Post on 30-Dec-2015
description
Psychology Practical (Year 2) PS2001
Correlation and other topics
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Correlation
A brief review
• It is a level of analysis between description and explanation– It can allow prediction
• Examination of relationships between two variables (for same individual)
• If a relationship (association) exists then this should allow us to predict the behaviour on one variable from the measure of behaviour on another variable (regression)
• A measure of consistency of relationship
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Correlation
Key points• No manipulation or control –not an experiment
– Can control when and where measured and sample, but no 'direct' control exercised
• Variables measured 'in situ'• Statistically you may find a relationship is indicated
between two variables, but you cannot determine ‘cause and effect’ – – There may be a number of other, unmeasured
variables that could be interrelated and responsible for the relationship found
– There may be an effect, but a correlation will not prove this - need an experimental design
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Techniques
• For interval data:– Pearson's Product-Moment Correlation – this is the
best known correlation and the most used.• For categorical data:
– Spearman's Rank Correlation Coefficient– Kendall's tau statistics
• In general:– Correlation examines the degree to which the two
variables change together: covary• Partial correlation:
– Uses Pearson’s– Allow examination of a relationship between two
variables while at the same time controlling for another variable
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Characteristics of a Relationship
• Direction– Positive (+) or negative (-)
• Form– Linear or non-linear
• Degree– How well data fit the form (consistency or
strength)– From 0 (no fit) to 1 (perfect fit)
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Visual Characteristics: an example
• 2 variables - X & Y• X on horizontal axis• Y on vertical axis• Look for a 'form' made by
the points representing the scores
• Rising to right is +• Falling left to right is -
Language Score
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Positive linear correlations – these are based on 1000 pairs of numbers. Each square with a number corresponds to its mirror graphical representation.
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Strength of a correlation
Cohen (1988) suggested the following interpretations of correlations:
But this depends on context. If this is in the context of a very highly controlled physics experiment one would expect high correlations, but not in the context of testing a general population’s attitudes. So judgements about the extent or strength of a correlation should if possible be made in the context of similar studies.
Interpretation correlation
Small 0.10 – 0.29
Medium 0.30 – 0.49
Large 0.50 – 1.00
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Why Use Correlations?
• Prediction– A relationship allows predictions to be made of one
behaviour from another• Validity
– To demonstrate a test scale is valid by showing a significant relationship between it and another accepted scale for a related construct
• Reliability– To show consistency of measurement on two
occasions (indirectly for internal consistency)• Theory verification
– Use to support hypotheses that predict relationships between variables
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Spearman's Correlation rS
• A non-parametric version of Pearson's correlation coefficient
• Uses ordinal data that is given a ranking to create numerical values
• Same general comments apply to this form of correlation as to Pearson's
• Can be used for ordinal data as can identify non-linear relationships - a measure of consistency independent of its specific form
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Correlation Matrix• SPSS produces a matrix to present correlation coefficients between
variables, if you are reporting a number of correlations, you should use a table in the form of a matrix
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Partial Correlation
• Similar to Pearson’s• Allows control of an additional variable• Usually one thought to influence the two other variables
of interest• Removal of this confounding variable permits better
examination of relationship between two variables of interest
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Two Correlation Coefficients
• Separate for two groups– Use Split File procedure
• Comparing– Use separate coefficients (and n) to determine if two r values
differ significantly– Convert r values to z values (table)– Calculate Zobs from formula– Is Zobs value equal to or greater than 1.96 - at either end of the
distribution?– If yes then two coefficients differ significantly
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Cronbach's Coefficient Alpha
• Measures internal consistency• Estimate of reliability of a scale• How well the items measure the same underlying
construct• Examines average correlation between all items in
the scale• Value from 0 to 1 (highest reliability)• Expect a minimum value of .70 for a moderate to
large scale
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Reliability Statistics
.571 .595 20
Cronbach'sAlpha
Cronbach'sAlpha Based
onStandardized
Items N of Items
SPSS Output - Alpha Value
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SPSS Output - Item Total StatisticsItem-Total Statistics
58.02 49.700 .526 .517 .498
57.66 56.260 .287 .316 .547
57.31 69.132 -.418 .591 .662
57.47 62.409 -.149 .488 .612
58.06 59.082 .040 .258 .579
58.29 61.779 -.113 .194 .599
57.87 50.407 .464 .538 .508
57.56 57.050 .207 .351 .556
57.75 52.608 .481 .539 .517
58.10 49.312 .598 .656 .489
57.46 53.911 .261 .601 .545
56.85 61.169 -.093 .687 .605
57.50 55.438 .238 .504 .550
57.32 60.603 -.054 .274 .594
56.58 58.527 .125 .381 .566
56.53 58.318 .134 .280 .565
57.24 54.987 .245 .594 .549
58.15 51.282 .469 .551 .512
58.11 48.085 .571 .805 .485
57.80 56.407 .343 .490 .544
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Scale Mean ifItem Deleted
ScaleVariance if
Item Deleted
CorrectedItem-TotalCorrelation
SquaredMultiple
Correlation
Cronbach'sAlpha if Item
Deleted
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SPSS Output - Item Total Statistics
• Corrected item-total correlation– Correlation of item to overall scale score– Low or ‘opposite direction’ item correlations suggest
ambiguous statement, statement that poorly reflects construct, or possibly failure to correctly score item
• Alpha if item deleted– Overall alpha value of scale if that item is deleted– Items that if omitted would improve alpha should be
examined - will be same items indicated by previous column output