Qualitative and quantitative data can provide different but complementary perspectives on research topics. While quantitative data relies on statistical analysis to identify patterns and relationships, qualitative data helps to describe and understand the context, experiences, and meanings behind those patterns. An effective way to integrate these two types of data is to use each method to corroborate, elaborate on, and bring greater depth to the findings from the other method.

In this study, we collected both survey responses (quantitative) and open-ended interview responses (qualitative) to understand students’ perceptions of and experiences with online learning during the COVID-19 pandemic. For the quantitative data, we surveyed 200 students about their satisfaction levels with different aspects of online instruction on a 5-point Likert scale. We then conducted statistical analysis to determine which factors had the strongest correlations with overall satisfaction. Our qualitative data involved one-on-one interviews with 20 students to elicit rich, narrative responses about their specific experiences in each online class.

In our findings and analysis section, we began by outlining the key results from our quantitative survey data. Our statistical analysis revealed that interaction with instructors, access to technical support when needed, and class engagement activities had the highest correlations with students’ reported satisfaction levels. We presented these results in tables and charts that summarized the response rates and significant relationships identified through our statistical tests.

Having established these overall patterns in satisfaction factors from the survey data, we then integrated our qualitative interview responses to provide greater context and explanation for these patterns. We presented direct quotations from students that supported and elaborated on each of the three significantly correlated factors identified quantitatively. For example, in terms of interaction with instructors, we included several interview excerpts where students described feeling dissatisfied because their professors were not holding regular online office hours, providing timely feedback, or engaging with students outside of lectures. These quotations brought the survey results to life by illustrating students’ specific experiences and perceptions related to each satisfaction factor.

We also used the qualitative data to add nuance and complexity to our interpretation of the quantitative findings. For instance, while access to technical support did not emerge as a prominent theme from the interviews overall, a few students described their frustrations in becoming dependent on campus tech staff to troubleshoot recurring issues with online platforms. By including these dissenting views, we acknowledged there may be more variables at play beyond what was captured through our Likert scale survey questions alone. The interviews helped qualify some of the general patterns identified through our statistical analysis.

In other cases, themes arose in the qualitative interviews that had not been measured directly through our survey. For example, feelings of isolation, distraction at home, and challenges in time management not captured in our quantitative instrument. We included a short discussion of these new emergent themes to present a more complete picture of students’ experiences beyond just satisfaction factors. At the same time, we noted these additional themes did not negate or contradict the specific factors found to be most strongly correlated with satisfaction through the survey results.

Our findings and analysis section effectively integrated qualitative and quantitative data by using each method to not only complement and corroborate the other, but also add context, depth, complexity and new insights. The survey data provided an overview of general patterns that was then amplified through qualitative quotations and examples. At the same time, the interviews surfaced perspectives and themes beyond what was measured quantitatively. This holistic presentation of multiple types of evidence allowed for a rich understanding of students’ diverse experiences with online learning during the pandemic. While each type of data addressed somewhat different aspects of the research topic, together they converged to provide a multidimensional view of the issues being explored. By strategically combining narrative descriptions with numeric trends in this way, we were able to achieve a more complete and integrated analysis supported by both qualitative and quantitative sources.