Statistical Analysis in the Behavioral Sciences
Edition: 2
Copyright: 2015
Pages: 436
Edition: 2
Copyright: 2015
Pages: 436
Choose Your Format
$121.55
Ebook
$63.67
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
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
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
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
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
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
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
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