Bitesize highlights from the Breaking Binaries Research 'Twilight Zone' Qualitative Research Training Sessions #qualitativeresearch #researchtips #qualitativeanalysis #phdlife
Open coding training in qualitative researchDenford G
1. The document discusses open coding in qualitative research, which is an inductive approach where codes emerge from the data rather than being predefined.
2. Open coding involves initially breaking down data line-by-line and assigning codes to summarize concepts, which can then be sorted into categories or themes through further analysis.
3. The open coding process typically involves an initial read-through of transcripts followed by multiple coders open coding a sample of transcripts to build an initial codebook, which is then tested and modified on additional transcripts through an iterative process.
Research seminar lecture_10_analysing_qualitative_dataDaria Bogdanova
This document provides an overview of qualitative data analysis. It discusses that qualitative data includes non-numeric texts, documents, visual and verbal data. Qualitative data collection methods include interviews, questionnaires, focus groups and observations. The analysis involves coding and categorizing the data to identify patterns and develop theories. The iterative process includes reading, memoing, describing, coding, categorizing and interpreting the data. Software can help organize the data during analysis. The goal is to gain an understanding and meaning from the data.
The document discusses grounded theory method and provides details on its key aspects:
- It defines grounded theory as a research method that generates or discovers a theory from data systematically obtained from social research.
- The main building blocks of grounded theory are discussed including coding, categories, concepts, theoretical sampling, constant comparison and memo writing.
- Strengths are that it effectively builds new theories and explains new phenomena, while weaknesses include the huge amount of time and data required for analysis.
This document provides guidance on qualitative data analysis methods, including:
- The process of immersion in qualitative data through repeated reading/listening to become familiar with the content.
- Coding qualitative data by applying abstract representations or labels to segments of data that are relevant to the research question.
- Developing codes that are data-derived (based on the explicit content) or researcher-derived (conceptual interpretations).
- Using analytical memos and diaries to document the analysis process, including emerging codes, themes, and interpretations.
- Identifying themes by examining codes for patterns and relationships that answer the research question. Themes capture broader meanings than codes.
1. Qualitative data analysis involves coding texts to identify patterns, which turns qualitative data into quantitative codes. The purpose is to produce findings by analyzing data, interpreting patterns, and presenting conclusions.
2. Analyzing qualitative data is challenging due to the massive amounts of information collected. The process involves reducing the volume of data, identifying significant patterns, and developing a framework to communicate what the data reveals.
3. Rigorous analysis depends on gathering high-quality data, the credibility of the researcher, and a philosophical belief in qualitative inquiry. Common stages of analysis include familiarization, coding, identifying themes, re-coding, developing categories, exploring relationships, and reporting findings.
This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves organizing, accounting for, and making sense of data by noting patterns, themes, and regularities. There is no single correct way to analyze qualitative data, as the method should fit the purpose. The researcher must be clear on what the analysis aims to do, such as describe, interpret, discover patterns, or explain. How the data is analyzed and presented will depend on the type of qualitative study and number of data sources. Analysis involves coding, categorizing, and grouping data to identify relationships and themes in order to draw conclusions. Displays are used to help make sense of relationships between codes and build themes.
Data analysis – qualitative data presentation 2Azura Zaki
The document discusses qualitative data analysis techniques such as coding, developing themes from qualitative data, and conducting content analysis. It provides examples of coding processes like developing initial codes and focused coding, as well as summarizing data and identifying themes and relationships across data sources. Qualitative data collection techniques mentioned include observation, interviews, and analyzing documents.
Open coding training in qualitative researchDenford G
1. The document discusses open coding in qualitative research, which is an inductive approach where codes emerge from the data rather than being predefined.
2. Open coding involves initially breaking down data line-by-line and assigning codes to summarize concepts, which can then be sorted into categories or themes through further analysis.
3. The open coding process typically involves an initial read-through of transcripts followed by multiple coders open coding a sample of transcripts to build an initial codebook, which is then tested and modified on additional transcripts through an iterative process.
Research seminar lecture_10_analysing_qualitative_dataDaria Bogdanova
This document provides an overview of qualitative data analysis. It discusses that qualitative data includes non-numeric texts, documents, visual and verbal data. Qualitative data collection methods include interviews, questionnaires, focus groups and observations. The analysis involves coding and categorizing the data to identify patterns and develop theories. The iterative process includes reading, memoing, describing, coding, categorizing and interpreting the data. Software can help organize the data during analysis. The goal is to gain an understanding and meaning from the data.
The document discusses grounded theory method and provides details on its key aspects:
- It defines grounded theory as a research method that generates or discovers a theory from data systematically obtained from social research.
- The main building blocks of grounded theory are discussed including coding, categories, concepts, theoretical sampling, constant comparison and memo writing.
- Strengths are that it effectively builds new theories and explains new phenomena, while weaknesses include the huge amount of time and data required for analysis.
This document provides guidance on qualitative data analysis methods, including:
- The process of immersion in qualitative data through repeated reading/listening to become familiar with the content.
- Coding qualitative data by applying abstract representations or labels to segments of data that are relevant to the research question.
- Developing codes that are data-derived (based on the explicit content) or researcher-derived (conceptual interpretations).
- Using analytical memos and diaries to document the analysis process, including emerging codes, themes, and interpretations.
- Identifying themes by examining codes for patterns and relationships that answer the research question. Themes capture broader meanings than codes.
1. Qualitative data analysis involves coding texts to identify patterns, which turns qualitative data into quantitative codes. The purpose is to produce findings by analyzing data, interpreting patterns, and presenting conclusions.
2. Analyzing qualitative data is challenging due to the massive amounts of information collected. The process involves reducing the volume of data, identifying significant patterns, and developing a framework to communicate what the data reveals.
3. Rigorous analysis depends on gathering high-quality data, the credibility of the researcher, and a philosophical belief in qualitative inquiry. Common stages of analysis include familiarization, coding, identifying themes, re-coding, developing categories, exploring relationships, and reporting findings.
This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves organizing, accounting for, and making sense of data by noting patterns, themes, and regularities. There is no single correct way to analyze qualitative data, as the method should fit the purpose. The researcher must be clear on what the analysis aims to do, such as describe, interpret, discover patterns, or explain. How the data is analyzed and presented will depend on the type of qualitative study and number of data sources. Analysis involves coding, categorizing, and grouping data to identify relationships and themes in order to draw conclusions. Displays are used to help make sense of relationships between codes and build themes.
Data analysis – qualitative data presentation 2Azura Zaki
The document discusses qualitative data analysis techniques such as coding, developing themes from qualitative data, and conducting content analysis. It provides examples of coding processes like developing initial codes and focused coding, as well as summarizing data and identifying themes and relationships across data sources. Qualitative data collection techniques mentioned include observation, interviews, and analyzing documents.
Analyzing observational data during qualitative researchWafa Iqbal
This document discusses qualitative data analysis methods. It explains that qualitative data analysis explores and interprets complex data from sources like interviews and observations to generate new understandings without quantification. The generic process of analysis involves organizing, reading, and coding the data by assigning labels to chunks of information to develop themes and descriptions. Coding is a primary element of analysis and allows the researcher to summarize and synthesize the data. Developing themes is also part of the analysis process and involves discovering core and peripheral elements of themes from the data.
This document provides an overview of qualitative data analysis. It defines qualitative research as research that describes phenomena through words rather than numbers. Common features of qualitative research include an in-depth understanding of social phenomena as experienced by subjects. There are various types of qualitative research like phenomenology, grounded theory, ethnography, and case study. The document outlines steps for analyzing qualitative data, which include organizing, transcribing, exploring, describing themes, coding, developing themes from codes, and connecting interrelated themes. Coding involves segmenting text and labeling segments with codes, which are then grouped into themes.
Grounded theory is a systematic qualitative research methodology that focuses on generating theory from data. It involves iterative collection and analysis of data to develop conceptual categories. The researcher codes data to identify concepts and looks for relationships between concepts to develop a theoretical understanding grounded in the views of participants. Key aspects of grounded theory include constant comparison of data, memo writing to develop ideas about codes and relationships, and allowing theory to emerge from the data rather than testing a pre-existing hypothesis. The goal is to develop a theory that explains processes, actions or interactions for a particular topic.
This document provides an introduction to research, including definitions of research, the differences between thesis and project work, steps in the research process such as identifying a topic and finding background information, research as a process involving conceptual approaches and data collection techniques, tracks in research, and qualities of a successful researcher.
This document discusses qualitative data analysis and representation. It begins by outlining ethical considerations and general steps to analysis, including preparing, reducing, and representing data. Common data analysis strategies are described, such as those from Madison, Huberman & Miles, and Wolcott. The data analysis spiral process is explained through collecting, analyzing and reporting data in an iterative process. Specific analysis procedures are covered for each qualitative approach, including managing data, coding, developing themes, interpreting findings, and visualizing results. Computer programs that can assist with analysis are also reviewed.
This document provides an overview of grounded theory, including its definition, uses, methodology, and key steps. Grounded theory is a systematic qualitative research method for developing theories about phenomena grounded in data. It involves collecting and analyzing data to generate concepts and theories, rather than testing a predetermined hypothesis. The methodology includes open, axial, and selective coding of data to group concepts into categories and identify core themes from which to build an explanatory theory.
The document provides an overview of grounded theory methodology for analyzing qualitative data. It discusses open, axial, and selective coding as the three stages of coding in grounded theory. Open coding involves preliminary labeling of raw data. Axial coding identifies relationships between open codes. Selective coding identifies broader themes by focusing on a core category and relating other categories to it. Coding frames, memos, and constant comparison are also important aspects of grounded theory analysis.
Qualitative research methodology and an introduction to NLP. There is also an example of how to use a pre-trained model to perform sentiment analysis on user feedback. A Google Colab Notebook is provided in the slides.
This presentation discusses about content analysis, its use, Types, Advantages, Issues of Reliability & Validity, Problems, Quantitative content analysis, coding, Qualitative content analysis, Creative synthesis, Data reduction and Constant comparison.,
This document provides an overview of qualitative data analysis software (QDAS) and the web-based software webQDA. It discusses the benefits of using QDAS to organize and analyze qualitative data. The document outlines the history of major QDAS programs and describes some of the key features and capabilities of webQDA, including its ability to code and categorize data from various sources to facilitate analysis and answer research questions. WebQDA allows for collaborative qualitative analysis in an online environment.
Bowling Green State University Digital Forensics Challenges Project.docxsdfghj21
This document outlines a digital forensics project that aims to familiarize students with encryption, anti-forensic techniques, and attacks on encrypted systems and passwords. The project involves creating a job aid to explain cryptography, password cracking, and interception attacks, as well as documenting the processing of files, partitions, and software in an investigative report. Students will apply skills related to organizing information, evaluating evidence, applying data analysis techniques, and accessing encrypted or anti-forensically altered data and systems. Upon completion, their work will be evaluated based on competencies in areas such as clear communication, logical reasoning, and technical understanding of computer systems and investigations.
This document provides an overview of a course on data warehousing, filtering, and mining. The course is being taught in Fall 2004 at Temple University. The document includes the course syllabus which outlines topics like data warehousing, OLAP technology, data preprocessing, mining association rules, classification, cluster analysis, and mining complex data types. Grading will be based on assignments, quizzes, a presentation, individual project, and final exam. The document also provides introductory material on data mining including definitions and examples.
This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves coding texts, identifying patterns, and reducing qualitative data into quantitative codes. It also outlines several stages of qualitative analysis including familiarization with data, transcription, organization, coding, identifying themes, recoding, developing categories, exploring relationships between categories, and developing theories. Finally, it discusses challenges of qualitative analysis including placing raw data into logical categories and communicating interpretations to others.
The ROER4D Curation & Dissemination team provides an overview of the ROER4D open data initiative as well as some key insights and challenges experienced.
Data analysis – using computers for presentationNoonapau
The document discusses using computer software for data analysis. It provides examples of different types of software including word processors, code-and-retrieve programs, and conceptual network builders. It emphasizes that the researcher should choose software based on their methodology and the type and amount of data, rather than which software is considered "best." The document also summarizes several research articles that used different software programs like MS Word, NVivo, and Qualrus to analyze qualitative data.
http://kulibrarians.g.hatena.ne.jp/kulibrarians/20170222
Presentation by Cuna Ekmekcioglu (The University of Edinburgh)
- Creating and Managing Digital Research Data in Creative Arts: An overview (2016)
CC BY-NC-SA 4.0
This document provides an overview of qualitative analysis methods for coding interview and document data. It begins with an agenda for covering two main qualitative approaches, coding exercises, slides on qualitative analysis, and potential brainstorming and affinity diagramming exercises if time allows. It then discusses common features of qualitative analytic methods including affixing codes, noting reflections, sorting materials to identify patterns, and gradually developing generalizations. Finally, it provides details on coding and categorization procedures, the iterative nature of qualitative analysis, and ensuring the credibility and rigor of qualitative findings.
The document provides guidance on planning dissertation research by outlining a 5-step process: 1) describing the research topic, 2) identifying keywords, 3) identifying relevant databases and sources, 4) searching additional sources, and 5) searching databases using Boolean logic, limiters, truncation and alternative spellings. It emphasizes building search strategies iteratively and searching across journal databases to access up-to-date peer-reviewed research. Key databases recommended include Compendex, Web of Science, Business Source Premier and Emerald.
This document discusses content analysis as a qualitative data analysis technique. It begins by defining content analysis as a method to systematically reduce and categorize textual data to identify patterns and relationships. The document then outlines the coding process, describing codes as labels assigned to segments of text that are then grouped into categories. It provides examples of different types of codes and discusses hierarchical coding structures. Steps in the content analysis process are also outlined, from defining research questions to data analysis and interpretation. Issues of reliability in content analysis are raised at the end.
Breaking Binaries Research Session on Coding and AnalysisKatrina Pritchard
This is the slide set for the Breaking Binaries Research Summer Session on Qualitative Coding and analysis delivered by Professor Katrina Pritchard and Dr Helen Williams
Analyzing observational data during qualitative researchWafa Iqbal
This document discusses qualitative data analysis methods. It explains that qualitative data analysis explores and interprets complex data from sources like interviews and observations to generate new understandings without quantification. The generic process of analysis involves organizing, reading, and coding the data by assigning labels to chunks of information to develop themes and descriptions. Coding is a primary element of analysis and allows the researcher to summarize and synthesize the data. Developing themes is also part of the analysis process and involves discovering core and peripheral elements of themes from the data.
This document provides an overview of qualitative data analysis. It defines qualitative research as research that describes phenomena through words rather than numbers. Common features of qualitative research include an in-depth understanding of social phenomena as experienced by subjects. There are various types of qualitative research like phenomenology, grounded theory, ethnography, and case study. The document outlines steps for analyzing qualitative data, which include organizing, transcribing, exploring, describing themes, coding, developing themes from codes, and connecting interrelated themes. Coding involves segmenting text and labeling segments with codes, which are then grouped into themes.
Grounded theory is a systematic qualitative research methodology that focuses on generating theory from data. It involves iterative collection and analysis of data to develop conceptual categories. The researcher codes data to identify concepts and looks for relationships between concepts to develop a theoretical understanding grounded in the views of participants. Key aspects of grounded theory include constant comparison of data, memo writing to develop ideas about codes and relationships, and allowing theory to emerge from the data rather than testing a pre-existing hypothesis. The goal is to develop a theory that explains processes, actions or interactions for a particular topic.
This document provides an introduction to research, including definitions of research, the differences between thesis and project work, steps in the research process such as identifying a topic and finding background information, research as a process involving conceptual approaches and data collection techniques, tracks in research, and qualities of a successful researcher.
This document discusses qualitative data analysis and representation. It begins by outlining ethical considerations and general steps to analysis, including preparing, reducing, and representing data. Common data analysis strategies are described, such as those from Madison, Huberman & Miles, and Wolcott. The data analysis spiral process is explained through collecting, analyzing and reporting data in an iterative process. Specific analysis procedures are covered for each qualitative approach, including managing data, coding, developing themes, interpreting findings, and visualizing results. Computer programs that can assist with analysis are also reviewed.
This document provides an overview of grounded theory, including its definition, uses, methodology, and key steps. Grounded theory is a systematic qualitative research method for developing theories about phenomena grounded in data. It involves collecting and analyzing data to generate concepts and theories, rather than testing a predetermined hypothesis. The methodology includes open, axial, and selective coding of data to group concepts into categories and identify core themes from which to build an explanatory theory.
The document provides an overview of grounded theory methodology for analyzing qualitative data. It discusses open, axial, and selective coding as the three stages of coding in grounded theory. Open coding involves preliminary labeling of raw data. Axial coding identifies relationships between open codes. Selective coding identifies broader themes by focusing on a core category and relating other categories to it. Coding frames, memos, and constant comparison are also important aspects of grounded theory analysis.
Qualitative research methodology and an introduction to NLP. There is also an example of how to use a pre-trained model to perform sentiment analysis on user feedback. A Google Colab Notebook is provided in the slides.
This presentation discusses about content analysis, its use, Types, Advantages, Issues of Reliability & Validity, Problems, Quantitative content analysis, coding, Qualitative content analysis, Creative synthesis, Data reduction and Constant comparison.,
This document provides an overview of qualitative data analysis software (QDAS) and the web-based software webQDA. It discusses the benefits of using QDAS to organize and analyze qualitative data. The document outlines the history of major QDAS programs and describes some of the key features and capabilities of webQDA, including its ability to code and categorize data from various sources to facilitate analysis and answer research questions. WebQDA allows for collaborative qualitative analysis in an online environment.
Bowling Green State University Digital Forensics Challenges Project.docxsdfghj21
This document outlines a digital forensics project that aims to familiarize students with encryption, anti-forensic techniques, and attacks on encrypted systems and passwords. The project involves creating a job aid to explain cryptography, password cracking, and interception attacks, as well as documenting the processing of files, partitions, and software in an investigative report. Students will apply skills related to organizing information, evaluating evidence, applying data analysis techniques, and accessing encrypted or anti-forensically altered data and systems. Upon completion, their work will be evaluated based on competencies in areas such as clear communication, logical reasoning, and technical understanding of computer systems and investigations.
This document provides an overview of a course on data warehousing, filtering, and mining. The course is being taught in Fall 2004 at Temple University. The document includes the course syllabus which outlines topics like data warehousing, OLAP technology, data preprocessing, mining association rules, classification, cluster analysis, and mining complex data types. Grading will be based on assignments, quizzes, a presentation, individual project, and final exam. The document also provides introductory material on data mining including definitions and examples.
This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves coding texts, identifying patterns, and reducing qualitative data into quantitative codes. It also outlines several stages of qualitative analysis including familiarization with data, transcription, organization, coding, identifying themes, recoding, developing categories, exploring relationships between categories, and developing theories. Finally, it discusses challenges of qualitative analysis including placing raw data into logical categories and communicating interpretations to others.
The ROER4D Curation & Dissemination team provides an overview of the ROER4D open data initiative as well as some key insights and challenges experienced.
Data analysis – using computers for presentationNoonapau
The document discusses using computer software for data analysis. It provides examples of different types of software including word processors, code-and-retrieve programs, and conceptual network builders. It emphasizes that the researcher should choose software based on their methodology and the type and amount of data, rather than which software is considered "best." The document also summarizes several research articles that used different software programs like MS Word, NVivo, and Qualrus to analyze qualitative data.
http://kulibrarians.g.hatena.ne.jp/kulibrarians/20170222
Presentation by Cuna Ekmekcioglu (The University of Edinburgh)
- Creating and Managing Digital Research Data in Creative Arts: An overview (2016)
CC BY-NC-SA 4.0
This document provides an overview of qualitative analysis methods for coding interview and document data. It begins with an agenda for covering two main qualitative approaches, coding exercises, slides on qualitative analysis, and potential brainstorming and affinity diagramming exercises if time allows. It then discusses common features of qualitative analytic methods including affixing codes, noting reflections, sorting materials to identify patterns, and gradually developing generalizations. Finally, it provides details on coding and categorization procedures, the iterative nature of qualitative analysis, and ensuring the credibility and rigor of qualitative findings.
The document provides guidance on planning dissertation research by outlining a 5-step process: 1) describing the research topic, 2) identifying keywords, 3) identifying relevant databases and sources, 4) searching additional sources, and 5) searching databases using Boolean logic, limiters, truncation and alternative spellings. It emphasizes building search strategies iteratively and searching across journal databases to access up-to-date peer-reviewed research. Key databases recommended include Compendex, Web of Science, Business Source Premier and Emerald.
This document discusses content analysis as a qualitative data analysis technique. It begins by defining content analysis as a method to systematically reduce and categorize textual data to identify patterns and relationships. The document then outlines the coding process, describing codes as labels assigned to segments of text that are then grouped into categories. It provides examples of different types of codes and discusses hierarchical coding structures. Steps in the content analysis process are also outlined, from defining research questions to data analysis and interpretation. Issues of reliability in content analysis are raised at the end.
Similar to BBR Twilight Highlights Coding and Analysis 24MAY23.pptx (20)
Breaking Binaries Research Session on Coding and AnalysisKatrina Pritchard
This is the slide set for the Breaking Binaries Research Summer Session on Qualitative Coding and analysis delivered by Professor Katrina Pritchard and Dr Helen Williams
How to use Babbage and Terry's Macro in Qualitative research - a short explanation.
Babbage, D. R., & Terry, G. (2023, April 19). Thematic analysis coding management macro. https://doi.org/10.17605/OSF.IO/ZA7B6
BBR Twilight Higlights- Interview Training 15JUN23.pptxKatrina Pritchard
Bitesize highlights from the Breaking Binaries Research 'Twilight Zone' Qualitative Research Training Sessions #qualitativeresearch #researchtips #qualitativeanalysis #phdlife
This document provides an overview of a qualitative thesis walkthrough session presented by Professor Katrina Pritchard and Dr. Helen Williams. The session covers key aspects of a qualitative thesis such as literature reviews, theoretical frameworks, methodology and methods, empirical findings, and discussion/conclusion. It also includes overviews of Pritchard and Williams' theses and tips for writing a qualitative thesis. The goal is to help participants thinking about structuring and writing their own qualitative theses.
BBR Twilight Zone Session 1 Introduction to Ontology and EpistemologyKatrina Pritchard
This is the first session from the 'Twilight Zone' delivered by Dr Helen Williams and Prof. Katrina Pritchard as part of the Breaking Binaries Research Programme.
You can read more about these sessions on our blog: https://breakingbinariesresearch.wordpress.com/
This document discusses ageing in the workplace. It begins with introductions from Professor Katrina Pritchard of Swansea University and Dr. Cara Reed of Cardiff University. The document then covers various ways of understanding age, including chronological, biological, functional, and subjective definitions. It also discusses generational categories and how attitudes towards age can influence stereotypes, prejudice, and discrimination. Finally, it explores hot topics regarding ageing such as retirement trends and the experience of older women workers.
This document outlines three sub-projects that analyze gendered constructions of entrepreneurship across online spaces: 1) Mapping visual representations of entrepreneurial masculinities and femininities, 2) Unpacking representations of entrepreneurial advice online, and 3) Analyzing the journey of a popular female entrepreneurial image. The researchers trace images and texts across platforms to understand how entrepreneurship is gendered. They discuss challenges of reflexively analyzing online images and platforms, tracing as an ongoing process, and using a montage approach. The second sub-project analyzes entrepreneurial advice through a framework of critical public pedagogy and examines how advice shapes subjects according to capitalist norms in a gendered way. Preliminary findings suggest advice constructs entrepreneurship
This document discusses qualitative research methods for analyzing online text and images. It describes the author's journey across different methodological approaches in human resource management, identity and diversity, and entrepreneurship research. These have included digital methods like tracking online data and trawling websites, as well as visual analysis techniques. Challenges of online research are noted around data volume, authenticity, and publishing multimodal findings. Future developments may involve more socially distanced research and combining digital and traditional methods as data becomes more complex, ephemeral and multimodal.
This document discusses the need for new directions in qualitative research methods. It argues that traditional qualitative research has become formulaic and fails to address important issues like reification of data and lack of consideration of concepts like temporality and materiality. The document then explores potential new directions, including personal reflection on one's research, developing method guides, and using creative and digital methods. It provides an example research project that maps across digital spaces and combines visual and semiotic analysis. Finally, it stresses that doctoral researchers should challenge assumptions, experiment with different knowledge generation techniques, and focus on methodology.
This document provides an overview of a research project analyzing web-based images of entrepreneurs. It discusses using a Combined Visual Analysis methodology to examine images from Google Image searches and stock image libraries. The analysis involves categorizing images, analyzing composition, semiotics, gaze and gesture. Preliminary conclusions found themes of masculinity reinforced in male images but adopted in female images, with stock images predominating. Challenges discussed include volume of data, platformization, and ethics. Key advice is to explore visual representations, notice stock image use, discuss ethics, and contribute seriously while having fun.
This document discusses generational stereotypes about young and older workers. It notes that while "young" and "old" are constructed categories in the labor market used to exclude workers, both groups face similar means and measures of exclusion based on chronological age. The document also examines how generations are defined but debates the evidence for lasting differences between birth cohorts. It concludes by calling for future research to better understand stereotypes, intersectional experiences, age as a competition, and the impact of COVID-19 across age groups.
This document provides an introduction to a keynote presentation about reimagining research in a digital age. It discusses how conducting research essentially involves extracting and abstracting meaning from data. When research moves online, issues like authenticity, hybridity, multimodality, temporality and sociomateriality must be critically engaged with. There are also practical challenges to consider regarding research ethics, skills, resources, and managing mixed methods. The document provides resources for conducting qualitative research on various digital platforms and methods.
This document provides an overview of a research seminar on age and work. It discusses several topics:
1) Generations are socially constructed cohorts that shape values and attitudes. Debates often conflate generations with age groups and present differences as natural rather than constructed.
2) Discussions of the "missing million" unemployed youth and the "missing million" unemployed older workers position different age groups in competition over limited jobs and resources.
3) Visual analyses of online news and stock photos reveal gendered discourses of ageing, with older men typically depicted in command roles and younger women as the focus of attention.
The seminar explores how notions of age and age identities are constructed online
Part of the British Academy of Management Research Methods SIG 'Sharing our Struggles' series.
The increased use of the Internet, social media and other virtual sites for discussing and accomplishing work and organization raises both new possibilities and new challenges for conducting organizational research. We have the opportunity to view work in a different way, to access the previously inaccessible and to gain insight into virtual organization through the utilisation of on-line research methods but we still know very little about how we might effectively and usefully do this. In this workshop speakers will discuss their own specific experiences of on-line research, revealing both their successes and the issues that arise.
See flyer for cost and booking details
Do you see what I see? Going beyond chronology by exploring images of age at work. Katrina Pritchard and Rebecca Whiting Paper presented at BPS conference, January 2013
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
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'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
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ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
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Committing to the careful, systematic analysis of all relevant reports and observations;
Coming to a descriptive-interpretive understanding of experiences and observations by carefully representing their meanings;
Enable us to organizing these understandings into clusters of similar experiences and observations
Integrating categories into some kind of coherent story and contribution (empirical and/or theoretical).