Correlational studies investigate whether certain characteristics of a population vary depending on whether the subjects have been exposed to a given event. In eHealth, correlational studies are often conducted to determine if the use of an eHealth system is associated with a particular set of user characteristics and/or quality of care patterns. For example, a computerized provider order entry (cpoe) study compared the background, usage, and performance of clinical users and non-users after the system was implemented in a hospital.
Comparative studies differ from correlational studies in that the evaluator does not control the allocation of subjects into comparison groups or the assignment of interventions to specific groups. Instead, the evaluator defines a set of variables including an outcome of interest, and tests for hypothesized relationships among them. The outcome is called the dependent variable, while the independent variables are those that are being tested for association.
Comparative studies are similar to correlational studies in that the variables can be defined, measured, and analyzed for potential relationships. In this way, correlational studies face the same challenges as comparative studies in terms of their internal and external validity. Design choices, selection bias, confounders, and consistency of reporting are of particular importance. In this article, we describe the basic types of correlational studies found in the eHealth literature and their methodological considerations. Three examples are provided to illustrate how these studies are conducted.
Correlational Study Or Research
Correlational research is a non-experimental method of studying the relationship between two variables using statistical analysis. Correlational research does not examine the effects of extraneous variables on the variables being studied.
The purpose of a correlational study in market research is generally to identify patterns, trends, or insights between consumer behavior and market variables, such as advertisements, promotions, or discounts on products.
Correlational research can be applied to all kinds of quantitative data sets, but it is most commonly employed for market research. In correlational analysis, researchers find it useful to examine Customer Effort Score Surveys and its association with sales; Customer Experience Surveys (CX) and their relationship with customer loyalty, as well as Net Promoter Score Surveys and their correlation with brand image or management.
The many relevant questions in these surveys make them ideal for correlational research. Correlational studies assist market researchers in identifying variables and determining how they interact.
Types of Data Collection Methods In Correlational Study Or Research
Three types of correlational research exist naturalistic observation, survey research, and archival research. Each has its own advantages and disadvantages.
In naturalistic observation, variables of interest are observed and recorded in a natural setting without interference or manipulation.
- Research ideas can be inspired by this study
- Option if lab experiment is not available
- Variables in a natural setting
- It can be time-consuming and expensive
- Extraneous variables cannot be controlled
- Variables are not scientifically controlled
- Observed subjects may behave differently if they are aware that they are being observed
This method is well suited to studies where researchers want to examine how variables behave in their natural settings. These observations can then serve as a source of inspiration for future avenues of research.
For example, if access, resources, or ethics prevent scientists from conducting laboratory experiments, this may be the only method available to them. This method can be costly and takes a long time, but it might be better than not being able to conduct research at all.
Naturalistic observation presents several challenges for researchers. For one, it does not allow them to control or influence the variables in any way nor can they change any possible external variables.
However, this does not mean that researchers will get accurate data from watching the variables, or that the data they gather will be biased-free.
Study subjects may act differently if they know they are being observed. Researchers might not understand that the behavior that they are observing is not necessarily the subject’s natural state (i.e., how they would act if they did not know they were being watched).
It is also important for researchers to be aware of their biases, which can affect how they observe and interpret a subject’s behavior.
The Survey Method
Surveys and questionnaires are among the most common methods used in psychological research. The survey method involves having a random sample of participants complete a survey, test, or questionnaire related to the variables of interest. Random sampling is vital to the generalizability of a survey’s results.
- Fast, easy, and cheap
- Easily collects large amounts of data in a short period of time
- The results of surveys can be affected by poor survey questions
- An unrepresentative sample can affect results
- Participants can influence outcomes
Whenever researchers need a large amount of data in a short period of time, surveys are likely to be the fastest, easiest, and cheapest option.
This method is flexible because it allows researchers to create data-gathering tools that will ensure they get the data they need (survey responses) from all the sources they want to use (a random sample of those taking the survey).
Although survey data might be cost-effective and easy to obtain, it has some downsides. Data is not always reliable – especially if the survey questions are poorly written or the design and delivery are weak. Data can also be affected by specific biases, such as underrepresented or unrepresented samples.
The use of surveys relies on participants to provide useful data. Researchers need to be aware of the specific factors related to the people taking the survey that will affect its outcome.
Some people might have difficulty understanding the questions, for example. Answering a particular way may be an attempt to please the researchers or to control how the researchers perceive them (such as trying to make themselves appear better).
In some cases, respondents may not even realize that their answers are incorrect or misleading as a result of misremembering.
It is beneficial to conduct psychological research in many areas by analyzing studies conducted long ago by other researchers, as well as reviewing historical records and case studies.
In an experiment called “The Irritable Heart,” researchers used digitalized records containing information on American Civil War veterans to study post-traumatic stress disorder (PTSD).
- There is a large amount of data
- It can be less expensive
- Participants cannot be changed by researchers
- It is possible that information is missing
- Data collection methods are not under our control
It can be helpful for researchers who do not have much money to support their research efforts to use records, databases, and libraries that are publicly accessible or accessible through their institutions.
Free and low-cost resources are available to researchers at all levels through academic institutions, museums, and data repositories around the world.
Another potential benefit is that these sources often provide a vast amount of data that was collected over a very long period of time, which can allow researchers to view trends, relationships, and outcomes that are relevant to their research.
The inability to change variables can be a disadvantage of some methods, but it can also be an advantage of archival research. Nonetheless, using historical records or information that was collected a long time ago also presents challenges. There might be missing or incomplete information in older studies, and some aspects of the studies might not be relevant to researchers today.
The primary issue with archival research is reliability. When reviewing old research, little information may be available about who conducted the research, how the study was designed, who participated, and how data was collected and interpreted.
Similarly, researchers can find themselves in ethical dilemmas – should they use data from unethical or questionable studies?
Different Types Of Correlational Study Or Research Outputs
- Positive correlation: A positive correlation indicates a positive relationship between two variables. The other variable also increases as one variable increases in this kind of relationship. An individual’s income is positively correlated with the number of cars they own. The higher the income, the more cars they own.
- Negative correlation: A positive correlation indicates a positive relationship between the two variables. A positive correlation occurs when one variable increases and a negative correlation occurs when the other variable decreases. Life satisfaction and stress, for example, are negatively correlated. Thus, as stress increases, life satisfaction decreases.
- Zero correlation: A zero correlation indicates that there is no relationship between two variables. If one variable changes, the other variable does not change. The relationship between intelligence and height is a good example of zero correlation. The height of an individual does not affect their intelligence.
Characteristics Of Correlational Study Or Research
There are three main characteristics of correlational research. These are:
- Non-experimental: A correlational study is a non-experimental study. A scientist does not have to manipulate variables with a scientific methodology to agree or disagree with a hypothesis. The researcher simply measures and observes the relationship between variables, without altering or conditioning them in any way.
- Backward-looking: Correlational research focuses only on historical data and observes past events. It assesses the relationship between two variables based on historical patterns. The correlation between two variables may be positive in a correlational study, but that could change in the future.
- Dynamic: There is always a change in the patterns between two variables from correlational research. A negative correlation between two variables in the past can have a positive correlation in the future depending on a variety of factors.
Correlational Research Design With Example
A correlational study is ideal for gathering data quickly from natural settings. This helps you generalize your findings to real-life situations in a valid way.
There are a few situations in which correlational research is appropriate.
- To investigate non-causal relationships: You want to find out if there is an association between two variables, but you don’t expect to find a causal relationship between them. Correlational research can provide insights into complex real-world relationships, helping researchers develop theories and make predictions.
As an example: If you want to find out whether people who vote for one party also have children. You don’t believe more children cause people to vote differently – it’s more likely that both are influenced by other factors, such as age, religion, ideology, and socioeconomic status. A strong correlation, however, may be useful in making predictions about voting patterns.
- To explore causal relationships between variables: You think there is a causal relationship between two variables, but it is impractical, unethical, or too costly to conduct experimental research that manipulates one of the variables. Correlational research can provide initial indications or additional support for theories about causal relationships.
As an example, you want to know whether greenhouse gas emissions cause global warming. Though it is impossible to perform an experiment that controls global emissions over time, you can show a strong correlation through observation and analysis.
- To test new measurement tools: You have developed a new instrument for measuring your variable, and you need to test its reliability or validity. Correlational research can be used to assess whether a tool consistently or accurately captures the concept it aims to measure.
During lockdowns, you develop a new scale to measure loneliness among young children based on anecdotal evidence. If you want to validate this scale, you should test whether it actually measures loneliness. You collect data on loneliness using three different measures, including the new scale and test the degree of correlation between them. High correlations indicate that your scale is valid.
Correlational Research Topics
- Correlation between quantitative traits and correlation between corresponding LOD scores: detection of pleiotropic effects.
- A Quantitative Correlational Study of Teacher Preparation Program on Student Achievement.
- Emotional Intelligence and Project Outcomes among Hispanics in Technology.
- Leadership Trust in Virtual Teams Using Communication Tools.
- Teachers’ Attitudes toward African American Vernacular English.
- Leadership Styles at Middle- and Early-College Programs.
- Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study.
- Correlates of stigma in adults with epilepsy: A systematic review of quantitative studies.
- Preparing Tomorrow’s Administrators: A Quantitative Correlation Study of the Relationship between Emotional Intelligence and Effective Leadership Practices.
- Student Proficiency in Spanish Taught by Native and Nonnative Spanish Instructors: A Quantitative Correlational Study.
- Are Quantitative Measures of Academic Productivity Correlated with Academic Rank in Plastic Surgery? A National Study.
- Semi-quantitative spectrographic analysis and rank correlation in geochemistry.
- A Quantitative Correlational Study of the Interaction between Assignment Response Times and Online Students’ Final Grades and Satisfaction.
- Quantitative Analysis of the Cervical Texture by Ultrasound and Correlation with Gestational Age.
- Correlation of quantitative sensorimotor tractography with clinical-grade of cerebral palsy.
- Correlation between quantitative and semiquantitative parameters in DCE-MRI with a blood pool agent in rectal cancer: can semiquantitative parameters be used as a surrogate for quantitative parameters?
- The quantitative study of the correlation between cerebellar retraction factors and hearing loss following microvascular decompression for hemifacial spasm.
- Quantitative fluorescence correlation spectroscopy on DNA in living cells.
- Correlation of visual in vitro cytotoxicity ratings of biomaterials with quantitative in vitro cell viability measurements.
- Correlative SEM SERS for quantitative analysis of dimer nanoparticles.
Examples Of Correlational Study Or Research
Several correlational research examples demonstrate how a correlational study may be carried out in order to determine a statistical trend pertaining to the variables under consideration. The following three examples illustrate correlational research in action.
- If wealthy people are less likely to be patient, you want to know why. From your experience, you think wealthy people are impatient. You need to establish a statistical pattern that proves or disproves your belief. Correlational research can be conducted in this case to identify a link between the variables.
- You want to know if there’s a correlation between how much people earn and how many children they have. People with more spending power don’t have more children than people with less spending power, do you?
Your belief is that how much people earn hardly determines the number of children they have. Even so, correlational research on both variables could reveal any relationship that exists between them.
- As a result of domestic violence, you believe there is a brain hemorrhage. Experiments cannot be conducted because deliberately exposing people to domestic violence would be unethical.
Correlational research can be used to determine whether victims of domestic violence suffer brain hemorrhage more often than non-victims.
Advantages Of Correlational Study Or Research
- A correlational study motivates and inspires researchers to ask relevant questions in the survey to assess the attitudes of customers.
- It allows researchers to identify the variables that have the strongest associations and make better decisions in the long run.
- Future research can also be guided by correlational studies.
- Researchers use correlational studies to determine the direction and strength of relationships among variables.
- Correlational research is easier to interpret, more cost-effective, and more applicable to day-to-day business decision-making.
Disadvantages Of Correlational Research
- Studies of correlation do not have the capacity to imply causality. They can only help us understand the relationship between two variables.
- The study does not ignore the possibility of other variables affecting the variables under study. Stress, for example, is not the only factor associated with happiness. Happiness is also affected by emotional intelligence, subjective well-being, and the quality of social relationships.
- Researchers cannot see the isolated effects of one variable on another using this technique.
In the definition of a correlational study, there is a crucial concept that you should have taken note of. It is the concept of a naturally occurring variable, which you’ll recall is a variable that was not manipulated by the researcher. In other words, correlational studies do not change experimental systems. Instead, they simply observe what is naturally happening (or what has happened).
As opposed to studies that, in essence, create variation artificially. Sometimes referred to as manipulative studies, these studies can also be called experimental studies. The purpose of these studies is to manipulate either the study environment or system in some way (introducing a change of some sort) in order to measure the effects the changes have had on a particular outcome.
Correlational Vs Experimental Study Or Research
In both correlational and experimental research, quantitative methods are used to study relationships between variables. But there are important differences between how data is collected and what conclusions you can draw.
What is an example of correlational research?
If there are multiple pizza trucks in the area and each one has a different jingle, we would memorize it all and relate the jingle to its pizza truck. This is what correlational research precisely is, establishing a relationship between two variables, “jingle” and “distance of the truck” in this particular example.
What are the 3 types of correlational studies?
There are three types of correlational research: naturalistic observation, the survey method, and archival research. Each type has its own purpose, as well as its pros and cons.
What is a correlational study vs experimental?
In correlational research, the researcher passively observes the phenomena and measures whatever relationship that occurs between them. However, in experimental research, the researcher actively observes phenomena after triggering a change in the behavior of the variables.
What are correlational design and an example?
A correlational research design investigates relationships between variables without the researcher controlling or manipulating any of them. A correlation reflects the strength and/or direction of the relationship between two (or more) variables.
How do you conduct a correlational study?
Here are five steps you can take to conduct a correlational study:
- Make a claim or create a hypothesis. Making a claim or a hypothesis is often the first step in any study.
- Choose a data collection method.
- Collect your data.
- Analyze the results.
- Conduct additional research.