Published on
4 May 2022
by
Pritha Bhandari.
Revised on
17 October 2022.
In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.
Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.
The independent variable is the cause. Its value is independent of other variables in your study.
The dependent variable is the effect. Its value depends on changes in the independent variable.
Example: Independent and dependent variablesYou design a study to test whether changes in room temperature have an effect on maths test scores.
Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.
Your dependent variable is maths test scores. You measure the maths skills of all participants using a standardised test and check whether they differ based on room temperature.
Published on
4 May 2022
by
Pritha Bhandari.
Revised on
5 December 2022.
Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning, where you start with specific observations and form general conclusions.
Deductive reasoning is also called deductive logic or top-down reasoning.
Note: Deductive reasoning is often confused with inductive reasoning. However, in inductive reasoning, you draw conclusions by going from the specific to the general.
Published on
4 May 2022
by
Pritha Bhandari.
Revised on
5 December 2022.
Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you go from general information to specific conclusions.
Inductive reasoning is also called inductive logic or bottom-up reasoning.
Note: Inductive reasoning is often confused with deductive reasoning. However, in deductive reasoning, you make inferences by going from general premises to specific conclusions.
Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing on the surface.
Types of measurement validityFace validity is one of four types of measurement validity. The other three are:
Construct validity: Does the test measure the concept that it’s intended to measure?
Content validity: Is the test fully representative of what it aims to measure?
Criterion validity: Do the results accurately measure the concrete outcome they are designed to measure?
Published on
3 May 2022
by
Pritha Bhandari.
Revised on
1 September 2022.
Construct validity is about how well a test measures the concept it was designed to evaluate. It’s crucial to establishing the overall validity of a method.
Assessing construct validity is especially important when you’re researching something that can’t be measured or observed directly, such as intelligence, self-confidence, or happiness. You need multiple observable or measurable indicators to measure those constructs.
Types of measurement validityConstruct validity is one of four types of measurement validity. The other three are:
Content validity: Is the test fully representative of what it aims to measure?
Face validity: Does the content of the test appear to be suitable to its aims?
Criterion validity: Do the results accurately measure the concrete outcome they are designed to measure?
Published on
3 May 2022
by
Pritha Bhandari.
Revised on
18 December 2023.
External validity is the extent to which you can generalise 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 generalisable knowledge about the real world. Without high external validity, you cannot apply results from the laboratory to other people or the real world.
In qualitative studies, external validity is referred to as transferability.
Published on
3 May 2022
by
Pritha Bhandari.
Revised on
10 October 2022.
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?
Published on
3 May 2022
by
Pritha Bhandari.
Revised on
5 December 2022.
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, organisations, countries, species, or organisms.
Population vs sample
Population
Sample
Advertisements for IT jobs in the UK
The top 50 search results for advertisements for IT jobs in the UK on 1 May 2020
Songs from the Eurovision Song Contest
Winning songs from the Eurovision Song Contest that were performed in English
Undergraduate students in the UK
300 undergraduate students from three UK 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
3 May 2022
by
Pritha Bhandari.
Revised on
20 January 2023.
In multistage sampling, or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups (units) at each stage. It’s often used to collect data from a large, geographically diverse group of people in national surveys.
Published on
19 April 2022
by
Pritha Bhandari.
Revised on
10 October 2022.
In experiments, researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment, all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.
