Experimental vs Quasi-Experimental Design: Differences and Real Examples

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Quick answerIn a true experiment, the researcher changes one factor and assigns participants or samples to groups at random, so differences in outcome can be attributed to that factor. A quasi-experiment also compares groups that receive and do not receive a treatment, but without random assignment, often because randomisation is impossible or unethical.

Below, we explain both designs in plain language, describe common quasi-experimental set-ups, and use live data from PhD researchers on ScholarsColab to show where experiments and simulations appear in real research.

True experiment vs quasi-experiment at a glance

True experiment Quasi-experiment
Random assignment Yes No
Control group Yes Usually, but groups may differ at the start
Researcher controls the treatment Yes Often, or uses a change that happened naturally
Evidence for cause and effect Strong Moderate; alternative explanations must be ruled out
Typical setting Laboratories, clinical trials Schools, hospitals, organisations, policy changes

The parts of an experiment

  • Independent variable: the factor the researcher changes (for example the dose, temperature or teaching method).
  • Dependent variable: the outcome measured.
  • Control group: the comparison that does not receive the change.
  • Random assignment: the step that makes the groups alike at the start.
  • Controlled conditions: everything else kept the same.

Common quasi-experimental designs

Design How it works Example
Non-equivalent groups Compare an existing group that receives the treatment with a similar group that does not Two classes, one taught with a new method
Pre-test / post-test with comparison Measure both groups before and after the treatment Productivity in two factories before and after training in one
Interrupted time series Many measurements before and after a change Monthly accident rates before and after a new traffic law
Natural experiment An event outside the researcher’s control creates the groups Districts that did and did not receive a new policy

Example: reading real experimental studies

To explain the two designs, we use real experimental studies published by PhD researchers on ScholarsColab. Our data: 41,792 papers, mostly published between 1998 and 2026, by 967 ScholarsColab mentors (data updated 15 October 2026).

Step 1
Pick a real experimental study. These studies by our mentors each changed something and measured what happened (the selection changes every day):

Published paperYearCitations
Design, synthesis, and in vitro antiplasmodial activity of 4-aminoquinolines containing modified amino acid conjugates
Dr. Pooja Agarwal		201612
In vitro antioxidant and cytotoxicity studies of Curcuma amada Roxb.(Mango ginger)
Prof.Dr.R.Udayakumar		201411
Studies on in vitro antioxidant and antistaphylococcal activities of some important medicinal plants
Bechan Sharma		2011155
Fighting arboviral diseases: low toxicity on mammalian cells, dengue growth inhibition (in vitro), and mosquitocidal activity of Centroceras clavulatum-synthesized …
Dr. P. Madhiyazhagan		2016108
Alteration in chloroplast structure and thylakoid membrane composition due to in vivo heat treatment of rice seedlings: correlation with the functional changes
Prof. P. Pardha Saradhi		200197

Step 2
Find what was changed and what was measured. What was changed is the independent variable (for example, a material, a temperature or a treatment). What was measured is the dependent variable (for example, strength, efficiency or recovery).

What the researcher changesindependent variable
→
What is measureddependent variable

Step 3
Ask how the groups were formed. If samples or participants were assigned to groups at random, it is a true experiment. If the researcher used groups that already existed, such as two classes or two factories, it is a quasi-experiment.

Step 4
Remember virtual experiments. 738 of the 41,792 papers in our data (1.8%) show this in their title: simulation / modelling. It is most common in Physical sciences & mathematics, where 4.4% of papers show it. In engineering and the physical sciences, a computer model often replaces a physical experiment: the researcher changes one input at a time and measures the output, with the same logic as a laboratory experiment.

Papers by methodSimulation / modelling7381.8% of all papersCase study3840.9% of all papersExperimental / laboratory study3560.9% of all papersRandomised / controlled trial770.2% of all papers
Number of papers whose title names each method or design (a paper can name more than one). Source: ScholarsColab Research Methods Index.

How to design an experiment or quasi-experiment

  1. State the hypothesis, naming the independent and dependent variables.
  2. Decide whether random assignment is possible. If yes, plan a true experiment; if not, choose a quasi-experimental design.
  3. Choose a comparison group that is as similar as possible to the treatment group.
  4. Measure before and after where you can, to account for starting differences.
  5. Keep all other conditions constant, and record any that change.
  6. Plan the analysis, including how you will adjust for differences between non-random groups.

Common mistakes reviewers point out

  • Calling a quasi-experiment a randomised experiment.
  • Ignoring starting differences between non-random groups.
  • Changing more than one factor at a time, so the cause of an effect is unclear.
  • Too few replicates or participants to detect a real effect.
  • Over-trusting simulation results without validating the model against real measurements.

Frequently asked questions

What is the difference between an experimental and a quasi-experimental design?

Both compare groups that do and do not receive a treatment. In a true experiment participants are assigned to groups at random; in a quasi-experiment they are not, so the groups may differ at the start.

Why use a quasi-experimental design?

Quasi-experiments are used when random assignment is impossible, impractical or unethical, for example when studying the effect of a policy, a school programme or a hospital procedure on existing groups.

What is random assignment?

Random assignment means using a chance process, such as a random number generator, to decide which participants receive the treatment. It makes the groups alike in both known and unknown characteristics.

Is a simulation study an experiment?

A simulation study applies experimental logic, changing inputs and measuring outputs, but in a computer model rather than the real world. Its conclusions depend on how well the model represents reality.

What is a natural experiment?

A natural experiment uses an event outside the researcher’s control, such as a new law or a natural disaster, that affects some groups and not others, creating a comparison similar to an experiment.

Use this data in your own work

Use this data in your own research. Download the aggregated figures behind these charts (counts, medians and shares by type of paper, period, method and discipline) as a CSV file. Please cite it as:ScholarsColab (2026). ScholarsColab Research Methods Index [Data set]. Vidhilekha Soft Solutions Pvt Ltd. Retrieved 15 October 2026, from https://scholarscolab.in/research-methodology/. Licensed under CC BY 4.0.Download the data (CSV) →

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About the data on this page. The ScholarsColab Research Methods Index is built from the Google Scholar publication lists of PhD researchers who mentor on ScholarsColab, and is updated daily as mentors and publications are added. Methods, designs and paper types are identified from titles, so papers whose titles do not mention them are not counted in those groups. The mentors are not a random sample of all researchers (engineering and computer science are strongly represented), so the figures describe this group of researchers and may not apply to research as a whole.