Qualitative vs Quantitative Research – Classification & Collection Of Data

Kinds Of Research:

There are generally two accepted forms of research – Quantitative Research and Qualitative Research. Qualitative research involves the collection of information through interviews, observations and by targeting focus groups. Quantitative research on the other hand relies more on surveys, collected data, experimentation and analysis.

Collection Of Data:

Collected data is usually of two forms: Primary and Secondary data. Primary data is first-hand data which is obtained by direct surveys, interviews, and the like. Secondary data on the other hand are those that are obtained from secondary sources such as old government records, published magazines, old newspapers, etc.

Data collection may furthermore be classified into internal and external. Internal data is that which is collected from company records, employees, etc. External sources of data comprise information collected from stakeholders, buyers, and the rest.

Quantitative vs Qualitative:

Qualitative research may appear to be faulty.

Since there are no numerical values in support of the statement of the researcher, the authenticity of the report depends largely on the researcher’s capability to gauge the situation. Contrary to qualitative research, quantitative research reports could be greatly biased without anyone ever being aware of it.

Factors Influencing Data Collection:

There are many factors including accuracy, time constraint, and budget constraints which influence data collection.

If accuracy is your priority, then you should opt for primary research since errors due to transcription and rounding are pretty much eliminated.

However, if you face a time and budget constraint, then you should go for secondary data from compiled sources. Although secondary sources of data aren’t as accurate as primary data but by using secondary data, one can work with a large sample size and perform random sampling.

Sampling and Non-Sampling Errors:

Sampling errors occur when a sample population is taken into consideration. Non-sampling errors occur due to inconsistent data or when population units are not correctly accounted for in the sample.

Sampling errors can be eliminated by using theoretical approaches, while non-sampling error results in biased results.

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