Importing and Organising Data in NVivo

Introduction

NVivo is a research software designed specifically for the qualitative research. Qualitative such as dealing with interview data, text data, audio, video and lots more. I found this application handy for dealing with interview while conducting thematic analysis to derive theme from pool of information.

We will be learning how NVivo can be used to analyse different research materials, which includes everything from primary sources like papers, films, or survey findings to memoranda, but the most important things are that we capture the thoughts and observations in NVivo.

In this article, we will learn how to organise data into file classification, cases, case classifications and much more; stay tuned.

ASSUMPTION

It is essential to have some assumptions before we start analysing, organising and coding our data and our assumptions are as follows.

• The Player information were collected at random.
• Most of the selected players are still actively playing.
• There is a disproportionate number of male and female players.
• The players were selected randomly and not from a particular football league.

Data Import

In NVivo, documents may be created from scratch or imported from text or Microsoft Word files.

However, NVivo will allow us to modify documents; for instance, we can format text, add photos, and apply paragraph styles directly to the document, but we will be exploring how this works.

Below is the link to the document files containing information about footballers.  We will be using NVivo to Import them and organise them to be ready for analysis.  Footballers Data

Figure 1: Importing 17 Players' documents into NVivo

From the highlighted Images in figure 1 above, click the link and import the data downloaded from the above links simultaneously.  We will see it easily.

File Classification and Folders

Our NVivo data view should look like the one in figure 1, and then, we need to classify our files into two categories by creating a file classification using label [1] as shown in the label [2].

Female footballers and male footballers, then male footballers, are selected from the file simultaneously and moved to male classification as labelled [3], which can be assessed by clicking on the labelled part [2]

Figure 2: File classification into Female and male footballers

 

Figure 3: Accessing File Classification

These classified files keep the record of data created and modified by date and time, which will help track the changes over time during research.

 

Annotation and Memo

Annotation notes observations about the data we are to analyse so that we will not forget when working on a project in the long run.

We can add annotations to data files (documents, datasets, audio and video files, photos and PDFs), externals, scripts, and memos to make notes on specific pieces of material.

The Annotations tab at the bottom of the window displays annotation text and highlights annotated information in blue or Red.

Memos are a particular kind of document that lets us keep track of our thoughts, revelations, interpretations or develop an understanding of the subject matter of our project.

They give us a technique to keep our analysis distinct from (but connected to) the source material.  Memos can develop into a crucial component of our research writing up phase of our project; for instance, they could serve as the basis for a book's chapters or a presentation's framework.

Memos may be tailored to our project's requirements.  We can, for instance, decide to make memoranda for our documents, noting any problems, criticisms, or new ideas.

Make memoranda for codes that explain the code's importance and any patterns or concepts from the references.  Moreover, this makes "unlinked" notes to keep track of additional project details like research goals or project progress.

Memos are used to record the general thought, ideas and steps taken in coding the project and are longer than annotations; however, they are stored in a separate memo folder, as shown in figure 5.

Moreover, it is important to keep different Memos for different participants in the analysis, which will help in the research and thesis writing after the analysis is done.

Figure 5: How memos are created

When writing our Memo, time can be of the essence, which can help us trace an event or thought in time during analysis, and timestamps can be imported into the Memo with CRTL + SHIFT + T as shown in Figure 6 below.

Figure 6: How to keep Memo active

However, a Memo can be created from the file folder directly, as shown in figure 7.  Right-click on the file participate, then use the Memo link to create a new Memo which will serve as a research journal throughout the project lifecycle.

Figure 7: Linking File to Memo

See-also-links: links information in our data files (documents, PDFs, datasets, externals, photos or media files), codes, cases, memos, and other associated project elements or information in other files.

In other to link the file Alex morgen's code extract to the research reflection contained in the General Memo folder, the material is highlighted and copied, then we need to go to our memo folder then, highlight the point and paste it as See-also-link, and then the related item appears in the Detail View is as shown in figure 8 below.

Figure 8: Linked text to the Memo for future reference.

SET

Project files, codes, cases, memoranda, framework matrices, search results, and coding matrices are grouped into sets.  These are a few examples of what they may be.

We can use sets to organise our work, gather objects connected to a particular subject, create photo galleries, or describe the parameters of a query or visualisation.  Once a set is created, we can quickly run various studies on it.

Static sets are manually formed by choosing each component of the set separately.  We can establish requirements for dynamic sets, and any objects that satisfy those requirements are added to the set.

Figure 9: create set data from the file classification

The screenshot below is the set created from the file folder, representing those data sources, as shown in figure 10.  

This is how the set is created; then, we can create another set from the already created set and name the set USA footballer who will be a case; this is where the beauty of using a set is paramount, as shown in figure 11.

Figure 10: Female footballers created as a set

 

Figure 11: Created USA footballer from the Already Existing Set

Case and Case Classification

So what we are going to be doing is to make sure that each of our participants is a separate case; creating our cases or classifying our current source file into cases is not very difficult; we can create the case from the source file by selecting all the football players and the right-clicking and select the Create as Case option as shown below in figure 11.

 

Figure 12: Creating cases for both female and male footballer

During case creation, we must decide whether our case is related to a person or organisation, but custom cases can also be created for the analysis and data organisations if need be, but we are going to use a pre-defined case classification as shown in figure 13.

Figure 13: Selecting a pre-defined case classification

Classification Sheet

Our project's files or cases are described in detail on a Classification sheet in figure 14, and we can also add the attribute and values we want.

We may, for instance, have a file Classification sheet for journal articles that includes information like the author and the date of publication which we can import the information we've acquired about the files outside of NVivo as a classification sheet, whether in a structured text file or spreadsheet.

Figure 14: Creating Classification sheet

Label [1a] is used to create new attributes and values, where label [2] is the row attribute and label [3] are  the inputted values which can be of any data type with respect to the attributes, as shown in figure 14 and 15 respectively.

Figure 15: Case classification of attributes and Values

Conclusion

In these readings, we have familiarised ourselves with some of the NVivo workspaces, designed and Imported data about footballers, Including using an assumption to kickstart our data organisation and analysis in NVivo.  We learnt how to use Memo and annotation, See-also-links in our analysis.

Secondly, We learned how to organise and classify data into folders, sets and cases, which made us identify their difference, and also we used the sheet to assign different attributes and values to each footballer, which will be used or can be used for thematic analysis in the subsequent readings.

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