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Correlation coefficient
 

Correlation coefficient

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    Correlation coefficient Correlation coefficient Presentation Transcript

    • Correlation Coefficient ELESTA1
    • Correlation
      • Measure of relationship between two variables
      • Ex. Grades in English tends to be related with Foreign Language
      • Height and weight
    • Nature of Correlation
      • Magnitude/direction of the relationship
      • Strength of the relationship
      • Variance explained
      • Significance of the relationship
    • Magnitude of the Relationship
      • Positive relationship – as one variable increases the other variable also increases
      • Ex. academic grades and intelligence
      • Negative relationship – as one variable increases, the other decreases or vice versa
      • Ex. procrastination and motivation
      • Absence of relationship between variables – denoted by .00
    • Strength of Relationship
      • A correlation coefficient is computed for a bivariate distribution using a statistical formula
      Correlation Coefficient Value Interpretation 0.80 – 1.00 Very strong relationship 0.6 – 0.79 Strong relationship 0.40 – 0.59 Substantial/marked relationship 0.2 – 0.39 Low relationship 0.00 – 0.19 Negligible relationship
    • Variance
      • How much of Y’s is explained/accounted for by X
      • Proportion explained
      • Square of the correlation coefficient value
    • Conditions in interpreting r
      • Linear regression – the points in a scatterplot should tend to fall along a straight line
      • The size of the r reflects the amount of variance that can be accounted for by a straight line
      • Homosedasticity – tendency of the standard deviation (or variances) of the arrays to be equal.
    • Correlational Techniques
      • Pearson Product-Moment correlation – (r) used for interval/ratio sets of variables
      • Spearman Rank-order correlation – two sets of data are ordinal
      • Phi coefficient – each of the variables is a dichotomy