Published on
June 12, 2020
by
Pritha Bhandari.
Revised on
June 22, 2023.
Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.
Quantitative research is the opposite of qualitative research, which involves collecting and analyzing 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
June 5, 2020
by
Pritha Bhandari.
Revised on
June 21, 2023.
Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem.
While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:
The aim of the research
The type of data that you will collect
The methods and procedures you will use to collect, store, and process the data
To collect high-quality data that is relevant to your purposes, follow these four steps.
Published on
May 20, 2020
by
Pritha Bhandari.
Revised on
March 17, 2023.
Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others. It is also called ascertainment bias in medical fields.
Sampling bias limits the generalizability of findings because it is a threat to external validity, specifically population validity. In other words, findings from biased samples can only be generalized to populations that share characteristics with the sample.
Published on
May 14, 2020
by
Pritha Bhandari.
Revised on
June 21, 2023.
A population is the entire group that you want to draw conclusions about.
A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.
In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organizations, countries, species, organisms, etc.
Population vs sample
Population
Sample
Advertisements for IT jobs in the Netherlands
The top 50 search results for advertisements for IT jobs in the Netherlands on May 1, 2020
Songs from the Eurovision Song Contest
Winning songs from the Eurovision Song Contest that were performed in English
Undergraduate students in the Netherlands
300 undergraduate students from three Dutch universities who volunteer for your psychology research study
All countries of the world
Countries with published data available on birth rates and GDP since 2000
Published on
May 8, 2020
by
Pritha Bhandari.
Revised on
December 18, 2023.
External validity is the extent to which you can generalize the findings of a study to other situations, people, settings, and measures. In other words, can you apply the findings of your study to a broader context?
The aim of scientific research is to produce generalizable knowledge about the real world. Without high external validity, you cannot apply results from the laboratory to other people or the real world. These results will suffer from research biases like undercoverage bias.
In qualitative studies, external validity is referred to as transferability.
Published on
May 1, 2020
by
Pritha Bhandari.
Revised on
June 22, 2023.
Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.
In other words, can you reasonably draw a causal link between your treatment and the response in an experiment?
