review attachment to complete the assignment

4

Major Considerations in QDA Software

Student’s name

Professor’s name

Course title

Institution

Date

Major Considerations in QDA Software

The organization, processing and analysis of data in organizations across various sectors and industries form integral parts of their operation. QDA plays an instrumental role in achieving the objectives of conducting data analysis. The effectiveness of utilizing the software forms one of the main reasons why it may be preferred over other tools. This quality along with the efficiency in analyzing large amounts of data make the tool the most suitable choice in many instances (Çayir & Saritaş, 2017). QDA provides users with a number of tools to choose from which is an added advantage as individuals can utilize any of them to their satisfaction. With the features that define the different QDA software, people can consider their preference qualities or specifications (Çayir & Saritaş, 2017). It is ideal and covenant that there are several QDA software which satisfactorily serve the analytical purpose.

There are other considerations that factor in applying QDA software in research. It is essential for researchers to code and sort through all findings and this should happen in a manner that is timely. QDA makes it much easier to break down information, and the text can be coded electronically making it possible to manipulate the data quickly (Çayir & Saritaş, 2017). The time factor is a major aspect in using the software as it helps enhance the speed of research. In the process, the software also helps ascertain that examination of research details is thorough. Sorting data into groups or categories influences the way researchers look at data (Çayir & Saritaş, 2017). Having different ways of assessing data can be vital in data analysis.

Coding using MS Word/Excel

The flexibility in using the QDA software is reflected in the role that Microsoft Office plays in conducting qualitative analysis. The experience of using Word and Excel has featured mixed outcomes. Utilizing word has been relatively simple to start given that the analysis relies on use of comments in the coding process. Use of the comments makes it easy to capture the themes that are evident across texts. As such, there have been no problems in regards to establishing particular codes. A key factor that has partly influenced success has entailed ensuring consistency in the comment labels as this is instrumental in accurate coding. One of the challenges has been the use of multi-word tags which requires that there is extraction before multi-layer tags can be retrofitted (Çayir & Saritaş, 2017). The use of multi-word tags has been integral in making the coding more comprehensive.

The first steps in applying Excel in the coding process are quite straightforward. The process generally entails activating the Developer tab and going to the Macros button. The Visual Basic for Applications should automatically come up. This factors in aiding the extraction of codes and this is where the process becomes relatively challenging especially for a beginner. Attention to detail is necessary for extraction of data from Microsoft Word (Çayir & Saritaş, 2017). The process gets relatively complicated but following the requisite step-by-step procedure generally leads to the desired outcomes. The major difference that makes Excel more involving during qualitative analysis as compared to Word is that there are several steps which if not followed to the latter can derail the analytical process. It was essentially challenging to swiftly code using Excel given that one of the major benefits of QDA software is to enhance the speed of analyzing data (Çayir & Saritaş, 2017). The situation has changed with more experience as much more practice has certainly made it easier to use the software.

Following the initial extraction of codes, adding more data is also relatively easier. It is possible to manually add columns which integrate more data for the analysis. Combining of all extracts into a singular sheet has been among the successes in the coding process. The Filter function serves a key role in aiding this process. It is vital to take advantage of this function as it helps in displaying certain codes specifically for a given subset. Overall, coding using Microsoft Word and Excel becomes easier and more efficient with time as one gets used to the different procedures and functions involved.

QDA Software Choices

The two choices amongst all QDA tools that are in use are ATLAS.ti and NVivo. The two tools have distinct features between them. The first things that pops up is the pricing of the software. ATLAS.ti is much cheaper compared to NVivo as the pricing of the former starts from as low as $10 per user while NVivo is quite expensive at $1249 flat rate. They however both have provision for using Microsoft Office programs. They both support analysis of qualitative and mixed method data. The similarities in other features can be seen in regards to data discovery, data visualization, query builder, reporting/analytics, self-service analytics and self-service data preparation (Friese, 2019; Phillips & Lu, 2018). The features which differentiate ATLAS.ti from NVivo include storytelling and natural language search. Overall, ATLAS.ti also has more functions as there are provisions for in person or live online training. Apart from the capability of using ATLAS.ti on smartphones, there is provision of phone support and chat support (Friese, 2019). NVivo is only web-based.

The choice of ATLAS.ti and NVivo is established on usability in qualitative analysis. They are two of the most commonly used tools and are effective in conducting data analysis. They allow data importation and exportation unlike some other tools. They have the features which are integral for analyzing data across small, medium or large businesses. It was also important that the focus solely lay in qualitative or mixed methods rather than inclusion of tools that integrate quantitative analysis. I am considering ATLAS.ti for my capstone project. The tool offers an alternative that is clearly advantageous.

References

Friese, S. (2019). Qualitative data analysis with ATLAS. ti. Sage.

Phillips, M., & Lu, J. (2018). A quick look at NVivo.  Journal of Electronic Resources Librarianship,  30(2), 104-106. https://doi.org/10.1080/1941126X.2018.1465535

Yakut Çayir, M., & Saritaş, M. T. (2017). Computer Assisted Qualitative Data Analysis: A Descriptive Content Analysis (2011-2016).  Necatibey Faculty of Education Electronic Journal of Science & Mathematics Education,  11(2). https://www.researchgate.net/profile/Melike-Yakut/publication/322201435_Nitel_Veri_Analizinde_Bilgisayar_Kullanimi_Bir_Betimsel_Icerik_Analizi_2011-2016/links/5ae6d305a6fdcc3bea97a5d5/Nitel-Veri-Analizinde-Bilgisayar-Kullanimi-Bir-Betimsel-Icerik-Analizi-2011-2016.pdf