The Best Analytical Techniques and Data display Methods

The Best Analytical Techniques and Data display Methods

Thematic Analysis

It is the most commonly used analytic technique in qualitative research studies. The researcher opts to use the method because it will help in identifying the patterns and themes contained in the data that the researcher collected during the study (Patricia, 2013). The technique allows the researcher to review the whole data gathered and make summaries of opinions on employee dissatisfaction from the various research participants involved in the study. By using this method, the researcher will conduct the preliminary reviews and get a feel of the whole data through making small annotations on the transcripts (Bryman, Alan & Burgess, 2011).

The method provides an opportunity for the researcher to conduct a more Indepth analysis of the data to establish the main themes. The method utilizes the identified theme to create a coding system that allows effective summary and analysis of the collected data, which enhances the quality, and validity of the findings (Patricia, 2013). The codes are applied on the data and the researcher may choose to write them on the margins of the transcripts or notes. The undertaking allows easier tracking of the summary of the participants views which minimizes the chances that the researcher will make an error.

Content Analysis

Content analysis is an effective alternative to thematic analysis because it will allow the researcher to conduct data analysis in a procedural manner. The behavioral and verbal data that the researcher has collected during the study is classified, summarized, categorized and tabulated (Patricia, 2013). The technique allows the researcher to use a descriptive approach where the research provides a descriptive summary of the collected data without having to come up with the theories or reason relating to how or why (Patricia, 2013). During the initial steps of analysis the researcher will describe the opinions of the employees on the reasons for job dissatisfaction without having to provide the how or why. By using the technique, the researcher will provide a more interpretive analysis through consideration of the participants both actual and implied responses therefore providing the researcher with a complete picture and clear understanding of employee dissatisfaction (Bryman et al., 2011).

Types of Data Display that will be most useful and Communicative

Pie charts

Pie charts are mostly used to show how a whole thing is divided into separate parts. Pie charts are easier to interpret which will minimizes the chances for errors since data is clearly provided (Smith, Thorpe & Jackson, 2012). In this research study, the pie chart will be used to identify, the percentage of dissatisfied employees, the percentage of employees who are satisfied with their jobs and the percentage of employees who are indifferent (Bryman et al., 2011). The researcher will provide clear proof about majority of employees being dissatisfied with their jobs. The undertaking will enhance the durability of the research findings.


Graphs are effective methods of displaying independent number of findings during data analysis (Oyeyemi, Adewara, & Adeyemi, 2010). Graphs avail visualized data that will enable the researcher and other academicians to decipher the collected data. The researcher will use the bar graphs to indicate the number of employees who independently affected by various factors that cause job dissatisfaction. Line graphs will show how various factors over the years have caused job dissatisfaction. The scenario will provide a clear display of data for the researcher to provide accurate interpretations that can be validated (Oyeyemi et al., 2010).


Bazeley, Patricia, (2013). Qualitative Data Analysis: Practical Strategies. Los Angeles [i.e. Thousand Oaks, Calif.]: SAGE Publications.

Bryman, Alan, and Robert G. Burgess (2011). “Reflections on qualitative data analysis.” Analyzing qualitative data. 216-226. doi: 10.4324/9780203413081_chapter_11.

Easterby- Smith, M, Thorpe & Jackson, P. (2012) Management research. 4th ed. London: Sage.

Oyeyemi, G., Adewara, A.A. & Adeyemi, R.A. (2010). Complex survey data analysis: a comparison of SAS, SPSS and STATA, Asian Journal of Mathematics & Statistics, 3 (1), pp. 33-39.

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