Last modified on 29 June 2010, at 03:00

Using SPSS and PASW/Creating Charts and Graphs

Charts and graphs are a useful way of organizing data so it can be read and interpreted more easily. In order to choose which type of chart or graph to use you must first decide the level of measurement, whether the variable is of nominal/ordinal or interval/ratio measurement. Next, you must consider the objective behind creating a chart. Finally, you must decide the target audience. For example if your targeted audience is the general public, you want your graph to be colorful, uncluttered and have an overview of the statistics you want to present. If your audience is a more technical audience, you may want to use tables instead.

One rule of thumb to follow is a “lost in the parking lot” test. The basic idea of this test is that if a relative stranger were to stumble upon your graph or chart it would include enough information in a simple enough format that this person would be able to interpret it.

As stated before, choosing which type of graph to create requires that you first determine the level of measurement. In statistics, the basic rules are as follows:

  • For nominal/ordinal variables, use pie charts and bar charts
  • For interval ratio/variables, use histograms.

Using the Genetic Counselors data set, we can create and interpret different types of graphs.

Pie ChartsEdit

This type of chart is particularly useful for conveying a sense of fairness, relative size, or inequality among categories.

To get started, click on the “Analyze” button and a drop-down menu will appear with various options.


After this you want to move your mouse to “Descriptive Statistics” and click on “Frequencies”. A separate window will pop-up.


Select “Frequencies.”

This is the box where you will determine which variable you want to use as well as the type of graph you want to use to illustrate the information.

To create a pie chart you must first decide which variable you want to use. (Remember that pie charts are used with nominal/ordinal variables.)

For this example we will use the nominal/ordinal variable of religion to determine the religious make-up of the people surveyed. To do this, you must scroll down to “relig”, highlight it by clicking on the variable name, and then move it over to the variable box using the blue arrow.


Next you want to click on the charts button and a separate window will pop-up, where you will be able to choose which type of graph to use. Click on pie chart. You can also determine if you would like frequencies or percentages to appear with your pie chart. For this example, clicking on percentages will be most useful. Do this, and then click continue.

This window, entitled “Frequencies: Charts” is where you will create not only pie charts, but also bar charts, and histograms.

This will return you to the “Frequencies” window. Click okay again. A separate window (the output window) will pop-up displaying the pie chart. This is called the “Output Window”. From this you will be able to interpret the pie chart.

From this pie chart we are able to interpret many different things. For example if someone were to ask what religion most people surveyed affiliate themselves with, you can look at the chart and see that “Protestant” takes up most of the chart and therefore, most people consider themselves to be Protestant. By using the box above the pie chart you can also determine exact percentages and frequencies. For example, if someone were to ask you what percentage of Genetic Counselors surveyed consider themselves to be Jewish, you can say 9.8%.

Bar ChartsEdit

Bar charts are useful for showing a sense of competition among categories. Like pie charts, bar charts are also used with variables of a nominal/ordinal level of measurement.

To create a bar chart you use the same steps to create a pie chart except when the window, entitled “Frequencies: Charts” pops-up, you want to click on Bar Charts instead of pie charts.


For this example we are going to ask the question of how often do the genetic counselors in this survey attend religious services and see the competition among the different categories.

To do this we go to “Analyze” → “Descriptive Statistics” → “Frequencies”

Next we choose our variable (which in this case is titled “attend”), move it over using the blue arrow, then click on charts, click on the chart type “bar charts”, and click continue and then okay. Your bar chart will be displayed in the Output window:

Here, just as in the pie chart output, you can see the frequencies and percentages in the box located above the bar chart. As you can also see, the most frequent answer when asked “How often do you attend religious services” is “several times a year”. You can also interpret that the most common answers are “several times a year”, “never” and “less than once a year”. With this we can conclude that the majority of the genetic counselors surveyed do not attend religious services very often.


Histograms, also known as frequency histograms, are similar to bar charts except that the columns of a histogram touch to account for real limits and the principal of inclusiveness. As stated before, interval/ratio variables are used when creating a histogram.

For this example we can use the interval/ratio variable of age to determine the general distribution of ages among the genetic counselors surveyed. To do this we go to “Analyze” → “Descriptive Statistics” → “Frequencies”

Next, just like before, you want to move your chosen variable (age) over using the blue arrow. Next click on charts, choose Histograms, click continue, and then okay. The output window will pop-up displaying the frequency histogram:

Within frequency histograms, the frequency and percentage table is often large. This is because it accounts for every score, and not just a range of scores.

Not only does this frequency histogram give you a chart, it also gives you the mean, standard deviation and sample size, located in the upper right hand corner of the chart.

Through this chart we can see that the most frequent answer when asked each person’s age was just under 30 years old. If you were to look under the frequency chart you would see that 57 people answered 28 years old and 56 people answer 29 years old. This explains why the bar directly before 30 is the highest one.

Remember that whenever you present a chart or graph you should provide a clear interpretation geared toward your audience.

Chapter contributed by Caitlin McGrath.