![]() SPSS screenshot © International Business Machines Corporation. The result is a matrix of scatterplots: SPSS screenshot © International Business Machines Corporation.įinally, for fun, pick 3D scatter in the first pop up menu and then pick three variables to get: SPSS screenshot © International Business Machines Corporation. Instead of picking Simple in the graph pop up menu, pick Matrix Scatter and move all of the variables over for analysis in the menu that pops up after that: SPSS screenshot © International Business Machines Corporation. The correlation matrix output above reported that the correlation between these two variables was not significant and it does not appear that the points in the scatterplot are contained in a longish ellipse. (Note that if we had a variable with subject names, we could move that variable into the labels slot and get scatter plots with each point labeled by the subject name.) The result, after hitting OK, is: SPSS screenshot © International Business Machines Corporation. Where two variables have been picked for plotting. This gives: SPSS screenshot © International Business Machines Corporation. ![]() Pick Graphs → Legacy dialogs → Scatter/Dot to get: SPSS screenshot © International Business Machines Corporation. First, a simple scatterplot of two variables. Then hit the big green triangle (“run”) to get: In the /VARIABLES line, add the word “with” between academic and general as shown: SPSS screenshot © International Business Machines Corporation. To get the associated partial correlation matrix, open the Analyze → Correlate → Bivariate dialog again, move all the variables over (if they are not already there) and hit Paste instead of OK. For example here we may want to lump the variables academic common friend and intimate together and see what their correlations are with the general variable. Sometimes you will not be interested in the complete correlation matrix but only in the correlations of one group of variables with another group. The -values themselves are also given in the SPSS output. SPSS puts ** by values that have and a * by those correlations with. This significance is determined using the statistic given in Section 14.2. Other thing to notice in the SPSS output is the significance of the correlation coefficients. The correlation matrix is at the heart of multivariate statistics in a way that standard deviation is at the heart of univariate statistics. We’ll be introduced to matrices more systematically in Chapter 17. The matrix is also symmetric which means that the numbers above the ones are the same as the ones directly across below the ones - the correlation between and is the same as the correlation between and. Note that the correlation matrix has ones on the diagonal - a variable is perfectly correlated with itself. Specifically, the correlation matrix for these four variables, looking at the SPSS output is: This result, when you just look at the Pearson correlation coefficients, is a correlation matrix. In the menu that pops up, move all the variables over: SPSS screenshot © International Business Machines Corporation.Īnd hit OK to get the following output: SPSS screenshot © International Business Machines Corporation. Open “Hypertension.sav” from the Data Sets and pick Analyze → Correlate → Bivariate: SPSS screenshot © International Business Machines Corporation. The trend is not strong which could be due to not having enough data or this could represent the actual relationship between these two variables.14.3 SPSS Lesson 10: Scatterplots and Correlation What this says is that as fertility rate increases, life expectancy decreases. Graph 2.5.3: Scatter Plot of Life Expectancy versus Fertility Rateįrom the graph, you can see that there is somewhat of a downward trend, but it is not prominent. Note: Always start the vertical axis at zero to avoid exaggeration of the data. The vertical axis needs to encompass the numbers 70.8 to 81.9, so have it range from zero to 90, and have tick marks every 10 units. The horizontal axis needs to encompass 1.1 to 3.4, so have it range from zero to four, with tick marks every one unit. In this case, it seems to make more sense to predict what the life expectancy is doing based on fertility rate, so choose life expectancy to be the dependent variable and fertility rate to be the independent variable. Sometimes it is obvious which variable is which, and in some case it does not seem to be obvious. ![]() To make the scatter plot, you have to decide which variable is the independent variable and which one is the dependent variable. \): Life Expectancy and Fertility Rate in 2013įertility Rate (number of children per mother) ![]()
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