This module describes quantitative data and examines common methods of data collection in quantitative studies.
Define quantitative data and its characteristics
Explain the difference between discrete and continuous data
List examples of quantitative data
Describe common methods of quantitative data collection
Quantitative data is data that can be counted or expressed numerically. It is commonly used to ask “how much” or “how many” and can be used to study events or levels of occurrence. Because it is numerical in nature, quantitative data is both definitive and objective. It also lends itself to statistical analysis and mathematical computations and therefore, is typically illustrated in charts or graphs.
There are two main types of quantitative data: discrete and continuous. Discrete data is described as having a finite number of possible values. For example, if a teacher gives an exam that has 100 questions, the exam scores reflect the number of answers that were correct out of the 100 possible questions. Discrete data may also be defined as data where there is space between values on a number line, thus values must be a whole number. For example, if a study examined the number of vehicles owned by households in America, the data collected would be whole numbers. Continuous data is defined as data where the values fall on a continuum and it is possible to have fractions or decimals. Continuous data is usually a physical measurement. Examples may include measurements of height, age, or distance.
Quantitative data collection may include ANY method that will result in numerical values. Common examples of quantitative data collection strategies may include:
Experiments and clinical trials
Surveys, interviews and questionnaires that collect numerical information or count data by using closed-ended questions
Observing or recording well-defined events such as the number of visits patients make to a doctor’s office each year
Obtaining information from a management information system.
The advantage of collecting quantitative data is that the numerical outcomes result in data that can be statistically analyzed that may be viewed as credible and useful in decision-making. However, the disadvantage of quantitative data is that it may be superficial and fail to fully capture explanatory information.
The following video, Quantitative vs. Qualitative Data, defines quantitative data, discusses the types of quantitative data and describes how the data can be analyzed.
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