Quantitative research and surveys

Easily gather quantitative data from your ideal audience.

Quantitative research has long been the most popular and widely recognized approach to data gathering and analysis. Learn how to use quantitative research to get the most out of your data.

When conducting empirical research, your first choice is whether to take a quantitative or a qualitative approach—or, some mixture of the two. In order to make that decision, you first need to figure out your research objectives. Do you want to detect trends and patterns in your data? Are you interested in questions like: what are customers buying? When are they buying? And from where? Alternatively, are you less interested in the what, when and where of decision making, but more in the why?

Qualitative research is research that involves gathering non-numerical data in order to obtain an in-depth understanding of the topic of research inquiry. By gathering narrative information such as opinions, and explanations, you will be able to understand in-depth why people do what they do. In contrast, quantitative research involves gathering numerical data that can be subjected to statistical analysis in order describe the qualities and characteristics of a dataset, to understand patterns and to find relationships or correlations between data points. We go into more detail about the differences between qualitative and quantitative research, but if you’re interested in quantitative research techniques specifically, read on. We go into detail about the advantages of this type of research, different quantitative research methods you can use, how to design surveys for effective quantitative research and more.

There are many advantages to quantitative forms of research. Let's take a look at a few of the reasons why you might want to conduct quantitative, over qualitative research. 

One of the main benefits of quantitative research is its versatility. You can gather quantitative data to answer a host of questions of different types. For example, some of the types of questions you can address through quantitative research include:

  • What are the prevalent characteristics of our customers?
  • Which of our products are the most and least popular?
  • What is the correlation between buying intentions and average level of spend?

By quantifying the answers to questions like these, you can gain some very useful critical insight into multiple areas of interest. To learn more, read our recent guide about how to conduct effective quantitative research.

Qualitative data is often very detailed, but the tradeoff is that it takes a very long time to collect and analyze. In contrast, quantitative research data can be captured very quickly, and if you use tools like SurveyMonkey’s analysis tools, statistical analysis can be almost immediate. This is very useful if you need insight critically. 

Quantitative research often (but not always!) makes use of samples selected through probability based methods, like random sampling or cluster sampling. Randomized sampling approaches are considered to be the best way to sample because they yield a sample that is representative of the wider population, which increases the ability to generalize the results of the research to the wider population. 

Many methods of gathering quantitative data involve replication of questions, which enables comparison of answers across multiple groups of individuals. In contrast, this duplication is difficult to control using qualitative methods of inquiry. For instance, interviews are organic discussions and direction, flow and content of the conversation is likely to evolve in different ways according to interests and experiences of different participants. 

Using surveys and other quantitative methods, however, you can ask the same series of questions, in the same order to different participants, thus controlling the research process, even when you are not physically present. This means it is possible to duplicate insight and perform comparisons. For instance, if you’re interested in comparing employee experience before and after the introduction of a new employee engagement initiative, you can simply readminister the same survey to the same sample, twice.

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When conducting quantitative research, you should be particularly wary of respondents providing what market researchers call socially desirable responses. In other words, people are often minded to give socially appropriate answers to questions in order to appear open-minded, unbiased and socially acceptable. This is especially the case where questions are on sensitive or controversial issues. Taking steps to ensure that responses are honest and a true representation of respondents’ beliefs is crucial if you are to avoid introducing errors into your research findings. One way that you can help to reduce social desirability is by gathering data using an anonymous data collection method, and that is possible using quantitative research.

One of the main advantages of quantitative research is that most quantitative methods of data collection can be self administered by the research subject or respondent, without the need for the researcher to be present. For example, you might distribute an online survey to a group of consumers, who are then able to fill in the answers in their own time, and at their own leisure. When respondents perceive that they have more space and time, and less pressure to supply research data, they are often more willing to do so - and their response will be more accurate and truthful.

The decision about whether to conduct quantitative or qualitative research is essentially a question about whether you’re seeking depth, or breadth of insight. Qualitative research allows you to gain a deeper understanding of a research phenomenon, but breadth of insight is sacrificed. One of the ways that that sacrifice occurs is in the sample size. This makes sense intuitively: if you’re conducting, say, one hour in-person interviews with customers, there’s a limit to the number of interviews you could possibly conduct within a certain timeframe. In contrast, the number of surveys you can administer is potentially unlimited, especially if you’re using a self-administered approach, such as an online survey. This means that the sample size for quantitative research tends to be higher than for qualitative research. That’s important for the credibility and validity of your research findings. The higher the sample size, the lower the chance that outliers in the sample can skew your results.

Related to sample size is the ability to generalize findings. As we explain in more detail here, when conducting quantitative research, you will typically extract a sample of research subjects from a wider population for manageability. When sample sizes are larger and representative of the population, you can extrapolate your research findings more generally. In other words, you can infer that the results yielded from the sample also apply to the research population. This is rarely possible with qualitative research.

In current times, the ability to gather data remotely has never been more important. Obtaining high quality, qualitative data often demands a significant period of rapport building between researcher and subject, and that may require in-person interaction. On the other hand, surveys can be performed remotely. This is especially useful if your population of interest is geographically dispersed. Over the past two decades, there has been a huge upsurge in internet penetration, which has increased access to global research subjects accessible through online surveys. 

So, now you know the various benefits to conducting quantitative research. However, there is no one correct way to undertake quantitative research, and in fact, several different methodological approaches are possible. Let’s take a look at some of the different quantitative research methods, the types of scenarios in which each might be used and the advantages and disadvantages of each.

Surveys are basically standardized data collection tools, designed to gather information from a specified set of respondents about some predetermined areas of interest. With a few exceptions, each respondent is presented with the same questions, allowing for the rapid collection of data, the pooling and aggregation of results, and the detection of patterns and trends. Since surveys are intended to be administered relatively quickly, the majority of the survey should focus on the gathering of quantitative data through different question types such as:

  • Categorical questions which ask respondents to tick boxes, and which can be used to count categories of individuals and to perform frequency analysis. For example, asking respondents to indicate their gender or their favorite foods from a prepared list are examples of categorical questions.
  • Interval rating scales are used to capture respondents’ feelings about the topic of interest. Typically, respondents are asked to indicate where they fall on a multiple point scale. For example, a Likert scale question that asks respondents to rate their agreement with a statement is an interval scale question. These questions can be subjected to frequency analysis, or measures of central tendency and dispersion.

Surveys are best used to gather information based on behaviors, characteristics, opinions, demographic information from your population of interest. They’re also useful where there is no existing data that can be used to address your research questions.

One of the main advantages of surveys is that you can exert full control over the research design, and the data to be collected. Based on what you need to know, you can create the best mix of questions that will suit your needs. Surveys are also practical, inexpensive and quick to administer. You can potentially gather large amounts of data from large samples quickly, which makes them ideal for quantitative research.

However, since surveys tend to be self-administered, there are risks that some people will not understand the question, some people may refuse to answer certain questions, and some people may drop out of the survey altogether. Some of these disadvantages can be controlled by carefully designing and testing your survey. However, other disadvantages, such as the inability for respondents to express themselves in depth on the survey, can only be avoided with qualitative research.

Secondary research involves conducting a re-analysis of data that is already in existence, having been collected for another purpose. That data might have been collected by you or your organization (such as employee records, or customer sales data), or might have been collected by another organization altogether, and be available in the public domain (e.g. publicly available unemployment data, or data weather patterns). 

The secondary approach is often preferred by inexperienced researchers conducting small-scale studies. This is because the data can be gathered relatively quickly, and is often of good quality. In contrast, primary data collection is resource-intensive and burdensome, and often researchers find that they are unable to collect sufficient amounts of data. Furthermore, sometimes collecting original data is tantamount to ‘reinventing the wheel’. If good, secondary data is available, it makes sense to use it.

The main advantage of secondary data collection is that it is cheap and may be quick to gather. For example, if you want to understand more about the diversity, equity and inclusion of your workforce, a relatively simple first step would be to download HR records, and compare salaries on the basis of demographic data like gender, age and race. 

However, one problem is that it is often very challenging to find data that is readily available and which can be used to address your research questions and objectives. Your HR data could tell you something about whether your workforce is being rewarded equitably, but it might not give you a sense of how people feel about the organizational culture or your DEI efforts. For that, you might want to use a survey.

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There are actually several types of survey approach you can use to gather quantitative data, and the type you should use will depend on a range of factors, such as time pressures and whether or not you are interested in determining causal relationships.

Cross-sectional surveys are those where the data is captured at a single point in time. This type of survey is useful if you want to learn more about the traits of a population of interest, or whether you want a snapshot of current events.

Longitudinal surveys involve gathering data over an extended period of time. Using this approach, data will be gathered at least twice, and possibly more. This type of survey is best when you want to determine whether an input variable (e.g. a diet) has an effect on a later outcome variable (e.g. weight loss). 

Retrospective surveys ask respondents to recall and report on historical events or behaviors. This type of survey is great at learning more about people’s habits, and why they do what they do. It’s a particularly popular approach in marketing and consumer behavior research, where you might be interested in people’s past buying habits and experiences.

If you are opting to use surveys to gather quantitative research data, use the following tips and advice on how to create and design effective surveys that have the potential to yield quality, usable results.

  • Identify what you want to know

It might seem obvious, but the very worst thing you can do is to start writing a survey without a clear understanding of your goals. You’ll either miss opportunities to gather the data that you need to fulfill your research goals, or you’ll seek superfluous information that will only make your survey longer, and less appealing to respondents. Are you seeking opinion level data? Attitudinal data? Sample characteristics? Habits and behaviors? We recommend starting with a list of survey objectives before you even think about writing questions.

  • Keep questions clear or succinct

As we’ve mentioned above, one of the benefits of the survey method is that the researcher need not be present when the respondent is completing it. Since the respondent does not have anyone to clarify understanding, it's crucial that questions are clear and easy to comprehend. Questions should also be succinct to prevent respondents from abandoning the survey due to boredom or apathy. 

  • Avoid confusing questions

Questions that confuse are one of the main sources of error in survey based data. A common way to confuse your respondents is to ask them two questions in one, known as a double-barreled question.  For instance, the question: how often and how much time do you spend going to coffee shops? actually has two different questions embedded within it, and respondents will find it difficult to answer accurately. 

  • Make questions relevant to respondents

A surefire way to prompt respondents to refuse to answer questions or abandon your survey altogether is to ask them questions that are not relevant. If you’re asking respondents about their preferred coffee brand, do you really need to know their marital status? Make sure all questions are appropriate and necessary before you send that survey.

  • Use filter questions as needed

Filter questions are helpful in addressing the point of relevance, because you can use them to help respondents avoid questions that are irrelevant to them. However, use them sparingly: use too many, and you will end up with only small subsets of your sample answering questions, which can cause validity problems when it comes to data analysis.

  • Get feedback

We recommend piloting your survey with a small sample prior to administering it in order to check that your questions are understood in the way you intended them.

So there you have it: quantitative research, how it’s used, and how to use surveys to gather it. If you’re looking for a research sample for quantitative research, buy targeted responses with SurveyMonkey Audience. Alternatively, if you would like to learn more about our expert services and our suite of market research solutions, visit Momentive. Momentive is an agile experience management company built for what’s next. Learn more about Momentive.

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