Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences [NOOK Book]

Overview

This classic text on multiple regression is noted for its nonmathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of ...

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Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences

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Overview

This classic text on multiple regression is noted for its nonmathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying CD with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT. Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.

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Editorial Reviews

Booknews
Since the publication of the first edition, multiple regression is increasingly used in the social and behavioral sciences. In this new edition, the material is not substantially changed aside from a down-playing of the use of analysis of variance, increased emphasis on graphical representation, and a moving away from reliance on correlational statistics. Chapters cover bivariate and multiple correlation and regression; data visualization; data analytic strategies; quantitative scales, curvilinear relationships, and transformations; continuous, nominal independent, and categorical variables; outliers and multicollinearity; missing data; causal models; alternative regression models; set correlation with multiple dependent values. The text does not make clear what the CD-ROM contains. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9781135468248
  • Publisher: Taylor & Francis
  • Publication date: 4/29/2014
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 2
  • Pages: 545
  • File size: 17 MB
  • Note: This product may take a few minutes to download.

Table of Contents

Preface
Ch. 1 Introduction 1
Ch. 2 Bivariate Correlation and Regression 19
Ch. 3 Multiple Regression/Correlation With Two or More Independent Variables 64
Ch. 4 Data Visualization, Exploration, and Assumption Checking: Diagnosing and Solving Regression Problems I 101
Ch. 5 Data-Analytic Strategies Using Multiple Regression/Correlation 151
Ch. 6 Quantitative Scales, Curvilinear Relationships, and Transformations 193
Ch. 7 Interactions Among Continuous Variables 255
Ch. 8 Categorical or Nominal Independent Variables 302
Ch. 9 Interactions With Categorical Variables 354
Ch. 10 Outliers and Multicollinearity: Diagnosing and Solving Regression Problems II 390
Ch. 11 Missing Data 431
Ch. 12 Multiple Regression/Correlation and Causal Models 452
Ch. 13 Alternative Regression Models: Logistic, Poisson Regression, and the Generalized Linear Model 479
Ch. 14 Random Coefficient Regression and Multilevel Models 536
Ch. 15 Longitudinal Regression Methods 568
Ch. 16 Multiple Dependent Variables: Set Correlation 608
App. 1 The Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements 631
App. 2 Determination of the Inverse Matrix and Applications Thereof 636
App.: Tables 643
References 655
Glossary 671
Statistical Symbols and Abbreviations 683
Author Index 687
Subject Index 691
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