In business, decision-making based on wrong conclusions can prove disastrous and costly. Interpretation of results will be faulty that will lead to wrong conclusions. If there are doubts about their collection, data analysis is compromised.

At other times, data accuracy is deliberately compromised because findings are inconsistent with the expected results.ĭata should be as accurate, truthful, or reliable as possible. Garbage in, garbage out (GIGO).įor many students who want to comply with their thesis requirements, rigorous and critical data analysis is almost always given much less attention than the other parts of the thesis. However, if the data used in such automated data analysis is faulty, the results are nothing more than just plain rubbish. Before performing data analysis, researchers must ensure that numbers in their data are as accurate as possible.Ĭlicking the menus and buttons of statistical software applications like SPSS, Stata, SAS, Statistica, SOFA Statistics, Jasp, and even those in accessible statistical software applications in office productivity tools like MS Excel Analysis ToolPak, and the statistical function of open-source Gnumeric, among others, is easy. This article enumerates the five essential steps to ensure data integrity, accuracy, and reliability.ĭata analysis is an integral part of the research process. Data analysis is only as good as the quality of data obtained during the data collection process.

Reliability in qualitative research