Using Statistics in the Social and Health Sciences with SPSS and Excel (inbunden)
Fler böcker inom
Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
592
Utgivningsdatum
2016-11-11
Upplaga
1
Förlag
John Wiley & Sons Inc
Medarbetare
Abbott
Dimensioner
236 x 157 x 36 mm
Vikt
931 g
Antal komponenter
1
Komponenter
HC gerader Rücken kaschiert
ISBN
9781119121046

Using Statistics in the Social and Health Sciences with SPSS and Excel

av Martin Lee Abbott
Inbunden,  Engelska, 2016-11-11
1492
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Provides a step-by-step approach to statistical procedures to analyze data and conduct research, with detailed sections in each chapter explaining SPSS (R) and Excel (R) applications This book identifies connections between statistical applications and research design using cases, examples, and discussion of specific topics from the social and health sciences. Researched and class-tested to ensure an accessible presentation, the book combines clear, step-by-step explanations for both the novice and professional alike to understand the fundamental statistical practices for organizing, analyzing, and drawing conclusions from research data in their field. The book begins with an introduction to descriptive and inferential statistics and then acquaints readers with important features of statistical applications (SPSS and Excel) that support statistical analysis and decision making. Subsequent chapters treat the procedures commonly employed when working with data across various fields of social science research. Individual chapters are devoted to specific statistical procedures, each ending with lab application exercises that pose research questions, examine the questions through their application in SPSS and Excel, and conclude with a brief research report that outlines key findings drawn from the results. Real-world examples and data from social and health sciences research are used throughout the book, allowing readers to reinforce their comprehension of the material. Using Statistics in the Social and Health Sciences with SPSS (R) and Excel (R) includes: Use of straightforward procedures and examples that help students focus on understanding of analysis and interpretation of findings Inclusion of a data lab section in each chapter that provides relevant, clear examples Introduction to advanced statistical procedures in chapter sections (e.g., regression diagnostics) and separate chapters (e.g., multiple linear regression) for greater relevance to real-world research needs Emphasizing applied statistical analyses, this book can serve as the primary text in undergraduate and graduate university courses within departments of sociology, psychology, urban studies, health sciences, and public health, as well as other related departments. It will also be useful to statistics practitioners through extended sections using SPSS (R) and Excel (R) for analyzing data.
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Fler böcker av Martin Lee Abbott

Övrig information

Martin Lee Abbott, PhD, is Professor of Sociology at Seattle Pacific University, where he has served as Executive Director of the Washington School Research Center, an independent research and data analysis center funded by the Bill & Melinda Gates Foundation. Dr. Abbott has held positions in both academia and industry, focusing his consulting and teaching in the areas of statistical procedures, program evaluation, applied sociology, and research methods. He is the author of Understanding Educational Statistics Using Microsoft Excel and SPSS, The Program Evaluation Prism: Using Statistical Methods to Discover Patterns, and Understanding and Applying Research Design, also from Wiley.

Innehållsförteckning

Preface xv Acknowledgments xix 1 Introduction 1 Big Data Analysis 1 Visual Data Analysis 2 Importance of Statistics for the Social and Health Sciences and Medicine 3 Historical Notes: Early Use of Statistics 4 Approach of the Book 6 Cases from Current Research 7 Research Design 9 Focus on Interpretation 9 2 Descriptive Statistics: Central Tendency 13 What is the Whole Truth? Research Applications (Spuriousness) 13 Descriptive and Inferential Statistics 16 The Nature of Data: Scales of Measurement 16 Descriptive Statistics: Central Tendency 23 Using SPSS (R) and Excel to Understand Central Tendency 28 Distributions 35 Describing the Normal Distribution: Numerical Methods 37 Descriptive Statistics: Using Graphical Methods 41 Terms and Concepts 47 Data Lab and Examples (with Solutions) 49 Data Lab: Solutions 51 3 Descriptive Statistics: Variability 55 Range 55 Percentile 56 Scores Based on Percentiles 57 Using SPSS (R) and Excel to Identify Percentiles 57 Standard Deviation and Variance 60 Calculating the Variance and Standard Deviation 61 Population SD and Inferential SD 66 Obtaining SD from Excel and SPSS (R) 67 Terms and Concepts 70 Data Lab and Examples (with Solutions) 71 Data Lab: Solutions 73 4 The Normal Distribution 77 The Nature of the Normal Curve 77 The Standard Normal Score: Z Score 79 The Z Score Table of Values 80 Navigating the Z Score Distribution 81 Calculating Percentiles 83 Creating Rules for Locating Z Scores 84 Calculating Z Scores 87 Working with Raw Score Distributions 90 Using SPSS (R) to Create Z Scores and Percentiles 90 Using Excel to Create Z Scores 94 Using Excel and SPSS (R) for Distribution Descriptions 97 Terms and Concepts 99 Data Lab and Examples (with Solutions) 99 Data Lab: Solutions 101 5 Probability and the Z Distribution 105 The Nature of Probability 106 Elements of Probability 106 Combinations and Permutations 109 Conditional Probability: Using Bayes' Theorem 111 Z Score Distribution and Probability 112 Using SPSS (R) and Excel to Transform Scores 117 Using the Attributes of the Normal Curve to Calculate Probability 119 "Exact" Probability 123 From Sample Values to Sample Distributions 126 Terms and Concepts 127 Data Lab and Examples (with Solutions) 128 Data Lab: Solutions 129 6 Research Design and Inferential Statistics 133 Research Design 133 Experiment 136 Non-Experimental or Post Facto Research Designs 140 Inferential Statistics 143 Z Test 154 The Hypothesis Test 154 Statistical Significance 156 Practical Significance: Effect Size 156 Z Test Elements 156 Using SPSS (R) and Excel for the Z Test 157 Terms and Concepts 158 Data Lab and Examples (with Solutions) 161 Data Lab: Solutions 162 7 The T Test for Single Samples 165 Introduction 166 Z Versus T: Making Accommodations 166 Research Design 167 Parameter Estimation 169 The T Test 173 The T Test: A Research Example 176 Interpreting the Results of the T Test for a Single Mean 180 The T Distribution 181 The Hypothesis Test for the Single Sample T Test 182 Type I and Type II Errors 183 Effect Size 187 Effect Size for the Single Sample T Test 187 Power Effect Size and Beta 188 One- and Two-Tailed Tests 189 Point and Interval Estimates 192 Using SPSS (R) and Excel with the Single Sample T Test 196 Terms and Concepts 201 Data Lab and Examples (with Solutions) 201 Data Lab: Solutions 203 8 Independent Sample T Test 207 A Lot of "Ts" 207 Research Design 208 Experimental Designs and the Independent T Test 208 Dependent Sample Designs 209 Between and Within Research Designs 210 Using Different T Tests 211 Independent T Test: The Procedure 213 Creating the Sampling Distribution of Differences 215 The Nature of the Sampling Distribution of Differences 216 Calculating the Estimated Standard Error of Difference with Equ

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