Correlation analysis is the chosen method when conducting research to understand the relationship between variables. Correlation analysis in statistics can take the form of partial correlation analysis and multiple correlation analysis.
Researchers commonly employ partial correlation analysis to determine the associative relationship between variables. In partial correlation analysis, we only establish a connection between each pair of variables.
There are various options for correlation analysis. As researchers, we need to select the appropriate correlation based on the measurement scale of the variables.
Approach to Measuring Variables with a Likert Scale
The use of the Likert scale to measure variables is well-known among researchers. Referring to its founder, the Likert scale can be used to measure non-parametric variables.
As we all know, the measurement scale of variables can be divided into nominal, ordinal, interval, and ratio scales. Based on this scale classification, variables measured using nominal and ordinal scales fall under non-parametric.
Measuring variables using the Likert scale approach is carried out based on the principles of ordinal scale measurement. In ordinal scale measurement, categorization is performed, wherein categories have levels or degrees.
Through ordinal scale measurement, we can clearly distinguish between what is higher and what is lower compared to other categories. Therefore, this ordinal measurement scale is a level above nominal measurement scales.
Creating a Likert Scale
Variables measured using a Likert scale can be designed in various scales depending on the research objectives. Typically, a close-ended Likert scale is constructed on a 1 to 5 scale with the following scoring technique:
Strongly Agree = score 5
Agree = score 4
Neutral = score 3
Disagree = score 2
Strongly Disagree = score 1
Researchers formulate statement items that represent the measured variable. Each statement item has response choices ranging from “Strongly Agree” to “Strongly Disagree.”
Subsequently, researchers can assign scores to each respondent’s answers using the above scoring. Based on the scores obtained from the Likert scale, researchers can then utilize them for quantitative data analysis.
Which Variables Are Measured Using a Likert Scale?
After understanding the scoring technique for variables that use the Likert scale, the next step is comprehending the types of variables typically measured using this scale.
As mentioned in the previous paragraph, variables measured using the Likert scale fall under non-parametric variables. Examples of variables commonly measured using the Likert scale include behaviour, motivation, performance, attitudes, competencies, and many other variables.
For instance, direct numerical values cannot be obtained when measuring the motivation variable. Hence, the Likert scale approach can be employed to measure and quantitatively analyze the motivation variable.
The construction of Likert scale statement items is tailored to the variable being measured. It means there is no limit on the number of statement items researchers must create to measure the motivation variable.
Researchers must identify as many indicators and sub-indicators as possible representing the motivation variable. Subsequently, researchers need to conduct validity and reliability testing based on these statement items.
Based on the constructed Likert scale items, it’s crucial to ensure the questionnaire is valid and reliable before researchers use it. Therefore, before fieldwork, validity and reliability testing of the research questionnaire/instrument must be conducted.
Researchers can then eliminate statement items that are not valid. As a result, the questionnaire used in the field will consist of statement items that are both valid and reliable.
Correlation Analysis of Likert Scale Variables
As per the title of this article, what is the appropriate correlation analysis for variables measured using a Likert scale? As we know, the choice of correlation analysis is contingent on the measurement scale of the data.
When we read scientific articles multiple times, we might come across correlation analyses such as Pearson correlation, Spearman rank, Kendall’s tau, chi-square, and other types of correlation tests.
The selection of the correlation analysis method is based on the measurement scale of the variables. If we measure variables using the Likert scale approach, we use an ordinal measurement scale.
We can consider employing the Spearman rank correlation analysis for suitable correlation analysis on variables measured using an ordinal scale.
In the Spearman rank correlation analysis, the data is not required to follow a normal distribution. Hence, if the variables we are correlating are measured using the Likert scale, we can consider using the Spearman rank correlation.
Interpreting the Results of Correlation Analysis for Likert Scale Variables
After conducting a Spearman rank correlation analysis, the next step is understanding how to interpret the output. The first thing to consider is the p-value of the correlation coefficient. This value tests a statistical hypothesis, either accepting or rejecting the null hypothesis.
The second aspect to interpret is the direction of the correlation coefficient. The positive or negative sign of the correlation coefficient can determine the direction. A positive sign indicates a positive relationship between the correlated variables, while a negative sign suggests an inverse relationship.
The third aspect of interpretation involves examining the magnitude of the correlation coefficient. A larger correlation coefficient signifies a stronger relationship between the variables. Conversely, a smaller correlation coefficient indicates a weaker relationship between the tested variables.
Based on our previous discussion, we can conclude that variables measured using the Likert scale are equivalent to variables measured using an ordinal scale. Thus, the appropriate correlation test to choose and consider is the Spearman rank correlation.
Validity and reliability tests should be conducted before collecting data in the field for variables measured using the Likert scale. It ensures that the questionnaire or instrument used in the field is valid, reliable, and truly represents the measured variables.
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