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Step 1: Open SPSS
For this example, we use the following research question: How do regime type and population size influence economic performance in Sub-Saharan Africa? Data for these variables were taken from Freedom House (freedomhouse.org) and the World Bank (data.worldbank.org). Regime type is measured by Freedom House on a scale from 0 to 100, with 0 representing no democracy and 100 representing full democracy. Population size is taken from the World Bank (in real numbers). Economic performance, measured as GDP per capita per annum, is also taken from the World Bank.
Step 2: After opening SPSS, select “New Dataset” located on the top right of the screen and then click “Open” at the bottom left of the screen, which will open a blank spreadsheet, as seen below.
Step 3: Click on Variable View at the bottom left to change the screen to create new variables
Once you have opened a new dataset, you will likely be in “Data View.” Click on “Variable View” so you can begin to enter in data. Before you can enter data in the “Data View,” you must first create variables in “Variable View.”
To begin, create variable names in “Variable View.” Because the first column will likely contain nonnumeric data (you will need a list for the names of each observation in the dataset—such as countries or states), the first variable must be transformed from a “numeric” variable that uses numbers to a “string” variable that uses letters so you can type in the name of each observation. To do this, use the following steps: Enter a name into the first cell under “Name.” Since we are using Sub-Saharan countries as the research population, for this example, “Country” was used.
Now click on the first cell under “Type” and click on the small blue box with three dots that appears in the cell.
Once you click on the little blue box, you will see a number of options. Select “String,” which will allow you to enter nonnumeric text for this column. (If desired, change the number of possible characters that will appear in the Country column by changing the number of ‘Characters’)). Once finished, click OK.
You can now create the variable names for the variables you will populate with data. For this example, new variables for the level of democracy “Democracy,” population size “Population” and economic performance “GDP per capita” were created. To do this, continue entering in variable names (under “Country”). Since these variables involve numeric data (and not letters), you do not need to change their Type (in other words, leave them as ‘Numeric’ variables). You may also opt to change the number of decimals in this screen from the default setting of ‘2’ to ‘0’ as shown in this example.
Step 4: Enter in the data to populate the variables
Once you have entered your variables’ names, you can begin to enter data into the spreadsheet. To do this, you must be in the Data View screen. Click on Data View at the bottom of the screen and you will see the variable names you have entered at the top. (You can also place your mouse in between the columns to elongate them if you wish.)
You can now insert the data you found online by clicking on the cells under the variable names and entering the value for each variable for each observation using your computer’s keyboard. Do this carefully to ensure you do not make mistakes. Double check your work to ensure that each value has been entered correctly. These instructions include data from only three countries, but you should ensure you have enough observations to conduct your analysis. Generally, as mentioned in the text, you should include at least 10 observations for every independent variable. That means, for an example like this one, you should include at least 20 Sub-Saharan countries. However, the best choice would be to include all possible observations for your research population; there are 46 countries for example in Sub-Saharan Africa.
Step 5: Save the dataset
After you enter the data, be sure to save the dataset. At the top, select File and then Save As. Give your file a name and save it to your computer.
Once these steps are complete (and your dataset is populated with data), you can perform quantitative analysis exactly as you did with the data from the Dataprac. First perform descriptive statistics on your variables, and then conduct bivariate and multivariate data analysis.