All chapters conclude with “Concluding Thoughts” and “Study Questions.”
Uses of Statistics.
Variables and Constants.
Value Categories and Values.
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.
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.
Bar graphs and Line Diagrams.
A Common Mistake in Displaying Data.
3 Measures of Central Tendency and Variability.
Measures of Central Tendency.
Which Measure of Central Tendency to Use?
Measures of Variability.
The Interquartile Range.
The Mean Deviation.
Reporting Measures of Variability.
Other Uses for Central Tendency and Variability.
4 The Normal Distribution.
Converting Raw Scores to z Scores and Percentiles.
Practical Uses of z Scores.
Deriving Raw Scores from Percentiles.
5 The Basics of Hypothesis Testing.
Research Design Flaws.
Probability and Inference.
Refuting Sampling Error.
More About Research Hypotheses.
The One-Tailed Research Hypothesis.
The Two-tailed Research Hypothesis.
The “No Relationship Research Hypothesis.
Testing the Null Hypothesis.
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.
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.
Deciding Which Test to Use.
More about Getting Help.
The Process of Hypothesis Testing.
Uses of Correlation.
Interpreting Linear Correlations.
Understanding Correlation Coefficients.
Very Strong Correlations.
Correlation Is Not Causation.
Using Correlation for Inference.
Computation and Presentation.
Spearman’s Rho and Kendall’s Tau.
Correlation with Three or More variables.
Variations of Multiple R.
Other Multivariate Tests that Use Correlation.
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.
Presentation of Y´.
The Standard Error.
Using Regression in Social Work Practice.
Regression with Three or More Variables.
Other Types of Regression Analyses.
The Chi-square Test of Association.
Degrees of Freedom.
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.
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.
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.
Answers Sought in Program Evaluations.
Needs Assessments and Formative 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.