The Steps of Quantitative Research

Quantitative research is a strategy which involves the collection of numerical data, a deductive view of the relationship between theory and research, a preference for a natural science approach (and for positivism in particular), and an objectivist conception of social reality.

It is important to note that quantitative research thus means more than the quantification of aspects of social life, it also has a distinctive epistemological and ontological position which distinguishes it from more qualitative research.

An ideal-typical outline of the stages of quantitative research:

quantitative research stages

1. Theory 

The fact that quantitative research starts off with theory signifies the broadly deductive approach to the relationship between theory and research in this tradition. The sociological theory most closely associated with this approach is Functionalism, which is a development of the positivist origins of sociology.

2. Hypothesis 

It is common outlines of the main steps of quantitative research to suggest that a hypothesis is deduced from the theory and is tested.

However, a great deal of quantitative research does not entail the specification of a hypothesis, and instead theory acts loosely as a set of concerns in relation to which social researcher collects data. The specification of hypotheses to be tested is particularly likely to be found in experimental research but is often found as well in survey research, which is usually based on cross-sectional design.

3. Research design 

The next step entails the selection of a research design which has implications for a variety of issues, such as the external validity of findings and researchers’ ability to impute causality to their findings.

4. Operationalising concepts

Operationalising concepts is a process where the researcher devises measure of the concepts which she wishes to investigate. This typically involves breaking down abstract sociological concepts into more specific measures which can be easily understood by respondents. For example, ‘social class’ can be operationalied into ‘occupation’ and ‘strength of religious believe’ can be measured by using a range of questions about ‘ideas about God’ and ‘attendance at religious services’.

5. selection of a research site or sites

With laboratory experiments, the site will already be established, in field experiments, this will involve the selection of a field-site or sites, such as a school or factory, while with survey research, site-selection may be more varied. Practical and ethical factors will be a limiting factor in choice of research sites.

6. Selection of respondents

Step six involves ‘choosing a sample of participants’ to take part in the study – which can involve any number of sampling techniques, depending on the hypothesis, and practical and ethical factors. If the hypothesis requires comparison between two different groups (men and women for example), then the sample should reflect this.

Step six may well precede step five – if you just wish to research ‘the extent of teacher labelling in schools in London’, then you’re pretty much limited to finding schools in London as your research site(s).

7. Data collection

Step seven,  is what most people probably think of as ‘doing research’.  In experimental research this is likely to involve pre-testing respondents, manipulating the independent variable for the experimental group and then post-testing respondents. In cross-sectional research using surveys, this will involve interviewing the sample members by structured-interview or using a pre-coded questionnaire. For observational research this will involve watching the setting and behaviour of people and then assigning categories to each element of behaviour.

8. Processing data

This means transforming information which has been collected into ‘data’. With some information this is a straightforward process – for example, variables such as ‘age’, or ‘income’ are already numeric.

Other information might need to be ‘coded’ – or transformed into numbers so that it can be analysed. Codes act as tags that are placed on data about people which allow the information to be processed by a computer.

9. Data analysis

In step nine, analysing data, the researcher uses a number of statistical techniques to look for significant correlations between variables, to see if one variable has a significant effect on another variable.

The simplest type of technique is to organise the relationship between variables into graphs, pie charts and bar charts, which give an immediate ‘intuitive’ visual impression of whether there is a significant relationship, and such tools are also vital for presenting the results of one’s quantitative data analysis to others.

In order for quantitative research to be taken seriously, analysis needs to use a number of accepted statistical techniques, such as the Chi-squared test, to test whether there is a relationship between variables. This is precisely the bit that many sociology students will hate, but has become much more common place in the age of big data!

10. Findings and conclusions 

On the basis of the analysis of the data, the researcher must interpret the results of the analysis. It is at this stage that the findings will emerge: if there is a hypothesis, is it supported? What are the implications of the findings for the theoretical ideas that formed the background of the research?

11. Writing up Findings 

Finally, in stage 11, the research must be written up. The research will be writing for either an academic audience, or a client, but either way, a write-up must convince the audience that the research process has been robust, that data is as valid, reliable and representative as it needs to be for the research purposes, and that the findings are important in the context of already existing research.

Once the findings have been published, they become part of the stock of knowledge (or ‘theory’ in the loose sense of the word) in their domain. Thus, there is a feedback loop from step eleven back up to step one.

The presence of an element of both deductivism (step two) and inductivism is indicative of the positivist foundations of quantitative research.


Bryman (2016) Social Research Methods

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