Introduction to SPSS

SPSS uses two main windows:

Data Editor: This is a spreadsheet-like window which contains the data to be analyzed. The data editor has two views:
- Data View contains the data and is the view you see when you open the Data Editor. Clicking the tab at the bottom of the window brings up the:
- Variable View This does not contain data, but displays information about the dataset that is stored with the dataset. From this window you can control how SPSS displays data.
Each Data Editor only contains one dataset, but you can open multiple Data Editors at one time, each of which contains a separate dataset. Datasets that are currently open are called working datasets and all data manipulations, statistical functions, and other SPSS procedures operate on these datasets.

Data View

Variable View

In the Data Editor, columns represent variables (categories which are measured or counted) while rows represent cases (individual observations for a variable). Variable names must begin with a letter, e.g A1 is allowed but 1A is not. You can create and manipulate variables in Variable View, and enter or edit data in Data View.

Viewer: This is where the results of any analysis appear. From the viewer, you can format the output in a wide range of ways. You can also export results in a variety of formats, e.g. SPSS, text, MSWord, MSExcel, etc.

Other windows: SPSS also has a number of other windows, the most important of which is the Syntax Editor. In early versions of SPSS, all analysis was done through the use of syntax commands (mini computer programs) which instructed SPSS on how to process your data. In current versions of SPSS, analysis is usually performed using the pull-down menus and dialog boxes which allow you to control SPSS without ever writing syntax. SPSS syntax is very powerful but not easy to learn. However, using SPSS syntax allows you access to additional commands which are not available through the menus and dialog boxes, and syntax files can be stored and rerun at a later date, allowing you to repeat an analysis. Although you should be aware of this powerful feature of SPSS, we will not be using SPSS syntax commands on this module.

Data Entry and Manipulation

Although you can type data directly into the SPSS Data View window, this is tedious for large datasets and liable to introduce errors! If data is already available to you in an electronic format, import it into SPSS, don't type it in! Although SPSS has extensive capacities for reformatting data, if you want to manipulate data before analysis, you will probably find easier to do this in Excel and/or a text editor and then import the result into SPSS.

Menus:

There are ten menus in the SPSS Data View window:

File Edit Data Transform Analyze Graphs Utilities Window Help

Apart from the obvious menu functions such as File and Help, for the purpose of this module, the two most important menus are:

Analyze: provides access to the analytical tools in SPSS.
Graphs: provides access SPSS's extensive graph-making capabilities. The basic procedure for plotting a graph in SPSS is:
- Select a variable for each axis - always put the independent variable (manipulated) on the x axis and the dependent variable (measured) on the y axis!

Interpreting SPSS Output:

The output from SPSS tests looks pretty confusing, but it isn't really. The main thing to look for is the Significance value. This is the probability that the null hypothesis is correct. Since we normally work with a significance (a) value of 0.05, i.e. a 95% certainty of getting the right answer:

If the Significance value is less than 0.05, REJECT the null hypothesis.
If the Significance value is greater than or equal to 0.05, ACCEPT the null hypothesis.

Of course this only works if you have the null hypothesis the right way round, or you'll still get the wrong answer.

Other things to remember about using SPSS:

The Significance value of any test needs to be less than 0.05 to be significant.
The Independent Variable is always the variable that you are predicting (i.e. what H_a predicts differences between).
The Dependent Variable is what you are measuring in order to tell if the groups (or conditions for repeated measures tests) are different. For correlations and chi-square, it does not matter which is the Independent or Dependent variable.
H_a always predicts a difference (for correlations, it predicts that r is different from zero, but another way of saying this is that there is a significant correlation) and H_o always predicts no difference.
If there is a WARNING box on your Output File, it is usually because you used the wrong test, or the wrong variables. Go back, think about it and double check.