Spss13 Correlation

7/28/2019 Spss13 Correlation

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ANALYZING DATA IN SPSS 13.0 USING CORRELATION

Tips before you begin:

• Make sure your data set is open before attempting to run any analyses.

• During analyses, right click on terms or buttons in the dialog boxes to learn about their functions.

• The Help button in the dialog boxes maybe clicked at any time during analyses for more

information on that particular procedure.

• Click the Reset button to clear the dialog box and begin a fresh analysis.

• Click the Cancel button to exit that dialog box without saving changes.

Choose a Procedure:

• Bivariate Correlations

• Partial Correlations

• Distances

BIVARIATE CORRELATIONS

In Bivariate Correlations, the relationship between two variables is measured. The degree of

relationship (how closely they are related) could be either positive or negative. The maximum number

could be either +1 (positive) or -1 (negative). This number is the correlation coefficient. A zero

correlation indicates no relationship.

Examples. Are a student’s grade and the amount of studying done correlated? You might find that

these variables are positively correlated. Or say, is the number of games won by a basketball team

correlated with the average number of points scored per game?

Procedure

1. On the menu bar of the SPSS Data Editor window, click Analyze > Correlate > Bivariate…

2. Select one or more variables that you want to analyze by clicking on the variable labels in the

Bivariate Correlations dialog box. To select multiple variables, hold down the Ctrl key and choose





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the variables you want. Click on the arrow button to add selected variables to the Variables

window.

List of Variables EmptyVariables

Window

Right arrow

button to add

selected

variable(s)

3. Check the type of correlation coefficients that you require (Pearson for parametric, and Kendall’s

tau-b and Spearman for non-parametric).

4. Click on the Options… button to

select statistics, and to control the

treatment of missing values. Click

on the Continue button.

5. Click the OK button in the Bivariate Correlations dialog box to run the analysis. The output will be

displayed in a separate SPSS Viewer window.

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PARTIAL CORRELATIONS

The Partial Correlations procedure computes partial correlation coefficients that describe the linear

relationship between two variables while controlling for the effects of one or more additional variables.

Correlations are measures of linear association. Two variables can be perfectly related, but if the

relationship is not linear, a correlation coefficient is not a proper statistic to measure their association.





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Example. Is there a relationship between healthcare funding and disease rates? Although you might

expect any such relationship to be a negative one, a study reports a significant positive correlation: as

healthcare funding increases, disease rates appear to increase. Controlling for the rate of visits to

healthcare providers, however, virtually eliminates the observed positive correlation. Healthcare

funding and disease rates only appear to be positively related because more people have access to

healthcare when funding increases, which leads to more reported diseases by doctors and hospitals.

Procedure

1. On the menu bar of the SPSS Data Editor window, click Analyze > Correlate > Partial…


Partial Correlations dialog box. Also select one or more numeric control variables. To select

multiple variables, hold down the Ctrl key and choose the variables you want. Click on the

respective arrow buttons to add selected variables to the Variables and Controlling for windows.

Empty

Controlling

for Window

Right arrow

buttons to

add selected

variable(s)

List of

Variables

Empty

Variables

Window





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3. Click on the Options… button to

select statistics, and to control the

treatment of missing values. Click

on the Continue button.

4. Click the OK button in the Partial Correlations dialog box to run the analysis. The output will be

displayed in a separate SPSS Viewer window.

Back to Top

DISTANCES

This procedure calculates any of a wide variety of statistics measuring either similarities or

dissimilarities (distances), either between pairs of variables or between pairs of cases. These similarity

or distance measures can then be used with other procedures, such as factor analysis, cluster

analysis, or multidimensional scaling, to help analyze complex data sets.

Example: Is it possible to measure similarities between pairs of automobiles based on certain

characteristics, such as engine size, MPG, and horsepower? By computing similarities between autos,

you can gain a sense of which autos are similar to each other and which are different from each other.

Procedure

1. On the menu bar of the SPSS Data Editor window, click Analyze > Correlate > Distances…


Distances dialog box. Optionally, select a single string variable for the Label Cases by window. To

select multiple variables, hold down the Ctrl key and choose the variables you want. Click on the

respective arrow buttons to add selected variables to the Variables and Label Cases by windows.





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3. Select the type of distance to compute and the type of measure that you require.

4. Click on the

Measures… button to

explicitly define o

for dissimilarity or

similarity measures.

Click on the Continue

button.

ptions

5. Click the OK button in the Distances dialog box to run the analysis. The output will be displayed in

a separate SPSS Viewer window.

Back to Top

Empty

Label Cases

by Window

List of

Variables

Right arrow

buttons to

add selected

variable(s)

Empty

Variables

Window

Spss13 Correlation

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