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They are used in describing the results, the relationships and the trends (Bavdekar 2015). For In and Lee (2017) tables are very suitable for giving separate information and can present jointly quantitative and qualitative information. Some of the advantages of using tables include:
i. Tables are useful for summarizing and comparing quantitative information of different variables;
ii. Information with different units can be presented together. For example, blood pressure, heart rate et cetera; and
iii. They can accurately present information that cannot be presented in a graph (In and Lee 2017).
In an extended opinion, In and Lee (2017) expound that graphs are excellent for summarizing, and exploring quantitative data because they employ pictures to simplify complex information and show data trends. On the other hand, theories are used to organize findings.
Theories/models help the researcher to see what lies behind the data. They offer explanations for why something is the way it is (Cronin, Coughlan and Smith 2015). Similarly, Bryman (2012) highlights that if data is not presented in an organized manner, this will impact on the study’s contribution to the field of knowledge and create confusion when it comes to interpretation. The next section discusses the SPSS software.
4.8.1. The Statistical Package for Social Sciences (SPSS)
Quantitative data collected was analyzed with SPSS software that facilitates the easy manipulation of statistical data (Eyaufe 2017).The SPSS Corporation made the SPSS software system package at the beginning of the 1980 and has recently released version twenty-five. In other words, SPSS is a valuable software tool used mainly by social scientists to investigate vital data quickly. Data analysis is a time-consuming and challenging activity, but it can be readily handled and operated with the help of SPSS by employing a variety of technical methods (Gogoi 2020). That is to say, the software can handle complex statistical issues. SPSS has the advantages one of which is the simple to use, handles a vast number of variables in a short amount of time by employing a variety of technical instructions to generate a set of appropriate outputs and so forth (Gogoi 2020). The section below discusses the NVivo software.
110 4.8.2. The NVivo software
Scholarship has it that computer-based qualitative data analysis software programmes have not gained full acceptance despite their potential (Cambra-Fierro and Wilson 2010). There are hopes, fears and fantasies associated with these technologies (Flick 2002; Curtis and Curtis 2011). This could be due to confusion about the difference between computer-aided data management and potential perception that software merely cuts corners and leads to numerical data analysis (Cambra-Fiero and Wilson 2010).
However, such use of software has been more common in academic settings (Cambra-Fierro and Wilson 2010). Therefore, software suppliers may need to reflect on the added value of their products aimed at both academic and practitioner products (Cambro-Fiero and Wilson 2010).
Ngulube (2015) states that computers could be used in such operations as making writing up or transcribing notes, editing, field notes, sorting, coding, memoing, storing, searching, data banking, indexing and retrieving qualitative materials.
On the other hand, Nvivo software was founded in 1995. Tom and Lyn Richards developed the earlier version of the software, which was released in 1981. NVivo 10 was released in 2012 with the first version for Apple operating systems released in 2013 (Paulus, Lester and Dempster 2014). According to Curtis and Curtis (2011) NVivo software entails a coding tree, then uploading transcripts to the software, followed by each piece of text being coded. The aggregation of data by code is very fast and simple. Furthermore, there is the tendency to quantify the data- assume that if a specific code than occurs frequently in the complete data set, it must be more essential than codes occurring less frequently. Some of the unique features of NVivo software packages include:
i. media file synchronization;
ii. supporting multimedia file coding;
iii. transcribing;
iv. Google earth integration;
v. It will import bibliographic data from citation management software;
vi. importing of survey data; and
vii. NCapture add-on- a web browser extension specifically designed to capture web and social media data and so on (Paulus et al. 2014).
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NVivo software supports various analysis tasks (Paulus et al.2014), as highlighted below;
i. Linking: allow you to represent the process (for instance storey lines, sequence of events, causes and effects in a procedure) and make connections between variable aspects of the data;
ii. Annotating: NVivo can displayed annotation as a footnote within a document with the relevant text highlighted;
iii. Coding: refers to the process of attaching a meaningful label to specific portion of data which can be created at any point of analysis;
iv. Searching: search tools can be used to find particular words or phrases (much like the find tool in Microsoft word) as well as provide word frequency counts;
v. Querying: Querying tools provide powerful ways of systematically exploring relationships between the codes; and
vi. Visualizing: being able to graphically represent the relationship between your data documents, quotations, memos, links, and codes provides an opportunity for greater analytic insight.
For this study, the data from a set of interviews held with (4) respondents were analyzed using NVivo. The interviews were transcribed and coded using NVivo that is specifically designed to attach meaning to a group of phrases (Paulus et al. 2014). Using visualization, the researcher immersed himself in the raw data and looked at how certain keywords were used by respondents through a wordlist. It is a list of all the words that occur in a given group of texts (Silverman 2017) and the frequency of distribution of these words in the transcripts was captured by the researcher. With regards to data management, NVivo as a Qualitative Data Analysis (QDA) software tool enabled the researcher to organize and inspect data and record thoughts on the data. Chapter 5 will elaborate further on this. The next section discusses the categorization of variables.