An introduction to SPSS
REQUESTING DATA ANALYSIS
You are now in a position to use SPSS to generate some useful tables and graphs on the data entered. You have two variables of interest here: the categorical variable of gender and the continuous variable of well-being. You are going to produce a frequency table, pie chart and bar chart for gender. And for well-being you will produce a table containing a variety of useful information, including the number of cases, the minimum value, maximum value, median, mean and standard deviation. You’ll also produce a histogram for well-being.
Before explaining how to do this, a little more background on SPSS will be provided. In SPSS there are essentially three types of page:
■ the Data Editor page which has both a Data View and the Variable View. You have already worked with this;
■ the Syntax page;
■ the Output page.
The Data Editor page is used to input data and to specify the characteristics of this data such as what the values of a given categorical variable refer to. It can also be used to request data analyses. This is achieved by using the pull-down menus at the top of the page. For example, if you click on Analyze at the top of the Data View you will see several options for analysing data.
The Syntax page can be used instead of the pull-down menus to give SPSS detailed instruc-tions for carrying out data analyses, and it has the advantage that you can save the instrucinstruc-tions written on the syntax page just like any other computer file. However, the disadvantage with using the syntax page is that the instructions have to be very precise and it is, there-fore, best suited to someone relatively experienced in the use of SPSS. Therethere-fore, in this book we will almost exclusively use the pull-down menus to request analyses.
The third type of SPSS page, the Output page, opens automatically when you request data analysis with the pull-down menus (or the syntax page). It is here that SPSS places the results of data analyses. Output pages can be saved just as the information in the Data Editor and Syntax pages can. However, they are often simply viewed on the monitor or, more frequently, printed out instead. It is in the Output page that you will find the tables, graphs and other statistical output from your analyses.
This will all become clearer with an example. Let’s start by requesting some frequency information on the categorical variable gender. To do this:
➢ Click on Analyze.
➢ Click on Descriptive Statistics. 1111
AN INTRODUCTION TO SPSS
➢ Click on Frequencies . . . .
➢ A dialog box will open. In the white rectangle to the left hand side of the box you will see your three variables.
➢ Click on Genderand then on the small black triangle to the left of the white rectangle headed Variable(s). The gender variable will be moved into the variables box as in Figure 4.8.
➢ Click on the Charts . . .button at the bottom of the dialog box to open another smaller dialog box that you can use to request a pie chart and a bar chart of the data in the gender variable.
➢ Click on the Bar Chartsradio button.
➢ Click on Continue. The small dialog box will disappear.
➢ Click on OK.
You will find that the following Output page automatically opens with the results of the analyses you have requested. You can toggle between this and the Data Editor page by clicking on Window at the top of the screen and then whichever of the two pages you want to see. For the time being, let’s stay with the Output page, shown in Figure 4.9.
Figure 4.8 Specifying the variable or variables to be analysed in SPSS Frequencies
Note that the page in Figure 4.9 is divided vertically into two parts. To the right are the tables, graphs, results etc. that you have requested. You can scroll up and down through these by clicking on and dragging the bar on the extreme right. On the left hand side of the page are a series of labelled icons. Each icon refers to a particular component of the results output. You can go to the table or graph you want by clicking on one of these. For example, if you click on the Bar chart icon you will see that the bar chart is displayed on the left hand side of the screen.
The results you have produced are shown below. They are annotated here to indicate what various parts of the output mean. Annotations are used in the same way throughout Chapters 5 and 6.
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AN INTRODUCTION TO SPSS
Figure 4.9 The SPSS Output page for the Frequencies analysis of gender
Shows the number of cases on which there is valid data for gender, and the number for which data are missing.
N Valid Missing
10 0 Gender
Statistics Frequencies
It is possible to copy any of these components of the SPSS output in Word. For example, to copy the bar chart:
➢ Click on the Bar chart iconto highlight it.
➢ Click on Edit(top left of screen).
➢ Click on Copy objects.
➢ Open a page in Word and on this click on Editand then Paste.
The bar chart will be pasted into Word. If you have any problems seeing the chart after you have pasted it into Word, try repeating the above process but this time in SPSS click on Copy instead of Copy objects. You can resize this if you want to. If you click on it you will see that a rectangle appears around it with a small black square at each corner and half way along each side. Place your cursor on the bottom right corner of this rectangle and a small line with arrows at each end will appear. Click on this and it will change to a small cross. By holding down the left button on your mouse and dragging the cross down and to the right or up and to the left you can adjust the size of the chart on the page.
OK, return to the SPSS Data Editor page. We will now request some detailed informa-tion on the continuous variable, well-being. To do so:
➢ Click on Analyze.
➢ Click on Descriptive Statistics.
➢ Click on Explore.
In this table you can see the frequency of males and females (6 and 4 respectively), the percentage of males and females, the valid percentage of males and females (i.e. the percentages for non-missing cases) and the cumulative percentage of males and females.
Valid
Frequency Male
Female Total
6 4 10
Per cent 60.0 40.0 100.0
Valid per cent 60.0 40.0 100.0
Cumulative per cent
60.0 100.0
7 6 5 4 3 2 1 0
Male
Gender
Female
Frequency
Gender
Gender
A dialog box will open. As you can see, it is laid out in a similar way to the Frequencies dialog box that you have already used, with the variables listed in the white rectangle to the left hand side.
➢ Click on Well-beingand then on the small black triangle to the left of the box headedDependent List. The well-being variable will be moved into the
Dependent Listbox as in Figure 4.10.
➢ Click on OK. 1111
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AN INTRODUCTION TO SPSS
Figure 4.10 Specifying the variable or variables to be analysed in SPSS Explore
The following output will be produced.
This is a very helpful table because it provides a great deal of summary information on the continuous variable in question. As you can see, much of the descriptive information outlined in Chapter 1 is provided: the mean, median, variance, standard deviation, range and inter-quartile range. In addition, the 95% confidence interval and standard error, discussed in Chapters 2 and 3, are also produced.
The 5% trimmed mean is the mean recalculated after the 5% most extreme data points, those furthest from it, are excluded. A comparison of the mean and the 5% trimmed mean is useful for checking that a few extreme values are not distorting the mean. If these two values are very different this suggests that such extreme values do exist and the data should be carefully checked.
It is likely that the extreme cases concerned will be specified in the box and whiskers plot below.
The skewness value is a numerical indication of how skewed the data are, and kurtosis refers to how flat or peaked the frequency distribution is. However, these figures are not used much in practice because most researchers prefer to examine the shape of the frequency distribution by eye when it is plotted on a graph.
This table shows the number of valid cases, the number of missing cases, and the total number of cases for the variable Well being.
The missing case is, of course, case number 6.
Well-being
Statistic Descriptives
Case processing summary Explore
Mean
95% confidence interval for mean 5% trimmed mean Median
Variance Std deviation Minimum Maximum Range
Interquartile range Skewness Kurtosis
3.4444 1.8546 5.0343 3.3272 3.0000 4.278 2.06828 1.00 8.00 7.00 2.5 1.409 2.472
Std error .68943
.717 1.400 Lower bound
Upper bound Cases
Valid Missing Total
N Per cent N Per cent N Per cent
Well-being 9 90.0% 1 10.0% 10 100.0%
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This stem and leaf plot is a useful way of examining the shape of the frequency distribution, and is often used to check that the distribution is roughly symmetrical and normal in shape. The ‘stem’ gives the values of the data points, and each ‘leaf’ indicates the number of cases which obtained the value in question. For example, here it is clear that four people have scores of 3 because there are four 0’s next to the stem of 3.
The stem and leaf plot is like a bar chart turned 90 degrees so that it is lying on its side, and the shape of the ‘leaves’ shows the shape of the distribution.
Well-being Stem-and-Leaf Plot Frequency Stem & Leaf
1.00 1 . 0
This is called a ‘box and whiskers’ plot. The ‘box’ is the rectangular area in the centre of the graph and the ‘whiskers’ are the two horizontal lines connected to the box by vertical lines. In a box and whiskers plot the top and bottom of the box are drawn at the 25th and 75th percentiles respectively.
The median is shown with the thick horizontal line (in this case it is 3).
A very useful feature of the box and whiskers plot is that it also shows outliers (defined as those data points more than 1.5 box-lengths above or below the box, marked with a circle) and extreme values (data points more than three box-lengths above or below the box and marked with a cross).
Helpfully, the number of the SPSS Data Editor row in which this outlier or extreme value occurs is also shown. In this example, case 5 is shown to have an outlying value, and it would be worth checking this to make sure it is a correct and valid data point.
The upper and lower whiskers are drawn in line with the highest and lowest values that are not outliers. The way to interpret it is quite simple: the box shows the limits and range of those 50% of the data points closest to the median, and the whiskers show the limits and range of all the data points bar those which are especially large or small.
We have now produced a great deal of interesting information about gender and well-being by using just Frequencies and Explore. We will complete this chapter by producing two more graphs, a pie chart for gender and a histogram for well-being. To do this make sure you are in the SPSS Data Editor and then:
➢ Click on Graphs.
➢ Click on Pie . . .A small dialog box will appear.
➢ Click on Define. Another dialog box will appear with the variables in the white rectangle to the left.
➢ Click on Genderand then on the button with the small black triangle on it to the left of the white box headed Define Slices by. Gender will be moved into theDefine Slices bybox.
➢ Click on OK.
A pie chart for gender will appear in the Output page.
To generate a histogram for well-being, return to the Data Editor page and then:
➢ Click on Graphs.
➢ Click on Histogram . . .A dialog box will appear with the variables in the white rectangle to the left.
➢ Click on Well-beingand then on the button with the small black triangle on it to the left of the white box headed Variable.Well-being will be moved into the
Variablebox.
➢ Click on OK.
A histogram for well-being will appear in the Output page.
As with the other SPSS output, these graphs can be examined, pasted into Word, printed out or saved along with the rest of the results of the analysis.
Well done! You have now carried out your first piece of SPSS data analysis, and in doing so have been through the four stages that are normally necessary whenever you intend to use SPSS to carry out your data analysis: defining variables, entering data, requesting data analysis and examining the output. This may all have seemed quite complicated and fiddly, but you will find that the steps you have used become second nature very quickly. What’s more, these steps will be essentially the same now whatever type of data, and whatever type of data analysis, you wish to do.
The next step is for you to go through these stages on your own. Close down SPSS by clicking on File and then Exit. You will be asked whether you wish to save your data file and output files. You can do so if you wish but this is not necessary.