[Ebook PDF] Essentials of Statistics for the Behavioral Sciences, 3rd Edition

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Essentials of Statistics for the Behavioral Sciences, 3rd Edition

ISBN-13: 978-1464107771
ISBN-10: 1464107777

Author: Susan A. Nolan (Author), Thomas Heinzen (Author)

Get introduced to the how and why of statistical practice in the behavioral sciences as Essentials of Statistics for the Behavioral Sciences uses storytelling and real-world examples to reinforce the material for you to comprehend easier.

PREFACE
Statistics are hot. According to an article in the New York Times, statistics is perhaps the most promising, adventurous career option you can choose right now—and the field is likely to expand significantly in the future, thanks to the large amounts of information (called big data) available to us in this digital age. Gone is the stereotype of boring (but influential) statistics geeks hiding behind their glowing screens. The new reality requires smart, reflective people who have been trained to explore big data, transforming them into something useful, while not losing sight of the people behind the numbers. This book trains you to find and create data, ask tough questions about a data set, interpret the feedback coming from data analysis, and display data in ways that reveal a precise, coherent, data-driven story. Statistical reasoning is not at the cutting edge of information; statistical reasoning is the cutting edge of information.
If you dare to embrace what your professor is teaching you, it will bring you to the brink of personal and social change. You will have to make many decisions about how you think—and that covers, well, your entire life. There are probably some natural boundaries to the benefits of statistical reasoning, such as the power of intuition. But every time we think we have bumped into a boundary, somebody busts through it, wins a Nobel Prize, and challenges the rest of us to become more creative as we learn how to live together on this beautiful planet.
We dare you to love this course.
Principles for Teaching Statistics
In their classic and persuasive article, Marsha Lovett and Joel Greenhouse (2000) present principles to teach statistics more effectively (all based on empirical research from cognitive psychology). And other researchers continue to build on their helpful work (see Benassi, Overson, & Hakala, 2014). We look to this body of research as we create every edition of this statistics text, from designing the pedagogy to deciding what specific examples to include. Six principles emerge from this research on teaching statistics and drive our text:
1. Practice and participation. Recent research has shown that active learning, broadly defined, increases student performance and reduces the failure rate in science courses, including psychology courses (Freeman et al., 2013). This principle pertains to work outside the classroom as well (Lovett & Greenhouse, 2000). Based on these findings, we encourage students to actively participate in their learning
throughout the text. Students can practice their knowledge through the many applied exercises, especially in the Applying the Concepts and Putting It All Together sections. In these sections, the source of the original data is often supplied, whether it is data from the Centers for Disease Control or a Marist poll, encouraging students to dig deeper. And students can take advantage of data sets from the General
Social Survey and EESEE Case Studies to “play” with statistics beyond the exercises in the book.
2. Vivid examples. Researchers have found that students are most likely to remember concepts illustrated with a vivid instructional tool (Vanderstoep, Fagerlin, & Feenstra, 2000). So, whenever possible, we use striking, vivid examples to make statistical concepts memorable, including the weights of cockroaches to explain standardization, destructive hurricanes in the discussion of confounding variables,
entertainment by a clown during in vitro fertilization to teach chi square, a Damien Hirst dot painting to explain randomness, and a house purchase by Beyoncé to highlight celebrity outliers. Vivid examples are often accompanied by photos to enhance their memorability. When such examples are drawn from outside the academic literature, we follow with engaging research examples from the behavioral
sciences to increase the memorability of important concepts.
3. Integrating new knowledge with previous knowledge. When connecting new material to existing student knowledge, students can more easily embed that new material into “a framework that will enable them to learn, retrieve, and use new knowledge when they need it” (p. 7, Ambrose & Lovett, 2014). Throughout the text, we illustrate new concepts with examples that connect to things most students already know. Chapter 1 includes an exercise that uses students’ knowledge of contemporary music, specifically the percentage of rhyming words in rap lyrics, to teach students how to operationalize variables. In Chapter 2, an example from Britain’s Got Talent uses students’ understanding of the ranking systems on reality shows to explain ordinal variables. In Chapter 5, we use students’ understanding of the potential fallibility of pregnancy tests to teach the difference between Type I and
Type II errors. And in Chapter 14, we use the predictive abilities of Facebook profiles
to teach regression. Learning in different contexts helps students to transfer knowledge to new situations, so we use multiple examples for each concept – typically
an initial one that is easier to grasp followed by more traditional behavioral science
research examples.
4. Confronting misconceptions. Conversely, some kinds of prior knowledge can slow students down (Lovett & Greenhouse, 2000). Students know many statistical words – from independent to variability to significant. But they know the “everyday” definitions of these words, and this prior knowledge can impede their learning of the statistical definitions. Throughout the book, we point out students’ likely prior understanding of these key terms, and contrast that with the newer statistical definitions. We also include exercises aimed at having students explain the various ways a given word can be understood. Plus, in Chapter 5, we introduce ways in which other types of misconceptions can emerge through illusory correlation, confirmation bias, and coincidence. Throughout the rest of the book, we highlight these types of flawed thinking with examples, and show how statistics can be the antidote to these kinds of misconceptions – whether it’s a belief that holiday weight gain is a serious problem, cheating is associated with better grades, or online personality quizzes are always accurate.
5. Real-time feedback. It’s not uncommon – in fact, it’s actually expected – for students to make mistakes when they first try their hand at a new statistical technique. Research demonstrates that one of the best ways to get past these errors is to provide students with immediate feedback (Kornell & Metcalfe, 2014). For this reason, we include solutions at the back of the book for all Check Your Learning exercises that fall after each section of a chapter and for the odd-numbered exercises at the end of each chapter. Importantly, we don’t just provide final answers. We offer fully worked-out solutions that show students all of the steps and calculations to arrive at the final answers. That way, students can figure out exactly where they went astray. Learning is simply more efficient when students can immediately correct their mistakes or receive validation that they answered correctly. This learning is also bolstered by other types of feedback embedded in the book that students can use as models. These include worked-out examples.
in the chapters and additional “How It Works” worked-out examples at the end of each chapter. As Lovett and Greenhouse (2000) explain “seeing worked examples before solving new problems makes the subsequent problem solving an easier task” (p. 201).
6. Repetition. There is a growing literature on the role of “desirable difficulty” in learning – that is, students learn better when they struggle with new material with support (Clark & Bjork, 2014). The three techniques of spacing, interleaving, and testing – all based on the central idea of repetition – help to create the right level of difficulty to help students learn more efficiently.
• Spacing involves repeated practice sessions with the same material with delays in between. Our book is set up to encourage spacing. For example, the Before You Go On sections at the beginning of each chapter offer students a chance to review previous material. Several sets of Check Your Learning questions are included across each chapter, and more exercises are included at the end of each chapter.
• Interleaving refers to the practice of mixing the types of exercises the student is practicing. Rather than practicing each new task in one block of exercises, students mix exercises on a new topic with repeats of exercises on earlier topics. This repetition of practice with earlier concepts increases retention of material. We build in exercises that encourage interleaving in the Putting It All Together sections, which ask students to return to concepts learned in earlier chapters.
• Testing is possibly the best way to learn new material. Simply studying does not introduce the desirable difficulty that enhances learning, but testing forces errors and drives efficient retention of new material. The tiered exercises throughout the chapter and at the end of the chapter provide numerous opportunities for testing – and then more testing. We encourage students to aim for repeated practice, completing more exercises than assigned, rather than by studying in more traditional, but less effective, ways.