The concept of pre-coding in qualitative data analysis has been highly focused in recent days. In regards to the developments in computer and internet technology, researchers and data analysts have diverted their efforts into pre-coding. Basically, pre-coding is the process of categorizing quantitative and qualitative data to facilitate analysis. In the process of pre-coding, each questionnaire, question, and the possible answer is numbered separately. Despite that pre-coding in qualitative data analysis has been widely acknowledged; there have been opposing viewpoints on its efficiency. In this case, the advantages and disadvantages of pre-coding will be discussed.
The process of pre-coding data makes the entire task of analyzing results from questionnaires easier. Coding makes it easier for the researcher to move the collected information into one sheet so as to produce useful statistics. As noted by Sapsford and Jupp (2006), pre-coding of qualitative data cuts the costs and time of data handling. This is very appropriate in handling large-scale surveys since it avoids minor errors. Unnecessary human or clerical efforts that might have otherwise been required in checking respondent errors are avoided. It is worth noting that pre-coding in qualitative data analysis enables the use of computers for tabulating and analyzing data. This is very desirable and enables the use of modern technology (Sapsford and Jupp, 2006).
On the contrary, pre-coding in qualitative data analysis has been highly criticized. Erdos (2002) indicated that it is not advantageous to pre-code questionnaires. The appearance of a pre-coded questionnaire is different and not appealing to all respondents. Pre-coding makes questionnaires to be crowded by adopting more printing and less space. This makes them look heavier and difficult as compared to un-coded questionnaires. On the other hand, pre-coding is only suitable for close-ended questionnaires as compared to open-ended questionnaires. With this in mind, pre-coding becomes inappropriate for addressing all issues in qualitative data analysis (Erdos, 2002).
The study on coding and data analysis has provided very insightful ideas for executing a comprehensive research process. The issues of data analysis and coding have been highlighted as very crucial aspects in a research study. Knowledge of these aspects is very crucial for every researcher. In the case of undertaking qualitative and quantitative research studies, coding becomes very essential. This is not only for utilizing the current developments in computer technology but also to ensure accuracy and efficiency in data analysis. As observed in the study by Patton (2001), coding is a very vital element in research in the sense that it assists in analysis. The study has clearly demonstrated that coding helps in simplifying data analysis. This is so because the respondents for the questionnaires can be effectively presented in datasheets for analysis. The overall cost and time which might have otherwise been required in handling data are minimized. On the contrary, it has been noted that coding is not fully efficient since it is not useful in open-ended questions. Coding also makes the questionnaires to be crowded thus discouraging the respondents (Patton, 2001).
In the case of data analysis, the discussion has offered insightful ideas concerning the various strategies of data analysis. In particular, the Pareto analysis has been the most focused. This strategy is based on the 20/80 principle which is very efficient in terms of time and resources. Since researchers are allowed to focus on small portions of the work of about 20%, a high sense of accuracy can be attained. This is so because the researchers are able to devote all their efforts to provide perfect results. Pareto analysis is also the most appropriate for large-scale data. The researcher can in this case concentrate on a small proportion of the work thus leading to high precise results. In light of these discussions, it is notable that coding and Pareto analysis should be incorporated in research studies based on their high levels of efficiency (Maxwell, 2004).
Reference list
Erdos, P. (2002). Professional Mail Surveys. New York: McGraw-Hill.
Sapsford, R. and Jupp, V. (2006). Data Collection and Analysis. New York: Sage Publications.
Maxwell, J. (2004). Qualitative Research Design: An Interactive Approach. New York: Sage Publications.
Patton, M. (2001). Qualitative Research & Evaluation Methods. London: Sage Publications.