Correlational Research Design
This type of research design is used for examining relationships between variables. A paper writer cannot manipulate or control any of them.
This correlation shows the strength of relationships or their direction. The direction can be positive or negative. You can also encounter a negative correlation.
| When the correlation is positive, the compared variables are changing in the same direction. For example: |
|---|
| ✔️ If you observe the increase in height, the weight will increase too. |
| When the correlation is positive, the compared variables are changing in the same direction. For example: |
| ✔️ If you observe the increase in height, the weight will increase too. |
| When the correlation is positive, the compared variables are changing in the same direction. For example: |
| ✔️ If you observe the increase in height, the weight will increase too. |
The Use of Correlational Research
You can use correlational research when you need to collect data from natural settings quickly. In this way, you can make generalizations about the findings related to real-life settings, too. These generalizations will be valid externally.
There are several situations where correlational research is quite helpful.
When You Do Research of Non-Causal Relationships
In this case, you do not need to find anything causal in the relationships between the two variables. You just use correlational research to help further researchers make predictions or formulate theories about some complex relationships in the real world.
You need to find out whether the number of children in families influence the way they vote in the elections. Obviously, you do not think that the political preferences of people are affected by the number of children. You are almost sure that there are other factors, which can influence this choice, such as ideology, religion, or socio-economic conditions. However, it is useful to check this factor as well to make valid predictions or exclude the factor completely.
When You Regard Causal Relationships Between Two Variables
There are situations when you believe that causal relationships between two variables do exist but you cannot do experimental research here because it looks unethical, costly, or too impractical. So, you need correlational research to make sure that there is enough support for a possible theory.
You want to correlate global warming and the emission of greenhouse gasses. It is clear that you cannot apply experimental research here. Though, you can make some observations and correlate them with the analysis to support this theory.
When You Need to Test New Measurement Instruments
If you have invented a new tool for measuring variables, you need to check how reliable and valid it is. You do correlational research here to identify the degree of consistency and accuracy your new instrument can show in revealing the concept you are investigating.
You have invented a new scale meant for measuring the level of excitement young kids can experience during lockdowns. You can use three measures to collect the data you need. One of them is your new scale, so you will see whether this scale is effective enough to measure excitement. In this way, you can test the correlation between various types of measurements and find out how high it is. If it is high, your scale is valid and worth using.
Collecting Correlational Data: Methods and Techniques
Correlational research can use different methods and techniques. Their choice depends on the field of study and complexity of research. For example, you can use secondary data, observations, and surveys in social science.
You need to know how to choose the research method correctly to be sure that it is the most valid and reliable one. That is why you have to select a representative sample carefully to minimize the bias in the data from the population you want to research.
Using Surveys
When you plan to measure the variables you are interested in, you can use questionnaires or surveys. You may have them online, in person, or by phone according to your needs and opportunities.
Surveys are effective for collecting data from many participants. However, be accurate in formulating questions to avoid bias and receive the most relevant insights.
If you want to research the relationship between the income and people’s food preferences, you have to send the questions about the diets to people from different social strata with various income brackets. In this way, you can identify, for instance, how much the income influences people who decide to become vegetarians.
Applying Naturalistic Observations
This is a kind of field research. You can collect information about the behavior or characteristics of a certain phenomenon in its natural environment. Here, you can use such techniques as counting, recording, categorizing, and describing events or actions. You can include both qualitative and quantitative elements of analysis here. Though, if you are looking for a correlation, a quantitative approach is more preferable. You can research amounts, frequencies, or durations in this case.
By using naturalistic observation, you will be able to generalize the data and relate them to the real-life context. The experiences you can test here are not available in the lab conditions. The minuses of this method include the researcher’s bias, which can affect the interpretation of results, or the analysis may be too time-consuming and lead to rather unpredictable results.
If you want to correlate gender and enthusiastic participation in class activities, you need to watch countless college classes and seminars, take notes about students’ participation in them, and categorize these contributions according to gender. Then, you make a statistical analysis and get the desired results.
Using Secondary Data
If you do not have time or means for collecting primary data, you can use the data that has been collected by someone else. For example, think about previous research in the field, polls, and official records.
This method is quite cheap and quick because you will not have to collect the data yourself. Though, you need to be careful about such data because it can be irrelevant, unreliable, or incomplete. You cannot control the validity of procedures used for collecting such data, either.
You have chosen to analyze the relationships between mental health of workers in a certain industry and the number of hours they have to work. For this purpose, you can use national records and statistics or previous research done in different countries. However, to make a relevant statistical analysis, you need to choose only official data which has been checked and verified already.
Ways of Analyzing Correlational Data
Data collection is only one stage of your research. Now, you need to analyze it and find relationships between variables. Regression or correlation analysis is helpful here. The types of correlation coefficients or regression indices are based on distributions and measurement levels.
Using Correlation Analysis
You need it for summarizing the relationship between the variables. The result is expressed by a correlation coefficient. It describes how strong and directed this relationship is. Then, you can calculate the degree of this relationship.
The most widely used correlation coefficient used in this case is the Pearson product-moment coefficient or Pearson’s r. It assesses the linear relationship between quantitative variables. However, you can also use the correlation coefficients for more than two variables.
What Regression Analysis Is
Here, you can predict the degree of influence of changes in one variable on changes in the other variable. On its basis, you can make up a regression equation. It is used to explain the line on the graph related to the variable you describe. This equation is useful for predicting the value of one variable compared to the values of other variables. The regression analysis is helpful after you have checked the correlation between the researched variables.
The Difference Between Causation and Correlation
When we speak about correlation, causation is excluded. Even if you can prove the correlation between two variables, you cannot say that one of them is the cause of the other.
What You Need to Know About Directionality
You cannot make any conclusions about the causation only because the two variables correlate with each other. Sometimes, one of them can be a cause and the other can be an effect but it is not mandatory. Correlation research is not meant to prove that.
If you have found out the correlation between vitamin D and levels of depression in people, you will never know whether the lack of vitamin D causes depression or depression causes reduced levels of vitamin D. The only thing you can find out is that there is a relationship between these two factors.
The Issue with the Third Variable
Sometimes, within the research process, the third variable appears. It is called a confounding variable, and it can affect other two variables. You may think that these variables are related while, in fact, they are not. On the other hand, there is a direct link between this third variable and each of the two variables under research, but they function separately.
Correlational research does not allow the direct control over external variables on part of a researcher, and it is its big minus. You may believe that you can control one confounder, but there are always some others that are hidden and unavailable for your control.
You may have found out already that people with more working hours have higher levels of stress and vice versa. Though, it does not mean that people with fewer working hours do not suffer from stress.
Here, you can find other variables that can influence the levels of stress, such as low income, poor working conditions or lack of safe workplaces. You can control these variables and still never conclude that only fewer working hours can reduce stress because all other variables make the statistical evidence less persuasive.
Everything said here means that correlational research cannot prove the causation. However, you can use this type of research for making out a causal hypothesis and further test it in an experimental way.
Final Thoughts
Now, when you know all the main features of correlational analysis and its difference from experimental and statistical research, you can choose the most appropriate method for your research and complete it with the best results.