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Test Bank for Statistics for Research in Psychology A Modern Approach Using Estimation By Rick Gurnsey, ISBN: 9781506305189

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Test Bank for Statistics for Research in Psychology A Modern Approach Using Estimation Gurnsey

Test Bank for Statistics for Research in Psychology A Modern Approach Using Estimation By Rick Gurnsey, ISBN: 9781506305189

Table Of Content

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