Evaluating data in research is choosing some statements that are more likely to be correct than others; therefore, this data is of high quality, so it can then be edited and coded into specific groups or patterns that can be understood by researchers and decision-makers (Thomas, 2006).
Statistical Analysis in Research
It refers to the process of collecting data to reveal patterns and trends and is one of the components of data analysis; it is a process that aims to remove bias through numerical analysis; statistical analysis helps in summarizing a large amount of data (Myers, Well, & Lorch, 2013).
Analyzing the Data in Research
According to (Jacelon & O’Dell, 2005), Data in Research aims to provide accurate data by avoiding statistical errors, missing or strange values, and data that can be represented graphically or in tabular form.
Data Editing in Research
This process improves survey data by finding and correcting data errors, whether intentional or unintentional; examples of editing include partial, macro, graphic, and interactive (Seale & Kelly, 2004).
Data Coding in Research
The coding process means assigning certain symbols to the data so that it is easier to analyze and can be interpreted perfectly, compared to if it was raw data; the conversion process is to produce coherent and meaningful data; the coding process is usually for qualitative data recorded and observed from samples (McDaniel Jr & Gates, 2018).
The Value of Editing and Coding
The editing and coding process works to maximize the usefulness of the data, which makes it accessible from errors and more coherent, consistent, and quality, in addition to the ease of browsing it and accessing the information that the researcher or decision-maker desires in a shorter time (Seale & Kelly, 2004).
Conclusion
Data analysis involves examining the available information and revealing the relationships best. Then comes the process of encoding this data to make it easier to browse and process optimally; these two processes take the researcher and the decision-maker to a level of reliability based only on the validity of the data and the ability to obtain answers to the questions posed.
References
Jacelon, C. S., & O’Dell, K. K. (2005). Analyzing qualitative data. Urologic Nursing, 25(3), 217-220.
McDaniel Jr, C., & Gates, R. (2018). Marketing research. John Wiley & Sons.
Myers, J. L., Well, A. D., & Lorch, R. F. (2013). Research design and statistical analysis. Routledge.
Seale, C., & Kelly, M. (2004). Coding and analysing data. Researching society and culture, 2, 304-321.
Thomas, D. R. (2006). A general inductive approach for analyzing qualitative evaluation data. American journal of evaluation, 27(2), 237-246.