Applied Multivariate Research: Design and InterpretationMultivariate designs were once the province of the very few exalted researchers who understood the underlying advanced mathematics. Today, through the sophistication of statistical software packages such as SPSS, virtually all graduate students across the social and behavioural sciences are exposed to the complex multivariate statistical techniques without having to learn the mathematical computations needed to acquire the data output. These students - in psychology, education, political science, etc. - will never be statisticians and appropriately so, their preparation and coursework reflects less of an emphasis on the mathematical complexities of multivariate statistics and more on the analysis and the interpretation of the methods themselves and the actual data output. This book provides full coverage of the wide range of multivariate topics in a conceptual, rather than mathematical, approach. The author gears toward the needs, level of sophistication, and interest in multivariate methodology of students in applied areas that need to focus on design and interpretation rather than the intricacies of specific computations. The book includes: - Coverage of the most widely used multivariate designs: multiple regression, exploratory factor analysis, MANOVA, and structural equation modeling. - Integrated SPSS examples for hands-on learning from one large study (for consistency of application throughout the text). - Examples of written results to enable students to learn how the results of these procedures are communicated. - Practical application of the techniques using contemporary studies that will resonate with students. |
Contents
List of Figures | xxv |
Preface | xxxi |
An Introduction to Multivariate Design | xxxvi |
Recommended Readings | xxxvi |
3A Data Screening | 32 |
Code and Value Cleaning | 44 |
Figure 3a 1 | 48 |
Dealing With Missing Values | 56 |
Figure 7a 1 Classification Table | 263 |
7B TwoGroup Discriminant Function Analysis Using SPSS | 267 |
THE DEPENDENT VARIABLE VARIATE | 279 |
8B Univariate Comparisons of Means Using SPSS | 315 |
65 | 337 |
Comparing Two Groups | 365 |
9B TwoGroup MANOVA Using SPSS | 385 |
Comparing Three or More Groups | 405 |
Outliers | 65 |
3B Data Screening Using SPSS | 75 |
Figure 3b 3 | 79 |
Figure 3b 15 Scatterplot Main Dialog Box and Its Matrix | 93 |
Figure 3b 22 | 100 |
Chapter 4 | 106 |
THE INDEPENDENT VARIABLE VARIATE | 107 |
Figure 4a 4 | 112 |
43 | 115 |
Figure 4a 7 | 122 |
44 | 124 |
Figure 4a 15 Actual Values of Y Associated With | 134 |
4B Bivariate Correlation | 137 |
5A Multiple Regression | 147 |
Figure 5a 1 SelfEsteem Dependent | 156 |
Figure 3b 6 | 175 |
5B Multiple Regression Using SPSS | 197 |
Figure 5b 1 Output From Explore Showing Extreme | 199 |
If Dialog Box | 204 |
Figure 4a 9 | 215 |
Chapter 6 | 220 |
6A Logistic Regression | 221 |
45 | 234 |
6B Logistic Regression Using SPSS | 243 |
Figure 6b 1 Logistic Regression Main Dialog | 244 |
7A Discriminant Function Analysis | 255 |
Comparing | 415 |
TwoWay Factorial | 439 |
TwoWay Factorial Using SPSS | 453 |
THE EMERGENT VARIATE | 465 |
Figure 3b 7 | 486 |
Figure 3b 9 | 499 |
Figure 3b 10 | 505 |
12B Principal Components | 515 |
Principal Axis Factoring With an Oblique Rotation | 527 |
Results | 533 |
13A Confirmatory Factor Analysis | 539 |
Figure 4a 12 | 566 |
13B Confirmatory Factor Analysis Using AMOS | 569 |
MODEL FITTING | 585 |
14B Path Analysis Using SPSS and AMOS | 619 |
Figure 4a 13 | 634 |
15A Applying a Model to Different Groups | 645 |
15B Assessing Model Invariance | 655 |
Figure 3b 12 Compute Variable Dialog | 664 |
Appendix | 673 |
Figure 4a 14 Predicting Values Using a Slope of 50 | 675 |
| 695 | |
| 701 | |
About the Authors | 721 |