What are the best 5 common data collection instruments?
In the age when information is power, how we gather that information should be one of our major concerns, right? Also, which of the many data collection methods is the best for your particular needs? Whatever the answer to the two questions above, one thing is for sure – whether you’re an enterprise, organization, agency, entrepreneur, researcher, student, or just a curious individual, data gathering needs to be one of your top priorities.
Still, raw data doesn’t always have to be particularly useful. Without proper context and structure, it’s just a set of random facts and figures after all. However, if you organize, structure, and analyze data obtained from different sources, you’ve got yourself a powerful “fuel” for your decision-making.
Data collection is defined as the “process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer queries, stated research questions, test hypotheses, and evaluate outcomes.”
It is estimated that, by 2025, the total volume of data created and consumed worldwide will reach 163 zettabytes. That being said, there are numerous reasons for data collection, but here we are going to focus primarily on those relevant to marketers and small business owners:
- It helps you learn more about your target audience by collecting demographic information
- It enables you to discover trends in the way people change their opinions and behavior over time or in different circumstances
- It lets you segment your audience into different customer groups and direct different marketing strategies at each of the groups based on their individual needs
- It facilitates decision making and improves the quality of decisions made
- It helps resolve issues and improve the quality of your product or service based on the feedback obtained
According to Clario, global top collectors of personal data among social media apps are:
And given how successfull they are when it comes to meeting their users’ needs and interests, it is safe to say that streamlined and efficient data collection process is at the core of any serious business in 2023.
Before we dive deeper into different data collection techniques and methods, let’s just briefly differentiate between the two main types of data collection – primary and secondary.
Primary vs. Secondary Data Collection
Primary data collection
Primary data (also referred to as raw data) is the data you collect first-hand, directly from the source. In this case, you are the first person to interact with and draw conclusions from such data, which makes it more difficult to interpret it.
According to reasearch, about 80% of all collected data by 2025. will be unstructured. In other words, unstructured daza collected as primary data but nothing meaningful has been done with it. Unstructured data needs to be organized and analyzed if it’s going to be used as in-depth fuel for decision-making.
Secondary data collection
Secondary data represents information that has already been collected, structured, and analyzed by another researcher. If you are using books, research papers, statistics, survey results that were created by someone else, they are considered to be secondary data.
Secondary data collection is much easier and faster than primary. But, on the other hand, it’s often very difficult to find secondary data that’s 100% applicable to your own situation, unlike primary data collection, which is in most cases done with a specific need in mind.
Some examples of secondary data include census data gathered by the US Census Bureau, stock prices data published by Nasdaq, employment and salaries data posted on Glassdoor, all kinds of statistics on Statista, etc. Further along the line, both primary and secondary data can be broken down into subcategories based on whether the data is qualitative or quantitative.
Quantitative vs. Qualitative data
This type of data deals with things that are measurable and can be expressed in numbers or figures, or using other values that express quantity. That being said, quantitative data is usually expressed in numerical form and can represent size, length, duration, amount, price, and so on.
Quantitative research is most likely to provide answers to questions such as who? when? where? what? and how many?
Quantitative survey questions are in most cases closed-ended and created in accordance with the research goals, thus making the answers easily transformable into numbers, charts, graphs, and tables.
The data obtained via quantitative data collection methods can be used to conduct market research, test existing ideas or predictions, learn about your customers, measure general trends, and make important decisions.
For instance, you can use it to measure the success of your product and which aspects may need improvement, the level of satisfaction of your customers, to find out whether and why your competitors are outselling you, or any other type of research.
As quantitative data collection methods are often based on mathematical calculations, the data obtained that way is usually seen as more objective and reliable than qualitative. Some of the most common quantitative data collection techniques include surveys and questionnaires (with closed-ended questions).
Compared to qualitative techniques, quantitative methods are usually cheaper and it takes less time to gather data this way. Plus, due to a pretty high level of standardization, it’s much easier to compare and analyze the findings obtained using quantitative data collection methods.
Unlike quantitative data, which deals with numbers and figures, qualitative data is descriptive in nature rather than numerical. Qualitative data is usually not easily measurable as quantitative and can be gained through observation or open-ended survey or interview questions.
Qualitative research is most likely to provide answers to questions such as “why?” and “how?”
As mentioned, qualitative data collection methods are most likely to consist of open-ended questions and descriptive answers and little or no numerical value. Qualitative data is an excellent way to gain insight into your audience’s thoughts and behavior (maybe the ones you identified using quantitative research, but weren’t able to analyze in greater detail).
Data obtained using qualitative data collection methods can be used to find new ideas, opportunities, and problems, test their value and accuracy, formulate predictions, explore a certain field in more detail, and explain the numbers obtained using quantitative data collection techniques.
As quantitative data collection methods usually do not involve numbers and mathematical calculations but are rather concerned with words, sounds, thoughts, feelings, and other non-quantifiable data, qualitative data is often seen as more subjective, but at the same time, it allows a greater depth of understanding.
Some of the most common qualitative data collection techniques include open-ended surveys and questionnaires, interviews, focus groups, observation, case studies, and so on.
5 Data Collection Methods
Before we dive deeper into different data collection tools and methods – what are the 5 methods of data collection? Here they are:
- Surveys, quizzes, and questionnaires
- Focus groups
- Direct observations
- Documents and records (and other types of secondary data, which won’t be our main focus here)
Data collection methods can further be classified into quantitative and qualitative, each of which is based on different tools and means.
Quantitative data collection methods
1. Closed-ended Surveys and Online Quizzes
Closed-ended surveys and online quizzes are based on questions that give respondents predefined answer options to opt for. There are two main types of closed-ended surveys – those based on categorical and those based on interval/ratio questions.
Categorical survey questions can be further classified into dichotomous (‘yes/no’), multiple-choice questions, or checkbox questions and can be answered with a simple “yes” or “no” or a specific piece of predefined information.
Interval/ratio questions, on the other hand, can consist of rating-scale, Likert-scale, or matrix questions and involve a set of predefined values to choose from on a fixed scale. To learn more, we have prepared a guide on different types of closed-ended survey questions.
Once again, these types of data collection methods are a great choice when looking to get simple and easily analyzable counts, such as “85% of respondents said surveys are an effective means of data collection” or “56% of men and 61% of women have taken a survey this year” (disclaimer: made-up stats).
If you’d like to create something like this on your own, learn more about how to make the best use of our survey maker.
It lets you segment your audience into different customer groups and direct different marketing strategies at each of the groups based on their individual needs (check out our quiz maker for more details).
Qualitative data collection methods
2. Open-Ended Surveys and Questionnaires
Opposite to closed-ended are open-ended surveys and questionnaires. The main difference between the two is the fact that closed-ended surveys offer predefined answer options the respondent must choose from, whereas open-ended surveys allow the respondents much more freedom and flexibility when providing their answers.
Here’s an example that best illustrates the difference:
When creating an open-ended survey, keep in mind the length of your survey and the number and complexity of questions. You need to carefully determine the optimal number of questions, as answering open-ended questions can be time-consuming and demanding, and you don’t want to overwhelm your respondents.
Compared to closed-ended surveys, one of the quantitative data collection methods, the findings of open-ended surveys are more difficult to compile and analyze due to the fact that there are no uniform answer options to choose from. In addition, surveys are considered to be among the most cost-effective data collection tools.
3. 1-on-1 Interviews
One-on-one (or face-to-face) interviews are one of the most common types of data collection methods in qualitative research. Here, the interviewer collects data directly from the interviewee. Due to it being a very personal approach, this data collection technique is perfect when you need to gather highly personalized data.
Depending on your specific needs, the interview can be informal, unstructured, conversational, and even spontaneous (as if you were talking to your friend) – in which case it’s more difficult and time-consuming to process the obtained data – or it can be semi-structured and standardized to a certain extent (if you, for example, ask the same series of open-ended questions).
4. Focus groups
The focus group data collection method is essentially an interview method, but instead of being done 1-on-1, here we have a group discussion.
Whenever the resources for 1-on-1 interviews are limited (whether in terms of people, money, or time) or you need to recreate a particular social situation in order to gather data on people’s attitudes and behaviors, focus groups can come in very handy.
Ideally, a focus group should have 3-10 people, plus a moderator. Of course, depending on the research goal and what the data obtained is to be used for, there should be some common denominators for all the members of the focus group.
For example, if you’re doing a study on the rehabilitation of teenage female drug users, all the members of your focus group have to be girls recovering from drug addiction. Other parameters, such as age, education, employment, marital status do not have to be similar.
5. Direct observation
Direct observation is one of the most passive qualitative data collection methods. Here, the data collector takes a participatory stance, observing the setting in which the subjects of their observation are while taking down notes, video/audio recordings, photos, and so on.
Due to its participatory nature, direct observation can lead to bias in research, as the participation may influence the attitudes and opinions of the researcher, making it challenging for them to remain objective. Plus, the fact that the researcher is a participant too can affect the naturalness of the actions and behaviors of subjects who know they’re being observed.
Interactive online data collection
Above, you’ve been introduced to 5 different data collection methods that can help you gather all the quantitative and qualitative data you need. Even though we’ve classified the techniques according to the type of data you’re most likely to obtain, many of the methods used above can be used to gather both qualitative and quantitative data.
While online quiz maker may seem like an inocuous tool for data collection, it’s actually a great way to engage with your target audience in a way that will result in actionable and valuable data and information. Quizzes can be more helpful in gathering data about people’s behavior, personal preferences, and more intimate impulses.
You can go for these options:
This type of quiz has been used for decades by psychologists and human resources managers – if administered properly, it can give you a great insight into the way your customers are reasoning and making decisions.
The results can come in various forms – they are usually segmented into groups with similar characteristics. You can use it to find out what your customers like, what their habits are, how they decide to purchase a product, etc.
This type of questionnaire lingers somewhere between a quiz and survey – but in this case, you can quantify the result based on your own metrics and needs. For example, you can use it to determine the quality of a lead.
You can use surveys to collect opinions and feedback from your customers or audience. For example, you can use it to find out how old your customers are, what their education level is, what they think about your product, and how all these elements interact with each other when it comes to the customer’s opinion about your business.
This type of quiz can help you test the user’s knowledge about the certain topic, and it differentiates from the personality quiz by having answers that are correct or false.
You can use it to test your products or services. For example, if you are selling a language learning software, a test quiz is a valuable insight into its effectiveness.
How to make data collection science-proof
However, if you want to acquire this often highly-sensitive information and draw conclusions from it, there are specific rules you need to follow. The first group of those rules refers to the scientific methodology of this form of research and the second group refers to legal regulation.
1. Pay Attention to Sampling
Sampling is the first problem you may encounter if you are seeking to research a demographic that extends beyond the people on your email list or website. A sample, in this case, is a group of people taken from a larger population for measurement.
To be able to draw correct conclusions, you have to say with scientific certainty that this sample reflects the larger group it represents.
Your sample size depends on the type of data analysis you will perform and the desired precision of the estimates.
Remember that until recently, users of the internet and e-mail were not truly representative of the general population. This gap has closed significantly in recent years, but the way you distribute your quiz or survey can also limit the scope of your research.
For example, a Buzzfeed type of quiz is more likely to attract a young, affluent demographic that doesn’t necessarily reflect the opinions and habits of middle-aged individuals.
You can use this software to calculate the size of the needed sample. You can also read more about sapling and post-survey adjustments that will guarantee that your results are reliable and applicable.
2. Ensure high-response rate
Online survey response rates can vary and sometimes can as low as 1%. You want to make sure that you offer potential respondents some form of incentive (for example, a discount for your product or an entertainment value for people who solve personality quizzes).
Response rate is influenced by interests of participants, survey structure, communication methods, and assurance of privacy and confidentiality. We will deal with the confidentiality in the next chapter, and here you can learn more about optimizing your quizzes for high response rates.
Now that you know what are the advantages and disadvantages of the online quizzes and surveys, these are the key takeaways for making a high-quality questionnaire.
3. Communicate clearly
Keep your language simple and avoid questions that may lead to confusion or ambiguous answers. Unless your survey or quiz target a specific group, the language shouldn’t be too technical or complicated.
Also, avoid cramming multiple questions into one. For example, you can ask whether the product is “interesting and useful,” and offer “yes” and “no” as an answer – but the problem is that it could be interesting without being useful and vice versa.
4. Keep it short and logical
Keep your quizzes and surveys as short as possible and don’t risk people opting out of the questionnaire halfway. If the quiz or survey have to be longer, divide them into several segments of related questions. For example, you can group questions in a personality quiz into interests, goals, daily habits etc. Follow a logical flow with your questions, don’t jump from one topic to another.
5. Avoid bias
Don’t try to nudge respondents’ answers towards a certain result. We know it feels easier to ask how amazing your product is, but try to stay neutral and simply ask people what they think about it.
Also, make sure that multimedia content in the survey or quiz does not affect responses.
6. Consider respondents’ bias
If you conduct personality quizzes, you may notice that you cannot always expect total accuracy when you ask people to talk about themselves. Sometimes, people don’t have an accurate perception of their own daily activities, so try to be helpful in the way you word the questions.
For example, it’s much easier for them to recall how much time they spend on their smartphone on a daily basis, then to as them to calculate in on a weekly or monthly basis.
Even then you may not get accurate answers, which is why you should cross-examine the results with other sources of information.
7. Respect Privacy and Confidentiality
As we previously mentioned, respecting users’ privacy and maintaining confidentiality is one of the most important factors that contribute to high response rates.
Until fairly recently, privacy and data protection laws were lagging almost decades behind our technological development. It took several major data-breach and data-mining scandals to put this issue on the agenda of the governments and legal authorities.
For a good reason – here are some stats showing how Internet users feel about privacy.
- 85% of the world’s adults want to do more to protect their online privacy
- 71% of the world’s adults have taken measures to protect their online privacy
- Two-thirds of the world’s consumers think that tech companies have too much control over their data
- According to consumers, the most appropriate type of collected data is brand purchase history
Many of global users’ concerns were addressed for the first time in the General Data Protection Regulation (GDPR) which was introduced on the 25th May 2018. It establishes different privacy legislation from European countries under one umbrella of legally binding EU regulation.
Although the law is European, each website that receives European visitors has to comply – and this means everyone. So what are your obligations under GDPR?
- you have to seek permission to use the customers’ data, explicitly and unambiguously
- you have to explain why you need this data
- you have to prove you need this data
- you have to document the ways you use personal data
- you have to report any data breaches promptly
- accessible privacy settings built into your digital products and websites
- switched on privacy settings
- regular privacy impact assessments
While the new rulebook may seem intimidating at first, in reality, it comes down to a matter of business ethics. Think about it in the simplest terms. Sleazy sliding into people’s email inbox may have its short-term benefits, but in the long run, it amounts to building an email list full of people who are uninterested in your product and irritated by your spam.
Actively seeking permission to send emails to your potential and existing customers is an excellent way to make sure that your list is full of high-quality leads that want to hear or buy from you.
Protecting your customers’ data or going to great lengths to explain how you’re going to use it establishes a long-term relationship based on trust.