Data Collection Methods

Data Collection Methods – Introduction



What Is Data Collection?

Data collection is “a process by which the researcher collects the information from all the relevant sources to find answers to the research problem, test the hypothesis and evaluate the outcomes” (“Data Collection”, n.d.).

There are numerous methods of data collection such as surveys, questionnaires, interviews, and observation. Managers can choose which techniques and approaches to use depending on their resources and organizational objectives.

There are two main types of data managers deal with-in data collection: Quantitative and Qualitative.

Quantitative Data

Quantitative data deals with measurable quantities, values, or numbers that are represented in numerical forms such as amount, length, size, price, or duration. This type of data is used to test hypotheses based on theories (The University of Wisconsin, n.d). For example, HR may have a theory that a lack of social support from coworkers is leading to reduced employee engagement. HR might ask the question, “To what extend does coworker social support impact employee engagement?” Then they would collect numeric data such as the percentage of employees who participate in clubs and events together outside of work and the average tenure and performance scores of those employees.

Using quantitative data can provide results that are broad and can be generally applied to specific populations. For example, an HR Manager might find exit interview survey results to reveal that 50% of all voluntary turnover is due to employees leaving for higher-paying jobs. The “50%” is what makes the data quantitative. While helpful, quantitative data rarely tells the whole story and is limited to the specific things being measured for specific participants. What if the HR Manager needs to explain the reasons for individual employees leaving in more detail? In that case, qualitative data would be needed (National Science Foundation (NSF), 2002).

Qualitative Data

Qualitative data deals with quality and is expressed using words instead of numbers. This type of data is more descriptive in nature than quantitative data. Unlike quantitative data, qualitative data is generally not measurable. Instead, managers learn about them via observation or by analyzing detailed written responses. Qualitative data includes descriptions such as color, appearance, texture, and other qualities. For example, if you ask a manager to describe an employee’s behavior, the data would be qualitative. If you ask the manager to instead rate the employee’s behavior on a scale of 1 to 10, then you have made the data quantitative.

Qualitative data cannot in most cases be applied generally to a broad group of people.  However, it is often used to generate the hypothesis on which quantitative data collection is then based (University of Wisconsin, n.d.). This type of data is also highly subjective, which provides valuable perspectives but can be overly colored by opinion versus actionable, factual information.

Once managers determine what type of data they need to collect, they must identify the most appropriate collection method.