Data analysis is a process of inspecting, cleaning, transforming and modeling data with the goal of underlining essential information, suggesting conclusions, and supporting decision making (Ader, 2008). It is the process which follows after data collection. For the purpose of this guide, two data analysis procedures, namely quantitative and qualitative are briefly highlighted:
Quantitative Data Analysis Procedure
Quantitative Data is information gathered in a numeric form. The basic instrument for collecting quantitative data is questionnaire. There are a number of steps that are involved in analyzing quantitative data. These include data cleaning, data coding, data presentation and data interpretation and discussion.
Date Cleaning: Collected data pass through the process of cleaning to remove ambiguous elements. Content analysis is also applied to capture information from the open-ended questions (items) which are also subject to quantification in a quantitative research.
Data Coding:Coding refers to the process of assigning numerals or other symbols to answers so that responses can be put into a limited number of categories (Connelly, 2000). Coding is a vital step where the collected data is translated into values suitable for computer entry and statistical analysis. Variables are created from data with the aim to simplifying the analysis. Basically variables are meant to summarize and reduce data, attempting to represent the “essential” information (Schoenbach, 2004). There are various applications which help the process of data coding and analysis. These include spreadsheets like Excel and statistical packages like SAS and SPSS (Coolican, 1994).
Data Presentation:Tables and Figures are used to summarize the coded data.When using computer programs such as MS Excel or advanced statistical packages like SPSS, functions are provided within the program for summarizing data into either tables or figures (Schoenbach, 2004). Quantitative data is summarized in order to help the process of data presentation which involves use of descriptive statistics such as frequencies, percentages, means and standard deviation. Data are also presented using inferential statistics such as t-tests, Analysis of Variance (ANOVA), Multiple Analysis of Variance (MANOVA), regression, factor analysis among others methods depending on the study design.
Data Interpretation and Discussion: Once the data is presented, the interpretation and discussion of the results follow. Data interpretation involves the provision of comments on the results obtained from the investigation. It is done based on the key findings of the study. Interpretation requires deep understanding of literature and issues under investigation. Such an understanding helps the researcher to avoid ‘shallowness’ and sweeping statements in the interpretation and discussion. The interpretation of the data must be within the framework of what the data analyzed, suggests and not an exaggeration. Statements that are not justified by the data do undermine the credibility of what is being presented. As such, interpretation should be done in context and supported by literature.
Another key point to note when interpreting data is to avoid alteration or skewing of the set objective. Vested interest should not be shown in data interpretation in order to maintain the credibility of the results and the whole report. Therefore it is important to ensure that interpretation and discussion of results are based strictly on what is evident in the presented data.
The following is an example of quantitative analysis procedure in a mixed research approach which was used in a study conducted by Ndaita (2013) on the influence of principals’ instructional quality assurance role on students’ academic performance in Kitui West District in Kenya:
The study employed quantitative data analysis procedure where descriptive statistics such as frequencies, percentages , means and standard deviations were used. The Statistical Package for Social Sciences (SPSS, Version 17) was used to run the descriptive statistics. Tables and figures were used to summarize data. The quantitative data were derived from the students’ and teachers’ questionnaires. The data were gathered about the principals’ role in the application of pedagogy, curriculum implementation, provision of instructional materials, teacher training and development, challenges facing principals as instructional quality assurance leaders and measures to improve the principals’ role of quality assurance.
Read about qualitative data analysis procedure
Adèr, H.J. & Mellenbergh, G.J. (2008). Advising on Research Methods: A consultant’s companion. Huizen, the Netherlands: Johannes van Kessel Publishing.
Coolican, H. (1994). Research methods and statistics in psychology (2nd ed.). London: Hodder & Stoughton.
Connlley, H. (2000). Hard book for research synthesis. New York: Russell Sage.
Schoenbach, V.J. (2004). Data analysis and interpretation. Retrieved from http://www.epidemiolog.net/evolving/DataAnalysis-and-interpretation.pdf