Recommendation of data analysis techniques
Bogdan and Biklen describe qualitative data analysis as working with facts, systematizing it, breaking it into convenient elements, amalgamating it, looking for prototypes, determining what is significant and what is to be studied, and settling on what to inform others(1982). Qualitative investigators seem to employ inductive examination of data implying that important topics come from data (Patton, 1990). Qualitative examination calls for some inventiveness since the challenge is to put the unprocessed data into reasonable momentous groups, scrutinize them in a holistic mode and look for a way to correspond to other scholars.
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Data analysis in qualitative research starts with classification of topics from unprocessed data, a procedure occasionally termed as open coding (Strauss, & Corbin, 1990). In open coding, the canvasser should categorize and tentatively name the theoretical types into which the occurrences experimented will be clustered. The aim is to generate explanatory and multi-dimensional classes, which form an introductory framework for examination. Expressions, idioms or actions that seem to be alike can be clustered into the same class. As unprocessed information is split into convenient units, the investigator should as well formulate an audit trail, which is specifically a method for classifying data units according to correspondents and perspectives.
The subsequent phase of analysis entails review of classes in order to establish how they are connected. It is an intricate process at times referred to as axial coding (Strauss, & Corbin, 1990). During axial coding, the canvasser is supposed to construct a theoretical model and establish whether adequate data is available to support clarification of results (Strauss, & Corbin, 1990).
Data collected through interview guides can be analyzed using Nvivo software. It is not a complex process hence saves on time and resources. Interview guides generate too much information that needs sorting and interpretation. The researcher classifies data to establish what is relevant to the study. One technique in which such precision could be realized is by utilizing the search facility in NVivo, which is perceived by the manufacturing designers as one of its key resources that enables examination of information (Lee, & Esterhuizen, 2000). This is definitely factual when information is searched in terms of characteristics. In this study for instance, the researcher seeks to know why individuals like certain persons. Obviously, analyzing such results through electronic means would generate more consistent results than conducting it physically. The software ensures that human slip-up is eliminated.
Coding using NVivo
NVivo augments some features of qualitative study process by facilitating data recording, systematization and text coding. It further increases methodical connection between texts and emerging hypothetical models. The software permits canvassers to record interpretations straight into Nivo or to convey them into the program. Once information have been ascribed or brought in, codes can be allocated to texts to start systematizing and handling information. Once formation of a folder comprising of texts with allied codes, researchers’ remarks and interpretations is over, the software permits easy institution and repossession of pieces of texts concurrent to ordinary codes or models. Consecutively, this permits the study to investigate prototypes in collected data and starts to conceptualize the results (Blank, 2004). The data can be clustered and prearranged using diverse techniques that helps the study in depicting key conclusions.
The NVivo software consists of a variety of equipments for presenting logical relationships between coded texts.
Software programs sustain manifold file configuration, such as doc, docx and pdf. This would imply that a researcher must not publish his/her works for interpretation and scrutiny since the software program can assist in interpreting and evaluating data all the way from within the program itself without changing the findings to a particular format (Robson, 2002). The next point that approves software program is that coding and examination process is automatic. All a researcher should do is to open the file in the software itself, underline the piece to be coded and haul the code from the code administrator. It is that uncomplicated because it generates quotation and quotes simultaneously. The researcher may perhaps say goodbye to the physical method of investigation that utilizes markers, highlighters and papers. The software comes in handy in any research.
Many researchers have embraced the use of software in analyzing data. The software facilitates supervision of documents including valuable information, be it primary or less important data. All a researcher is supposed to do is to consign the file as ‘main document’ and he or she is able to recover the document straight from within the software program itself. However, the most important advantage is the friendly system device, which is helpful to the researcher in terms of presenting data to the audiences. The system product can be saved as picture folder, as well as easily slotted into researcher’s work, for example MS PowerPoint and MS word. Last, though not essentially least is the fact that software program is reliable. Through software program, a researcher would have access to online assistance from the support team who are regularly reachable through online chat or responsive electronic message (Rubin, & Rubin, 2005). The researcher is rest assured that the administrative team could deal with all mechanical problems.
Critics have their own problems with software utilization. The major one pertains to inflexibility of the software meaning that it cannot handle all forms of data. As the name suggests, the program handles only soft data, the software cannot interpret other forms of data mainly in hard copy (Lee, & Esterhuizen, 2000). Conversion of writings in books into soft copies is time consuming and expensive.
- Bogdan, R.C., & Biklen, S. K. (1982). Qualitative research for education: An introduction to theory and methods. Boston: Allyn and Bacon.
- Patton, M. Q. (1990). Qualitative Evaluation and Research Methods (2nd ed.). Newbury Park, CA: Sage Publications.
- Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Newbury Park, CA: Sage Publications.
- Blank, G. (2004). Teaching qualitative data analysis to graduate students. Social Science Computer Review, 22(2), 187-196.
- Lee, R. M., & Esterhuizen, L. (2000). Computer software and qualitative analysis: Trends, issues, and responses. International Journal of Social Research Methodology, 3, 231-243.
- Robson, C. (2002). Real world research: A resource for social scientists and practitioner-researchers (2nd ed.). Malden MA: Blackwell Publishing.
- Rubin, H. J., & Rubin, I. S. (2005). Qualitative interviewing: The art of hearing data (2nd ed.). Thousand Oaks, CA: Sage.