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Statistics for Research in Psychology
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Statistics for Research in Psychology
A Modern Approach Using Estimation

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September 2017 | 720 pages | SAGE Publications, Inc

Statistics for Research in Psychology offers an intuitive approach to statistics based on estimation for interpreting research in psychology. This innovative text covers topic areas in a traditional sequence but gently shifts the focus to an alternative approach using estimation, emphasizing confidence intervals, effect sizes, and practical significance, with the advantages naturally emerging in the process. Frequent opportunities for practice and step-by-step instructions for using Excel, SPSS, and R in appendices will help readers come away with a better understanding of statistics that will allow them to more effectively evaluate published research and undertake meaningful research of their own.

 
Preface
 
Acknowledgments
 
About the Author
 
PART I • INTRODUCTION TO STATISTICS AND STATISTICAL DISTRIBUTIONS
 
Chapter 1 • Basic Concepts
Statistics in Psychology  
Variables, Values, and Scores  
Measurement  
Populations and Samples  
Sampling, Sampling Bias, and Sampling Error  
A Preview of What’s Ahead  
Summary  
Key Terms  
Exercises  
Appendix 1.1: Introduction to Excel  
Appendix 1.2: Introduction to SPSS  
Appendix 1.3: An Introduction to R  
 
Chapter 2 • Distributions of Scores
Introduction  
Distributions of Qualitative Variables  
Distributions of Discrete Quantitative Variables  
Distributions of Continuous Variables  
Probability  
Probability Distributions  
Summary  
Key Terms  
Exercises  
Appendix 2.1: Grouped Frequency Tables and Histograms in Excel  
Appendix 2.2: Grouped Frequency Tables and Histograms in SPSS  
 
Chapter 3 • Properties of Distributions
Introduction  
Central Tendency  
Dispersion (Spread)  
Shape  
Summary  
Key Terms  
Exercises  
Appendix 3.1: Basic Statistics in Excel  
Appendix 3.2: Basic Statistics in SPSS  
 
Chapter 4 • Normal Distributions
Introduction  
Normal Distributions  
The Standard Normal Distribution: z-Scores  
Area-Under-the-Curve Problems: Approximate Solutions  
The z-Table  
Area-Under-the-Curve Problems: Exact Solutions  
Critical Value Problems  
Applications  
Summary  
Key Terms  
Exercises  
Appendix 4.1: NORM.DIST and Related Functions in Excel  
 
Chapter 5 • Distributions of Statistics
Introduction  
The Distribution of Sample Means  
Area-Under-the-Curve Questions  
Critical Value Problems  
The Distribution of Sample Variances  
Summary  
Key Terms  
Exercises  
Appendix 5.1: Statistical Distribution Functions in Excel  
 
PART II • ESTIMATION AND SIGNIFICANCE TESTS (ONE SAMPLE)
 
Chapter 6 • Estimating the Population Mean When the Population Standard Deviation Is Known
Introduction  
An Example  
Point Estimates Versus Interval Estimates  
95% Confidence Intervals  
(1-a)100% Confidence Intervals  
Cautions About Interpretation  
Estimating µ When Sample Size Is Large  
Assumptions  
Planning a Study  
A Word About Jerzy Neyman  
Summary  
Key Terms  
Exercises  
Appendix 6.1: Computing Confidence Intervals in Excel  
 
Chapter 7 • Significance Tests
Introduction  
A Scenario: Whole Language Versus Phonics  
Significance Tests  
Computing Exact p-Values: Directional and Non-directional Tests  
The Alternative Hypothesis  
p-Values Are Conditional Probabilities  
Using s to Estimate s (An Approximate z-Test)  
Statistical Significance Versus Practical Significance  
Review of Significance Tests  
Summary  
Key Terms  
Exercises  
Appendix 7.1: Significance Tests in Excel  
 
Chapter 8 • Decisions, Power, Effect Size, and the Hybrid Model
Introduction  
Statistical Decisions  
Neyman and Pearson  
The Determinants of Power  
Prospective Power Analysis: Planning Experiments  
Interpreting Effect Size  
The Hybrid Model: Null Hypothesis Significance Testing  
Summary  
Key Terms  
Exercises  
 
Chapter 9 • Significance Tests: Problems and Alternatives
Introduction  
Significance Tests Under Fire  
Criticisms of Significance Tests  
Confidence Intervals  
Estimating µ1 - µ0  
Estimating d = (µ1 - µ0)/s  
Estimation Versus Significance Testing  
Summary  
Key Terms  
Exercises  
 
Chapter 10 • Estimating the Population Mean When the Standard Deviation Is Unknown
Introduction  
t-Scores: sm Versus sm  
t-Distributions  
Confidence Intervals: Estimating µ  
An Example  
Estimating the Difference Between Two Population Means  
Estimating d  
Significance Tests  
Summary  
Key Terms  
Exercises  
Appendix 10.1: Confidence Intervals and Significance Tests in Excel  
Appendix 10.2: Confidence Intervals and Significance Tests in SPSS  
Appendix 10.3: Exact Confidence Intervals for d Using MBESS in R  
 
PART III • ESTIMATION AND SIGNIFICANCE TESTS (TWO SAMPLES)
 
Chapter 11 • Estimating the Difference Between the Means of Independent Populations
Introduction  
The Two-Independent-Groups Design  
An Example  
Theoretical Foundations for the (1-a)100% Confidence Interval for µ1 - µ2  
Effect Size d  
Significance Testing  
Interpretation of Our Riddle Study  
Partitioning Variance  
Meta-Analysis  
Summary  
Key Terms  
Exercises  
Appendix 11.1: Estimation and Significance Tests in Excel  
Appendix 11.2: Estimation and Significance Tests in SPSS  
 
Chapter 12 • Estimating the Difference Between the Means of Dependent Populations
Introduction  
Dependent Versus Independent Populations  
The Distributions of D and mD  
Repeated Measures and Matched Samples  
Estimating d for Dependent Populations  
Significance Testing  
Partitioning Variance  
Summary  
Key Terms  
Exercises  
Appendix 12.1: Estimation and Significance Tests in Excel  
Appendix 12.2: Estimation and Significance Tests in SPSS  
 
Chapter 13 • Introduction to Correlation and Regression
Introduction  
Associations Between Two Scale Variables  
Correlation and Regression  
The Correlation Coefficient  
The Regression Equation  
Many Bivariate Distributions Have the Same Statistics  
Random Variables, Experiments, and Causation  
Summary  
Key Terms  
Exercises  
Appendix 13.1: Correlation and Regression in Excel  
 
Chapter 14 • Inferential Statistics for Simple Linear Regression
Introduction  
Regression When Values of x Are Fixed: Theory  
Regression When x Values Are Fixed: An Example  
Regression When x Is a Random Variable  
Regression When x Is a Random Variable: An Example  
Estimating the Expected Value of y: E(y|x)  
Prediction Intervals  
Summary  
Key Terms  
Exercises  
Appendix 14.1: Inferential Statistics for Regression in Excel  
Appendix 14.2: Inferential Statistics for Regression in SPSS  
 
Chapter 15 • Inferential Statistics for Correlation
Introduction  
An Example  
The Sampling Distribution of r  
Significance Tests  
What Is a Big Correlation and What Is the Practical Significance of r?  
The Correlation Coefficient Is a Standardized Effect Size: Meta-Analysis  
The Generality of Correlation  
Summary  
Key Terms  
Exercises  
Appendix 15.1: Correlation Analysis in Excel  
Appendix 15.2: Correlation Analysis in SPSS  
 
PART IV • THE GENERAL LINEAR MODEL
 
Chapter 16 • Introduction to Multiple Regression
Introduction  
An Example  
Parameters and Statistics in Multiple Regression  
Significance Tests  
Using SPSS to Conduct Multiple Regression  
Degrees of Freedom  
Comparing Regression Models  
Confidence Intervals for yˆ and Prediction Intervals for yNEXT  
Discussion of Our Example: To Add TIE or Not to Add TIE  
Summary  
Key Terms  
Exercises  
Appendix 16.1: Bootstrapped Confidence Intervals for ?R2  
 
Chapter 17 • Applying Multiple Regression
Introduction  
The Regression Coefficients  
Statistical Control  
Mediation  
Moderation  
Summary  
Key Terms  
Exercises  
Appendix 17.1: Installing the PROCESS Macro in SPSS  
 
Chapter 18 • Analysis of Variance: One-Factor Between-Subjects
Introduction  
The One-Factor, Between-Subjects ANOVA  
Planned Contrasts  
Sources of Variance  
Trend Analysis  
Corrections for Multiple Contrasts  
Regression and ANOVA Are the Same Thing  
Power  
Summary  
Key Terms  
Exercises  
 
Chapter 19 • Analysis of Variance: One-Factor Within-Subjects
Introduction  
An Example: The Posner Cuing Task  
The Omnibus Analysis  
Confidence Intervals and Significance Tests for Contrasts  
Conducting the One-Factor Within-Subjects ANOVA in SPSS  
Summary  
Key Terms  
Exercises  
 
Chapter 20 • Two-Factor ANOVA: Omnibus Effects
Introduction  
Main Effects and Interactions in a 3 × 4 Design  
Partitioning Variability Among Means: Orthogonal Decomposition  
An Example: The Texture Discrimination Task  
The Two-Factor Between-Subjects Design  
The Two-Factor Within-Subjects Design  
The Two-Factor Mixed Design  
Unequal Sample Sizes and Missing Data  
Why Bother With Main Effects and Interactions?  
Summary  
Key Terms  
Exercises  
 
Chapter 21 • Contrasts in Two-Factor Designs
Introduction  
An Overview of First-Order and Second-Order (Interaction) Contrasts  
The Two-Factor, Between-Subjects Design  
The Two-Factor, Within-Subjects Design  
The Two-Factor Mixed Design  
Summary  
Key Terms  
Exercises  
 
Selected Answers to Chapter Exercises
 
Appendix A
 
Appendix B
 
Appendix C
 
Appendix D
 
Glossary
 
References
 
Index

Supplements

Instructor Resource Site

The password-protected Instructor Resources site features author-created tools designed to help instructors plan and teach their course. These include an extensive test bank, chapter-specific PowerPoint presentations, and lecture notes.

For even more coverage and support, visit the site for an additional chapter on Meta-Analysis and numerous appendices for using Excel and SPSS.

Student Study Site
The open-access Student Resources site provides eFlashcards, web quizzes, access to full-text SAGE journal articles with accompanying assessments, and multimedia resources.
Key features
KEY FEATURES:

  • Chapters focus on estimation and include a discussion of how confidence intervals and test statistics can be used to test null hypotheses.
  • Model reports of statistical analyses that follow APA guidelines are presented in each chapter.
  • Learning Checks after each section allow students to assess their understanding before moving on to new material.
  • End-of-chapter exercises with definitions and concepts, true or false statements, and scenarios provide many opportunities for students to test their knowledge and master material.
  • A chapter devoted to the problems associated with significance tests, including file-drawer problem, p-hacking, and basic misunderstandings about p-values, shows students that statistical reform in behavioral research will be the responsibility and accomplishment of their generation.
  • Numerous appendices offer a discussion of useful tools for Excel, SPSS, and R.

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ISBN: 9781506305189