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More About This Textbook
Overview
Emphasizing meaning and concepts, not just symbols and numbers
Statistics for Psychology, 6^{th} edition places definitional formulas center stage to emphasize the logic behind statistics and discourage rote memorization. Each procedure is explained in a direct, concise language and both verbally and numerically.
MyStatLab is an integral part of the Statistics course. MyStatLab gives students practice with hundreds of homework problems. Every problem includes tools to help students understand and solve each problem  and grades all of the problems for instructors. MyStatLab also includes tests, quizzes, eText, a Gradebook, a customizable study plan, and much more.
Learning Goals
Upon completing this book, readers should be able to:
Note: This is the standalone book if you want the book/access card please order the ISBN below;
0205924174 / 9780205924172 Statistics for Psychology Plus NEW MyStatLab with eText  Access Card Package
Package consists of:
0205258158 / 9780205258154 Statistics for Psychology
0205923860 / 9780205923861 New MyStatLab for Social Sciences with Pearson eText  ValuePack Access Card
Emphasizes definitional formulas & teaches verbally, numerically & visually w/ same examples.
Editorial Reviews
From the Publisher
“I like the order in which the topics are presented. The order is the same way that I would choose if I were writing a text myself.”
 Kathleen Denson, Texas A&M Commerce
“Aron et al. strives to make statistical concepts accessible to students who may not have a background or interests in math.”
 Brock Kirwan, Brigham Young University
“This text is at a level that I would feel comfortable assigning to my intro stats students.”
 Osvaldo Morera, University of Texas at El Paso
“I like that [the text] introduced students to more complex techniques in an effort to help them to understand the articles that they are reading with more complex statistics in them”
Stacey Williams, East Tennessee State University
“.The Level of writing is it’s strongest assets. Very clear step by step instructions. The level of writing is very appropriate to my students”
Stephen Armeli, Farleigh Dickinson
“Highlevel, clear, comprehensive. The book is effective at helping students understand the material”
 Kristin Lane, Bard College
Booknews
An introductory text combining features of traditional and newer treatments of statistics, incorporating emphasis on definitional formulas, statistical methods, and preparation for research in an intuitive approach. Each procedure is explained with verbal and numerical, and often visual, examples. Pedagogical features include chapter summaries, key terms, and practice problems, plus boxes on topics of interest. This second edition contains new material on probability, repeated measures analysis of variance, and confidence intervals, and includes more multicultural examples. Annotation c. by Book News, Inc., Portland, Or.Product Details
Table of Contents
In this Section:
1. Brief Table of Contents
2. Full Table of Contents
1. BRIEF TABLE OF CONTENTS
Chapter 1 Displaying the order in a group of numbers
Chapter 2 Central tendency and variability
Chapter 3 Some key ingredients for inferential statistics: Z scores, the normal curve, sample versus
population, and probability
Chapter 4 Introduction to hypothesis testing
Chapter 5 Hypothesis testing with means of samples
Chapter 6 Making sense of statistical significance: Effect size and statistical power
Chapter 7 Introduction to the t test: Single sample and dependent means
Chapter 8 The t test for independent means
Chapter 9 Introduction to the analysis of variance
Chapter 10 Factorial analysis of variance
Chapter 11 Correlation
Chapter 12 Prediction
Chapter 13 Chisquare tests
Chapter 14 Strategies when population distributions are not normal: Data transformations and rankorder
tests
Chapter 15 Integration and the general linear model and Making sense of advanced statistical procedures in
research articles
2. FULL TABLE OF CONTENTS
Chapter 1: Displaying the order in a group of numbers
The Two Branches of Statistical Methods
Some Basic Concepts
Frequency Tables
Histograms
Shapes of Frequency Distributions
Controversy: Misleading Graphs
Frequency Tables and Histograms in Research Articles
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Chapter 2: Central tendency and variability
Central Tendency
Variability
Controversy: The Tyranny of the Mean
Central Tendency and Variability in Research Articles
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Chapter 3: Some key ingredients for inferential statistics: Z scores, the normal curve, sample versus
population, and probability
Z Scores
The Normal Curve
Sample and Population
Probability
Controversies: Is the Normal Curve Really So Normal? And Using Nonrandom Samples
Z Scores, Normal Curves, Samples and Populations, and Probabilities in Research Articles
Advanced Topics: Probability Rules and Conditional Probabilities
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Chapter 4: Introduction to hypothesis testing
A HypothesisTesting Example
The Core Logic of Hypothesis Testing
The HypothesisTesting Process
OneTailed and TwoTailed Hypothesis Tests
Controversy: Should Significance Tests Be Banned?
Hypothesis Tests in Research Articles
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Chapter 5: Hypothesis testing with means of samples
The Distribution of Means
Hypothesis Testing with a Distribution of Means: The Z Test
Controversy: Marginal Significance
Hypothesis Tests About Means of Samples (Z Tests) and Standards Errors in Research Articles
Advanced Topic: Estimation, Standard Errors, and Confidence Intervals
Advanced Topic Controversy: Confidence Intervals versus Significance Tests
Advance Topic: Confidence Intervals in Research Articles
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Chapter 6: Making sense of statistical significance: Effect size and statistical power
Decision Errors
Effect Size
Statistical Power
What Determines the Power of Study
The Role of Power Interpreting the Results of a Study
Controversy: Statistical Significance versus Effect Size
Decision Errors, Effect Size, and Power in Research Articles
Advanced Topics; Figuring Statistical Power
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Chapter 7: Introduction to the t test: Single sample and dependent means
The t Test for a Single Sample
The t Test for Dependent Means
Assumptions of the t Test for a Single Sample and the t Test for Dependent Means
Controversy: Advantages and Disadvantages of RepeatedMeasures Designs
Single Sample t Tests and Dependent Means t Tests in Research Articles
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Chapter 8: The t test for independent means
The Distribution of Differences Between Means
Hypothesis Testing with a t Test for Independent Means
Assumptions of the t Test for Independent Means
Effect Size and Power for the t Test for Independent Means
Review and Comparison of the Three Kinds of t Tests
The t Test for Independent Means in Research Articles
Advanced Topic: Power for the t Test for Independent Means When Sample Sizes Are Not Equal
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Chapter 9: Introduction to the analysis of variance
Basic Logic of the Analysis of Variance
Carrying Out an Analysis of Variance
Hypothesis Testing with the Analysis of Variance
Assumptions in the Analysis of Variance
Planned Contrasts
Post Hoc Comparisons
Effect Size and Power for the Analysis of Variance
Controversy: Omnibus Tests versus Planned Contrasts
Analyses of Variance in Research Articles
Advanced Topic: The Structural Model in the Analysis of Variance
Principles of the Structural Model
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Chapter 10: Factorial analysis of variance
Basic Logic of Factorial Designs and Interaction Effects
Recognizing and Interpreting Interaction Effect
Basic Logic of the TwoWay Analysis of Variance
Assumptions in the Factorial Analysis of Variance
Extensions and Special Cases of the Analysis of Variance
Controversy: Dichotomizing Numeric Variables
Factorial Analysis of Variance in Research Articles
Advanced Topic: Figuring a TwoWay Analysis of Variance
Advanced Topic: Power and Effect Size in the Factorial Analysis of Variance
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Chapter 11: Correlation
Graphing Correlations: The Scatter Diagram
Patterns in Correlation
The Correlation Coefficient
Significance of a Correlation Coefficient
Correlation and Causality
Issues in Interpreting the Correlation Coefficient
Effect Size and Power for the Correlation Coefficient
Controversy: What is a Large Correlation?
Correlation in Research Articles
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Chapter 12: Prediction
Predictor (X) and Criterion (Y) Variables
The Linear Prediction Rule
The Regression Line
Finding the Best Linear Prediction Rule
The Least Squared Error Principle
Issues in Prediction
Multiple Regression
Limitations of Prediction
Controversy: Unstandardized and Standardized Regression Coefficients; Comparing Predictors
Prediction in Research Articles
Advanced Topic: Error and Proportionate Reduction in Error
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Chapter 13: Chisquare tests
The ChiSquare Statistic and the ChiSquare Test for Goodness of Fit
The ChiSquare Test for Independence
Assumptions for ChiSquare Tests
Effect Size and Power for ChiTests for Independence
Controversy: The Minimum Expected Frequency
ChiSquare Tests in Research Articles
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Chapter 14: Strategies when population distributions are not normal: Data transformations and
rankorder tests
Assumptions in the Standard HypothesisTesting Procedures
Data Transformations
RankOrder Tests
Comparison of Methods
Controversy: ComputerIntensive Methods
Data Transformations and RankOrder Tests in Research Articles
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Chapter 15: Integration and the general linear model and Making sense of advanced statistical
procedures in research articles
The General Linear
Partial Correlation
Reliability
Multilevel Modeling
Factor Analysis
Casual Modeling
Procedures That Compare Groups
Analysis of Covariance (ANCOVA)
Multivariate Analysis of Variance (MANOVA) and Multivariate Analysis of Covariance (MANCOVA)
Overview of Statistical Techniques
Controversy: Should Statistics Be Controversial?
How to Read Results Using Unfamiliar Statistical Techniques
Summary
Key Terms
Example WorkedOut Problems
Practice Problems
Using SPSS
Chapter Note
Preface
Our approach was developed over three decades of successful teaching—successful not only in the sense that students have consistently rated the course (a statistics course, remember) as a highlight of their major, but also in the sense that students come back to us later saying, "I was lightyears ahead of my fellow graduate students because of your course," or "Even though I don't do research, your course has really helped me read the journals in my field."
The response to the first and second edition has been overwhelming. We have received hundreds of thankyou emails and letters from instructors (and from students themselves!) from all over the Englishspeaking world. Of course, we were also delighted by the enthusiastic review in Contemporary Psychology (Bourgeois, 1997).
In this third edition we have tried to maintain those things that have been especially appreciated, while reworking the book to take into account the feedback we have received, our own experiences, and advances and changes in the field. We have also added new pedagogical features to make the book even more accessible for students. However, before turning to the third edition, we want to reiterate what we said in thefirst edition about how this book from the beginning has been quite different from other statistics texts.
A BRIEF HISTORY OF THE STATISTICS TEXT GENRE
In the 1950s and 1960s statistics texts were dry, daunting, mathematical tomes that quickly left most students behind. In the 1970s, there was a revolution—in swept the intuitive approach, with much less emphasis on derivations, proofs, and mathematical foundations. The approach worked. Students became less afraid of statistics courses and found the material more accessible, even if not quite clear.
The intuitive trend continued in the 1980s, adding in the 1990s some nicely straightforward writing. A few texts have now also begun to encourage students to use the computer to do statistical analyses. However, discussions of intuitive understandings are becoming briefer and briefer. The standard is a cursory overview of the key idea and sometimes the associated definitional formula for each technique. Then come the procedures and examples for actually doing the computation, using another "computational" formula.
Even with all this streamlining, or perhaps because of it, at the end of the course most students cannot give a clear explanation of the logic behind the techniques they have learned. A few months later they can rarely carry out the procedures either. Most important, the three main purposes of the introductory statistics course ark, not accomplished: Students are not able to make sense of the results of psychology research articles, they are poorly prepared for further courses in statistics (where instructors must inevitably spend half the semester reteaching the introductory course), and the exposure to deep thinking that is supposed to justify the course's meeting general education requirements in the quantitative area has not occurred.
WHAT WE HAVE DONE DIFFERENTLY
We continue to do what the best of the newer books are already doing well: emphasizing the intuitive, deemphasizing the mathematical, and explaining everything in direct, simple language. But what we have done differs from these other books in 11 key respects.
1. The definitional formulas are brought to center stage because they provide a concise symbolic summary of the logic of each particular procedure. All our explanations, examples, practice problems, and test bank items are based on these definitional formulas. (The amount of data to be processed in practice problems and test bank items are reduced appropriately to keep computations manageable.)
Why this approach? To date, statistics texts have failed to adjust to technological reality. What is important is not that the students learn to calculate a t test with a large data set—computers can do that for them. What is important is that students work problems in a way that they remain constantly aware of the underlying logic of what they are doing. Consider the population variance—the average of the squared deviations from the mean. This concept is directly displayed in the definitional formula (once the student is used to the symbols): Variance = Σ(I M)^{2}/N. Repeatedly working problems using this formula engrains the meaning in the student's mind. In contrast, the usual computational version of this formula only obscures this meaning: Variance = ΣX^{2} (ΣX)^{2}/N/N. Repeatedly working problems using this formula does nothing but teach the student the difference between ΣX^{2} and (ΣX)^{2}!
Teaching the old computational formulas today is an anachronism. Researchers do their statistics on computers now. At the same time, the use of statistical software makes the understanding of the basic principles, as they are symbolically expressed in the definitional formulas, more important than ever. Students still need to work lots of problems by hand to learn the material. But they need to work them using the definitional formulas that reinforce the concepts, not using the computational formulas that obscure them. Those formulas once made some sense as timesavers for researchers who had to work with large data sets by hand, but they were always poor teaching tools. (Because some instructors may feel naked without them, we still provide the computational formulas, usually in a brief footnote, at the point in the chapter where they would traditionally have been introduced.)
2. Each procedure is taught both verbally and numerically—and usually visually as well. In fact, when we introduce every formula, it has attached to it a concise statement of the formula in words. Typically, each example lays out the procedures in workedout formulas, in words (often with a list of steps), and illustrated with an easytograsp figure. Practice problems and test bank items, in turn, require the student to calculate results, write a short explanation in layperson's language of what they have done, and make a sketch (for example of the distributions involved in a t test). The chapter material completely prepares the student for these kinds of practice problems and test questions.
It is our repeated experience that these different ways of expressing an idea are crucial for permanently establishing a concept in a student's mind. Many psychology students are more at ease with words than with numbers. In fact, some have a positive fear of all mathematics. Writing the formula in words and providing the laylanguage explanation gives them an opportunity to do what they do best.
3. A main goal of any introductory statistics course in psychology is to prepare students to read research articles. The way a procedure such as a t test or an analysis of variance is described in a research article is often quite different from what the student expects from the standard textbook discussions. Therefore, as this book teaches a statistical method, it also gives examples of how that method is reported in the journals (excerpts from current articles). And we don't just leave it there. The practice problems and test bank items also include excerpts from articles for the student to explain.
4. The book is unusually up to date. For some reason, most introductory statistics textbooks read as if they were written in the 1950s. The basics are still the basics, but statisticians and researchers think far more subtly about those basics now. Today, the basics are undergirded by a new appreciation of effect size, power, the accumulation of results through metaanalysis, the critical role of models, the underlying unity of difference and association statistics, the growing prominence of regression and associated methods, and a whole host of new orientations arising from the central role of the computer. We are much engaged in the latest developments in statistical theory and application, and this book reflects that engagement. For example, we devote an entire early chapter to effect size and power and then return to these topics as we teach each technique.
5. We capitalize on the students' motivations. We do this in two ways. First, our examples emphasize topics or populations that students seem to find most interesting. The very first example is from a real study in which 151 students in their first week of an introductory statistics class rate how much stress they feel they are under. Other examples emphasize clinical, organizational, social, and educational psychology while being sure to include sufficient interesting examples from cognitive, developmental, behavioral and cognitive neuroscience, and other areas to inspire students with the value of those specialties. (Also, our examples continually emphasize the usefulness of statistical methods and ideas as tools in the research process, never allowing students to feel that what they are learning is theory for the sake of theory.)
Second, we have worked to make the book extremely straightforward and systematic in its explanation of basic concepts so that students can have frequent "aha" experiences. Such experiences bolster selfconfidence and motivate further learning. It is quite inspiring to us to see even fairly modest students glow from having mastered some concept like negative correlation or the distinction between failing to reject the null hypothesis and supporting the null hypothesis. At the same time, we do not constantly remind them how greatly oversimplified we have made things, as some books do. Instead, we show students, in the controversy sections in particular, how much there is for them to consider deeply, even in an introductory course.
6. We emphasize statistical methods as a living, growing field of research. We take the time to describe the issues, such as the recent upheaval about the value of significance testing. In addition, each chapter includes one or more "boxes" about famous statisticians or interesting sidelights. The goal is for students to see statistical methods as human efforts to make sense out of the jumble of numbers generated by a research study; to see that statistics are not "given" by nature, not infallible, not perfect descriptions of the events they try to describe but rather constitute a language that is constantly improving through the careful thought of those who use it. We hope that this orientation will help them maintain a questioning, alert attitude as students and later as professionals.
7. Chapter 16 integrates the major techniques that have been taught, explaining that the t test is a special case of the analysis of variance and that both the t test and the analysis of variance are special cases of correlation and regression. (In short, we introduce the general linear model.) In the past, when this point has been made at all, it has usually been only in advanced texts. But many students find it valuable for digesting and retaining what they have learned, as well as for sensing that they have penetrated deeply into the foundations of statistical methods.
8. The final chapter looks at advanced procedures without actually teaching them in detail. It explains in simple terms how to make sense out of these statistics when they are encountered in research articles. Most psychology research articles today use methods such as analysis of covariance, multivariate analysis of variance, hierarchical multiple regression, factor analysis, or structural equation modeling. Students completing the ordinary introductory statistics course are illequipped to comprehend most of the articles they must read to prepare a paper or study a course topic in further depth. This chapter makes use of the basics that students have just learned (along with extensive excerpts from current research articles) to give a rudimentary understanding of these advanced procedures. This chapter also serves as a reference guide that students can keep and use in the future when reading such articles.
9. The accompanying Student's Study Guide and Computer Workbook focuses on mastering concepts and also includes instructions and examples for working problems on the computer. Most study guides concentrate on plugging numbers into formulas and memorizing rules (which is consistent with the emphasis of the textbooks they accompany). For each chapter, our Student's Study Guide and Computer Workbook provides learning objectives, a detailed chapter outline, the chapter's formulas (with all symbols defined), and summaries of steps of conducting each procedure covered in the chapter, plus a set of self tests, including multiplechoice, fillin, and problem/essay questions. In addition, for each procedure covered in the chapter, the study guide furnishes a thorough outline for writing an essay explaining the procedure to a person who has never had a course in statistics (a task they are frequently given in the practice problems and test bank items.).
Also, our Student's Study Guide and Computer Workbook provides the needed support for teaching students to carry out analyses on the computer. First, there is a special appendix on getting started with SPSS. Then, in each chapter corresponding to the text chapters, there is a section showing in detail how to carry out the chapter's procedures with SPSS. (These sections include stepbystep instructions, examples, and illustrations of how each menu and each output appears on the screen.) There are also special activities for using the computer to strengthen understanding. As far as we know, no other statistics textbook package provides this much depth of explanation.
10. We have written an Instructor's Resource Manual that really helps teach the course. The manual begins with a chapter summarizing what we have gleaned from our own teaching experience and the research literature on effectiveness in college teaching. The next chapter discusses alternative organizations of the course, including tables of possible schedules and a sample syllabus. Then each chapter, corresponding to the text chapters, provides full lecture outlines and additional workedout examples not found in the text (in a form suitable for copying onto transparencies or for student handouts). These workedout examples are especially useful to new instructors or those using our book for the first time, since creating good examples is one of the most difficult parts of preparing statistics lectures.
11. Our Test Bank makes preparing exams easy. We supply approximately 40 multiplechoice, 25 fillin, and 10 to 12 problem/essay questions for each chapter. Considering that the emphasis of the course is so conceptual, the multiplechoice questions will be particularly useful for those of you who do not have the resources to grade essays.
INFLUENCES ON THE THIRD EDITION
We did the revision for the third edition over a summer in Tiburon, a small town overlooking the San Francisco Bay. We hope that this has not resulted in a loss of whatever romance the first edition gained from being written in Paris. On the other hand, this edition has been leavened by some beautiful Bay views.
More important, this revision is enriched by what we learned teaching with the first and second editions and by what we learned from the many instructors and students who have written to us about their experiences using the book. This revision is also informed by our own use of statistical methods. The last several years have been quite productive for the two of us in our own research programs in personality and social psychology. (For overviews of our main research programs, see A. Aron et al., 2001; E. Aron, 2000.) Our most recent adventure has been in social neuroscience, learning brainimaging techniques, which it turns out are almost as fascinating for the statistical analysis challenges they pose as for the opportunities they provide for deepening knowledge of the issues we were previously studying with more conventional methods. Perhaps particularly useful has been that one of us (A. A.) has been serving as an associate editor for the Journal of Personality and Social Psychology. This has kept us in touch with how the best researchers are using statistics (as well as how reviewers assess their colleagues' use of statistics). In addition to reworking the book to keep it up to date in obvious and subtle ways, we have made a special effort in this edition to bring in to the text significant new pedagogical features.
SPECIFIC CHANGES IN THE THIRD EDITION
Some changes we have not made. The 11 points listed earlier in this Preface remain as the central, unique features of this book. Also, except in a few cases where we felt we could make a significant improvement in pedagogy, we have not changed each chapter's major teaching examples. Instructors using the second edition told us they have built their lectures around these examples and don't want to have to start from scratch with new ones.
KEEP IN TOUCH
Our goal is to do whatever we can to help you make your course a success. If you have any questions or suggestions, please write or email. Also, if you should find an error somewhere, for everyone's benefit, please let us know right away. When errors have come up in the past, we have usually been able to fix them in the very next printing.