Which of the following is a method in which both qualitative and quantitative data are collected and analyzed at the same time?

We often talk of qualitative OR quantitative research.

Inhaltsverzeichnis Show

Explanatory Sequential Design
Exploratory Sequential Design
Convergent Parallel Design
When to use mixed methods research
Mixed methods research designs
Convergent parallel
Explanatory sequential
Exploratory sequential
Advantages of mixed methods research
“Best of both worlds” analysis
Method flexibility
Disadvantages of mixed methods research
Differing or conflicting results
Frequently asked questions
What of the following is a method in which both qualitative and quantitative data are collected and analyzed at the same time?
Which method collects both quantitative and qualitative data?
What of the following is a method in which both qualitative and quantitative data are collected and analyzed at the same time quizlet?
What type of research might collect quantitative data first and then use a qualitative study to help interpret the results?

You needn’t think of this as an either-or situation. You can often optimize customer research with a mix of the two.

While it might seem unorthodox to mix seemingly different fields, it turns out to be a common practice.

Mixing qualitative and quantitative methods is neither new nor controversial. In fact, there’s a journal dedicated to mixed-method research, aptly named, The Journal of Mixed Method Research.

Customer research lends itself well to the triangulating that a mixed-methods approach offers: identifying areas of convergence among methods to, in turn, increase the usefulness and validity of the findings.

While you can combine qualitative and quantitative methods at various points—data collection or data analysis, for example—we typically use the following three research designs (also called topologies).

Explanatory Sequential Design

An explanatory sequential design emphasizes quantitative analysis, which we follow with interviews or observation (qualitative measures) to help explain the quant findings.

For example, we conducted a large comparative branding study with an internet retailer on attitudes toward the shopping experience on five mobile websites. After statistical analysis and cross-tabbing on experience levels to gauge brand attitudes, we came up with topics to further explore. We then recruited a new set of 16 participants for 1-on-1 sessions in which participants interacted with the sites used earlier and discussed their attitudes toward those sites.

This enabled us to look more closely into trends we observed in the larger sample. In this study we used a new set of 16 participants; you can also use a subset of participants from the first survey phase and dig deeper into any interesting patterns. To remember, the explanatory sequential design, think of qual explaining quant.

Exploratory Sequential Design

An exploratory sequential design starts with the qualitative research and then uses insights gained to frame the design and analysis of the subsequent quantitative component.

For example, to develop a new questionnaire, start with a qualitative phase where you interview participants and identify phrases, questions, or terms used to help derive the items used. We used this approach to develop the SUPR-Q®.

Exploratory sequential design lends itself well to usability testing. We often start with 5 to 10 participants in a classic think-aloud, moderated usability test. This exposes problem areas for which to create new tasks and survey questions, which in turn helps us refine our understanding of customer attitudes. We then launch a larger-scale, unmoderated study to get a better idea of the magnitude of the problems in the larger customer population.
To remember the exploratory sequential approach, think of qual to enable research questions followed by quant for validation.

Convergent Parallel Design

If you collect qualitative data and quantitative data simultaneously and independently, and if you then analyze the results, you’re executing a convergent parallel design. In the analysis phase, you often give equal weight to the quant and qual data—you look to compare and contrast the results to look for patterns or contradictions.

For example, one team may conduct ethnographic research at customer locations while another launches a survey to a set of global customers on the same product experience. The teams then converge and compile the findings to generate insights.

In Summary

If you’re setting up a customer-research project and wondering whether to take a quantitative or a qualitative approach, consider a third option: use both, and take advantage of the opportunities afforded by mixing the two methods.

Published on August 13, 2021 by Tegan George. Revised on July 21, 2022.

Mixed methods research combines elements of quantitative research and qualitative research in order to answer your research question. Mixed methods can help you gain a more complete picture than a standalone quantitative or qualitative study, as it integrates benefits of both methods.

Mixed methods research is often used in the behavioral, health, and social sciences, especially in multidisciplinary settings and complex situational or societal research.

Mixed methods research question examples

To what extent does the frequency of traffic accidents (quantitative) reflect cyclist perceptions of road safety (qualitative) in Amsterdam?
How do student perceptions of their school environment (qualitative) relate to differences in test scores (quantitative)?
How do interviews about job satisfaction at Company X (qualitative) help explain year-over-year sales performance and other KPIs (quantitative)?
How can voter and non-voter beliefs about democracy (qualitative) help explain election turnout patterns (quantitative) in Town X?
How do average hospital salary measurements over time (quantitative) help to explain nurse testimonials about job satisfaction (qualitative)?

When to use mixed methods research

Mixed methods research may be the right choice if your research process suggests that quantitative or qualitative data alone will not sufficiently answer your research question. There are several common reasons for using mixed methods research:

Generalizability: Qualitative research usually has a smaller sample size, and thus is not generalizable. In mixed methods research, this comparative weakness is mitigated by the comparative strength of “large N,” externally valid quantitative research.
Contextualization: Mixing methods allows you to put findings in context and add richer detail to your conclusions. Using qualitative data to illustrate quantitative findings can help “put meat on the bones” of your analysis.
Credibility: Using different methods to collect data on the same subject can make your results more credible. If the qualitative and quantitative data converge, this strengthens the validity of your conclusions. This process is called triangulation.

As you formulate your research question, try to directly address how qualitative and quantitative methods will be combined in your study. If your research question can be sufficiently answered via standalone quantitative or qualitative analysis, a mixed methods approach may not be the right fit.

Research exampleYou want to research cycling safety in high-traffic areas of Amsterdam. If you’re interested in the frequency of accidents and where they occur, this could be a straightforward quantitative analysis. If you’re interested in the nature of complaints submitted by cyclists, or their perceptions about cycling in particular areas, then a qualitative approach may fit best.

But mixed methods might be a good choice if you want to meaningfully integrate both of these questions in one research study.

For example, you could use a mixed methods design to investigate whether areas perceived as dangerous have high accident rates, or to explore why specific areas are more dangerous for cyclists than others.

Keep in mind that mixed methods research doesn’t just mean collecting both types of data; you need to carefully consider the relationship between the two and how you’ll integrate them into coherent conclusions. Mixed methods can be very challenging to put into practice, so it’s a less common choice than standalone qualitative or qualitative research.

Mixed methods research designs

There are different types of mixed methods research designs. The differences between them relate to the aim of the research, the timing of the data collection, and the importance given to each data type.

As you design your mixed methods study, also keep in mind:

Your research approach (inductive vs deductive)
Your research questions
What kind of data is already available for you to use
What kind of data you’re able to collect yourself.

Here are a few of the most common mixed methods designs.

Convergent parallel

In a convergent parallel design, you collect quantitative and qualitative data at the same time and analyze them separately. After both analyses are complete, compare your results to draw overall conclusions.

Example: Convergent parallel designIn your research on cycling safety in Amsterdam, you undertake both sides of your research simultaneously:

On the qualitative side, you analyze cyclist complaints via the city’s database and on social media to find out which areas are perceived as dangerous and why.
On the quantitative side, you analyze accident reports in the city’s database to find out how frequently accidents occur in different areas of the city.

When you finish your data collection and analysis, you then compare results and tie your findings together.

Embedded

In an embedded design, you collect and analyze both types of data at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.

This is a good approach to take if you have limited time or resources. You can use an embedded design to strengthen or supplement your conclusions from the primary type of research design.

Example: Embedded designAs part of a quantitative study testing whether the number of cyclist complaints about an area correlates with the number of accidents, you could “embed” a series of qualitative interviews with cyclists who submitted complaints to further strengthen your argument. The bulk of your research remains quantitative.

Explanatory sequential

In an explanatory sequential design, your quantitative data collection and analysis occurs first, followed by qualitative data collection and analysis.

You should use this design if you think your qualitative data will explain and contextualize your quantitative findings.

Example: Explanatory sequentialYou analyze the accident statistics first and draw preliminary conclusions about which areas are most dangerous. Based on these findings, you conduct interviews with cyclists in high-accident areas and analyze complaints qualitatively.

You can utilize the qualitative data to explain why accidents occur on specific roads, and take a deep dive into particular problem areas.

Exploratory sequential

In an exploratory sequential design, qualitative data collection and analysis occurs first, followed by quantitative data collection and analysis.

You can use this design to first explore initial questions and develop hypotheses. Then you can use the quantitative data to test or confirm your qualitative findings.

Example: Exploratory sequential designYou first interview cyclists to develop an initial understanding of problem areas, and draw preliminary conclusions. Then you analyze accident statistics to test whether cyclist perceptions line up with where accidents occur.

Advantages of mixed methods research

“Best of both worlds” analysis

Combining the two types of data means you benefit from both the detailed, contextualized insights of qualitative data and the generalizable, externally valid insights of quantitative data. The strengths of one type of data often mitigate the weaknesses of the other.

For example, solely quantitative studies often struggle to incorporate the lived experiences of your participants, so adding qualitative data deepens and enriches your quantitative results.

Solely qualitative studies are often not very generalizable, only reflecting the experiences of your participants, so adding quantitative data can validate your qualitative findings.

Method flexibility

Mixed methods are less tied to disciplines and established research paradigms. They offer more flexibility in designing your research, allowing you to combine aspects of different types of studies to distill the most informative results.

Mixed methods research can also combine theory generation and hypothesis testing within a single study, which is unusual for standalone qualitative or quantitative studies.

Disadvantages of mixed methods research

Workload

Mixed methods research is very labor-intensive. Collecting, analyzing, and synthesizing two types of data into one research product takes a lot of time and effort, and often involves interdisciplinary teams of researchers rather than individuals. For this reason, mixed methods research has the potential to cost much more than standalone studies.

Differing or conflicting results

If your analysis yields conflicting results, it can be very challenging to know how to interpret them in a mixed methods study. If the quantitative and qualitative results do not agree or you are concerned you may have confounding variables, it can be unclear how to proceed.

Due to the fact that quantitative and qualitative data take two vastly different forms, it can also be difficult to find ways to systematically compare the results.

Frequently asked questions

What is data collection?

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

What are the main types of mixed methods research designs?

These are four of the most common mixed methods designs:

Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions.
Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

This Scribbr article

George, T. (July 21, 2022). Mixed Methods Research | Definition, Guide & Examples. Scribbr. Retrieved October 5, 2022, from https://www.scribbr.com/methodology/mixed-methods-research/

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What of the following is a method in which both qualitative and quantitative data are collected and analyzed at the same time?

A mixed methods research design is a procedure for collecting, analyzing, and “mixing” both quantitative and qualitative research and methods in a single study to understand a research problem.

Which method collects both quantitative and qualitative data?

In mixed methods research, you use both qualitative and quantitative data collection and analysis methods to answer your research question.

What of the following is a method in which both qualitative and quantitative data are collected and analyzed at the same time quizlet?

What is triangulation mixed method design: Quantitative and qualitative data collection and analysis are conducted separately but concurrently in one phase, the findings are integrated and equal priority is given to both quantitative and qualitative method.

What type of research might collect quantitative data first and then use a qualitative study to help interpret the results?

Explanatory Sequential Design - Two phase design where quantitative data is collected and analyzed first, then qualitative data is collected and analyzed based on the quantitative results.