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Increasing the rigor and efficiency of research through the use of  qualitative data analysis software

Increasing the rigor and efficiency of research through the use of qualitative data analysis software






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    Increasing the rigor and efficiency of research through the use of  qualitative data analysis software Increasing the rigor and efficiency of research through the use of qualitative data analysis software Document Transcript

    • Paper presented at the 2nd Asia-Pacific conference on Computer-Aided Qualitative Research 24 & 25 February 2011, Macau SAR Increasing the rigor and efficiency of research through the use of qualitative data analysis software Dr Suria Baba and Prof Marohaini Yusoff, University of Malaya, Malaysia.AbstractThe purpose of this study is to examine the creativity of instructional leaders (IL) ingenerating innovative environment during teaching and learning process. Methods for datacollection reflected a qualitative case study approach and included audio-recordedobservations of project work, focus group interviews, and students journals. Data showedthat students preparatory activities inside and outside the classroom included negotiating taskdefinition and teacher expectations, sharing experiences, collaborative dialogue in preparingpresentation materials, and rehearsing and peer-coaching and thus resulting the innovativeenvironment. Analysis shed useful light on students contextualization of and orientation tothe task and the interdependence of doing task. To support the humoungous data gathered,analysis were done using Nvivo. The use of this software has proven to benefit and enrich thefindings of the study and a pathway to increase rigor and efficient in research. Data has beenmanaged, analyse and code from various sources and has features that are well suited to aidin analyzing throughout field work. In sum, procedural instruction and friendly user featureshad reduce the fear of the researchers in using software as it leads to wider spectrum becausein using the software the researchers are assured of a full assistance. To relate this to themethodology used, transcricpt from observation, interviews, picture, audio video are archivedsystematically and thus developed patterns through the themes provided.Key Words:Computer Based Data Analysis, NVivo 8 & 9, Constructivist Instructional Leadership ,Creative and innovative classroomIntroductionThis article describes how a qualitative data analysis package, NVivo, was used in a study ofcreative instuctional leadership (IL) enhancing an innovative and constructivist classroom.The paper starts with a summary of CAQDA (Computer Aided Qualitative Data Analysis )that starts the era in assisting qualitative data analysis and followed by the research study inwhich NVivo was used to analyze the data and overviews the methodology that was adoptedin this study. It, then, describes how NVivo was used in the analysis of observational (video) 21
    • data, interviews and field notes and finally how it meet to the criteria rigor and efficienceusage.Weitzman & Miles (1995) noted that CAQDAS has been used in social research, since theearly 1980s. The findings was supported by Creswell (2007) saying that the development ofqualitative data analysis software are well-conceived and is an assistance to expedite andenhance the process of qualitative research as a whole. The inquirer identifies a textsegments, assign code label, and then searches through the database for all text segments thathave the same code label. In this process the researcher, nor the computer program, does thecoding and categorizing.There is a substantial literature on the advantages of CAQDAS. In particular, it can facilitate:data reduction; systematic coding; effective searching; the analysis of large data sets; thetesting of hypotheses; and the identification of negative cases Creswell (2007) claim thatusing a computer for the more mechanical aspects of the process allows the researcher todevote more energy to analytic and interpretive work.Data analysis as a core businessAs we know Miles and Huberman (1994) divide data analysis into three stages: datareduction, data display and verification but coding may be part of the analysis process, itshould not be thought of as a substitute for analysis as quoted by Atkinson, Coffey andDelamonts (2003). Coding links data fragments to concepts, but the important analytic worklies in establishing and thinking about such linkages. However, Tech (1990) claimed that thecoding process does not merely consist of a random division into smaller units, but requiresskilled perception and artful transformation. Richards (2002) regards that coding is atheorizing process. Different analysts may use different coding systems for the same data,and the same analyst may apply different coding systems at different stages; there is no oneideal coding structure. Furthermore to gain thick and rich description of data, analysis mustbe done rigorous so the report will be sufficient and in depth not as suferficial as quoted“student engage in learning activity and they relate their experience” (very thin andsuperficial explaination).Coffey and Atkinson (1996) viewed that the process of analysis and coding is iterative,because the identification of relevant concepts and codes depends on analysis, but in analysis, 22
    • codes are used as tools to “think with”. Eisenhardt (1989) detail out that it is analysis is aniterative process started with the development and presentation of an initial set of theoreticalpropositions based on evidence from the first phase of data collection during fieldwork andthe theoretical assumptions associated with the theoretical framework. The initialpropositions then became a vehicle for generalizing to the phenomenon studied. As a secondstep, the emergent propositions from the first phase were systematically compared withevidence from the second phase. The theoretical propositions were either supported by theevidence, revised, or not supported for lack of sufficient evidence. As a final step, the processwas repeated when refined theoretical propositions were systematically compared withevidence from the previous phase. The central idea was to iterate toward a theory that fits thedata, where projects which supported the emergent theory enhance confidence in its validity,while projects which did not support the theory often provide an opportunity to refine andextend the theoretical model.The method of generalization adopted here is "analytic generalization," in which previouslydeveloped theory is used as a template with which to compare the empirical results of thecase study. Under such logic, when two or more cases are shown to support the same theory,replication may be claimed (Yin, 1994).Early steps in data analysisOne of the main and striking feature in the collection of data is the frequent overlap of dataanalysis in the process to build theory from case studies (Eisenhardt, 1989). The analyticaltechniques adopted in the first stage of data analysis in our own research are listed below.Note that these techniques were used to help us identify themes, develop categories, andexplore similarities and differences in the data, and relationships among them. The processare as follows;First, field notes were an important means of accomplishing this overlap in our study. Asdescribed by Van Maanen (1988), field notes are an ongoing stream-of-consciousnesscommentary about what is happening in the research. By reviewing, refining with reflexiveapproach our field notes frequently, important issues or conflicting answers provided bydifferent individuals were identified immediately. Then follow up with several visitations andinterview with the selected key informants to clear up any questions and to provide any 23
    • additional information that was missing and also to bridge the gap. The field notes also wereuseful in revising the interview guides and protocol as the study progress . Second, once aninterview was transcribed, reflective remarks were directly entered into the transcripts withinbrackets.( As in NVivo it is done with Memos file). These statements were ways of gettingideas down and of use as a way to facilitate reflection and analytic insight. They were a wayto convert the researchers perceptions and thoughts into a visible form that allows reflection(Miles & Huberman, 1994; Strauss & Corbin, 1990). In short, reflective remarks helped usstart thinking, making deep thinking and more general sense of what was happening, andexplaining things in a conceptually coherent way. Finally, a document summary form wascreated for each document collected and then filled out in the database. This form put thedocument in context, explained its significance, and gave a brief content summary (Miles &Huberman, 1994).In sum, overlapping data analysis with data collection not only gives the researcher a headstart in analysis but, more importantly, allows researchers to take advantage of flexible datacollection. Indeed, a key feature of theory-building case research is the freedom to makeadjustments during the data collection process. In our study, adjustments included addingquestions to interview protocol, reviewing more data sources, observe activities in the and outthe classroom whichever viable to the study especially when the opportunity arose to do so,and interviewing previously unknown individuals who were identified during the study asimportant actors in the study.Thus, this paper specifically addresses how NVivo was used in this research study to analyzethe qualitative data.Using NVivo in Qualitative Data Analysis: Literature ReviewIn the mid- 1980’s, a reform in using computer to aid analyzing of qualitative data whereCAQDAS was developed. After this development, qualitative data analysis became quitedifferent. Richards (2002) claimed that this development had meet to this criteria, (1)Computing has enabled new and assist in making things easier, previously unavailablequalitative techniques, (2) there are no support in computerization before until this reformoccurs as an eye opener, at least until recently, and (3) this innovation of computerizationencouraged some biases in qualitative techniques, NVivo does everything, it has character-based coding, rich text capabilities, edit-while-you-code, multimedia data, and splitting upthe information load that nodes were being asked to carry. Lyn Richards, (2009) regards 24
    • NVivo is designed for the researchers who wish to display and develop rich data in dynamicdocuments. The rich data she refers to is a wide range of data collected over a period of time.Needless to say, computer based programs served poorly in the previous years to deal withthis kind of data. NVivo addresses this need with the features like rich text, memos,DataBites (media files such as video, audio, images, literature review, external data fromvarious sources.), and new capabilities embedded into document and node browsers. Byusing qualitative data analysis software (QDAS) basically helps and assists researchersduring labor-intensive process of qualitative data analysis.Not only are there many different approaches and debates on qualitative research methodsand techniques, but also computer-assisted analysis of data was discussed widely. Forinstance, (Welsh, 2002) expresses his concern that the software may “guide” researchers in aparticular direction. There are also many other comments like “using QDAS may serve todistance the researcher from the data, encourage quantitative analysis of qualitative data,and create a homogeneity in methods across the social sciences” (Welsh, 2002).Some others believe that using computers in the qualitative analysis process may add rigorand prestige to research study, also to the thrustwortiness and quality of the analysis. This istrue if we think about how NVivo and other similar programs help organize and manage datafiles as well as support the representation of coding in a neat manner. However, it is still theresearchers who will make the decisions for their data organization, coding, or analysis.Nevertheless, computer analysis programs do not add rigor per se, but the way researchershandle their data using these programs does add rigor.Study done by Asensio (2000) using phenomenographic towards students’ experiences ofnetworked learning in higher education in the U.K., describes the process and rationale ofchoosing QDAS. As a group of researchers they investigate and contrast the three most wellknown software packages, Atlas/ti, QSR NUD*IST, and QSR NUD*IST Vivo (NVivo) andexplain why they have chosen NVivo in their research. This study is also a very interesting inthat it gives an example of phenomenographic analysis which is completely different fromthe grounded theory approach. Asensio (2000) thinks that “the outcome ofphenomenographic research is a set of categories of description which describe the variationin experiences of phenomena in ways that they were allowed to deepen their understandingon students’ learning”.The aforementioned study aims at understanding the students’ experiences of participating ina networked learning course. The basis of the study is phenomenological and draws on 25
    • individual interviews of 60 students plus observations of the face-to-face classes and onlineenvironments. The study is also complemented by a survey of 300 students and mappingexercise for wide range of teaching staff to show the examples of the use of networkedlearning in higher education in the UK.The existence of large and varied amounts of data and a research team geographicallydistributed require using a software program to support the management of the data. Afterusing NVivo, the research team agreed that the software increased their speed and flexibilityin coding, retrieving, and linking the data. They also discuss that this new version ofNUD*IST is really advanced and flexible as compared to other versions. One of the advancedfeatures of NVivo is enabling researchers to work collaboratively on the same project fromdifferent geographic regions of the U.K (Asensio, 2000).Whilst, Di Gregorio (2000) in her interesting paper discusses how to use NVivo for literaturereviews which are often overlooked as a form of qualitative analysis. She acknowledges thebenefits of bibliographical software such as EndNote, Reference Manager, ProCite, and theirunique biographical tools. However these packages do not support the analysis processes ofliterature review. “Of all the qualitative analysis software packages, only NVivo has aparticular set of tools that is ideal for analyzing literature”. She uses “proxy documents”(documents created in NVivo) to summarize the particular authors’ argument or quotationwhich may be retrieved later. “Memos” attached to proxy documents can be used to writereflections on a particular paper and then use these first reflections to build one’s critique.“Document” and “node links” of NVivo may be used as reference to other works. DiGregorio (2000), also suggests the use of “attributes” and “sets” as organizers of the existentdocuments since these are also useful for restricted searches to particular documents byauthor, date, or discipline. She also presents some other strategies for the researchers to use inelaborating their literature review.Video Intervention/Prevention Assessment (VIA) was used in the study done by Rich andPatashnick (2002) which investigates health conditions from the patient’s perspective. Eachparticipant use the devices and they create a personal “video diary” of living with theirmedical condition. Therefore, VIA “examines the illness experience from the outsidein”.Rich and Patashnick (2002) try to adopt constructivist theory in their study as well byasking patients to interview their family members or friends to capture the whole picture oftheir condition (multiple perspectives and holistic approach). Also, they believe that socialrealities can be represented best by using a variety of media such as words, sounds, and 26
    • images through VIA. In the analysis of their data, they found NVivo as the software packagethat responds best to the nature of VIA data. According to them, data can be coded easily inNVivo and the software supports analysis of different types of data. They also think thatNVivo is ideal in “parallel analysis of visual and audio components, objective and subjectiveinformation, or a variety of types of information that can exist simultaneously in video data”(Rich & Patashnick, 2002).The above few research using NVivo gives us some eye opening on the advantages andauthenticity in using computer aided software data analysis. Basically the initial purpose ofthis software is to assist the researchers but it had turn into an important package when theresearcher plan out their qualitative research.Purpose of the StudyIn general, there’s a lot of “nuts and bolts” when writing qualitative data analysis, it needs alot of effort and courage. It is supported by Dickie (2003) that suggests a different approachin qualitative research reporting and calls for less jargon and more detailed description withinthe data analysis process.As seen in the previous section, there are only a few studies which exemplify how aqualitative software package can be used in the analysis of qualitative classroom data. Inorder to address a more open approach to reporting and to help researchers better understandhow NVivo is used in an actual classroom study, we will share our experienced on how it wasdone in this study. In the following sections, the research study will be give background forthis article and then how and why NVivo was chosen for the data analysis will be explained.Finally, the researcher will share her experiences with the readers on the advantages ofNVivo software to place rigor and their efficience during data analysisIn recent years constructivism and its implications for instruction have been researchedwidely since it was seen as one of the best ways of renewing and restructuring learningenvironments.. Authentic learning is, for instance, one good way to ensure constructivism inthe learning environments .Therefore, it is described, defended, and advocated in theliterature. After reviewing the literature in authentic learning, one of the major implications isthat authentic learning has to be used more often during the instruction. What is missing,however, is knowledge about what successful instructional leadership, especially authentic 27
    • learning, do in the classroom and how students behave in such contexts in propelling studensability to create innovative environmentThus this study is to discuss the findings from the research objectives; (1) to investigate thecreative of IL in enhancing innovative classroom to compliment research question which is“How does creative IL enhance innovative classroom”. This is based from the statement ofproblem of this study on the process of innovative classroom develop by IL. And how do weuse it for our data analysis in the research project. So in this paper basically is to explain howanalysis of data was done in a rigorous and efficient manner. This is due to the statement thatunderlying problem in analyzing data that is “ hearing what the data have to say rather thansplicing them into an arbitrary units before searching for the themes, categories or meanings”..Methodology and Data TypesThe study used qualitative, case study design. One way to summarize the researchmethodology is to describe it as an effort to develop a rich, thick description of how creativeIL enhancing innovative classroom, with data drawn from different sources. Qualitativemethod is considered to be the best for this study, because it meets the descriptive nature ofthe research problems and gives the best picture of the learning environment studied. Eisner(1998) states that “qualitative experience depends on qualitative forms of inquiry. We learnto see, hear, and feel.”Because this study is about the qualities of learning environments being studied, qualitativeinquiry best fits in this framework. Data was gathered in several ways including classroomobservations, informal and formal interviews of students and teachers, field notes, workcompleted by students including projects, student self assessments, reflective journal logs,teacher’s comments and notes.Multisource in the collection of dataInterviews and Observations: Information about students’ responses was gathered throughinterviews and observations. Focus groups interview among student interviews wereconducted at the conclusion of school visitations and also instructional leadership (IL)opinions’ about the constructivist-learning environment were gathered through in-depth 28
    • interviewing. IL and student were interviewed during the school visitations especially afterclassroom observation. The interview focused specifically on the use of authentic materialsand activities that lead by the constructivist and creative instructional leadership that able topropel innovative environment in classroom. Finally, in addition to these formal interviews,informal questions were asked of IL and students during observations in the classrooms aswell as videotaping them working on the learning tasks.Field Notes: During each classroom observation short notes were taken and expanden intolong and reflexive notes were written to clarify what was observed in the classrooms ( Kirk& Miller) Researchers’ observations in the classrooms might be considered an example ofnonparticipatory observation. However, while trying to be as unobtrusive and unbiased aspossible, the researcher did participate in some activities with teachers and students.Student Products: A sample of student products (reflective journals, concrete products suchas computer print-outs, pictures, and self or peer evaluation rubrics) were collected and usedin the data analysis.As for the data analysis approach, the interpretivist research paradigm was used as to guidethe data analysis. This holistic approach of data analysis and a strategy that could be termed“reflective-interpretive” fits well with the use of NVivo. The software package does not forcethe use of certain data analysis strategies, but provides various tools for the researchers whichthey can choose based on their research goals and ways of approaching their data.NVivo as a ToolThe data were analyzed using a qualitative data analysis program, QSR NUD*IST(Nonnumerical Unstructured Data Indexing Searching and Theorizing), also called NVivowhich was launched in May 1999 (the screenshots used in this paper are from version 8 and 9Now, NVivo has an updated to version 9). NVivo was chosen as best fit for the study as wellfor the researcher’s ease of use of the program. More specifically, these reasons are:The structural design of the software. One who sees NVivo’s main menu for the first timemay assume that this is a very smart program to deal with. However, as the first impressionfades away, some of the terms used in the NVivo, help to uplift and creates a learning curve. 29
    • In fact, this is the case in most of the other qualitative data analysis software reviewed. Ittakes some time to understand some basic concepts like links, nodes, memos, and attributes,sets, classification, queries to get acquainted with the terminology, and learn how to use someimportant functions like coding, searching, uncode or developing a model using graphicfeatures of the software. However, once the basic features are understood, the process ofanalyzing large amounts of qualitative data becomes much easier and more powerful thanmanual approaches.The nature of the research study. NVivo is a powerful way to do sophisticated data codingand it supports several ways to build theories, either local or more general. These capabilitiesfit well with this study’s research goals and the approach to data analysis. NVivo also enabledthe researcher to look at coded segments of the data in context so that it was possible toexplore coded passages without separating them from the material before and after. NVivowas also very helpful in easily organizing different data types and sources used in the study.NVivo BasicsNVivo has three main menus: Navigation menu,detail and list view menu supported byribbon where the icon laid.Snapshot 1. Navigation menu is the place where one can create, edit, view, manage,archieveand explore project documents. Using NVivo it is possible to create and work with differentkinds of documents as much as its needed, either in internal or external source. For example,documents can be created or imported from a computer hard disk into NVivo (internaldocuments) and (external documents). Before this, documents is to convert them into richtext or plain text format in order to work with them in NVivo.But its being simplify in NVivo8 and 9.Another type of the document is “memos” which is extended notes about the data. All kind ofdocuments can be coded in NVivo including memos. Writing memos, however, is not merelya support to the memory of the researcher. It is important because it forces the researcher toreflect, to make explicit all the ideas, perceptions and decisions that have arisen duringobservation and analysis. Writing down and recording these mental leaps in memos is animportant tool for making analysis cumulative. In the Navigating menu all the documents can 30
    • be viewed in a database with short descriptions of each document, the time it was created ormodified, and how many other documents are linked to each document. (Appendix i)The second menu in Snapshot 2 is List view where we can add new items, open existingitems and edit item properties. And when we open the an item from List View it is displayedin Detail View. This three main menu are interconnected as working a platform of the databeing coded (Appendix i).In other words, a node is coded to a related data to the study (In NVivo there are options tocode data: nodes (coded but not categorized nodes), tree codes (codes in a hierarchicalmode), and case nodes (codes categorized under different cases). Using NVivo, it is alsopossible to search the documents or nodes in the project. In fact, NVivo has a verysophisticated search tool which might be very useful while working with a group ofresearchers or while dealing with very large data files. The second step is to utilize themodels feature and draw visuals based on the patterns, or any other relationship researcherswish to see based on their data.Relationships development. It was also very useful to look at the data emphasizing therelationships within it. Using NVivo, it was easy to do cross-case analyses, to re-order thecodes and add memos about potential relationships to files, and to “play” with the data. Theadvanced features of NVivo helped to develop concepts and do complex thinking about thedata. The sophisticated search option of NVivo, for example, allowed the researcher toexplore complex ideas and connect it in a quickly and easily mode. Even the data beingcoded can be automated into model feature or even can be displayed in many forms like Tagcloud, clusters, 3-Dimension features one of new innovative features in NVivo, which ismore meaningful)Time Consumed As we know doing qualitative research need patience, perseverance andtolerance. It evolve and time consumed especially during the development of pattern of thephenomenon studied. As it progress, analyzing of data occurs and it is a “to and forth”process as its also potrayed as nonlinear and recursive activities. In this condition, NVivohelped to automate and speed up many data management and analysis tasks. To someresearchers, this might be the most important feature of any computer program. Most QDA 31
    • programs provide tools to organize data, help shape the data in ways researchers reflect uponit, and give opportunities to see data from different angles; and all these happen in seconds.Rigor and thorough as it progress. Overall, NVivo was very helpful while building arigorous database for the data analyzed. It demonstrated very clearly all the data coded andthe way it had been coded. The relationships explored by the researcher among the datasources could be seen easily in the menus of NVivo. Also, the management of these long datafiles was very easy using NVivo. These were the things that helped increase the rigor of theentire data analysis process. Welsh (2002) emphasizes another important feature of NVivo interms of its adding rigor to the qualitative studies; search facility that enables researchers tointerrogate their data. “However, the software now is also a useful tool addressing issues ofvalidity and reliability in the thematic ideas that emerge during the data analysis process”Most researchers have no problem with the idea of being rigorous; a rigorous study isregarded as thorough, as opposed to sloppy, and purposively complete, as opposed tohaphazard. Qualitative researchers, however, commonly avoid the term, because inqualitative research, overemphasis on rigidity of the study resists its adaption to discoveredmeanings. Rather than thorough, “rigorous” may be seen as meaning undiscriminating,treating all experience as the same. Rather than ensuring completeness, a fixed researchdesign can impede discovery from the data.Qualitative RigorQualitative researchers, however, are very alert to the risks of inadequate and unpersuasiveresearch. They evaluate their work by criteria for qualitative rigor usually expressed indifferent terms from those for a survey or experimental study. We localized the analysisprocess in such a way we believed it will add rigor and data meet its validity suggested byMeriam (2009) that to enable the researcher to be “experience near” it has to be done in away the data were collected through multisource or multitechnique; Data were collected through triangulations of non participation observation, face to face depth interviews and documents that include vignettes, reflection, memo writing and daily lesson plan 32
    • Peer check on the verbatim of the transcript from the view of participants itself being conducted as to validate the data continously Collaboration with the participant being set from the initial of the project till the data meet the saturation point. As always being in the researcher frame of work the validity aspects were put front in every steps of the fieldwork this is done when researcher itself will remain unbiased on any incidens during collection of data, so the data being stored are specifically picturing the events in the field.Much of the work done are archieve, manage and blend into NVivo file and it helps inbreaking up the data into their specific themes under Nvivo navigator’s icon/buttonQualitative techniques for ensuring rigor include the following:Framework scope. In qualitative research design, principles of rigor require ongoingassessment in the scope of study (which, unlike a predetermined sample, changes constantly)and the fabric of the data (the sources, richness, adequacy, persuasiveness, and complexity ofthe records studied). Analogically, it is said that analysis is just like a loom that facilitates theknitting together of the tapestry. As a matter it reduce and limit the weaver’s error.Assessing completeness. Rigor in such respondents involves are reliable, strict applicationof a prior design but persistent,thorough revisiting of a problem or theme with constantcomparison of cases.The study need to look at every angle of the phenomenon studied withinthe theoretical framework.Establishing saturation. Perhaps the most dramatic development of qualitative coding hasbeen the ability of software to support exploration of context and dimensionalizing ofconcepts. These methods enhanced the rigor of code-based analysis, supporting claims thatthe themes adequately represent the data and “dimensionalizing” of a concept. exhaustion of sources: -little information of relevance gained if prolong engagement Saturation of categories- continuing data collection will only gathereed tiny increments of new information emerging of regularities – sufficient consistencies in the data that had been developed and the phenomena is represented 33
    • Overextension- a new information is far removed from the central core of viable categories that have emerged and does not representing the phenomenaAs (Wolcott,1994,b) claimed that working closely through emic perspective is to ensure thereal picture from the perspectives of the participant view because it will describe feeling thatthey experienced thus to produce a rich thick description (Geertz, 1973) and according toMerriam (2009) rich, thick description provide enough description so that readers will be ableto determine how closely their situations match the research situation and hence findings canbe transferredComputer Solutions to the Time ChallengeTime framework : Speed and Qualitative ResearchSuch challenges require not the condensing or dodging of analytical processes, but theefficient handling of those that qualitative researchers, however, are very alert to the risks ofinadequate and unpersuasive. Qualitative research faces 3 particular challenges of speed:Data collection. Computer tools cannot remove the time required to conduct a narrativeinterview, but they can support rapid assessment of the adequacy of records and automateprocessing. An indicator of the relevance of questions asked or the appropriateness of sitesstudied can be rapidly obtained as documents can be viewed, reported on, and profiled..Data preparation For the researcher in a hurry, the labor of qualitative data “collection” arehigh compares dramatically with data-collection methods that are not face-to-face. Any of themethods of making qualitative records, focus groups, in depth interviews, field research, takesubstantial time, even for small scale research. Even the first generation of qualitativecomputer tools remade qualitative coding for many researchers. Coding on paper was boring,burdensome work, more clerical than creative. All computer software for qualitative researchsupports coding, and it is always faster than the same task done manually. Some software caneffectively remove descriptive coding tasks (autocoding by command file or section coding inNVivo codes all the answers to a question, or everything said by a respondent). Demographicor other background data can be input by table import from spreadsheet or statistics package.Interpretive coding is easier, swifter, and more visual. 34
    • Pursuit and validation of conclusions. :Arrival at conclusions in qualitative research is rarelyrapid, and in most studies undue haste risks superficial or incomplete analysis. Significantly,qualitative software has met resistance from researchers to the sorts of searching is supported.But the processes of pursuing conclusions and establishing their robustness are helped bysoftware tools that provide ways of gaining rapid access to data. Text search or keywordsearch are mechanical processes that can support interpretative goals by providing all relevantdata for consideration. In NVivo 9, an innovative tools to display the excessive work doneand looking at the themes’ frequency of the data gathered by clicking the button and thedisplay in either at matrices, three dimension feature, three charts or tag cloud and datacluster. It means the fear for not finishing aren’t happen in NVivo because the anxiety inlooking at the finishing parts is high. As conclusions are pursued, researchers can commanditerative searches through different areas of the data to hasten assessment of explanations orcreate live matrices offering a new sort of assessment of patterns. These and all other profilesof data can be exported to statistical software or spreadsheet in Excell if this is appropriate.Computer-aided ReliabilityReliabilityReliability in social research usually refers to the assertion that a measurement procedureyields consistent scores when the phenomenon being measured is not changing. If reliabilityrequires exact replication, this will be difficult, arguably impossible, to achieve in aqualitative study, because all qualitative methods require situated study of changing ideas andbehaviours. Not surprisingly, therefore, qualitative researchers frequently express concern atthe concept.Positivist notions of reliability assume an underlying universe where inquiry could, quitelogically, be replicated. This assumption of an unchanging social world is in direct contrast tothe qualitative/interpretative assumption that the social world is always changing and theconcept of replication is itself problematic. Such negativism about positivism has brandedqualitative research in some areas as defiantly unreliable. What is reliability in qualitativeresearch?Qualitative Reliability 35
    • Qualitative researchers have clear standards for reliability. Reliable studies have methods ofmaking and interpreting data that are transparent, properly documented, and clearly adequateto the question asked and the claims made. However the concept (like “validity”) has beenseen as problematic, and there are few texts in which techniques for establishing reliabilityare set outTechniques for ensuring qualitative reliability are emphasized as mentoned by Merriam (2009):;As we looked into the table below some of the flow of the procedures done in the NVivofile to show the rigorous process and it is supported and determined by a scholarly principlein data analyzing.Coding reliability. Ways of establishing the reliability of interpretation as through coding bymany researchers of the same data or one over time Qualitative researchers would rarelyexpect identical coding across coders or across time, because the goal is to learn from thedata, but differences, especially gross differences in coding require discussion,interpretation,and often concept development. Software can assist the researcher with thistask, though it is almost impossible to do manually. (N9 provides for automating viewing ofareas of difference and similarity, within specified tolerance, between 2 researchers’ codingof the same document or 2 coding processes by the same researcher at different times.)Comparison of coding patterns provides a firm basis for concept clarification and teamtraining and is necessary for a claim that coding is reliable.Triangulation. Ways of showing data from different sources and technique but lead to thesame conclusion. This is a (much misused) term for “sighting” a phenomenon by differentmethods. It requires the dovetailing of studies, very difficult or takng times to achieve bymanual methods. Its supports coding, and it is always faster than the same task donemanually. (focus-group transcripts) for thorough comparison or detailed searching issupported by import and export of tables from any table-based software. Theresearcher“tells” NVivo what the statistic spackage “knows” about a case or site and can thenuse that information in seeking and verifying patterns in the qualitative data. Export of tables 36
    • permits the output of qualitative analysis to be “told” to the statistics package for furtherpursuit. Merging of 2 or more qualitative projects for comparative analysis or collaborativework is supported by software that investigates all aspects of the databases being merged andallows the researcher to construct the best fit of projects. (With Merge for NVivo, this abilityis extended to aligning projects in great detail for thorough comparison of their emerginganalyses.)Auditing and log trails. Ways of accounting or the steps by steps in analysis, the crucialprocesses of theory emergence and theory construction. Most qualitative research basesclaims to reliability on the ability of the researcher to show clearly how a concept wasdeveloped and discovered, its recurrence in the data traced and place in a growing theory andhow its significance was investigated. The researcher using computer software can logemergence of a category, date memos or other documents, archive images of analysis at eachstage. (In NVivo, the researcher can create and edit a memo telling its history and trailing itsoccurrence in other data by hyperlinking documents and coded data.) This can provide fulldocumentation of how the category grows in significance and is tested through the data.Therefore, it can be said that the NVivo package provided a tremendous help in the dataanalysis process and some facilities of the software helped increase rigor in terms of datamanagement. The researcher used the term “validity and reliability” appropriate terms forqualitative research studies as many scholar in qualitative has been using for decade(Merriam 2009, Creswell 2005). The things that ensured the validity of the conclusions in thisresearch study such as triangulation of data sources, extended or long term collaborationexperience in the environment, and researcher journaling had nothing to do with NVivosoftware but the way this study was conducted by the researcher. The material used are keptand managed effiently in NVivo.CONCLUSIONSUsing CAQDA especially NVivo in qualitative data analysis have strong standards andpositive mechanisms of rigor and efficient. However, the difficulty of achieving thesestandards and unevenness in research outcomes may come from te user and how they dealwith it. If steps and procedure were used properly and systematically it will lead to a 37
    • successful work. This paper has identified computer assisted techniques, but it was beyond itsscope to assess and critique them or to discuss the unanticipated consequences of rapidmethodological change. These include dramatic increase in the acceptability of qualitativeresearch in areas where it is not taught and hitherto has not been widely accepted. The needfor appropriate literature in these areas is urgent. So too is the need for a full and criticaldiscussion of the impact of these changing techniques and the directions of softwaredevelopment. Thus, outreaching software user in qualitative research should be donecontinuously and in timely it will develop.ReferencesAsensio, M (2000) . Choosing NVivo to support phenomenographic research in networked learning. Proceeding of a symposium conducted at the meing of the second International on Networked learning, Lancaster , EnglandAtkinson, P., Coffey, A., and Delamonts,S. (2003).Key themes in qualitative research: continuities and changes. Walnut Creek, CA: AltaMiraCreswell,J.W., (2007). Qualitative inquiry & research design. Choosin among five approaches. Thousands Oak.Sage.Dickie, V. A. (2003). Data analysis in qualitative research: A plea for sharing the magic andthe effort . American Journal of Occupational Therapy, 57(1), 49-56.Di Gregorio, S. (2000, September). Using NVivo for your literature review. Paper presentedat the conference of the Strategies in Qualitative Research: Issues and results fromanalysis using QSR NVivo and NUD*IST at the Institute of Education, London.Eisner, E. W. (1998). The enlightened eye: Qualitative inquiry and the enhancement of educational practice. Upper Saddle River, NJ: Prentice-Hall.Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532-550.Geertz,C.(1973).Deep play: Noteson the Balinese cockfight. In C. Geertz (Ed.). Theinterpretation of cultures: Selected essays ( pp 412-435). New York Basic Books. 38
    • Guba, E. G., & Lincoln, Y. S. (1989). Fourth generation evaluation. Newbury Park, CA:Sage.Hammersley, M., & Atkinson, P.(1983). Ethnography: Principles in practice. London, UK: Tavistock.Merriam, S.B,(2009).Qualitative Research. A guide to design and implementation..Jossey- Bass, CAMiles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expandedsourcebook. Beverly Hills, CA: Sage.Patton, M. Q. (1999). Enhancing the quality and credibility of qualitative analysis. Health Services Research, 34(5), Part II, 1189-1208.Rich, M., & Patashnick, J. (2002). Narrative research with audiovisual data: VideoIntervention / Prevention Assessment (VIA) and NVivo. International Journal of Social Research Methodology, 5(3), 245-261. 603Richards L. (2000). The NVivo Qualitative Project Book. London: Sage.Strauss, A. L., & Corbin, J. (1990). Basics of qualitative research: Grounded theoryprocedures and techniques. Newbury Park, CA: Sage.Tech,R. (1990). Qualitative research: Analysis types and software tools. Bristol PA.Falmer PressVan Maanen, J. (1988). Tales of the field: On writing ethnography. Chicago, IL: Universityof Chicago Press.Weitzman, E.A.,& Miles,M.B., (1995). Computer programs for qualitative data analysis. Thousands Oaks, CA: SageWelsh, Elaine (2002, May). Dealing with data: Using NVivo in the qualitative data analysis process [12 paragraphs]. Forum Qualitative Sozialforschung / Forum: QualitativeSocial 39
    • Research [On-line Journal], 3(2). Retrieved July 16, 2002 from http://www.qualitativeresearch.net/fqs-texte/2-02/2-02welsh-e.htmWolcott.,H.F. (1994,b).Transforming qualitative data: Descriptipn, analysis andinterpretation.Thousands Oak,CA: Sage.Yin, R. K. (1994). Case study research, Design and methods Beverly Hills, CA: Sage. 40
    • Appendix i 41