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Statistics and Data Interpretation for Social Work

Statistics and Data Interpretation for Social Work


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About the Book

Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to provide a solid foundation in statistics. It also addresses tools used by researchers to describe and summarize data ranging from single variables to assessing the relationship between variables and cause and effect among variables. The second section focuses on inferential statistics, describing how researchers draw conclusions about whole populations based on data from samples. This section also covers confidence intervals and a variety of significance tests for examining relationships between different types of variables. Additionally, tools for multivariate analyses and data interpretation are presented. Key Features: Addresses the role of statistics in evidence-based practice and program evaluation Features examples of qualitative and quantitative analysis Each chapter contains exercise problems and questions to enhance student learning Includes electronic data sets taken from actual social work arenas Offers a full ancillary digital packet including a student guide to SPSS with accompanying Data Set, an Instructor's Manual, PowerPoint slides, and a Test Bank

Table of Contents:
STATISTICS AND DATA INTERPRETATION FOR SOCIAL WORK TABLE OF CONTENTS PART ONE: INTRODUCTION AND DESCRIPTIVE STATISTICS CHAPTER 1 INTRODUCTION AND OVERVIEW 1. 1 CHAPTER OVERVIEW 1.2 STATISTICS AND SOCIAL WORK 1.3 SCIENCE AND RESEARCH 1.4 VARIABLES AND MEASUREMENT 1.5 SAMPLES AND POPULATIONS 1.6 DESCRIPTIVE AND INFERENTIAL STATISTICS 1.7 UNIVARIATE, BIVARIATE, AND MULTIVARIATE STATISTICS 1.8 RANDOM ASSIGNMENT 1.9 LEVELS OF MEASUREMENT 1.9.1 Basics 1.9.2 Fine Points 1.10 CHAPTER SUMMARY 1.11 PROBLEMS AND QUESTIONS CHAPTER 2 DATA PRESENTATION 2.1 CHAPTER OVERVIEW 2.2 FREQUENCY DISTRIBUTIONS AND TABLES 2.3 FIGURES 2.4 CHAPTER SUMMARY 2.5 PROBLEMS AND QUESTIONS CHAPTER 3 CENTRAL TENDENCY 3.1 CHAPTER OVERVIEW 3.2 KEY CONCEPTS IN UNIVARIATE DESCRIPTIVE STATISTICS 3.3 THREE KEY MEASURES OF CENTRAL TENDENCY 3.4 THE MODE 3.5 THE MEDIAN 3.6 THE MEAN 3.7 CHOOSING BETWEEN MEASURES 3.8 CHAPTER SUMMARY 3.9 PROBLEMS AND QUESTIONS CHAPTER 4 MEASURES OF VARIABILITY 4.1 CHAPTER OVERVIEW 4.2 THE CONCEPT OF VARIABILITY 4.3 ASSESSING THE VARIABILITY OF CATEGORICAL VARIABLES 4.4 THE RANGE 4.5 THE INTERQUARTILERANGE 4.6 THE MEAN DEVIATION 4.7 THE STANDARD DEVIATION 4.8 THE VARIANCE 4.9 CHAPTER SUMMARY 4.10 PROBLEMS AND QUESTIONS CHAPTER 5 SHAPE OF DISTRIBUTION 5.1 CHAPTER OVERVIEW 5.2 THE NORMAL DISTRIBUTION 5.3 SKEWED DISTRIBUTIONS 5.3.1 Characteristics 5.3.2 Skewness and Measures of Central Tendency 5.4 KURTOSIS 5.5 UNIFORM AND BIMODAL DISTRIBUTIONS 5.6 PERCENTAGES AND THE NORMAL DISTRIBUTION 5.7 INTRODUCTION TO Z SCORES 5.7.1 z Score Calculation 5.7.2 Basics of z Scores 5.7.3 Uses of z Scores 5.8 z SCORES AND THE NORMAL DISTRIBUTION 5.8.1 Problems about Percentages of Cases 5.8.2 Reminders and Cautions 5.9 CHAPTER SUMMARY 5.10 PROBLEMS AND QUESTIONS CHAPTER 6 THE CONCEPT OF RELATIONSHIP AND RELATIONSHIP BETWEEN CATEGORICAL VARIABLES 6.1 CHAPTER OVERVIEW 6.2 DEFINITION OF RELATIONSHIP 6.3 COMMENTS ON RELATIONSHIP 6.4 CONTINGENCY TABLES AND CATEGORICAL VARIABLES 6.4.1 Reading a Contingency (Crosstabs) Table 6.4.2 Assessing Relationship Using a Contingency Table 6.5 SIZE OF ASSOCIATION 6.6 DIFFERENCE IN PERCENTAGES (D%) 6.7 QUALITATIVE DESCRIPTORS OF SIZE OF ASSOCIATION 6.8 RISK RATIO (RR) 6.9 DIFFERENCE IN PERCENTAGES OR RISK RATIO? 6.10 CHAPTER SUMMARY 6.11 PROBLEMS AND QUESTIONS CHAPTER 7 THE ODDS RATIO AND OTHER MEASURES FOR CATEGORICAL VARIABLES 7.1 Chapter Overview 7.2 ODDS RATIO (OR): 7.2.1 Basics and Formula 7.2.2 Interpretation 7.2.3 Advantages 7.3 RELATIONSHIP IN CONTINGENCY TABLES LARGER THAN 2 ˚ 2 7.4 DIRECTIONAL RELATIONSHIP 7.5 MEASURE OF DIRECTIONAL ASSOCIATION BETWEEN CATEGORICAL VARIABLES. 7.6 CHAPTER SUMMARY 7.7 PROBLEMS AND QUESTIONS CHAPTER 8 CORRELATION AND REGRESSION 8.1 CHAPTER OVERVIEW 8.2 POSITIVE AND NEGATIVE CORRELATION 8.3 SCATTERPLOTS 8.4 FORMULA FOR THE CORRELATION COEFFICIENT, r 8.5 UNDERSTANDING r 8.6 INTERPRETATIONS USING r AND z SCORES 8.6.1 Predictions with z Scores 8.6.2 Predicted Change in Standard Deviation Units 8.7 CURVILINEAR RELATIONSHIP 8.8 A CAUTION IN INTERPRETING r 8.9 REGRESSION 8.9.1 Regression Equation and Regression Line 8.9.2 Contrast between r and B 8.10 CORRELATION FOR NOMINAL AND ORDINAL-LEVEL VARIABLES 8.11 CHAPTER SUMMARY 8.12 PROBLEMS AND QUESTIONS CHAPTER 9 STANDARDIZED MEAN DIFFERENCE 9.1 CHAPTER OVERVIEW 9.2 INTRODUCTION TO THE SMD 9.3 GRAPHICAL INTERPRETATION OF THE SMD 9.4 A CAUTION REGARDING THE SMD 9.5 MORE MEASURES OF DIFFERENCES BETWEEN TWO MEANS 9.6 DIFFERENCES BETWEEN THREE OR MORE MEANS 9.7 CHAPTER SUMMARY 9.8 PROBLEMS AND QUESTIONS CHAPTER 10 RESEARCH DESIGN AND CAUSALITY 10.1 CHAPTER OVERVIEW 10.2 INTRODUCTION TO CAUSALITY 10.3 WHAT DOES “CAUSE” MEAN IN SOCIAL SCIENCE? 10.4 CONFOUNDING VARIABLES 10.5 EXPERIMENTAL AND SURVEY DESIGNS 10.5.1 Experiments vs. Surveys 10.5.2 Random Assignment to Groups 10.6 RANDOM ASSIGNMENT AND CAUSALITY 10.7 CHAPTER SUMMARY 10.8 PROBLEMS AND QUESTIONS CHAPTER 11 CONTROLLING FOR CONFOUNDING VARIABLES 11.1 CHAPTER OVERVIEW 11.2 CONTROLLING FOR A VARIABLE 11.2.1 Basic Concepts 11.2.2 Different Patterns Following Control 11.2.3 Initial Relationship Persists 11.2.4 Initial Relationship Weakens 11.2.5 Initial Relationship Disappears 11.3 CAUSAL MODELS AND ADDITIONAL CONSIDERATIONS 11.4 CONTROL FOR MULTIPLE VARIABLES AND CAUSALITY 11.5 INTERACTION EFFECTS 11.6 CHAPTER SUMMARY 11.7 PROBLEMS AND QUESTIONS PART 2: INFERENTIAL STATISTICS AND DATA INTERPRETATION CHAPTER 12 AN INTRODUCTION TO INFERENTIAL STATISTICS 12.1 CHAPTER OVERVIEW 12.2 DESCRIPTIVE VERSUS INFERENTIAL STATISTICS 12.3 CHARACTERISTICS OF RANDOM SAMPLES 12.4 THE ADVANTAGE OF RANDOM SAMPLES 12.5 STATISTICS AND PARAMETERS 12.6 CHARACTERISTICS OF ESTIMATORS 12.7 SAMPLING ERROR AND SAMPLING DISTRIBUTIONS 12.7.1 Concepts 12.7.2 Sampling Distribution of the Mean. 12.8 SAMPLE SIZE AND THE SAMPLING DISTRIBUTION OF 12.9 CHAPTER SUMMARY 12.10 PROBLEMS AND QUESTIONS CHAPTER 13 CONFIDENCE INTERVALS FOR MEANS AND PROPORTIONS 13.1 CHAPTER OVERVIEW 13.2. WHAT IS A CONFIDENCE INTERVAL? 13.3 CONFIDENCE INTERVALS FOR MEANS 13.3.1 Theory 13.3.2 Formulas and Computation 13.4 CONFIDENCE INTERVALS FOR PROPORTIONS AND PERCENTAGES 13.4.1 Theory 13.4.2 Formulae and Application 13.5 FIVE MORE THINGS TO KNOW 13.5 CHAPTER SUMMARY 13.6 PROBLEMS AND QUESTIONS CHAPTER 14 THE LOGIC OF STATISTICAL SIGNIFICANCE TESTS 14.1 CHAPTER OVERVIEW 14.2 INTRODUCTION TO SIGNIFICANCE TESTING 14.3 PROBABILITY 14.3.1 Definition and Formula 14.3.2 Probability and the Normal Distribution 14.4 NULL AND RESEARCH HYPOTHESES 14.5 A COMMON PATTERN FOR HYPOTHESIS PAIRS 14.6 DIRECTIONAL AND NONDIRECTIONAL HYPOTHESIS PAIRS 14.6.1 Definitions and Examples 14.6.2 Guidelines for Usage 14.7 SAMPLING ERROR AND THE NULL HYPOTHESIS 14.8 STATISTICAL SIGNIFICANCE LEVELS 14.9 EXAMPLES OF STATISTICAL SIGNIFICANCE TESTING 14.9.1 An Example in Which We Reject the Null 14.9.2 An Example in Which We Fail to Reject (Accept) the Null 14.9.3 More interpretations of p 14.9.4 What Does it Mean to Reject the Null? 14.9.5 What Does It Mean to Fail to Reject (Accept) the Null? 14.10 NULL AND ALTERNATIVE HYPOTHESES AND SCIENTIFIC INQUIRY 14.11 WHAT IS A STATISTICALLY SIGNIFICANT RESULT? 14.12 CHAPTER SUMMARY 14.13 PROBLEMS AND QUESTIONS CHAPTER 15 THE LARGE SAMPLE TEST OF THE MEAN AND NEW CONCEPTS 15.1 CHAPTER OVERVIEW 15.2 A MODEL FOR HYPOTHESIS TESTING 15.3 ASSUMPTIONS OF STATISTICAL SIGNIFICANCE TESTS 15.3.1 Definition and Assumptions of the Large Sample Test of 15.3.2 Assumptions Common to All Tests 15. 4 THE FIRST TWO STEPS OF THE HYPOTHESIS TESTING MODEL 15.5 STATISTICAL TESTS AND SAMPLING DISTRIBUTIONS 15.6 DIFFERENCES: DIRECTIONAL VS. NONDIRECTIONAL HYPOTHESIS PAIRS 15.6.1 Overview 15.6.2 Nondirectional Hypothesis Pair: 15.6.3 Directional Hypothesis Pair: 15.7 CARRYING OUT THE SIGNIFICANCE TEST USING THE SAMPLING DISTRIBUTION 15.8 FINISHING THE EXAMPLE USING THE FORMULA 15.8.1 Step 3: Carry Out the Test 15.8.2 Decision Rules 15.8.3 Step 4: Make a Decision 15.9 EFFECT OF CHOICE OF SIGNIFICANCE LEVEL ON DECISION MAKING 15.10 TYPE I AND TYPE II ERRORS 15.11 TWO-TAILED VS. ONE-TAILED TESTS 15.11.1 Carrying out our Example 15.11.2 Determining the Exact Value of the Study Sample Result (p) 15.12 MORE DECISION RULES FOR THE ONE-TAILED, LARGE SAMPLE TEST OF 15.13 REJECTING THE NULL AND “REAL” THINGS 15.15 CHAPTER SUMMARY 15.16 PROBLEMS AND QUESTIONS CHAPTER 16 STATISTICAL POWER AND SELECTED TOPICS 16.1 CHAPTER OVERVIEW 16.2 DEFINITION OF POWER 16.3 SAMPLE SIZE AND POWER 16.4 EXAMPLES OF HOW SAMPLE SIZE AFFECTS POWER 16.4.1 The Parenting Skills Example 16.4.2 More Examples of Sample Size and Power 16.5 FACTORS OTHER THAN SAMPLE SIZE THAT INFLUENCE POWER 16.5.1 Overview 16.5.2 Size of Relationship or Difference in the Population 16.5.3 Reduced Variability of Independent or Dependent Variable 16.5.4 Control for Third Variables 16.5.5 Significance Level 16.5.6 Directional Hypotheses and One-tailed Tests 16.5.7 Statistical Significance Test 16.6 HOW MUCH POWER IS ENOUGH? 16.7 HOW LARGE SHOULD SAMPLE SIZE BE? 16.8 NONRANDOM SAMPLES AND SIGNIFICANCE TESTS 16.9 REPORTING STATISTICAL SIGNIFICANCE 16.10 WHAT STATISTICAL SIGNIFICANCE IS (AND IS NOT) 16.11 CHAPTER SUMMARY 16.12 PROBLEMS AND QUESTIONS CHAPTER 17 THE t DISTRIBUTION AND ONE-SAMPLE PROCEDURES FOR MEANS 17.1 CHAPTER OVERVIEW 17.2 SMALL SAMPLE SIZE AND DISTRIBUTIONS 17.3 DEGREES OF FREEDOM 17.4 THE FAMILY OF t DISTRIBUTIONS 17.5 CONFIDENCE INTERVALS FOR MEANS FOR SMALL SAMPLES (AND LARGE) 17.6 INTRODUCTION TO THE ONE-SAMPLE t TEST 17.6.1 Assumptions, Hypothesis Pairs, and Formula 17.6.2 Decision Rules 17.7 CARRYING OUT THE ONE-SAMPLE t TEST 17.8 CHAPTER SUMMARY 17.9 PROBLEMS AND QUESTIONS CHAPTER 18 INDEPENDENT SAMPLES t TEST AND DEPENDENT SAMPLES t TEST 18.1 CHAPTER OVERVIEW 18.2 INTRODUCTION TO THE INDEPENDENT SAMPLES t TEST 18.2.1 Purpose and Sampling Distribution 18.2.2 Hypothesis Pairs 18.2.3 Assumptions and Formulas 18.3 CARRYING OUT THE INDEPENDENT SAMPLES t TEST USING THE SPSS SOFTWARE PACKAGE 18.4 THE INDEPENDENCE ASSUMPTION 18.5 THE DEPENDENT SAMPLES t TEST 18.5.1 Dependent Samples and Statistical Power 18.5.2 Requirements 18.5.3 Example Demonstrating Increase in Power from Positive Correlation 18.6 CHAPTER SUMMARY 18.7 PROBLEMS AND QUESTIONS CHAPTER 19 SINGLE SAMPLE TESTS OF PROPORTIONS 19.1 CHAPTER OVERVIEW 19.2 ONE-SAMPLE TEST OF A PROPORTION 19.2.1 Introduction 19.2.2 Theory and Basics 19.2.3 Carrying Out the Behavior Problems Example 19.3 THE BINOMIAL TEST 19.3.1 Background 19.3.2 Carrying Out the Test. 19.4 THE ONE VARIABLE CHI-SQUARE TEST 19.4.1 Background 19.4.2 The Chi-Square Distribution 19.3.3 Observed and Expected Frequencies 19.4.4 Particulars for the Test 19.4.4 Completing Our Example 19.5 CHAPTER SUMMARY 19.6 PROBLEMS AND QUESTIONS CHAPTER 20 THE CHI-SQUARE TEST OF INDEPENDENCE 20.1 Chapter Overview 20.2 INTRODUCTION TO THE CHI-SQUARE TEST OF INDEPENDENCE 20.3 SELECTED CHARACTERISTICS OF ?2 TEST 20.3.1 Hypothesis Pair 20.3.2 Distribution and Degrees of Freedom 20.3.3 Observed and Expected Proportions 20.3.4 Formula, Observed and Expected Frequencies, Decision Rule, and Requirements 20.4 CARRYING OUT THE ?2 TEST 20.4.1 Hypothesis Testing Model and Calculations 20.4.2 Comments on the Example 20.5 COMMENTS ON THE ?2 TEST 20.6 THE ?2 TEST IS NOT A MEASURE OF SIZE OF ASSOCIATION 20.7 CHAPTER SUMMARY 20.8 PROBLEMS AND QUESTIONS CHAPTER 21 ANALYSIS OF VARIANCE 21.1 CHAPTER OVERVIEW 21.2 INTRODUCTION TO ANOVA 21.3 THE LOGIC OF ANOVA 21.3.1 Hypothesis and Overview 21.3.2 Two Estimates of the Population Variance 21.3.3 The F Distribution 21.4 PARTICULARS OF ANOVA 21.4.1 Assumptions and Level of Measurement 21.4.2 Hypothesis Pair 21.4.3 Critical Values and Decision Rules 21.5 CALCULATION EXAMPLE USING SPSS 21.6 MULTIPLE COMPARISON PROCEDURES 21.7 FISHING EXPEDITIONS 21.7.1 The Dangers of Fishing 21.7.2 The Dangers of Not Fishing 21.8 CHAPTER SUMMARY 21.9 PROBLEMS AND QUESTIONS CHAPTER 22 MORE SIGNIFICANCE TESTS AND REASONING WITH TEST RESULTS 22.1 CHAPTER OVERVIEW 22.2 STATISTICAL SIGNIFICANCE TEST OF PEARSON’S r 22.2.1 Basic Logic 22.2.2 Assumptions and Levels of Measurement 22.2.3 Hypothesis Pair 22.2.4 Decision Rules and Degrees of Freedom 22.2.5 Carrying Out the Hypothesis Testing Model 22.3 A CORRELATION MATRIX 22.4 COMMENTS ON HYPOTHESIS TESTING AND CONFIDENCE INTERVALS 22.4.1 Hypothesis Testing 22.4.2 Confidence Intervals 22.5 PARAMETRIC AND NONPARAMETRIC TESTS 22.6 SELECTED PARAMETRIC TESTS 22.6.1 Significance Test of Spearman’s r 22.6.2 Tests of Association Between Two Ordinal-Level Categorical Variables 22.6.3 Tests for Independent Samples 22.6.4 Tests for Dependent Samples 22.7 Parametric or Nonparametric Test? 22.8 DATA TRANSFORMATION 22.9 SINGLE-CASE DESIGNS 22.9.1 Basic Applications: 22.9.3 Serial Dependency: 22.10 QUALITATIVE METHODS AND STATISTICS 22.11 REASONING WITH DATA: A BRIEF REVIEW 22.12 CHAPTER SUMMARY 22.13 PROBLEMS AND QUESTIONS CHAPTER 23 AN OVERVIEW OF SELECTED MULTIVARIATE PROCEDURES 23.1 CHAPTER OVERVIEW 23.2 MULTIPLE REGRESSION ANALYSIS 23.2.1 Equation and Introduction 23.2.2 Assumptions of Multiple Regression 23.3 ADVANTAGES OF MULTIVARIATE ANALYSES. 23.4 LOGISTIC REGRESSION 23.5 FACTORIAL ANALYSIS OF VARIANCE 23.6 MULTIVARIATE PROCEDURES RELATED TO ANOVA 23.7 A GLIMPSE AT SELECTED PROCEDURES 23.8 CHAPTER SUMMARY 23.9 PROBLEMS AND QUESTIONS CHAPTER 24 GENERALIZABILITY, IMPORTANCE, AND A DATA INTERPRETATION MODEL 24.1 CHAPTER OVERVIEW 24.2 Generalizability 24.2.1 Inferential Statistics and Generalizability 24.2.2 Generalization using Nonstatistical Tools 24.3 IMPORTANCE 24.4 THE BALANCED MODEL FOR DATA INTERPRETATION 24.5 CHAPTER SUMMARY 24.6 PROBLEMS AND QUESTIONS APPENDIX A TABLES A.1 PERCENTAGE OF CASES IN SELECTED AREAS OF THE NORMAL DISTRIBUTION A.2 CRITICAL VALUES FOR THE T DISTRIBUTION AND VALUES FOR CONFIDENCE INTERVALS A.3 CRITICAL VALUES (FREQUENCIES) FOR THE BINOMIAL DISTRIBUTION: ONE-TAILED TEST , ALPHA = .05 A.4 CRITICAL VALUES FOR THE CHI-SQUARE DISTRIBUTION A.5 CRITICAL VALUES FOR THE F DISTRIBUTION A.6 CRITICAL VALUES FOR PEARSON'S r APPENDIX B REVIEW OF BASIC MATH APPENDIX C APPROPRIATE MEASURES FOR DIFFERENT SITUATIONS APPENDIX D SYMBOLS IN THE TEXT APPENDIX E FORMULAS IN THE TEXT APPENDIX F ANSWERS TO END OF CHAPTER PROBLEMS AND QUESTIONS

About the Author :
Jim A. Rosenthal, PhD, is a professor at the University of Oklahoma School of Social Work and Coordinator of the Graduate Program.


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Product Details
  • ISBN-13: 9780826107206
  • Publisher: Springer Publishing Co Inc
  • Publisher Imprint: Springer Publishing Co Inc
  • Height: 254 mm
  • No of Pages: 512
  • Weight: 915 gr
  • ISBN-10: 0826107206
  • Publisher Date: 16 Dec 2011
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Width: 178 mm


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