Introduction
Qualitative coding is the systematic classification of extracts from qualitative data to identify themes and patterns. It enables the analysis of unstructured or semi-structured data, such as transcripts from in-depth interviews or focus groups (Kuckartz, & Rädiker, 2019). Through qualitative data analysis, researchers can examine and interpret qualitative data to ascertain its meaning.
Discussion
Qualitative data is any type of unstructured information gathered from the fields in the form of pross. This can be speeches, discussions, songs, video recordings, or interviews (Kozinets, 2019). For instance, the NPS measure can be simply quantitative. However, when interested in why consumers give businesses a rating, researchers need qualitative data analysis procedures to decipher the comments they provide alongside numerical replies.
Qualitative data coding techniques fall into automatic and manual coding categories. Automatic coding has been driven by advancements in machine learning and algorithms such as those seen in targeted advertisements. Big data companies such as Facebook have been at the forefront of data analysis automation as the company deals with numerous qualitative data (Kozinets, 2019). This foot would not be accomplished using a hands-on approach in time to make the relevant decisions. Although it takes considerable computer resources, it would take more humans months and years to go through all the data.
The hands-on analysis is more advanced than any QDA software available. However, while it remains popular due to its perceived high accuracy, more advanced artificial intelligence systems are being designed. Consequently, as automated analysis becomes more accurate, so is it rapidly gaining ground. Furthermore, automated analysis is mostly assumed to be free of bias.
Interview Coding: Summary
To understand how lived experiences and social skills of Ghanaian undergraduate and graduate students are associated with the student’s information retention, the two interviews were conducted and coded. As for the latter, the inductive method was applied, meaning that the codes were developed based on the raw data (Cohen et al., 2017). Then, the codes were grouped into categories (Adu, 2019). They include social skills evaluation, social interaction catalysts, social interaction barriers, teacher traits, student traits, the impact of social life on performance, and the impact of religious involvement on performance and social activity, to name a few.
For example, three codes are included in the category of social skills evaluation: namely extraverted, introverted, and neutral. Extraversion implies that a person has relatively developed social skills and high involvement with the life of a higher educational institution’s community. The interviewees used such words and phrases as ‘sociable,’ ‘able to maintain a presence,’ and ‘I communicated with my fellow students.’ On the other hand, introversion means that an individual has a problem communicating with others which was formulated by interviewees as ‘conservative,’ ‘I had limited communication with lecturers,’ and ‘I found it very difficult to approach my lecturers.’ Finally, one of the interviewees assessed social skills as ‘fair,’ which was coded as a ‘neutral’ evaluation.
Generally, the coding and categorization revealed that although the two interviewees possess different social skills, their experiences were quite similar. Both respondents mentioned that starting a higher education was associated with significant difficulties because it necessitated personal transformation. Additionally, the two interviewees indicated that social life was an important part of their life but had positive as well as negative impacts on their performance. Moreover, both interviews show that positive interaction can increase students’ motivation and performance, whereas negative interactions, as well as a lack of communication between teachers and students, lead to reduced incentives to study. Therefore, the intermediate results suggest that social skills are important determinants of success in the studies, including information retention. However, as students experience difficulties entering higher education institutions, teachers should help them to break the communication barriers and provide support.
QDA Software in Qualitative Data Coding: Summary
MAXQDA and Nvivo are two of the most extensively used commercial QDAS solutions. MAXQDA allows researchers to organize and code data electronically and visualize patterns that emerge from codes and classifications. The QDA software enables research teams to record and transcribe audio/video data. When working with NVivo, the researcher can import, analyze, and examine almost any data source in one location. As and when the researcher requires the functionality of the NVivo software, they may purchase cloud-based modules such as NVivo Collaboration Cloud.
Both have a feature-based pricing model that directly links the product’s price to the value the brand delivers to its clients. The products are jam-packed with functionality, with NVivo having the most access to data from all data sources. There are several valuable features in MAXQDA, including an integrated Twitter data importation mechanism. Twitter integration is particularly beneficial for qualitative analysis of Twitter data; the feature is relatively simple to use straight out of the box. The hashtag auto-coding function is also beneficial (Product features MAXQDA 2018. (n.d.)). Even though the Nvivo program featured more functionality than QDA, it was costly to use most of the functions. Modern QDA software is here to stay, and no single model suites all the data research requirements of the world.
Comparing Qualitative Hand-Coding Experience With QDA Software
Manual data analysis is sometimes the most manageable with small research projects and allows for lower costs in getting the ball rolling. However, in large-scale data collection, QDA software is necessary. Despite the ease of automation derived from most QDA software vendors, the experience of hand-coding has one key advantage, accuracy. Manual data analysis allows the researcher to maintain an oversight that would not have been possible with automation.
Conclusion
Additionally, manual coding does not require expensive system spending. A manual processing system allows a business to get into data analysis without investing much in QDA software and its dependencies. However, these costs can be overcome by the labor costs of manual data analysis. It costs money to pay people to enter data manually. It keeps entrepreneurs and other employees distracted. The time spent dealing with these distractions affects the productivity of the company.
References
Evers, J. C. (2018). Current issues in qualitative data analysis software (QDAS): A user and developer perspective. The Qualitative Report, 23(13), 61-73.
Kuckartz, U., & Rädiker, S. (2019). Analyzing qualitative data with MAXQDA (pp. 1-290). Basel, Switzerland:: Springer International Publishing.
Kozinets, R. V. (2019). Netnography: The essential guide to qualitative social media research. Sage.