Correlational Studies

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Correlational Studies
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• the presence of a correlation does not indicate a
  cause-effect relationship primarily
• a minimum of 30 participants is acceptable
• larger samples are used if validity and
  reliability are important

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Types of correlation studies

• relationship studies

attempt to gain insight into variables that are
related to more complex variables

• prediction studies

conducted to test variables believed to be good
predictors of a criterion
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Data analysis and interpretation
the two or more scores are obtained for each
member of the sample, and the paired scores are
then correlated
the correlation coefficient indicates the degree
of relationship between the variables of interest
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Correlation coefficient
-1.00
1.00
0.00
strong positive
strong negative
no relationship
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A positive correlation
y
x
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A negative correlation
y
x
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No correlation
y
x
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No correlation
y
x
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• The method for computing a correlation
  coefficient

depends upon the type of data represented by
each variable
types of data
nominal (dichotomous)
interval (continuous)
ordinal (rank)
ratio (continuous)
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Scatter plot
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Measurement Scales

• Nominal scales allow for only qualitative
  classification and have no arithmetic value. E.g.
  gender, race, color, city, etc
• Ordinal scales allow us to rank order the items
  in terms of which has less and which has more but
  still they do not allow us to say "how much
  more." E.g. Likert scale, class rank, letter
  grade
• Interval variables allow us not only to rank
  order the items that are measured, but also to
  quantify and compare the sizes of differences
  between them. E.g. temperature, test scores.
• Ratio scales are like interval scales but they
  feature an identifiable absolute zero point,
  thus, they allow for statements such as x is two
  times more than y. E.g. age, height, weight.
  (Most statistical data analysis procedures do not
  distinguish between the interval and ratio
  scales).

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• with continuous data

use the product moment correlation, Pearson r
(?, rho)

• with rank data

use the rank difference correlation, Spearman r
(?, rho)
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• with dichotomous data

use the phi correlation (?)

• with curvilinear data

use the eta correlation (?)