

Age, height, weight, body mass index, serum creatinine, heart rate, systolic blood pressure, and diastolic blood pressure are some examples. It is two types: discrete and continuous.ĭiscrete variable: Discrete variable is the quantitative data, but its values cannot be expressed or presented in the form of a decimal for example, number of males, number of females, number of patients, and family size cannot expressed in decimal in meaningful way.Ĭontinuous data: Data are measured in values and can be quantified and presented in decimals. Age, blood pressure, body temperature, hemoglobin level, and serum creatinine level are some examples of quantitative data. Quantitative data are the numeric variables (e.g., how many, how much, or how often). Quantitative variable is the data that show some quantity through numerical value. All the ranking data including Likert scales, Bristol stool scale, and all the other scales which are ranked between 0 and 10 are also called ordinal data. For example, ordinal scales are seen in questions that call for ratings of quality (very good, good, fair, poor, very poor), agreement (strongly agree, agree, disagree, strongly disagree), economic status (low, medium, and high), etc. The difference between the two is that there is a clear ordering in the data, i.e., ordinal data, unlike nominal data, have some order. Ordinal variable: An ordinal variable is similar to a nominal variable. For example, gender (male and female) and marital status (married/unmarried) have two categories, but these categories have no natural order or ranking. Nominal variable: Nominal data are simply names or properties having two or more categories, and there is no intrinsic ordering to the categories, i.e., data have no natural ranking or ordering. These scales are mutually exclusive (no overlap) and none of them have any numerical significance. It is represented by a name, a symbol, or a number code. Qualitative variable (also called categorical variable) shows the quality or properties of the data. The first two (nominal and ordinal) are assessed in terms of words or attributes called qualitative data, whereas discrete and continuous variables are part of the quantitative data. The first two are called qualitative data and the last two are quantitative data. There are four types of variables: nominal, ordinal, discrete, and continuous. The objective of this paper is to discuss the statistical data type (Section A) and its presentation (Section B), which is an important part of biomedical research. There are various tabulation and graphical methods used to present the data, which are not possible without proper knowledge of data types. If done properly, they not only reduce word count but also convey an important message in a meaningful way so that the readers can grasp it easily. Similarly, in the data analysis, statistical tests/methods differ from one data type to another.ĭata presentation is an important step to communicate our information and findings to the audience and readers in an effective way. In the data collection, the type of questionnaire and the data recording tool differ according to the data types.

Measurement scale (data type) is an important part of data collection, analysis, and presentation. Statistics is a branch of mathematics dealing with the collection, analysis, presentation, interpretation, and conclusion of data, while biostatistics is a branch of statistics, where statistical techniques are used on biomedical data to reach a final conclusion.
