SPSS On-Line Training Workshop

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Carl Lee
Felix Famoye

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Correlation

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In this Tutorial: 

Bivariate

Partial

Analysis of Data

Correlate includes Bivariate and Partial Correlations. The procedure also computes distance statistics that measure similarities or dissimilarities                      

 

camera.gif (1166 bytes) MOVIE: Bivariate and Partial Correlation camera.gif (1166 bytes)

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In this on-line workshop, you will find many movie clips. Each movie clip will demonstrate some specific usage of SPSS.

Bivariate: This analysis is used to obtain correlation coefficients, a measure of linear relationship between two variables. It computes Pearsonís correlation coefficient, Spearmanís rho and Kendallís tau-b. You can request for one-tailed or two-tailed significance probabilities.

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Options: Gives us the means and standard deviations of the variables. For missing values, you can choose to exclude cases pairwise or listwise.

Partial: This analysis is used to obtain correlation coefficients after controlling for one or more variables. Partial correlation describes the relationship between two variables while controlling for the effects of one or more variables

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Options: Gives us the means and standard deviations of the variables. You can request for zero-order correlations which are the simple correlations between any pair of variables, including the controlled variables. For missing values, you can choose to exclude cases pairwise or listwise

Distances Calculates similarities and dissimilarities between pairs of cases or pairs of variables. These distance measures can be used in other procedures like factor analysis or cluster analysis.

The data set used for this demonstration is the Body Fat data set. See Data Set page for details. The body fat data has 4 variables; the amount of body fat, triceps skin fold thickness, thigh circumference, and mid-arm circumference.

click here to watch Bivariate and Partial Correlations

 

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This online SPSS Training Workshop is developed by Dr Carl Lee, Dr Felix Famoye , student assistants Barbara Shelden and Albert Brown , Department of Mathematics, Central Michigan  University. All rights reserved.