Statistics for Social Workers

ISBN-10: 0205484220

ISBN-13: 9780205484225

Edition: 7th 2007 (Revised)

List price: $99.80
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This widely acclaimed statistics text, emphasizing a conceptual understanding of the topic and its contribution to evidence-based practice, requires no prior knowledge of statistics. It helps students by discussing the types of statistical analyses that are most likely to be encountered by social work practitioners and researchers. Book jacket.
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Book details

List price: $99.80
Edition: 7th
Copyright year: 2007
Publisher: Allyn & Bacon, Incorporated
Publication date: 3/22/2006
Binding: Paperback
Pages: 320
Size: 7.25" wide x 9.75" long x 0.75" tall
Weight: 0.990
Language: English

Robert W. Weinbach is Distinguished Professor Emeritus at the University of South Carolina, College of Social Work, where he has taught for over thirty years in the areas of research and management. He is also co-author of Research Methods for Social Workers (six editions), Statistics for Social Workers (7 editions),and Applying Social Work Research Knowledge, and is the author of Evaluating Social Work Services and Programs, all books published by Allyn & Bacon, and over 75 other publications. His management focus is in health/mental health programs and in program evaluation.Lynne M. Taylor is a full-time faculty member at Radford University in the School of Social Work. She teaches practice, social policy and management courses, and an elective course on loss and grief. Prior to joining the faculty there in 2008, she worked in adoptions, child protection and medical social work and (most recently) as a manager in three different non-profit organizations, while also teaching part-time for over ten years in most all areas of the curriculum at the University of South Carolina, College of Social Work. She is a co-author of Applying Social Work Research Knowledge, third edition, published by Allyn & Bacon.

Uses of Statistics
Methodological Terms
Variables and Constants
Research Hypotheses
Measurement Levels
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
Concluding Thoughts
Study Questions
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
Bar Graphs and Line Diagrams
Pie Charts
Frequency Polygons
Stem-and-Leaf Plots
A Common Mistake in Displaying Data
Concluding Thoughts
Study Questions
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
Standard Deviation
Reporting Measures of Variability
Other Uses for Central Tendency and Variability
Concluding Thoughts
Study Questions
The Normal Distribution
Normal Distributions
Converting Raw Scores to Z Scores and Percentiles
Practical Uses of z Scores
Deriving Raw Scores from Percentiles
Concluding Thoughts
Study Questions
The Basics of Hypothesis Testing
Alternative Explanations
Rival Hypotheses
Research Design Flaws
Sampling Error
Probability and Inference
Refuting Sampling Error
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
Concluding Thoughts
Study Questions
Sampling Distributions and the Null Hypothesis Testing
Sample Size and Sampling Error
Sampling Distributions and Inference
Comparing an Experimental Sample with Its Population
Comparing a Non-Experimental Sample with Its Population
Sampling Distribution 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
Concluding Thoughts
Study Questions
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
Concluding Thoughts
Study Questions
Uses of Correlation
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
Concluding Thoughts
Study Questions
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
Concluding Thoughts
Study Questions
Cross Tabulation
The Chi-Square Test of Association
Observed Frequencies
Expected Frequencies
Degrees of Freedom
Using Chi-Square
Presentation of Findings
Interpreting the Results of a Chi-Square Analysis
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
Concluding Thoughts
Study Questions
t 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: T[superscript 2]
Simple Analysis of Variance (Simple Anova)
Additional Data Analyses
A Nonparametric Alternative: Kruskal-Wallis
Multivariate Analysis of Variance
Concluding Thoughts
Study Questions
Other Contributions of Statistics to Evidence-Based Practice
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
Concluding Thoughts
Study Questions
Beginning to Select a Statistical Test
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