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Marie-Hélène Paré teaches qualitative research methods at the Open University of Catalonia (UOC) and is a freelance methodologist in qualitative data analysis. She was educated in Quebec, Beirut and Oxford where she read social work. A clinician by training, she worked as a mental health officer in humanitarian missions for MSF, MDM and UNWRA in psychosocial aid programs for survivors of war trauma in East Africa and the Middle East. Her clinical work led her to research the harm that INGOs can do in the name of doing good when imposing Western paradigms in culturally and politically different contexts. She is an NVivo Certified Platinum Trainer and is part of the NVivo Academy training team for the NVivo online courses. Marie-Hélène is a sought-after methodologist, having taught qualitative data analysis in more than fifty universities and research centres worldwide, including universities in Qatar and Iran. Since 2009, she has taught the introductory and advanced courses in qualitative data analysis at this Methods School, and she teaches similar courses the IPSA-NUS Summer School in Singapore. Her methodological interests range from advances in qualitative data analysis to qualitative systematic reviews, postcolonial epistemology and participatory methodologies. for NVivo 11 for Mac as this version is incomplete compared to Windows. You can run NVivo 11 Pro for Windows on a Mac using Apple Boot Camp or Parallels if, and only, your Mac meets the system requirements here. You must ensure that NVivo works well on your machine regardless of the OS as no technical assistance will be provided at the Winter School. You can find more on installation instructions in the section Software and Hardware below. This course is designed for participants who plan to use NVivo for the management, coding, analysis and visualisation of qualitative data. The course content is spread over four modules and includes to set up a project and organise data, manage a literature review, code and analyse data, and present qualitative findings using graphic displays. The course is entirely hands-on and uses sample data to learn NVivo’s basic and advanced functionalities. This course does not cover how to analyse qualitative data in NVivo based on specific methods such as thematic analysis, grounded theory, or content analysis. If you are looking for such course, see the outline of the course Please see the full course outline, including diagrams and demos here NVivo is software programme for qualitative data analysis. It is a powerful platform that supports text, multimedia, pictures, PDFs, open-ended surveys from Excel and Survey Monkey, reference libraries, webpages, social media data from Facebook, Twitter, Linked In, and You Tube, notes from Evernote and One Note, and emails from Outlook. NVivo supports a range of inductive and deductive methods to qualitative analysis such as thematic and content analysis, within and cross-case analysis, discourse, conversational and narrative analysis, grounded theory, analytical induction, and qualitative research synthesis. The objective of this course is to provide participants with knowledge and skills to use the basic and advanced features of NVivo in their own research. The course content is spread over four modules and includes to set up a project and organise data, work with multimedia, manage a literature review, autocode and code data inductively, generate hypotheses, seek patterns and discover relationships, and present qualitative findings. Module 1 Data Management The course opens with notions of qualitative research designs and their application in a NVivo project. In turn, we review how data can be organised in comparative and non-comparative designs, coding approaches developed, and types of analyses conducted. We then move in NVivo and import and organise a range of qualitative data. We learn the key features that support a literature review so sources can be annotated and cross-referenced to highlight a line of arguments and connections across sources. Our attention then turns to the transcribing possibilities of NVivo, starting with transcribing media recordings in-full or working only with sound and video sequences. We see that one can work directly on pictures or generate a log to associate comments with specific picture regions. We move on and create externals that link a NVivo project to outside information, as well as creating memos where the analytic process is recorded. Module 1 concludes with lexical queries which search for frequency, occurrence, and context of keywords in textual data. We analyse the outputs using word clouds, dendograms, and wordtrees. Module 2: Data Coding Module 2 introduces the different techniques to autocode and code data inductively in NVivo. We start by autocoding questions from structured interviews, so the responses of each question are gathered in one node. Such data sorting - known as broad-brush coding - is very useful when one wants to examine everything that was said about a question or a theme across a dataset without having to open each and every source. We move on with inductive coding and learn the different tools to code data manually. Key notions underlying the coding process such as coding unit, semantic exclusiveness, semantic exhaustiveness, and coding cooccurrence are discussed and exemplified. The use of relationship nodes is introduced to formalise relationships between codes when working towards hypothesis generation or falsification. Module 2 concludes with visualisations that support the coding process from inception to the end. Module 3: Data Analysis Module 3 covers the range of functionalities to prepare and conduct qualitative analysis. Since a large number of social research gather qualitative data, as well as variables, so comparison can be made across cases and sub-sets of cases, we first look at the procedures to create cases from interview data, import variables from Excel, and merge these to the cases. We extend our use of cases to policy documents where comparisons are made on document data, and not cases of individuals. In both instances, we use the functionality of source and node classifications to define type of sources and cases in the dataset. With the cases created, we turn to the NVivo search tools that efficiently retrieve cases that match a specific search string. This allows us to create sets of cases and documents for comparative analysis. We then move on with coding-based queries which retrieve data based on codes overlap, proximity, sequence, or exclusion patterns. We first run coding queries that search for data coded at some nodes but only when mentioned by cases of a given profile. For cross-case analysis, we run matrix queries which cross-tabulate cases with codes, and we interpret the results using different numerical readings: coding density, number of cases, relative percentage, etc. Our interpretation is recorded in memos and is linked back to theory. Content analysis research method with Nvivo-6 software in a Ph D thesis: an approach to the long-term psychological effects on Chilean ex-prisoners survivors of experiences of torture and imprisonment. Module 3 concludes with running group query to find out association between coded items across a dataset. Zapata-Sepúlveda, P., López-Sánchez, F., & Sánchez-Gómez, M. This course description may be subject to subsequent adaptations (e.g. Module 4: Data Visualisation Module 4 proposes different graphic displays to effectively communicate one’s research findings. Maximising transparency in a doctoral thesis: the complexity of writing about the use of QSR* NVIVO within grounded theory study. taking into account new developments in the field, participant demands, group size, etc). We first discuss the rationales for choosing certain displays against others. Registered participants will be informed at the time of change. We learn to generate maps, charts, diagrams, and dendograms. By registering for this course, you confirm that you possess the knowledge required to follow it. Moving on to building a solid audit trail to back up results and substantiate one’s claims, we learn how to export qualitative findings out of NVivo, so these can be used in Word, Excel, and Power Point. The instructor will not teach these prerequisite items. The usefulness of generating nodes summary reports, which provide detailed synthesis of the scope of a node in a project, is also covered. When working with colleagues who don’t use NVivo, the possibility to export project data in mini websites using HTML files is presented. Development of a Decision Tree to Determine Appropriateness of NVivo in Analyzing Qualitative Data Sets. Module 4 concludes with the ABC of coordinating team work, with a particular emphasis on the golden rules for successful data management, splitting and merging project files in a master project, and the measurement of intercoder reliability. The NVivo 11 Pro Started Guide (see here for download) is the main text of the course. Those who wish to deepen understanding of using NVivo in qualitative research can do the optional readings of Bazeley & Jackson (2013) Optional text Bazeley & Jackson: format data: 59-61; download data with NCapture: 173-177; import data: (internals) 24-34; 45-46; 61-66; (open-ended surveys) 199- 203; (social media) 171-176; 209-211; (multimedia) 154-167; transcription: 167-169; externals: 62-63; literature review: 178-194; links and memos: 34-45; text-based queries: 110-117; 249-250 Optional text Bazeley & Jackson: autocoding: 108-110; (datasets) 207-208; codes and coding: 68-94; coding scheme: 95-106; 117-119; relationship nodes: 230-234; cases and variables: 50-56; (from surveys) 122-139; 205-207 Optional text Bazeley & Jackson: sets: 106-107; 146-153; coding-based queries: 141-146; 242-248; 250-257; cross-case analysis and theory-building: 257- 265; visualisations: (model) 28-30; 217-230; 234-241; reports: 265-269; export content out of NVivo: 119-121; 139-140; team work: 270-296 This course requires that you run NVivo 11 Pro for Windows on your laptop or, alternatively, NVivo 11 Plus. DO NOT COME TO THE COURSE WITH NVIVO 11 FOR MAC as this version is incomplete compared to NVivo 11 Pro for Windows. Mac users should consult the compatibility options and system requirements to run NVivo 11 Pro for Windows using Boot camp or Parallels on their Mac. It is your responsibility to ensure that NVivo works well on your laptop as no troubleshooting will be provided at the Winter School. Once NVivo is installed on your laptop, verify that it works properly. As per QSR International Processor: 2.0 GHz Pentium 4-compatible processor or faster Memory: 2 GB RAM or more Hard disk: Approximately 4 GB Auld, G. Marie-Hélène Paré teaches qualitative research methods at the Open University of Catalonia (UOC) and is a freelance methodologist in qualitative data analysis. She was educated in Quebec, Beirut and Oxford where she read social work. A clinician by training, she worked as a mental health officer in humanitarian missions for MSF, MDM and UNWRA in psychosocial aid programs for survivors of war trauma in East Africa and the Middle East. Her clinical work led her to research the harm that INGOs can do in the name of doing good when imposing Western paradigms in culturally and politically different contexts. She is an NVivo Certified Platinum Trainer and is part of the NVivo Academy training team for the NVivo online courses. Marie-Hélène is a sought-after methodologist, having taught qualitative data analysis in more than fifty universities and research centres worldwide, including universities in Qatar and Iran. Since 2009, she has taught the introductory and advanced courses in qualitative data analysis at this Methods School, and she teaches similar courses the IPSA-NUS Summer School in Singapore. Her methodological interests range from advances in qualitative data analysis to qualitative systematic reviews, postcolonial epistemology and participatory methodologies. for NVivo 11 for Mac as this version is incomplete compared to Windows. You can run NVivo 11 Pro for Windows on a Mac using Apple Boot Camp or Parallels if, and only, your Mac meets the system requirements here. You must ensure that NVivo works well on your machine regardless of the OS as no technical assistance will be provided at the Winter School. You can find more on installation instructions in the section Software and Hardware below. This course is designed for participants who plan to use NVivo for the management, coding, analysis and visualisation of qualitative data. The course content is spread over four modules and includes to set up a project and organise data, manage a literature review, code and analyse data, and present qualitative findings using graphic displays. The course is entirely hands-on and uses sample data to learn NVivo’s basic and advanced functionalities. This course does not cover how to analyse qualitative data in NVivo based on specific methods such as thematic analysis, grounded theory, or content analysis. If you are looking for such course, see the outline of the course Please see the full course outline, including diagrams and demos here NVivo is software programme for qualitative data analysis. It is a powerful platform that supports text, multimedia, pictures, PDFs, open-ended surveys from Excel and Survey Monkey, reference libraries, webpages, social media data from Facebook, Twitter, Linked In, and You Tube, notes from Evernote and One Note, and emails from Outlook. NVivo supports a range of inductive and deductive methods to qualitative analysis such as thematic and content analysis, within and cross-case analysis, discourse, conversational and narrative analysis, grounded theory, analytical induction, and qualitative research synthesis. The objective of this course is to provide participants with knowledge and skills to use the basic and advanced features of NVivo in their own research. The course content is spread over four modules and includes to set up a project and organise data, work with multimedia, manage a literature review, autocode and code data inductively, generate hypotheses, seek patterns and discover relationships, and present qualitative findings. Module 1 Data Management The course opens with notions of qualitative research designs and their application in a NVivo project. In turn, we review how data can be organised in comparative and non-comparative designs, coding approaches developed, and types of analyses conducted. We then move in NVivo and import and organise a range of qualitative data. We learn the key features that support a literature review so sources can be annotated and cross-referenced to highlight a line of arguments and connections across sources. Our attention then turns to the transcribing possibilities of NVivo, starting with transcribing media recordings in-full or working only with sound and video sequences. We see that one can work directly on pictures or generate a log to associate comments with specific picture regions. We move on and create externals that link a NVivo project to outside information, as well as creating memos where the analytic process is recorded. Module 1 concludes with lexical queries which search for frequency, occurrence, and context of keywords in textual data. We analyse the outputs using word clouds, dendograms, and wordtrees. Module 2: Data Coding Module 2 introduces the different techniques to autocode and code data inductively in NVivo. We start by autocoding questions from structured interviews, so the responses of each question are gathered in one node. Such data sorting - known as broad-brush coding - is very useful when one wants to examine everything that was said about a question or a theme across a dataset without having to open each and every source. We move on with inductive coding and learn the different tools to code data manually. Key notions underlying the coding process such as coding unit, semantic exclusiveness, semantic exhaustiveness, and coding cooccurrence are discussed and exemplified. The use of relationship nodes is introduced to formalise relationships between codes when working towards hypothesis generation or falsification. Module 2 concludes with visualisations that support the coding process from inception to the end. Module 3: Data Analysis Module 3 covers the range of functionalities to prepare and conduct qualitative analysis. Since a large number of social research gather qualitative data, as well as variables, so comparison can be made across cases and sub-sets of cases, we first look at the procedures to create cases from interview data, import variables from Excel, and merge these to the cases. We extend our use of cases to policy documents where comparisons are made on document data, and not cases of individuals. In both instances, we use the functionality of source and node classifications to define type of sources and cases in the dataset. With the cases created, we turn to the NVivo search tools that efficiently retrieve cases that match a specific search string. This allows us to create sets of cases and documents for comparative analysis. We then move on with coding-based queries which retrieve data based on codes overlap, proximity, sequence, or exclusion patterns. We first run coding queries that search for data coded at some nodes but only when mentioned by cases of a given profile. For cross-case analysis, we run matrix queries which cross-tabulate cases with codes, and we interpret the results using different numerical readings: coding density, number of cases, relative percentage, etc. Our interpretation is recorded in memos and is linked back to theory. Content analysis research method with Nvivo-6 software in a Ph D thesis: an approach to the long-term psychological effects on Chilean ex-prisoners survivors of experiences of torture and imprisonment. Module 3 concludes with running group query to find out association between coded items across a dataset. Zapata-Sepúlveda, P., López-Sánchez, F., & Sánchez-Gómez, M. This course description may be subject to subsequent adaptations (e.g. Module 4: Data Visualisation Module 4 proposes different graphic displays to effectively communicate one’s research findings. Maximising transparency in a doctoral thesis: the complexity of writing about the use of QSR* NVIVO within grounded theory study. taking into account new developments in the field, participant demands, group size, etc). We first discuss the rationales for choosing certain displays against others. Registered participants will be informed at the time of change. We learn to generate maps, charts, diagrams, and dendograms. By registering for this course, you confirm that you possess the knowledge required to follow it. Moving on to building a solid audit trail to back up results and substantiate one’s claims, we learn how to export qualitative findings out of NVivo, so these can be used in Word, Excel, and Power Point. The instructor will not teach these prerequisite items. The usefulness of generating nodes summary reports, which provide detailed synthesis of the scope of a node in a project, is also covered. When working with colleagues who don’t use NVivo, the possibility to export project data in mini websites using HTML files is presented. Development of a Decision Tree to Determine Appropriateness of NVivo in Analyzing Qualitative Data Sets. Module 4 concludes with the ABC of coordinating team work, with a particular emphasis on the golden rules for successful data management, splitting and merging project files in a master project, and the measurement of intercoder reliability. The NVivo 11 Pro Started Guide (see here for download) is the main text of the course. Those who wish to deepen understanding of using NVivo in qualitative research can do the optional readings of Bazeley & Jackson (2013) Optional text Bazeley & Jackson: format data: 59-61; download data with NCapture: 173-177; import data: (internals) 24-34; 45-46; 61-66; (open-ended surveys) 199- 203; (social media) 171-176; 209-211; (multimedia) 154-167; transcription: 167-169; externals: 62-63; literature review: 178-194; links and memos: 34-45; text-based queries: 110-117; 249-250 Optional text Bazeley & Jackson: autocoding: 108-110; (datasets) 207-208; codes and coding: 68-94; coding scheme: 95-106; 117-119; relationship nodes: 230-234; cases and variables: 50-56; (from surveys) 122-139; 205-207 Optional text Bazeley & Jackson: sets: 106-107; 146-153; coding-based queries: 141-146; 242-248; 250-257; cross-case analysis and theory-building: 257- 265; visualisations: (model) 28-30; 217-230; 234-241; reports: 265-269; export content out of NVivo: 119-121; 139-140; team work: 270-296 This course requires that you run NVivo 11 Pro for Windows on your laptop or, alternatively, NVivo 11 Plus. DO NOT COME TO THE COURSE WITH NVIVO 11 FOR MAC as this version is incomplete compared to NVivo 11 Pro for Windows. Mac users should consult the compatibility options and system requirements to run NVivo 11 Pro for Windows using Boot camp or Parallels on their Mac. It is your responsibility to ensure that NVivo works well on your laptop as no troubleshooting will be provided at the Winter School. Once NVivo is installed on your laptop, verify that it works properly. As per QSR International Processor: 2.0 GHz Pentium 4-compatible processor or faster Memory: 2 GB RAM or more Hard disk: Approximately 4 GB Auld, G.

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