To analyse social and behavioral phenomena in our digitalized world, it’s necessary to understand the main research opportunities and challenges specific to online and digital social research. This book presents an overview of the many techniques that are part of the fundamental toolbox of the digital social scientist.
Placing online methods within the wider tradition of social research methods, Giuseppe Veltri discusses the methodological principles and related frameworks that underlie each technique of digital research. This useful guide covers methodological issues such as different digital data types, sampling for big data, construct validity, and representativeness. It looks at different forms of unobtrusive data collection methods (web scraping, social media mining) as well as obtrusive methods (qualitative methods, web surveys, and experiments). Special attention is given to three key analytical approaches, explored in more detail: computational approaches to statistical analysis, text mining, and network analysis.
Digital Social Research Methods will be a welcome and essential resource for students and researchers across the social sciences and humanities carrying out digital research (or interested in the future of social research).
Giuseppe Alessandro Veltri is associate professor at the University of Trento, Department of Sociology and Social Reserach
From Chapter 1: Self-Reported and Behavioural Data (pag. 8 - 11)
All social research methods have underlying assumptions about human nature and, in particular, about the way people make decisions, create their opinions and behave. In fact, this is one of the aspects that differentiates between the various different disciplines within the social sciences. Psychology, economics and sociology all have different models of how people behave. Besides the theoretical implications of such differences, the consequences for the type of methodology employed are substantial. Depending on which underlying model is selected, a particular research method is considered appropriate to study human behaviour.
For a long time, in economics but not only, a model of how people make decisions, known as the ‘rational choice theory’, has been considered the baseline. According to this model, people’s preferences have a well-defined structure and the choice between courses of action is an almost automatic mechanism in which the individual applies his or her system of preferences to a limited set of options (for example, the set of products that fall within the budget available). In other social sciences, the most common underlying model of human behaviour was unbalanced by an ‘oversocialized view’, ‘a conception of people as so overwhelmingly sensitive to the opinions of others, and hence obedient to the dictates of consensually developed norms and values, internalized through socialization, that obedience is not burdensome but unthinking and automatic’ (Granovetter, 2017: 11; see also Di Maggio, 1997).
Both models look primarily at people’s conscious thought processes and determine what they think, believe and how they act. Deviation from either economic rationality or forms of ‘sociological rationality’ were labelled as ‘irrational’. In such a context, the way to elicit data and study people’s behaviour relies on what people themselves report, about their opinions, social norms, attitudes and beliefs. These are often defined as ‘self-reported’ data, meaning that researchers rely on participants of a study to report on something they have done or on what they think or believe. Surveys and interviews of all sorts are examples of self-reported data. In contrast to this approach, there are observational or behavioural data, data about the actual actions and behaviour carried out by someone. To better appreciate the difference, let’s take the example of asking someone how many times she or he goes to the gym every month, and compare the response to the actual tracking of their movements, for example, on a GPS phone or watch. The two pieces of information can differ dramatically. Researchers in the social sciences have learned to live with limitations of self-reported data, such as the social desirability bias (Kreuter et al., 2009). The concept of social desirability rests on the notion that there are social norms governing some behaviours and attitudes and that people may misrepresent themselves in order to appear to comply with these norms. This is the reason why participants might provide inaccurate information about their behaviour to researchers. At the same time, people have difficulty in verbalizing accurately what they have done, felt and thought. Recalling events from memory is not easy either (Gaskell et al., 2000). In other words, self-reported measures have their limitations, but they have been the most common way of conducting social research related to human behaviour.
However, the biggest challenge to self-reported data has come from a shift in the model of human behaviour.
[…] A more precise model of human behaviour and decision-making has implications for social science research methodology and in particular for the aforementioned distinction between self-reported and observational/behavioural data. The dual mode of thinking brings back the importance of unconscious thought processes, but also of contextual and environmental influences on human behaviour, something that is highly problematic in studies using self-reported measures and instruments only. Traditionally, collecting behavioural data has been very difficult and expensive for social scientists. To keep track of people’s actual behaviour could be done only for small groups of people and for a very limited amount of time. The availability of digital data has brought us a large increase in behavioural data; we now have digital traces of people’s actual behaviour that were quite simply never available before. The combined effect of a relatively new and powerful foundational model of human behaviour and decision-making offered by the dual model together with the availability of behavioural data thanks to the digital traces recorded by a multitude of services and tools is very promising for social scientists.
Courtesy by Polity