Transcript Correlational Research

Correlational Research
Relationships are everywhere, but
are they strong ones…?
Descriptive Stats Recap
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Two major aspects?
Outlier?
Concepts
– Central tendency (mean, median, mode)
– Variability (range, standard deviation)
– Outlier
– Skew
Abbreviations
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Mean: 
Median: Mdn
Standard Deviation: SD or σ
Variation: S2 or σ2
Name that Skew
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Where is the mean, median, mode?
Correlations
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Which of the following are related to
one’s happiness?
– gender
– money
– marital status
– religious involvement
– relationships with family, friends
– community involvement
Correlations
Statistics consistently show
that body weight and reading
achievement are positively
correlated. A principal worried
about her school’s test scores
decides to feed her students
Twinkies and sodas to increase
the scores. What do you think
of her idea?
Correlational Design
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Determines whether and to what
degree a relationship exists between
two or more quantifiable variables.
Example of Correlation
Correlational Design
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The degree of the relationship is
expressed as a coefficient of correlation
Examples
– Relationship between math achievement
and math attitude
– Relationship between degree of a school’s
racial diversity and student use of
stereotypical language
– Your topics?
Correlation coefficient…
-1.00
strong negative
0.00
+1.00
strong positive
no
relationship
Advantages of Correlational
Design
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Analysis of relationships among a large
number of variables in a single study
Information about the degree of the
relationship between the variables
being studied
Muris and Meesters
Article
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What do all the negative correlations
mean?
The strongest relationship is found
between which variables?
What can be claimed?
What questions do you have?
Thought Questions
Cautions
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A relationship between two variables
does NOT mean one causes the other
Correlation ≠ Causation
Cautions
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Lack of variability in scores (e.g.
everyone scoring very, very low;
everyone scoring very, very high; etc.)
makes it difficult to identify
relationships
Large sample sizes and/or using many
variables can identify significant
relationships for statistical reasons and
not because the relationships really
exist (Avoid shotgun approach)
Correlational Designs
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Guidelines for interpreting the size of
correlation coefficients
– Much larger correlations are needed for
predictions with individuals than with
groups
 Crude group predictions can be made
with correlations as low as .40 to .60
 Predictions for individuals require
correlations above .75
Correlational Designs
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Guidelines for interpreting the size of
correlation coefficients
– Exploratory studies
 Correlations of .25 to .40 indicate the
need for further research
 Much higher correlations are needed to
confirm or test hypotheses
Think…
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If you were going to take your action
research project, and create a
correlational study, what would it look
like?