Using Statistics to Describe Variables

Using Statistics to Describe Variables


There are two major classes of statistics: descriptive statistics and inferential statistics. Descriptive statistics are computed to reveal characteristics of the sample data set. Inferential statistics are computed to gain information about effects in the population being studied. For some types of studies, descriptive statistics are the only approach to analysis of the data. For other studies, descriptive statistics are the first step in the data analysis process, to be followed by inferential statistics. For all studies that involve numerical data, descriptive statistics are crucial in understanding the fundamental properties of the variables being studied. This chapter focuses on descriptive statistics and includes the most common descriptive statistics conducted in nursing research with examples from clinical studies.

Using Statistics to Summarize Data

Frequency Distributions

A basic yet important way to begin describing a sample is to create a frequency distribution of the variable or variables being studied. A frequency distribution can be displayed in a table or figure. A line graph figure can be used to plot one variable, whereby the x-axis consists of the possible values of that variable, and the y-axis is the tally of each value. The frequency distributions presented in this chapter include values of continuous variables. With a continuous variable, the higher numbers represent more of that variable, and the lower numbers represent less of that variable. Common examples of continuous variables are age, income, blood pressure, weight, height, pain levels, and perception of quality of life.

The frequency distribution of a variable can be presented in a frequency table, which is a way of organizing the data by listing every possible value in the first column of numbers and the frequency (tally) of each value in the second column of numbers. For example, consider the following hypothetical age data for patients from a primary care clinic. The ages of 20 patients were:



First, we must sort the patients’ ages from lowest to highest values:





















Next, each age value is tallied to create the frequency. This is an example of an ungrouped frequency distribution. In an ungrouped frequency distribution, researchers list all categories of the variable on which they have data and tally each datum on the listing (Corty, 2007). In this example, all the different ages of the 20 patients are listed and then tallied for each age.

Age Frequency
26 2
28 1
31 1
32 1
36 1
39 1
42 2
43 1
45 1
47 1
48 1
51 1
52 1
55 1
57 1
59 1
61 1
67 1

Because most of the ages in this data set have frequencies of “1,” it is better to group the ages into ranges of values. These ranges must be mutually exclusive. A patient’s age can be classified into only one of the ranges. In addition, the ranges must be exhaustive, meaning that each patient’s age fits into at least one of the categories. For example, we may choose to have ranges of 10, so that the age ranges are 20 to 29, 30 to 39, 40 to 49, 50 to 59, and 60 to 69. We may choose to have ranges of 5, so that the age ranges are 20 to 24, 25 to 29, 30 to 34, and so on. The grouping should be devised to provide the greatest possible meaning to the purpose of the study. If the data are to be compared with data in other studies, groupings should be similar to groupings of other studies in this field of research. Classifying data into groups results in the development of a grouped frequency distribution (Munro, 2005). Table 22-1 presents a grouped frequency distribution of patient ages classified by ranges of 10 years. The range starts at “20” because there are no patient ages lower than 20; also, there are no ages higher than 69.

Table 22-1 also includes percentages of patients with an age in each range and the cumulative percentages for the sample, which should add to 100%. This table provides an example of a percentage distribution that indicates the percentage of the sample with scores falling in a specific group or range (Corty, 2007; Munro, 2005). Percentage distributions are particularly useful in comparing the data of the present study with results from other studies.

As discussed earlier, frequency distributions can be presented in figures. Frequencies are commonly presented in graphs, charts, histograms, and frequency polygons. Figure 22-1 is a line graph of the frequency distribution for age ranges, where the x-axis (horizontal line) represents the different age ranges, and the y-axis (vertical line) represents the frequencies of patients with ages in each of the ranges.

A frequency table is also an important method to represent nominal data (Corty, 2007; Munro, 2005; Tukey, 1977). For example, a common nominal variable is smoking history. Many researchers assess subjects’ history of smoking using nominal categories such as “never smoked,” “former smoker,” and “current smoker.” Table 22-2 presents frequency and percentage distributions for data extracted from a sample of veterans with rheumatoid arthritis (Tran, Hooker, Cipher, & Reimold, 2009).

As shown in Table 22-2, the frequencies indicate that 6 of 10 (60%) veterans were former smokers, and 3 (30%) never smoked. For nominal variables such as smoking status, tables are a helpful method to inform researchers and others about the variable being studied. Graphically representing the values in a frequency table can yield visually important trends. Figure 22-2 is a histogram that was developed to represent the smoking status data visually.

Measures of Central Tendency

A measure of central tendency is a statistic that represents the center or middle of a frequency distribution (Corty, 2007; Glass & Stanley, 1970; Grove, 2007). The three measures of central tendency commonly reported in nursing studies include mode, median (MD), and mean (image) (Corty, 2007; Munro, 2005). The mode, median, and mean are defined and calculated in this section using data collected from veterans with rheumatoid arthritis (Tran et al., 2009). The data were extracted from a larger sample of veterans who had a history of biologic medication use. Examples of common biologic medications used to treat rheumatoid arthritis are adalimumab, etanercept, and infliximab (Deighton, O’Mahony, Tosh, Turner, & Rudolf, 2009). Table 22-3 contains the data collected from 10 veterans who had stopped taking biologic medications, and the variable represents the number of years that each veteran had taken the medication before the veteran stopped. Because the number of study subjects represented is 10, the correct statistical notation to reflect that number is:



The letter “n” is lowercase because we are referring to a sample of veterans. If the data being presented represented the entire population of veterans, the correct notation would be uppercase “N” (Zar, 1999). Because most nursing research is conducted using samples, not populations, all formulas in Chapters 22 to 25 incorporate the sample notation, n.


The mode is the numerical value or score that occurs with the greatest frequency in a data set. It does not indicate the center of the data set. The data in Table 22-3 contain two modes: 1.5 years and 3.0 years of medication use. Each of these numbers occurred twice in the data set. When two modes exist, the data set is referred to as bimodal (see Chapter 21). A data set that contains more than two modes is referred to as multimodal (Zar, 1999).


The mean is the arithmetic average of all the values of a variable in a study and is the most commonly reported measure of central tendency. The mean is the sum of the scores divided by the number of scores being summed. Similar to the MD, the mean may not be a member of the data set. The formula for calculating the mean is as follows:




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Feb 17, 2017 | Posted by in NURSING | Comments Off on Using Statistics to Describe Variables

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