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The aims of this workshop are to introduce you to SPSS and Data entry.
By the end of this workshop you will:
SPSS stands for ‘Statistical Package for the Social Sciences’. It is one of the main data analysis packages used for research and education. SPSS has a multitude of commands and covers a wide range of quantitative analysis of which only a fraction will be covered for the purposes of this course. As well as being found in the Psychology Data Analysis Laboratories, SPSS can be found in the Learning Resources Centre.
The two main windows in SPSS are the Data Entry Window, which comprises of the data view and variable view windows, and the Output Window. The Data Entry Window is where you enter and edit data whilst the Output Window shows your results, tables and graphs.
NB: The Data Entry Window and the Output Window are two separate windows as such they must be saved independently. Thus the Output file cannot be opened or viewed in the Data Entry Window.
You will see ‘VAR’ at the top of each column, which is where the name of your variables will appear
The data view window is where the variable data is entered. The variables are defined using the variable view window and is viewed using the functions in the bottom left hand corner of the page
The output window will become active when you ask SPSS to analyse data.
The Pull-down menu options and short cut icons on the Toolbar are used in the same way as in Microsoft Word. Most of you will be familiar with the pull-down menu options File and Edit, which are used for opening, closing, saving and generally editing files. The Two menus you need to make yourselves familiar with are Analyze & Graphs as these are where you will carry out most of your statistical functions. More details about these menus will be covered in future weeks.
It is important to understand levels of data in order to correctly choose future tests of significance
Non-parametric data is used in situations in which we don’t measure or test our subjects. Instead, non-parametric data places participants either into groups and/or categories or rates (not measures) participants abilities in an arbitrary fashion. Non-parametric data encompasses nominal and ordinal data types .
Parametric data involves the assumption that the data we work with are drawn from normally distributed population; an easier definition is that all data must be scores or measurements of some sort. Within the auspices of parametric data are two levels or types: interval and ratio.
Nominal (non-parametric) - numbers used to label categories e.g. an eye colour variable may assign label brown eyes as 1, blue eyes as 2 etc. These numbers cannot be used to calculate statistics - what is the mean of brown eyes?
Ordinal (non-parametric) - this data uses numbers to define an order of performance. This data is ranked and is not measured in any scientific way; therefore means and standard deviation cannot be calculated. e.g. I can rank John as greedier than Bob, and Mary greedier than both.
Interval data (parametric) - time, speed, distance can all be measured by interval scales as we have clocks, speedometers and measures etc. These are all interval data because the difference between the consecutive numbers are at equal intervals i.e. the difference between 1&2 is the same as between 5&6.
Ratio data (parametric) - like interval data but with an absolute zero. For instance temperature is interval as it does not have an absolute zero. Therefore 80 degrees is not twice as hot as 40 degrees. Ratio data has a genuine zero so a score of 40 can be considered twice as good as a score of 20.
The first task is to give your variables names. Imagine that we wish to see if there is a reaction time difference between males and females. To do this we need to record gender and reaction time, so two columns are required in the data editor. The first column could be ‘gender’ and the second column ‘reaction time’.
Missing: Is any of our data missing?
Columns & Alignment: Refer to positioning and can be left as the default setting.
Measure: What level of data is the variable? Scale, ordinal or nominal?
The finished product should look something like this…
| Participant | Gender | Height | Weight | Shoe | Appetite |
| 1 | 1.00 | 148.00 | 175.00 | 12.00 | 4.00 |
| 2 | 1.00 | 118.00 | 145.00 | 10.00 | 3.00 |
| 3 | 1.00 | 133.00 | 150.00 | 8.00 | 2.00 |
| 4 | 1.00 | 151.00 | 180.00 | 6.00 | 6.00 |
| 5 | 1.00 | 156.00 | 170.00 | 9.00 | 5.00 |
| 6 | 1.00 | 167.00 | 162.00 | 8.00 | 7.00 |
| 7 | 1.00 | 151.00 | 155.00 | 9.00 | 9.00 |
| 8 | 1.00 | 136.00 | 155.00 | 10.00 | 8.00 |
| 9 | 1.00 | 140.00 | 165.00 | 8.00 | 5.00 |
| 10 | 1.00 | 156.00 | 210.00 | 9.00 | 6.00 |
| 11 | 2.00 | 240.00 | 171.00 | 5.00 | 5.00 |
| 12 | 2.00 | 223.00 | 150.00 | 6.00 | 6.00 |
| 13 | 2.00 | 120.00 | 156.00 | 9.00 | 4.00 |
| 14 | 2.00 | 165.00 | 185.00 | 5.00 | 5.00 |
| 15 | 2.00 | 145.00 | 171.00 | 2.00 | 5.00 |
| 16 | 2.00 | 136.00 | 160.00 | 4.00 | 1.00 |
| 17 | 2.00 | 139.00 | 160.00 | 6.00 | 3.00 |
| 18 | 2.00 | 140.00 | 159.00 | 6.00 | 8.00 |
| 19 | 2.00 | 125.00 | 140.00 | 5.00 | 7.00 |
| 20 | 2.00 | 135.00 | 120.00 | 5.00 | 5.00 |
