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“Quantitative methods involve the processes of collecting, analysing, interpreting, and writing the results of a study . Specific methods exist in both survey and experimental research that relate to identifying a sample and population, specifying the strategy of inquiry, collecting and analysing data, presenting the results, making an interpretation, and writing the research in a manner consistent with a survey or experimental study” (Creswell 2018, 2009).

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Scroll down to find information on:

  • How does big data fit within quantitative approach? Quantitative approach vs. big data

  • When to use quantitative research and core characteristics

  • What are the advantages of using a quantitative approach for urban design?

  • What limitations and/or challenges should I be aware of?

  • Research methods and techniques

  • What are necessary skills for me to acquire?

  • Key literature


How does big data fit within quantitative approach? 

Big data contains a large volume of data that are often collected as part of operating systems through a variety of sensors (traffic counts, CCTV, credit card machines, ticket machines etc); they are typically not collected for research as part of scientifically designed inquiries. Big Data often allow the researcher to process data from a variety of sources in near-real time to study people’s behaviour or make predictions. The key difference is that, as far as understanding people is concerned, survey data can record individuals’ subjective, discursive accounts (beliefs, values, preferences); in Big Data, such information can only be accessed indirectly through interpreting the activities that have been recorded through sensing technologies. For example, emotions may be queried in a survey; in Big Data they may be predicted e.g. by employing machine learning techniques on facial expression captured on CCTV.  Surveys are costly to run; they collect data on smaller samples of the population on often narrowly defined topics. By contrast, Big Data captures data on larger samples, sometimes the entire population,  in a cost-effective manner and regularly in real time.


When to use quantitative research and core characteristics?

  • The research tests theories based on the “scientific method”, i.e. hypothesis testing based on the evidence generated from the analysis of data.

  • It has its roots in a positivist research paradigms and deductive reasoning.

  • It focuses on describing and explaining social phenomena; and thus seeks to contribute generalisable knowledge.

  • The goals of quantitative research is to remain as objective as possible by means of replicable methods.

  • The purpose of quantitative approach is typically specific and narrowly centered on hypotheses, which are investigated in a formal step-by-step procedure.

  • It seeks to control for alternative factors influencing the association of interest

  • It follows carefully designed surveys (observational studies) or experiments


What are the advantages of using a quantitative approach for urban design?

  • Allows analysis of larger samples, sometimes even entire populations, in a cost-effective manner (breadth)

  • Is based on replicable measurement with defined standards of validity and reliability, which are difficult to meet in qualitative research

  • Observational and experimental studies rely on replicable statistical procedures to confirm or reject a hypothesis, making it a highly transparent form of research.


What limitations and/or challenges should I be aware of?

  • Lack of depth due to generally numeric information gathered on scales of instruments for a larger sample;

  • Limitations of predetermined and bordered/ closed-ended questions, which affect data quality.

  • Observational studies in particular do contain the participant’s voice and is mostly researcher-driven

  • Impersonal accounts due to dry interpretation of statistical results or patterns; thus it identifies patterns rather than delivering thorough understanding of social phenomena.

  •  In some cases, financial constraints due to the resources needed for quantitative data collection

  • Some methods/techniques can be time consuming and require expertise and technology that is not swiftly or easily acquired.

  • No full confirmation of hypotheses possible; causality rests on correlations.

  • In observational studies, the focus is often on population averages, correlations and general predictions; limited potential to truly understand social phenomena.

  • Experimental studies, where the outcome is purposively influenced by the researcher, require careful design, are costly to run and often subject to bias.



  • Experimental (e.g. clinical trials)

  • Non-Experimental, observational (e.g. surveys)

  • Survey (and polls)

  • Sampling (of Population and/or other)

  • (Systematic) observation, which can include photography and film

  • Interviewing (structured questionnaires)

  • (Systematic) Mapping

  • Focus groups protocols


What are necessary skills for me to acquire?


key literature

  • Clough, P., Nutbrown, C. (2002) A Student’s Guide to Methodology: Justifying Enquiry, London: Thousands Oaks, New Delhi: Sage Publications

  • Creswell, J.W. (2018) Research Design: Qualitative, Quantitative and Mixed Methods Approaches. Sage: London.

  • Campbell D.T, Stanley, J. C. (1963) Experimental and Quasi-experimental Designs for Research. Chicago: Rand McNally

  • SAGE Research Methods Video has over 480 videos, including hours of tutorials, interviews, video case studies and mini-documentaries covering the entire research process.

  • Shmueli, G. (2010) To Explain or to Predict?, in: Statistical Science 25(3), 289–310.