Quantitative Research
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]
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. [6]
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. [7]
Survey research designs[edit | edit source]
Survey designs are most frequently employed in healthcare epidemiology research.[8] 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. [8]
Cross-sectional surveys[edit | edit source]
Cross-sectional survey research studies exposures and outcomes at a certain point in time. [9] These observational designs are commonly used for population-based studies and investigations of disease prevalence. [9]
LongiTudinal surveys[edit | edit source]
Longitudinal survey research measures at different points in time (continuously or repeatedly). [10]This design is very useful in identifying the relationship between risk factors and disease onset, or treatment outcomes over time. [10]
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. [11]
Cohort studies[edit | edit source]
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.[11]
Case control studies[edit | edit source]
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. [11]
cross sectional studies[edit | edit source]
These designs are a type of cohort study where only one comparison is made between exposed and unexposed subjects. [11]
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. [12]
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[edit | edit source]
Randomised controlled trials are considered top methods in the hierarchy of evidence when testing the link between cause and effect in clinical interventions. [13][14]
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. [15]
Step 4: Data analysis[edit | edit source]
Data analysis in quantitative research involves the use of statistics to investigate numerical data. [16] This may involve descriptive statistics for distributions and relationships between variables and inferential statistics. [16]
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]
| Descriptive / Normative | Explanatory | |
|---|---|---|
| Type of information sought | Quantitative | Quantitative |
| 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]
- ↑ 1.0 1.1 1.2 Watson R. Quantitative research. Nurs Stand. 2015 Apr 1;29(31):44-8.
- ↑ 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
- ↑ 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.
- ↑ Hedrick T., Bickman L., Rog D. Applied Research Design: A Practical Guide. Sage Publications, Newbury Park. 1993
- ↑ Hazra A., Gogtay N. Biostatistics Series Module 2: Overview of Hypothesis Testing. Indian J Dermatol. 2016 Mar-Apr;61(2):137-45.
- ↑ Kapoor M. Types of studies and research design. Indian J Anaesth. 2016 Sep; 60(9): 626–630.
- ↑ Crowe S., Cresswell K., Robertson, A., Huby G., Avery A., Sheikh A. The case study approach. BMC Med Res Methodol 2011,11:100.
- ↑ 8.0 8.1 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.
- ↑ 9.0 9.1 Setia MS. Methodology Series Module 3: Cross-sectional Studies. Indian J Dermatol. 2016 May- Jun;61(3):261-4.
- ↑ 10.0 10.1 Caruana E., Roman M., Hernández-Sánchez J., Solli P. Longitudinal studies. J Thorac Dis. 2015 Nov;7(11):E537-40.
- ↑ 11.0 11.1 11.2 11.3 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)
- ↑ 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.
- ↑ Evans D. Hierarchy of evidence: a framework for ranking evidence evaluating healthcare interventions. J Clin Nurs. 2003 Jan;12(1):77-84.
- ↑ Wallace S., Barak G., Truong G., Parker M. Hierarchy of Evidence Within the Medical Literature. Hosp Pediatr. 2022 Aug 1;12(8):745-750.
- ↑ Hernandez AV, Marti KM, Roman YM. Meta-Analysis. Chest. 2020 Jul;158(1S):S97-102
- ↑ 16.0 16.1 Sheard J. Chapter 18 - Quantitative data analysis. Research methods (second edition) 2018; 429-52.
