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Statistics for social workers

Author: Robert W Weinbach; Richard M Grinnell
Publisher: Boston : Pearson/Allyn & Bacon, ©2007.
Edition/Format:   Print book : English : 7th edView all editions and formats
Database:WorldCat
Summary:

This widely acclaimed text focuses on a conceptual understanding of the topic and its contribution to evidence-based practice which requires no prior knowledge of statistics.

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Document Type: Book
All Authors / Contributors: Robert W Weinbach; Richard M Grinnell
ISBN: 0205484220 9780205484225
OCLC Number: 62732863
Description: xvi, 301 pages : illustrations ; 24 cm
Contents: 1. Introduction --
Uses of Statistics --
Methodological Terms --
Data --
Variables and Constants --
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 --
Common Mistake in Displaying Data --
3. Measures of Central Tendency and Variability --
Measures of Central Tendency --
Mode --
Median --
Mean --
Which Measure of Central Tendency to Use? --
Measures of Variability --
Range --
Interquartile Range --
Mean Deviation --
Variance --
Standard Deviation --
Reporting Measures of Variability --
Other Uses for Central Tendency and Variability --
4. 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. 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 --
One-Tailed Research Hypothesis --
Two-Tailed Research Hypothesis --
"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 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 --
7. Selecting a Statistical Test --
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 --
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 --
Least-Squares Criterion --
Interchanging X and Y Variables --
Interpreting Results --
Presentation of Y' --
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 --
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 --
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 --
Median Test --
11. t Tests and Analysis of Variance --
Use of t Tests --
Misuse of t --
One-Sample t Test --
Determining If a Sample Is Representative --
Hypothesis Testing --
Presentation of Findings --
Nonparametric Alternative: Chi-Square Goodness of Fit --
Dependent t Test --
Use with Two Connected (or Matched) Samples Measured Once --
Use with One Sample Measured Twice --
Nonparametric Alternative: Wilcoxon Sign --
Independent t Test --
Nonparametric Alternatives: U and K-S --
Multivariate Alternative: T[superscript 2] --
Simple Analysis of Variance (Simple Anova) --
Additional Data Analyses --
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.
Responsibility: Robert W. Weinbach, Richard M. Grinnell, Jr.

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