Richard M. Grinnell, Jr. is a Professor of Social Work and holds the Clair and Clarice Platt Jones/Helen Frays Endowed Chair of Social Work Research in the School of Social Work at Western Michigan University. He received his Ph.D. in social work from the University of Wisconsin at Madison and for over the past 40 years has held academic and senior university administrative appointments in Australia, Canada, and the United States. He has published over 100 journal articles, book chapters, and conference presentations, and 36 books. His two latest books are Evaluation for Social Workers: Foundations of Evidence–Based Programs, 6th edition (Oxford University Press, 2012, with Peter Gabor and Yvonne Unrau) and Social Work Research and Evaluation: Foundations of Evidence-Based Practice, 10th edition (Oxford University Press, 2014, with Yvonne Unrau).

    I) Brief Table of Contents

    II) Detailed Table of Contents

I) Brief Table of Contents

II) Detailed Table of Contents

Chapter 1. Introduction

Useful Terms You Will Need to Know

Measurement iSSUES

Additional Measurement Classifications      

Research Hypotheses

Classification of Variables and Their Relationship

Categories of Statistical Analyses   

Statistics and data collection

Research designs and statistics

Chapter 2. Frequency Distributions and Graphs

Frequency Distributions      

Grouped Frequency Distributions    

Using Frequency Distributions to Analyze Data     

Misrepresentation of Data   

Graphs         

A Caution: Computer-generated graphs    

Chapter 3. Measures of Central Tendency and Variability

Measures of Central Tendency

Measures of Variability       

Other Uses for Central Tendency and Variability   

Chapter 4. Normal Distributions

Skewness     

Kurtosis        

The Normal Curve

The Standard Normal Distribution

Converting Raw Scores to z Scores and Percentiles

Deriving raw Scores From Percentiles

Chapter 5. Testing Hypotheses

Alternative Explanations for Relationships Within Samples

Probability and Inference

Refuting Sampling Error

Statistical Significance

Testing the Null Hypothesis

Errors in Drawing Conclusions About Relationships

Statistically Significant Relationships and Meaningful Findings

The Hypothesis Testing process

Chapter 6. Sampling Distributions

Sample Size and Sampling Error

What are Sampling Distribution?

Rejection Regions and hypothesis testing

Estimating Parameters

Selecting Statistical Tests    

Deciding Which Test to use  

Chapter 7. t Tests and Analysis of Variance     

The use of t Tests    

The One-sample t Test       

The Dependent t Test         

The Independent tTest        

Misuse of t tests

Simple Analysis of Variance (Simple Anova)        

Multivariate Analysis of Variance    

Chapter 8.  The Chi-Square Test of Association between Variables           

When Chi-Square is Appropriate

Cross-Tabulation Tables      

Using Chi-Square

When Chi-square is not appropriate

Using chi-square in social work practice

Cross-Tabulation With Three or More Variables

Special Applications of The Chi-Square Formula    

Chapter 9. Correlation

Uses of Correlation  

Scattergrams

Nonperfect Correlations

Interpreting Linear Correlations      

Using Correlation for inference

Pearson’S r   

Nonparametric Alternatives to Pearson’S r

Correlation With Three or More Variables  

Other Multivariate analyses That use Correlation  

Chapter 10. Regression    

Prediction and Evidence-Based Practice     

Prediction and Statistical Analysis   

What is Simple Linear Regression?  

Computation of the Regression Equation   

More About the Regression Line     

Interpreting Results  

Using regression in social work practice

When is regression analysis appropriate

Regression With Three or More Variables  

Other Types of Regression Analyses         

Chapter 11. Other Ways That Statistical Analyses Contribute to Evidence-Based Practice

Meta-analyses

Program evaluations

Single-System designs        

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