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Statistics for Research in Psychology: A Modern Approach Using Estimation

by Rick Gurnsey SAGE Publications, Inc
Pub Date:
09/2017
ISBN:
9781506305189
Format:
Hbk 720 pages
Price:
AU$226.00 NZ$236.52
Product Status: Available in Approx 14 days
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Statistics for Psychological Research is a core text for use in introductory and advanced undergraduate statistics courses in Psychology. In contrast with texts that base their approach on null hypotheses significance testing, Rick Gurnsey provides an alternative approach, one that emphasizes estimation (confidence intervals, effect sizes, and practical significance), a general approach that is more intuitively grasped by students. Built on the same statistical foundations as traditional approaches to the content, this text covers both decision making (e.g., are two population means identical or not?), and estimation (e.g., how big is the difference between two population means?). By covering both approaches, instructors are able to easily shift the emphasis from the more tradition null hypothesis significance testing to estimation.

Preface
Acknowledgments
About the Author
PART I GÇó INTRODUCTION TO STATISTICS AND STATISTICAL DISTRIBUTIONS
Chapter 1 GÇó 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 GÇó 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 GÇó 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 GÇó 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 GÇó 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 GÇó ESTIMATION AND SIGNIFICANCE TESTS (ONE SAMPLE)
Chapter 6 GÇó 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 -¦ 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 GÇó 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 GÇó 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 GÇó Significance Tests: Problems and Alternatives
Introduction
Significance Tests Under Fire
Criticisms of Significance Tests
Confidence Intervals
Estimating -¦1 - -¦0
Estimating d = (-¦1 - -¦0)/s
Estimation Versus Significance Testing
Summary
Key Terms
Exercises
Chapter 10 GÇó Estimating the Population Mean When the Standard Deviation Is Unknown
Introduction
t-Scores: sm Versus sm
t-Distributions
Confidence Intervals: Estimating -¦
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 GÇó ESTIMATION AND SIGNIFICANCE TESTS (TWO SAMPLES)
Chapter 11 GÇó 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 -¦1 - -¦2
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 GÇó 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 GÇó 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 GÇó 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 GÇó 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 GÇó THE GENERAL LINEAR MODEL
Chapter 16 GÇó 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 y-å 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 GÇó 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 GÇó 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 GÇó 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 GÇó Two-Factor ANOVA: Omnibus Effects
Introduction
Main Effects and Interactions in a 3 +ù 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 GÇó 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