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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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Correlational analysis is a statistical technique used to assess the degree and direction of association between two variables
This method is used when controlled experimentation is not possible due to ethical or practical constraints
Correlational analysis is commonly used to evaluate the consistency of measurements, such as in test-retest reliability studies for scales or questionnaires
A positive correlation occurs when an increase in one variable corresponds with an increase in another
A negative correlation means that an increase in one variable is associated with a decrease in the other
Zero correlation indicates no apparent relationship between the variables
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
The conventional thresholds for interpreting 'r' are used to evaluate the significance of the correlation observed
Scatterplots are essential tools for visualizing the relationship between two continuous variables
Correlational research is advantageous for its non-invasive nature and its ability to validate the reliability and validity of measurement instruments
A major limitation of correlational research is its inability to establish causal relationships and its susceptibility to confounding variables