QUALITATIVE DATA ANALYSIS WITH NVIVO PDF

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of qualitative data analysis software (QDAS) has influenced the current trajec Using NVivo during the analysis of qualitative data will help you: files), *.txt ( text files), *.rtf (rich text files), and *.pdf (portable data format files). PDF | On Jan 1, , Richard Lakeman and others published Qualitative Data Analysis with NVivo. PDF | On Jan 1, , Ralph Godau and others published Qualitative Data Analysis Software: NVivo.


Qualitative Data Analysis With Nvivo Pdf

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Add data sources to NVivo (text, image, audio visual). • Create thematic nodes for Doing qualitative data analysis with NVivo. 2nd .. room). You may instead select to 'print' as a pdf file for storage and sharing - a pdf writer. Apex Centre for Training and Research P.O. Box – , Kikuyu – Kenya Nurturing Talents and Developing Skills Tel: + Key words: Qualitative research data analysis, NVivo Wong LP. Data analysis in qualitative research: a brief guide to using NVivo. Malaysian Family Physician.

In the conclusion to her book, Tesch candidly acknowledged that the rapid pace of software development combined with the time that elapses between conceptualizing and distributing a book meant her book was already out of date. By establishing an internet presence and a location where more recent advancements could be posted without the delays of paper publishing, the CAQDAS site became a cutting-edge source of information about qualitative software, without formal financial ties to any developer.

Shortly thereafter, Weitzman and Miles produced comprehensive comparison tables of the range of tools provided in 24 programs available at the time. Common tools across current programs include the ability to write memos and track ideas, index or code data with thematic or conceptual labels, add demographic or other categorical information for the purpose of comparing subgroups, run searches to examine constellations or patterns, develop visual models or charts, and generate reports or output from the data.

Lewins and Silver provided a good overall map of these common tools and the common research activities they support. Although the early presence of these programs represented a great diversity of features, purposes and software platforms, the software development trajectory since then has become fairly typical Norman, The early diversity of programs and their notable limitations in handling only a narrow methodological approach or data type gave way to programs containing more features.

A few products took the lead around , some have fallen by the wayside, and as of today the CAQDAS networking site provides reviews of only nine qualitative analysis programs. These participants were in widespread agreement that they came up with very similar conclusions regarding the primary research questions and that the impact of a particular QDAS in analysing the data was negligible. This corroborates the claims by Gilbert, di Gregorio, and Jackson that over the last 20 years QDAS software has simultaneously become more comprehensive, more applicable to a diverse range of methodologies, and more homogeneous.

Issues raised by using software for qualitative data analysis Tools extend and qualitatively change human capabilities Gilbert, Users of NVivos tools can face opposition from those who express doubts about using software for analysis of qualitative data, or who simply have an aversion to technological solutions. Nonetheless, the development of software tools and technology in general has a significant impact on how research is done.

The constantly expanding use of the web to provide access to data is now extending and changing the range of qualitative source data as well as the structure of surveys and survey samples. The advent of social networking will have an as yet unknown influence on social research and method.

Historically, the widespread use of tape recorders in interpretive research changed both the level and kind of detail available in raw material for analysis, and as video recording became more common, data and method changed again. Given this context, it is dangerous to adopt a simplistic understanding of the role of QDAS.

Tools range in purposes, power, breadth of functions, and skill demanded of the user. The effectiveness with which you can use tools is partly a software design issue because software can influence your effectiveness by the number or complexity of steps required to complete a task, or by how information is presented to the user. It is also a user issue because the reliability or trustworthiness of results obtained depends on the skill of the user in both executing method and using software.

Statistical & Qualitative Data Analysis Software: About NVivo

The danger for novices using a sophisticated tool is that they can mess up without realizing they have done so Gilbert, Historically, the use of QDAS has facilitated some activities such as coding and limited others such as seeing a document as a whole or scribbling memos alongside text. In so doing, early computer programs somewhat biased the way qualitative data analysis was done.

Historically, also, qualitative researchers were inclined to brand all code-and-retrieve software as supporting grounded theory methodology a methodology which has become rather ubiquitously and inaccurately associated with any data-up approach with the implication that if you wanted to take any other kind of qualitative approach, software would not help. Closeness and Early critiques of QDAS suggested that users of software lost closeness to data through poor screen display, segmentation of text, and loss of context, thereby risking alienation from their data.

Despite enormous changes in technology and in software, these attitudes persist in some communities of practice. The alternative argument is that the combination of full transcripts and software can give too much closeness, and so users become caught in the coding trap, bogged down in their data, and unable to see the larger picture Gilbert, ; Johnston, Qualitative software was designed on the assumption that researchers need both closeness and distance Richards, : closeness for familiarity and appreciation of subtle differences, but distance for abstraction and synthesis, and the ability to switch between the two.

Closeness to data at least as much as can be had using manual methods is assisted by enlarged and improved screen display, improved management of and access to multiple sources and Kelle traced the assumption that programs were written to support grounded theory to the need for a methodological underpinning for analysis, and grounded theory is one of the few methodologies where authors have been prepared to be explicit about what it is they actually do in analysis although, as Kelle goes on to point out, a closer look at the concepts and procedures of Grounded Theory makes clear that Glaser, Strauss and Corbin provide the researcher with a variety of useful heuristics, rules of thumb and a methodological terminology rather than with a set of precise methodological rules paragraph 3.

Other tools are designed to provide distance, for example, tools for modelling ideas, interrogating the database to generate and test theory, or summarizing results.

These take the researcher beyond description to more broadly applicable understanding. Tacking back and forth between the general and the specific, exploiting both insider and outsider perspectives, is characteristic of qualitative methods and contributes to a sophisticated analysis.

Domination of code and retrieve as a method The development of software for textual data management began when qualitative researchers discovered the potential for text storage and retrieval offered by computer technology. Hence, early programs became tools for data storage and retrieval rather than tools for data analysis, because that was what computers were best able to do. The few programs that went beyond retrieval to facilitate asking questions about the association of categories in the data, particularly non-Boolean associations such as whether two concepts occurred within a specified level of proximity to each other, were less rather than more common, and in these early stages were given special status as second-generation theorybuilding programs Tesch, Computers removed much of the drudgery from coding cutting, labelling and filing ; they also removed the boundaries which limited paper-based marking and sorting of text.

When recoding data involves laborious collation of cut-up slips and creation of new hanging folders, there is little temptation to play with ideas, and much inducement to organize a tight set of codes into which data are shoved without regard to nuance.

When an obediently stupid machine cuts and pastes, it is easier to approach data with curiosity asking what if I cut it this way? Marshall, 67 Simply making coding more efficient was not, in itself, a conceptual advance from manual methods of data sorting. Criticism that segments of text were removed from the whole, creating a loss of perspective, was frequently levelled at computer software apparently without recognition that cutting up paper did the same thing, with even greater risk of not having identified the source of the segment.

Fears were expressed that computers would stifle creativity and reduce variety as code and retrieve became the dominant approach to working with data. Most problematically, the facility for coding led to what Lyn Richards commonly referred to as coding fetishism a tendency to code to the exclusion of other analytic and interpretive activities, which biases the way qualitative qualitative computing research is done, and which often contributes to a report that comprises only themes from the data.

Prior to the development of computer software for coding, more emphasis was placed on reading and rereading the text as a whole, on noting ideas that were generated as one was reading, on making links between passages of text, on reflecting on the text and recording those reflections in journals and memos, and on drawing connections seen in the data in doodles and maps.

Improvements in the memoing, linking, and modelling tools within current qualitative software now provide ample capacity for these approaches to analysis, allowing the user to strike a balance between coding and reflecting and linking as they work with data. Clarkes Space Odyssey series, might take over the decisions and start controlling the process of analysis stem in part from the historical association of computers with numeric processing. Adding to that concern is the computers capacity to automate repetitive processes or to produce output without making obvious all the steps in the process.

There are software programs designed to automate the coding process entirely, using complex dictionaries and semantic rule books to guide that process, but these are specifically designed for quantitative purposes, and the results of their coding are generally interpreted through the use of statistics with minimal recourse to the original text. Keyword searches within qualitative analysis will almost always be preliminary to or supplemental to interactive coding of the data, if they are used at all.

Automated coding processes have a place in handling routine tasks such as identifying the speakers in a focus group, or what question was being answered , in facilitating initial exploration of texts, or in checking thoroughness of coding. These remove drudgery without in any way hindering the creativity or interpretive capacity of the researcher. They do not substitute for interpretive coding that still needs to be done interactively live on screen. One of the goals of this book is to ensure that researchers using NVivo understand what the software is doing as they manipulate their data, and the logic on which its functions are based just as artisans need to understand their tools.

Such metacognitive awareness ensures researchers remain in control of the processes they are engaging in and are getting the results they think they asked for Gilbert, More aware, creative, and adventurous users can experiment with new ways of using NVivos tools to work with their data, just as the good artisan knows how to make his or her tools sing to produce a creative piece of work.

The oversimplification of qualitative methods has occurred and continues to occur whether software is involved or not. Researchers talk about doing qualitative as if to imply there is just one general approach to the analysis of qualitative data. While there are some generally accepted emphases, different approaches to qualitative analysis are shaped by differences in foundational philosophies and understandings of the nature of social reality, the nature of the questions being asked, and the methodological approaches adopted.

Researchers must integrate their chosen perspective and conceptual framework into their choices regarding what tools they will use, what and how they might code, and what questions to ask of the data. This is the role of the researcher whether or not they use software. Exploring an NVivo project Throughout this book we will be illustrating the principles and activities being discussed with examples from a number of our own projects, those undertaken by colleagues or students, projects from the literature, and some practiceinformed vignettes.

To give you an overview of the tools available for working in an NVivo project and of what you might be working towards, we will start by taking a look at the sample project that comes with the software.

Because this is a moderately mature project, these instructions are not designed to show you how to make a start on working in your NVivo project, but rather what will become possible as you progress through your analysis.

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As you read these instructions and others in later chapters, you will encounter a number of special icons: indicates these are steps actions for you to follow. Access NVivo Help by clicking on the question mark near the top right-hand side of the screen when NVivo is open.

NVivo Help also provides a glossary, should you come across unfamiliar terms you might also check for these in the index of this book as it will point you to where they are described.

Group names within the ribbon are in italic text.

The three main views in the interface Navigation, List, and Detail are in italic text. Source names and node names are written in italics. Words that are copied from the screen as part of a click instruction are in bold.

Installing the software If you dont already have the software on your computer, then your first step to using NVivo will be to install either a fully licensed or a trial version on your computer.

These are available through the QSR website: www. Basically, insert a disk or double-click the downloaded software and follow the steps as they appear on screen after launch. It is likely that you will be required, as part of this process, to install several supporting programs prior to installing NVivo itself: the installation wizard will guide you through the necessary steps.

Once you have completed the installation, if you own the software, or your institution has a site licence, you will need to have available the licence number that came with your software or is available through your institution. A brief guide to using NVivo. Address for correspondence: Analysing qualitative data entails reading a large amount of transcripts looking for similarities or differences, and subsequently finding themes and developing categories.

Recently, the use of software specifically designed for qualitative data management greatly reduces technical sophistication and eases the laborious task, thus making the process relatively easier. This paper illustrates the ways in which NVivo can be used in the qualitative data analysis process. The basic features and primary tools of NVivo which assist qualitative researchers in managing and analysing their data are described.

Key words: Malaysian Family Physician. In some cases, qualitative data can also include pictorial display, audio or video clips e. Data analysis is the part of wide range of medical and health disciplines, including health qualitative research that most distinctively differentiates from services research, health technology assessment, nursing, quantitative research methods.

It is not a technical exercise and allied health. Coding merely involves subdividing the huge amount of raw information or data, and Qualitative research yields mainly unstructured text-based subsequently assigning them into categories.

These textual data could be interview transcripts, codes are tags or labels for allocating identified themes or observation notes, diary entries, or medical and nursing topics from the data compiled in the study. Given software developer.

This software allows for qualitative inquiry the advancement of software technology, electronic methods beyond coding, sorting and retrieval of data. It was also of coding data are increasingly used by qualitative researchers. The following sections discuss the Nevertheless, the computer does not do the analysis for the fundamentals of the NVivo software version 2.

Users still have to create the categories, code, the primary tools in NVivo which assist qualitative researchers decide what to collate, identify the patterns and draw meaning in managing their data. The use of computer software in qualitative data analysis is limited due to the nature of qualitative research Key features of NVivo itself in terms of the complexity of its unstructured data, the To work with NVivo, first and foremost, the researcher has to richness of the data and the way in which findings and theories create a Project to hold the data or study information.

Once a emerge from the data. The the marking, cutting, and sorting tasks that qualitative project pad of NVivo has two main menus: Document browser researchers used to do with a pair of scissors, paper and note and Node browser.

In any project in NVivo, the researcher cards. It helps to maximise efficiency and speed up the process can create and explore documents and nodes, when the data of grouping data according to categories and retrieving coded is browsed, linked and coded. Both document and node themes. Ultimately, the researcher still has to synthesise the browsers have an Attribute feature, which helps researchers data and interpret the meanings that were extracted from the to refer the characteristics of the data such as age, gender, data.

Therefore, the use of computers in qualitative analysis marital status, ethnicity, etc. The qualitative data analysis Figure 2. Project pad with documents tab selected process is illustrated in Figure 1. Figure 1. Qualitative data analysis flowchart The document browser is the main work space for coding documents Figure 3. It can also be imported as a plain text file.

Transcripts of interview data and observation notes are examples of documents that can be saved as individual documents in NVivo. In the document browser all the documents can be viewed in a database with short descriptions of each document. A hyperlink is very much like a footnote.

Document browser with coder and coding stripe activated Figure 4. Browsing a node The second menu is Node explorer Figure 4 , which document turns bold. Multiple codes can be assigned to the represents categories throughout the data. The codes are same segment of text using the same process. Coding Stripes saved within the NVivo database as nodes.

Nodes created in can be activated to view the quotes that are associated with NVivo are equivalent to sticky notes that the researcher places the particular nodes.

With the guide of highlighted text and on the document to indicate that a particular passage belongs coding stripes, the researcher can return to the data to do to a certain theme or topic. Unlike sticky notes, the nodes in further coding or refine the coding. NVivo are retrievable, easily organised, and give flexibility to the researcher to either create, delete, alter or merge at any Coding can be done with pre-constructed coding schemes stage.

There are two most common types of node: Alternatively, a bottom-up nodes free standing and not associated with a structured approach can be used where the researcher reads the framework of themes or concepts. Once the coding process documents and creates nodes when themes arise from the is complete, the researcher can browse the nodes. To view all data as he or she codes. Making and using memos In analysing qualitative data, pieces of reflective thinking, ideas, Coding in NVivo using Coder theories, and concepts often emerge as the researcher reads Coding is done in the document browser.

Coding involves the through the data. NVivo allows the user the flexibility to record desegregation of textual data into segments, examining the ideas about the research as they emerge in the Memos. A memo itself can button at the bottom of document browser window. To code a segment of the text in a project document under a particular node, highlight the particular segment and drag the Creating attributes highlighted text to the desired node in the coder window Figure Attributes are characteristics e.

The segments that have been coded to a particular node educational level, etc. Search results can attributes to either document or node. Items in attributes can be displayed in matrix form and it is possible for the researcher be added, removed or rearranged to help the researcher in to perform quantitative interpretations or simple counts to making comparisons.

Attributes are also integrated with the provide useful summaries of some aspects of the analysis.

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Using models to show relationships Models or visualisations are an essential way to describe and explore relationships in qualitative research. NVivo provides Search operation a Modeler designated for visual exploration and explanation The three most useful types of searches in NVivo are Single of relationships between various nodes and documents.

In item text, node, or attribute value , Boolean and Proximity Model Explorer, the researcher can create, label and connect searches. Single item search is particularly important, for ideas or concepts.

Every paragraph in which this word is used the stages in the model-building over time Figure 7. Any can be viewed. The results of the search can also be compiled documents, nodes or attributes can be placed in a model and into a single document in the node browser and by viewing clicking on the item will enable the researcher to inspect its the coding stripe.

The researcher can check whether each of properties. This is particularly useful for the researcher to further determine whether conducting further coding is necessary.

Model explorer showing the perceived risk factors of cervical cancer Note: A model showing the relationships of the perceived risk factors of cervical cancer as related by the respondents. The mechanisms through which the respondents thought they might obtain an infection are divided into two categories; sexual transmitted infection and unknown mechanism.

The respondents considered feminine hygiene, trauma to the cervix, carelessness in health care and the use contraceptives could lead to infection and subsequently causes cervical cancer via a mechanism unknown to them. The website also carries and much easier. In addition, NVivo is ideal for researchers information about the latest versions of NVivo. Free working in a team as the software has a Merge tool that enables demonstrations and tutorials are available for download.

The newly released NVivo 7 released March The examples in this paper were adapted from the data of the and NVivo 8 released March are even more study funded by the Ministry of Science, Technology and sophisticated, flexible, and enable more fluid analysis.

Qualitative Data Analysis with NVivo

In addition, the user can also import and work on rich text files in character based languages Attributes: An attribute is a property of a node, case or such as Chinese or Arabic. It is equivalent to a variable in quantitative analysis.

An attribute e. To sum up, qualitative research undoubtedly has been Malay, Chinese, Indian, etc.Understand the historical context and ongoing development of this type of software. Formatting strategies to facilitate case construction from different types of sources Arranging data sources in NVivo. A document memo might include: Where the text illustrates something you read in the literature, create a see also link from that text to the relevant material in a reference document, such as a passage in a pdf article you imported.

Having everything together in one NVivo project will allow you to gather together everything you know on any topic, regardless of source, and to make instant comparisons across different sources, phases, types of data, or cases.

Many of us who use, teach and write about QDAS encounter both positive and negative claims regarding the software that are obsolete but have survived as part of the discourse among qualitative methods instructors and scholars.

You might also want to retain copies from impor- tant transition points, for example, before and after a major node restructuring, before and after combining the work of team members, or when youve devel- oped key models or understandings of the project.

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