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What Kind of Meeting Data Should You Capture?

EM Team Member
Posted byEM Team Member
on 02/28/17 02:30 PM

What kind of data is better, quantitative or qualitative? This question has often been asked and answered, but the comparison really isn't of which type of data is better, rather, which suits a given situation more. 

It can be challenging to know exactly what meeting data your organization should capture. We hope to provide clarity on the subject and help any professional make a more informed decision about obtaining the most appropriate information for their live meeting.

Consider the following five elements that determine what kind of data is best based on your needs.


1. Large vs. Small Sample Size

Quantitative research is frequently used to yield data that’s projectable to a larger population. For that reason, the larger the sample size, the more viable the data collected. Quantitative research makes use of questionnaires, surveys, audience polling and other equipment to collect numerical or measurable data. Quantitative research forms can also be easily distributed to large groups.

Qualitative research, on the other hand, is not interested in making a generalization about a larger population and typically utilizes more laborious data collection methods, which necessitate a smaller sample size. Since qualitative data is composed of non-numerical, verbal data, the presenter or speaker is the primary data collection source. In-depth interviews, focus groups, extensive field notes and open-ended survey questions are all methods for collecting qualitative data.

Though each kind of data provides different types of information, both are incredibly useful in understanding more about live meeting attendees as a whole and at an individual level. While qualitative demographic data can be expressed in regards to the entire group, it is also meaningful at an individual level. Qualitative data can then deepen progressive profiling by allowing individuals to provide information about themselves that is less structured and defined.

2. Quantifiable vs. Indefinite

One of the most fundamental differences between quantitative and qualitative data is that quantitative data can be measured and the results are expressed numerically. Qualitative data, on the other hand, is virtually any data captured that is not numerically expressed.

For instance, a follow-up survey from a CME conference reveals that 63% of physicians who attended incorporated the recommended treatment strategies that were taught into their practice. That result is measurable and, therefore, represents quantitative data.

However, let’s say the same survey provides physicians the opportunity to explain reasons for not implementing the new techniques and responses include: too few resources, uncooperative patients and too little time. Such responses represent qualitative data. Though qualitative data cannot be statistically analyzed, the explanations provided by attendees provide valuable information that paints a more complete picture of the impact of the CME activity on treatment protocols. 

3. Precision vs. Detail

Quantitative data is appealing due to its precise, observable, and easy-to-interpret characteristics. The primary purpose for obtaining quantitative data is to answer the question, “How many?” How many physicians have a comprehensive understanding of Type 2 Diabetes? How many healthcare professionals attend live CME meetings versus online CME activities? Quantitative data produces accurate, reliable measurements that can be statistically analyzed and placed in a graph.

Qualitative data, on the other hand, is appropriate for answering the question, “Why?” Why were some CME seminars more successful than others? Why did some physicians fail to incorporate new techniques into their practice? This data is helpful when seeking an understanding to the rationale behind certain actions and behavior. Though this data does not lend itself to mathematical analysis or easy observation, it provides informative details that quantitative data cannot provide. 

4. Conclusive Outcomes vs. Inconclusive Assumptions

One of the strengths of the quantitative approach lies in the reliability of the data collected. If a large sample size is utilized, the same measurements should yield the same results time after time. If 442 of 500 cardiologists attending a symposium for Congenital and Acquired Heart Disease implement new diagnostic methods into their practice, it can be assumed that the education was informative, beneficial and applicable. 

If 20 of the physicians who did not adopt new protocols into their practice were interviewed, their responses would add more depth and understanding for their behavior, but the responses would not be considered reliable enough to assume that all physicians who fail to adopt new techniques would do so for the same reasons. Qualitative data can; however, provide valuable insight into what potential challenges should be addressed in future CME activities. 


Though a great deal of debate has attempted to answer the question of whether a quantitative or qualitative approach is superior, the reality is that it depends on the answers sought after by the researcher. The goals of quantitative and qualitative research are very different. When measurable, numerical and reliable data is preferred, quantitative research should be utilized. If, however, a researcher desires to delve a little deeper than “What?” or “How many?” and seeks to understand the rationale behind a response or behavior, qualitative research is the answer. 

Qualitative and quantitative data complement one another and integrating the two can provide a more well-rounded picture of your data. A qualitative study can follow up on the results of a quantitative study to provide insight on what the raw numbers mean. What was it about the speaker that generated a low rating from meeting attendees? How could the overall activity be improved? Likewise, a quantitative study can follow up qualitative feedback to determine if greater trends can be identified and reliably projected to a larger population.

Either approach can be used alone or in conjunction with one another to provide valuable insight into the data. Deciding which method to use relies more on the types of questions a professional seeks to answer than an inherent superiority of one method over the other.

5 Ways to Use Live Meeting AnalyticsThis article was written by our DTS  (Data and Technical Services) department who work with live meeting data, specifically with EM Insights. This division is dedicated to providing clean, accurate, and precise data to clients. As our experts in this area, we found it only fitting to have one of our own discuss this matter. 

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