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Correlational analysis is a statistical method used to determine the relationship between two variables. It identifies positive, negative, or zero correlations and uses the correlation coefficient 'r' to quantify their strength. Scatterplots visually represent these relationships, and while correlational research is insightful, it cannot establish causality.

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## Definition and Purpose

### Statistical Technique

Correlational analysis is a statistical technique used to assess the degree and direction of association between two variables

### Non-Manipulated Variables

This method is used when controlled experimentation is not possible due to ethical or practical constraints

### Test-Retest Reliability Studies

Correlational analysis is commonly used to evaluate the consistency of measurements, such as in test-retest reliability studies for scales or questionnaires

## Types of Correlation

### Positive Correlation

A positive correlation occurs when an increase in one variable corresponds with an increase in another

### Negative Correlation

A negative correlation means that an increase in one variable is associated with a decrease in the other

### Zero Correlation

Zero correlation indicates no apparent relationship between the variables

## Correlation Coefficient

### Definition and Range

The correlation coefficient, symbolized by 'r', is a numerical index that ranges from -1 to +1, representing the strength and direction of a linear relationship between two variables

### Interpretation

The conventional thresholds for interpreting 'r' are used to evaluate the significance of the correlation observed

### Visual Representation

Scatterplots are essential tools for visualizing the relationship between two continuous variables

## Advantages and Limitations

### Advantages

Correlational research is advantageous for its non-invasive nature and its ability to validate the reliability and validity of measurement instruments

### Limitations

A major limitation of correlational research is its inability to establish causal relationships and its susceptibility to confounding variables

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