Content analysis was focused on identifying themes that emerged from the responses across the four-time points. Later, additional random 20 comments were collected to check the data saturation. We randomly collected 100 comments from the relevant articles on new organisations’ Facebook and Instagram pages and conducted preliminary coding. In the study, we examined public responses to the government-announced “circuit breaker” (a local term for lockdown) at four-time points in Singapore: (1) entry, (2) extension, (3) exit of lockdown ‘phase 1’ and (4) entry of lockdown ‘phase 2’. Public health risk communication about the justification and scope of a lockdown was challenging as it involved a conflict between solidarity and individual liberty and a trade-off between various values across groups with different socioeconomic statuses. When control of the coronavirus disease 2019 (COVID-19) required strong precautionary measures, lockdowns were necessarily implemented in countries around the globe. Users need to think critically about ways in which these themes emerge and relate to each other to gain a deeper understanding of the data.Mobility restriction is the most effective measure to control the spread of infectious disease at its early stage, especially if a cure and vaccine are not available.
Once individual themes in a data set are found, NVivo doesn’t provides tools to map out how these themes relate to one another, making it difficult to visualize the inter-relationships of the nodes and topics across data sets.
#Nvivo review manual#
Manual searches and evaluation of automatic word searches help to ensure that the data are, in fact, thoroughly examined. While it is alluring to do so, in-depth analyses and critical thinking skill are needed for meaningful data analysis.Īlthough it is possible to search for particular words and derivations of those words, various ways in which ideas are expressed make it difficult to find all instances of a particular usage of words or ideas. It may be tempting to limit the data analysis process to automatic word searches that yield a list of nodes and themes.
#Nvivo review software#
While NVivo seems to be a great software that serves to provide a reliable, general picture of the data, it is important to be aware of its limitations. At the same time, the use of word clouds and cluster analysis also provides insight into prevailing themes and topics across data sets. NVivo allows users to create clusters of nodes and organize their data into categories and themes, making it easy for researchers to identify patterns. Given the rise of social media and increased interest in studying its impact on our society, this capability of NVivo may become more heavily employed.
NVivo allows users to import Tweets, Facebook posts, and Youtube comments and incorporate them as part of their data. Teachers and students can quickly become proficient in use of this software.
#Nvivo review code#
Users can import documents directly from word processing packages in various forms, including Word documents and pdfs, and code these documents easily on screen via the point-and-click system. Furthermore, NVivo proves to be really useful with large data sets – it would be extremely time-consuming to code hundreds of documents by hand with a highlighter pen.
Doing word search or coding using NVivo will produce more reliable results than doing so manually since the software rules out human error. Using software like NVivo in content analysis can add rigor to qualitative research. Given the increasing demand for qualitative data analysis in academic research and teaching, this blog post will discuss the strengths and weaknesses of NVivo as a teaching tool in qualitative analysis.
#Nvivo review series#
For the past few weeks, DASIL has been publishing a series of blog posts comparing the two presidential candidates this year – Hillary Clinton and Donald Trump – using NVivo, a text analysis software.
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