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Atlas.ti version 7

Manufactured by ATLAS.ti
Sourced in Germany

Atlas.ti version 7 is a qualitative data analysis software that supports researchers in organizing, analyzing, and interpreting complex non-numerical data. It provides a range of tools for coding, annotating, and visualizing data from various sources, including interviews, documents, and multimedia files.

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46 protocols using Atlas.ti version 7

For quantitative study, the data were checked for completeness, cleaned and entered in EPI INFO version 7 and exported to SPSS version 23 for data cleaning and analysis. Tables and pie charts were used to present results. The goodness-of-fit model (Hosmer and Lemshow) was used for the fitness of the model. Bivariate and multivariate logistic regression analysis was performed to see the association between MHM and independent variables. Variables with a p-value < 0.25 at bivariate logistic regression were entered into multivariate logistic regression. Finally, AOR with 95% CI and p-value < 0.05 were used to declare a statistically significant association. The qualitative study data were first transcribed verbatim. The next step was to translate the transcript from the local languages (Afan Oromo and Amharic) into the English language. The transcript was copied to ATLAS.ti version 7 for analysis. Then ATLAS.ti version 7 was used for developing categories and themes. The researchers conducted qualitative data analysis using inductive thematic analysis, which aimed to identify a set of main themes that captured the diverse views and feelings expressed by participants. Direct quotations were presented with a thick description of the findings to triangulate the quantitative results.
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Data analysis included simple descriptive statistics, as well as subanalyses of data by awardee characteristics of interest identified a priori. In the last year of the program, NPHII funded 73 public health agencies/organizations (48 states, the District of Columbia, and 9 local, 8 territorial, and 7 tribal agencies/organizations). While sample sizes were small for some agency types, there were some notable differences across findings. Throughout the “Results” section, data are reported in the aggregate. If meaningful variation was identified, specifically by awardee agency/organization type, then cross-tabulations are shared.
Colleagues from ICF conducted qualitative analysis of responses to the Impact and Sustainability section of the final APR and KIIs to identify themes, patterns, and interrelationships. For each set of data, Atlas.ti version 7.0 qualitative data management software was used to conduct a thematic analysis using a deductive set of a priori codes. Prior to independent coding, the team of 3 analysts reached 80% intercoder reliability across all coders. Throughout analysis, team members met regularly to discuss any questionable code assignments and worked to achieve group consensus before including newly emergent or further refined codes in the analysis.
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3

Qualitative Analysis of Women's Preferences for Proposed Intervention

All qualitative data were transcribed, translated to English, and coded in Atlas-Ti Version 7.0 software (ATLAS.ti Scientific Software Development GmbH, Berlin, Germany; http://atlasti.com/). The FGD transcripts were iteratively reviewed and a codebook was developed based on the study’s objective and emerging themes in the data. A content thematic analysis approach was then used to identify the common preferences and concerns among women regarding the proposed intervention.19 Descriptive statistics (eg, mean, frequency, proportions) were used to summarize data from the structured survey using STATA, Version 11 (StataCorp LP, College Station, TX, USA).
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Interviews with four experts in MF substantiated symptom and impact level concepts of measurement. Experts were identified from a list of clinicians who specialized in hematology. Interviews were conducted over the phone by trained interviewers, followed a semi-structured interview guide, and were each 60 min. The interviews were analyzed using semi-quantitative and qualitative data analytic methods via ATLAS.ti Version 7.0 (ATLAS.ti Scientific Software Development GmbH, Berlin) [10 ].
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Researchers analyzed qualitative data collected during the interviews using a combination of grounded theory methods of data analysis involving constant comparison (ie, simultaneous and iterative data collection and analysis) and traditional content analysis, in which responses were tabulated based on questions in the semi-structured interview guide.39 In these methods of data collection and analysis, the meaning of a concept was determined through the words of participants from the ground up rather than on the basis of an a priori theory.40 Qualitative data were analyzed using ATLAS.ti version 7.0 (Atlas.ti GmbH, Berlin), a software package designed specifically for the analysis of qualitative data.41 Researchers developed a coding scheme to be applied to all transcripts. The preliminary coding scheme was based on the FLO-11 and the semi-structured interview guide. It was modified as coders analyzed the transcripts and added or modified codes. Independent coders read each transcript and coded relevant text regarding the FLO-11’s ability to measure psychological impacts. The data were aggregated around each item in the FLO-11 questionnaire. Any issues encountered in the FLO-11, as well as recommendations to address the issues, were documented.
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Analysis of qualitative data collected during interviews involved constant comparison and traditional content tabulations. Qualitative data were analyzed using ATLAS.ti version 7.0 (Atlas.ti GmbH, Berlin, Germany), a software package designed specifically for the analysis of qualitative data. Researchers trained in qualitative methods and coding developed a coding scheme that was applied to all transcripts. A saturation grid was developed to compare the amount of new information elicited and analyzed periodically during the interview process to determine that saturation had been reached. The goal of the saturation grid was to compare the amount of new or “novel” information that was observed. Responses from the interviews were grouped by domain, concept, and patient. Further analysis compared the number of responses elicited from the first 25% of patients to the next 25% of patients, then from the first 50% of patients to the next 25% of patients, and finally from the first 75% of patients to the last 25% of patients. This allowed researchers to determine if and when new concepts were elicited as data collection neared completion and, ultimately, to determine if saturation was met within the number of interviews conducted. The saturation results determined whether or not additional interviews should be conducted.
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A total of 1,283 comments were submitted to the US Federal Register in response to the rule. As in many research studies, the study team was restricted by time and financial resources to analyzing all the comments; therefore, we downloaded a list of all of the comments and used a random number generator to select 20% of the comments to be analyzed (n¼303). After selecting the public comments to be analyzed, we downloaded each individual comment electronically and approach to analyzing public comments because it allows for the capturing of themes that might have been overlooked if using a preestablished codebook with themes already created. In addition, it goes beyond code counting and focuses more on the interpretation and meaning of themes. 18 Public comments were analyzed using Atlas.ti version 7.0. 19 Frequency of arguments was calculated based on the number of times a theme or subtheme was mentioned in a comment; this resulted in the same argument potentially being coded multiple times in one comment.
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Two coding schemes were used during data analysis: descriptive coding, in which passages of data are coded using words or short phrases, and in vivo coding, in which direct quotes are used to name specific concepts (Saldaña 2013 ). The coding process was conducted by two members of the research team and included the establishment of codes and meanings and the cross-checking of code choices. Discussions between the coders allowed for the refinement of the coding scheme and agreement on code choices (Barbour 2001 (link)). Analysis of codes employed a descriptive qualitative approach (Colorafi and Evans 2016 ). Recurrent meanings and patterns in the data were used to generate descriptive themes. Atlas.ti version 7 software was used for data management, coding, and analysis.
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Two coding schemes were used during data analysis: descriptive coding, in which passages of data are coded using words or short phrases, and In vivo coding, in which direct quotes are used to name specific concepts (Saldaña, 2013 ). The coding process was conducted by two members of the research team and included the establishment of codes and meanings and the cross-checking of code choices. Discussions between the coders allowed for the refinement of the coding scheme and agreement on code choices (Barbour, 2001 (link)). Analysis of codes employed a descriptive qualitative approach (Colorafi & Evans, 2016 ). Recurrent meanings and patterns in the data were used to generate descriptive themes. Atlas.ti version 7 software was used for data management, coding, and analysis.
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Qualitative data were analysed using Atlas‐ti version 7.1. Open coding and thematic analysis were applied.42 Open questions from the questionnaires and interviews were analysed independently by two members of the research team. Transcripts were coded (open coding) and discussed together with the first and third author for clarification. Codes were grouped into categories (axial coding) and then into themes (selective coding). Themes and selected quotations were translated into English for this article.
The questionnaires were analysed using SPSS version 22. The transcription and analysis of the observation field notes were executed immediately after each MM so, if necessary, both the MM manual and the topic list could be adjusted for the next MM based on the qualitative observations.33, 39 The three student observers discussed their findings with the research team on a weekly basis.
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