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Correlation - Coggle Diagram
Correlation
Correlation Coefficient
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Use a table of significance to see how big the coefficient needs to be in order for the correlation to be significant
Correlational Hypothesis
States the expected relationship between the co-variables.
In an experiment, the differences is between conditions instead.
X and Y are:
positively correlated (directional)
negative correlation (directional)
correlated (non-directional)
not correlated (non-directional)
Scattergrams
Each individual score, or data, is depicted as a dot on an xy-axis grid. The dot indicates the degree of correlation between the co-variables.
Positive - bottom left to top right.
Negative - top left to bottom right.
No correlation - randomly scattered.
Evaluation
Strengths:
Used to investigate trends in data.
Rule out a causal relationship if not significant.
Usually be easily repeated to confirm findings.
Limitations:
Variables are only measured, no change is made.
No conclusion can be made about causality.
People assume causal relationships so leads to misinterpretation.
Supposed causal connections could be due to intervening variables instead.
Like experiments, correlations could lack internal or external validity
Linear and Curvilinear
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Curvilinear - curved, still a predictable relationship, e.g. stress and performance, as performance decreases when stress is too high or too low