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Nvivo 11 plus

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NVivo 11 Plus is a qualitative data analysis software application. It provides tools for organizing, analyzing, and visualizing qualitative data, such as interviews, focus group discussions, and textual documents. The software allows users to manage, explore, and discover insights from various types of unstructured data.

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Lab products found in correlation

Sas v9, by SAS Institute (1 mentions)

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NVivo 11 (406 citations) NVivo Plus (3 citations)

7 protocols using nvivo 11 plus

1

Qualitative Exploration of Provider Perspectives

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This study employed thematic analysis to explore providers' perspectives as it offers a theoretically flexible but rigorous approach to identifying and organizing patterns of meaning in qualitative data (Braun & Clarke, 2006) (link). Data analysis was supported by the use of NVivo 11 Plus (QSR International, 2018) . Following initial reading of transcripts, coding proceeded inductively; generated directly from recurrent issues and often the participants' own language (in vivo) and continued until no new codes were identified.
Codes were applied to units of meaning, rather than line-by-line, and were refined into themes that permitted recognition of relationships between themes. Clusters and themes were constantly interrogated within and across cases to ensure they were grounded in these data. To support quality and data validation, cultivate reflexivity, and minimize biases on the part of the author, key themes were discussed with the research assistant to verify or modify interpretations and incorporated Braun and Clarke's (2006) (link) quality criteria for good thematic analysis.
Palfreyman A. (2019). Helping or Heightening Vulnerability? Midwives as Arbiters of Risk for Women Experiencing Self-Directed Violence in Urban Sri Lanka. Qualitative health research, 29(10).
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2

Thematic Analysis of Translated Interviews

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The audio-recorded interviews were transcribed and later translated into English by the same team that conducted the interviews, with each member transcribing and translating their own interviews. For analysis, we followed the steps of thematic analysis outlined by Braun and Clarke [24 ]. This process included reading through the data repeatedly to understand its meaning, coding, identifying themes, and establishing the relationships between them. Interview transcripts were coded using NVivo 11 Plus (QSR International, Memphis, USA). PW led the open coding, which included coding and presenting emerging themes at the manifest level. Following that, the research team conducted a series of discussions to review themes in order to give them meaning and identify underlying patterns [24 ]. These discussions also included double-checking with the codes, and transcripts were required. HMA, a medical anthropologist, guided the process.
Wanduru P., Hanson C., Waiswa P., Kakooza-Mwesige A, & Alvesson H.M. (2023). Mothers’ perceptions and experiences of caring for sick newborns in Newborn Care Units in public hospitals in Eastern Uganda: a qualitative study. Reproductive Health, 20, 106.
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3

Qualitative Analysis of Focus Group Data

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Focus group sessions were audio recorded and transcribed verbatim. We used a modified version of Silverman (2016) transcription conventions. Transcripts were imported into NVivo 11Plus (QSR International Pty Ltd.). The first two authors (JA, CG) read each transcript and independently conducted line-by-line coding. JA and CG met to discuss codes for each focus group transcript, used differences to clarify code names and descriptions, and resolved differences of opinions. They engaged in an iterative process to ensure codes, categories, and themes represented data from each group. Final analysis yielded themes as well as categories and sub-categories for themes. Given the divergence in meaning across categories and themes by group, JA and CG created memos with definitions to represent the meaning of each category and sub-category. The third author (RK) assessed internal validity and reliability by independently examining the codes, sub-categories, categories, and themes for clarity, consistency, credibility, and meaning (Cho & Trent, 2014 ; Lincoln & Guba, 1985 ).
Anderson J.N., Graff C., Krukowski R.A., Schwartzberg L., Vidal G.A., Waters T.M., Paladino A.J., Jones T.N., Blue R., Kocak M, & Graetz I. (2020). “Nobody will tell you. You’ve got to ask!”: An examination of patient-provider communication needs and preferences among Black and White women with early-stage breast cancer. Health communication, 36(11), 1331-1342.
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4

Qualitative Analysis of Focus Groups

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Focus group sessions were audio recorded and professionally transcribed verbatim. Transcripts were imported into NVivo 11Plus (QSR International Pty Ltd.). Researchers (JA and CG) read each transcript and conducted line-by-line coding independently. They engaged in an iterative process of developing codes for each focus group, discussing differences to clarify codes names and descriptions, and referencing transcript texts and audio recordings to resolve differences. Final analysis yielded themes as well as categories and sub-categories for themes. RK assessed internal validity and reliability by independently examining thematic analyses for clarity, consistency, credibility, and meaning (25 ,26 ).
Anderson J.N., Krukowski R.A., Paladino A.J., Graff J.C., Schwartzberg L., Curry A.N., Vidal G.A., Jones T.N., Waters T.M, & Graetz I. (2021). THRIVE intervention development: using participatory action research principles to guide a mHealth app-based intervention to improve oncology care. Journal of hospital management and health policy, 5, 5.
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5

Qualitative Analysis of Focus Groups

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Focus group sessions were audio recorded and professionally transcribed verbatim. Transcripts were imported into NVivo 11Plus (QSR International Pty Ltd.). Researchers (JA and CG) read each transcript and conducted line-by-line coding independently. They engaged in an iterative process of developing codes for each focus group, discussing differences to clarify codes names and descriptions, and referencing transcript texts and audio recordings to resolve differences. Final analysis yielded themes as well as categories and sub-categories for themes. RK assessed internal validity and reliability by independently examining thematic analyses for clarity, consistency, credibility, and meaning (25 ,26 ).
Anderson J.N., Krukowski R.A., Paladino A.J., Graff J.C., Schwartzberg L., Curry A.N., Vidal G.A., Jones T.N., Waters T.M, & Graetz I. (2021). THRIVE intervention development: using participatory action research principles to guide a mHealth app-based intervention to improve oncology care. Journal of hospital management and health policy, 5, 5.
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6

Thematic Analysis of Qualitative Data

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Thematic data analysis was conducted by OLA 36 alongside data collection, providing an opportunity to refine the topic guide in response to findings. 37 Initial coding was reviewed by DK and MC prior to further analysis supported by the Nvivo 11 Plus software package by QSR International. As data analysis progressed, OLA grouped the codes into categories, subthemes and themes. These were regularly discussed with members of the research team and modified where deemed appropriate. Data collection and analysis continued until data saturation was achieved for both participant groups. 39 40 At this point, no new conceptrelevant information was elicited from analysing additional interview data. 41 42 Saturation tables, which documented the initial and subsequent occurrences of codes, were used to assess data saturation. 41
Aiyegbusi O.L., Kyte D., Cockwell P., Marshall T., Dutton M., Walmsley-Allen N., Slade A., McMullan C, & Calvert M. (2019). Patient and Clinician Perspectives on Electronic Patient-Reported Outcome Measures in the Management of Advanced CKD: A Qualitative Study. American journal of kidney diseases : the official journal of the National Kidney Foundation, 74(2).
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7

Menopausal Symptoms and Lifestyle Preferences

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All analyses were performed using SAS v9.4 software (SAS Institute,
Inc.) with a significance level of α=0.05. Overall participant
characteristics are expressed as mean ± standard deviation (SD).
Chi-square analyses were used to compare differences in frequency distribution
between menopause groups. Ranking of symptoms, exercise preferences, holistic
remedies, and important components of a lifestyle program were treated as
continuous measures and modeled using the least-squares means (LSM). T-tests
constructed from LSM were used to compare differences between menopause groups
for all continuous variable assessments. For the two open-ended questions,
thematic analysis was used to develop coding schemes using NVivo 11 Plus (QSR
International, Melbourne, Australia). In all analyses, we did not adjust for age
due to the presence of a high association (goodness of fit;
X2=696, p<0.0001)
between age categories and menopause stage.
Marlatt K.L., Beyl R.A, & Redman L.M. (2018). A qualitative assessment of health behaviors and experiences during menopause: a cross-sectional, observational study. Maturitas, 116, 36-42.
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