What is the term for the group you generalize to in your study?

The goal of scientific research is to increase our understanding of the world around us. To do this, researchers study different groups of people or populations. These populations can be as small as a few individuals from one workplace or as large as thousands of people representing a cross-section of Canadian society. The results of this research often provide insights into how work and health interact in those groups. But how do we know if a study's results can be applied to another group or population?

To answer this question, we first need to understand the concept of generalizability.

In its simplest form, generalizability can be described as making predictions based on past observations.

In other words, if something has often happened in the past, it will likely occur in the future. In studies, once researchers have collected enough data to support a hypothesis, they can develop a premise to predict the outcome in similar circumstances with a certain degree of accuracy.

Two aspects of generalizability

Generalizing to a population. Sometimes when scientists talk about generalizability, they are applying results from a study sample to the larger population from which the sample was selected. For instance, consider the question, “What percentage of the Canadian population supports the Liberal party?” In this case, it would be important for researchers to survey people who represent the population at large. Therefore they must ensure that the survey respondents include relevant groups from the larger population in the correct proportions. Examples of relevant groups could be based on race, gender or age group.

Generalizing to a theory. More broadly, the concept of generalizability deals with moving from observations to scientific theories or hypotheses. This type of generalization amounts to taking time- and place-specific observations to create a universal hypothesis or theory. For instance, in the 1940s and 1950s, British researchers Richard Doll and Bradford Hill found that 647 out of 649 lung cancer patients in London hospitals were smokers. This led to many more research studies, with increasing sample sizes, with differing groups of people, with differing amounts of smoking and so on. When the results were found to be consistent across person, time and place, the observations were generalized into a theory: “cigarette smoking causes lung cancer.”

Requirements for generalizability

For generalizability we require a study sample that represents some population of interest — but we also need to understand the contexts in which the studies are done and how those might influence the results.

Suppose you read an article about a Swedish study of a new exercise program for male workers with back pain. The study was performed on male workers from fitness centres. Researchers compared two approaches. Half of the participants got a pamphlet on exercise from their therapist, and half were put on an exercise program led by a former Olympic athlete. The study findings showed that workers in the exercise group returned to work more quickly than workers who received the pamphlet.

Assuming the study was well conducted, with a strong design and rigorous reporting, we can trust the results. But to what populations could you generalize these results?

Some factors that need to be considered include: How important is it to have an Olympian delivering the exercise program? Would the exercise program work if delivered by an unknown therapist? Would the program work if delivered by the same Olympian but in a country where he or she is not well-known? Would the results apply to employees of other workplaces that differ from fitness centres? Would women respond the same way to the exercise program?

To increase our confidence in the generalizability of the study, it would have to be repeated with the same exercise program but with different providers in different settings (either worksites or countries) and yield the same results.

Source: At Work, Issue 45, Summer 2006: Institute for Work & Health, Toronto

  • Entry
  • Reader's guide
  • Entries A-Z
  • Subject index

Generalization

Generalization refers to the extent to which findings of an empirical investigation hold for a variation of populations and settings. The definition of generalization is closely related to the concept of external validity, which concerns whether findings of one particular study can be applied to unexamined subjects and contexts. Some scholars, such as William Shadish and colleagues, argued that construct validity is another type of generalization, which concerns how well the variables operationalized in a study represent the abstract constructs they are supposed to represent. Overall, it is common for researchers to use the term generalization to refer to external validity in a broad sense. Generalization pertains to various aspects of a research design, including participants, settings, measurements, and experimental treatments. This entry ...

locked icon

Sign in to access this content

Sign in

Get a 30 day FREE TRIAL

  • What is the term for the group you generalize to in your study?

    Watch videos from a variety of sources bringing classroom topics to life

  • Read modern, diverse business cases

  • Explore hundreds of books and reference titles

sign up today!

What do you call the group that you want to generalize?

The group you wish to generalize to is often called the population in your study.

What do you call a group of people in a study?

Sampling is the process of selecting a representative group from the population under study. The target population is the total group of individuals from which the sample might be drawn. A sample is the group of people who take part in the investigation. The people who take part are referred to as “participants”.

What do you call a sample group?

In statistics, a sample group can be defined as a subset of a population. The population, or target population, is the total population about which information is required.

What is the term for the entire group you want to study in a survey?

A population is the entire group that you want to draw conclusions about. A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.