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Preface

In almost any class at my college, there is a natural gap between the teacher’s enthusiasm for the subject matter and the students’ level of enthusiasm for that same material. This makes sense. The teacher has made a career of that subject matter, whereas students are still learning to appreciate it. Perhaps in no class within psychology is this enthusiasm gap wider than it is in statistics classes. This is unfortunate, as statistics are useful not only for interpreting and conducting research but also in navigating many of life’s everyday situations. I make no qualms about my love for statistics. They can be, once understood, powerful tools not only in research but also in many life situations more generally. Most of my students come into this class with dread and apprehension about what’s to come. Most of these same students leave the class with reactions such as “It wasn’t that bad,” and some even admit “It was pretty interesting.” Indeed, I want all students to see statistics as at least “pretty interesting,” and I am hoping this book can help you not only learn statistics but also see the practical value they hold.

Guiding philosophy of this book

Teachers in the social sciences are fortunate to have inherently interesting material to discuss with students. However, the research process used to systematically investigate our subject matter is of lesser interest to many students. I can understand why. To paraphrase from a conversation with a colleague at another college, when learning about various statistics, students get lost in a myriad of symbols, numbers, and formulas, and when they finish calculating a statistic, they often have no idea what it means or how to use it. Indeed, to many students, statistics courses tend to be an evil necessity. Therefore, this book attempts to use the inherently interesting content of the discipline as the basis for teaching the statistical techniques we use to learn about the subject matter. In a sense, this book starts its discussions of statistical tools with what people often find interesting and then discusses the statistical tools needed to discern such information.

This book is written in the same tone as I teach in the classroom. I mention this because there are attempts at humor throughout (notice I said “attempts at humor”). Indeed, statistical information does not come naturally to many of us (yes, that does include me, as you’ll see in Chapter 1). Anything to add some lightheartedness to a potentially intimidating subject matter, I firmly believe, can only hold your attention. I know when I read books as an undergraduate student, I never appreciated that there was a person on the other end trying to teach me. I hope you will be able to have that appreciation reading this book.

Most psychology and social science majors will not go into research-specific careers. You may or may not pursue this career path. Regardless of your eventual career route, this book allows you to become skilled at using and interpreting statistical information with which you are presented. Being able to competently interpret statistical information is a skill needed in almost any career and is essential to being a liberally educated citizen. For those who do go into research-related careers, this book will show you how to conduct and interpret statistical analyses as they are conducted when one engages in research. If you are thinking about a research-oriented career, you will want to gain research experience outside the classroom. Indeed, after reading this book, you will be able to make valuable contributions to a faculty member’s research lab with respect to both conceptual understanding of tools and the ability to conduct and interpret the statistical tools presented in this book.

Content organization

Chapter 1 begins with an overview of why learning about statistics is important, not so much in research, but in life more generally. As human beings, we are remarkably efficient at navigating the world around us. However, such efficiency comes with certain drawbacks that are rooted in our difficultly processing quantitative information. In Chapter 1, you will learn about your efficient mind, how it naturally makes inferences about the world, where it can lead you astray, and how competence at interpreting statistical information can help you compensate for the drawbacks that come with being the efficient thinker that you are. In addition, basic statistical concepts that will recur throughout the book will be introduced in this opening chapter.

Chapter 2 through Chapter 5 contain information about descriptive statistics. The statistical tools in these chapters form the foundation for understanding large amounts of data, something you will likely need to do at some point in your career. Chapter 2 uses a study on gender differences in aggression to describe basic considerations in defining and measuring variables in research. Chapter 3 uses a study on academic burnout to help us learn how to interpret and construct frequency distributions and visual displays of large sets of data. Chapter 4 begins our discussion of quantitative information. By using the same study as we used in the previous chapter, we will learn about summarizing large sets of data in ways that are easy to understand. We will learn the pros and cons of various statistical tools that serve the purpose of summarizing data. Finally, Chapter 5 will introduce the notion of data distributions and locating individual scores within a large dataset. Each of these chapters will help you develop your ability to use a statistical software package commonly used in psychological and social science research.

Chapter 6 through Chapter 14 contain information about inferential statistics. Chapter 6 introduces the notion of inferential statistics and how they are related to descriptive statistics covered in the previous four chapters. Each of the next eight chapters presents one or more inferential statistical tools. Each chapter is divided into two major themes. The first major theme will be Conceptual Understanding of the Tool. Within this major theme will be three subthemes. The first is called “The Study.” Each chapter opens with a description of a fairly mundane situation. For instance, Chapter 11 opens with a discussion of making pizza for dinner. That situation leads into a description of a research study that is used to begin discussion of a statistical tool. As a study is introduced, basic methodological information is presented in an effort to bridge the gap between research methods and the resulting statistical analyses. The second subtheme is called “The Tool,” and it introduces the statistical tool used to answer the research question. The conceptual logic of the statistic and associated formula are presented in detail. As appropriate for a given chapter, information regarding the hypothesis/ses being tested will be provided in this subsection. Finally, the third subtheme is called “Interpreting the Tool,” and it presents pertinent portion(s) of the results sections of published articles. Explanations of these results presentations allow you to connect what has been learned in the first two subsections of the chapter to a published research study. You do not have to read entire primary source journal articles to understand the statistical information presented in a given chapter.

The second major theme in each chapter will be Using Your New Statistical Tool and will contain two subthemes. The first subtheme will be “Hand-Calculating the Statistical Tool Under Consideration,” where there will be guidance on how to do just that. Although not always the case, efforts will be made to use a hand-calculation example that is related to the study that was used to open the chapter. For instance, in Chapter 7, when hand-calculating the independent samples t test, a dependent variable related to but not used in the actual research study will be introduced. Only a limited number of datapoints will be used in hand-calculations to make doing so manageable. After each hand-calculation subsection, there will be opportunities to practice calculating the statistical tool being presented. The second subtheme will be “Statistical Tool Under Consideration and SPSS.” Here, you will learn how to set up a spreadsheet using the software program IBM® SPSS® Statistics^* for the statistical tool in that chapter. Then, you will read step-by-step instructions of how to do the appropriate statistical analysis in SPSS with screenshots to point out precisely what should be done at each step. Given that SPSS is often how data are analyzed in “real” research (as opposed to computing statistics by hand), this feature will be one that you can refer to in any situation in which you have to analyze data, even after this class is over. Finally, you will learn how to interpret the SPSS output, with call-out bubbles to highlight what the relevant numbers mean on the SPSS printout and how they relate to the statistic under consideration in that chapter.

Chapter 15, the last chapter of the book, does not present new statistical information. Rather, it focuses on published research studies and the role of statistics in those studies. It starts with a flowchart that helps you decide what inferential statistical tool to use, given the research hypothesis presented and type of research design used. You can use your kit of statistical tools to help determine the appropriate analysis(ses) to use in a given situation. There are descriptions of six published research studies that used various statistical tools covered in the earlier chapters. These descriptions will include a research hypothesis and a description of the methodology. For each of these six studies, we will walk through the flowchart and determine which tool(s) should be used to answer the research question given the stated hypothesis and methodology used. After we finish discussing these six studies, you will read about three additional published studies and answer a series of questions after each one. This series of questions, in conjunction with the flowchart provided, will help you determine the appropriate statistical tool(s) to use to analyze the data in each study.

Helpful features

1 Each chapter begins with a series of learning objectives, that is, what you should be able to do after reading and thinking about that chapter. I know as a student I typically never looked at, much less thought about, such learning objectives; however, they are helpful in previewing and organizing what you are about to read. Please read and think about them (i.e., don’t do what I did).

2 Each chapter contains technical terminology that is highlighted in marginal definitions. As a general rule, don’t simply memorize these definitions but try to think about how they relate to other information in the chapter. Doing so will help you accomplish the learning objectives at the beginning of each chapter.

3 Throughout each of the first 14 chapters are periodic Learning Checks to help assure you’re understanding the material up to that point in the chapter. The answers to these questions appear in each Learning Check, so you don’t need to flip around the book to locate the answers. Please don’t just look at the answers and say “Yeah, I get it.” Test yourself because if you don’t get the correct answer, that is a signal to go back and reread that section.

4 At the end of each of these chapters are Chapter Application Questions that help you integrate the information in that chapter. This feature is like a massive Learning Check that we just discussed. These end-of-chapter questions provide a good way to make sure you “get it” after reading each chapter. They contain a variety of short-answer and multiple-choice questions.

5 After the Chapter Application Questions are Questions for Class Discussion. Try to answer these questions, as your teacher can use them to help you make sure you understand the material and work with you in case there is any confusion that needs to be ironed out.

Suggestions welcomed!

If you have any comments or suggestions that could improve this book, I would very much like to hear them. Please feel free to email me at achristopher@albion.edu with any ideas and observations that you have. I look forward to hearing from you. Have a great course!

* SPSS is a registered trademark of International Business Machines Corporation.