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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.
This acclaimed statistics text requires no prior knowledge of statistics, emphasizing a conceptual understanding of the topic and its usefulness to social work practice and research. Rather than focusing on mathematical computation, Statistics for Social Workers instead focuses 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. The authors have used this approach to teaching statistics for over 25 years, and it remains the most widely used book of its kind.
Data
Information
Variables and Constants
Conceptualization
Operationalization
Reliability
Validity
Three Forms of Research Hypotheses
Causal and Non-causal Research Hypotheses
Independent and Dependent Variables
Nominal
Ordinal
Interval
Ratio
Discrete and Continuous Variables
Dichotomous, Binary, and Dummy Variables
Number of Variables in an Analysis
Primary Purpose of the Analysis
2 Frequency Distributions and Graphs
FREQUENCY DISTRIBUTIONS
Absolute Frequency Distributions
Cumulative Frequency Distributions
Percentage Frequency Distributions
Cumulative Percentage Frequency Distributions
Bar Graphs and Line Diagrams
Pie Charts
Histograms
Frequency Polygons
Stem-and-Leaf Plots
3 Measures of Central Tendency and Variability
MEASURES OF CENTRAL TENDENCY
The Mode
The Median
The Mean
Which Measure of Central Tendency to Use?
The Range
The Interquartile Range
The Mean Deviation
Variance
Standard Deviation
Reporting Measures of Variability
4 Normal Distributions
SKEWNESS
KURTOSIS
NORMAL DISTRIBUTIONS
CONVERTING RAW SCORES TO Z SCORES AND PERCENTILES
Practical Uses of z Scores
5 Hypothesis Testing: Basic Principles
ALTERNATIVE EXPLANATIONS FOR RELATIONSHIPS WITHIN SAMPLES
Rival Hypotheses
Research Design Flaws
Sampling Error
Replication
Statistical Analyses
p values
Rejection Levels (Alpha)
Avoiding Type I Errors
Avoiding Type II Errors
More about Effect Size
Is the Relationship Valuable?
Complex Interpretations of Statistically Significant Relationships
6 Sampling Distributions, Rejection Regions, and Statistical Test Selection
SAMPLE SIZE AND SAMPLING ERROR
SAMPLING DISTRIBUTIONS AND INFERENCE
Comparing an Experimental Sample with Its Population
Comparing a Non-Experimental Sample with Its Population
Samples Drawn from Normal Distributions
Samples Drawn from Skewed Distributions
Constructing a 95 Percent Confidence Interval
Constructing a 99 Percent Confidence Interval
The Importance of Selecting the Correct Test
Factors to Consider in Selecting the Correct Test
More about Getting Help
7 t Tests and Analysis of Variance
THE USE OF t TESTS
Misuse of t
Determining If a Sample Is Representative
Seeking Support for a Research Hypothesis
Presentation of Findings
A Nonparametric Alternative: Chi-Square Goodness of Fit
Use with Two Connected (or Matched) Samples Measured Once
Use with One Sample Measured Twice
A Nonparametric Alternative: Wilcoxon Sign
Nonparametric Alternatives: U and K-S
A Multivariate Alternative: T2
Additional Data Analyses
A Nonparametric Alternative: Kruskal-Wallis
8 The Chi-square Test of Association Between Variables
WHEN CHI-SQUARE IS APPROPRIATE
CROSS-TABULATION TABLES
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
Problems with Sizes of Expected Frequencies
Effects of Introducing Additional Variables
McNemar’s Test
The Median Test
9 Correlation Analyses
USES OF CORRELATION
SCATTERGRAMS
PERFECT CORRELATIONS
NONPERFECT CORRELATIONS
INTERPRETING LINEAR CORRELATIONS
Understanding Correlation Coefficients
Very Strong Correlations
Remember,Correlation Is Not Causation!
Using Correlation For Inference
Computation and Presentation
Spearman’s Rho and Kendall’s Tau
Partial r
Multiple R
Variations of Multiple R
Factor Analysis
Cluster Analysis
10 Regression Analyses
PREDICTION AND EVIDENCE BASED PRACTICE
PREDICTION AND STATISTICAL ANALYSIS
WHAT IS SIMPLE LINEAR REGRESSION?
Research Questions in Simple Linear Regression
Limitations of Simple Linear Regression
The Least-Squares Criterion
Interchanging X and Y Variables
Presentation of Y ?
The Standard Error
Using Regression in Social Work Practice
Options for Entering Variables
Discriminant Analysis
Logistic Regression
11Other Ways that Statistical Analyses Contribute 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
Hypothesis Testing in Single-System Research
Statistical Analyses of Single-System Data
Using Familiar Statistical Tests
Two Other Popular Tests
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