Published on
11 April 2022
by
Pritha Bhandari.
Revised on
17 October 2022.
In experiments, you test the effect of an independent variable by creating conditions where different treatments (e.g. a placebo pill vs a new medication) are applied.
In a between-subjects design, or a between-groups design, every participant experiences only one condition, and you compare group differences between participants in various conditions. It’s the opposite of a within-subjects design, where every participant experiences every condition.
A between-subjects design is also called an independent measures or independent-groups design because researchers compare unrelated measurements taken from separate groups.
In experiments, a different independent variable treatment or manipulation is used in each condition to assess whether there is a cause-and-effect relationship with a dependent variable.
In a within-subjects design, or a within-groups design, all participants take part in every condition. It’s the opposite of a between-subjects design, where each participant experiences only one condition.
A within-subjects design is also called a dependent groups or repeated measures design because researchers compare related measures from the same participants between different conditions.
All longitudinal studies use within-subjects designs to assess changes within the same individuals over time.
Published on
8 April 2022
by
Pritha Bhandari.
Revised on
16 January 2023.
Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.
Published on
4 April 2022
by
Pritha Bhandari.
Revised on
10 October 2022.
Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations.
Quantitative research is the opposite of qualitative research, which involves collecting and analysing non-numerical data (e.g. text, video, or audio).
Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.
Quantitative research question examples
What is the demographic makeup of Singapore in 2020?
How has the average temperature changed globally over the last century?
Does environmental pollution affect the prevalence of honey bees?
Does working from home increase productivity for people with long commutes?
Published on
4 April 2022
by
Pritha Bhandari.
Revised on
30 January 2023.
Qualitative research involves collecting and analysing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.
Qualitative research is the opposite of quantitative research, which involves collecting and analysing numerical data for statistical analysis.
Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, and history.
Qualitative research question examples
How does social media shape body image in teenagers?
How do children and adults interpret healthy eating in the UK?
What factors influence employee retention in a large organisation?
How is anxiety experienced around the world?
How can teachers integrate social issues into science curriculums?
Published on
18 January 2021
by
Pritha Bhandari.
Revised on
2 February 2023.
In statistics, a Type I error is a false positive conclusion, while a Type II error is a false negative conclusion.
Making a statistical decision always involves uncertainties, so the risks of making these errors are unavoidable in hypothesis testing.
The probability of making a Type I error is the significance level, or alpha (α), while the probability of making a Type II error is beta (β). These risks can be minimized through careful planning in your study design.
Example: Type I vs Type II errorYou decide to get tested for COVID-19 based on mild symptoms. There are two errors that could potentially occur:
Type I error (false positive): the test result says you have coronavirus, but you actually don’t.
Type II error (false negative): the test result says you don’t have coronavirus, but you actually do.
Published on
22 December 2020
by
Pritha Bhandari.
Revised on
2 February 2023.
Effect size tells you how meaningful the relationship between variables or the difference between groups is. It indicates the practical significance of a research outcome.
A large effect size means that a research finding has practical significance, while a small effect size indicates limited practical applications.
NoteThere are several ways to report your results. In this article, we follow APA guidelines.
