Challenges faced during data analysis

Data analysis is a systematic technique that assists in the analytical exploration of the study undertaken during research. The objective of data collection, calculation and evaluation are to find out an accurate and apposite result of the problem. However, there are many hitches on the way to reach the result. Below discussed are some of the leading challenges faced in the process –

• Sample size – Fixating sample size is the first challenge faced during data analysis. For quantitative analysis, sample size can still have a limit of 10-20 in variation, however, for qualitative analysis, there is no such fixed sample size, as it may increase up to a limit where one can get a relevant response. Also, a big sample size is another dare to take on, as it becomes difficult to manage and reach up to the result. Data biasness during sample collection tempts up with the population being reluctant in accepting the sample from outside their circle, this, in turn, dribs a barrier in the study.
• Reluctance from response – There are times when would be population deny from permitting their sample to participate in the research-based inquiry. This, therefore, is one of the biggest challenges, as you can’t conduct a study, in spite of having the samples.
• Hawthorne effect challenge – Participants perform and react differently when they feel that they are observed, then their normal behavior. This, however, does not give the real result.
• Reliability and validity – Data collected should meet the standard of being stable over a period of time, reproductive up to a multiple stages and accurate enough to be applicable statistically.
• Choosing appropriate methods for calculation – Although brought into action after sample data collection, yet choosing methods for calculation is the initial step to be planned. If planned on starting, it will guide best on the way to collect data and its analysis, however, if not it effects entire study.
• Biases in inference – Biasness in inference occurs right from the initial stage of sample selection. Selection of a familiar sample may create some sort of off-the-cuff response that hinders the actual result as required.

Furthermore, data analysis should be planned from every prospect, right from the very first phase of the study.