Applied Multivariate Research: Design and Interpretation

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SAGE, 2006 - Social Science - 722 pages
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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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Contents

List of Figures
xxv
Preface
xxxi
FOUNDATIONS
xxxvi
Some Fundamental Research Design Concepts
17
3A Data Screening
43
Figure 3a 1 Histogram Showing
50
3B Data Screening Using SPSS
75
Figure 3b 1 Frequencies Main Dialog Box
77
7B TwoGroup Discriminant Function Analysis Using SPSS
267
THE DEPENDENT WARIABLE VARIATE
279
8B Univariate Comparisons of Means Using SPSS
315
Comparing Two Groups
365
9B TwoGroup MANOVA Using SPSS
385
Comparing Three or More Groups
405
Comparing
415
TwoWay Factorial
439

Figure 3b 12 Compute Variable Dialog
90
Figure 3b 15 Scatterplot Main Dialog Box and Its Matrix Box
93
Chapter 4
106
THE INDEPENDENT VARIABLE VARIATE
107
Figure 4a 7 Scatterplot of SelfEsteem
122
Figure 4a 15 Actual Values of YASSOciated With
134
4B Bivariate Correlation
137
5A Multiple Regression
147
Figure 5a 1 SelfEsteem Dependent
156
Figure 5a 6 Unique Contribution of Variable
175
5B Multiple Regression Using SPSS
197
Figure 5b 1 Output From Explore Showing Extreme
199
If Dialog Box
204
Chapter 6
220
6A Logistic Regression
221
6B Logistic Regression Using SPSS
243
Figure 6b 1 Logistic Regression Main Dialog
244
7A Discriminant Function Analysis
255
Figure 7a 1 Classification Table
263
TwoWay Factorial Using SPSS
453
THE EMERGENT WARIATE
465
12B Principal Components
515
13A Confirmatory Factor Analysis
539
13B Confirmatory Factor Analysis Using AMOS
569
MODEL FITTING
585
Structuring the Path Analyses
594
The Multiple Regression Strategy to Perform a Path Analysis
600
Structural Equation Modeling
611
Recommended Readings
617
14B Path Analysis Using SPSS and AMOS
619
15A Applying a Model to Different Groups
645
15B Assessing Model Invariance
655
Appendix
673
Name Index
695
Subject Index
701
About the Authors
721
Copyright

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academic self-doubt alpha level ANOVA assess beta coefficient beta weights between-subjects box plots Chapter checkbox Client Ethnicity coded computed covariance criterion variable data file degrees of freedom dent variable dependent variable Descriptive Statistics dialog box dichotomous discriminant analysis discriminant function equal error estimate evaluated example GAF Posttest GAF score gafpost gender independent indicates interaction interpretation kurtosis latent variables logistic regression Mahalanobis distance Main Dialog Box main effect matrix mean measured variables method missing data missing values model fits multiple regression multivariate normality null hypothesis outliers output path analysis path coefficients Pearson correlation plots post hoc predicted predictor variables principal components procedure pushbutton Q-Q Plot QL-SF quantitative regression analysis relationship represent researchers residuals rotation sample scale scatterplot seeking therapy self-esteem shown in Figure ſº SPSS standard deviation statistically significant structural equation modeling Table univariate variance variate within-subjects zero

References to this book

About the author (2006)

Lawrence S. Meyers earned his doctorate in experimental psychology and has been a Professor in the Psychology Department at California State University, Sacramento, for a number of years. He supervises research students and teaches research design courses as well as history of psychology at both the undergraduate and graduate levels. His areas of expertise include test development and validation.

Glenn Gamst is Professor and Chair of the Psychology Department at the University of La Verne, where he teaches the doctoral advanced statistics sequence. His research interests include the effects of multicultural variables on clinical outcome. Additional research interests focus on conversation memory and discourse processing. He received his PhD in experimental psychology from the University of Arkansas.

A. J. Guarino is a professor of biostatistics at Massachusetts General Hospital, Institute of Health Professions. He is the statistician on numerous National Institutes of Health grants and a reviewer on several research journals. He received his BA from the University of California, Berkeley, and a PhD in statistics and research methodologies from the Department of Educational Psychology, the University of Southern California.

Bibliographic information

QR code for Applied Multivariate Research
TitleApplied Multivariate Research: Design and Interpretation
AuthorsLawrence S. Meyers, Glenn Gamst, A.J. Guarino
Editionillustrated
PublisherSAGE, 2006
ISBN1412904129, 9781412904124
Length722 pages
Subjects › 

Social Science / Research
  
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