Data Collection and Assimilation
Misinterpretation is a common problem when using information. It may be caused by a number of factors.
It could be due to misunderstanding the data or due to usage of incomparable data.
When there is too much data on the table it is something like too much food in your mouth; you can’t chew, you can’t swallow; hence you don’t know what to do with it.
That is when tools come in handy.
It could be prevented by adopting spot checking, eye balling and logic checks etc.
Data collection must be done in a systematic manner to start with, so you know where to go to get what.
Something as small as naming the file in relevance to the topic can save one tons of time which would otherwise go into the treasure hunt of finding that one picture or one document.
Even when collecting information one must be careful to keep bias or self tabbed notions aside and keep one’s eyes and ears open.
A proper segregation must be done so when it is time to analyze it; it doesn’t look like a clutter of pointers thrown into a heap.
Once that is done, picking what to keep and filtering out the rest is to be achieved.
Data can be represented either quantitatively or qualitatively.
Now here the tricky part is to choose which part of your collected, filtered data goes where.
Sometimes data is better presented when you marry both the data; quantitative and qualitative and create an amalgamation of the two.
Synthesizing of data happens in this stage; where in your interpretations as you see it and note it; are broken down in to a structured manner.
Next you codify it. With all that collected you must have strong evidence backing it up.
Once that has been tackled taking information that would help your final analysis and presenting it; keeping optimum in mind.
The misinterpretation of data can snatch away the entire purpose of the research problem undertaken.
Hence systematic data collection, segregation, synthesizing, analyzing and presentation is the way to go about; the key word being systematic.