Quantitative research

Introduction

In our last article, we explored the various layers of evidence and how they align within the hierarchy of evidence. Broadly, this evidence falls into two main categories: quantitative and qualitative research. While quantitative and qualitative research have distinct approaches, they can work together to provide a more complete understanding of a topic (Evans, 2023). Quantitative methods aim to describe and measure, whereas qualitative approaches focus on exploring and interpreting the effects of an intervention or phenomenon (Ellis, 2016). In the next two articles, we will examine the key differences between these research types and discuss when each is most appropriately used in clinical settings.

What is quantitative research?

Quantitative research is a systematic investigation that focuses on quantifying data and phenomena to answer a research question (Greenhalgh, 2019). It uses numerical data, statistical analysis, and structured tools (like surveys or experiments) to test hypotheses, measure variables, and identify patterns or cause and effect relationships (Ellis, 2016; Ghanad, 2023).

When using quantitative research, it is important to understand key terms such as hypothesis, independent variable, and dependent variable. A hypothesis is an idea or prediction that has not yet been tested using a scientific method (Ghanad, 2023). The independent variable is what the researcher changes on purpose in an experiment, this is often referred to as the exposure (Ellis, 2016). The dependent variable is what is measured as the result—this is the outcome (Ellis, 2016).

For example, if the hypothesis is: Do anti-embolic stockings prevent or reduce deep vein thrombosis (DVT) after surgery? the study would investigate the effect of wearing these stockings (independent variable/exposure) to see if it reduces the occurrence of postoperative DVT (dependent variable/outcome).

Some other terminologies that you may want to familiarise yourself with are listed in Table 1 (This list is not exhaustive):

Table 1. Common terminologies used in research studies (adapted from Ellis, 2016, Polit and Beck, 2021)

Term	Explanation
Attrition	Loss of participants during the course of a study
Control group	Usually in a randomised controlled trial, the control group is the group that does not have the intervention that is under investigation applied
Confounding variable	A variable which is independent of the exposure or outcome being measured
External validity	How well the results of a study can be applied to other people, settings, or situations outside the study
Hawthorne effect	People change their behaviour because they know they are being observed in a study
Heterogeneity	Diversity of participants
Interrater reliability	How much agreement there is between different people (raters) when they observe or rate the same thing
Internal validity	How confidently you can say that the results of a study were caused by the intervention, not by other factors
p-value (probability value)	Tells you how likely it is that the results you found happened by random chance, A small p value (less than 0.5) indicates that it is unlikely the results happened by chance while larger P value indicates the results could have happened anyway so there is not enough evidence to say there is a real effect

 

Key characteristics of quantitative research

• Objective measurement: Aims for precision and replicability.
• Structured tools: Uses surveys, questionnaires, tests, or instruments with fixed response options.
• Statistical analysis: Involves statistical techniques to analyse and interpret the data.
• Large sample sizes: Often uses large, representative samples to allow findings to be generalised.
• Hypothesis testing: Begins with a clear hypothesis and uses data to confirm or refute it.

(Polit and Beck, 2021; Ghanad, 2023)

Common uses of quantitative research

• Measuring behaviours, attitudes, or opinions numerically.
• Comparing groups or tracking changes over time.
• Establishing correlations or cause-effect relationships.

(Ellis, 2016; Polit and Beck, 2021)

Quantitative research usually falls into two main categories:

1.Interventional or experimental studies are used to determine cause and effect, so when researchers actively do something such as giving a treatment or changing a condition to see what effect it has (Ellis, 2016). The most common type of experimental research is the randomised control trial (RCT) which is considered the gold standard when considering the evidence hierarchy (Al Noman et al, 2024).

2. Observational studies are studies where researchers collect and analyse data without manipulating or intervening in patient care. These studies aim to observe outcomes as they naturally occur, using numbers and statistics to find patterns or relationships (Ellis, 2016). Observational studies use quantitative data (like measurements, rates, or statistical associations) but do not involve experimental treatment or random assignment (Polit and Beck, 2021).

There are two common types of observational studies:

1. Descriptive studies – These aim to describe characteristics or behaviours of a population
2. Correlational studies – These look at the relationship between two or more variables but still do not involve any changes or interventions (Ellis, 2016; Polit and Beck, 2021).

Table 2 highlights some examples of common types of quantitative research.

Table 2 - Examples of quantitative studies (adapted from Ellis, 2016; Greenhalgh, 2019; Al Noman et al, 2024)

Interventional/experimental studies
Type of study	How and why it is conducted	Pros	Cons
Randomised controlled trial	Uses randomisation to assign participants into groups to test the effect of an intervention. One group receives the treatment, while another (the control group) does not. Often used to test efficacy and safety of new drugs, treatments or procedures	Reduces selection bias Strong evidence for cause and effect Randomisation increases reliability Controlled conditions reduce confounding variables	Can be expensive and time-consuming May raise ethical concerns Not always generalisable to real-world cases Complex to design and implement
Quasi-experimental	Experimental design without randomisation. Often used in real-world settings to study outcomes while trying to compare groups fairly	More practical to conduct Useful when randomisation is not possible of ethical	More prone to bias Harder to prove the intervention alone caused the outcome Other confounding variables may influence the results
Observational studies
Cohort study	Follow a group of people over time to see how exposure to a risk factor affects outcomes over time	Easy to see what happens after exposure Useful when studying something not common Multiple outcomes from one exposure can be studied	Time consuming and expensive Participants may be lost to follow up which can affect results Not good for rare outcomes where very large samples are needed to get useful results
Cross-sectional studies	Look at data from a population at one specific point in time. Useful for measuring how common a disease or risk factor is and identifying patterns or associations	Fast and cost-effective Good for measuring prevalence Simple to design and implement	Cannot show cause and effect Only shows one moment in time May be subject to recall or self-reporting bias
Case-controlled study	Compares people with a condition to those without to find a possible cause/risk factor	Good for studying rare diseases or outcomes Quick and inexpensive Require fewer people than other studies Good for studying multiple exposure	Cannot confirm cause and effect Can be subject recall bias Prone to selection bias

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References

Al Noman A, Sarkar O, Mohsin Mita T, Siddika K, Afrose F (2024) Simplifying the concept of level of evidence in lay language for all aspects of learners: In brief review. Intelligent Pharmacy. 2:270-273. doi: 10.1016/j.ipha.2023.11.002

Ellis P (2016) Understanding Research for Nursing Students. 3^rd edn. Sage Publications Ltd, London

Evans D (2023) Isn’t it time to end the quantitative versus qualitative research war? https://www.nursingtimes.net/research-and-innovation/isnt-it-time-to-end-the-quantitative-versus-qualitative-research-war-14-06-2023/ (accessed 30 May 2025)

Ghanad A (2023) An overview of quantitative research methods. International Journal of Multidisciplinary Research and Analysis. 6(8): 3794-3803. doi: 10.47191/ijmra/v6-i8-52

Greenhalgh T (2019) How to read a paper. The basics of evidence-based medicine and health care. 6^th edn. Wiley Blackwell, Oxford

Polit D, Beck C (2021) Essentials of Nursing Research: Appraising evidence for Nursing Practice. 10^th edn. Wolters Kluwer Health, Philadelphia