Essay on Qualitative vs. Quantitative Research

Research Strategy and Data Analysis

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The most common characteristic of quantitative data is that it is all expressed in numerical form, and it differs from qualitative in a similar sense that the latter is never shown in the binary form (McLeod, 2008). For example, when counting the number of students in the class, you can report the data as a numerical figure, for instance saying 34 students participated in an exam. However, it is different when describing the conduct of students in the class. One can describe students to be rude, friendly, understanding or just brilliant. Those are qualities which define qualitative data. The above can be extrapolated to several other data, to identify them as either quantitative or qualitative.

The data provided for the instructors performance has majorly quantitative data. Most of the issues addressed are using numerical values, and they include sections taught, students enrolled in the course, those who withdrew from the course, and the closed-ended questions where they were told to rate what they felt towards what the course was offered. However, there is an optional comment section which is purely qualitative. Qualitative data is more about what someone thinks or feel at an individual level about something or someone, and in this case, it was what the students felt towards the instructors (McCurdy & Ross, 2017).

Descriptive Statistics of Quantitative Data

As noted above, quantitative data is described using numerical values. There are several numerical values which represent a quantitative data, and below we explain some of them. The first four quantitative variables gave the numbers of students in the respective variable. The following table shows the data value of quantitative data.

Descriptive Basic Mathematics Computer English

Section taught 3 6 2

Students Enrolled 45 120 38

Withdrawals 12 6 8

Teacher knows material 4.3 2.9 4.7

Teacher helped me understand material 3.1 2.8 3.5

I would recommend this teacher to others 3.0 1.8 3.8

The course materials were helpful 2.8 3.0 1.8

The time allotted for each was sufficient 3.5 1.8 3.5

Grades A B C D F A B C D F A B C D F

5 9 12 3 4 18 20 14 37 25 27 1 0 0 2

Quantitative data descriptive reveal several issues about the data. For instance, in the above data, we can see the number of the section that was taught on each subject by each instructor. The same for students who were enrolled in the course and those who withdrew along the way before completion.

However, other than the data values there are much more descriptive. For instance, we can get the average score of each instructor. In the above table, the average for specific closed-ended questions provided the average for each instructor. From the average figures, we can say that English literature instructor was the best when it came to helping students understand the material, and the same professor maintained the same for those who students can recommend to others and regarding knowing the materials he or she was using. For time allotted for each unit, the average score was a tie between mathematics and English instructors. The computer instructor took the lead on the provision of helpful study materials.

It is evident also that more students enrolled for basic computer course than other courses, and such a variable can alter the average rating. A proportional consideration of students who passed or failed in each class indicates computer basics had the most students failing. In total, 25 had a fail against 4 and 2 for mathematics and English respectively. Ideally, these are things that describe the data, and putting it in an organized manner makes it easy to be analyzed and compared. Id probably argue, even though not with surety that a general average for all the metrics would take to measure the effectiveness of each instructor. In that case, every value under each instructor can be added and then divided by total, and an average used to be gauged to determine the best instructor.

Trends Identified in the Analysis of Qualitative Data

Closed-end questions, particularly those that use a scale to provide individual approval give some information about the quality of an individual. For example, students have different reasons as to why the can recommend an instructor to another student, but this cannot be included in the questionnaire with a standardized scale which considers the same elements across every learner. For analysis purposes, it is a quantities data. But a deeper look at it will provide more qualitative aspects. With that in mind, we can easily say all the closed end questions provided some level of qualitative features across the three instructors.

Other than the above, the last section provided a comment section where each learner would provide what he or she feels about each her instructor. However, this section was largely for justifying the rating given in the above, closed-ended questionnaires questions. For instance, it is a section where students had an opportunity to reveal why they thought the teachers material werent sufficient for their use. Unlike the closed-ended question or quantitative questions, it is not possible to give the average opinions of each student. Therefore, the qualitative data can only be gauged on what is answered and rated quantitatively as done on the questions that learners rated out of five.

Recommendations

Quantitative and qualitative data reveal a lot about something or someone. For example, a revelation that most students withdrew from mathematics class can aid the school to intervene and know the exact reasons behind that, and then addressing them. Poor rating for instructors conduct, material, and time management among others can help in self-improvement. In essence, there would be more closed-ended questions, each dealing with specific issues to broaden the research area.

Also, there are more descriptive statistics than mentioned above, and some are critical in decision making. Variance, standard deviation, range and many others are crucial. They, however, thrive best where there is a uniform scale for gauging a particular variable. We could expand this data, and analyze each section separately to enable the use of those other descriptive statistics.

Conclusion

Data collection varies in complexity, but the analysis is often more complicated based on what is the actual requirements. But for data like this one, collection and analysis are relatively manageable. However, most researchers often look for ways to put qualitative data into quantitative nature, and by doing that the entire process of analysis is eased. They then do analysis and report the data back, which can be used for various decision-making actions.

References

McCurdy, S., & Ross, M. (2017). Qualitative Data Are Not Just Quantitative Data With Text but Data With Context: On the Dangers of Sharing Some Qualitative Data: Comment on DuBois et al. (2017). Qualitative Psychology. http://dx.doi.org/10.1037/qup0000088

McLeod, S. (2008). Qualitative vs Quantitative Data | Simply Psychology. Simplypsychology.org. Retrieved 11 June 2017, from https://www.simplypsychology.org/qualitative-quantitative.html