Let’s start with an example
I’m going to use this example to help you understand how to enter the data. Suppose you want to study the effect of beverage type (IV) on number of hours participants sleep (DV). You have three conditions in your experiment, caffeine, juice and beer. Each participant participates in all conditions of the experiment. Each condition is separated by one week’s time. Because the participants in each condition are related, they are actually the same exact participants in each condition, we will use the 1-Way Within Subjects ANOVA. Here are the data. You can see participants in each condition and the average number of hours they sleep each night.
What you want to know
In this experiment, you want to know if there is a significant different between the data collected from each condition. You want to know if beverage type really does have an effect on the amount of sleep that participants get. Do certain types of beverages significantly increase or decrease the amount of sleep that people get? Is there no difference in the amount of sleep that participants will get if they drink caffeinated beverages, juice or beer? Why not just look at the data?
Just looking at the data, you can probably see that there is a difference in amount of sleep between the three conditions. You can probably see that amount of sleep in the beer condition appears to be greater than the amount of sleep in the caffeine and juice condition. People generally appear to get more sleep when they have consumed beer. So why do I have to conduct this ANOVA? The reason is that we are not just trying to figure out if there is a difference in amount of sleep between each group. We want to know if there is a statistically significant difference. That is, a real difference as defined by statistics. The 1-Way Within Subjects will be able to tell us that.
You will use the first three columns of your SPSS data file to enter the data. These columns will contain the data collected in your experiment.
What goes in each column?
Enter the DV data collected in the first condition of the experiment in column 1. Enter the DV data collected in the second condition in column 2 and the third condition in column 3. In our example experiment, the first condition was one in which participants were administered caffeine. So, we enter all the data collected for this condition into the first column. See the number 6 in the first cell of the first column? That indicates that the first participant in condition 1 (caffeine) averaged 6 hours of sleep. In our experiment, the second condition was the one in which participants were administered juice. So, we enter all the data collected for this condition into the second column. See the number 8 in the first cell of the second column? That indicates that the first participant in condition 2 (juice) averaged 8 hours of sleep.
Name the columns
When you are finished entering the data, double click on the top of each column to name it. The Define Variable box will pop up and you can enter a new name for each variable in the Variable Name area. Give each variable a meaningful name. This will make your life a lot easier when you analyze the data and interpret the results. Click OK when you are finished using the Define Variable box and it will disappear.
Because each column represents DV data collected from each condition of this experiment, it is a good idea to name the variable after its condition. In the below example, I decided to name my first column “caffeine.” I decided on this name because this column has data that was collected in the caffeine condition. I decided to name my second column “juice” because it represents the juice condition and my third column “beer.”
Save the data!
Always remember to save your data file to a meaningful place with a meaningful name. You don’t want to lose this file and enter the data all over again. I decided to name my data file “Effect of Drink Type on Number of Hours of Sleep Data.sav.” It’s a long name but this file will be very easy for me to identify in the future.
Background | Enter Data | Analyze Data | Interpret Data | Report Data
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