What Is Generalizability In Research?
By Moradeke Owa
| Last updated:
Dec 13
|
13 min read
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Generalizability is making sure the conclusions and recommendations from your research apply to more than just the population you studied. Think of it as a way to figure out if your research findings apply to a larger group, not just the small population you studied.
In this guide, we explore research generalizability, factors that influence it, how to assess it, and the challenges that come with it.
So, let’s dive into the world of generalizability in research!
Generalizability refers to the extent to which a study’s findings can be extrapolated to a larger population. It’s about making sure that your findings apply to a large number of people, rather than just a small group.
Generalizability ensures research findings are credible and reliable. If your results are only true for a small group, they might not be valid.
Also, generalizability ensures your work is relevant to as many people as possible. For example, if you were to test a drug only on a small number of patients, you could potentially put patients at risk by prescribing the drug to all patients until you are confident that it is safe for everyone.
Here are some of the factors that determine if your research can be adapted to a large population or different objects:
1. Sample Selection and Size
The size of the group you study and how you choose those people can affect how well your results can be applied to others. Think of it like asking one person out of a friendship group of 16 if a game is fun, doesn’t accurately represent the opinion of the group.
2. Research Methods and Design
Different methods have different levels of generalizability. For example, if you only observe people in a particular city, your findings may not apply to other locations. But if you use multiple methods, you get a better idea of the big picture.
3. Population Characteristics
Not everyone is the same. People from different countries, different age groups, or different cultures may respond differently. That’s why the characteristics of the people you’re looking at have a significant impact on the generalizability of the results.
4. Context and Environment
Think of your research as a weather forecast. A forecast of sunny weather in one location may not be accurate in another. Context and environment play a role in how well your results translate to other environments or contexts.
You can only generalize a study when it has high validity, but there are two types of validity- internal and external. Let’s see the role they play in generalizability:
1. Understanding Internal Validity
Internal validity is a measure of how well a study has ruled out alternative explanations for its findings. For example, if a study investigates the effects of a new drug on blood pressure, internal validity would be high if the study was designed to rule out other factors that could affect blood pressure, such as exercise, diet, and other medications.
2. Understanding External Validity
External validity is the extent to which a study’s findings can be generalized to other populations, settings, and times. It focuses on how well your study’s results apply to the real world.
For example, if a new blood pressure-lowering drug were to be studied in a laboratory with a sample of young healthy adults, the study’s external validity would be limited. This is because the study doesn’t consider people outside the population such as older adults, patients with other medical conditions, and more.
3. The Relationship Between Internal and External Validity
Internal validity and external validity are often inversely related. This means that studies with high internal validity may have lower external validity, and vice versa.
For example, a study that randomly assigns participants to different treatment groups may have high internal validity, but it may have lower external validity if the participants are not representative of the population of interest.
Several strategies enable you to enhance the generalizability of their findings, here are some of them:
1. Random Sampling Techniques
This involves selecting participants from a population in a way that gives everyone an equal chance of being selected. This helps to ensure that the sample is representative of the population.
Let’s say you want to find out how people feel about a new policy. Randomly pick people from the list of people who registered to vote to ensure your sample is representative of the population.
2. Diverse Sample Selection
Choose samples that are representative of different age groups, genders, races, ethnicities, and economic backgrounds. This helps to ensure that the findings are generalizable to a wider range of people.
3. Careful Research Design
Meticulously design your studies to minimize the risk of bias and confounding variables. A confounding variable is a factor that makes it hard to tell the real cause of your results.
For example, you are studying the effect of a new drug on cholesterol levels. Even if you take a random sample of participants and randomly select them to receive either a new drug or placebo if you don’t control for the participant’s diet, your results could be misleading. You could be attributing cholesterol balance to drugs when it is due to their diet.
4. Robust Data Collection Methods
Use robust data collection methods to minimize the risk of errors and biases. This includes using well-validated measures and carefully training data collectors.
For instance, an online survey tool could be used to conduct online polls on how voters change their minds during an election cycle rather than relying on phone interviews, which would make it harder to get repeat voters to participate in the study and review their views over time.
Sample bias happens when the group you study doesn’t represent everyone you want to talk about. For example, if you’re researching ice cream preferences and only ask your friends, your results might not apply to everyone because your friends are not the only people who take ice cream.
2. Ethical Considerations
Ethical considerations can limit your research’s generalizability because it wouldn’t be right or fair. For example, it’s not ethical to test a new medicine on people without their permission.
3. Resource Constraints
Having a limited budget for a project also restricts your research’s generalizability. For example, if you want to conduct a large-scale study but don’t have the resources, time, or personnel, you opt for a small-scale study, which could make your findings less likely to apply to a larger population.
4. Limitations of Research Methods
Tools are just as much a part of your research as the research itself. If you an ineffective tool, you might not be able to apply what you’ve learned to other situations.
Evaluating generalizability allows you to understand the implications of your findings and make realistic recommendations. Here are some of the most effective ways to assess generalizability:
Several statistical tools and methods allow you to assess the generalizability of your study. Here are the top two:
A confidence interval is a range of values that is likely to contain the true population value. So if a researcher looks at a test and sees that the mean score is 78 with a 95% confidence interval of 70-80, they’re 95% sure that the actual population score is between 70-80.
The p-value indicates the likelihood that the results of the study, or more extreme results, will be obtained if the null hypothesis holds. A null hypothesis is the supposition that there is no association between the variables being analyzed.
A good example is a researcher surveying 1,000 college students to study the relationship between study habits and GPA. The researcher finds that students who study for more hours per week have higher GPAs.
The p-value below 0.05 indicates that there is a statistically significant association between study habits and GPA. This means that the findings of the study are not by coincidence.
Reviewers and experts can look at sample selection, study design, data collection, and analysis methods to spot areas for improvement. They can also look at the survey’s results to see if they’re reliable and if they match up with other studies.
Clearly and concisely report the survey design, sample selection, data collection methods, data analysis methods, and findings of the survey. This allows other researchers to assess the quality of the survey and to determine whether the results are generalizable.
Generalizability refers to the degree to which the findings of a study can be applied to a larger population or context. Specificity, on the other hand, refers to the focus of a study on a particular population or context.
a. When Generalizability Matters Most
Generalizability comes into play when you want to make predictions about the world outside of your sample. For example, you want to look at the impact of a new viewing restrictions policy on the population as a whole.
b. Situations Where Specificity is Preferred
Specificity is important when researchers want to gain a deep understanding of a specific group or phenomenon in detail. For example, if a researcher wants to study the experiences of people with a rare disease.
Finding the Right Balance Between Generalizability and Specificity
The right balance between generalizability and specificity depends on the research question.
Case 1- Specificity over Generalizability
Sometimes, you have to give up some of their generalizability to get more specific results. For example, if you are studying a rare genetic condition, you might not be able to get a sample that’s representative of the population.
Case 2- Generalizability over Specificity
In other cases, you may need to sacrifice some specificity to achieve greater generalizability. For example, when studying the effects of a new drug, you need a sample that includes a wide range of people with different characteristics.
Keep in mind that generalizability and specificity are not mutually exclusive. You can design studies that are both generalizable and specific.
Here are a few real-world examples of studies that turned out to be generalizable, as well as some that are not:
1. Case Studies of Research with High Generalizability
We’ve been talking about how important a generalizable study is and how to tell if your research is generalizable. Let’s take a look at some studies that have achieved this:
This is a long-running study that has been tracking the health of over 15,000 participants since 1948. The study has provided valuable insights into the risk factors for heart disease, stroke, and other chronic diseases
The findings of the Framingham Heart Study are highly generalizable because the study participants were recruited from a representative sample of the general population.
b. The Cochrane Database of Systematic Reviews
This is a collection of systematic reviews that evaluate the evidence for the effectiveness of different healthcare interventions. The Cochrane Database of Systematic Reviews is a highly respected source of information for healthcare professionals and policymakers.
The findings of Cochrane reviews are highly generalizable because they are based on a comprehensive review of all available evidence.
2. Case Studies of Research with Limited Generalizability
Let’s look at some studies that would fail to prove their validity to the general population:
Research generalizability has significant effects in the real world, here are some ways to leverage it across different fields:
1. Medicine and Healthcare
Generalizability is a key concept of medicine and healthcare. For example, a single study that found a new drug to be effective in treating a specific condition in a limited number of patients might not apply to all patients.
Healthcare professionals also leverage generalizability to create guidelines for clinical practice. For example, a guideline for the treatment of diabetes may not be generalizable to all patients with diabetes if it is based on research studies that only included patients with a particular type of diabetes or a particular level of severity.
2. Social Sciences
Generalizability allows you to make accurate inferences about the behavior and attitudes of large populations. People are influenced by multiple factors, including their culture, personality, and social environment.
For example, a study that finds that a particular educational intervention is effective in improving student achievement in one school may not be generalizable to all schools.
3. Business and Economics
Generalizability also allows companies to conclude how customers and their competitors behave. Factors like economic conditions, consumer tastes, and tech trends can change quickly, so it’s hard to generalize results from one study to the next.
For example, a study that finds that a new marketing campaign is effective in increasing sales of a product in one region may not be generalizable to other regions.
Let’s take a look at new and future developments geared at improving the generalizability of research:
1. Evolving Research Methods and Technologies
The evolution of research methods and technologies is changing the way that we think about generalizability. In the past, researchers were often limited to studying small samples of people in specific settings. This made it difficult to generalize the findings to the larger population.
Today, you can use various new techniques and technologies to gather data from a larger and more varied sample size. For example, online surveys provide you with a large sample size in a very short period.
2. The Growing Emphasis on Reproducibility
The growing emphasis on reproducibility is also changing the way that we think about generalizability. Reproducibility is the ability to reproduce the results of a study by following the same methods and using a similar sample.
For example, you publish a study that claims that a new drug is effective in treating a certain disease. Two other researchers replicated the study and confirmed the findings. This replication helps to build confidence in the findings of the original study and makes it more likely that the drug will be approved for use.
3. The Ongoing Debate on Generalizability vs. Precision
Generalizability refers to the ability to apply the findings of a study to a wider population. Precision refers to the ability to accurately measure a particular phenomenon.
For some researchers, generalizability matters more than accuracy because it means their findings apply to a larger number of people and have an impact on the real world. For others, accuracy matters more than generalization because it enables you to understand the underlying mechanisms of a phenomenon.
The debate over generalizability versus precision is likely to continue because both concepts are very important. However, it is important to note that the two concepts are not mutually exclusive. It is possible to achieve both generalizability and precision in research by using carefully designed methods and technologies.
Generalizability allows you to apply the findings of a study to a larger population. This is important for making informed decisions about policy and practice, identifying and addressing important social problems, and advancing scientific knowledge.
With more advanced tools such as online surveys, generalizability research is here to stay. Sign up with Formplus to seamlessly collect data from a global audience.
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