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RESEARCH PRODUCT

Descriptive Statistics

subject

description

A set of medical data is based on a collection of the data of individual cases or objects, also called observation units or statistical units. Every case, for example every study participant, patient, every experimental animal, every tooth or every cell shows comparable parameters (such as body weight, gender, erosion, pH). Each of these parameters, also called variables, has a specific parameter value (gender = male, age = 30 years, weight = 70 kg) for each observation unit (for example the patient). The aim of descriptive statistics is to summarize the data, so that they can be clearly illustrated (1–3). The property of a parameter is specified by its so-called scale of measure. Generally two types of parameters are distinguished. A variable has a metric level (= quantitative data) if it can be counted, measured or weighed in a physical unit (as in cm or kg) or at least can be recorded in whole numbers. Data with a metric scale of measure can be further classified into continuous and discrete variables. In contrast to discrete variables, continuous variables can take any value. Examples for metric continuous parameters are body height in cm, blood pressure in mmHg or the creatinine concentration in mg/L. One example for a metric discrete parameter is the number of erythrocytes per microliter of blood. The gender of man cannot be measured, but is classified into two categories. Parameters which can be classified into two or more categories are described as categorical parameters (= qualitative data). A further classification of a categorical parameter is into nominal characteristics (unordered) and ordinal characteristics (ordered according to rank). Good basic portrayals of the descriptive statistics of medical data can be found in text books (4–9). Figure 1 gives a review of types of parameters, as well as graphs to be used and statistical measures. Figure 1 Sketch of parameter types and suitable statistical measures for descriptive presentation Different procedures are necessary for the statistical evaluation of metric and categorical parameters in graphic and tabular forms. The graphs used here and the evaluation tables were created with the statistics package SPSS for WINDOWS (Version 15). As example, we are using data of 176 sportsmen and women.