Full texts of included articles were imported into NVivo for Windows (V12, QSR International, Burlington, MA; see Appendix 2 for attributes extracted). Studies' samples were evaluated for overlap and collated into one NVivo case where this occurred. SP reviewed each article line-by-line, storing information within codes corresponding to the research questions. Only content within the results section was coded. However, content pertaining to conceptualisations was also drawn from articles' introduction and methods sections.
Nvivo for windows
Manufactured by Lumivero
NVivo for Windows is a qualitative data analysis software that allows users to organize, analyze, and visualize non-numeric data, such as interview transcripts, open-ended survey responses, and social media content. It provides tools for coding, categorizing, and identifying patterns within these types of data.
Automatically generated - may contain errors
Lab products found in correlation
2 protocols using nvivo for windows
1
Systematic Review of Conceptualizations
2
Evaluating App Usability and User Experience
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The data collected (eg, internet server data, observation, audiovisual recordings, and interviews) corresponded to the 5 app components. Quantitative laboratory usability test (eg, task completion, time, errors) measures were evaluated based on users’ interactions with the app and to assess the app’s ease of use and efficiency. In both usability tests, quantitative data from the SUS questionnaire (10 questions, each scored from 0 to 4 points) were transformed by multiplying by 2.5 to convert scores to a 0 to 100 range and were categorized adjectivally [42 ]. Descriptive statistics were analyzed using IBM SPSS Statistics for Windows, version 25.0 (IBM Corp). Both usability tests followed the same semistructured interview guide, comprising 14 open-ended questions. The 5 predefined theory-based app components (ie, self-monitoring, goal setting, coping skills training, social support, and pain education library) were the basis for developing a structured categorization matrix using deductive content analysis [43 (link)]. The collected data were coded according to 8 predefined categories, including the 5 components, potential improvements, usage considerations, and coping. Interview responses were transcribed verbatim using NVivo for Windows (QSR International Pty Ltd, version 12, 2018).
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!