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Statistics for Social Workers

by ;
Edition:
5th
ISBN13:

9780801333125

ISBN10:
0801333121
Format:
Paperback
Pub. Date:
7/1/2000
Publisher(s):
Pearson College Div

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Summary

This is the leading text for social work statistics courses because of its accessibility and the fact that it assumes no prior background in statistics. This text can be used in introductory-level statistics courses or as part of the research methods sequence in social work. It includes numerous social work practice examples, and discusses types of statistical analyses that are most likely to be encountered by social work practitioners and researchers. This edition has been reorganized and updated, with new content added on statistical power, effect size, and measures of association. New topics have been incorporated, including logistic regression and other methods of analysis not in prior editions. New figures and new content on sampling distributions and standard scores (z-scores) have also been added.

Table of Contents

Preface xiii
Introduction to Statistical Analysis
1(19)
Uses of Statistical Analysis
2(1)
General Methodological Terms
3(6)
Data
3(1)
Information
3(1)
Variables and Constants
3(2)
Conceptualization
5(1)
Operationalization
5(2)
Reliability
7(1)
Validity
7(1)
Research Hypotheses
8(1)
Levels of Measurement
9(5)
Nominal
10(1)
Ordinal
11(2)
Interval
13(1)
Ratio
13(1)
Other Measurement Classifications
14(1)
Discrete Variables and Continuous Variables
14(1)
Dichotomous, Binary, and Dummy Variables
14(1)
Levels of Measurement and Analysis of Data
15(1)
Categories of Statistical Analyses
16(1)
Number of Variables Analyzed
16(1)
Descriptive and Inferential
16(1)
Concluding Thoughts
17(1)
Study Questions
17(3)
Frequency Distributions and Graphs
20(19)
Frequency Distributions
20(4)
Absolute Frequency Distributions
22(1)
Cumulative Frequency Distributions
22(1)
Percentage Frequency Distributions
23(1)
Cumulative Percentage Distributions
24(1)
Grouped Frequency Distributions
24(2)
Using Frequency Distributions to Analyze Data
26(2)
Misrepresentation of Data
28(1)
Example: An Administrator's Efforts to Hire More Women
28(1)
Graphical Presentation of Data
29(6)
Bar Graphs
30(1)
Pie Charts
30(2)
Histograms
32(1)
Frequency Polygons
32(2)
Pareto Charts
34(1)
Stem-and-Leaf Plots
34(1)
A Common Mistake in Displaying Data
35(1)
Concluding Thoughts
36(1)
Study Questions
37(2)
Central Tendency and Variability
39(19)
Central Tendency
39(9)
The Mode
40(2)
The Median
42(1)
The Mean
43(2)
Which Measure of Central Tendency to Use?
45(3)
Variability
48(8)
The Range
48(1)
The Interquartile Range
49(1)
The Mean Deviation
50(1)
The Variance
51(1)
The Standard Deviation
51(4)
Reporting Measures of Variability
55(1)
Concluding Thoughts
56(1)
Study Questions
56(2)
Normal Distributions
58(18)
Skewed Distributions
58(2)
Normal Distributions
60(4)
Converting Raw Scores to z Scores and Percentiles
64(8)
Practical Uses of z Scores
69(1)
Example: Student Anxiety
70(2)
Deriving Raw Scores from Percentiles
72(2)
Concluding Thoughts
74(1)
Study Questions
74(2)
Introduction to Hypothesis Testing
76(22)
Alternative Explanations
76(3)
Rival Hypotheses
77(1)
Design Bias
77(2)
Sampling Error
79(1)
Probability
79(3)
Refuting Sampling Error
82(1)
Replication
82(1)
Statistical Analyses
83(1)
Research Hypotheses
83(2)
The One-Tailed Research Hypothesis
84(1)
The Two-Tailed Research Hypothesis
84(1)
The Null Research Hypothesis
85(1)
Testing the Null Hypothesis
85(3)
Statistical Significance
88(2)
p-Values
88(1)
Rejection Levels
88(2)
Errors in Drawing Conclusions About Relationships
90(2)
Avoiding Type I Errors
91(1)
Statistically Significant Relationships and Meaningful Findings
92(3)
Assessing Strength (Effect Size)
92(2)
Is the Relationship Surprising?
94(1)
Complex Interpretations of Statistically Significant Relationships
95(1)
Concluding Thoughts
95(1)
Study Questions
96(2)
Sampling Distributions and Hypothesis Testing
98(18)
Sample Size and Sampling Error
98(2)
Sampling Distributions and Inference
100(2)
Comparing an Experimental Sample with Its Population
100(1)
Comparing a Nonexperimental Sample with Its Population
101(1)
Sampling Distribution of Means
102(9)
Samples Drawn from Normal Distributions
105(5)
Samples Drawn from Skewed Distributions
110(1)
Estimating Parameters from Statistics
111(3)
Constructing a 95 Percent Confidence Interval
112(1)
Constructing a 99 Percent Confidence Interval
112(2)
Other Distributions
114(1)
Concluding Thoughts
114(1)
Study Questions
115(1)
Selecting a Statistical Test
116(18)
The Importance of Selecting the Correct Statistical Test
116(2)
Factors to Consider When Selecting a Statistical Test
118(7)
Sampling Method(s) Used
118(1)
Distribution of the Variables within the Population
119(1)
Level of Measurement of the Independent and Dependent Variables
120(1)
Amount of Statistical Power That is Desirable
121(3)
Robustness of Tests Being Considered
124(1)
Parametric and Nonparametric Tests
125(1)
Multivariate Statistical Tests
126(1)
General Guidelines for Test Selection
127(2)
Getting Help With Data Analyses
129(1)
Concluding Thoughts
130(2)
Study Questions
132(2)
Correlation
134(30)
Uses of Correlation
135(3)
Scattergrams
135(3)
Perfect Correlations
138(2)
Nonperfect Correlations
140(2)
Interpreting Linear Correlations
142(4)
The Range of Correlation Coefficients
143(1)
Interpreting Very Strong Correlations
143(1)
The Coefficient of Determination
144(1)
Correlation is Not Causation
145(1)
Computation and Presentation of Pearson's r
146(2)
Using Pearsons's r for Inference
148(6)
Example: Verbal Participation among Female Group Members
149(3)
Example: Worker Experience and Error Rates
152(2)
Nonparametric Alternatives
154(1)
Spearman's rho and Kendall's tau
154(1)
Example: Caregiver Attitudes and Longevity of Hospice Patients
154(1)
Using Correlation with Three or More Variables
155(4)
Partial r
155(1)
Multiple R
156(3)
Other Multivariate Tests That Use Correlation
159(3)
Factor Analysis
159(2)
Cluster Analysis
161(1)
Concluding Thoughts
162(1)
Study Questions
162(2)
Regression Analyses
164(23)
What is Prediction?
164(3)
What is Simple Linear Regression?
167(2)
Formulating a Research Question
168(1)
Limitations of Simple Linear Regression
168(1)
Computation of the Regression Equation
169(3)
More About the Regression Line
172(4)
The Least-Squares Criterion
172(2)
The Regression Coefficient (b)
174(1)
The y-Intercept (a)
174(1)
Predicted Y (Y')
175(1)
Interchanging X and Y Variables
176(1)
Interpreting Results
176(1)
Presentation of Y'
177(1)
The Standard Error
177(1)
Using Regression Analyses in Social Work Practice
177(3)
Example: Socializing with Family Members and Life Satisfaction
177(2)
Example: Worker's Case Load Size and Number of Sick Days Taken
179(1)
Regression with Three or More Variables
180(2)
Other Types of Regression Analyses
182(2)
Discriminant Analysis
182(1)
Logistic Regression
183(1)
Concluding Thoughts
184(1)
Study Questions
185(2)
Cross-Tabulation
187(28)
The Chi-Square Test of Association
187(15)
Observed Frequencies
189(2)
Expected Frequencies
191(2)
Degrees of Freedom
193(3)
Computation of Chi-Square
196(1)
Presentation of Chi-Square
197(1)
Interpreting the Results of a Chi-Square Analysis
197(1)
Meaningfulness and Sample Size
198(2)
Related Indicators of the Strength of a Relationship
200(1)
Restrictions on the Use of Chi-Square
201(1)
An Alternative to Chi-Square: Fisher's Exact Test
202(1)
Using Chi-Square in Social Work Practice
202(4)
Example: Discharge Planning and Readmission
202(3)
Example: Legislators' Voting Patterns and Tax Issues
205(1)
Chi-Square with Three or More Variables
206(3)
Problems with Sizes of Expected Frequencies
207(1)
Effects of Introducing Additional Variables
208(1)
Special Applications of the Chi-Square Formula
209(4)
McNemar's Test
209(2)
Median Test
211(2)
Concluding Thoughts
213(1)
Study Questions
214(1)
t Tests and Analysis of Variance
215(28)
The Use of t Tests
216(2)
The One-Sample t Test
218(6)
Comparing a Sample's Mean to a Population's Mean
218(2)
Hypothesis Testing
220(1)
Presentation of Findings
221(1)
A Nonparametric Alternative: The Chi-Square Goodness-of-Fit Test
221(3)
The Dependent t Test
224(3)
Use with Two Connected (or Matched) Samples Measured Once
224(1)
Use with One Sample Measured Twice
224(1)
A Nonparametric Alternative: The Wilcoxon Sign Test
225(2)
The Independent t Test
227(11)
Example: Study Guide for the State Merit Exam
228(1)
Example: Treatment of Marital Problems
229(3)
Example: Staff Turnover
232(3)
Nonparametric Alternatives: U and K-S
235(3)
Simple Analysis of Variance (One-Way Anova)
238(2)
Computation
239(1)
A Nonparametric Alternative: The Kruskal-Wallis Test
240(1)
Multiple Analysis of Variance
240(1)
Concluding Thoughts
241(1)
Study Questions
241(2)
References and Further Reading 243(2)
Glossary 245(17)
Index 262


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