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Correlational Research

An Insightful Guide To Correlational Research!

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Correlational Research
 John  19 Sep, 2020  Wriitng Service

Correlational Research

Correlational research is a potent avenue for acquiring and analyzing information. It is a non-experimental research method wherein researchers investigate & assess the statistical relationships between two research variables without controlling any influencers or the variables involved.

This write-up dwells deep into the intricacies of the correlational research design.

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Defining Characteristics of Correlational Research Design

Correlational research designs are effective on all kinds of quantitative data sets. For example, widely used in market research, another key reason for utilizing correlational research is when variable manipulation is impossible or unethical. In such cases, using correlational studies, ascertaining statistical relationships between two variables involves just measuring instances.

The basic characteristics of correlational research are as follows:

  • Correlational analysis unearths relations among the behavior, occurrences, and other aspects of concerned research variables naturally. Hence, correlational research can help validate & investigate the reliability of measurements. A standard observation acts as the reference for validating another.

For example,

Researchers might evaluate the validity of a brief extraversion test by tallying with the results & nature of another validated extraversion test. The researcher can check to see if the new test scores have a strong correlation with the results of the validated study. As neither test scores affect the other, there are no independent variables to manipulate.

There are no dependent and independent variables in correlational research per se. The outcomes themselves are dependent variables, while those under test are the independent variables.

  • A key correlational research characteristic is that they have higher external validity than experimental studies. This means that the findings of correlational research can be generalized to other situations, people, scenarios, etc.

The downside is that they have low internal validity since variables cannot be manipulated. As the researchers cannot manipulate or control the variables of an experiment, the results are more likely to reflect real-world relationships.

  • Engaging in correlational research generally involves a trade-off between the benefits & the drawbacks. However, using correlational study in tandem with experiments having high internal validity can provide converging evidence regarding a theory.

If a theory is supported by the findings of an actual experiment with high internal validity, then correlational research can boost the confidence in the assumptions achieved.

  • From a statistical standpoint, correlation research can help compare the characteristics & aspects of two different populations. For example, populations exposed to a specific event of interest are compared against those that have not been.
  • Correlational studies differ substantially from comparative studies. This is because evaluators cannot manipulate the concerned variables and need to study subjects in a naturalistic setting. They cannot control the allocation of subjects or the interventions & factors involved in the study. Instead, evaluators define a particular set of variables and an outcome of interest and then test the hypothesized relationships among them.
  • Correlational studies are objectivist. Variables can be studied, analyzed, and defined concerning their hypothesized relationships. As such, correlational studies suffer from similar issues to those of comparative studies, with selection bias, design choices, confounders, and consistency of reporting, as key constraints.

Those were the most significant characteristics of the correlational research design.

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Types of Correlational Studies

As discussed, correlational research designs ascertain the population's features, properties, and characteristics.

One of the most prevalent fields of application is epidemiology, studying disease distributions and determinants in a certain population. We will use the context of epidemiological studies to look at the three different types of correlational research.

  • Cohort Studies: Here, a sample of subjects is observed over time. Those exposed or not exposed to a certain determinant are compared via one or more predefined outcomes. Most cohort studies are prospective, where subjects are observed for a particular period.

Specific comparisons are made at the beginning of the study as baseline measures and then repeated overtime at predefined time intervals to evaluate differences & reveal trends. Most cohort studies involve a single group of subjects and describe subject characteristics through variables.

  • Cross-Sectional Studies: These studies are somewhat akin to cohort studies, with only a single comparison between the different subjects classes. Nevertheless, they provide a glimpse of the outcomes and the associated characteristics of the cohort.
  • Case-Control Studies: Subjects in a sample exposed to a certain event of interest are compared with their unexposed counterparts. Their differences are then compared with a predefined outcome. Case-control studies are generally retrospective, and historical cases refer to tallying similar subject characteristics.

Next up, let’s look at the nuances of data collection in the correlational research method.

Data Collection in Correlational Research

Correlational studies allow researchers to evaluate the statistical relationship without engaging in any expansive experiment. Researchers are generally not interested in determining causal relationships; correlational strategies aid in describing and predicting the strength & direction of the variables involved.

Correlational studies' most prominent defining feature is that no variables are manipulated. Irrespective of the acquisition details, no variable manipulation is allowed during the assessment of relationships. No independent variable manipulation helps researchers link the information between two different tasks or samples.

Visualizing Correlated Data

The most prominent manner of visually representing the correlation among variables is through scatterplots. Scatterplots reveal the underlying relationships among the variables, which can primarily be of three kinds.

  • Positive Correlation: A positive correlation is indicative of a positive or direct proportionality between the variables involved. In this case, when a variable increases, the other variable also increases. A simple positive correlational study example would relate the number of cars a person owns with their income. The more a person earns, the more cars they can own.
  • Negative Correlation: Negative correlation indicates a negative relationship between variables. Stress and life satisfaction are negatively correlated with one another.
  • No correlation: Here, changes in a variable do not affect any other variable. For example, one's height does not correlate with one's intelligence.

As we wrap up, let’s mull over the fundamental ideas of correlational research.

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Reiterating the Key Ideas

  • Naturalistic Observations: Subjects or participants are observed in their natural environment. Correlational studies are akin to field studies, and researchers collect data as unobtrusively as possible. Researchers want to view subjects in their natural state as they do not intend to influence their behavior.
  • Archival Data as Reference: Existing studies or findings refer to correlational studies. Information from naturalistic observations is compared with more straightforward info from natural observations.
  • Correlation DOES NOT Imply Causation: It can be that the other variable does not influence a variable under study, or they have an inverse relationship. There is no manipulation of any variable involved.
  • Key Benefits: While correlational research has certain drawbacks, there are prominent benefits. Establishing reliability & validity of an assumption, providing converging evidence, describing relationships, if any, and making predictions count as enormous benefits.

And that finally wraps up this content. Hope this write-up acts as a handy guide to correlational research design. Bookmark this write-up for some quick reference any time you need it.

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