lab2 - Psychology 321(SP08 Lab#2 Content A Group-wise data...

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1 Psychology 321 (SP08) – Lab #2 Content: A. Group-wise data exploration in SPSS B. Dot Plots and Histograms (Data Visualization in SPSS) C. Computing independent Samples t-test by hand and in SPSS D. Importing data (e.g. ‘.dat’ or ‘.txt’ files) into SPSS A. Group-wise data exploration in SPSS When you want to perform an independent samples t-test, there are assumptions that the data have to meet before you can perform the test. They include that the observations in the groups are normally distributed and that the variances of the two groups are about equal (technically called homogeneity of variance). To get this information you have to tell SPSS that you want to compare groups of data. 1. Select ‘ANALYZE’ barb2right ‘DESCRIPTIVE STATISTICS’ barb2right ‘EXPLORE…’ 2. Select the dependent variable by clicking on it and move it into the ‘DEPENDENT LIST’ box. 3. Bring the variable that defines your testing groups (in this case ‘gastype’) into the box labeled ‘FACTOR LIST’. 4. In the area below where all your variables were listed, there is a ‘DISPLAY’ section. Click on ‘STATISTICS’ so you don’t get a bunch of graphs you don’t need. 5. Click ‘OK’.
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2 If you did everything correctly, you will get the following Output: Descriptives Gas filling of the football Statistic Std. Error Mean 26.385 .9950 Lower Bound 24.370 95% Confidence Interval for Mean Upper Bound 28.399 5% Trimmed Mean 26.620 Median 28.000 Variance 38.611 Std. Deviation 6.2138 Minimum 11 Maximum 39 Range 28.0 Interquartile Range 6.0 Skewness -.877 .378 helium Kurtosis .852 .741 Mean 25.923 .7505 Lower Bound 24.404 95% Confidence Interval for Mean Upper Bound 27.442 5% Trimmed Mean 26.026 Median 26.000 Variance 21.968 Std. Deviation 4.6870 Minimum 15 Maximum 35 Range 20.0 Interquartile Range 6.0 Skewness -.366 .378 distance kicked in yards air Kurtosis -.012 .741 B. Dot Plots and Histograms (Data Visualization with SPSS) Summary statistics (mean, variance, etc.) are useful, but they don’t really tell you a whole lot about the shape of the distribution. Plots of the data are the best way to do that. Using the ‘CARS’ dataset that is provided by SPSS and that was also used in homework #1 as example, you can make:
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3 Histograms – show the data distribution with respect to the variable of interest. The variable of interest is organized into classes, or ‘bins’. Each bar of a histogram represents the cases that fall into each class or ‘bin’. Cases in each bin can represent either number of cases or relative values (i.e. percent) This histogram plots the frequency (number of cases) for each of the different weight classes in the ‘Cars’ dataset.
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