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This is a great way to collect quantitative data as it involves quick and to-the-point questions and answers. These strata are made using a common attribute that defines that set of people, for example, income or occupation. Stratified random sampling -In this method, you divide the population into smaller sub-groups called strata.For example, you select the first target member for sampling randomly and then select the rest in a predetermined fashion thereafter, say every third member of the group or say, every fifth member of the population. Systematic random sampling – In this, each member of the population is selected from a preset or ordered sampling frame.Simple random sampling – In this, each member of the targeted population has an equal probability of being selected for sampling.It allows you to reach out to your target population and collect data in the most effective way possible with a representative sample.Īdditionally, you can opt for any of the following sampling techniques as per your convenience and requirement. Probability sampling is a great way to eliminate sampling bias. You can collect quantitative data in many different ways. Statistically, the geometric or harmonic mean is calculated in ratio data while the arithmetic mean is calculated in interval data.Ī few examples of continuous data include: This type of quantitative data is usually represented using a line graph as a line graph aptly illustrates the data changes occurring over a period of time.Ĭontinuous data can be further divided into two types, namely, ratio data and interval data. Thus, the value will vary over a given period of time, depending on when you seek the data. This value has a tendency to fluctuate over time. On the other hand, continuous data is data that can take any value. This type of data is usually represented using tally charts, bar charts, and pie charts. Thus, you can easily identify discrete quantitative data by questioning whether the given data can be counted or not. Discrete data is also known as attribute data. These values are typically counted in whole numbers and cannot be broken down into smaller units. Let’s explore the two types of data in detail.ĭiscrete data is data that can be expressed in specific values. In simple terms, Discrete data is countable and Continuous data is measurable. There are two main types of quantitative data. However, you must try and identify relevant groups and descriptions to make sense of the data. Such data cannot be used for statistical analysis. Answers to questions like ‘ How much ’, ‘ How many ’, ‘ What percentage ’, and ‘ How often ’ are what constitutes quantitative data. Quantitative data refers to data that can be expressed in numerical terms. Ensure Precision with Solid Quantitative Data Collection.Difference Between Quantitative Data and Qualitative Data.Steps to Conduct Quantitative Data Analysis.