Describe the process of framing and testing hypotheses.
By the end of this chapter, you should be able to:
Outline the key features of descriptive, correlational, and experimental research designs. Explain the importance of reliability and validity in designing research studies. Compare and contrast the different scaling methods for measuring variables. Identify the pros and cons of behavioral, physiological, and self-report measures. Describe the process of framing and testing hypotheses.
In the early 1950s, Canadian physician Hans Selye introduced the term stress into both the medical and popular lexicons. By that time, it had been accepted that humans have a well-evolved �ight-or-�light response, which prepares them either to �ight back or �lee from danger, largely by releasing adrenaline and mobilizing the body’s resources more ef�iciently. While working at McGill University, Selye began to wonder about the health consequences of adrenaline and designed an experiment to test his ideas using rats. Selye injected rats with doses of adrenaline over a period of several days and then euthanized the rats in order to examine the physical effects of the injections. Just as he had hypothesized, rats that were exposed to adrenaline had developed ill effects, such as ulcers, increased arterial plaques, and decreases in the size of reproductive glands—all now understood to be
2 Design, Measurement, and Testing Hypotheses
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consequences of long-term stress exposure. But there was just one problem. When Selye took a second group of rats and injected them with a placebo, they also developed ulcers, plaques, and shrunken reproductive glands.
Fortunately, Selye was able to solve this scienti�ic mystery with a little self-re�lection. Despite all his methodological savvy, he turned out to be rather clumsy when it came to handling rats, occasionally dropping one when he removed it from its cage for an injection. In essence, the experience for both groups of rats was one that we would now call stressful, and it is no surprise that they developed physical ailments in response. Rather than testing the effects of adrenaline injections, Selye was inadvertently testing the effects of being handled by a clumsy scientist. It is important to note that if Selye ran this study in the present day, ethical guidelines would dictate much more stringent oversight of his procedures to protect the welfare of the animals.
This story illustrates two key points about the scienti�ic process. First, as Chapter 1 discussed, researchers should always be attentive to apparent mistakes because they can lead to valuable insights. Second, it is absolutely vital that researchers actually measure what they think they are measuring—Selye ended up measuring the effects of stress rather than just adrenaline injections. This chapter explains what it means to do research in a more concrete way, beginning with a broad look at the three types of research design. The goal at this stage is to obtain a general sense of what these designs are, when they are used, and the main differences between them. (Chapters 3, 4, and 5 are each dedicated to one type of research design and will elaborate on each one.) Following the overview of designs, this chapter covers a set of basic principles that are common to all research designs. Regardless of the particulars of a given design, all research studies involve making sure measurements are accurate and consistent and that they are captured using the appropriate type of scale. Finally, the chapter will discuss the general process of hypothesis testing, from laying out predictions to drawing conclusions.
2.1 Overview of Research Designs As Chapter 1 explained, scientists can have a wide range of goals when they begin a research project, everything from describing a phenomenon to changing people’s behavior. It turns out that these goals will dictate different approaches to answering a research question. That is, researchers will approach the problem of describing voting patterns differently than they would approach the problem of how to increase voter turnout. These approaches are called research designs, or the speci�ic methods that are used to collect, analyze, and interpret data. The choice of a design is not one to be made lightly; the way an investigator collects data trickles down to decisions about how to analyze the data and about the kinds of conclusions that can be drawn from the results. This section provides a brief introduction to the three main types of design—descriptive, correlational, and experimental.
Recall from Chapter 1 that a research study can have the basic goal of describing a phenomenon. If a research question centers around description, then the research design falls under the category of descriptive research, in which the primary goal is to describe thoughts, feelings, or behaviors. Descriptive research provides a static picture of what people are thinking, feeling, and doing at a given moment in time, as the following examples of research questions illustrate:
What percentage of doctors prefer Xanax for the treatment of anxiety? (thoughts) What percentage of registered Republicans vote for independent candidates? (behaviors) What percentage of Americans blame the president for the economic crisis? (thoughts) What percentage of college students experience clinical depression? (feelings) What is the difference in crime rates between Beverly Hills and Detroit? (behaviors)
What these �ive questions have in common is an attempt to get a broad understanding of a phenomenon without trying to delve into its causes.
The crime-rate example highlights the main advantages and disadvantages of descriptive designs. On the plus side, descriptive research is a good way to achieve a broad overview of a phenomenon and may inspire future research. It is also a good way to study things that are dif�icult to translate into a controlled experimental setting. For example, crime rates can affect every aspect of people’s lives, and this importance would likely be lost in an experiment that staged a mock crime in a laboratory. On the downside, descriptive research provides a static overview of a phenomenon and cannot explore the reasons for it. A descriptive design might tell us that Beverly Hills residents are half as likely as Detroit residents to be assault victims, but it would not reveal the underlying reasons for this discrepancy. (If we wanted to understand why this was true, we would use one of the other designs.)
Descriptive research can be either qualitative or quantitative; in fact, the large majority of qualitative research falls under the category of descriptive designs. Descriptions are quantitative when they attempt to make comparisons or to present a random sampling of people’s opinions. The majority of our example questions above would fall into this group because they quantify opinions from samples of households, or cities, or college students. Good examples of quantitative description appear in the “snapshot” feature on the front page of USA Today. The graphics represent poll results from various sources; the snapshot for May 15, 2015, reported that 90% of Americans crave more “variety” in their home-cooked meals (i.e., thoughts). View a current gallery of these snapshot graphs here: http://www.usatoday.com /services/snapshots/gallery/ (http://www.usatoday.com/services/snapshots/gallery/)
Descriptive designs are qualitative when they attempt to provide a rich description of a particular set of circumstances. A powerful example of this approach can be found in the work of the late neurologist Oliver Sacks. Sacks wrote several books exploring the ways that people with neurological damage or de�icits are able to navigate the world around them. In one selection from The Man Who Mistook His Wife for a Hat, Sacks (1998) relates the story of a man he calls
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Dr. Oliver Sacks studied how people with neurological damage formed and retained memories.
William Thompson. As a result of chronic alcohol abuse, Thompson developed Korsakov’s syndrome, a brain disease marked by profound memory loss. The memory loss was so severe that Thompson had effectively “erased” himself and could remember only scattered fragments of his past.
Whenever Thompson encountered people, he would frantically try to determine who he was. He would develop hypotheses and test them, as in this excerpt from one of Sacks’s visits:
I am a grocer, and you’re my customer, right? Well, will that be paper or plastic? No, wait, why are you wearing that white coat? You must be Hymie, the kosher butcher. Yep. That’s it. But why are there no bloodstains on your coat? (p. 112)
Sacks concluded that Thompson was “continually creating a world and self, to replace what was continually being forgotten and lost” (p. 113). With this story, Sacks helps illuminate Thompson’s experience and fosters readers’ gratitude for the ability to form and retain memories. This story also illustrates the trade-off in these sorts of descriptive case studies: Despite all its richness, we cannot generalize these details to other cases of brain damage; we would need to study and describe each patient individually.
Recall from Chapter 1 that research studies can also have the goal of trying to predict a phenomenon. If a research question centers around prediction, then the research design falls under the category of correlational research, in which the primary goal is to understand the relationships among various thoughts, feelings, and behaviors. Examples of correlational research questions include:
Are people more aggressive on hot days? Are people more likely to smoke when they are drinking? Is income level associated with happiness? What is the best predictor of success in college? Does television viewing relate to hours of exercise?
What these questions have in common is the goal of predicting one variable based on another. If we know the temperature, can we predict aggression? If we know a person’s income, can we predict her level of happiness? If we know a student’s SAT scores, can we predict his college GPA?
These predictive relationships can turn out in one of three ways (Chapter 4 will provide more detail about each): A positive correlation means that higher values of one variable predict higher values of the other variable. For instance, more money is associated with higher levels of happiness, and less money is associated with lower levels of happiness. The key is that these variables move up and down together, as the �irst row of Table 2.1 shows. A negative correlation means that higher values of one variable predict lower values of the other variable. For example, more television viewing is associated with fewer hours of exercise, and fewer hours of television is associated with more hours of exercise. The key is that one variable increases while the other decreases, as the second row of Table 2.1 illustrates. Finally, worth noting is a third possibility, which is no correlation between two variables, meaning that we cannot predict one variable based on another. In brief, changes in one variable are not associated with changes in the other, as seen in the third row of Table 2.1.
Table 2.1: Three possibilities for correlational research
Figure 2.1: Correlation is not causation
Outcome Description Visual
Variables go up and down together. For example: Taller people have bigger feet, and shorter people have smaller feet.
One variable goes up, and the other goes down. For example: As a driver’s speed goes up, the time it takes to �inish the trip decreases.
The variables have nothing to do with one another. For example: Shoe size and number of siblings are completely unrelated.
Correlational designs are about testing predictions, but we are still unable to make causal, explanatory statements (that comes next). A common mantra in the �ield of psychology is that correlation does not equal causation. In other words, just because variable A predicts variable B does not mean that A causes B. This is true for two reasons, which we refer to as the directionality problem and the third variable problem. (See Figure 2.1.)
First, when we measure two variables at the same time, we have no way of knowing the direction of the relationship. Take the relationship between money and happiness: It could be true that money makes people happier, because they can afford nice things and fancy vacations. It could also be true that happy people have the con�idence and charm to obtain higher-paying jobs, resulting in more money. In a correlational study, we are unable to distinguish between these possibilities. Or, take the relationship between television viewing and obesity: It could be that people who watch more television get heavier, because TV makes them snack more and exercise less. It could also be that people who are overweight lack the energy to move around and end up watching more television as a consequence. Once again, we cannot identify a cause–effect relationship in a correlational study.
Second, when we measure two variables as they naturally occur, a third variable that actually causes both of them is always a possibility. For example, imagine we �ind a correlation between the number of churches and the number of liquor stores in a city. Do people build more churches to offset the threat of liquor stores? Do people build more liquor stores to rebel against churches? Most likely, the link involves a third variable, population size, that causes changes in both variables: The more people who are living in a city, the more churches and liquor stores they can support. As another example, imagine a correlation between ice cream sales and homicide rates is discovered. Does ice cream lead people to commit murder? Do murderers like to buy ice cream on the way home from the scene of the crime? Most likely, the link involves a third variable, temperature, that causes changes in both variables: The hotter it gets outside, the more people want ice cream, and the greater likelihood that disagreements will turn violent.