Correlation Analysis Help

Correlation analysis is used to measure the strength and direction of the association between two or more variables. The following types of correlation are commonly used:

Pearson Correlation

Measures the linear relationship between two continuous variables. The Pearson correlation coefficient ranges from -1 to +1. A value of +1 indicates a perfect positive linear relationship, -1 indicates a perfect negative relationship, and 0 indicates no linear correlation.

Use when: The data is continuous and normally distributed, and there is a linear relationship between variables.

Spearman Rank Correlation

A non-parametric measure of correlation that assesses the relationship between two variables based on their ranks. It does not assume a linear relationship between the variables.

Use when: The data is ordinal, not normally distributed, or when you are unsure if there is a linear relationship.

Kendall's Tau Correlation

A non-parametric correlation, measuring the strength of association between two variables by comparing the ranks of the data.

Use when: The data is small, or there are ties in ranks.

Data Entry Instructions

Data Format: The data should be entered in tab or space-delimited format in the text area provided. Each column represents a variable (or character), and each row represents an observation. Ensure all variables have the same number of observations.

Example of Data Entry

In the text area for data, you may enter:

23 56 12
45 67 15
34 78 16
            

In the side text area for character names, you may enter:

Height
Weight
Age
            

Procedure for Using the Tool

1. Data Entry: Copy and paste your data into the first text area. Enter the corresponding variable names into the second text area.
2. Specify Inputs: Enter the total number of variables (characters), observations per variable, and the variable positions for analysis.
3. Choose the Correlation Type: Select the correlation type(s) you want to compute (Pearson, Spearman, or Kendall's Tau) by checking the corresponding boxes.
4. Submit for Analysis: Click on the Analyse button to generate the correlation analysis.

Example Workflow

1. Enter data in the main text area:

175 80 24
160 70 30
180 90 26
165 75 22
                

2. Enter character names in the side text area:

Height
Weight
Age
            

3. Specify:

• Total Variables: 3
• Observations per Variable: 4
• Variables Positions for Analysis: 1 2

4. Choose Pearson Correlation and Spearman Rank Correlation by selecting the corresponding checkboxes.

5. Click Analyse to get the results.

Error Handling

• Data validation: Ensure that the number of observations matches across all variables.
• Empty fields: You must provide values in all required fields. If any field is left empty, the form will alert you to complete it before submission.
• Numeric values: The variables and observations should only contain numeric values, and any non-numeric entries will trigger an error message.