LO 8 Recognize the forms of descriptive research.

Suppose a researcher is interested in studying how the sudden arrival of drugs, sweets, and other prohibited goods affects the miners’ social interactions. This is a great topic for descriptive research, the type of investigation psychologists conduct when they need to explore a subject of interest. Descriptive research is primarily concerned with describing, and is useful for studying new or unexplored topics when researchers might not have specific expectations about outcomes. But there are some things descriptive research cannot achieve. This method provides us with clues to the causes of behaviors, but it cannot unearth cause-and-effect relationships, a point that we will return to later in the chapter. But first, we will explore four descriptive research methods.

One form of descriptive research is naturalistic observation, which involves studying participants in their natural environments using systematic observation. And when we say “natural environments,” we don’t necessarily mean the “wild.” It could be an office, a home, or even a preschool. In one naturalistic study, researchers explored the interactions that occurred when parents dropped off their children at preschool. One variable of interest was the length of time it took parents to leave the children at school. The researchers found that the longer parents or caregivers took to leave, the less time the children played with their peers and the more likely they were to stand by and watch other kids playing. The researchers also noted that women were somewhat more likely than men to hold the children and physically pick them up during drop-off. These drop-off interactions seemed to get in the way of children “settling into” the classroom (Grady, Ale, & Morris, 2012).

NATURALLY, IT’S A CHALLENGE As with any type of research, naturalistic observation centers around variables, and those variables must be pinned down with operational definitions. Let’s say a researcher is interested in studying aggressiveness in enclosed spaces and decides to examine aggression among Los 33: an intriguing topic given that research has linked stress and hot temperatures to more aggressive behaviors (Anderson, 2001; Fay & Maner, 2014; Kruk, Halász, Meelis, & Haller, 2004). At the beginning of the study, the researcher would need to operationally define aggression, including detailed descriptions of specific behaviors that illustrate it. Then she might create a checklist of aggressive behaviors like shouting and pushing, and a coding system to help keep track of them.

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Naturalistic observation allows psychologists to observe participants going about their business in their normal environments, without the disruptions of artificial laboratory settings. Perhaps the most important requirement of naturalistic observation is that researchers must not disturb the participants or their environment, so participants won’t change their normal behaviors (particularly those behaviors the researchers wish to observe). Some problems arise with this arrangement, however. Natural environments are cluttered with a variety of unwanted variables, and removing them can alter the natural state of affairs the researchers are striving to maintain. And because the variables in natural environments are so hard to control, researchers may have trouble replicating findings. Suppose the researcher opted to study the behavior of Los 33 during their heated dominoes matches. In this natural setting, she would not be able to control who played and when; whoever showed up at the dominoes table would become a participant in her study.

OBSERVER BIAS How can we be sure observers will do a good job recording behaviors? A researcher who detests dominoes might pay attention to very different aspects of domino-related behaviors than a researcher who relishes the game. One way to avoid such problems is to include multiple observers and then determine how similarly they record the behaviors. If the observers don’t execute this task in the same way, there may be observer bias, which refers to errors introduced as a result of an observer’s value system, expectations, attitudes, and so forth.

Another type of descriptive research method is the case study, a detailed examination of an individual or small group. Case studies typically involve collecting a vast amount of data on one person or group, often using multiple avenues to gather information. The process might include in-depth interviews with the person being studied and her friends, family, and coworkers, and questionnaires about medical history, career, and mental health. The goal of a case study is to provide a wealth of information from a variety of resources. Case studies are invaluable for studying rare events, like 33 Chilean miners cooped up in a subterranean hole. Unlike naturalistic observation, where the researcher assumes the role of detached spectator, the case study may require complete immersion in the participant’s environment. How do you think this might impact the researcher’s observations and the conclusions of the study? See Table 1.5 for some examples of classic case studies, many of which we will examine in later chapters, helping us answer this question.

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No matter how colorful or thought-provoking a case study may be, it cannot be used to support or refute a hypothesis (Stanovich, 2013). Like naturalistic observation, the case study method can help to further develop theories, but it can’t provide answers to what is causing behaviors. This descriptive method can offer guidance for designing new studies on topics that may be relatively underexplored (Stanovich, 2013). But as knowledge advances and researchers are more concerned with testing hypotheses, the case study becomes increasingly irrelevant. Hypothesis testing involves drawing comparisons between different conditions (Stanovich, 2013). Case studies do not allow us to compare conditions to determine if there are other explanations for behaviors.

Suppose you are trying to examine how parent–child interactions at home might relate to preschoolers’ transitions during morning drop-off. What would happen if you limited your research to a case study of a family with two working parents and 10 children? The dynamics of this family may not be representative of those in other families. We should not make sweeping generalizations based on our observations of a single person or group.

One of the fastest ways to collect descriptive data is the survey method, which relies on questionnaires or interviews. A survey is basically a series of questions that can be administered on paper, in face-to-face interviews, via telephone, or through a few mouse clicks. Your college might send out surveys to gauge student attitudes about new online classes and e-books (Some possible questions you may see on a college survey might be: How often do you encounter technical difficulties with your online courses? How would you rate your overall satisfaction with an assigned e-book?). The benefit of the survey method is that one can gather data from numerous people in a short period of time. Surveys can be used alone or in conjunction with other research methods.

WORDING AND HONESTY Like any research design, the survey method has its limitations. The wording of surveys can lead to biases in responses. A question with a positive or negative spin may sway a participant’s response one way or the other: Do you prefer a half-full glass of soda or a half-empty glass of soda? Take a look at Figure 1.4 for an example of how the wording of a survey can influence participant responses.

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More importantly, participants in studies using the survey method are not always forthright in their responses, particularly when they have to admit to things they are uncomfortable discussing face-to-face. In short, people lie. In one study, psychologists asked thousands of American women if they had ever cheated on their husbands. When a researcher questioned them in person, 1% of the wives admitted cheating; but when asked through a computer-based questionnaire, 6% confessed their infidelity (Whisman & Snyder, 2007). People often exhibit a desire not to reveal attitudes or behaviors that are embarrassing or deal with sensitive topics (Tourangeau & Yan, 2007). This may lead to an inaccurate representation of participants’ attitudes and beliefs.

SKIMMING THE SURFACE Another disadvantage of the survey method is that it tends to skim the surface of people’s beliefs or attitudes, failing to tap into the complex issues underlying responses. Ask 1,000 people if they intend to exercise regularly, and you might get more than a few affirmative responses. But yes might mean something quite different from one person to the next (“Yes, it crosses my mind, but I can never go through with it” versus “Yes, I have a specific plan, and I have been able to follow through”). To obtain more precise responses, researchers often ask people to respond to statements using a scale that indicates the degree to which they agree or disagree (for example, a 5-point scale ranging from strongly agree to strongly disagree), or the degree to which they have had an experience (for example, a 5-point scale ranging from never to almost always).

REPRESENTATIVE SAMPLES AND SURVEYS The representativeness of survey samples can also be called into question. Some surveys fail to achieve representative samples because their response rates fall short of ideal. If a researcher sends out 100 surveys to potential participants and only 20 people return them, how can we be sure that the answers of those 20 responders reflect those of the entire group? Without a representative sample, we cannot generalize the survey findings.

The final form of descriptive research we will cover is the correlational method, which examines relationships among variables and assists researchers in making predictions. When researchers collect data on many variables, it can be useful to determine if these variables are related to each other in some way. A correlation represents a relationship or link between variables (Infographic 1.4, below). For example, there is probably a correlation between the years of working in a mine and yearly salary. The more time a miner has put in, the more cash he takes home. This is an example of a positive correlation. As one variable increases, so does the other. A negative correlation, on the other hand, means as one variable goes up the other goes down (an inverse relationship). A good example would be the number of days trapped in the mine and body weight (at least before rescuers started delivering food). As the number of days increased, the miners’ weight decreased. You have probably noticed correlations between variables in your own life. Increase the hours you devote to studying, and you will likely see your grades go up (a positive correlation). The more you hold a baby, the less she cries (a negative correlation).

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CORRELATION COEFFICIENT Some variables are not related to each other, whereas others have a link between them. Correlations indicate this type of information by representing the strength of the relationship between variables. Some relationships are strong and clear. Others are weak and barely noticeable. Lucky for researchers, there is one number that indicates the relationship’s strength and direction (positive or negative): a statistical measure called the correlation coefficient, which is symbolized as r. Correlation coefficients range from +1.00 to −1.00, with positive numbers indicating a positive relationship between variables and negative numbers indicating an inverse relationship between variables. The closer r is to +1.00 or to −1.00, the stronger the relationship. The closer r is to .00, the weaker the relationship. When the correlation coefficient is very close to zero, there may be no relationship at all between the variables. For example, consider the variables of shoe size and intelligence. Are people with bigger (or smaller) feet more intelligent? Probably not; there would be no link between these two variables, so the correlation coefficient between them (the r value) is around zero. Take a look at Infographic 1.4 to see how correlation coefficients are portrayed on graphs called scatterplots.

THIRD VARIABLE Even if there is a very strong correlation between two variables, this does not indicate there is a causal link between them. No matter how high the r value is or how logical the relationship seems, it does not prove a cause-and-effect connection. If you discover a positive relationship between the years worked in the mine and salary, it’s easy to jump to the conclusion that experience causes higher salary. But is it also possible that higher salary leads to greater experience (people keep jobs that pay well)? The direction of the relationship, or directionality, matters. It is also important to consider that some other variable or characteristic may be influencing both experience and salary. What additional variables might “cause” increased salary? One possibility is productivity, which could increase with the number of years on the job; more productive workers are rewarded with higher salaries. Here, we have a third variable, some unaccounted for characteristic of the participants or their environment that explains the changes in the two other variables. When you observe strong links between variables, consider other factors that could be related to both.

Remember, correlational and other descriptive methods can’t identify the causes of behaviors. But this type of research can provide clues to what underlies behaviors, and thus it serves as a valuable tool when other types of experiments are unethical or impossible to conduct (in the previous example, researchers couldn’t ethically manipulate real-world variables like salary or employment decisions). Moreover, descriptive research can produce interesting results. Just consider the following example.

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across the WORLD

Question 1

1. An operational definition

2. Observer bias

3. Descriptive research

4. A correlation

Question 2

1. naturalistic observation.

2. representative sampling.

3. informed consent.

4. applied research.

Question 3