helps students to approach classes in statistics with the mind-set that statistics are tools we need to understand research. Just as we need a screwdriver to tighten a loose screw or scissors to cut paper, we need statistics to understand scientific research. The goal of a research study will guide the type of statistic (or tool) that the researcher needs to use. We now examine four goals of scientific research.
Scientific research in psychology has four overriding goals: (1) to describe, (2) to predict, (3) to explain, and (4) to apply behavioral and cognitive phenomena. The first three goals have different statistical tools associated with them, which we will discuss as we present each goal in turn now.
Goal: To Describe
Descriptive research aims to communicate variables as they exist in the world. To conduct descriptive research, we need to make observations and measurements of the phenomena we want to study. If you want to know the temperature outside, a thermometer located outside can provide this information. The temperature is descriptive information. If we want to describe the health-related behaviors of college students, we can take measurements of how much sleep they get each night, how many times per week they exercise, and their daily fruit and vegetable consumption. We could then describe the health-related behaviors of college students along these dimensions.
How might we go about collecting data to describe the health behaviors of college students? First, we can conduct observational studies. By using naturalistic observation, we could sit in the student cafeteria and record what students eat. Likewise, we can go to the student recreational facility and see how many students work out there, including the types of exercises that they perform. Naturalistic observation will be more difficult to use to record sleep habits; however, we might use laboratory observations, in which we observe behavior in a more controlled setting, such as a research laboratory. In this case, we could have a bed available and record how long students sleep.
In addition to observational research, we can use case studies to describe behavior. A case study involves studying one or more people in great depth. We could examine the health behaviors of a small number, perhaps three or four, college students. An advantage of using the case study method over observational studies is that we can go into great detail on the behaviors of this small sample. In addition, case studies are particularly useful when studying rare phenomena, such as certain diseases that occur in only a few people. The disadvantage, though, is that it is extremely time-consuming to conduct a large number of case studies, especially if we want the research to be representative of the college student population.
Finally, surveys can be used to describe behavior. In our case, we would ask questions of college students about their health-related behaviors. Surveys can be administered through questionnaires via campus mail, over the Internet, or in a research lab setting. Likewise, surveys can be administered as interviews either in person or over the phone. Questionnaires are advantageous because the researcher asks respondents to provide the same information. Interviews are advantageous because the researchers can ask follow-up questions depending on a respondent’s answers. Surveys are particularly helpful because it is easier to collect more data than with case studies or most observational research. However, researchers must pay close attention to the wording of the questions to make sure respondents are interpreting them correctly. We must also be sure that the sample of survey respondents is representative of the population we want to study. In this case, it might only be the more health-conscious students who complete a survey. If that happens, our sample data will not reflect the population of college students.
Descriptive research: depicts variables as they exist in the world.
Observational studies: consist of watching behaviors in naturalistic and laboratory settings.
Case studies: examine in depth one or more people with a certain characteristic.
Surveys: series of questions to which people respond via a questionnaire or an interview.
Description is typically the first step in conducting predictive and explanatory research. In the next section of this chapter, we will preview what are called descriptive statistics. In Chapters 2 through 5, we will look extensively at these types of statistics. Their overriding purpose is to help researchers describe data from a sample.
Goal: To Predict
Predictive research aims to make forecasts about future events. In the case of the weather, if we know the time of year it is, wind flow patterns, and barometric pressure, we can predict the temperature and likelihood of precipitation. Returning to our health-behaviors research, if we have data on college students’ health behaviors, we can use those data to predict outcomes such as grade-point average and satisfaction with college, both of which most colleges are keenly interested in. There are two methods of conducting predictive research. First, using the correlational method, researchers measure the extent to which two or more variables are related to each other (i.e., co-related). In our example, if we know how much sleep a college student gets each night, how many times per week he or she exercises, and his or her daily fruit and vegetable consumption, we can predict, to some extent, outcomes such as GPA and satisfaction with college.
Figure 1.3 What a Positive Correlation Looks Like
We will explore correlational research in more detail in Chapters 12 and 13. For now, understand that there are positive correlations, in which increases (or decreases) in the frequency of one behavior tend to be accompanied by increases (or decreases) in the frequency of a second behavior. To illustrate what a positive correlation looks like, consider Figure 1.3, which displays the nature of the relationship between weekly exercise habits and GPA (Bass, Brown, Laurson, & Coleman, 2013). Each dot on this scatterplot represents one student’s weekly aerobic exercise time (x-axis) and the student’s corresponding GPA (y-axis). In general, as weekly aerobic exercise time increases, GPA increases. This does not happen for every student, but in general, this is the case. Therefore, weekly aerobic exercise and GPA are positively correlated.
Figure 1.4 What a Negative Correlation Looks Like
In addition, the second type of correlation is a negative correlation, which results when increases in the frequency of one behavior tend to be accompanied by decreases in the frequency of a second behavior. To illustrate what a negative correlation looks like, consider Figure 1.4, which displays the hypothetical relationship between weekly alcohol consumption and GPA. In general, as weekly alcohol consumption increases, GPA decreases (Singleton & Wolfson, 2009).
Finally, the third type of correlation is a zero correlation. As you might have guessed, a zero correlation exists when there is no pattern between the frequency of one behavior and the frequency of a second behavior. I am aware of no research that suggests any relationship between physical height and frequency of flossing one’s teeth. I doubt that taller people floss more often than shorter people (which would have been a positive correlation) or that shorter people floss more often than taller people (which would have been a negative correlation).
In addition to the
