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Explore the significance of quantitative methods in social sciences and their role in understanding social phenomena, making evidence-based decisions, and addressing social problems. Discover the importance of theories, hypothesis testing, statistical inference, and the limitations of measurement in social research.
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QS101 – Introduction to Quantitative Methods in Social ScienceWeek 2: The Importance of Quantitative Methods in Social SciencesStella Chatzitheochari Assistant Professor in Sociology Course Director: BA Sociology and Quantitative Methodss.chatzitheochari@warwick.ac.uk
Questions for you: What are quantitative methods? Do you know any other methods?
So: why are QM important for you? • Data are everywhere! • Evidence-based policy • You as a citizen (Lies, lies and damned statistics!) • Job market: huge demand • Large-scale data and statistical analysis are essential for the scientific study of social groups and societies (this is the focus of today’s lecture) .
Research in the Social Sciences “The natural sciences talk about their results. The social sciences talk about their methods” • “paradigm wars”, qualitative/quantitative divide • The nature of foci of social research is abstract rather than concrete. Think about democracy or poverty. Henri Poincaré, philosopher of science
Generally, social research wants to: • describe the structure and organization of societies • Identify regularities that are worth explaining • Construct and test explanations for such patterns and regularities • Address social problems, implement changes So we are interested in theories = stories on why certain things happen. Causes and effects. But remember: the term theory is often used loosely by social scientists…(well, by everybody)
Theoriesand explanations * Testable stories about relationship between different things (hypotheses) Theories are falsified, not verified & theories are never final – “the game of science is without end”
Two types of reasoning Deductive Inductive Theory Theory ↓ ↑ Hypothesis Hypothesis ↓ ↑ Observations Pattern ↓ ↑ Confirmation Observations Sowhere do quantitative methods and statistical inference fit it?
Induction in everyday life and in the social sciences • my friend’s love life all men are cheaters • Greek recession and the rise of Golden Dawn austerity provokes right-wing populism BUT • Is generalization (external validity) appropriate in the above cases? • Induction often relies on an inadequate number of cases that do not represent the population of interest (i.e. men, countries)
Sampling and statistical inference • We cannot study everyone from the population of interest. So we select a sample. • Sampling is vital for inductive logic; it allows us to make inferences from the sample to the population • Representative, large-scale samples obtained by social surveys • Thekeypoint is that they capture sufficient variability in the characteristics that we are interested in (variables) • Falsification with a degree of certainty
And implications (?) Both slides from “The Spirit Level” (Wilkinson and Pickett, 2009)
Examples of research questions in quantitative social sciences • What proportion of the UK population is poor? • Has social mobility declined over time? • Is quality of life higher in liberal welfare regimes? • Does marriage make people happier? • Does early political socialization predict political identification in adulthood? ** descriptive or CAUSAL **
Making sense of relationships… Married people are consistently found to have better health than unmarried people in social surveys. Does this mean marriage improves health? What do you think?
Making sense of relationships… Not, necessarily. Married people may have been healthier than divorced people even before they were married. And those with worse health may have been less likely to marry. But it may also be that married people are more wealthy and this is the main driver behind their better health. Hmm…oh, and… There is extensive social research literature trying to answer this question with QM and ascertain whether there is a “marriage premium” in relation to health
Correlation is not causation! We can only say that A causes B if: 1. There is a link between A and B 2. A took place before B (what happened first?) 3. The relationship is not driven by C (another variable)
Causation and quantitative methods • detect spurious correlations (third variable involved?) • establish temporal order between causes and effects • Multiple causal factors can be distinguished • Test potential mechanisms that bring about an identified effect …..but measurement limitations… Small N-cases, case studies: in depth picture but low generalizability
Methodological pluralism • Each research question should be tackled on the basis of its own particular features • Mixed Methods Research (MMR): qual and quant methodologies can be combined to provide rich insights (vs methodological purism focusing on irreconcilable differences) • Examples: study of riots & social mobility of different ethnic groups & even social survey design .
MMR in the social sciences Source: asociologist.com
On intellectual craftsmanship “Be a good craftsman: avoid any rigid set of procedures. Above all, seek to use the sociological imagination. Avoid the fetishism of method and technique…Let every man be his own methodologist; let every man be his own theorist; let theory and method again become part of the practice of a craft. Stand for the primacy of the individual scholar; stand opposed to the ascendancy of research teams of technicians. Be one mind that is on its own confronting the problems of man and society” C.W.Mills .
