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Applied Statistics From Bivariate Through Multivariate Techniques

ISBN-10: 0761927727
ISBN-13: 9780761927723
Edition: 2007
List price: $111.00
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Description: This textbook provides a clear introduction to widely used topics in bivariate and??multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and  More...

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Book details

List price: $111.00
Copyright year: 2007
Publisher: SAGE Publications, Incorporated
Publication date: 9/6/2007
Binding: Hardcover
Pages: 1128
Size: 7.00" wide x 10.00" long x 1.75" tall
Weight: 3.828

This textbook provides a clear introduction to widely used topics in bivariate and??multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked to think about the meaning of equations. For example, "How do researchers' decisions about treatment dosage levels and sample size tend to influence the magnitude of??t and F ratios?" Each chapter presents a complete empirical research example to illustrate the application of a specific method, such as multiple regression. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions. The robust ancillaries include??datasets in SPSS and??Excel; answers to all comprehension questions; Microsoft(R) PowerPoint?? slides for each chapter; a listing of useful Web sites; and more.?? Visit www.sagepub.com/warnerstudy??for more information.Key Features: The text begins with a clear review and a fresh perspective on concepts including effect size, variance partitioning, and statistical control. Depending on student background and the level of the course, instructors can begin with chapters that review basic material, or they can begin with more advanced topics and use earlier chapters as supplemental review material.Chapters 10 through 13 examine three variable research situations in detail and teach students how to think about statistical control: How does the nature and strength of the association between an??X1 predictorvariable and a Y outcome variable change when we statistically control for another (X2) variable? This understanding of statistical control is essential for comprehension of multivariate analyses.The book includes a chapter on reliability, validity, and multiple item scales, and draws extensively on path models to illustrate theories about possible causal and non-causal associations among variables, beginning with simple three variable research situations.Graphics are used to explain concepts such as variance partitioning, statistical control, and factor rotation.A glossary and extensive practice exercises help readers to digest the material presented. ??

Rebecca M. Warner received a B.A. from Carnegie-Mellon University in Social Relations in 1973 and a Ph.D. in Social Psychology from Harvard in 1978. She has taught statistics for more than 25 years: from Introductory and Intermediate Statistics to advanced topics seminars in Multivariate Statistics, Structural Equation Modeling, and Time Series Analysis. She is currently a Full Professor in the Department of Psychology at the University of New Hampshire. She is a Fellow in the Association for Psychological Science and a member of the American Psychological Association, the International Association for Relationships Research, the Society of Experimental Social Psychology, and the Society for Personality and Social Psychology. She has consulted on statistics and data management for the World Health Organization in Geneva and served as a visiting faculty member at Shandong Medical University in China.

Preface
Acknowledgments
Review of Basic Concepts
Introduction
A Simple Example of a Research Problem
Discrepancies Between Real and Ideal Research Situations
Samples and Populations
Descriptive Versus Inferential Uses of Statistics
Levels of Measurement and Types of Variables
The Normal Distribution
Research Design
Parametric Versus Nonparametric Statistics
Additional Implicit Assumptions
Selection of an Appropriate Bivariate Analysis
Summary
Comprehension Questions
Introduction to SPSS: Basic Statistics, Sampling Error, and Confidence Intervals
Introduction
Research Example: Description of a Sample of HR Scores
Sample Mean (M)
Sum of Squared Deviations and Sample Variance (s2)
Degrees of Freedom (df) for a Sample Variance
Why Is There Variance?
Sample Standard Deviation (s)
Assessment of Location of a Single X Score Relative to a Distribution of Scores
A Shift in Level of Analysis: The Distribution of Values of M Across Many Samples From the Same Population
An Index of Amount of Sampling Error: The Standard Error of the Mean (oM)
Effect of Sample Size (N) on the Magnitude of the Standard Error (oM )
Sample Estimate of the Standard Error of the Mean (SEM)
The Family of t Distributions
Confidence Intervals
Summary
Appendix on SPSS
Comprehension Questions
Statistical Significance Testing
The Logic of Null Hypothesis Significance Testing (NHST)
Type I Versus Type II Error
Formal NHST Procedures: The z Test for a Null Hypothesis About One Population Mean
Common Research Practices Inconsistent With Assumptions and Rules for NHST
Strategies to Limit Risk of Type I Error
Interpretation of Results
When Is a t Test Used Instead of a z Test?
Effect Size
Statistical Power Analysis
Numerical Results for a One-Sample t Test Obtained From SPSS
Guidelines for Reporting Results
Summary
Comprehension Questions
Preliminary Data Screening
Introduction: Problems in Real Data
Quality Control During Data Collection
Example of an SPSS Data Worksheet
Identification of Errors and Inconsistencies
Missing Values
Empirical Example of Data Screening for Individual Variables
Identification and Handling of Outliers
Screening Data for Bivariate Analyses
Nonlinear Relations
Data Transformations
Verifying That Remedies Had the Desired Effects
Multivariate Data Screening
Reporting Preliminary Data Screening
Summary and Checklist for Data Screening
Comprehension Questions
Comparing Group Means Using the Independent Samples t Test
Research Situations Where the Independent Samples t Test Is Used
A Hypothetical Research Example
Assumptions About the Distribution of Scores on the Quantitative Dependent Variable
Preliminary Data Screening
Issues in Designing a Study
Formulas for the Independent Samples t Test
Conceptual Basis: Factors That Affect the Size of the t Ratio
Effect Size Indexes for t
Statistical Power and Decisions About Sample Size for the Independent Samples t Test
Describing the Nature of the Outcome
SPSS Output and Model Results Section
Summary
Comprehension Questions
One-Way Between-Subjects Analysis of Variance
Research Situations Where One-Way Between-Subjects Analysis of Variance (ANOVA) Is Used
Hypothetical Research Example
Assumptions About Scores on the Dependent Variable for One-Way Between-S ANOVA
Issues in Planning a Study
Data Screening
Partition of Scores Into Components
Computations for the One-Way Between-S ANOVA
Effect Size Index for One-Way Between-S ANOVA
Statistical Power Analysis for One-Way Between-S ANOVA
Nature of Differences Among Group Means
SPSS Output and Model Results
Summary
Comprehension Questions
Bivariate Pearson Correlation
Research Situations Where Pearson r Is Used
Hypothetical Research Example
Assumptions for Pearson r
Preliminary Data Screening
Design Issues in Planning Correlation Research
Computation of Pearson r
Statistical Significance Tests for Pearson r
Setting Up CIs for Correlations
Factors That Influence the Magnitude and Sign of Pearson r
Pearson r and r2 as Effect Size Indexes
Statistical Power and Sample Size for Correlation Studies
Interpretation of Outcomes for Pearson r
SPSS Output and Model Results Write-Up
Summary
Comprehension Questions
Alternative Correlation Coefficients
Correlations for Different Types of Variables
Two Research Examples
Correlations for Rank or Ordinal Scores
Correlations for True Dichotomies
Correlations for Artificially Dichotomized Variables
Assumptions and Data Screening for Dichotomous Variables
Analysis of Data: Dog Ownership and Survival After a Heart Attack
Chi-Square Test of Association (Computational Methods for Tables of Any Size)
Other Measures of Association for Contingency Tables
SPSS Output and Model Results Write-Up
Summary
Comprehension Questions
Bivariate Regression
Research Situations Where Bivariate Regression Is Used
A Research Example: Prediction of Salary From Years of Job Experience
Assumptions and Data Screening
Issues in Planning a Bivariate Regression Study
Formulas for Bivariate Regression
Statistical Significance Tests for Bivariate Regression
Setting Up Confidence Intervals Around Regression Coefficients
Factors That Influence the Magnitude and Sign of b
Effect Size/Partition of Variance in Bivariate Regression
Statistical Power
Raw Score Versus Standard Score Versions of the Regression Equation
Removing the Influence of X From the Y Variable by Looking at Residuals From Bivariate Regression
Empirical Example Using SPSS
Summary
Comprehension Questions
Adding a Third Variable: Preliminary Exploratory Analyses
Three-Variable Research Situations
First Research Example
Exploratory Statistical Analyses for Three-Variable Research Situations
Separate Analysis of X1, Y Relationship for Each Level of the Control Variable X2
Partial Correlation Between X1 and Y Controlling for X2
Understanding Partial Correlation as the Use of Bivariate Regression to Remove Variance Predictable by X2 From Both X1 and Y
Computation of Partial r From Bivariate Pearson Correlations
Intuitive Approach to Understanding Partial r
Significance Tests, Confidence Intervals, and Statistical Power for Partial Correlations
Interpretation of Various Outcomes for rY1.2 and rY1
Two-Variable Causal Models
Three-Variable Models: Some Possible Patterns of Association Among X1, Y, and X2
Mediation Versus Moderation
Model Results
Summary
Comprehension Questions
Multiple Regression With Two Predictor Variables
Research Situations Involving Regression With Two Predictor Variables
Hypothetical Research Example
Graphic Representation of Regression Plane
Semipartial (or "Part") Correlation
Graphic Representation of Partition of Variance in Regression With Two Predictors
Assumptions for Regression With Two Predictors
Formulas for Regression Coefficients, Significance Tests, and Confidence Intervals
SPSS Regression Results
Conceptual Basis: Factors That Affect the Magnitude and Sign of B and b Coefficients in Multiple Regression With Two Predictors
Tracing Rules for Causal Model Path Diagrams
Comparison of Equations for B, b, pr, and sr
Nature of Predictive Relationships
Effect Size Information in Regression With Two Predictors
Statistical Power
Issues in Planning a Study
Use of Regression With Two Predictors to Test Mediated Causal Models
Results
Summary
Comprehension Questions
Dummy Predictor Variables and Interaction Terms in Multiple Regression
Research Situations Where Dummy Predictor Variables Can Be Used
Empirical Example
Screening for Violations of Assumptions
Issues in Planning a Study
Parameter Estimates and Significance Tests for Regressions With Dummy Variables
Group Mean Comparisons Using One-Way Between-S ANOVA
Three Methods of Coding for Dummy Variables
Regression Models That Include Both Dummy and Quantitative Predictor Variables
Tests for Interaction (or Moderation)
Interaction Terms That Involve Two Quantitative Predictors
Effect Size and Statistical Power
Nature of the Relationship and/or Follow-Up Tests
Results
Summary
Comprehension Questions
Factorial Analysis of Variance
Research Situations and Research Questions
Screening for Violations of Assumptions
Issues in Planning a Study
Empirical Example: Description of Hypothetical Data
Computations for Between-S Factorial ANOVA
Conceptual Basis: Factors That Affect the Size of Sums of Squares and F Ratios in Factorial ANOVA
Effect Size Estimates for Factorial ANOVA
Statistical Power
Nature of the Relationships, Follow-Up Tests, and Information to Include in the Results
Factorial ANOVA Using the SPSS GLM Procedure
Summary
Appendix: Nonorthogonal Factorial ANOVA (ANOVA With Unbalanced Numbers of Cases in the Cells or Groups)
Comprehension Questions
Multiple Regression With More Than Two Predictors
Research Questions
Empirical Example
Screening for Violations of Assumptions
Issues in Planning a Study
Computation of Regression Coefficients With k Predictor Variables
Methods of Entry for Predictor Variables
Variance Partitioning in Regression for Standard or Simultaneous Regression Versus Regressions That Involve a Series of Steps
Significance Test for an Overall Regression Model
Significance Tests for Individual Predictors in Multiple Regression
Effect Size
Changes in F and R as Additional Predictors Are Added to a Model in Sequential or Statistical Regression
Statistical Power
Nature of the Relationship Between Each X Predictor and Y (Controlling for Other Predictors)
Assessment of Multivariate Outliers in Regression
SPSS Example and Results
Summary
A Review of Matrix Algebra Notation and Operations and Application of Matrix Algebra to Estimation of Slope Coefficients for Regression With More Than k Predictor Variables
Tables for Wilkinson and Dallal (1981) Test of Significance of Multiple R2 in Method = Forward Statistical Regression
Comprehension Questions
Analysis of Covariance
Research Situations and Research Questions
Empirical Example
Screening for Violations of Assumptions
Variance Partitioning in ANCOVA
Issues in Planning a Study
Formulas for ANCOVA
Computation of Adjusted Effects and Adjusted Y* Means
Conceptual Basis: Factors That Affect the Magnitude of SSAadj and SSresidual and the Pattern of Adjusted Group Means
Effect Size
Statistical Power
Nature of the Relationship and Follow-Up Tests: Information to Include in the Results Section
SPSS Analysis and Model Results
Additional Discussion of ANCOVA Results
Summary
Appendix: Alternative Methods for the Analysis of Pretest/Posttest Data
Comprehension Questions
Discriminant Analysis
Research Situations and Research Questions
Introduction of an Empirical Example
Screening for Violations of Assumptions
Issues in Planning a Study
Equations for Discriminant Analysis
Conceptual Basis: Factors That Affect the Magnitude of Wilks's Lambda
Effect Size
Statistical Power and Sample Size Recommendations
Follow-Up Tests to Assess What Pattern of Scores Best Differentiates Groups
Results
One-Way ANOVA on Scores on Discriminant Functions
Summary
Appendix: Eigenvalue/Eigenvector Problem
Comprehension Questions
Multivariate Analysis of Variance
Research Situations and Research Questions
Introduction of the Initial Research Example: A One-Way MANOVA
Why Include Multiple Outcome Measures?
Equivalence of MANOVA and DA
The General Linear Model
Assumptions and Data Screening
Issues in Planning a Study
Conceptual Basis of MANOVA and Some Formulas for MANOVA
Multivariate Test Statistics
Factors That Influence the Magnitude of Wilks's Lambda
Effect Size for MANOVA
Statistical Power and Sample Size Decisions
SPSS Output for a One-Way MANOVA: Career Group Data From Chapter 16
A 2 x 3 Factorial MANOVA of the Career Group Data
A Significant Interaction in a 3 x 6 MANOVA
Comparison of Univariate and Multivariate Follow-Up Analyses for MANOVA
Summary
Comprehension Questions
Principal Components and Factor Analysis
Research Situations
Path Model for Factor Analysis
Factor Analysis as a Method of Data Reduction
Introduction of an Empirical Example
Screening for Violations of Assumptions
Issues in Planning a Factor Analytic Study
Computation of Loadings
Steps in the Computation of Principal Components or Factor Analysis
Analysis 1: Principal Components Analysis of Three Items Retaining All Three Components
Analysis 2: Principal Component Analysis of Three Items Retaining Only the First Component
Principal Components Versus Principal Axis Factoring
Analysis 3: PAF of Nine Items, Two Factors Retained, No Rotation
Geometric Representation of Correlations Between Variables and Correlations Between Components or Factors
The Two Multiple Regressions
Analysis 4: PAF With Varimax Rotation
Questions to Address in the Interpretation of Factor Analysis
Results Section for Analysis 4: PAF With Varimax Rotation
Factor Scores Versus Unit-Weighted Composites
Summary of Issues in Factor Analysis
Optional: Brief Introduction to Concepts in Structural Equation Modeling
Appendix: The Matrix Algebra of Factor Analysis
Comprehension Questions
Reliability, Validity, and Multiple-Item Scales
Assessment of Measurement Quality
Cost and Invasiveness of Measurements
Empirical Examples of Reliability Assessment
Concepts From Classical Measurement Theory
Use of Multiple-Item Measures to Improve Measurement Reliability
Three Methods for the Computation of Summated Scales
Assessment of Internal Homogeneity for Multiple-Item Measures
Correlations Among Scores Obtained Using Different Methods of Summing Items
Validity Assessment
Typical Scale Development Study
Summary
Appendix: The CESD Scale
Comprehension Questions
Analysis of Repeated Measures
Introduction
Empirical Example: Experiment to Assess Effect of Stress on Heart Rate
Discussion of Sources of Within-Group Error in Between-S Versus Within-S Data
The Conceptual Basis for the Paired Samples t Test and One-Way Repeated Measures ANOVA
Computation of a Paired Samples t Test to Compare Mean HR Between Baseline and Pain Conditions
SPSS Example: Analysis of Stress/HR Data Using a Paired Samples t Test
Comparison Between Independent Samples t Test and Paired Samples t Test
SPSS Example: Analysis of Stress/HR Data Using a Univariate One-Way Repeated Measures ANOVA
Using the SPSS GLM Procedure for Repeated Measures ANOVA
Screening for Violations of Assumptions in Univariate Repeated Measures
The Greenhouse-Geisser e and Huynh Feldt e Correction Factors
MANOVA Approach to Analysis of Repeated Measures Data
Effect Size
Statistical Power
Planned Contrasts
Results
Design Problems in Repeated Measures Studies
More Complex Designs
Alternative Analyses for Pretest and Posttest Scores
Summary
Comprehension Questions
Binary Logistic Regression
Research Situations
Simple Empirical Example: Dog Ownership and Odds of Death
Conceptual Basis for Binary Logistic Regression Analysis
Definition and Interpretation of Odds
A New Type of Dependent Variable: The Logit
Terms Involved in Binary Logistic Regression Analysis
Analysis of Data for First Empirical Example: Dog Ownership/Death Study
Issues in Planning and Conducting a Study
More Complex Models
Binary Logistic Regression for Second Empirical Analysis: Drug Dose and Gender as Predictors of Odds of Death
Comparison of Discriminant Analysis to Binary Logistic Regression
Summary
Comprehension Questions
Proportions of Area Under Standard Normal Curve
Critical Values for t Distribution
Critical Values of F
Critical Values of Chi-Square
Critical Values of the Correlation Coefficient
Critical Values of the Studentized Range Statistic
Transformation of r (Pearson Correlation) to Fisher Z
Glossary
References
Index
About the Author

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