Generally speaking, both sociology and anthropology are considered disciplines that study social interaction, and often it is hard to determine the point at which one begins and the other ends. Both make use of similar research methods, and they tend to borrow results, methods, and idea from each other as easily as next door neighbors. Traditionally, however, the two have exhibited popularly understood differences in their focus and research methods. The field of anthropology has generally emphasized the importance of understanding human development and social interaction in a cultural and historical context, whereas sociology has (particularly in recent years) come to embrace a more methodically rigorous, quantitative version of social analysis. Anthropology seeks to tap the universal consciousness generated by social norms, values, and shared history, while sociology seeks to use hard data to analyze social trends and institutions. As mentioned before, these are not firm distinctions, but for the purposes of this paper this line will be maintained in order to present a comparison of quantitative and qualitative approaches in social sciences. The paper will conclude in a discussion of the ways in which each discipline complements and enhances the academic value of the other.
Surveys are the most commonly used method of research in sociology, and their benefits are logically obvious. Through questionnaires, telephone surveys, online answer forms, etc., sociologists can query populations for their opinions on a wide range of issues. This method offers both control and breadth. Researchers are able to the specifically choose the questions and they are able to control the answers. More importantly, depending upon the technology used to survey recipients, researchers can draw data from large sectors of the population.
Secondary data analysis is a related methodology, but it provides an opportunity to talk about the recent “big data” trend in modern society. Secondary data analysis is the use of data already collected by another organization for some research purpose. For example, a researcher can tap the databases of census.gov to retrieve a demographic breakdown of a given US city, data that they can then use in comparison with other variables in order to analyze correlations or articulate trends.
Beyond that, the philosophical justifications for secondary data analysis rest upon two factors. First is the economy of the method. If a researcher has already collected the data previously, then it is much cheaper and less time-consuming to use that research. The second, and perhaps more interesting justification, is the breadth of raw data available in this day and age, especially in the digital landscape. Savage and Burrows (2007) argues that big data (as these mass amounts of information are referred to) has a potential to invigorate sociology with large and complicated data sets that are there for the taking. The potential for drawing correlations and monitoring conscious and unconscious human behavior has never been greater, especially given the digital footprints that people leave in society today. It is a new wave of data collection, one that has many researchers concerned about the lack of context and rigor in much of this research (Uprichard, 2012; Dale 2006, pp. 146), but even they recognize that these data sets can be useful if observed in the appropriate historical and social context and with the appropriate quantitative rigor (Uprichard 2012, pp. 127).
Participation observation is the most common research method in anthropology and is often thought necessary when studying a culture in order to gain the appropriate depth and historical context that raw data lacks (Kawulich 2005). Participant observation requires that researchers become part of the actual group they intend to study, both a as a subjective participant and an objective observer. This is an interactive, qualitative method that gives researchers deep insights into the thought processes and shared historical biases of various societies if they are able to maintain a good level of objectivity.
In the same way that participant observation emphasizes the importance of cultural and historical motivators, so does content analysis. Content analysis seeks to understand societies through the cultural content that they produce, such as literature, news, and even social media today. While this analysis can be both quantitative and qualitative, many scholars argue that neither approach is fully complete without the other (Macnamara 2005, pp 4-7). What content analysis does provide, however, is a tangible and analyzable field of data that is often equally as complex as the society that is studies. It is a method of studying the distribution and meaning of information in the information era, as such can provide a relatively accurate reflection of social interaction. If nothing else, content analysis provides a codex for analyzing how we interact with the information at our disposal and the ideas that we publish to wider society, which have become much more influential in the modern world.
Although sociology and anthropology generally fall on different sides of the quantitative/qualitative debate respectively, it is important to point out the way in which IT advances have affected both disciplines. For example, a sociologist’s use of secondary data analysis in his or her research draws on a wealth of digital information that was previously unavailable. Content analysis, as well, is able to make use of new types of content, such as social media texts, that allow researchers to draw inferences from the content of everyday interactions. Quantitative analysis is necessary to capture the breadth of information available to researchers, but qualitative analysis is equally necessary to determine how we ourselves interact with this very same information, thereby providing space for both approaches.
Dale, A. (2006). Quality Issues with Survey Research. International Journal of Social Research Methodology, 9(2), 143-158.
Kawulich, B. (2005). Participant observation as a data collection method. Qualitative Social Research, 6(2). Retrieved from http://www.qualitative-research.net/index.php/fqs/article/view/466/997
Macnamara, J. (2005). Media content analysis: Its uses; benefits and best practice methodology. Asia Pacific Public Relations Journal , 6(1), 1-34.
Savage, M., & Burrows, R. (2007). The coming crisis of empirical sociology. Sociology, 41(5), 885-899.
Uprichard, E. (2012). Being stuck in (live) time: the sticky sociological imagination. The Sociological Review, 60, 124-138.