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5 Questions to Help You Choose Between Qualitative and Quantitative Approaches

  • October 20, 2020

  • Eyes4Research

When starting a research project, deciding between qualitative and quantitative approaches can be difficult. Here are some questions to consider to choose the best option for your overall research objectives. 

How Complex is Your Concept?

Topic complexity plays a large role in finding the most suitable research approach. Quantitative research is best used for simple, measurable topics, as numerical data may not provide sufficient insights for abstract concepts such as emotion, thoughts, or attitudes. The use of operational definitions also limits interpretations of complex insights, reducing conclusions to numerical data. 

Taking a qualitative approach helps develop an in-depth understanding of topics that are difficult to quantify. This method is best for concepts that are not widely understood and help researchers explore new ideas with more flexibility in their interpretations. Instead of reducing insights to a number, qualitative data provides researchers with detailed perspectives that can identify new patterns and understand intangible concepts. 

How Many Participants Do You Need? 

When choosing a research approach, one must consider the number of participants needed. For studies looking to generalize a large population, quantitative research may be the best option, as researchers can use the internet to send surveys to thousands of respondents quickly and efficiently. Quantitative research also simplifies data analysis, as researchers can use software to help organize and spot numerical trends, helping to confirm or dispute hypotheses. 

For studies requiring a smaller number of respondents, one should use qualitative research. This approach allows researchers to gather in-depth insights about a topic through one-on-one interviews, focus groups, and observations. Seeing how this approach often requires researchers to coordinate with individual respondents, this method works best with small sample sizes. Considering their objectivity, the results of these studies also need a more hands-on analysis to obtain insights. With this, it remains the best practice to limit the number of respondents to save themselves time when interpreting qualitative results.  

Are You Forming or Testing a Hypothesis?

When beginning a research project, one must consider the overall purpose of the initiative. For researchers looking to confirm or test a hypothesis or theory, quantitative data remains the ideal choice. This approach provides researchers with the subjective, numerical results they need to conclude correlational, descriptive, and experimental methods. 

Qualitative research remains best for those looking to form a hypothesis, providing researchers with in-depth insights on concepts, experiences, and thoughts while helping explore and develop new ideas. Seeing that the results are objective, this method remains best suited for researchers looking to understand a topic rather than test or confirm it. 

 Do You Need to Replicate the Study?

Researchers must consider if their study is repeatable when deciding on their approach. Quantitative research allows for replication, as data collection standardized protocols make it easy to track numerical data over time. On the other hand, qualitative data is unreplicable, as insights are created through the researcher’s interpretation, invalidating the conclusions of other research professionals.  

How Much Does the Setting Affect Your Research Initiative? 

The overall results of a study may be influenced by its setting, making it crucial for researchers to pick the best possible approach for their overall research objectives. Quantitative research often disregards context, taking place in unnatural settings that may deter respondents from providing accurate information. On the other hand, qualitative research takes place in a naturalistic setting with a real-world context. Despite this, uncontrollable stimuli in the environment make it difficult to draw definite conclusions, leading to unreliable results. 

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