Social Research Glossary


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Citation reference: Harvey, L., 2012-20, Social Research Glossary, Quality Research International,

This is a dynamic glossary and the author would welcome any e-mail suggestions for additions or amendments. Page updated 19 December, 2019 , © Lee Harvey 2012–2020.


A fast-paced novel of conjecture and surprises



core definition

Quantitative social research refers to any research that uses quantitative indicators of social phenomena.

explanatory context

Quantitative social research is usually closely linked to the search for causal relations of generalisable concepts. Inferential statistical techniques are often used by which phenomena are measured and correlated in order to discover any association between them and thereby suggest causal relationships.

This approach requires the operationalisation of concepts. Operationalisation involves an attempt at objectifying concepts through the specification of criteria that determine the presence or absence or extent of a given concept. Operationalisation involves constructing definitions of theoretical concepts that are measurable using observable phenomena (frequently, answers to specific questions designed to illicit the extent to which a sociological concept manifests itself).

Such operational definitions must be reliable and valid. Reliability and validity are key concerns for quantitative research. Reliability is about measuring the same thing consistently, while validity is about making sure that the thing being measured represents the theoretical concept under investigation. Reliability, being consistent in measurement, although a major concern of some quantitative researchers, is not enough in itself. A questionnaire, for example, may be consistent, it may be consistently measuring the wrong thing.

The major problem with operationalisation is the problem of validity, how can one be sure that the operational measurement still measures the theoretical concept? There are no ways in which validity can be 'tested' because of the break between theory and practice (empirical data), which is integral to the quantitative research tradition.

The choice of questions is ultimately a subjective decision of the researcher based on preference and prior experience and mediated by the practical trial (and error) process of the pre-pilot and pilot survey. At each stage in the operationalisation process, maximum care (in theory) is taken in breaking down concepts into component parts. Similarly the process of recombining the elements into a single indicator is a considered process that is often 'validated' through statistical analysis, particularly in respect of the weighting of different elements in the final index.

Operationalisation looses integrity of concept because framing it in a given (measurable) structure means that it becomes constructed for the purposes of pragmatic research concerns. The concept becomes fitted to the framework, not vice versa.

Ultimately, the legitimacy of operationalisation rests on the pragmatic (and theoretically weak) notion of the interchangeability of indicators. This idea, proposed by Lazarsfeld and others, suggests that one indicator is as good as another for the purposes of multivariate analysis, provided that both have some theoretical legitimacy and both are reliable.

The identification of causal links is primarily dependent on the establishment of non-spurious correlation. This involves the adoption of a pragmatic model of causality the attribution of which is dependent on four criteria:

i. Time priority (the dependent variable occurs after the independent one)
ii. Correlation between independent and dependent variables

iii. Checking for non-spurious correlations

iv. A theoretical rational for the observed relationship between the dependent and independent variables.

The quantitative tradition in sociology is underpinned by a falsificationist approach. The bold conjecture of falsificationism is the theoretically informed hypothesis that is tested probabilistically (using statistical techniques). The theoretical conjecture, through operationalisation, is made empirically testable, although a probability approach makes positive disproof impossible.

Hence the quantitative research tradition in social research only approaches the normative prescriptions of falsificationism. Refutation is not as clear-cut as is the case (supposedly) in the natural sciences where controlled environments in experiments allow for positive assertions of disproof. (Provided one ignores epistemological problems of the theory laden nature of observation). A falsificationist model of science has underpinned the quantitative research tradition in sociology since the 1930s.

In its refined form, as developed, for example, at the Columbia School, the quantitative approach has been labelled abstracted empiricism, notably by C. Wright Mills.


Durkheim referred to statistial data as social facts and his work still informs quantitative approaches.


analytical review

Colorado State University (1993–2013) defines:

Quantitative Research: Empirical research in which the researcher explores relationships using numeric data. Survey is generally considered a form of quantitative research. Results can often be generalized, though this is not always the case.

Wilmott (2005, p. 1) wrote:

Quantitative research, by definition, implies a measurement or numerical approach. The methodology employed is based on the testing of hypotheses deduced from theory. Using statistical inference the results may be generalised to the population.


Richard Schaefer (2017):

Quantitative research: Research that collects and reports data primarily in numerical form.


The NHS Health News Glossary, (NHS, undated) refers to quantitative research:

Quantitative research uses statistical methods to count and measure outcomes from a study. The outcomes are usually objective and predetermined. A large number of participants are usually involved to ensure the results are statistically significant.


associated issues

Quantitative data

Quantitative data is any data that is used for research purposes that is in numerical form. However, this numerical data does need to be constructed meaningfully (operationalised) and represent the concept being investigated in a valid way.


Non-numerical data is referred to as qualitative data.

related areas

See also


interchangeability of indicators

multivariate analysis

Researching the Real World Section 8


Colorado State University, 1993–2013, Glossary of Key Terms available at, accessed 3 February 2013, still available 1 June 2019.

NHS, undated, Health News Glossary, available at, accessed 1 June 2019.

Schaefer, R. T., 2017, 'Glossary' in Sociology: A brief introduction, Fourth Edition, originally c. 2000, McGraw-Hill. Available at, site dated 2017, accessed 11 June 2017, 'not dound' 1 June 2019.

Wilmot. A., 2005, 'Designing sampling strategies for qualitative social research: with particular reference to the Office for National Statistics’ Qualitative Respondent Register', available at services/dcm/downloads/AW_Sampling.pdf, no longer available at this address 28 June 2013.

copyright Lee Harvey 2012–2020


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