Psychology.pdf - Discover Psychology 2.0 A Brief Introductory Text NOBA Copyright R Biswas-Diener E Diener(Eds Noba Textbook Series Psychology Champaign

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Unformatted text preview: Discover Psychology 2.0 - A Brief Introductory Text NOBA Copyright R. Biswas-Diener & E. Diener (Eds), Noba Textbook Series: Psychology. Champaign, IL: DEF Publishers. DOI: nobaproject.com Copyright © 2019 by Diener Education Fund. This material is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit . The Internet addresses listed in the text were accurate at the time of publication. The inclusion of a Website does not indicate an endorsement by the authors or the Diener Education Fund, and the Diener Education Fund does not guarantee the accuracy of the information presented at these sites. Contact Information: Noba Project [email protected] Contents About Noba & Acknowledgements 4 Research in Psychology 5 1 6 Research Designs Christie Napa Scollon 2 Conducting Psychology Research in the Real World 22 Matthias R. Mehl 3 The Replication Crisis in Psychology 40 Edward Diener & Robert Biswas-Diener Learning 53 4 54 Conditioning and Learning Mark E. Bouton 5 Judgment and Decision Making 80 Max H. Bazerman Memory 95 6 96 Memory (Encoding, Storage, Retrieval) Kathleen B. McDermott & Henry L. Roediger 7 Forgetting and Amnesia 120 Nicole Dudukovic & Brice Kuhl 8 Eyewitness Testimony and Memory Biases 132 Cara Laney & Elizabeth F. Loftus Intelligence 145 9 146 Intelligence Robert Biswas-Diener Sensation and perception 161 10 Sensation and Perception 162 Adam John Privitera Developmental Psychology 187 11 Cognitive Development in Childhood 188 Robert Siegler 12 Attachment Through the Life Course 203 R. Chris Fraley 13 Adolescent Development 219 Jennifer Lansford 14 Emerging Adulthood 232 Jeffrey Jensen Arnett 15 Aging 246 Tara Queen & Jacqui Smith 16 Social and Personality Development in Childhood 263 Ross Thompson Personality 278 17 Personality Traits 279 Edward Diener & Richard E. Lucas 18 Personality Assessment 297 David Watson 19 The Psychodynamic Perspective 317 Robert Bornstein 20 Personality Stability and Change 332 M. Brent Donnellan 21 Self and Identity 351 Dan P. McAdams Social Psychology 370 22 Social Cognition and Attitudes 371 Yanine D. Hess & Cynthia L. Pickett 23 Conformity and Obedience 392 Jerry M. Burger 24 Prejudice, Discrimination, and Stereotyping Susan T. Fiske 406 25 Aggression and Violence Brad J. Bushman 421 26 Helping and Prosocial Behavior Dennis L. Poepsel & David A. Schroeder 439 Psychological Disorders 457 27 Anxiety and Related Disorders David H. Barlow & Kristen K. Ellard 458 28 Social Anxiety Todd Kashdan 478 29 Mood Disorders Anda Gershon & Renee Thompson 499 30 Schizophrenia Spectrum Disorders Deanna M. Barch 522 31 Personality Disorders Cristina Crego & Thomas Widiger 543 32 Psychopathy Chris Patrick 558 Therapies 581 33 Therapeutic Orientations Hannah Boettcher, Stefan G. Hofmann & Q. Jade Wu 582 34 Psychopharmacology Susan Barron 602 Index 616 About Noba The Diener Education Fund (DEF) is a non-profit organization founded with the mission of reinventing higher education to serve the changing needs of students and professors. The initial focus of the DEF is on making information, especially of the type found in textbooks, widely available to people of all backgrounds. This mission is embodied in the Noba project. Noba is an open and free online platform that provides high-quality, flexibly structured textbooks and educational materials. The goals of Noba are three-fold: • • To reduce financial burden on students by providing access to free educational content To provide instructors with a platform to customize educational content to better suit their curriculum • To present material written by a collection of experts and authorities in the field The Diener Education Fund is co-founded by Drs. Ed and Carol Diener. Ed is the Joseph Smiley Distinguished Professor of Psychology (Emeritus) at the University of Illinois. Carol Diener is the former director of the Mental Health Worker and the Juvenile Justice Programs at the University of Illinois. Both Ed and Carol are award- winning university teachers. Acknowledgements The Diener Education Fund would like to acknowledge the following individuals and companies for their contribution to the Noba Project: The staff of Positive Acorn, including Robert BiswasDiener as managing editor and Peter Lindberg as Project Manager; The Other Firm for user experience design and web development; Sockeye Creative for their work on brand and identity development; Arthur Mount for illustrations; Chad Hurst for photography; EEI Communications for manuscript proofreading; Marissa Diener, Shigehiro Oishi, Daniel Simons, Robert Levine, Lorin Lachs and Thomas Sander for their feedback and suggestions in the early stages of the project. Research in Psychology 1 Research Designs Christie Napa Scollon Psychologists test research questions using a variety of methods. Most research relies on either correlations or experiments. With correlations, researchers measure variables as they naturally occur in people and compute the degree to which two variables go together. With experiments, researchers actively make changes in one variable and watch for changes in another variable. Experiments allow researchers to make causal inferences. Other types of methods include longitudinal and quasi-experimental designs. Many factors, including practical constraints, determine the type of methods researchers use. Often researchers survey people even though it would be better, but more expensive and time consuming, to track them longitudinally. Learning Objectives • • • • • • Articulate the difference between correlational and experimental designs. Understand how to interpret correlations. Understand how experiments help us to infer causality. Understand how surveys relate to correlational and experimental research. Explain what a longitudinal study is. List a strength and weakness of different research designs. Research Designs Research Designs 6 In the early 1970’s, a man named Uri Geller tricked the world: he convinced hundreds of thousands of people that he could bend spoons and slow watches using only the power of his mind. In fact, if you were in the audience, you would have likely believed he had psychic powers. Everything looked authentic—this man had to have paranormal abilities! So, why have you probably never heard of him before? Because when Uri was asked to perform his miracles in line with scientific experimentation, he was no longer able to do them. That is, even though it seemed like he was doing the impossible, when he was tested by science, he proved to be nothing more than a clever magician. When we look at dinosaur bones to make educated guesses about extinct life, or systematically chart the heavens to learn about the relationships between stars and planets, or study magicians to figure out how they perform their tricks, we are forming observations—the foundation of science. Although we are all familiar with the saying “seeing is believing,” conducting science is more than just what your eyes perceive. Science is the result of systematic and intentional study of the natural world. And psychology is no different. In the movie Jerry Maguire, Cuba Gooding, Jr. became famous for using the phrase, “Show me the money!” In psychology, as in all sciences, we might say, “Show me the data!” One of the important steps in scientific inquiry is to test our research questions, otherwise known as hypotheses. However, there are many ways to test hypotheses in psychological research. Which method you choose will depend on the type of questions you are asking, as well as what resources are available to you. All methods have limitations, which is why the best research uses a variety of methods. Most psychological research can be divided into two types: experimental and correlational research. Experimental Research If somebody gave you $20 that absolutely had to be spent today, how would you choose to spend it? Would you spend it on an item you’ve been eyeing for weeks, or would you donate the money to charity? Which option do you think would bring you the most happiness? If you’re like most people, you’d choose to spend the money on yourself (duh, right?). Our intuition is that we’d be happier if we spent the money on ourselves. Knowing that our intuition can sometimes be wrong, Professor Elizabeth Dunn (2008) at the University of British Columbia set out to conduct an experiment on spending and happiness. She gave each of the participants in her experiment $20 and then told them they had to spend 7 Research Designs the money by the end of the day. Some of the participants were told they must spend the money on themselves, and some were told they must spend the money on others (either charity or a gift for someone). At the end of the day she measured participants’ levels of happiness using a self-report questionnaire. (But wait, how do you measure something like happiness when you can’t really see it? Psychologists measure many abstract At the Corner Perk Cafe customers routinely pay for the drinks of concepts, such as happiness and strangers. Is this the way to get the most happiness out of a cup of intelligence, by beginning with operational coffee? Elizabeth Dunn's research shows that spending money on definitions of the concepts. See the others may affect our happiness differently than spending money Noba modules on Intelligence [ ­ on ourselves. [Image: The Island Packet, ] oba.to/ncb2h79v] and Happiness [http­ ://noba.to/qnw7g32t], respectively, for more information on specific measurement strategies.) In an experiment, researchers manipulate, or cause changes, in the independent variable, and observe or measure any impact of those changes in the dependent variable. The independent variable is the one under the experimenter’s control, or the variable that is intentionally altered between groups. In the case of Dunn’s experiment, the independent variable was whether participants spent the money on themselves or on others. The dependent variable is the variable that is not manipulated at all, or the one where the effect happens. One way to help remember this is that the dependent variable “depends” on what happens to the independent variable. In our example, the participants’ happiness (the dependent variable in this experiment) depends on how the participants spend their money (the independent variable). Thus, any observed changes or group differences in happiness can be attributed to whom the money was spent on. What Dunn and her colleagues found was that, after all the spending had been done, the people who had spent the money on others were happier than those who had spent the money on themselves. In other words, spending on others causes us to be happier than spending on ourselves. Do you find this surprising? But wait! Doesn’t happiness depend on a lot of different factors—for instance, a person’s upbringing or life circumstances? What if some people had happy childhoods and that’s why they’re happier? Or what if some people dropped their toast that morning and it fell jam-side down and ruined their whole day? It is correct to recognize that these factors and many more Research Designs 8 can easily affect a person’s level of happiness. So how can we accurately conclude that spending money on others causes happiness, as in the case of Dunn’s experiment? The most important thing about experiments is random assignment. Participants don’t get to pick which condition they are in (e.g., participants didn’t choose whether they were supposed to spend the money on themselves versus others). The experimenter assigns them to a particular condition based on the flip of a coin or the roll of a die or any other random method. Why do researchers do this? With Dunn’s study, there is the obvious reason: you can imagine which condition most people would choose to be in, if given the choice. But another equally important reason is that random assignment makes it so the groups, on average, are similar on all characteristics except what the experimenter manipulates. By randomly assigning people to conditions (self-spending versus other-spending), some people with happy childhoods should end up in each condition. Likewise, some people who had dropped their toast that morning (or experienced some other disappointment) should end up in each condition. As a result, the distribution of all these factors will generally be consistent across the two groups, and this means that on average the two groups will be relatively equivalent on all these factors. Random assignment is critical to experimentation because if the only difference between the two groups is the independent variable, we can infer that the independent variable is the cause of any observable difference (e.g., in the amount of happiness they feel at the end of the day). Here’s another example of the importance of random assignment: Let’s say your class is going to form two basketball teams, and you get to be the captain of one team. The class is to be divided evenly between the two teams. If you get to pick the players for your team first, whom will you pick? You’ll probably pick the tallest members of the class or the most athletic. You probably won’t pick the short, uncoordinated people, unless there are no other options. As a result, your team will be taller and more athletic than the other team. But what if we want the teams to be fair? How can we do this when we have people of varying height and ability? All we have to do is randomly assign players to the two teams. Most likely, some tall and some short people will end up on your team, and some tall and some short people will end up on the other team. The average height of the teams will be approximately the same. That is the power of random assignment! Other considerations In addition to using random assignment, you should avoid introducing confounds into your experiments. Confounds are things that could undermine your ability to draw causal Research Designs 9 inferences. For example, if you wanted to test if a new happy pill will make people happier, you could randomly assign participants to take the happy pill or not (the independent variable) and compare these two groups on their self-reported happiness (the dependent variable). However, if some participants know they are getting the happy pill, they might develop expectations that influence their self-reported happiness. This is sometimes known as a placebo effect. Sometimes a person just knowing that he or she is receiving special treatment or something new is enough to actually cause changes in behavior or perception: In other words, even if the participants in the happy pill condition were to report being happier, we wouldn’t know if the pill was actually making them happier or if it was the placebo effect—an example of a confound. A related idea is participant demand. This occurs when participants try to behave in a way they think the experimenter wants them to behave. Placebo effects and participant demand often occur unintentionally. Even experimenter expectations can influence the outcome of a study. For example, if the experimenter knows who took the happy pill and who did not, and the dependent variable is the experimenter’s observations of people’s happiness, then the experimenter might perceive improvements in the happy pill group that are not really there. One way to prevent these confounds from affecting the results of a study is to use a doubleblind procedure. In a double-blind procedure, neither the participant nor the experimenter knows which condition the participant is in. For example, when participants are given the happy pill or the fake pill, they don’t know which one they are receiving. This way the participants shouldn’t experience the placebo effect, and will be unable to behave as the researcher expects (participant demand). Likewise, the researcher doesn’t know which pill each participant is taking (at least in the beginning—later, the researcher will get the results for data-analysis purposes), which means the researcher’s expectations can’t influence his or her observations. Therefore, because both parties are “blind” to the condition, neither will be able to behave in a way that introduces a confound. At the end of the day, the only difference between groups will be which pills the participants received, allowing the researcher to determine if the happy pill actually caused people to be happier. Correlational Designs When scientists passively observe and measure phenomena it is called correlational research. Here, we do not intervene and change behavior, as we do in experiments. In correlational research, we identify patterns of relationships, but we usually cannot infer what causes what. Importantly, with correlational research, you can examine only two variables at a time, no more and no less. 10 Research Designs So, what if you wanted to test whether spending on others is related to happiness, but you don’t have $20 to give to each participant? You could use a correlational design—which is exactly what Professor Dunn did, too. She asked people how much of their income they spent on others or donated to charity, and later she asked them how happy they were. Do you think these two variables were related? Yes, they were! The more money people reported spending on others, the happier they were. More details about the correlation To find out how well two variables correspond, we can plot the relation between the two scores on what is known as a scatterplot (Figure 1). In the scatterplot, each dot represents a data point. (In this case it’s individuals, but it could be some other unit.) Importantly, each dot provides us with two pieces of information—in this case, information about how good the person rated the past month (x-axis) and how happy the person felt in the past month (yaxis). Which variable is plotted on which axis does not matter. The association between two variables can be summarized statistically using the correlation coefficient (abbreviated as r). A correlation coefficient provides information about the direction and strength of the association between two variables. For the example above, the direction of the association is positive. This means that people who perceived the past month as being good reported feeling more happy, whereas people who perceived the month as being bad reported feeling less happy. With a positive correlation, the two Figure 1. Scatterplot of the association between happiness and variables go up or down together. In a ratings of the past month, a positive correlation (r = .81). Each dot scatterplot, the dots form a pattern that represents an individual. extends from the bottom left to the upper right (just as they do in Figure 1). The r value for a positive correlation is indicated by a positive number (although, the positive sign is usually omitted). Here, the r value is .81. 11 Research Designs A negative correlation is one in which the two variables move in opposite directions. That is, as one variable goes up, the other goes down. Figure 2 shows the association between the average height of males in a country (y-axis) and the pathogen prevalence (or commonness of disease; x-axis) of that country. In this scatterplot, each dot represents a country. Notice how the dots extend from the top left to the bottom right. What does this mean in real-world terms? It means that people are shorter in parts of the world where there is more disease. The r value for a negative correlation is indicated by a negative number—that is, it has a minus (–) sign in front of it. Here, it is –.83. The strength of a correlation has to do with how well the two variables align. Recall that in Professor Dunn’s correlational study, spending on others positively correlated with happiness: The more money people reported spending on others, the happier they reported to be. At this point you may be th...
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