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SPSS Computing Exercise: Correlation
Correlation Analysis -- QUESTIONS
Introduction
In this week’s exercise, SPSS procedures relevant to correlation analysis are demonstrated, using continuous and
dichotomous variables. Also, you will be introduced to Scatterplots using SPSS. The data come from a general
social survey conducted in the US (file gss.sav). In particular, this exercise uses the following variables:
SATJOB:
a measure of job satisfaction with values from 1 (very satisfied) to 4 (very dissatisfied);
HAPMAR: a measure of happiness of marriage with values from 1 (very happy) to 3 (not too happy);
LIFE:
a response to the question “Is life exciting or dull?” with values from 1 (exciting) to 3 (dull);
DEGREE: highest degree received ranging from 1 (less than high school) to 4 (a postgraduate degree);
INCOME: total family income with values from 1 (under $1,000) to 17 ($50,000 or over);
HEALTH: a measure of condition of health ranging from 1 (excellent) to 4 (poor);
EDUCINDX: a dichotomous variable representing the education level of the respondent:1 = didn’t finish high
school, 2 = completed or went beyond high school;
PAEDUC: the highest year of education that the respondent’s father reached ranging from 1 (first grade) to
20 (postgraduate);
MAEDUC: the highest year of education that the respondent’s mother reached ranging from 1 (first grade) to
20 (postgraduate);
PAEINDEX: a dichotomous variable signifying the education level of the respondents’ fathers:1 = father didn’t
finish high school, 2 = father completed or went beyond high school;
MAEINDEX: a dichotomous variable representing the education level of the respondent’s mother:1 = mother
didn’t finish high school, 2 = mother completed or went beyond high school;
SEX:
respondent’s sex, 1 = male and 2 = female.
Figure 1
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Task 1: Correlations between continuous variables.
Compute bivariate correlations between the following variables: SATJOB, HAPMAR, LIFE, DEGREE,
INCOME and HEALTH. To do this, click on the Analyze pull down menu and then click on the Correlate
option. Now select Bivariate. The window presented in Figure 1 will appear.
On the left-hand side of the window you will see the list of the variables in the data file. Select the variables
SATJOB, HAPMAR, LIFE, DEGREE, INCOME and HEALTH into the Variables: box. Also, select the
Spearman and Pearson options in the Correlation Coefficients frame (the Pearson option should already be
selected). Once you have done this, click on OK.
Questions for Task 1
Q1)
Q2)
Q3)
Q4)
Q5)
Q6)
Q7)
Q8)
What are the correlations between INCOME and each of the following: SATJOB, LIFE and DEGREE
(report both Pearson and Spearman coefficients). On how many cases is each of the correlations based?
Correlation coefficients give information on the magnitude and the direction of the relationship between
two variables. Describe the magnitude and direction of the relationships for the correlations in question
1 (use only Pearson correlations).
What are the significance levels for each of the correlations in question 1? Report p-levels for both
Spearman and Pearson correlations.
In light of the significance levels, how do you interpret the correlations in question 1?
In terms of their magnitudes, which pairs of variables in the correlation matrix are most and least
strongly correlated?
Generally, how do the results of Pearson correlations differ from Spearman correlations?
Why do the significance levels of Pearson correlations differ from Spearman correlations?
For these variables, are Pearson or Spearman correlations most appropriate? Why?
Task 2: The Scatterplot
Produce a scatterplot graph plotting the highest education level of the respondent’s father (PAEDUC) against
that of the respondent’s mother (MAEDUC). To do this, click on the Graphs pull down menu and then on the
Scatter option. Then click on the Simple selection and click on Define. In the window that appears, enter the
variable MAEDUC in the Y-Axis box, and enter the variable PAEDUC in the X Axis box. Click on OK and
your graph will now appear. Enter your name as a footnote. Print this scatterplot.
Questions for Task 2
Q1)
Q2)
Q3)
From looking at this graph, do you think there is a linear relation between PAEDUC and MAEDUC?
If so, make a rough guess as to the direction and magnitude of their correlation.
What are the exact magnitude, direction and significance level of the correlation between PAEDUC and
MAEDUC? (carry out the procedure you followed in Task 1 but only for these two variables)
Task 3: Correlations with Dichotomous Data
3(a) Testing the correlation between EDUCINDX and PAEINDEX.
Here you are going to carry out correlational analysis on two dichotomous variables (such as “male-female”,
“didn’t finish high school - finished high school” and so on). The first two variables you are going to look at are
EDUCINDX (whether or not the respondent finished high school) in relation to PAEINDEX (whether or not the
respondent’s father finished high school). To do this, select the Analyze pull down menu and click on the
Descriptive Statistics option. Then select Crosstabs. In the window that appears, enter the variable
EDUCINDX into the Row(s) box, and PAEINDEX into the Column(s) box. Then click on Cells and make sure
both the Observed option and the Expected option are selected in the Counts frame (Observed should already
be selected). Then click on Continue. Now click on Statistics and in the window that appears,.select the
Correlations option and in the Nominal Data frame select "Phi and Cramer’s V" then click on Continue.
Click on OK and your analysis will run.
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3(b) Testing the correlation between EDUCINDX and PAEINDEX, again.
Using the procedures described under Task 1, generate the Pearson correlation between EDUCINDX and
PAEINDEX.
Questions for Task 3:
Q1)
Q2)
Q3)
Q4)
State the direction, magnitude and significance level for the correlations in 3 (a) and 3(b) (use the
Pearson and Phi coefficients).
What does the correlation in 3 (a) mean? Guess at some reasons for this correlation.
What are the direction, magnitude and significance level for the Pearson correlation in 3(b)?
Compare the Pearson and Phi coefficient generated in 3(a) with the Pearson correlation generated in
3(b). Are they similar or different? What does this suggest about the relationship between these three
statistics?