7.1.1 Evaluation of Assumptions
754(1)
17.7.1.2 Baseline Model Identification and Estimation
755(3)
17.7.1.3 Baseline Model Diagnosis
758(1)
17.7.1.4 Intervention Analysis
758(4)
17.7.2 Time-Series Analysis of Introduction of a Dashboard to an Educational Computer Game
762(9)
17.7.2.1 Evaluation of Assumptions
763(2)
17.7.2.2 Baseline Model Identification and Diagnosis
765(1)
17.7.2.3 Intervention Analysis
766(5)
17.8 Comparison of Programs
771(4)
17.8.1 IBM SPSS Package
771(3)
17.8.2 SAS System
774(1)
17.8.3 SYSTAT System
774(1)
18 An Overview of the General Linear Model 775(8)
18.1 Linearity and the General Linear Model
775(1)
18.2 Bivariate to Multivariate Statistics and Overview of Techniques
775(7)
18.2.1 Bivariate Form
775(2)
18.2.2 Simple Multivariate Form
777(1)
18.2.3 Full Multivariate Form
778(4)
18.3 Alternative Research Strategies
782(1)
Appendix A A Skimpy Introduction to Matrix Algebra 783(8)
A.1 The Trace of a Matrix
784(1)
A.2 Addition or Subtraction of a Constant to a Matrix
784(1)
A.3 Multiplication or Division of a Matrix by a Constant
784(1)
A.4 Addition and Subtraction of Two Matrices
785(1)
A.5 Multiplication, Transposes, and Square Roots of Matrice
785(1)
A.6 Matrix "Division" (Inverses and Determinants)
786(2)
A.7 Eigenvalues and Eigenvectors: Procedures for Consolidating Variance from a Matrix
788(3)
Appendix B Research Designs for Complete Examples 791(6)
B.1 Women's Health and Drug Study
791(2)
B.2 Sexual Attraction Study
793(1)
B.3 Learning Disabilities Data Bank
794(1)
B.4 Reaction Time to Identify Figures
794(1)
B.5 Field Studies of Noise-Induced Sleep Disturbance
795(1)
B.6 Clinical Trial for Primary Biliary Cirrhosis
795(1)
B.7 Impact of Seat Belt Law
795(1)
B.8 The Selene Online Educational Game
796(1)
Appendix C Statistical Tables 797(11)
C.1 Normal Curve Areas
798(1)
C.2 Critical Values of the t Distribution for α = .05 and .01, Two-Tailed Test
799(1)
C.3 Critical Values of the F Distribution
800(4)
C.4 Critical Values of Chi Square (x2)
804(1)
C.5 Critical Values for Squares Multiple Correlation (R2) in Forward Stepwise Selection: α = .05
805(2)
C.6 Critical Values for FMAX (S2MAX/S2MIN) Distribution for α = .05 and .01
807(1)
References 808(7)
Index 815
About our authors Barbara G. Tabachnick is Professor Emerita of Psychology at California State University, Northridge. She has published over 80 articles and technical reports and participated in over 60 professional presentations, many invited. She currently presents workshops in computer applications in univariate and multivariate data analysis and has consulted in a variety of research areas, including professional ethics in and beyond academia, effects of such factors as age and substances on driving and other performance, educational computer games, effects of noise on annoyance and sleep, and fetal alcohol syndrome. She is the recipient of the 2012 Western Psychological Association Lifetime Achievement Award and a 2015 Western Psychological Association Presidential Citation.

Linda S. Fidell is Professor Emerita of Psychology at California State University, Northridge. She has published several articles and given numerous professional presentations. She taught research design and statistics at CSUN for 32 years and retired in 2001. In 2015 she received a Western Psychological Association Presidential Citation. She now lives in Morro Bay, where she is delighted to contribute to the community.