Ryan And Bernard Techniques To Identify Themes In Qualitative Data Pdf
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Citas duplicadas. Citas combinadas.
- Techniques to Identify Themes
- Coding (social sciences)
- YRM-30806 Research Methods and Data Analysis in Communication and Health
- Semiautomated text analytics for qualitative data synthesis
Techniques to Identify Themes
The original transcripts from the primary research studies included in this secondary analysis were only accessible to the authors for the length and use of this project and are therefore not available to third parties; the corresponding author can be of assistance to liaise with the original institutions that hold the data.
Approaches to synthesizing qualitative data have, to date, largely focused on integrating the findings from published reports. However, developments in text mining software offer the potential for efficient analysis of large pooled primary qualitative datasets. We applied Leximancer v4. The limitations of using Leximancer were the substantial data preparation time involved and the contextual knowledge required from the researcher to turn lines of inquiry into meaningful insights.
There are increasing calls to make use of existing qualitative and quantitative data, increasing availability of large qualitative data and growth in demand for and approaches to data and evidence synthesis. Synthesis of large textual data is labor intensive and requires novel approaches. We present the utility of text analytics as an independent method for contributing to qualitative data synthesis, facilitating more efficient, comprehensive, and transparent data familiarization and coding. The method enables analysis across various levels of supervision to modify in line with project objectives.
Text analytics software such as Leximancer can facilitate qualitative data synthesis of unusually large datasets in any field and invites further reflection and critique by social scientists. In recent years, technological advances in automation have enhanced the efficiency of the review and analysis process.
These advances have primarily focused on expediting the identification and synthesis of quantitative data. Thus, there is a growing body of research and guidance that describe possible approaches to conducting and evaluating qualitative synthesis in meaningful ways. Traditional approaches to analyzing or synthesizing the findings of large qualitative datasets are time and resource heavy.
Expediting the process has been the subject of recent investigation, and potential approaches include the application of artificial intelligence and machine learning. Contrasting or conflicting language between machine learning experts and qualitative social scientists and the difficulty of capturing complex concepts using decontextualized features such as word occurrence are examples of the challenges of integrating automated techniques and qualitative data.
Several overlapping terms are used to describe software tools that might help in the analysis of textual data. Text mining is an umbrella term, which refers to the activity of retrieving information from unstructured text and then enabling users to view and interpret the results. There are numerous technologies used in text mining, which include natural language processing NLP and machine learning. The former tends to be used when the activity of programming computers to process text in semantically informed ways eg, accounting for grammatical rules is being considered.
Machine learning refers to statistical approaches to text mining where the text is transformed into numeric form, and statistical interrelationships are analyzed. Text mining has been applied to improve reviewing efficiency in systematic reviews and used to identify, categorize, and summarize data for rapid evidence synthesis.
The approach to text mining used in this paper mostly utilizes statistical machine learning approaches. There are two common divisions of the machine learning: supervised and unsupervised. The two are distinguished by the level of input and a priori direction required from the researcher. For example, the results of primary study analysis can be used to devise a classification scheme to synthesize further data.
The unsupervised machine learning approach, on the other hand, does not require any rules, training sets, or key term dictionaries; structures and patterns are entirely driven from the input data and, in our case, transcripts. The process automatically extracts terms contained within the text or other data and develops a list of keywords; it performs the coding stage of the analysis without the need for any researcher input.
Until recently, these analyses were based in simple algorithms to produce a list of words that are then used as labels to code the rest of the data. Leximancer is a text mining software application that was developed by researchers at the University of Queensland, Australia, to code automatically large qualitative datasets, and has since been validated and applied in various research dimensions. The text analytics tool performs an automatic unsupervised analysis of texts that are imported as individual files or folders.
The text excerpts are grouped into chunks of two sentences and can be viewed in their original context to facilitate the interpretation of the data. The outputs of Leximancer analyses can be presented in two ways. The first is a conceptual map sometimes referred to as an intertopic distance map , which provides a bird's eye view of the semantic data.
Within the bubbles are collections of interlinked dots that represent the concepts that make up each theme. Tags can be allocated to specific data folders, files, or dialogue, and these tags displayed on the map in a similar way to the concepts. The proximity of the bubbles, concept dots, or tags to one another indicates conceptually similarity, with those clustered together most closely related.
The second visualization is a quantitative data summary that provides an overall bar chart of the data as frequency counts.
Each theme links to a list of associated concepts, and five text extracts to support each concept are displayed; however, all text examples are also available to view if required. The bars are also heat mapped to correspond with colored bubbles of the conceptual map and to provide an integrative summary of the quantitative and semantic data for example, see Data S1 and S2. Presentation of findings tagged by primary study [Colour figure can be viewed at wileyonlinelibrary.
Presentation of findings classified by gender [Colour figure can be viewed at wileyonlinelibrary. In this case study, we describe how we applied an unsupervised machine learning approach to a pooled set of textual qualitative data from five primary research studies that explored practices and experiences of transportation, including everyday walking, cycling, driving and using public transport.
Thus, the wider aim of applying a semiautomated text analysis approach to the pooled data was to uncover networks or patterns that have not emerged from the original and more traditional forms of qualitative analysis of the individual datasets. In doing so, we aimed to explore simultaneously Leximancer and its possibilities as an approach to qualitative data synthesis, which was the primary focus of this case study. Study contexts ranged from commuting in Cambridge, 34 , 36 , 37 cycling in London 38 and free bus passes for young people in London, 39 , 40 to the impact of a new motorway in Glasgow 41 and a graduated drivers license scheme in Northern Ireland.
We used Leximancer Desktop 4. Ethical approval for secondary analysis of the data was granted by the original ethics committees, where necessary, and overseen by the University of Exeter Ethics Committee as the lead institution.
Formatting transcripts : Each transcript was edited to a standardized format in Microsoft Word to ensure compatibility with the software and to help Leximancer to distinguish between the interviewer and interviewee, as presented in the transcript template in Data S1.
A unique identification number was developed using the basic contextual information available to us from the primary datasets and assigned to each anonymized transcript to enable mapping of gender, age range, location, study, and whether the transcript was derived from an interview or focus group.
Classification of transcripts for analysis : Each transcript was copied into relevant subfolders for analysis according to the participant's demographic information gender and age range and the study source. Automatic text processing and concept seed generation : Tags were assigned at folder level for gender, age, and study to enable subgroup analysis eg, women versus men and young people versus older people.
Concept editing : Only automatically defined concepts were used, and no tags or concepts were defined by the user. Output : The social network Gaussian map was chosen over the topic network linear map to emphasize the conceptual context in which the words appear and maximize the discovery of indirect relationships.
As this paper aims to provide a guide to the opportunities and limitations of applying the software to qualitative analysis, we describe the findings of our case study through a process, rather than content, lens. Leximancer delivers this overview as a visual, easy to read illustration.
Here, the data have been organized and analyzed in subfolders according to each primary study. This facilitates data tagging to illustrate the clustering of concepts and indicate conceptual similarity and variation between the different datasets included in the synthesis in this case between the studies. In this regard, tags can facilitate comparative analysis of the findings between any subgroup allowed by the demographic information available and providing that the data are arranged to distinguish between these subgroups.
The slider presents fewer broader themes or a greater number of defined themes depending on the granularity required by the user. Leximancer also provides a platform to focus in on the data and follow lines of enquiry to analyze specific subgroups according to the available descriptors such as demographic information. The program allowed us to identify similarities and differences between the subgroup findings.
Further interpretive analysis, however, then requires the qualitative researcher to return to the primary data. Leximancer can also be a tool for this via specific functions for exploring concepts in context. For example, one useful function provides an exportable list of all text extracts that contributed to the development of a concept or theme, which can be used by the analyst to facilitate their interpretive work.
Additionally, the software allows the analyst to investigate the cooccurrence of terms within the data. However, researchers might want to revert to these more commonly used software packages for these more familiar analysis steps. In this paper, we provide a guide on how to use text analytics software, in this case Leximancer, to synthesize primary qualitative datasets.
We provide a case study of using Leximancer to analyze a pooled dataset of UK transportation studies. In this discussion, we set out the opportunities and limitations of this software that we encountered in our case study.
The Leximancer software promises time efficiency, comprehensiveness, and relative ease of qualitative content analysis. A key advantage is the extensiveness of the analysis. Despite the efficiency and extensiveness of the coding phase, it is important to consider the general efficiency of the process as a whole. One key consideration here is the initial challenge of obtaining and preparing the data from multiple studies.
We then edited each transcript against a standard template see Data S1 to ensure compatibility with the software and consistency across the pooled studies. The length of interviews, and therefore the size of the word files, varied greatly between and within studies. This process might, of course, vary greatly in other projects but is an important indication of the considerable time required to prepare the data.
By discounting researcher input in this phase of pattern recognition, the software does not allow for the grouping of more interpretative or theoretical ideas that could be related to one another. As we had very close knowledge of the used datasets, we deliberately opted for this approach to allow us to step back from previous analyses and research questions that shaped the original primary data collection and analysis.
We aimed for this to generate new lines of potential enquiry, and the functionality of the software enables the researcher to follow such lines using subgroup analyses presented in both a broad or refined manner. However, while this machine learning approach can uncover previously unanticipated patterns and clusters, researcher input and interpretative work is then necessary to make meaning from these. It is important to recognize that Leximancer only conducts the initial stage of the analysis and can only point to avenues for further interpretation.
The software provides a helpful starting point to this interpretative work by providing a summary of text excerpts to support each concept that can be used to investigate what the findings of the initial Leximancer analysis actual mean in the context of the transcripts.
Further interpretation of text excerpts is an essential phase to arrive at meaningful qualitative findings. We do not present findings from this further analytical work in this paper, but would like to emphasize that the software is a tool to facilitate the first steps of qualitative analysis, familiarization with and initial coding of large textual data, rather than a tool to replace the work of judgement, inference, and interpretation.
This analysis was intentionally focused on the unsupervised functions of Leximancer, given our aim of uncovering latent themes. However, the program also has the capacity to facilitate a range of more supervised machine learning approaches. Although these functions allowed for a more focused analysis, we acknowledge the limitations of these decisions and recognize that information about what the interviewer asked about or prompted for or the vocabulary used may provide valuable information for complementary analyses about interview content or conversational style.
If a more supervised approach is required, then analysts can define their own concepts or tags and direct the analysis to follow specific lines of enquiry. For example, we could have used the software to interrogate specific findings from the primary studies at greater scale across the pooled dataset. Alternatively, an initial unsupervised analysis may highlight conceptually similar terms through clustering, which can then be explored further for cooccurrence in the context of the text.
These semiautomatic investigations of identified terms may be particularly useful when working with very large volumes of data and support the value of the tool in wider contexts than that demonstrated by this case study.
The utility of Leximancer lies in this flexibility of the software to enable analyses of various levels of automaticity or supervision. This framing was guided by our own theoretical interests in the topic and previous research, in particular social practice approaches that understand transportation as a relational activity or behavior that tends to be performed or enacted with others, learned from others, and through the life course.
The outputs of analysis are also inevitably constrained by the scope and content of the primary research studies, and the lacking contextual insight usually gained during data collection as a primary researcher. This is a feature of any method of data synthesis, given the findings are inherently bound to the specific contexts of the primary studies, and whatever question, sample or data generation limitations shaped their production.
However, when pooled, as we have done here, we have the potential to compare across contexts and derive insights that speak to broader, varied contexts. In this case study, we have reported the functionality of the software and used Leximancer's explicitly defined terminology to do so.
We have therefore attempted to describe and clarify how these Leximancer terms relate to common terminology of qualitative thematic analysis in the following way.
Coding (social sciences)
Gery W. Ryan, H. Russell Bernard. Theme identification is one of the most fundamental tasks in qualitative research. It also is one of the most mysterious. Explicit descriptions of theme discovery are rarely found in articles and reports, and when they are, they are often relegated to appendices or footnotes.
PDF | Theme identification is one of the most fundamental tasks in qualitative research. It also is one of the most mysterious. Explicit descriptions of | Find, read and cite all the research you need on ResearchGate. H. Russell Bernard at University of Florida. H. Russell ryan and bernard themes. FM.
YRM-30806 Research Methods and Data Analysis in Communication and Health
Crabtree Editor , William L. Miller Editor One of the most-cited and used textbooks for conducting qualitative research in primary care, as well as in health care and public health research more broadly See Amazon Record. Donnelly, Ph.
Research into Communication, Health and Life Sciences addresses complex issues that involve multiple stakeholders. Usually, adequate research in the field requires contributions from different disciplines. The design of interdisciplinary research, good research conduct and research integrity are central to this course. A selection of types of research questions, appropriate study designs and data analysis techniques will be discussed in detail. These types of research questions are illustrated with published papers and work in progress.
Kay E. This article contributes to the debate about the use of reliability assessments in qualitative research in general, and health promotion research in particular. All qualitative articles published in the journal Health Promotion International from to employing reliability assessments were examined.
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Semiautomated text analytics for qualitative data synthesis
In the social sciences , coding is an analytical process in which data, in both quantitative form such as questionnaires results or qualitative form such as interview transcripts are categorized to facilitate analysis. One purpose of coding is to transform the data into a form suitable for computer-aided analysis. This categorization of information is an important step, for example, in preparing data for computer processing with statistical software. Prior to coding, an annotation scheme is defined. It consists of codes or tags.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Ryan Published Theme identification is one of the most fundamental tasks in qualitative research. It also one of the most mysterious. Explicit descriptions of theme discovery are rarely described in articles and reports and if so are often regulated to appendices or footnotes. Techniques are shared among small groups of social scientists and are often impeded by disciplinary or epistemological boundaries.
The original transcripts from the primary research studies included in this secondary analysis were only accessible to the authors for the length and use of this project and are therefore not available to third parties; the corresponding author can be of assistance to liaise with the original institutions that hold the data. Approaches to synthesizing qualitative data have, to date, largely focused on integrating the findings from published reports. However, developments in text mining software offer the potential for efficient analysis of large pooled primary qualitative datasets. We applied Leximancer v4. The limitations of using Leximancer were the substantial data preparation time involved and the contextual knowledge required from the researcher to turn lines of inquiry into meaningful insights. There are increasing calls to make use of existing qualitative and quantitative data, increasing availability of large qualitative data and growth in demand for and approaches to data and evidence synthesis. Synthesis of large textual data is labor intensive and requires novel approaches.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Ryan and H. Ryan , H.
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