The cornerstone of the research process is correlational research. It investigates the correlation among the variables without establishing the cause.
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The Foundation of Correlational Research
1.1 Understanding Correlation
The heart of correlation is correlational research. It is a statistical technique that gauges the degree of correlation between the variables. The range of correlation is from -1 (perfect negative correlation) to 1 (perfect positive correlation), while 0 indicates no correlation.
1.2 Differentiating Correlation from Causation
Correlation is different from causation. Correlation measures the correlation between the variables. It doesn’t imply causation. This fundamental concept needs to be highlighted by the researchers.
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Types of Correlational Study
2.1 Positive Correlation
Take the example of a study that measures that the frequency of exercise has a positive correlation with overall health. It indicates that as exercise increases, health becomes better, and vice versa.
2.2 Negative Correlation
Contrary to positive correlation, there is a concept of negative correlation. For example, a research study reveals that as the level of stress increases, academic performance decreases, and vice versa.
2.3 Zero Correlation
In some of these situations, zero correlation also occurs. For example, a study might find no correlation between sleep and creativity levels, which indicates a zero correlation between these two variables.
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Significance of Correlational Study
3.1 Real-World Applications
The concept of correlational research is applied in many fields, such as psychology, economics, education, sociology, and health.
3.2 Predictive Power
In certain situations, correlational research is used to predict the outcomes. For example, how can the relationship between academic achievement and habits of study help the experts in education identify students at risk of falling behind? So, correlational research can prove to be a tool to predict the results.
Steps for conducting correlational research
1. Data collection:
In the beginning of correlational research, the researcher collects the data from the respondents. For example, the data may be about their academic performance, like grades and test scores, and their study habits.
2. Data analysis:
After the collection of data, the next step is the analysis of the data by using the correlational coefficient. The most commonly used technique is the Pearson correlation coefficient, which quantitatively finds the strength and direction of the relationship among the variables.
Statistical Methods Used In Correlational Research |
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Statistical Methods |
Type of Data |
When to Use |
| 1. Pearson’s Correlation Coefficient (Pearson’s r):
This coefficient calculates the linear relationship between continuous variables. It ranges between -1 and +1.
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The Pearson correlation is used when we have two continuous variables (ratio or interval scales) which show linear relationship. | The correlation is used to assess the relationship between variables having linear relationship. For instance, Pearson correlation is used to find association between ice cream sales and temperature. |
| 1. Spearman’s Rank Correlation Coefficient (Spearman’s ρ or rho):
This correlation calculates the direction and strength of correlation among the two variables. This correlation coefficient is suitable for both ordinal and continuous variables. Its range is from -1 to +1. |
When the researcher has ordinal or when the relationship between variables is monotonic but not necessarily linear. | . It indicates that when a non-linear relationship exists between the variables or when the information is ranked rather than measured on a continuous scale. For example, the correlation can be found between exam ranks and students’ study hours. |
| 2. Kendall’s Tau (τ):
This is a non-parametric correlation coefficient and measures the relationship between the two ordinal variables. It calculates the similarity of rankings or orders of data points. This coefficient also ranges from -1 to +1. |
When there are two ordinal variables or when one wants to calculate the link between the ranked information. | This coefficient is suitable in situations where the researcher wants to assess the direction and strength of correlation among the two variables, specifically when the data is normally distributed or when the researcher wants to minimize the influence of outliers. |
3. Point-Biserial Correlation Coefficient:The situation in which there is one continuous variable and the other is a dichotomous variable requires the use of point-Biserial correlation coefficient. |
When one is a continuous variable and the other is dichotomous variable
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It is beneficial when the researcher intends to find the relationship between a binary categorical and continuous variable. |
| 4. Biserial Correlation Coefficient:
This coefficient is used when one variable is dichotomous and the other is a continuous variable. But it considers that there is a normal distribution of the continuous variables. |
It considers the normal distribution of the continuous variables. | When there are normally distributed continuous variables along with a dichotomous variable, the researcher wants to measure the relationship. |
| 5. Cramer’s V:
This coefficient is used to analyse the correlation between the categorical variables. |
When the researcher has two categorical variables in the contingency table, then we use Cramer’s V. | The environment in which there are categorical variables, like gender, and mode of transportation (car, bus, or train). |
| 6. Phi Coefficient (ϕ):
When both variables are binary (dichotomous),. |
This coefficient is used when there are two binary variables. | It is used to measure the correlation between the two dichotomous variables. |
| 7. Bivariate Correlation Ratio (Eta, η):
When one variable is continuous and the other is categorical, then this variable is used. |
When one variable is continuous and the other is categorical, it has more than two categories. |
When one variable is continuous and the other is categorical, having more than two categories |
Interpretation of the results of correlation:
In the third step, the researcher analyses the correlation coefficients. Positive values indicate a positive correlation, whereas negative values show a negative correlation among the variables. On the other hand, no correlation is indicated by the value “0.”
When to Use Correlational Study
The correlation research is an important approach which used in various disciplines when we used relationship among various variables. Here are some situations when correlational research is particularly useful:
5.1 Exploration of Associations:
The correlation is used to explore the relationship between two or more variables. This research is more valuable when the researcher has not developed any specific hypothesis regarding causation but intends to explore the connections between the variables.
5.2 Prediction:
Correlation can also be used for prediction of the outcomes. For instance, the correlation can be used to predict how the variations in one variable can impact the other variables. This prediction is useful in economics, finance, and forecasting.
5.3 Identifying Trends:
The correlation also helps in revealing the directions of the movement of the variables. Thus it provides it provides insights into the essential trends.
5.4 Preliminary Research:
In the initial phase of research, this research provides help in generating the hypotheses and guides us in further investigations.
5.5 Ethical Considerations:
When the manipulation of the variables is not considered ethical, the correlational research helps the researchers to study the naturally occurring relationship without intervention.
5.6 Longitudinal Studies:
When studies are conducted for a longer period of time, then the correlation can be effectively used to analyse the correlation among the variables across different points of time.
5.7 Comparative Studies:
In situations of cross-cultural research, help is provided by correlational research to examine variables to relate to one another across various regions and groups.
5.8 Complex Systems:
In many areas such social sciences, in which numerous factors affect the results, correlational research helps to unravel the complexities of systems.
5.9 Construct Validation:
The situations of validation of new research tools or those of existing ones, the correlation can provide help.
5.10 Resource Constraints:
As correlational research doesn’t require experimental manipulation, it can be practical choice in situations of limited resources, time.