Generalization is applied by researchers in the academic field. It can be defined as the extension of the results and conclusions of an investigation conducted in a population sample to the general population. Although the reliability of this extension is not absolute, it is statistically likely.
Since good generalization requires data on large populations, quantitative research—experimental, for example—provides the best basis for producing a broad generalization. The larger the population of the sample, the more the results can be generalized. For example, a comprehensive study of the role computers play in the writing process could reveal that students who make up most of the text on a computer are statistically likely to move more pieces of text than non-computer students.
Transferability is applied by the readers of the research. Although generalization usually applies only to certain types of quantitative methods, transferability can be applied to varying degrees to most types of research. Unlike generalization, transferability does not involve broad statements, but invites readers of research to make connections between the elements of a study and their own experience. For example, high school teachers might selectively apply to their own classrooms the results of a study showing that heuristic writing exercises help students at the college level.
Interrelations between Generalization and Transferability
Generalization and transferability are important elements of any research methodology, but they are not mutually exclusive. Generalization, to varying degrees, is based on the transferability of research results. It is important for researchers to understand the implications of these two aspects of research before designing a study. Researchers who intend to make a generalizable statement should carefully examine the variables involved in the study.
Among them are the sample of the population used and the mechanisms of formulation of a causal model. In addition, if researchers want the results of their study to be transferable to another context, they must maintain a detailed account of the environment surrounding their research, and include a rich description of that environment in their final report. With the knowledge that the sample population was large and varied, as well as detailed information about the study itself, readers of the research can generalize and transfer the results to other situations with greater confidence.
Definitions of Both Concepts
Generalization is not only common to research, but also to everyday life. In this section, we establish a practical definition of generalization as it applies within and outside of academic research. We also define and consider three different types of generalization and some of their likely applications. Finally, we look at some of the potential shortcomings and limitations of generalization that researchers should consider when preparing a study that they hope will produce potentially generalizable results.
In many ways, according to Shavelson et al (1991), generalization is nothing more than making predictions based on recurrent experience. If something happens frequently, we expect it to continue to happen in the future. Researchers use the same kind of reasoning when generalizing the results of their studies. Once researchers have collected enough data to support a hypothesis, a premise can be formulated about the behavior of that data. This is what makes it generalizable to similar circumstances. However, because of its basis in probability, such a generalization cannot be considered conclusive or exhaustive.
Although generalization can occur in informal and non-academic contexts, in academic studies it usually only applies to certain research methods. Quantitative methods allow for some generalization. Experimental research, for example, often produces generalizable results. However, this experimentation must be rigorous in order to obtain generalizable results.
Generalization Example 1
An example of generalization in everyday life is driving. Driving a car in traffic requires drivers to make assumptions about the likely outcome of certain actions. When approaching an intersection where a driver is about to turn left, the driver traversing the intersection assumes that the driver who is going to turn left will give way to him before turning. The driver passing through the intersection applies this assumption with caution, recognizing the possibility that the other driver may turn prematurely.
American drivers also generalize that everyone drives on the right side of the road. However, if we try to generalize this assumption to other environments, such as England, we will be making a potentially disastrous mistake. It is therefore clear that generalization is necessary to form coherent interpretations in many different situations. However, we don’t expect our generalizations to work the same way in all circumstances. With enough evidence we can make predictions about human behavior. At the same time we must recognize that our assumptions are based on statistical probability.
Generalization Example 2
Consider this example of generalizable research in the field of English studies. A study on instructors’ assessments of student composition could reveal that there is a strong correlation between the grade students expect to get in a course and the fact that they give their instructor high marks.
The study could find that 95% of students who expect to receive a “C” or less in their class give their instructor a grade of “average” or lower. Therefore, there would be a high probability that prospective students expecting a “C” or less would not give their instructor high marks. However, the results would not necessarily be conclusive. Some students might challenge the trend.
In addition, a number of different variables could also influence students’ assessments of an instructor. This includes instructor experience, class size, and relative interest in a particular topic. These variables – and others – would have to be addressed for the study to yield potentially valid results. However, even if virtually all variables were isolated, the results of the study would not be 100% conclusive. At best, researchers can make educated predictions of future events or behaviors, but not guarantee prediction in all cases. Therefore, before generalizing, the results should be tested by rigorous experimentation, which allows researchers to confirm or reject the premises that govern their dataset.
Types of Generalization
According to Babbie (1979), there are three types of generalization that interact to produce probabilistic models. All of them involve the generalization of a treatment or measurement to a population outside the original study. Researchers who wish to generalize their claims should attempt to apply all three forms to their research, or the strength of their claims will be weakened (Runkel and McGrath, 1972).
Generalization Type 1
In one type of generalization, researchers determine whether a specific treatment will produce the same results under different circumstances. To do this, they must decide whether an aspect within the original environment, a factor beyond the treatment, generated the particular result. In this way, the degree of flexibility with which the treatment adapts to new situations will be established. Greater adaptability means that treatment is generalizable to a greater variety of situations.
For example, let’s imagine a new set of pre-writing heuristic questions designed to encourage freshmen college students to consider the audience more fully. This works so well that students write fully developed rhetorical analyses of their target audiences. To responsibly generalize that this heuristic is effective, a researcher would have to test the same pre-writing exercise in a variety of educational settings at the university level, using different faculty, students, and settings. If the same positive results occur, treatment is generalizable.
Generalization Type 2
A second form of generalization focuses on measurements rather than treatments. For a result to be considered generalizable outside the test group, it must produce the same results with different forms of measurement. In terms of the heuristic example above, the results will be more generalizable if the same results are obtained when evaluated “with questions that have slightly different wording, or when we use a six-point scale instead of a nine-point scale” (Runkel and McGrath, 1972, p.46).
A third type of generalization concerns the subjects of the test situation. Although the results of an experiment may be valid internally, i.e. applicable to the group being tested, in many situations the results cannot be generalized beyond that particular group. Researchers who hope to generalize their results to a wider population should ensure that their test group is relatively large and chosen at random. However, researchers should take into account the fact that test populations of more than 10,000 subjects do not significantly increase generalizability (Firestone, 1993).
However carefully these three forms of generalizability are applied, there is no absolute guarantee that the results obtained in a study will occur in all situations outside the study. To determine causal relationships in a test environment, accuracy is of paramount importance. However, if researchers want to generalize their findings, scope and variance must take precedence over accuracy. Therefore, it is difficult to test accuracy and generalizability simultaneously, as focusing on one of them reduces the reliability of the other. One solution to this problem is to make a greater number of observations. This has a double effect: firstly, it increases the population of the sample, which increases generalizability. Second, accuracy can reasonably be maintained because random errors between observations will be averaged (Runkel and McGrath, 1972).
Transferability describes the process of applying research results in one situation to other similar situations. In this section, we establish a working practical definition of transferability as applied in and out of academic research. We also expose the important considerations that researchers must take into account in order for their results to be potentially transferable, as well as the fundamental role that the reader plays in this process. Finally, we look at the potential shortcomings and limitations of transferability that researchers should consider when planning and conducting a study that produces potentially transferable results.
Transferability is a process performed by research readers. Readers take note of the details of the research situation and compare them to the details of an environment or situation with which they are familiar. If there are enough similarities between the two situations, readers can deduce that the results of the research would be the same or similar in their own situation. To do so effectively, readers need to know as much as possible about the original situation of the research to determine if it is similar to yours.
The results of any type of research method can be applied to other situations. However, transferability is more relevant to qualitative research methods, such as ethnography and case studies. But, because they often only take into account one subject or one group, researchers conducting these studies rarely generalize the results to other populations. However, the detailed nature of the results makes them ideal for transferability.
Transferability is easy to understand considering that we constantly apply this concept to aspects of our daily lives. If, for example, you are an inexperienced composition teacher and read a study in which a veteran writing teacher found that extensive pre-writing exercises helped the students in his classes work on much more defined work topics, you might wonder to what extent the teacher’s classroom resembled yours. If there were many similarities, you could try to draw conclusions about how increasing pre-writing by your students would affect their ability to come up with sufficiently narrowed work topics. In doing so, he would be trying to transfer the techniques of the composition researcher to his own class.
Generalization and transferability: Synthesis
Generalization, according to Crocker and Algina (1986), allows us to form coherent interpretations in any situation and act with determination and effectiveness in everyday life. Transferability gives us the opportunity to analyze the methods and conclusions given to decide what to apply to our own circumstances. In essence, both generalization and transferability allow us to make comparisons between situations. For example, we can generalize that most people in America drive on the right side of the road, but we cannot translate this conclusion to England or Australia without finding ourselves in a treacherous situation. Therefore, it is important to always take into account the context when generalizing or transferring the results.
The fact that a study emphasizes transferability or generalization is closely related to the objectives of the researcher and the needs of the audience. Studies conducted for a magazine like Time or a newspaper tend toward generalization, as editors want to provide relevant information for a large portion of the population. A research project aimed at a small group of specialists studying a similar problem may emphasize transferability, as specialists in the field have the ability to transfer aspects of the study results to their own situations without overt generalizations provided by the researcher. Ultimately, the topic, audience, and objectives of the researcher will determine the method the researcher uses to conduct a study, which in turn will determine the transferability or generalizability of the results.
Comparison of generalization and transferability
Although generalization has been the preferred method of research for quite some time, transferability according to Fyans (1983) is a relatively new idea. However, in theory, it has always accompanied research topics. It is important to note that generalizability and transferability are not necessarily exclusive, but may overlap.
From an experimental study to a case study, readers transfer the methods, results, and ideas of the research to their own context. Therefore, a generalizable study can also be transferable. For example, a researcher may generalize the results of a survey of 350 people at a university to the entire university population; readers of the results can apply, or transfer, the results to their own situation.
They will wonder, basically, whether they enter the majority or not. However, a transferable study is not always generalizable. For example, in case studies, transferability allows readers the option of applying the results to external contexts, while generalizability is basically impossible because a person or a small group of people is not necessarily representative of the general population.
Controversy, value and function
Research in the natural sciences has a long tradition of valuing empirical studies; experimental research has been considered “the” way of conducting research. When social scientists adapted the research methods of the natural sciences to their own needs, they adopted this preference for empirical research. Therefore, studies that are generalizable have long been considered to be more valuable; the value of research was often determined based on whether a study was generalizable to a population as a whole. However, more and more social scientists realize the value of using a variety of research methods, and the value of transferability is recognized.
It is important to recognize that generalization and transferability alone do not determine the value of a study. They perform different functions in research, depending on the topic and objectives of the researcher. While generalizable studies usually indicate phenomena that apply to broad categories such as gender or age, transferability can provide part of the how and why of these results.
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Babbie, Earl R. (1979). The practice of social research. Belmont: Wadsworth Publishing Company, Inc.
Crocker, Linda & Algina, James. (1986). Introduction to classical & modern test theory. New York: Holt, Rinehart and Winston.
Fyans, Leslie J. (Ed.). (1983). Generalizability theory: Inferences and practical applications. In New Directions for Testing and Measurement: Vol. 18. San Francisco: Jossey-Bass.
Shavelson, Richard J. & Webb, Noreen M. (1991). Generalizability theory: A primer. Newbury Park, CA: Sage Publications.