Research is becoming digitalized like all other aspects of education. According to Bazeley, 2019, researchers are increasingly relying on computers to run qualitative data analysis software (QDAS), particularly when working with digital media files. NVivo and MAXQDA, two of the most extensively used commercial QDAS solutions, are among the most widely used applications because they support qualitative methodological approaches. These goods are well-established, introduced in 1997 and 1989 (Woods et al., 2016). These current software packages provide effective methods for organizing and coding data electronically and visualizing the patterns that emerge from codes and classifications. Even though both software are meant to generate the same effect, the methods they use are rather different.
MAXQDA is a qualitative data analysis tool designed to assist researchers, educational institutions, and non-governmental organizations (NGOs) in systematically organizing, evaluating, interpreting, and publishing textual and multimedia data from various sources such as websites, tweets, surveys, and group discussions (Bazeley, 2019). The QDA software enables research teams to record and transcribe audio/video data. Additionally, it helps to incorporate media assets. However, its gem is in comparing and contrasting information across documents. Finally, it can display data in a manner that enables easy visualization. Managers may also export data in MS Word and HTML forms, among other formats.
For organizing, storing, analyzing, and getting insights from large amounts of data and varied sources of information, NVivo is an intuitive and powerful QDA software. When working with NVivo, the researcher can import, analyze, and examine almost any data source in one location, ranging from quantitative demographic information to qualitative open-ended questions and interviews (Empowering discovery. advancing knowledge, n.d). As and when the researcher requires them, they may extend the functionality of the NVivo software by purchasing cloud-based modules such as NVivo Collaboration Cloud or NVivo Transcription.
When it comes to cost, both MAXQDA and Nvivo provide a 14-day free trial period prior to purchasing. Another resemblance is that both did not provide a free version and were sold at a higher price. However, the pricing models of the two firms were somewhat different. The pay-per-feature pricing approach is used by MAXQDA, whereas workers use a flat fee pricing model. Flat rate pricing is a subscription model in which consumers are charged a fixed amount each month or year for all services and access levels, regardless of how much they use. When a company uses feature-based pricing, the consumers grow in tandem with the product as it grows in popularity (Cypress, 2019). They may require new answers to their difficulties during their progress, necessitating an upgrade to the next level. By employing a feature-based pricing approach, the company directly links the product’s prices to the value the brand delivers to its clients.
Both software packages are jam-packed with functionality, with NVivo having the most. Nvivo stands out with its ability to integrate data from all sources of data collection used by researchers. Additionally, researchers can analyze data with numerous tools such as advanced management and query. Furthermore, Nvivo comes with visualization tools that present researchers with a clear picture in order to ask complex questions about collected data. These questions aid in identifying themes and drawing clear conclusions in less time. These are intended to assist researchers in increasing their productivity by shortening the time it takes to identify common themes and evidence-based conclusions. Additional benefits include discovering deeper insights by discovering patterns that are not feasible to reveal using typical coding approaches (Cabrera, 2018). When articulating results or assessing effect, researchers are provided with a comprehensive picture thanks to the use of these instruments. In a similar vein, the conclusion is legitimate, defendable, and results in a successful outcome.
There are several valuable features in MAXQDA, including an integrated Twitter data importation mechanism. The Twitter integration is particularly beneficial for qualitative analysis of Twitter data; the feature is fairly simple to use straight out of the box, and the hashtag auto-coding function is also very useful (Product features MAXQDA 2018. (n.d.). As well as easy code diagramming and linking, the creative coding board is useful for visualizing code in the form of a “brain map.” On the other hand, the actual coding process may be difficult to master. Most first-time users have realized it has a steep learning curve. Cleaning Twitter data after utilizing the integrated import tool is also a time-consuming and tough task that is nearly impossible to complete with any ease. As a result, it is proposed that researchers make certain that the search query is as restricted and accurate as they want it to be before attempting any imports.
Even though the Nvivo program featured more functionality than MAXQDA, using most of the functions was extremely expensive. Another disadvantage of the Nvivo software was its incompatibility with multiple operating systems, and a similar issue exists with QDA. For example, the Mac version of Nvivo does not have all of the functionality included in the Windows version. Finally, while working with other team members, all members must have access to the same software and the know-how to utilize it. This training will take time and money since training a large workforce would necessitate more resources.
Modern QDA software is here to stay, and no single model suites all the data research requirements of the world. However, depending on the type of qualitative research required, different software offers different features. However, as the industry grows and more free and cheaper versions of QDA software become available, the major players will be forced to lower their prices, which is the biggest limitation of using all features of QDA.
References
Bazeley, P. (2019). Using qualitative data analysis software (QDAS) to assist data analyses. Handbook of Research Methods in Health Social Sciences, 917–934.
Cabrera, G. A. (2018). The use of computer applications in qualitative research: A review. Asia Pacific Journal of Academic Research in Social Sciences, 3, 35-42.
Cypress, B. S. (2019). Data analysis software in qualitative research: Preconceptions, expectations, and adoption. Dimensions of critical care nursing, 38(4), 213-220.
Maxqda.com. (2022).
Woods, M., Paulus, T., Atkins, D. P., & Macklin, R. (2016). Advancing qualitative research using qualitative data analysis software (QDAS)? Reviewing potential versus practice in published studies using atlas. ti and NVIVO, 1994–2013. Social Science Computer Review, 34(5), 597–617.
QSR International – World-Class Technology & Software Solutions for Universities and Researchers. Qsrinternational.com. (2022).