3 Social Research Methods That You Need to Know Before Starting Your Career as Social Researcher

Choosing social research as a viable career has always been seen with a lot of apprehension in the past. Individuals have always assumed natural sciences to be the rightful way ahead with focus on pragmatic career choices such as medicine, engineering etc. Albeit slowly, with time there has been a flurry of acceptance for social sciences and interdisciplinary studies. To be a social researcher, ample experience in research is required. One of those requirements can be understood by gaining knowledge on social research methods. While some of the methods are more nuanced and require advance level of studies, they can be disintegrated and clubbed into three types of methods. This article talks about the type of methods one should definitely know about before they decide to become a social researcher.

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Quantitative vs. Qualitative

It is important to understand how you want your research outputs to look like. Here one good question to ask is whether your data would rely more on numbers or more on textual data. Both the research methods have their own pros and cons. While qualitative data gives a lot of flexibility to adjust new knowledge and works great with smaller samples, quantitative research methods can be used with ease on systematically large samples and can generate reproducible/generalizable knowledge. However, using quantitative methods would require advance knowledge of statistical training to analyse data.

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Primary vs. secondary data

sing primary data sources (such as surveys, observations and experiments) help in collecting on-ground information to address the research questions whereas secondary data is the information that is already collected and available in public domain or with some other agencies. These can be government census, scientific studies on any thematic area. Which data source to use depends on what a researcher is looking for? If one is looking to address a novel research question, use of primary data is optimal. To synthesize already available information and identify patterns and trends, secondary data is a better choice.

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Descriptive vs. experimental data

Descriptive data focuses on collecting data in a situation where an intervention is not planned. The use of descriptive data is insightful and may be generalizable if the sample is large enough. Experimental methods are best used to understand cause and effect. There is a need to vary your independent variable in order to measure the dependent variable and controlling for confounding variables (variables that hide the true effect of a variable in your experiment). While descriptive data is crisp and can help cover large samples, it is best to use experimental methods in order to establish cause and effect. Experimental methods go hand-in-hand with already planned interventions.

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