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Overview This widely acclaimed statistics text requires no prior knowledge of statistics, emphasizing a conceptual understanding of the topic and its contribution to evidence-based practice. It helps students by discussing the types of statistical analyses that are most likely to be encountered by social work practitioners and researchers. What Reviewers Are Saying About The 7th Edition "Weinbach and Grinnell have an engaging and interesting book that is useful to all students no matter what their previous level of statistical knowledge and ability….Rather than focusing on formulas and hand-calculating data, it presents examples of the use of statistical analyses related to social work practice. In this way, students learn that by understanding statistics they can become critical consumers of articles that can inform their practice." --Carolyn Turturro, University of Arkansas at Little Rock "I believe Statistics for Social Workers is the perfect textbook for training social welfare students….[My students] rather enjoy reading this text because it is easy to read and there are real application examples they can identify with in their agency placements." --Julia F. Hastings, University of California, Berkeley [ Insert MyHelpingLab Advertisement ]

Tabla de contenidos provista por Syndetics

• Preface(p. xiii)
• 1 Introduction(p. 1)
• Uses of Statistics(p. 2)
• Methodological Terms(p. 3)
• Data(p. 3)
• Information(p. 3)
• Variables and Constants(p. 4)
• Conceptualization(p. 5)
• Operationalization(p. 6)
• Reliability(p. 7)
• Validity(p. 7)
• Research Hypotheses(p. 8)
• Measurement Levels(p. 10)
• Nominal(p. 11)
• Ordinal(p. 12)
• Interval(p. 13)
• Ratio(p. 14)
• Measurement Levels and Data Analysis(p. 14)
• Additional Measurement Classifications(p. 16)
• Discrete and Continuous Variables(p. 16)
• Dichotomous, Binary, and Dummy Variables(p. 16)
• Categories of Statistical Analyses(p. 17)
• Number of Variables in an Analysis(p. 17)
• Primary Purpose of the Analysis(p. 17)
• Analysis of Qualitative Data(p. 18)
• Concluding Thoughts(p. 19)
• Study Questions(p. 20)
• 2 Frequency Distributions and Graphs(p. 22)
• Frequency Distributions(p. 23)
• Absolute Frequency Distributions(p. 24)
• Cumulative Frequency Distributions(p. 25)
• Percentage Frequency Distributions(p. 25)
• Cumulative Percentage Frequency Distributions(p. 26)
• Grouped Frequency Distributions(p. 27)
• Using Frequency Distributions to Analyze Data(p. 28)
• Misrepresentation of Data(p. 30)
• Graphs(p. 31)
• Bar Graphs and Line Diagrams(p. 32)
• Pie Charts(p. 32)
• Histograms(p. 34)
• Frequency Polygons(p. 35)
• Stem-and-Leaf Plots(p. 36)
• A Common Mistake in Displaying Data(p. 37)
• Concluding Thoughts(p. 38)
• Study Questions(p. 38)
• 3 Measures of Central Tendency and Variability(p. 40)
• Measures of Central Tendency(p. 40)
• The Mode(p. 41)
• The Median(p. 41)
• The Mean(p. 43)
• Which Measure of Central Tendency to Use?(p. 46)
• Measures of Variability(p. 48)
• The Range(p. 49)
• The Interquartile Range(p. 50)
• The Mean Deviation(p. 51)
• Variance(p. 52)
• Standard Deviation(p. 52)
• Reporting Measures of Variability(p. 56)
• Other Uses for Central Tendency and Variability(p. 57)
• Concluding Thoughts(p. 58)
• Study Questions(p. 58)
• 4 The Normal Distribution(p. 60)
• Skewness(p. 60)
• Kurtosis(p. 62)
• Normal Distributions(p. 63)
• Converting Raw Scores to Z Scores and Percentiles(p. 68)
• Practical Uses of z Scores(p. 72)
• Deriving Raw Scores from Percentiles(p. 75)
• Concluding Thoughts(p. 77)
• Study Questions(p. 77)
• 5 The Basics of Hypothesis Testing(p. 79)
• Alternative Explanations(p. 80)
• Rival Hypotheses(p. 80)
• Research Design Flaws(p. 81)
• Sampling Error(p. 83)
• Probability and Inference(p. 84)
• Refuting Sampling Error(p. 85)
• Replication(p. 85)
• Statistical Analyses(p. 86)
• More About Research Hypotheses(p. 87)
• The One-Tailed Research Hypothesis(p. 88)
• The Two-Tailed Research Hypothesis(p. 88)
• The "No Relationship" Research Hypothesis(p. 88)
• Testing the Null Hypothesis(p. 89)
• Statistical Significance(p. 91)
• p Values(p. 92)
• Rejection Levels ("Alpha")(p. 93)
• Errors in Drawing Conclusions About Relationships(p. 94)
• Avoiding Type I Errors(p. 95)
• Statistically Significant Relationships and Meaningful Findings(p. 96)
• Assessing Strength of Relationships (Effect Size)(p. 97)
• Is the Relationship Surprising?(p. 98)
• Complex Interpretations of Statistically Significant Relationships(p. 99)
• Concluding Thoughts(p. 99)
• Study Questions(p. 100)
• 6 Sampling Distributions and the Null Hypothesis Testing(p. 102)
• Sample Size and Sampling Error(p. 103)
• Sampling Distributions and Inference(p. 104)
• Comparing an Experimental Sample with Its Population(p. 105)
• Comparing a Non-Experimental Sample with Its Population(p. 106)
• Sampling Distribution of Means(p. 107)
• Samples Drawn from Normal Distributions(p. 109)
• Samples Drawn from Skewed Distributions(p. 114)
• Estimating Parameters(p. 115)
• Constructing a 95 Percent Confidence Interval(p. 116)
• Constructing a 99 Percent Confidence Interval(p. 116)
• Concluding Thoughts(p. 118)
• Study Questions(p. 118)
• 7 Selecting a Statistical Test(p. 119)
• The Importance of Selecting the Correct Test(p. 119)
• Where Can We Go Wrong?(p. 120)
• Factors to Consider(p. 121)
• Sampling Method(s) Used(p. 122)
• Distribution of the Variables within the Population(p. 123)
• Level of Measurement of the Variables(p. 123)
• Desirable Amount of Statistical Power(p. 124)
• Robustness of Tests Being Considered(p. 128)
• Parametric and Nonparametric Tests(p. 128)
• Multivariate Tests(p. 130)
• Deciding Which Test to Use(p. 131)
• More About Getting Help(p. 132)
• The Process of Hypothesis Testing(p. 133)
• Concluding Thoughts(p. 135)
• Study Questions(p. 135)
• 8 Correlation(p. 137)
• Uses of Correlation(p. 137)
• Scattergrams(p. 138)
• Perfect Correlations(p. 141)
• Nonperfect Correlations(p. 143)
• Interpreting Linear Correlations(p. 143)
• Understanding Correlation Coefficients(p. 144)
• Very Strong Correlations(p. 145)
• Correlation Is Not Causation(p. 147)
• Using Correlation For Inference(p. 148)
• Pearson's r(p. 150)
• Computation and Presentation(p. 150)
• Nonparametric Alternatives(p. 155)
• Spearman's Rho and Kendall's Tau(p. 155)
• Correlation with three or More Variables(p. 157)
• Partial r(p. 157)
• Multiple R(p. 158)
• Variations of Multiple R(p. 159)
• Other Multivariate Tests that Use Correlation(p. 160)
• Factor Analysis(p. 161)
• Cluster Analysis(p. 162)
• Concluding Thoughts(p. 163)
• Study Questions(p. 164)
• 9 Regression Analyses(p. 165)
• What is Prediction?(p. 165)
• What is Simple Linear Regression?(p. 168)
• Formulating a Research Question(p. 169)
• Limitations of Simple Linear Regression(p. 170)
• Computation of the Regression Equation(p. 170)
• More About the Regression Line(p. 172)
• The Least-Squares Criterion(p. 174)
• Interchanging X and Y Variables(p. 177)
• Interpreting Results(p. 178)
• Presentation of Y'(p. 178)
• The Standard Error(p. 178)
• Using Regression in Social Work Practice(p. 179)
• Regression with Three or More Variables(p. 181)
• Other Types of Regression Analyses(p. 185)
• Discriminant Analysis(p. 185)
• Logistic Regression(p. 186)
• Concluding Thoughts(p. 188)
• Study Questions(p. 189)
• 10 Cross Tabulation(p. 190)
• The Chi-Square Test of Association(p. 190)
• Observed Frequencies(p. 192)
• Expected Frequencies(p. 194)
• Degrees of Freedom(p. 196)
• Using Chi-Square(p. 196)
• Presentation of Findings(p. 199)
• Interpreting the Results of a Chi-Square Analysis(p. 200)
• Meaningfulness and Sample Size(p. 201)
• Restrictions on the Use of Chi-Square(p. 204)
• An Alternative: Fisher's Exact Test(p. 204)
• Using Chi-Square in Social Work Practice(p. 205)
• Cross Tabulation with Three or More Variables(p. 208)
• Problems with Sizes of Expected Frequencies(p. 210)
• Effects of Introducing Additional Variables(p. 210)
• Special Applications of the Chi-Square Formula(p. 211)
• McNemar's Test(p. 211)
• The Median Test(p. 213)
• Concluding Thoughts(p. 215)
• Study Questions(p. 216)
• 11 t Tests and Analysis of Variance(p. 217)
• The Use of t Tests(p. 218)
• Misuse of t(p. 218)
• The One-Sample t Test(p. 219)
• Determining If a Sample Is Representative(p. 221)
• Hypothesis Testing(p. 222)
• Presentation of Findings(p. 223)
• A Nonparametric Alternative: Chi-Square Goodness of Fit(p. 223)
• The Dependent t Test(p. 226)
• Use with Two Connected (or Matched) Samples Measured Once(p. 226)
• Use with One Sample Measured Twice(p. 227)
• A Nonparametric Alternative: Wilcoxon Sign(p. 228)
• The Independent t Test(p. 230)
• Nonparametric Alternatives: U and K-S(p. 239)
• A Multivariate Alternative: T[superscript 2](p. 242)
• Simple Analysis of Variance (Simple Anova)(p. 243)
• Additional Data Analyses(p. 245)
• A Nonparametric Alternative: Kruskal-Wallis(p. 246)
• Multivariate Analysis of Variance(p. 246)
• Concluding Thoughts(p. 247)
• Study Questions(p. 247)
• 12 Other Contributions of Statistics to Evidence-Based Practice(p. 249)
• Meta-Analysis(p. 250)
• Answers Sought in Program Evaluations(p. 253)
• Needs Assessments and Formative Evaluations(p. 253)
• Outcome Evaluations(p. 254)
• Hypothesis Testing in Outcome Evaluations(p. 254)
• Statistical Analyses of Outcome Evaluation Data(p. 257)
• Answers Sought in Single-System Research(p. 259)
• Hypothesis Testing in Single-System Research(p. 259)
• Statistical Analyses of Single-System Data(p. 260)
• Using Familiar Statistical Tests(p. 261)
• Two Other Popular Tests(p. 262)
• Concluding Thoughts(p. 266)
• Study Questions(p. 267)
• Appendix A Beginning to Select a Statistical Test(p. 268)
• Glossary(p. 270)
• Index(p. 289)