Applied Multivariate Research: Design and Interpretation
Multivariate 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.
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List of Figures
Some Fundamental Research Design Concepts
3A Data Screening
Figure 3a 1 Histogram Showing
3B Data Screening Using SPSS
Figure 3b 1 Frequencies Main Dialog Box
7B TwoGroup Discriminant Function Analysis Using SPSS
THE DEPENDENT WARIABLE VARIATE
8B Univariate Comparisons of Means Using SPSS
Comparing Two Groups
9B TwoGroup MANOVA Using SPSS
Comparing Three or More Groups
Figure 3b 12 Compute Variable Dialog
Figure 3b 15 Scatterplot Main Dialog Box and Its Matrix Box
THE INDEPENDENT VARIABLE VARIATE
Figure 4a 7 Scatterplot of SelfEsteem
Figure 4a 15 Actual Values of YASSOciated With
4B Bivariate Correlation
5A Multiple Regression
Figure 5a 1 SelfEsteem Dependent
Figure 5a 6 Unique Contribution of Variable
5B Multiple Regression Using SPSS
Figure 5b 1 Output From Explore Showing Extreme
If Dialog Box
6A Logistic Regression
6B Logistic Regression Using SPSS
Figure 6b 1 Logistic Regression Main Dialog
7A Discriminant Function Analysis
Figure 7a 1 Classification Table
TwoWay Factorial Using SPSS
THE EMERGENT WARIATE
12B Principal Components
13A Confirmatory Factor Analysis
13B Confirmatory Factor Analysis Using AMOS
Structuring the Path Analyses
The Multiple Regression Strategy to Perform a Path Analysis
Structural Equation Modeling
14B Path Analysis Using SPSS and AMOS
15A Applying a Model to Different Groups
15B Assessing Model Invariance
About the Authors