7.3 Unit of analysis and unit of observation

When designing a research project, it is imperative to consider units of analysis and units of observation. These may differ slightly in quantitative and qualitative research designs. These two items concern what the researcher observes in their data collection and what they hope to say about those observations. A unit of analysis is the entity that you wish to say something about at the end of your study, and it is considered the focus of your study. A unit of observation is the item (or items) that you observe, measure, or collect while trying to learn something about your unit of analysis.

In some studies, the unit of observation may be the same as the unit of analysis. For example, a study on electronic gadget addiction may interview undergraduate students (our unit of observation) for the purpose of saying something about undergraduate students (our unit of analysis) and their gadget addiction. Perhaps, if we were investigating gadget addiction in elementary school children (our unit of analysis), we might collect observations from teachers and parents (our units of observation) because younger children may not report their behavior accurately. In this case and many others, units of analysis are not the same as units of observation. However, researchers are required to clearly define their units of analysis and units of observation to themselves and their audiences.

More specifically, your unit of analysis will be determined by your research question. Your unit of observation, on the other hand, is determined largely by the method of data collection that you use to answer that research question. We’ll take a closer look at methods of data collection later on in the textbook. For now, let’s consider our previous example study that sought to address students’ addictions to electronic gadgets. We’ll consider first how different types of research questions about this topic may yield different units of analysis. Then, we’ll think about how those questions might be answered and with what kinds of data. This leads us to a variety of units of observation.

Let’s say that we are going to explore which students are most likely to be addicted to their electronic gadgets. Our unit of analysis would be the individual students. We would likely mail a survey to students on campus. We would classify individuals based on social group membership to see how membership in certain specific social groups correlates with electronic gadget addiction. For example, we might find that students majoring in new media, students that identify as men, and students with high socioeconomic status are more likely than other students to become addicted to their electronic gadgets. We could also explore how students’ gadget addictions differ and how are they similar. In this case, we could conduct observations of addicted students and record when, where, why, and how they use their gadgets. Whether the information about students’ addictions to electronic gadgets is collected by survey response or by direct observation, data are collected from individual students. Thus, the unit of observation in both examples is the individual.

Another common unit of analysis in social science inquiry is the group. Of course, groups vary in size, but almost no group is too small or too large to be of interest to social scientists. Families, friendship groups, and group therapy participants are some common examples of micro-level groups examined by social scientists. Employees in an organization, professionals in a particular domain (e.g., chefs, lawyers, social workers), and members of clubs (e.g., Girl Scouts, Rotary, Red Hat Society) are all meso-level groups that social scientists might study. Finally, at the macro-level, social scientists sometimes examine citizens of entire nations or residents of different continents or other regions.

A study of student addictions to their electronic gadgets at the group level might consider whether certain types of social clubs have more or fewer gadget-addicted members than other sorts of clubs. Perhaps we would find physical fitness clubs, such as the rugby club and the scuba club, have fewer gadget-addicted members than cerebral activity clubs, like the chess club and the women’s studies club. Our unit of analysis in this example is groups because groups are what we hope to say something about. If we had asked whether individuals who join cerebral clubs are more likely to be gadget-addicted than those who join social clubs, then our unit of analysis would have been individuals. In either case, however, our unit of observation would be individuals.

Organizations are yet another potential unit of analysis that social scientists might wish to say something about. Organizations include entities like corporations, colleges and universities, and even nightclubs. At the organization level, a study of students’ electronic gadget addictions might explore how different colleges address this social issue. In this case, our interest lies not in the experience of individual students but instead in the campus-to-campus differences in confronting gadget addictions. A researcher conducting a study of this type might examine schools’ written policies and procedures, so their unit of observation would be documents. However, because they ultimately wish to describe differences across campuses, the college would be their unit of analysis.

In sum, there are many potential units of analysis that a social worker might examine, but some of the most common units include the following:

• Individuals
• Groups
• Organizations

One common error people make when it comes to both causality and units of analysis is something called the ecological fallacy. This occurs when claims about one lower-level unit of analysis are made based on data from some higher-level unit of analysis. In many cases, this occurs when claims are made about individuals, but only group-level data have been gathered. For example, we might want to understand whether electronic gadget addictions are more common on certain campuses than others. Perhaps different campuses around the country have provided us with their campus percentage of gadget-addicted students, and we learn from these data that electronic gadget addictions are more common on campuses that have business programs than on campuses without them. We then conclude that business students are more likely than non-business students to become addicted to their electronic gadgets. However, this would be an inappropriate conclusion to draw. We only have addiction rates by campus, so we can only draw conclusions about campuses, not about the individual students on those campuses. Perhaps the social work majors on the business campuses are the ones that caused the addiction rates on those campuses to be so high. The point is we simply don’t know because we only have campus-level data. Therefore, we run the risk of committing the ecological fallacy if we draw conclusions about students when our data are about the campus.

In addition, another mistake to be aware of it reductionism. Reductionism occurs when claims about some higher-level unit of analysis are made based on data from some lower-level unit of analysis. In this case, claims about groups or macro-level phenomena are made based on individual-level data. An example of reductionism can be seen in some descriptions of the civil rights movement. On occasion, people have proclaimed that Rosa Parks started the civil rights movement in the United States by refusing to give up her seat to a White person while on a city bus in Montgomery, Alabama, in December 1955. Although Parks played an invaluable role in the movement and her act of civil disobedience inspired courage in others, it would be reductionist to credit her with starting the movement. Surely, many factors contributed to the rise and success of the American civil rights movement, including legalized racial segregation, the historic 1954 Supreme Court decision to desegregate schools, and the creation of the Student Nonviolent Coordinating Committee to name a few. In other words, the movement is attributable to many factors—some social, others political and others economic. Rosa Parks played a very important role in this development in American history, but to say that she caused the entire civil rights movement would be reductionist.

The preceding discussion was not meant to deter you from making claims about data or relationships between levels of analysis. While it is important to be attentive to the possibility for error in causal reasoning about different levels of analysis, this warning should not prevent you from drawing well-reasoned analytic conclusions from your data. The point is to be cautious and conscientious in making conclusions between levels of analysis. Errors in analysis stem from a lack of rigor and deviation from the scientific method.