Skip to Main Content



  • imageAll observations of subjects in a study are evaluated on a scale of measurement that determines how the observations should be summarized, displayed, and analyzed.

  • imageNominal scales are used to categorize discrete characteristics.

  • imageOrdinal scales categorize characteristics that have an inherent order.

  • imageNumerical scales measure the amount or quantity of something.

  • imageMeans measure the middle of the distribution of a numerical characteristic.

  • imageMedians measure the middle of the distribution of an ordinal characteristic or a numerical characteristic that is skewed.

  • imageThe standard deviation is a measure of the spread of observations around the mean and is used in many statistical procedures.

  • imageThe coefficient of variation is a measure of relative spread that permits the comparison of observations measured on different scales.

  • imagePercentiles are useful to compare an individual observation with a norm.

  • imageStem-and-leaf plots are a combination of frequency tables and histograms that are useful in exploring the distribution of a set of observations.

  • imageFrequency tables show the number of observations having a specific characteristic.

  • imageHistograms, box plots, and frequency polygons display distributions of numerical observations.

  • imageProportions and percentages are used to summarize nominal and ordinal data.

  • imageRates describe the number of events that occur in a given period.

  • imagePrevalence and incidence are two important measures of morbidity.

  • imageRates must be adjusted when populations being compared differ in an important confounding factor.

  • imageThe relationship between two numerical characteristics is described by the correlation.

  • imageThe relationship between two nominal characteristics is described by the risk ratio, odds ratio, and event rates.

  • imageNumber needed to treat is a useful indication of the effectiveness of a given therapy or procedure.

  • imageScatterplots illustrate the relationship between two numerical characteristics.

  • imagePoorly designed graphs and tables mislead in the information they provide.


Presenting Problem 1

Life expectancy varies across regions of the United States. Davids et al (2014) examined the Community Health Status Indicators (CHSI) to Combat Obesity, Heart Disease, and Cancer to determine opportunities to improve health status and life expectancy based on known social determinants of health. They found a link between life expectancy and poverty, educational level, and the racial composition of the county.

The data may be accessed using the following link:

Details regarding the content of the data may be accessed here:

Presenting Problem 2

Many patients with chronic diseases do not engage in self-management activities. Bos-Touwen and associates (2015) investigated the characteristics of patients that participate in self-management programs for a number of chronic diseases including: type-2 Diabetes Mellitus (DM-II), Chronic Obstructive Pulmonary Disease (COPD), Chronic Heart Failure (CHF), and Chronic Renal Disease (CRD). They used a survey tool called the 13-item Patient Activation Measure (PAM-13) as well as demographic, clinical, and psychosocial variables.


Pop-up div Successfully Displayed

This div only appears when the trigger link is hovered over. Otherwise it is hidden from view.