SPSS Statistical Software

SPSS utilizes 2 windows while you're working in it. The first window is the Data file window and the other is the Output file window. These 2 windows work in tandem and you'll generally have them both opened as you work.

The Data file contains your data and allows you to perform different actions and analyses with the data. The Output file keeps a running record of everything you do to the data, and is also where any analysis results and visuals (e.g., charts, graphs, tables) will display.

Let's go through the specifics of both of these windows and their interfaces!

The Data File window contains your data and looks kind of like a spreadsheet. You can see your variable names at the top of each column and the values of each variable within those columns. Depending on what your data is, you may see numbers and/or text as the values.

The Data File window has 2 different views. You will notice 2 tabs at the bottom of the screen. One is labelled Data View, which is what we are currently in. The other tab is labelled Variable View. If you click on Variable View, you will see the following interface:

What is the difference between Data View and Variable View?

Data View:

• Shows your dataset.
• Looks like an Excel/CSV data file.
• Variable names are at the top of each column, with the values of each variable going down each column.
• Case number is on the left. Each case number represents an individual participant/case. 
• The values in the columns represent each participant's/case's data for a particular variable.

Variable View:

• Shows more detailed info about each of your variables.
• Does not show the data values of participants.
• Each variables is listed in the first column, with each subsequent column showing information and settings for each variable.
• Let's go through what each column is:

1. Name = your variable names.
2. Type = what type of data it is. Like Numeric (which is numbers), String (which is letters or words), Dates, Dollars, or others. Click on the right-hand side of the cell to see a little button with 3 dots appear. Click the 3 dots and then you'll see a popup window that allows you to change the variable's Type.
3. Width = the number of digits or characters possible for the variable. So for example, Work_Field allows for 6 characters. If we go back to Data View, we can see the Work Fields have at most 6 characters, like HEALTH, (but can have less than 6, like GOV or TECH).
4. Decimals = how many decimal places you want for each variable. 
5. Labels = The next column is Labels. In contrast to Variable names, which are messy, shorthand names, Labels are clean, nicer looking names you can give your variables. Labels are also what will display in any tables or graphs you create. So if your variables have very non-descript names, like Q1 or Q2 or a series of random letters, be sure to add Labels so you don’t get confused when you’re running analyses or looking at the output.
6. Values = these are important when your data consists of Likert-based questions or demographic questions where the participants had multiple options to choose from (like Race/Ethnicity or Gender). For example, if you have questions where the scale was Strongly Agree to Strongly Disagree, but the responses were all recorded as numeric values 1-5, it can get confusing trying to remember what each number meant. This is where Data Values come into play. You can assign meaningful text to each number, so when you run analyses, the output will display these text values next to the results so it’s easier to interpret.
   • Click on the right-hand side of a cell to see a little button with 3 dots appear. Click the 3 dots and then you'll see a popup window that allows you to add text names for each of your numeric values. You have to manually input each numeric value and associated text.
7. Missing = if you coded your missing data a specific way when you were cleaning your data, here is where you can tell SPSS what the code or value is that you’ve assigned to missing data. This way SPSS will take this into account when running analyses and not just consider these values as valid responses from participants which could then skew or bias your results.
   • For example, let’s say our dataset was missing some responses to the Age question. If we already cleaned our data and entered -1 for all of those individuals who didn’t answer this question, we can add that information here in the Missing Data column.
8. Columns = the column width of your variables on the Data View. This is purely aesthetic and doesn’t affect your data or analyses. You generally don’t need to touch this.
9. Align = another completely aesthetic option; this just lets you choose the alignment of how your variables appear in their columns in the Data Viewer (Left, Right, or Center).
10. Measure = is the Measurement Level of the variable. It tells you how each variable was measured – nominal, ordinal, or scale.
    • Nominal data is categorical in nature, so we see for example that Gender and Ethnicity are both nominal because these variables are just categorizing genders and ethnicities, they’re not measuring anything numerically.
    • Ordinal variables are similar to Nominal as they are just categories but these categories have a rank order to them, meaning there is hierarchy to the response options. An example could be Education Level because a Bachelor’s degree is a higher level of education than an Associate’s Degree which is higher than a High School Diploma, so there is a hierarchy to which is more or less Education.
    • Scale variables are variables that are measured at the interval or ratio-level and use numbers in the traditional sense versus just for categorizing data. So for example, Age is scale, and so is Years Employed because these numbers measure amount of time in years, both of which involve using numbers in a traditional, quantitative manner, wherein the numbers have meaning as well as equal intervals/distance between each numeric value.
11. Role = this is for defining what role each variable will play in your analyses. By default they should all be set to Input (which means that the variables will be used as a predictor or independent variable).
    • Target means the variable will be used as an outcome or dependent variables.
    • Both means the variables will be used as both a predictor and outcome.
    • None is if the variable has no role assignment.
    • Partition means the variable will partition the data into separate samples. You won’t typically need to use this.
    • Split is not used here, it’s used in the SPSS Modeler which is something separate that is not covered in this guide.
    • As a note, Typically, you do not change Role from Input on any variables because it’s not necessary - you can still run any analyses on any of your variables and SPSS does not care which is your predictor or outcome.

As a final note here, all of these options in the Variable View are adjustable. If you want change the number of decimals for a variable, you can do that here. If you imported data and it categorized some of the Measurement levels incorrectly, you can fix that here.

The Output window will initially look blank until you do something with your data, like run an analysis or create a graph/chart. It will look like this:

How to Display Syntax/Commands in the Output Window

One thing that may be helpful before you start running any analyses is to turn on the option for displaying commands in the output window log. By default, SPSS does not display commands in the output window, however it can helpful to see the commands to have a running record of everything you do to your data in SPSS.

Let's go over how to turn on the Display Commands option:

1. Click on Edit at the top of the window, then select Options.
   
   
2. Now the Options window will pop up. Click on the Viewer tab at the top. Now check the box for Display commands in the log.
   
   
3. Click OK. Now anytime you do anything in SPSS, the output window will display the commands/syntax for the action you just performed.

Having the commands displayed in the log (output window) is especially helpful when you are doing things such as cleaning your data or making transformations to your variables, as these actions otherwise are not noted and you may forget if you have already done them. When you run an analysis or create a graph, you have the visual result of those actions, but with cleaning or transforming your data, there are not necessarily any obvious visual indicators that you have done these actions, so the Display commands in the log option is your visual indicator and record that you have done these actions. 

Having the commands displayed is also helpful to maintain a record of every action you do to your data so in the future (even if you haven't looked at your SPSS files in a long time), you will know exactly what actions you have performed on your data and can easily pick back up where you left off.

What Can You Do in the Output Window?

You will notice the same toolbar and menubar at the top of the output window as you see at the top of the data viewer window. You have all those same capabilities from the output window, so, for example, you can run analyses from the output window by clicking the appropriate buttons.

To help give you an idea of what the output window can display, here are some examples:

If you have Display commands in the log turned on, here's what the output window will display when you import an Excel file:

Here's an example of the output tables from running a Frequencies analysis:

Here's an example of the output tables from running a Descriptives analysis:

Here's an example of creating a bar graph:

Here's an example of creating a scatterplot: