Description

Features

• This text helps students by discussing the types of statistical analyses that are most likely to be encountered by social work practitioners and researchers.
• This text helps students by being accessible to students who have never taken a course in statistics.
• This text helps students by containing numerous real-life examples of how statistical analysis can be helpful in practice decision-making.
• This text helps students by de-emphasizing mathematical computations, focusing instead on providing an understanding of the logical underpinnings of statistical analysis and how to apply the results of analysis in a social work practice environment.

New To This Edition

• A new chapter focuses on how statistical analyses, including meta-analysis, are used in evaluation research.
• Includes new content on statistical analyses of qualitative research data.
• Features ten additional figures, tables and boxes, as well as new cases.

Table of Contents

All chapters conclude with “Concluding Thoughts” and “Study Questions.”

Preface.


1. Introduction.

             Uses of Statistics.

             Methodological Terms.

                          Data.

                          Information.

                          Variables and Constants.

                          Value Categories and Values.

                          Conceptualization.

                          Operationalization.

                          Reliability.

                          Validity

                          Research Hypotheses.

             Measurement Levels.

                          Nominal.

                          Ordinal.

                          Interval.

                          Ratio.

             Measurement Levels and Data Analysis.

             Additional Measurement Classifications.

                          Discrete and Continuous Variables.

                          Dichotomous, Binary, and Dummy Variables.

             Categories of Statistical Analyses.

                          Number of Variables in an Analysis.

                          Primary Purpose of the Analysis.

             Analysis of Qualitative Data.

2  Frequency Distributions and Graphs.

             Frequency Distributions.

                          Absolute Frequency Distributions.

                          Cumulative Frequency Distributions.

                          Percentage Frequency Distributions.

                          Cumulative Percentage Frequency Distributions.

             Grouped Frequency Distributions.

             Using Frequency Distributions to Analyze Data.

             Misrepresentation of Data.

             Graphs.

                          Bar graphs and Line Diagrams.

                          Pie Charts.

                          Histograms.

                          Frequency Polygons.

                          Stem-and-Leaf Plots.

             A Common Mistake in Displaying Data.

3  Measures of Central Tendency and Variability.

             Measures of Central Tendency.

                          The Mode.

                          The Median.

                          The Mean.

                          Which Measure of Central Tendency to Use?

             Measures of Variability.

                          The Range.

                          The Interquartile Range.

                          The Mean Deviation.

                          Variance.

                          Standard Deviation.

                          Reporting Measures of Variability.

             Other Uses for Central Tendency and Variability.

4  The Normal Distribution.

             Skewness.

             Kurtosis.

             Normal Distributions.

             Converting Raw Scores to z Scores and Percentiles.

                          Practical Uses of z Scores.

             Deriving Raw Scores from Percentiles.

5  The Basics of Hypothesis Testing.

             Alternative Explanations.

                          Rival Hypotheses.

                          Research Design Flaws.

                          Sampling Error.

             Probability and Inference.

             Refuting Sampling Error.

                          Replication.

                          Statistical Analyses.

             More About Research Hypotheses.

                          The One-Tailed Research Hypothesis.

                          The Two-tailed Research Hypothesis.

                          The “No Relationship Research Hypothesis.

             Testing the Null Hypothesis.

             Statistical Significance.

                          p-values.

                          Rejection Levels (“Alpha”).

             Errors in Drawing Conclusions About Relationships.

                          Avoiding Type I Errors.

             Statistically Significant Relationships and Meaningful Findings.

                          Assessing Strength of Relationships (Effect Size).

                          Is the Relationship Surprising?

                          Complex Interpretations of Statistically Significant Relationships.

6  Sampling Distributions and Testing the Null Hypothesis.

             Sample Size and Sampling Error.

             Sampling Distributions and Inference.

                          Comparing an Experimental Sample with Is Population.

                          Comparing a Non-experimental Sample with Its Population.

             Sampling Distributions of Means.

                          Samples Drawn from Normal Distributions.

                          Samples Drawn from Skewed Distributions.

             Estimating Parameters.

                          Constructing a 95 Percent Confidence Interval.

                          Constructing a 99 Percent Confidence Interval.

7  Selecting a Statistical Test.

             The Importance of Selecting the Correct Test.

                          Where Can We Go Wrong?

             Factors to Consider.

                          Sampling Method(s) Used.

                          Distribution of the Variables within the Population.

                          Level of Measurement of the Variables.

                          Desirable Amount of Statistical Power.

                          Robustness of Tests Being Considered.

             Parametric and Nonparametric Tests.

             Multivariate Tests.

             Deciding Which Test to Use.

             More about Getting Help.

             The Process of Hypothesis Testing.

8  Correlation.

             Uses of Correlation.

             Scattergrams.                 

             Perfect Correlations.

             Nonperfect Correlations.

             Interpreting Linear Correlations.

                          Understanding Correlation Coefficients.

                          Very Strong Correlations.

                          Correlation Is Not Causation.

                          Using Correlation for Inference.

             Pearson’s r.

                          Computation and Presentation.

                          Nonparametric Alternatives.

                                      Spearman’s Rho and Kendall’s Tau.

             Correlation with Three or More variables.

                          Partial r.

                          Multiple R.

                          Variations of Multiple R.

             Other Multivariate Tests that Use Correlation.

                          Factor Analysis.

                          Cluster Analysis.

9  Regression Analyses.

             What Is Prediction?

             What Is Simple Linear Regression?

                          Formulating a Research Question.

                          Limitations of Simple Linear Regression.

             Computation of the Regression Equation.

             More About the Regression Line.

                          The Least Squares Criterion.

                          Interchanging X and Y Variables.

             Interpreting Results.

                          Presentation of Y´.

                          The Standard Error. 

                          Using Regression in Social Work Practice.

             Regression with Three or More Variables.

             Other Types of Regression Analyses.

                          Discriminant Analysis.

                          Logistic Regression.

10  Cross-Tabulation.

             The Chi-square Test of Association.            

                          Observed Frequencies.

                          Expected Frequencies.

                          Degrees of Freedom.

                          Using Chi-square.

                          Presentation of Findings.

                          Meaningfulness and Sample Size.

                          Restrictions on the Use of Chi-square.

                          An Alternative : Fisher’s Exact Test.

                          Using Chi-square in Social Work Practice.

             Cross-Tabulation with Three or More Variables.

                          Problems with Sizes of Expected Frequencies.

                          Effects of Introducing Additional Variables.

             Special Applications of the Chi-square Formula.

                          McNemar’s Test.

                          The Median Test.

11  tests and Analysis of Variance.

The Use of t Tests.

             Misuse of t.

The One-Sample t Test.

             Determining If a Sample Is Representative.

             Hypothesis Testing.

                          Presentation of Findings.

                          A Nonparametric Alternative: Chi-Square Goodness-of-Fit.

             The Dependent t Test.

                          Use with Two Connected (or Matched) Samples Measured Once.

                          Use with One Sample Measured Twice.

                          A Nonparametric Alternative: Wilcoxon Sign.

             The Independent t Test.

                          Nonparametric Alternatives: U and K-S.

                          A Multivariate Alternative: T2.

             Simple Analysis of Variance: Simple ANOVA.

                          Additional Data Analyses.

                          A Nonparametric Alternative: Kruskal-Wallis.

             Multivariate Analysis of Variance.

12  Other Contributions of Statistics to Evidence-Based Practice.

             Meta-Analysis.

             Answers Sought in Program Evaluations.

             Needs Assessments and Formative Evaluations.

             Outcome Evaluations.

             Hypothesis Testing in Outcome Evaluations.

             Statistical Analyses of Outcome Evaluation Data.

             Answers Sought in Single System Research.

             Hypothesis Testing in Single System Research.

             Statistical Analyses of Single System Data.

                          Using Familiar Statistical Tests.

                          Two Other Popular Tests.

Appendix A    Beginning to Select A Statistical Test.

Glossary.

Index.

Courses

Statistics for Social Workers  (Social Work / Family Therapy / Human Services)

Next Edition(s)

• Statistics for Social Workers, 8/E
  Weinbach & Grinnell
  ©2010  |  Pearson  |  Unknown / Other; 312 pp  |  Instock
  ISBN-10: 0205739873  |  ISBN-13: 9780205739875
  Suggested retail price: $112.40  Buy from myPearsonStoreNet price: $84.30  (What's this?)Brief Description  | 

  

  Now in its eighth edition, this widely used text covers the types of statistical analyses that are most likely to be encountered by social work practitioners and researchers.  It requires no prior knowledge of statistics and only basic mathematical competence.

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Overview

This widely acclaimed statistics text requires no prior knowledge of statistics, emphasizing a conceptual understanding of the topic and its contribution to evidence-based practice. It helps students by discussing the types of statistical analyses that are most likely to be encountered by social work practitioners and researchers.

What Reviewers Are Saying About The 7th Edition

“Weinbach and Grinnell have an engaging and interesting book that is useful to all students no matter what their previous level of statistical knowledge and ability….Rather than focusing on formulas and hand-calculating data, it presents examples of the use of statistical analyses related to social work practice. In this way, students learn that by understanding statistics they can become critical consumers of articles that can inform their practice.”
--Carolyn Turturro, University of Arkansas at Little Rock

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