Psychologists arm their scientific attitude with the scientific method—a self-correcting process for evaluating ideas with observation and analysis. In its attempt to describe and explain human nature, psychological science welcomes hunches and plausible-sounding theories. And it puts them to the test. If a theory works—if the data support its predictions—so much the better for that theory. If the predictions fail, the theory will be revised or rejected.

2-2 How do theories advance psychological science?

In everyday conversation, we often use theory to mean “mere hunch.” Someone might, for example, discount evolution as “only a theory”—as if it were mere speculation. In science, a theory explains behaviors or events by offering ideas that organize what we have observed. By organizing isolated facts, a theory simplifies. By linking facts with deeper principles, a theory offers a useful summary. As we connect the observed dots, a coherent picture emerges.

A theory about sleep effects on memory, for example, helps us organize countless sleep-related observations into a short list of principles. Imagine that we observe over and over that people with good sleep habits tend to answer questions accurately in class and do well at test time. We might therefore theorize that sleep improves memory. So far so good: Our principle neatly summarizes a list of facts about the effects of a good night’s sleep on memory.

Yet no matter how reasonable a theory may sound—and it does seem reasonable to suggest that sleep could improve memory—we must put it to the test. A good theory produces testable predictions, called hypotheses. Such predictions specify what results (what behaviors or events) would support the theory and what results would oppose it. To test our theory about the effects of sleep on memory, our hypothesis might be that when sleep deprived, people will remember less from the day before. To test that hypothesis, we might assess how well people remember course materials they studied before a good night’s sleep, or before a shortened night’s sleep (FIGURE 2.1 below). The results will either support our theory or lead us to revise or reject it.

Our theories can bias our observations. Having theorized that better memory springs from more sleep, we may see what we expect: We may perceive sleepy people’s comments as less insightful. The urge to see what we expect is ever-present, both inside and outside the laboratory, as when people’s views of climate change influence their interpretation of local weather events.

As a check on their biases, psychologists report their research with precise operational definitions of procedures and concepts. Sleep deprived, for example, might be defined as “X hours less” than one’s natural sleep. Using these carefully worded statements, others can replicate (repeat) the original observations with different participants, materials, and circumstances. If they get similar results, confidence in the finding’s reliability grows. The first study of hindsight bias aroused psychologists’ curiosity. Now, after many successful replications with different people and questions, we feel sure of the phenomenon’s power. Although “mere replications” of others’ research are unglamorous—they seldom make headline news—today’s science is placing greater value on replication studies. International research teams are repeating high-profile studies. In one project, which attempted replications of 13 studies, researchers convincingly replicated 10 findings with similar or greater effects. They replicated one with a weaker effect. And they failed to replicate two studies (Klein et al., 2014). Such replication forms an essential part of good science. Replication = confirmation.

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In the end, our theory will be useful if it (1) organizes observations and (2) implies predictions that anyone can use to check the theory or to derive practical applications. (Does people’s sleep predict their retention?) Eventually, our research may (3) stimulate further research that leads to a revised theory that better organizes and predicts.

As we will see next, we can test our hypotheses and refine our theories using descriptive methods (which describe behaviors, often through case studies, naturalistic observations, or surveys), correlational methods (which associate different factors), and experimental methods (which manipulate factors to discover their effects). To think critically about popular psychology claims, we need to understand these methods and know what conclusions they allow.

2-3 How do psychologists use case studies, naturalistic observations, and surveys to observe and describe behavior, and why is random sampling important?

The starting point of any science is description. In everyday life, we all observe and describe people, often drawing conclusions about why they act as they do. Professional psychologists do much the same, though more objectively and systematically, through

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THE CASE STUDY Among the oldest research methods, the case study examines one individual or group in depth in the hope of revealing things true of us all. Some examples: Much of our early knowledge about the brain came from case studies of individuals who suffered a particular impairment after damage to a certain brain region. Jean Piaget taught us about children’s thinking after carefully observing and questioning only a few children. Studies of only a few chimpanzees revealed their capacity for understanding and language. Intensive case studies are sometimes very revealing, and they often suggest directions for further study.

But atypical individual cases may mislead us. Both in our everyday lives and in science, unrepresentative information can lead to mistaken conclusions. Indeed, anytime a researcher mentions a finding (Smokers die younger: 95 percent of men over 85 are nonsmokers) someone is sure to offer a contradictory anecdote (Well, I have an uncle who smoked two packs a day and lived to be 89). Dramatic stories and personal experiences (even psychological case examples) command our attention and are easily remembered. Journalists understand that, and often begin their articles with personal stories. Stories move us. But stories can mislead. Which of the following do you find more memorable? (1) “In one study of 1300 dream reports concerning a kidnapped child, only 5 percent correctly envisioned the child as dead” (Murray & Wheeler, 1937). (2) “I know a man who dreamed his sister was in a car accident, and two days later she died in a head-on collision!” Numbers can be numbing, but the plural of anecdote is not evidence. A psychologist’s single case of someone who reportedly changed from gay to straight is not evidence that sexual orientation is a choice. As psychologist Gordon Allport (1954, p. 9) said, “Given a thimbleful of [dramatic] facts we rush to make generalizations as large as a tub.”

The point to remember: Individual cases can suggest fruitful ideas. What’s true of all of us can be glimpsed in any one of us. But to discern the general truths that cover individual cases, we must employ other research methods.

NATURALISTIC OBSERVATION A second descriptive method records behavior in natural environments. These naturalistic observations range from watching chimpanzee societies in the jungle, to videotaping and analyzing parent-child interactions in different cultures, to recording racial differences in students’ self-seating patterns in a school lunchroom.

Naturalistic observation has mostly been “small science”—science that can be done with pen and paper rather than fancy equipment and a big budget (Provine, 2012). But new technologies, such as smart-phone apps, body-worn sensors, and social media, are enabling “big data” naturalistic observation. Using such tools, researchers can track people’s location, activities, and opinions—without interference. The billions of people on Facebook, Twitter, and Google have also created a huge but sometimes controversial new opportunity for big-data naturalistic observation. Meanwhile, the data pour in. One research team studied the ups and downs of human moods by counting positive and negative words in 504 million Twitter messages from 84 countries (Golder & Macy, 2011). As FIGURE 2.2 shows, people seem happier on weekends, shortly after arising, and in the evenings. (Are late Saturday evenings often a happy time for you, too?) Another study found that the proportion of negative emotion (especially anger-related) words in 148 million tweets from 1347 U.S. counties predicted the counties’ heart disease rates, and did so even better than other predictors such as smoking and obesity (Eichstaedt et al., 2015).

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Like the case study, naturalistic observation does not explain behavior. It describes it. Nevertheless, descriptions can be revealing. We once thought, for example, that only humans use tools. Then naturalistic observation revealed that chimpanzees sometimes insert a stick in a termite mound and withdraw it, eating the stick’s load of termites. Such unobtrusive naturalistic observations paved the way for later studies of animal thinking, language, and emotion, which further expanded our understanding of our fellow animals. Thanks to researchers’ observations, we know that chimpanzees and baboons use deception: Psychologists repeatedly saw one young baboon pretending to have been attacked by another as a tactic to get its mother to drive the other baboon away from its food. “Observations, made in the natural habitat, helped to show that the societies and behavior of animals are far more complex than previously supposed,” chimpanzee observer Jane Goodall noted (1998).

Naturalistic observations also illuminate human behavior. Here are three findings you might enjoy:

Naturalistic observation offers interesting snapshots of everyday life, but it does so without controlling for all the factors that may influence behavior. It’s one thing to observe the pace of life in various places, but another to understand what makes some people walk faster than others. Even so, the observation of natural everyday behavior is an important part of psychological science.

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THE SURVEY A survey looks at many cases in less depth by asking people to report their behavior or opinions. Questions about everything from sexual practices to political opinions are put to the public. In recent surveys:

But asking questions is tricky, and the answers often depend on question wording and respondent selection.

WORDING EFFECTS Even subtle changes in the order or wording of questions can have major effects. People are much more approving of “aid to the needy” than of “welfare,” of “affirmative action” than of “preferential treatment,” of “not allowing” televised cigarette ads and pornography than of “censoring” them, and of “revenue enhancers” than of “taxes.” Because wording is such a delicate matter, critical thinkers will reflect on how the phrasing of a question might affect people’s expressed opinions.

RANDOM SAMPLING In everyday thinking, we tend to generalize from cases we observe, especially vivid cases. Given (a) a statistical summary of a professor’s student evaluations and (b) the vivid comments of a biased sample (two irate students), an administrator’s impression of the professor may be influenced as much by the two unhappy students as by the many favorable evaluations in the statistical summary. The temptation to ignore the sampling bias and to generalize from a few vivid but unrepresentative cases is nearly irresistible.

So how do you obtain a representative sample of, say, the students at your college or university? It’s not always possible to survey the whole group you want to study and describe. How could you choose a group that would represent the total student population? Typically, you would seek a random sample, in which every person in the total group has an equal chance of being included in the sample group. You might number the names in the general student listing and then use a random number generator to pick your survey participants. (Sending each student a questionnaire wouldn’t work because the conscientious people who returned it would not be a random sample.) Large representative samples are better than small ones, but a small representative sample of 100 is better than an unrepresentative sample of 500.

Political pollsters sample voters in national election surveys just this way. Using some 1500 randomly sampled people, drawn from all areas of a country, they can provide a remarkably accurate snapshot of the nation’s opinions. Without random sampling (also called random selection), large samples—including unrepresentative call-in phone samples and TV or website polls—often give misleading results.

The point to remember: Before accepting survey findings, think critically. Consider the sample. The best basis for generalizing is from a representative sample. You cannot compensate for an unrepresentative sample by simply adding more people.

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2-4 What are positive and negative correlations, and why do they enable prediction but not cause-effect explanation?

Describing behavior is a first step toward predicting it. Naturalistic observations and surveys often show us that one trait or behavior relates to another. In such cases, we say the two correlate. A statistical measure (the correlation coefficient) indicates how closely two things vary together, and thus how well either one predicts the other. Knowing how much aptitude test scores correlate with school success tells us how well the scores predict school success.

A positive correlation (above 0 to +1.00) indicates a direct relationship, meaning that two things increase together or decrease together. For example, height and weight are positively correlated.

A negative correlation (below 0 to −1.00) indicates an inverse relationship: As one thing increases, the other decreases. The weekly number of hours spent in TV watching and video gaming correlates negatively with grades. Negative correlations could go as low as −1.00, which means that, like people on opposite ends of a teeter-totter, one set of scores goes down precisely as the other goes up.

Though informative, psychology’s correlations usually explain only part of the variation among individuals. As we will see, there is a positive correlation between parents’ abusiveness and their children’s later abusiveness when they become parents. But this does not mean that most abused children become abusive. The correlation simply indicates a statistical relationship: Most abused children do not grow into abusers, but nonabused children are even less likely to become abusive. Correlations point us toward predictions, but usually imperfect ones.

The point to remember: A correlation coefficient helps us see the world more clearly by revealing the extent to which two things relate.

CORRELATION AND CAUSATION Consider some recent newsworthy correlations:

What shall we make of these correlations? Do they indicate that students would achieve more if their parents supported them less? That stopping smoking would improve mental health? That abstaining from video games would make reckless teen drivers more responsible?

No, because such correlations do not come with built-in cause-effect arrows. But correlations do help us predict. An example: Self-esteem correlates negatively with (and therefore predicts) depression. (The lower people’s self-esteem, the more they are at risk for depression.) So, does low self-esteem cause depression? If, based on the correlational evidence, you assume that it does, you have much company. A nearly irresistible thinking error is assuming that an association, sometimes presented as a correlation coefficient, proves causation. But no matter how strong the relationship, it does not. As FIGURE 2.3 indicates, we’d get the same negative correlation between self-esteem and depression if depression caused people to be down on themselves, or if some third factor—such as heredity or distressing events—caused both low self-esteem and depression.

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This point is so important—so fundamental to thinking smarter with psychology—that it merits another example. A survey of over 12,000 adolescents found that the more teens feel loved by their parents, the less likely they are to behave in unhealthy ways—having early sex, smoking, abusing alcohol and drugs, exhibiting violence (Resnick et al., 1997). “Adults have a powerful effect on their children’s behavior right through the high school years,” gushed an Associated Press (AP) story reporting the finding. But again, correlations come with no built-in cause-effect arrow. The AP could as well have reported, “Well-behaved teens feel their parents’ love and approval; out-of-bounds teens more often think their parents are disapproving.”

The point to remember (turn the volume up here): Correlation does not prove causation. Correlation indicates the possibility of a cause-effect relationship but does not prove such. Remember this principle and you will be wiser as you read and hear news of scientific studies.

2-5 What are the characteristics of experimentation that make it possible to isolate cause and effect?

Happy are they, remarked the Roman poet Virgil, “who have been able to perceive the causes of things.” How might psychologists perceive causes in correlational studies, such as the correlation between breast feeding and intelligence? Is breast really best?

Intelligence scores of children who were breast-fed as infants are somewhat higher than the scores of children who were bottle-fed (Angelsen et al., 2001; Mortensen et al., 2002; Quinn et al., 2001). Moreover, the longer they breast-feed, the higher their later IQ scores (Jedrychowski et al., 2012).

What do such findings mean? Do the nutrients of mother’s milk, as some researchers believe, contribute to brain development? Or do smarter mothers have smarter children? (Breast-fed children tend to be healthier and higher achieving than other children. But their bottle-fed siblings, born and raised in the same families, tend to be similarly healthy and high achieving [Colen & Ramey, 2014].) To find answers to such questions—to isolate cause and effect—researchers can experiment. Experiments enable researchers to isolate the effects of one or more factors by (1) manipulating the factors of interest and (2) holding constant (controlling) other factors. To do so, they often create an experimental group, in which people receive the treatment, and a contrasting control group whose members do not receive the treatment. To minimize any preexisting differences between the two groups, researchers randomly assign people to the two conditions. Random assignment—whether with a random numbers table or flip of the coin—effectively equalizes the two groups. If one-third of the volunteers for an experiment can wiggle their ears, then about one-third of the people in each group will be ear wigglers. So, too, with ages, attitudes, and other characteristics, which will be similar in the experimental and control groups. Thus, if the groups differ at the experiment’s end, we can surmise that the treatment had an effect.

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To experiment with breast feeding, one research team randomly assigned some 17,000 Belarus newborns and their mothers either to a control group given normal pediatric care, or to an experimental group that promoted breast feeding, thus increasing expectant mothers’ breast-feeding intentions (Kramer et al., 2008). At 3 months of age, 43 percent of the experimental group infants were being exclusively breast-fed, as were 6 percent in the control group. At age 6, when nearly 14,000 of the children were restudied, those who had been in the breast-feeding-promotion group had intelligence test scores averaging six points higher than their control group counterparts.

With parental permission, one British research team directly experimented with breast milk. They randomly assigned 424 hospitalized premature infants either to formula feedings or to breast-milk feedings (Lucas et al., 1992). Their finding: For premature infants’ developing intelligence, breast was best. On intelligence tests taken at age 8, those nourished with breast milk scored significantly higher than those who were formula-fed.

No single experiment is conclusive, of course. But randomly assigning participants to one feeding group or the other effectively eliminated all factors except nutrition. This supported the conclusion that for developing intelligence, breast is indeed best. If test performance changes when we vary infant nutrition, then we infer that nutrition matters.

The point to remember: Unlike correlational studies, which uncover naturally occurring relationships, an experiment manipulates a factor to determine its effect.

Consider, then, how we might assess therapeutic interventions. Our tendency to seek new remedies when we are ill or emotionally down can produce misleading testimonies. If three days into a cold we start taking vitamin C tablets and find our cold symptoms lessening, we may credit the pills rather than the cold naturally subsiding. In the 1700s, bloodletting seemed effective. People sometimes improved after the treatment; when they didn’t, the practitioner inferred the disease was too advanced to be reversed. So, whether or not a remedy is truly effective, enthusiastic users will probably endorse it. To determine its effect, we must control for other factors.

And that is precisely how investigators evaluate new drug treatments and new methods of psychological therapy. They randomly assign participants either to the group receiving a treatment (such as a medication), or to a group receiving a pseudotreatment—an inert placebo (perhaps a pill with no drug in it). The participants are often blind (uninformed) about what treatment, if any, they are receiving. If the study is using a double-blind procedure, neither the participants nor those who administer the drug or placebo and collect the data will know which group is receiving the treatment.

In double-blind studies, researchers check a treatment’s actual effects apart from the participants’ and the staff’s belief in its healing powers. Just thinking you are getting a treatment can boost your spirits, relax your body, and relieve your symptoms. This placebo effect is well documented in reducing pain, depression, and anxiety (Kirsch, 2010). Athletes have run faster when given a supposed performance-enhancing drug (McClung & Collins, 2007). Drinking decaf coffee has boosted vigor and alertness—for those who thought it had caffeine in it (Dawkins et al., 2011). People have felt better after receiving a phony mood-enhancing drug (Michael et al., 2012). And the more expensive the placebo, the more “real” it seems to us—a fake pill that costs $2.50 works better than one costing 10 cents (Waber et al., 2008). A pain-reducing placebo effect, if repeatedly experienced, can persist. Even when people learn they received a placebo, they continue to report reduced pain (Schafer et al., 2015). To know how effective a therapy really is, researchers must control for a possible placebo effect.

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INDEPENDENT AND DEPENDENT VARIABLES Here is an even more potent example: The drug Viagra was approved for use after 21 clinical trials. One trial was an experiment in which researchers randomly assigned 329 men with erectile disorder to either an experimental group (Viagra takers) or a control group (placebo takers given an identical-looking pill). The procedure was double-blind—neither the men nor the person giving them the pills knew what they were receiving. The result: At peak doses, 69 percent of Viagra-assisted attempts at intercourse were successful, compared with 22 percent for men receiving the placebo (Goldstein et al., 1998). Viagra performed.

This simple experiment manipulated just one factor: the drug dosage (none versus peak dose). We call this experimental factor the independent variable because we can vary it independently of other factors, such as the men’s age, weight, and personality. Other factors which could influence a study’s results are called confounding variables. Random assignment controls for possible confounding variables.

Experiments examine the effect of one or more independent variables on some measurable behavior, called the dependent variable because it can vary depending on what takes place during the experiment. Both variables are given precise operational definitions, which specify the procedures that manipulate the independent variable (in this study, the exact drug dosage and timing) or measure the dependent variable (the questions that assessed the men’s responses). These definitions answer the “What do you mean?” question with a level of precision that enables others to replicate the study. (See FIGURE 2.4 for the British breast milk experiment’s design.)

Let’s pause to check your understanding using a simple psychology experiment: To test the effect of perceived ethnicity on the availability of rental housing, Adrian Carpusor and William Loges (2006) sent identically worded e-mail inquiries to 1115 Los Angeles-area landlords. The researchers varied the ethnic connotation of the sender’s name and tracked the percentage of positive replies (invitations to view the apartment in person). “Patrick McDougall,” “Said Al-Rahman,” and “Tyrell Jackson” received, respectively, 89 percent, 66 percent, and 56 percent invitations.

Experiments can also help us evaluate social programs. Do early childhood education programs boost impoverished children’s chances for success? What are the effects of different antismoking campaigns? Do school sex-education programs reduce teen pregnancies? To answer such questions, we can experiment: If an intervention is welcomed but resources are scarce, we could use a lottery to randomly assign some people (or regions) to experience the new program and others to a control condition. If later the two groups differ, the intervention’s effect will be supported (Passell, 1993).

Let’s recap. A variable is anything that can vary (infant nutrition, intelligence, TV exposure—anything within the bounds of what is feasible and ethical). Experiments aim to manipulate an independent variable, measure a dependent variable, and control confounding variables. An experiment has at least two different conditions: an experimental condition and a comparison or control condition. Random assignment works to minimize preexisting differences between the groups before any treatment effects occur. In this way, an experiment tests the effect of at least one independent variable (what we manipulate) on at least one dependent variable (the outcome we measure).

TABLE 2.1 compares the features of psychology’s main research methods. You will read later about other research designs, including cross-sectional and longi-tudinal research, and twin studies. To help you understand how researchers design their studies, we have created activities that invite you to play the role of researcher (see Thinking Critically About Research Design: How Would You Know?)

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2-6 Can laboratory experiments illuminate everyday life?

When you see or hear about psychological research, do you ever wonder whether people’s behavior in the lab will predict their behavior in real life? Does detecting the blink of a faint red light in a dark room say anything useful about flying a plane at night? After viewing a violent, sexually explicit film, does an aroused man’s increased willingness to push buttons that he thinks will electrically shock a woman really say anything about whether violent pornography makes a man more likely to abuse a woman?

Before you answer, consider: The experimenter intends the laboratory environment to be a simplified reality—one that simulates and controls important features of everyday life. Just as a wind tunnel lets airplane designers re-create airflow forces under controlled conditions, a laboratory experiment lets psychologists re-create psychological forces under controlled conditions.

An experiment’s purpose is not to re-create the exact behaviors of everyday life, but to test theoretical principles (Mook, 1983). In aggression studies, deciding whether to push a button that delivers a noise blast may not be the same as slapping someone in the face, but the principle is the same. It is the resulting principles—not the specific findings—that help explain everyday behaviors.

When psychologists apply laboratory research on aggression to actual violence, they are applying theoretical principles of aggressive behavior, principles refined through many experiments. Similarly, it is the principles of the visual system, developed from experiments in artificial settings (such as looking at red lights in the dark), that researchers apply to more complex behaviors such as night flying. And many investigations have demonstrated that principles derived in the laboratory do typically generalize to the everyday world (Anderson et al., 1999).

The point to remember: Psychological science focuses less on particular behaviors than on seeking general principles that help explain many behaviors.