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Applied Univariate, Bivariate, and Multivariate Statistics (Hardcover) (Daniel J. Denis)
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A clear and efficient balance between theory and applications of statistical modeling techniques in the social and behavioral sciences
Written as a general and accessible introduction, Applied Univariate, Bivariate, and Multivariate Statistics provides an overview of statistical modeling techniques used in fields in the social and behavioral sciences. Providing a unique balance of statistical theory and methodology, the book surveys both the technical and theoretical aspects of good data analysis.
Featuring applied resources at various levels, the book includes statistical techniques such ast-tests and correlation as well as more advanced procedures such as MANOVA, factor analysis, and structural equation modeling. To promote a more in-depth interpretation of statistical techniques across the sciences, the book surveys some of the technical arguments underlying formulas and equations. Applied Univariate, Bivariate, and Multivariate Statistics also features:
- Demonstrations of statistical techniques using software packages such as R and SPSS®
- Examples of hypothetical and real data with subsequent statistical analysis
- Historical and philosophical insights into many of the techniques used in modern social science
- A companion website that includes further instructional details, additional data sets, solutions to selected exercises, and multiple programming options
An ideal textbook for courses in statistics and methodology at the upper-undergraduate and graduate-level in psychology, political science, biology, sociology, education, economics, communications, law, and survey research,Applied Univariate, Bivariate, and Multivariate Statistics is also a useful reference for practitioners and researchers in their field of application.
The aim of this book is to provide advanced undergraduate and beginning graduate students in the social and behavioral sciences with an applied resource for a variety of statistical techniques from introductory ones such as t-test and correlation coefficients to more advanced procedures such as factor analysis and structural equation modeling. Though on the applied side, this book does not shy away from presenting formulas and equations that are necessary for an accurate interpretation of statistical methodology. It is assumed that the reader has had at least one introductory course in statistics, and, that he is familiar with statistical significance testing, confidence intervals, power, and the logic of ANOVA. An author web site is available, which includes further instructional details, solutions to selected problems in the book, and multiple programming options.