The most common method of social science data collection is a survey based on a probability sample. The survey design was, and still is, quite complex to ensure equal probability of selection to ensure that the findings can be generalized to a specified universe. The survey data is used for quantitative empirical analysis and its output is often precise correlations using various statistical tools. Theory drives such data to specify policy recommendations for government or business. Not all data analysis is, however, driven by theory that is so highly formalized. Thus, the data are also used via repeat analysis to establish empirical regularities, i.e., patterns in the data repeated across time or space, which become part of social knowledge.
Although, the advantages of quantitative survey analysis are formidable, yet there are several drawbacks. The nature of information gathered by survey techniques is extractive and researchers are concerned with publishing findings based on the data collected. At times, the quality of data is viewed as being poor since a detached expert through an unmotivated field team reflects upon the data and there is lack of bonding between the researcher and the field team. Another serious limitation of this method is when a structured questionnaire is utilized. It is assumed that the expert knows what is important and that is often not the case, and this happens when close-ended questions are used.
Due to these shortcomings of quantitative research, alternative research methods, focusing on qualitative information collection, have gained currency. These techniques are more popular because of the fact that they are cheaper and more sensitive to human behaviour.
[ 2 ]. Robert Chambers, the PRA Guru has critiqued the conventional social science research. He terms research as extractive if it is meant only to produce reports for donors and the subjects i.e., the people who...
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