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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.

For instructors

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