Lesson 2: Summarizing Data

Section 3: Frequency Distributions

Look again at the data in Table 2.1. How many of the cases (or case-patients) are male?

When a database contains only a limited number of records, you can easily pick out the information you need directly from the raw data. By scanning the 5^th column, you can see that 12 of the 20 case-patients are male.

With larger databases, however, picking out the desired information at a glance becomes increasingly difficult. To facilitate the task, the variables can be summarized into tables called frequency distributions.

A frequency distribution displays the values a variable can take and the number of persons or records with each value. For example, suppose you have data from a study of women with ovarian cancer and wish to look at parity, that is, the number of times each woman has given birth. To construct a frequency distribution that displays these data:

• First, list all the values that the variable parity can take, from the lowest possible value to the highest.
• Then, for each value, record the number of women who had that number of births (twins and other multiple-birth pregnancies count only once).

To create a frequency distribution from a data set in Analysis Module:

Select frequencies, then choose variable.

Table 2.4 displays what the resulting frequency distribution would look like. Notice that the frequency distribution includes all values of parity between the lowest and highest observed, even though there were no women for some values. Notice also that each column is clearly labeled, and that the total is given in the bottom row.

Table 2.4 displays the frequency distribution for a continuous variable. Continuous variables are often further summarized with measures of central location and measures of spread. Distributions for ordinal and nominal variables are illustrated in Tables 2.5 and 2.6, respectively. Categorical variables are usually further summarized as ratios, proportions, and rates (discussed in Lesson 3).