Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences
Albers, Michael J.
219 p. 24 cm
Presents the entire data analysis process as a cyclical, multi-phase process and addresses the processes of exploratory analysis, decision-making for performing parametric or non-parametric analysis, and practical significance determination.
Guides readers through the quantitative data analysis process including contextualizing data within a research situation, connecting data to the appropriate statistical tests, and drawing valid conclusions Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences presents a clear and accessible introduction to the basics of quantitative data analysis and focuses on how to use statistical tests as a key tool for analyzing research data. The book presents the entire data analysis process as a cyclical, multiphase process and addresses the processes of exploratory analysis, decision-making for performing parametric or nonparametric analysis, and practical significance determination. In addition, the author details how data analysis is used to reveal the underlying patterns and relationships between the variables and connects those trends to the data s contextual situation. Filling the gap in quantitative data analysis literature, this book teaches the methods and thought processes behind data analysis, rather than how to perform the study itself or how to perform individual statistical tests. With a clear and conversational style, readers are provided with a better understanding of the overall structure and methodology behind performing a data analysis as well as the needed techniques to make informed, meaningful decisions during data analysis. The book features numerous data analysis examples in order to emphasize the decision and thought processes that are best followed, and self-contained sections throughout separate the statistical data analysis from the detailed discussion of the concepts allowing readers to reference a specific section of the book for immediate solutions to problems and/or applications. Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences also features coverage of the following: The overall methodology and research mind-set for how to approach quantitative data analysis and how to use statistics tests as part of research data analysis A comprehensive understanding of the data, its connection to a research situation, and the most appropriate statistical tests for the data Numerous data analysis problems and worked-out examples to illustrate the decision and thought processes that reveal underlying patterns and trends Detailed examples of the main concepts to aid readers in gaining the needed skills to perform a full analysis of research problems A conversational tone to effectively introduce readers to the basics of how to perform data analysis as well as make meaningful decisions during data analysis Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences is an ideal textbook for upper-undergraduate and graduate-level research method courses in the behavioral and social sciences, statistics, and engineering. This book is also an appropriate reference for practitioners who require a review of quantitative research methods. Michael J. Albers, Ph.D., is Professor in the Department of English at East Carolina University. His research interests include information design with a focus on answering real-world questions, the presentation of complex information, and human information interaction. Dr. Albers received his Ph.D. in Technical Communication and Rhetoric from Texas Tech University.
Table of Contents
Preface ix About the Companion Website xiii 1 Introduction 1 Basis of How All Quantitative Statistical Based Research 1 Data Analysis, Not Statistical Analysis 3 Quantitative Versus Qualitative Research 8 What the Book Covers and What It Does Not Cover 9 Book Structure 10 References 11 Part I Data Analysis Approaches 13 2 Statistics Terminology 15 Statistically Testing a Hypothesis 15 Statistical Significance and p-Value 19 Confidence Intervals 26 Effect Size 27 Statistical Power of a Test 31 Practical Significance Versus Statistical Significance 34 Statistical Independence 34 Degrees of Freedom 36 Measures of Central Tendency 37 Percentile and Percentile Rank 41 Central Limit Theorem 42 Law of Large Numbers 44 References 48 3 Analysis Issues and Potential Pitfalls 49 Effects of Variables 49 Outliers in the Dataset 53 Relationships Between Variables 53 A Single Contradictory Example Does Not Invalidate a Statistical Relationship 60 References 62 4 Graphically Representing Data 63 Data Distributions 63 Bell Curves 64 Skewed Curves 68 Bimodal Distributions 71 Poisson Distributions 75 Binomial Distribution 77 Histograms 79 Scatter Plots 80 Box Plots 81 Ranges of Values and Error Bars 82 References 85 5 Statistical Tests 87 Inter-Rater Reliability 87 Regression Models 92 Parametric Tests 93 Nonparametric Tests 95 One-Tailed or Two-Tailed Tests 96 Tests Must Make Sense 99 References 103 Part II Data Analysis Examples 105 6 Overview of Data Analysis Process 107 Know How to Analyze It Before Starting the Study 107 Perform an Exploratory Data Analysis 108 Perform the Statistical Analysis 109 Analyze the Results and Draw Conclusions 110 Writing Up the Study 111 References 112 7 Analysis of a Study on Reading and Lighting Levels 113 Lighting and Reading Comprehension 113 Know How the Data Will Be Analyzed Before Starting the Study 113 Perform an Exploratory Data Analysis 115 Perform an Inferential Statistical Analysis 122 Exercises 132 8 Analysis of Usability of an E-Commerce Site 135 Usability of an E-Commerce Site 135 Study Overview 135 Know How You Will Analyze the Data Before Starting the Study 136 Perform an Exploratory Data Analysis 138 Perform an Inferential Statistical Analysis 147 Follow-Up Tests 151 Performing Follow-Up Tests 153 Exercises 157 Reference 158 9 Analysis of Essay Grading 159 Analysis of Essay Grading 159 Exploratory Data Analysis 160 Inferential Statistical Data Analysis 165 Exercises 173 Reference 175 10 Specific Analysis Examples 177 Handling Outliers in the Data 177 Floor/Ceiling Effects 182 Order Effects 183 Data from Stratified Sampling 184 Missing Data 184 Noisy Data 186 Transform the Data 187 References 188 11 Other Types of Data Analysis 189 Time-Series Experiment 189 Analysis for Data Clusters 192 Low-Probability Events 193 Metadata Analysis 193 Reference 195 A Research Terminology 197 Independent, Dependent, and Controlled Variables 197 Between Subjects and Within Subjects 199 Validity and Reliability 200 Variable Types 201 Type of Data 201 Independent Measures and Repeated Measures 203 Variation in Data Collection 205 Probability What 30% Chance Means 212 References 214 Index 215