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Psychology Practical (Year 2) PS2001 Correlation and other topics.

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1 Psychology Practical (Year 2) PS2001 Correlation and other topics

2 2 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

3 3 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

4 4 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

5 5 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)

6 6 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 -

7 7 Positive linear correlations – these are based on 1000 pairs of numbers. Each square with a number corresponds to its mirror graphical representation.

8 8 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. Interpretationcorrelation Small0.10 – 0.29 Medium0.30 – 0.49 Large0.50 – 1.00

9 9 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

10 10 Spearman's Correlation r S 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

11 11

12 12 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

13 13 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

14 14 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 at either end of the distribution? –If yes then two coefficients differ significantly

15 15 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

16 16 SPSS Output - Alpha Value

17 17 SPSS Output - Item Total Statistics

18 18 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


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