Quantitative Research

Original Editor - Angeliki Chorti Top Contributors - Angeliki Chorti

Introduction[edit | edit source]

Quantitative research is a type of research that assumes that the phenomena under study can be measured and involves methods that:

  • gather data using measurement (numerical data)
  • analyse data by using quantitative statistical analysis techniques.


Quantitative Research Methodology[edit | edit source]

Methodology refers to the overall approach taken in a piece of research. [2] In quantitative research, it encompasses the general principles of investigation that guide how such a study is designed and conducted in order to answer a quantitative research question.

The methodological steps for quantitative investigations are found below.

Step 1: The Research Question[edit | edit source]

The core of any research is the research question(s). [2] Research question(s) guide the design and methods used in a study, being key to not only identifying gaps in knowledge but also refining and adjusting existing knowledge. [2]

In quantitative research, the nature of the research question may be descriptive / normative or explanatory.

Descriptive / normative research questions[edit | edit source]

Descriptive research questions provide a descriptive account of a phenomenon within an established framework of knowledge; this approach is often used when aiming to develop a fuller account of an observation and is sometimes combined with identifying some relationships of potential interest. [2] For example, a researcher may choose to utilise survey methods to investigate the characteristics, perceptions and behavious about a particular condition in a specific population. [3]

Normative research questions are similar in their purpose to descriptive research questions, but also include an additional objective of comparing data gathered with a criterion or standard. [4]

Explanatory research questions[edit | edit source]

Explanatory research questions usually test a hypothesis i.e. a prediction that the study sets out to either retain or reject, by means of statistical inference testing. [5][6]

Step 2: Research strategy[edit | edit source]

Quantitative investigations may be primary or secondary, depending on the source of research knowledge.

Primary research[edit | edit source]

Primary investigations involve the actual research study i.e. information gathered through self-conducted research methods. The decision on primary research is influenced by the number / quality of available information. A search for available evidence is common before any research study. Primary research study is usually based on gaps in available knowledge.

Secondary research[edit | edit source]

Secondary research accesses primary data through previously conducted quantitative research studies. Systematic reviews and meta-analyses that gather and analyse clinical/experimental primary studies rank higher than single quantitative studies in the hierarchy of evidence. [7]

Step 3: Designs[edit | edit source]

Primary research[edit | edit source]

There are five main types of primary quantitative research designs: [1]

  • Descriptive
  • Survey
  • Correlational
  • Quasi-experimental
  • Experimental
Descriptive research designs[edit | edit source]

As the name implies, descriptive research aims to observe and measure a phenomenon e.g. identify characteristics, categories or describe a patient’s journey e.g. case study. [8]

Survey research designs[edit | edit source]

Survey designs are most frequently employed in healthcare epidemiology research.[9] Surveys may be used to gain insights into opinions and practices in large samples; they can be descriptive and/or be used to test associations. [9]

Cross-sectional survey research studies exposures and outcomes at a certain point in time. [10] These observational designs are commonly used for population-based studies and investigations of disease prevalence. [10]

Longitudinal survey research measures at different points in time (continuously or repeatedly). [11]This design is very useful in identifying the relationship between risk factors and disease onset, or treatment outcomes over time. [11]

Correlational research designs[edit | edit source]

Correlational research designs identify relationships between variables without implying causation. This can be done in terms of direction and/or strength of the relationship between two variables with no influence of any extraneous variable, or manipulation. [12]

Cohort studies are prospective or retrospective in nature. A sample of participants is observed over time where those exposed and those not exposed are compared for differences in one or more predefined outcomes, such as adverse event rates.[12]

Case-control studies are retrospective in nature, participants already exposed to the event are selected, then matched with unexposed participants, using historical cases to ensure they have similar characteristics. [12]

Cross-sectional studies are a type of cohort study where only one comparison is made between exposed and unexposed subjects. [12]

Quasi-experimental research designs[edit | edit source]

Aims to identify a cause-and-effect relationship between independent and dependent variables but based on no random criteria. Commonly found as non-randomised, pre-post intervention studies. [13]

Experimental research designs[edit | edit source]

In experimental research designs, the researcher can manipulate one (or more) variable(s), the independent variable, and study the effect on a dependent variable. [1] There are many types of experimental designs; one of the most important is the randomised controlled trial.

Randomised controlled trials are considered top methods in the hierarchy of evidence when testing the link between cause and effect in clinical interventions. [14][15]

Secondary research[edit | edit source]

Meta-analyses[edit | edit source]

Meta-analyses synthesise results from multiple studies in a quantitative manner to determine the average effectiveness of interventions. [16]

Step 4: Data analysis[edit | edit source]

Data analysis in quantitative research involves the use of statistics to investigate numerical data. [17] This may involve descriptive statistics for distributions and relationships between variables and inferential statistics. [17]

Step 5: Data reporting[edit | edit source]

Reporting and interpreting quantitative research findings usually follows a set of guidelines. [18] Recommendations may involve the reporting of designs, methods and procedures, data and analyses, usually with a aim of producing a publishable result. [19]

Key differences between different forms of quantitative research[edit | edit source]

Key differences between different forms of quantitative research are found below. Distinctions have been simplified for clarity and some overlap may exist between characteristics. [2][6]

Descriptive / Normative Explanatory
Ontological assumption Reality is objective and singular apart from the researcher. Reality is objective and singular apart from the researcher.
Nature of research question Fairly specific and largely definite Highly specific and definite; Declarative
Research design Structured, sequential and largely predetermined Highly structured, sequential and predetermined
Data collected Quantitative Quantitative
Relationship to theory Aims to develop or elaborate theory Hypothesis testing

References[edit | edit source]

  1. 1.0 1.1 1.2 Watson R. Quantitative research. Nurs Stand. 2015 Apr 1;29(31):44-8.
  2. 2.0 2.1 2.2 2.3 2.4 Sim J., Wright C. Research in Health Care: concepts, designs and methods. Nelson Thornes: Cheltenham, UK. 2002
  3. Mouchtouri V., Agathagelidou E., Kofonikolas K., Rousou X., Dadouli K., Pinaka O., et al. Nationwide Survey in Greece about Knowledge, Risk Perceptions, and Preventive Behaviors for COVID-19 during the General Lockdown in April 2020. Int J Environ Res Public Health. 2020 Nov 28;17(23):8854.
  4. Hedrick T., Bickman L., Rog D. Applied Research Design: A Practical Guide. Sage Publications, Newbury Park. 1993
  5. Hazra A., Gogtay N. Biostatistics Series Module 2: Overview of Hypothesis Testing. Indian J Dermatol. 2016 Mar-Apr;61(2):137-45.
  6. 6.0 6.1 Sukamolson S. Fundamentals of quantitative research. Available: https://www.researchgate.net/profile/Vihan-Moodi/post/What_are_the_characteristics_of_quantitative_research/attachment/5f3091d0ed60840001c62a27/AS%3A922776944787456%401597018576221/download/SuphatSukamolson.pdf (accessed 27/3/2023)
  7. Kapoor M. Types of studies and research design. Indian J Anaesth. 2016 Sep; 60(9): 626–630.
  8. Crowe S., Cresswell K., Robertson, A., Huby G., Avery A., Sheikh A. The case study approach. BMC Med Res Methodol 2011,11:100.
  9. 9.0 9.1 Safdar N., Abbo L., Knobloch M., Seo S.Research Methods in Healthcare Epidemiology: Survey and Qualitative Research. Infect Control Hosp Epidemiol. 2016 Nov; 37(11): 1272–1277.
  10. 10.0 10.1 Setia MS. Methodology Series Module 3: Cross-sectional Studies. Indian J Dermatol. 2016 May- Jun;61(3):261-4.
  11. 11.0 11.1 Caruana E., Roman M., Hernández-Sánchez J., Solli P. Longitudinal studies. J Thorac Dis. 2015 Nov;7(11):E537-40.
  12. 12.0 12.1 12.2 12.3 Lau F. Chapter 12: Methods for correlational studies. Handbook of eHealth evaluation: an evidence-based approach. Available: https://www.ncbi.nlm.nih.gov/books/NBK481614/ (Accessed 26/03/2023)
  13. Harris A., McGregor J., Perencevich E., Furuno J., Zhu J., Peterson D., Finkelstein J. The use and interpretation of quasi-experimental studies in medical informatics. J Am Med Inform Assoc. 2006 Jan-Feb;13(1):16-23.
  14. Evans D. Hierarchy of evidence: a framework for ranking evidence evaluating healthcare interventions. J Clin Nurs. 2003 Jan;12(1):77-84.
  15. Wallace S., Barak G., Truong G., Parker M. Hierarchy of Evidence Within the Medical Literature. Hosp Pediatr. 2022 Aug 1;12(8):745-750.
  16. Hernandez AV, Marti KM, Roman YM. Meta-Analysis. Chest. 2020 Jul;158(1S):S97-102
  17. 17.0 17.1 Sheard J. Chapter 18 - Quantitative data analysis. Research methods (second edition) 2018; 429-52.
  18. Larson-Hall J., & Plonsky L. Reporting and Interpreting Quantitative Research Findings: What Gets Reported and Recommendations for the Field. Language Learning 2015; 65(S1): 127-159.
  19. Norris J., Plonsky L., Ross S., Schoonen R. Guidelines for Reporting Quantitative Methods and Results in Primary Research. Language Learning 2015; 65(2):470-476.