Since the purpose of the present study was to test the feasibility of using Q-sort, the brief interviews regarding the sorting procedure were deductively analyzed using qualitative content analysis (Elo & Kyngäs, 2008) (link). The content was analyzed by Arnell into three categories: the sorting procedure, statement comprehension, and preferred mapping procedure. Observations and field notes supplemented this analysis. The NVivo software program (version 12; QSR International, 2018) was used in both the qualitative content analysis and in the card content analysis.
Nvivo software program
Manufactured by Lumivero
Sourced in United States
NVivo is a qualitative data analysis software program that allows users to organize, analyze, and understand unstructured data, such as interviews, focus groups, and social media posts. The software provides tools for coding, querying, and visualizing data, enabling researchers and analysts to identify patterns and insights from complex information.
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Lab products found in correlation
6 protocols using nvivo software program
1
Feasibility of Q-Sort Methodology Evaluation
2
Qualitative Analysis of De-identified Transcripts
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De-identified transcripts were analysed using thematic and inductive analysis, enabling the identification, coding and categorisation of primary patterns within the data. All transcripts were independently analysed and coded by two investigators (SK and KM) in accordance with the predetermined constructs. In depth discussion of emerging themes took place before a final iteration of the results was developed and agreed upon across investigators. An independent qualitative researcher was included to counteract the dual role of the researcher delivering and evaluating the program. Thematic coding of data and development of models were assisted by the NVivo Software program (QSR International Pty Ltd. Version 10, 2012, Victoria, Melbourne). Verbatim quotes from interviews that best represented the key findings for each theme were highlighted for subsequent reporting purposes.
3
Analyzing Participant Retention in Longitudinal Studies
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Statistical analysis was performed using STATA version 12 for Windows (STATA, Texas, USA) and descriptive statistics used to explore participant demographic and anthropometric data at baseline (recruitment data). Continuous variables are presented as mean (SD) and categorical data as relative frequencies. Program retention data were assessed via Student’s t-test to determine whether there were differences in characteristics between participants that remained in the study (completers) compared to those that did not remain in the study (non-completers) at 12 months. However, categorical data (income, education and employment status) were compared between completers and non-completers using Fisher’s exact test because of the reduced numbers of participants within each category. Therefore, the categorical tests need to be interpreted with caution, as applying more robust statistical measures was not possible.
Qualitative transcripts were analyzed independently by two investigators via thematic analysis and key themes and patterns in the data identified. Thematic coding of data and development of models were assisted by the NVivo software program (QSR International Pty Ltd., version 10, 2012, Victoria, Melbourne).
Qualitative transcripts were analyzed independently by two investigators via thematic analysis and key themes and patterns in the data identified. Thematic coding of data and development of models were assisted by the NVivo software program (QSR International Pty Ltd., version 10, 2012, Victoria, Melbourne).
4
Thematic Analysis of Weight Loss Data
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The statistical analysis plan has been previously reported as has statistical methods applied [42 (link), 49 (link)]. Data analysis was conducted using STATA and SPSS version 19.0 for Windows. The trial was designed to have a statistical power of 80% to detect a difference of 1.0 kg in weight between groups at 1-year with the use of a two-sided test at a significance level of 0.05. Results are presented as mean (SD) for continuous and relative frequencies for categorical data.
Qualitative transcripts were analyzed thematically, with grounded theory principles of analysis enabling the identification, coding, and categorization of primary data patterns. All transcripts were independently analyzed and coded by two investigators, assisted by the NVivo Software program (QSR International Pty Ltd. Version 10, 2012, Victoria, Melbourne).
Qualitative transcripts were analyzed thematically, with grounded theory principles of analysis enabling the identification, coding, and categorization of primary data patterns. All transcripts were independently analyzed and coded by two investigators, assisted by the NVivo Software program (QSR International Pty Ltd. Version 10, 2012, Victoria, Melbourne).
5
Bowen Model Indicator Analysis
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For the Bowen model, we will analyse the indicators shown in table 2 using quantitative data from the teen surveys and qualitative data from the KIIs, FGD and other observations by vendors and researchers, referring to previous studies.49 52 (link) All qualitative data will be audiorecorded and transcribed. The interview data will be analysed using a general inductive approach.53 (link) The transcripts will be read repeatedly by two researchers until they have fully familiarised themselves with the contents, and codes and themes will be extracted independently using the NVivo software program, V.1.6.1 (QSR International, MA, USA). A study team with field researchers will discuss and finalise abstract themes and subthemes. The study team will analyse the indicators set by the study design.
6
Telephone Interviews with AOD Nurses in Australia
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Participants were recruited through an online survey designed to capture demographic information about AOD nurses across Australia. During this process, survey participants were asked if they wished to participate in a telephone interview, providing contact details for further follow‐up by the research assistant. Complete details of the survey study are described elsewhere (Searby et al. 2021 (link)). Semi‐structured interviews were conducted by telephone between August and October 2019 by the second author (D.B.), a research assistant with extensive experience collecting data for similar studies. The semi‐structured interview guide was developed after a scan of existing grey literature and checked for content validity by the management committee of the Drug and Alcohol Nurses of Australia (DANA), the peak professional association for nurses with a professional interest in AOD issues.
Participants were asked to agree to a verbal consent script read verbatim by the research assistant, and after consenting, all participants completed their interview. Interviews continued beyond theoretical data saturation to attempt to gather responses from a wide geographical area. Interviews were transcribed verbatim by a professional transcription company and transcripts were analysed using the NVivo software program (QSR International, Version 20).
Participants were asked to agree to a verbal consent script read verbatim by the research assistant, and after consenting, all participants completed their interview. Interviews continued beyond theoretical data saturation to attempt to gather responses from a wide geographical area. Interviews were transcribed verbatim by a professional transcription company and transcripts were analysed using the NVivo software program (QSR International, Version 20).
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