PY1PR1 Workshop 3: Introduction to SPSS (Statistical Package for the Social Sciences)

In today’s workshop you will learn to use a computer program called SPSS to calculate some of the descriptive statistics and graphs that you were drawing and calculating by hand in Workshop 1.

Chapter 3 of the course textbook, Andy Field, Discovering Statistics Using SPSS, 3rd edition contains a good introduction to using SPSS, and all the chapters contain SPSS exercises to help you learn.

If you get stuck during the workshop ask one of the demonstrators for help

The first step is to locate the files you will need for today’s workshop

STEP1: Finding the files you need

Click on Start, and click on My Computer:

Look for the K: drive, and double-click on it:

Find the Pracs folder and double-click on it (folders are arranged alphabetically):

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In the Pracs folder, find the Workshop_3_SPSS folder and right click on it. From the menu that appears select Send to and then My documents:

You should now have a copy of the folder in your My documents folder in the M: drive. You can access My documents by clicking start, then clicking My documents. There is also a My Documents icon on the desktop.

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STEP 2: Starting SPSS

Start SPSS by clicking on the Start menu at the bottom left of the screen; select All Programs, SPSS for Windows, SPSS 15.0/16.0/17.0 for Windows. Click.

This opens the SPSS Data Viewer window, as shown below. An SPSS for windows dialogue box may also appear. This gives you the option to open a file that has been used recently. As the file we are using has not been used recently, it is easiest to cancel this dialogue box. Click Cancel.

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STEP 3: Opening an SPSS data file

From the main menu, choose File / Open / Data

All data files of the expected type (*.sav) that are found in your current directory are listed in the dialogue box. SPSS should automatically look in My Documents. The folder called workshop_3_spss that you copied should be found here. Open that folder. Click on blueberry_expt.sav, and then click Open. If SPSS is not looking in the folder, My Documents, click on the arrow at the right hand side of the area headed Look in: My documents.

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The data file will be opened in the SPSS Data Editor. As suggested by the file name this example contains some imaginary data from the example in Lecture 2 in which some researchers randomly sampled a group of students, and randomly divided the initial sample into two groups. The diet of the first group was supplemented with 200 grams of blueberries per day, and the diet of the second group was supplemented with sugar cubes. At the end of term the researchers recorded the exam scores of the two groups to find out if the flavonoids in blueberries improved their exam performance.

The SPSS Data Editor is a spreadsheet, similar to Microsoft Excel. Each column contains a single variable. The names of the variables can be seen at the top of the column, e.g. exam_score.

Each row contains data from a single participant on all the study variables. So, reading across row 6 of the SPSS Data Editor, you can see that this participant had an exam score of 74%, and was in group 1 of the experiment. Group 1 is the label for the blueberry group and group 2 is the label for the sugar cube group. Each individual number is shown in a rectangle called a cell.

There are more participants in this study than you can see on the screen at any one time. Use the scroll bar on the right hand side of the SPSS Data Editor window to scroll down to the bottom of the data file.

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QUESTION A: How many participants are there in the study______________________________________________________

At the bottom left of the SPSS Data Editor window there are two tabs. The currently active one is called “Data View” and the other one is called “Variable View”

STEP 4: Switch to “Variable View” by clicking on the tab

In this view you can edit properties of the variables, as well as creating new variables. The most commonly edited properties are the columns “Name”, “Label”, and “Values”. Labels and value labels are particularly useful because by entering information here you can get SPSS to put meaningful labels on graphs and table headings, which makes the SPSS output much easier to read and more useful for copying and pasting into your reports.

For the variable “group” click on the values cell. You need to click on the small grey square with 3 dots in it. This will bring up a window showing you that the number 1 is the numeric code for the blueberry group and the number 2 is the code for the sugar cube group. Later on in the workshop you will create some variables and enter value labels yourself.

Click Cancel on the “Value Labels” dialogue box and then use the tabs at the bottom left of the window to switch back to “Data View”

STEP 5: Producing histograms and descriptive statistics for the blueberry experiment

From the Analyze menu select Descriptive Statistics and then Explore (see below)

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In the Explore dialogue box you need to add one variable to the “Dependent List” and one variable the “Factor” list. The dependent variable is the measured thing and factor has a similar meaning to independent variable. In this case SPSS will produce separate sets of descriptive statistics and graphs for each level of the factor.

Notice the 3 arrows in the dialogue box. Use the top one to send the “percent exam score” across into the “Dependent List”. Use the middle arrow to send the “experimental group” across into factor. (Note that this step is optional and if you don’t do it SPSS will just create one set of statistics for each variable in the “Dependent List”)

Once you have sent the appropriate variables across the dialogue box should look like this:

Notice the 3 buttons at the bottom of the dialogue box. Click on the button called Plots. Under “Boxplots” select “none”, and under “Descriptive” select Histogram, and remove the tick in the box next to “Stem-and-Leaf”. (Note: SPSS has a habit of producing huge amounts of unwanted output and it is sometimes useful to switch some of it off). The completed dialogue box should look like this before you click “Continue”.

You have now returned to the main “Explore” dialogue box. Click “OK” to obtain your first SPSS output:

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A new window will open called the SPSS Viewer. If the window is not maximised then maximise it now. The SPSS Viewer window is divided into left and right panes.

The left pane contains an outline view of the SPSS viewer contents, which functions in a similar way to the left pane of a Windows Explorer window. Click on one of the small – (minus) signs and see what happens. Now click on the small + (plus) sign that replaces it to expand the selection. This left pane often gets cluttered with a lot of output and it can be useful to collapse parts of it using the – sign.

Now use the mouse to grab the vertical grey bar that separates the two pains. You can change the proportion of the screen that each pane takes up. You will sometimes need to do this in order to see your output in the right pane properly, or to get a better view of the overview in the left pane.

Look at the left pane again and make a single click on the word “Output” at the top of it. Now make one more single click. The word “Output” is now highlighted and you can change it to something like “My first SPSS Output”. If you are going to save the contents of the SPSS viewer it is useful to first label some of the output in this way so that you will be reminded what analysis each section refers to when you open the file again.

To save the SPSS viewer contents click on the File menu and choose Save. SPSS automatically gives the filename an extension of .spo, identifying it as an SPSS For Windows Output Viewer file, so you can just type the filename and the appropriate extension will be added.

Tip: It is useful to label corresponding data (.sav) and output (.spo) files with the same name, so that you can identify which data files were used to create each output.

Remember that saving the data file only saves the data not the output.

Now let’s find the output that we want to look at. In the left hand pane click the mouse on “Descriptives”. Notice that the red arrow moves and now points at “Descriptives”. The red arrow indicates which portion of the statistical output is currently being displayed in the right hand pane.

The “Descriptives” table contains means, and SD etc for each level of the factor you specified in the Explore dialogue box. Fill in the appropriate descriptive statistics in Table 1 below:

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Table 1: Descriptive statistics for exam scores in blueberry exptBlueberry group exam scores

Mean 57.47

Lower bound of 95% confidence interval

53.61

Upper bound of 95% confidence interval

61.33

Standard deviation 10.335

Median 57.50

Sugar cube group exam scores

Mean 50.77

Lower bound of 95% confidence interval

47.13

Upper bound of 95% confidence interval

54.40

Standard deviation 9.737

Median 49.50

The Explore dialogue box should also have produced a histogram for each of the groups. You can find the frequency histogram for the blueberry group by clicking on the icon labelled “group = blueberries” in the left pane of the SPSS viewer.

The frequency histogram of exam scores for the sugar cube group is just underneath it.

SPSS auto-scales the minimum and maximum values on both the x and y axes of all graphs that it produces. It’s often a good idea to change the choices that SPSS makes automatically. In this case SPSS has chosen a different range of x axis values for the two histograms it has produced. This makes it hard to make an instant visual comparison between the two histograms. If you were producing a report and you wanted to include these two histograms it would be essential to set the range on the x axis to be the same for both histograms, which would make the histograms much more useful for the reader of your report.

You are going to change the x axis minimum and maximum values to 20 and 90 respectively for both graphs. Begin by double clicking on the blueberry group graph. This will open up the Chart Editor and the Property Editor. The way this works is that the options available for editing in the property editor change depending where you click on the graph. You can edit almost every conceivable property of the graph by clicking in the correct place and then selecting the correct tab in the property editor.

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Now double click on one of the x axis labels (e.g. on 50). Then, in the Property Editor window select the tab called Scale. At the top of the window under the heading “Range” you can remove the tick in the auto box, if present, for Minimum and Maximum. Enter 20 as the new value for Minimum and 90 as the new value for Maximum.

Now double click on one of the histogram bars. This will cause a new set of tabs to be displayed in the Property Editor. Select the tab called Binning. Here you can alter the interval or bin widths for the frequency histogram. Now change the x axis option from Automatic to Custom and specify a new interval width of 10. Click “Apply”. Now close the Chart Editor window by clicking on the red x in the top right hand corner of the window.

Now repeat the above steps for the sugar cube group and then answer the following questions:

QUESTION B: Find the bar with the maximum frequency in the blueberry group histogram. What is its frequency?______________________________________________________

QUESTION C: Find the bar with the maximum frequency in the sugar cube group histogram. What is its frequency?

______________________________________________________

Tip: if you were writing a report and you wanted to paste the histograms into your report you could select Copy Chart from the Edit menu in the Chart Editor. (Of course, you’d probably want to tidy up some of the other aspects of the chart first, such as font sizes and the scale on the y axis).

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STEP 6: Producing an error bar chart for the blueberry experiment

You can get SPSS to produce an error bar chart with 95% confidence intervals around the mean, just like the ones you saw in Lecture 3.

First, you need to switch back to the SPSS Data Editor window. One of several ways to do this is to Use the Windows menu that can see at the top of the SPSS Viewer window. As with most Windows programs you can see a list of files that are open here. In this case it should look like the following figure, and you can select the 2nd window in the list.

To obtain the error bar chart you go via the Graphs menu, and the Legacy Dialogs sub menu as illustrated below

In the first dialogue box that appears you can accept the default options that data are “summaries of groups of cases” and the bar chart is “simple”. You use “summaries of groups of cases” when you have one data variable that is divided up according to another variable that specifies an independent variable, as in this data file. On the other hand, if each group you want an error bar plot for has its data in a separate variable you would choose the “Summaries of separate variables” option. (continued over page…)

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Click “Define” to bring up the second dialogue box. Use the appropriate arrow to ‘send’ percent exam score across into the “Variable” box, and then use the other arrow to ‘send’ experimental group across into the “Category Axis” box. Notice that where it says “Bars represent” 95% confidence interval for mean is selected, which in this case is what we want. You can now click on “Ok” and the view will switch automatically to the SPSS Viewer.

Look at the error bar chart and answer the following question.

QUESTION D: Do the 95% confidence intervals for the two group means overlap? Does this graph establish that the experimenters have failed to reject the null hypothesis?__________________________________________________________________________________________________________________________________________________________________

STEP 7: Adding some more data by hand to explore the effects of outlying values on the mean and the median.

QUESTION E: Using the values you filled in table 1 earlier in this worksheet; work out the difference between the mean and the median for the sugar cube group. Write down the difference, in percentage points on the exam scale:____________________________________________________

Switch the active window back to the SPSS Data Editor. Now scroll down to the end of the existing data.

Imagine that the Experimenters sampled 3 more students, and they all got randomly allocated to the sugar cube group. By a fluke of chance these 3 students all have statistics ‘A’ Level, and so when they come to sit the statistics exam they all score 100%.

To begin adding this new data click on row 61 column 1 (“exam_score”) of the spreadsheet. Type 100 and press “Enter”. Do the same for rows 62 and 63.

Now in the second column, which is the variable “group” enter a 2 in the 3 cells that currently have a dot instead of a number. (Note: in SPSS the dot means “missing data”)

Now, repeat the first step you performed to obtain descriptive statistics and histograms for the blueberry and sugar cube groups. To find this again use Analyze menu -> Descriptives -> Explore. SPSS will have remembered the various options you selected and will have the correct variables in the correct boxes, so you can just press ‘OK’.

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You can now look at the descriptive statistics and histograms for the sugar cube group with the 3 extra participants

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QUESTION F: Using the descriptive statistics you just obtained using Explore; work out the difference between the mean and the median for the sugar cube group with the 3 extra participants you just added? Write down the difference below. Is it bigger or smaller than before?________________________________________________________________________________________________________

You have now reached the end of the “official” workshop and can ask one of the demonstrators to sign you off. STEP 8 is an optional exercise for those of you who get excited about sampling distributions?! Complete it now if you have time, or at some other point. It also introduces more SPSS functions and familiarizes you with random sampling.

STEP 8: Using the height data from Workshop 1 and SPSS to illustrate the concept of sampling distributions and the SE

Recall that in Workshop 1 you measured each others height. The combined data from the two Workshop 2008 workshop groups is available as an SPSS data file (170 cases).

We will define the full set of 170 measurements as the population. We are going to use SPSS to take multiple samples of different sizes from the population. By repeating the sampling exercise several times we will build up small sampling distributions. This should allow us to demonstrate the relationship between sample size and standard error (SE) directly instead of using the usual estimation formula for that we rely on when we only have one sample.

First, open the data file containing the height data from 170 Psychology students. Choose File Menu->Open->Data and open height_Psych_students.sav This data file contains just one variable.

First, let’s calculate some descriptive statistics for the full population. To do this you can use the Explore dialogue box again (Analyze menu -> Descriptives -> Explore). Send the variable height Psych students across into the “Dependent List”. This time, you can leave the “Factor List” empty, as we are just interested in descriptive statistics for all the data in the variable together. Click on the Plots button to bring up the Explore: Plots dialogue box and deselect the Stem-and-leaf and select the Histogram option. Also set Boxplots to “none” unless you really want to see a Boxplot. Click ‘continue’ and then ‘OK’.

QUESTION G: Write down the mean height and SD of the population of students in cm ________________________________________________________________________________________________________

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Now we have the mean and SD of the population we are going to select a number of seperate random samples and calculate their means and SD too.

Make the SPSS Data Editor the active window again. From the Data menu choose Select cases… as you can see below

This brings up the Select Cases dialogue bow, in which you should tick the box “Random Sample of cases” (see below)

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Now click on the “Sample…” button to bring up a small dialogue box (see below). Tell SPSS to randomly choose exactly 16 cases from the first 170 cases (i.e., from all of them because there are only 170):

Now switch the active window back to the SPSS Data Editor to see what effect your Select Cases options have had. An additional variable has been created called filter $. This variable has a value of 1 if a particular row of the data file is included in the randomly selected sample of 16, and a value of 0 if it does not. Also, those cases that are not part of the randomly selected sample of 16 have a horizontal line drawn through their row number. Your data file should now look a bit like this, but with exact details determined by the specific random selection that has been made by SPSS in your case:

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While the select cases options you have chosen are in force any other statistic SPSS performs will be performed only on the subset of cases that have a 1 not a 0 in the filter variable. So, now you can run Explore again and obtain the mean height of the random sample of 16 students. Do this and enter it into the table 2 below as the mean for sample 1 with sample size 16. You can round the mean to a single decimal place if you wish.

To complete the table you need to repeat the procedure 9 more times for sample size 16, and then do it 10 times with randomly selected samples of 4 cases. To speed up the process use the Dialogue Recall button: this allows you to return rapidly to recently used dialogue boxes. The button you need to press in the SPSS viewer window is highlighted with a thick black arrow below.

Use the dialogue recall to bring back the Select cases dialogue. You just need to click OK and SPSS will produce a new random sample of 16 from the total of 170. Then use dialogue recall to bring back the Explore dialogue and run that again to get the mean height for the new sample of 16 and enter it in Table 2 below.

Now repeat that process 8 more times. Then change the sample size to 4 and create 10 sample means for sample size 4 to enter into Table 2 over the page.

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Table 2. Creating a mini sampling distribution using SPSSMeans of random samples of size 16

Means of random samples of size 4

sample 1

sample 2

sample 3

sample 4

sample 5

sample 6

sample 7

sample 8

sample 9

sample 10

Mean of sample means

SE of mini sampling distribution (same as SD of the 10 samples)

Once the table is complete go to Select Cases one more time and tick the circular box for all cases.

Now you are going to enter your sample means as two new variables in the SPSS Data Editor so that we can calculate the mean and SD of your mini sampling distributions.

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Click on the Variable View tab at the bottom of the SPSS Data Editor and on row 3 type a short new variable name that does not include any spaces, something like “samples4”. Then create another new variable “samples16”. You can also enter a longer description of the variables in the Label column. See the screen shot below for how this should look.

Now you can click on the Data View tab again and enter the data from Table 2 above into SPSS. Make sure that you enter the means of the samples of 4 into the first new variable. Don’t worry about the extra missing cases from 11 onwards. SPSS deals with those automatically.

Finally, you need to use the Explore dialogue box one more time. Send both the new variables across into the Dependent List and click ‘OK’. This will give you the mean of the 10 sample means for the two different sample sizes. It will also give you the SD of the 10 data points. Enter these values in the bottom two rows of Table 2 above.

If statistical theory is correct then the mean of the sample means should be very similar for the two sample sizes and both should be very close to the population mean. More importantly, according to statistical theory the SD of the sample means when the sample size is 4 (called standard error SE because this is a distribution of sample means rather than a distribution of raw scores) should be approximately double the SD of the sample means when the sample size is 16.

To remind you of the relationship between an original population distribution and sampling distributions of varying sizes a slide from Lecture 2 is reproduced below

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QUESTION H: Did statistical theory turn out to be more or less correct, even though there were only a small number of samples in your sampling distribution?__________________________________________________________________________________________________________________________________________________________________

Finally, if you have time, you might like to familiarise yourself with one more very useful function that SPSS has. Return to the SPSS Data Editor and from the Transform menu select Compute Variable (it is at the top of the menu). Here you can create new variables by performing computations on existing variables. In “Target variable” enter a name like heightMet. In “Numeric Expression” type “height / 100”. You can type this or use the list and arrow and calculator style interface to enter it. Then press ‘OK’ – you have created a new variable where the heights are in units of meters instead of centimetres. This is a trivial operation, but it illustrates the basic principle of how to use Compute Variable.

It’s also worth exploring the Help menu, which documents everything SPSS can do.

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