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# Statistical Analysis in the Behavioral Sciences

**Author(s):** * James Raymondo *

** Edition: ** 2

** Copyright: ** 2015

** Pages: ** 436

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** Statistical Analysis in the Behavioral Sciences** presents a basic understanding of statistical analysis, by incorporating real-world examples and exposing readers to current technology.

Designed for students in all the disciplines of the behavioral sciences, ** Statistical Analysis in the Behavioral Sciences** gives the reader a far better understanding of what statistics is, what the statistical procedures really mean, and just as importantly, what they do not mean.

** Statistical Analysis in the Behavioral Sciences**:

- Is organized into four major sections: Some Basics, The Bridge to Inferential Statistics, The Bridge to Inferential Statistics, and Inferential Statistics.
- Features statistical procedures that can be performed in popular spreadsheet software or by software dedicated to statistical applications.
- Provides instructors with a data set consisting of an excerpt from the 2000 to 2012 General Social Survey and an ongoing project by the National Opinion Research Center of the University of Chicago.
- Incorporates suggested computer exercises in most chapters.
- Provides readers with directions and tips for those with access to Statistical Package for the Social Sciences (SPSS).

**PART I Some Basics
Chapter 1 Basic Issues in Statistics **

Introduction

What Is Statistics?

The Role of Statistics in the Research Process

The Research Process

Basic Terms in Statistical Analysis

Measurement

Scales of Data or the Levels of Measurement

Why Does the Level of Data Matter?

Common Symbols and Mathematics Used in Statistics

Commonly Used Statistical Symbols

Introduction to Computer Applications

Computer Applications

Summary of Key Points

Questions and Problems for Review

**Chapter 2 Sampling**

Introduction

What Is Sampling, and Why Do It?

Sampling Strategies

Some Basic Sampling Concepts

Nonprobability Sampling Methods

Probability Sampling Methods

The Sampling Distribution

How Much Change Is in Your Pocket?

What Is the Sampling Distribution?

How Many Sample Means Can Be Drawn from a Given Population?

Calculate the Odds of Winning the Lottery!

Computer Applications

Summary of Key Points

Questions and Problems for Review

**PART II Descriptive Statistics**

Chapter 3 Data Reduction: Frequency Distributions, and the Graphic Display of Data

Chapter 3 Data Reduction: Frequency Distributions, and the Graphic Display of Data

Introduction

The Construction of Frequency Distributions

What are the Advantages of a Simple Frequency Distribution?

Frequency Distributions Where i > 1

Guidelines for the Construction of a Frequency Distribution

An Example of Constructing a Frequency Distribution

Frequency Distributions May Be Created with Any Type of Data

Midpoints and Limits in a Frequency Distribution

Proportions, Percentiles, Deciles, and Quartiles

The Relationship between Percentiles, Deciles, and Quartiles

Finding the Score Corresponding to a Given Percentile

Steps in Finding a Percentile

Finding the Percentile Corresponding to a Given Score

The Graphic Display of Data

The Bar Graph, the Histogram, and the Frequency Polygon

Some Common Graphic Patterns Seen in Data

Computer Applications

Summary of Key Points

Questions and Problems for Review

**Chapter 4 Measures of Central Tendency**

Introduction

Mode

Median

Mean

The Mode, Median, and Mean Compared

The Grand Mean

Summary of Key Points

Questions and Problems for Review

**Chapter 5 Measures of Variation**

Introduction

The Range, Interquartile Range, and Semi-interquartile Range

The Variance and the Standard Deviation

Computing Variance and Standard Deviation for Frequency Distributions

Variance as Prediction Error (or Cabo San Lucas Here I Come!)

Computer Applications

Summary of Key Points

Questions and Problems for Review

**PART III The Bridge to Inferential Statistics**

Chapter 6 The Normal Distribution

Chapter 6 The Normal Distribution

Introduction

The Normal Distribution

The Z Table, Areas Under the Normal Curve

A Normal Distribution

Predicting the Distribution of Scores in A Normal Distribution

Types of Normal Distributions

With So Many Possible Samples How do We Know We Have a “Good” One?

Areas Under the Sampling Distribution

Point Estimation and Interval Estimation

Formula for Computing a Confidence Interval

A Computational Example of a Confidence Interval

Computing the 95% Confidence Interval

The Sampling Distribution Is the Foundation for Two Important Statistical Concepts

Sometimes the Sampling Distribution is Not Normal: The t Distribution

Computer Applications

Summary of Key Points

Questions and Problems for Review

**Chapter 7 Probability**

Introduction

Origins of Probability Theory

Probability

The Link Between Probability, Hypothesis: Testing, and Statistical Inference

Summary of Key Points

Questions and Problems for Review

**Chapter 8 Hypothesis Testing**

Introduction

The Sampling Distribution

Hypotheses and Types of Relationships

A One-Tail Test of the Hypothesis or a Two-Tail Test of the Hypothesis

Devising a Research Strategy

Hypothesis Testing is Conducted Indirectly: the Research Hypothesis and the Null Hypothesis

A Summary of the Steps in Testing a Statistical Hypothesis

Some Key Points to Keep in Mind

Two Examples of the Z test

A Second Example (with an Important Twist)

Types of Error in Hypothesis Testing: Type I and Type II Error

Why Do We Need Statistical Tests, and What Does a Finding of Statistical Significance Really Mean?

The Need For Statistical Tests

What Does a Finding of Statistical Significance Mean?

What Does Statistical Significance NOT Mean?

Summary of Key Points

Questions and Problems for Review

**PART IV Inferential Statistics**

Chapter 9 Correlation

Chapter 9 Correlation

Introduction

A Brief Review of Levels of Measurement

Choosing the Proper Correlation Coefficient

Bivariate Data Plots–Graphing Two Variables to Reveal the Relationship between Two Variables

The Pearson Correlation Coefficient

Logic of the Pearson r

Computing the Pearson r

Testing the Pearson r for Statistical Significance

Brief Review of the Steps in Testing a Pearson r for Statistical Significance

Computation and Interpretation of r 2

Some General Guidelines for the Interpretation of Correlation Coefficients

A Pearson r Example Using Education and Income

The Point-Biserial Correlation Coefficient

The Logic of the Point-Biserial Correlation Coefficient

The Formula for the Point-Biserial Correlation Coefficient

Computing the Point-Biserial Correlation Coefficient

Testing the Point-Biserial Correlation for Statistical Significance

Using the Point-Biserial Correlation Coefficient to Measure the Relationship between Sex and Physical Dexterity

The Spearman Rank Order Correlation Coefficient

The Logic of the Spearman Rank Order Correlation Coefficient

The Formula for the Spearman Rank Order Correlation Coefficient

Computing the Spearman Rank Order Correlation Coefficient

Testing the Spearman Rank Order Correlation Coefficient for Statistical Significance

An Example of the Spearman rs Involving Tied Ranks

An Example of the Spearman Correlation Coefficient rs with Rank on Physical Attractiveness and Rank on Perceived Intelligence

Using a Correlation Coefficient to Control for the Effects of a Third Variable

The Partial Correlation Coefficient

Formula for the Partial Correlation Coefficient

Computer Applications

Summary of Key Points

Questions and Problems for Review

**Chapter 10 Linear Regression**

Introduction

Fitting a Straight Line to Describe a Linear Relationship

The Regression Equation

Formula for “b”, the Regression Coefficient

Predicting Values of Y

Assessing the Quality of the Regression Model

The Standard Error of the Estimate

A Conceptual Formula for the Standard Error of the Estimate

Explained and Unexplained Variance

A Second Example: Using Education to Predict Income

Standardized Regression Analysis and Outliers

Outliers

An Alternative Method of Calculating “b”

Assumptions for Linear Regression

Two Important Alternatives to Linear Regression

A Conceptual Example of Multiple Regression

Predicted Annual Sales

Computer Applications

Summary of Key Points

Questions and Problems for Review

**Chapter 11 Hypothesis Tests for Means**

Introduction

A Brief Review of the Logic of Hypothesis Testing

A Quick Review of the Steps in Testing a Statistical Hypothesis

The Z Test for Comparing a Sample Mean X to a Known Population Mean µ

A Finding of Statistical Significance Involves More than the Mean

The t Distribution

The t-Test for Comparing a Sample Mean X to a Known Population Mean µ

Statistical Tests for Two Independent Sample Means

The Z test for Two Independent Samples

The Z test for Significant Differences between Two Proportions

Computing Proportions and the Standard Error of the Difference

Formula for the Z test for a Significant Difference between Two Proportions

The t-Test for Two Related Samples

Computer Applications

Summary of Key Points

Questions and Problems for Review

**Chapter 12 Analysis of Variance**

Introduction

One-Way Analysis of Variance

Basic Terms and Assumptions for Analysis of Variance

Computing the Sum of Squares

A Computational Example of Analysis of Variance

Computing the F Statistic

The ANOVA Summary Table

Obtaining the F Critical Values

Post Hoc Tests for Significant Differences

Tukey’s HSD Multiple Comparison Test

Variance Explained by the Independent Variable: Eta Squared, and Omega Squared

Computational Formula for Eta Squared

Computational Formula for Omega Squared

An Example of ANOVA with Unequal Sample Sizes

Fisher’s Protected t-Test

Comparing the Placebo Group and the High-Dose Group

Comparing the Placebo Group and the Low-Dose Group

Comparing the Low-Dose Group and the High-Dose Group

Computing Eta Squared and Omega Squared to Estimate Variance Explained

Variations on a Theme in Analysis of Variance

Computer Applications

Summary of Key Points

Questions and Problems for Review

**Chapter 13 Nonparametric Statistics**

Introduction

The Construction and Presentation of Data in a Contingency Table

Difficult to Interpret Tables

Degrees of Freedom in a Contingency Table

The Chi Square Test for Independence

Assumptions for the Chi Square Test

A Computational Example of Chi Square

The Easier Way to Compute Expected Results

The Formula for The Chi Square Test for Independence

Critical Values from the Chi Square Distribution

Some Additional Issues Regarding Chi Square

Yate’s Correction for Continuity for 2 × 2 Tables

Measures of Association for Contingency Tables

The Coefficient of Contingency (c)

The Phi Coefficient

Guttman’s Coefficient of Predictability, Lambda

Goodman’s and Kruskal’s Gamma

A Second Example of Chi Square and Measures of Association

Computing the Measures of Association, How Strong Is the Relationship?

Computing Gamma

Nonparametric Tests of Significance

The Mann–Whitney U Test for Two Independent Samples

An Application of the Mann–Whitney U Test

The Wilcoxon T Test for Two Related Samples

An Application of the Wilcoxon T Test

Computer Applications

Summary of Key Points

Questions and Problems for Review

**Appendices**

Appendix A: Statistical Tables

Appendix B: Answers to Selected Problems

Appendix C: The National Opinion Research Center General Social Survey

Appendix D: How to Use SPSS

**Glossary**

Index

Index

**James Raymondo**

#### Related ISBN's: 9781465269676, 9781465270023

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** ISBN ** 9781465269676

** Details ** Print Product Only (SA,CP, Pak, all Print)