Key data collection methods and when you should use them

By Indeed Editorial Team

Published 25 April 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Quality data analysis is a fundamental part of modern business operation, guiding and targeting future operations and strategy in the long run. Collecting this data is therefore key, as valid data leads to correct conclusions and vice versa. This is where using the right collection methods comes in handy. In this article, we discuss the data collection methods available to you and the cases in which it's most appropriate to use the data methods, improving your data practices in the long term.

Related: Research skills: definition and examples

What data collection methods are available?

There's a wide range of data collection methods available to analysis departments. All work with different types of data in specific situations, offering tailored responses to the problem the team is responding to. Some key collection methods, alongside information about why they are important and when they are implemented, are as follows:

Observation

The first method of data collection is through observation. This is visible in a range of contexts and is compatible with both qualitative and quantitative data. For example, in football scouting, observation is for counting the number of passes, shots and possessions a player has, in addition to qualitative aspects about how they complete the actions. As a basic data collection method, observation is an excellent cover-all to ascertain data of all types.

When completing observational data collection, keep a distance between data collectors and data subjects. The act of observation interferes with the phenomena of observation. Data subjects ideally act within a vacuum, unaware that they are under observation thus retaining the same behaviour as prior.

Example: Management staff is launching an investigation into the conduct and performance of staff on the factory floor. Initial data collection entails watching from a distance to better establish the rate at which employees work and the quality of work at hand. The way employees work is additionally observed, finding flaws in workflows.

Related: Inductive reasoning in the workplace

Survey

The survey method emphasises asking respondents a range of questions. This includes multiple-choice, quantitative and written response questions such as employee opinions on a new policy or the number of products a customer buys from a company monthly. Surveys are a method of gathering quick and easy internal and external feedback on a range of issues. Customer feedback is simpler through this route than observation as the general public is difficult to observe when using your products.

When sending out surveys you ensure a representative audience. Targeting overwhelmingly leading or biased parts of a customer base leads to not every view being appropriately represented. This data is invalid and does not give a complete picture of the situation.

Example: Squibl Corporation's sales are dwindling and management sends out a survey to establish customer opinions on the product. Questions include quantitative queries such as the number of magazines users bought previously in addition to qualitative questions such as customer views on the quality of products. Through this survey, the management team learns which customers have dropped off and why acting to remedy the issue.

Focus groups

Akin to surveys, focus groups are selections of staff or customers created from a range of demographics and sections of the population or work team. Asking questions to the group leads to debate and discussion of the topics at hand. Furthermore, focusing entirely on qualitative questions in a focus group is key as a qualitative question provides more open responses debated between the group going forwards.

Creating a script asks the right questions throughout and keeps the conversation on the topic. With a script and a guiding hand in the room, the discussion is relevant and helpful throughout. All data in focus groups are helpful when you understand the demographic and audience each individual falls within.

Example: MediaInc releases a new political advertising campaign and wants to know what customers think of their product. They pose the question to the group and open the floor for discussion whilst listening in. As the group settles, the minder asks probing questions to encourage further discussion. The MediaInc team has information on the views each demographic holds but has a greater idea of the tensions and arguments between the specific views in question.

Related: 7 example group interview questions (plus sample answers)

Interview

Interviews convert the focus group environment into a one-to-one discussion. This method of data collection provides detailed responses to queries, as researchers ask scripted questions in greater depth than those asked in a focus group environment. Respondents in these cases are typically part of the prior focus group, as continuity is key in data collection. This is not always the case, but building a detailed database from a focus group foundation is a helpful route forwards.

Conducting multiple interviews is key. It removes the effect outliers have on the process, as interviewing a single member of a focus group that's unaligned with the rest of the discussants runs the risk of misleading data. Interview at least three individuals and see more valid, reliable data in the long term.

Example: MediaInc's prior focus group is helpful, although management asks a respondent back to build on their critiques and guide the advertising campaign forwards. The interviewer asks about the respondent's points in the focus group, encouraging greater detail. From the interview responses, a coherent image of their issues arises and tweaks to the campaign can be actioned, receiving improved responses from the target audience.

Research

Research entails discovering data from secondary sources and implementing it into your report and findings. Known as secondary data, researchers use other writers' books, scholarly articles and information from the research methods as listed above as a tool when improving their work. The benefit of implementing research is that expenditure on the part of the researcher is limited, although due diligence is required when assessing the reliability of the data in question.

Research is ideally followed by further primary data collection. Secondary data is reliable, although some sources are shaky and confirming them with data collected in the organisation is key to cementing the decision made. Although primary research is more resource-intensive, it gives solutions the company relies on rather than making inferences around the secondary data of others.

Example: BuckCo is entering a new market, and requires information surrounding customer behaviour in the market. By conducting research, BuckCo researchers discover little elasticity in the market, as one company is operating an effective monopoly. This research informs BuckCo executives not to enter the market, as barriers to entry are incredibly high.

Types of data

A strong understanding of the types of data is at the heart of good research practices. By fully understanding the types of data available, the positives and the drawbacks of each you avoid pitfalls in research practice. Below are the four types of data and what they mean for your data collection:

Primary data

Primary data collection is where researchers obtain information directly from original sources. For example, completing tests into the abrasiveness of materials and conducting interviews with subjects is primary research. Primary data is ideal for many situations as organisations tailor and adjust questions to collect appropriate data. Information is appropriate to the question and finding the correct response is comparatively simple.

Related: What is quantitative analysis? (With definitions and examples)

Secondary data

Secondary data refers to information gathered from previous research, whether from the organisation itself or an external source. Such sources include fellow organisations that make resources and data public or government organisations under a mandate releasing information from departmental work. Data, in this event, is rarely raw and has undergone a degree of analysis already. Some key secondary data sources include:

  • books

  • academic articles and papers

  • data collation websites

  • informative videos, podcasts and documentaries

Qualitative data

Qualitative data refers to immeasurable information. For example, thoughts, concepts and the feelings of respondents are qualitative data, as words describe them rather than numbers measuring them. Qualitative data is common in humanities research. Collecting qualitative data is easiest through the implementation of interviews and focus groups.

Quantitative data

Quantitative data collection is the antithesis of qualitative data and collects numerical and statistical data over descriptive information. Surveys and observation best record quantitative data. Finance and the sciences benefit from quantitative research, using it as a foundation for conclusions and long-term projections (in the event that data collection occurs over a long period of time).

Please note that none of the companies mentioned in this article are affiliated with Indeed.

Disclaimer: The model shown is for illustration purposes only, and may require additional formatting to meet accepted standards.

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