The researcher manipulates one or more variables to see how affect other variable. Research has an influence and is able to interfere.
Non-experimental methods
The researcher does not manipulate variables, but instead observes and studies them in their natural environment.
Qualitative data
A type of data that describes characteristics or qualities of something, and is not represented by numbers non-numerical.
Quantitative data
Data that can be counted or measured in numerical values.
Independent variable
Variable that can be manipulated (changed) by the experimenter.
Dependent variable
The variable which is measured by the experimenter.
Lab experiment
A research method where a researcher intentionally changes a variable to see how it affects another variable in a controlled environment.
E.g. a university lab
Field experiment
IV is manipulated by researcher, takes place in a real-world setting.
Natural experiment
A research method that involves observing a natural situation as it unfolds, without manipulating, researcher takes advantage of a pre-existing IV.
E.g. naturally hit by a car
Quasi experiment
Participant are usually observed in their natural environment before and after an intervention. Aims to establish a cause-and-effect relationship between variables.
Directional hypothesis (one-tailed)
Use this when fairly certain of outcome due to prior research.
E.g. loud noise will significantly reduce a persons ability to read.
Non-directional hypothesis (two-tailed)
Use if are unsure of the directions of results as no prior research.
E.g. loud noise will have a significant effect on a persons ability to read.
Operationalism
When a variable is defined by the researcher and a way of measuring that variable is developed for the research.
Extraneous variable
A factor which can affect the results of a research study, but is not being investigated.
E.g. age, gender, or environmental factors, like noise or lighting.
Confounding variable
Something that potentially affects the results of a study but is not accounted for in the study itself.
E.g. some people may have drink more caffeine throughout the day.
Mundane realism (everyday realism)
a measure of how closely a research study resembles real life.
Situational variable
A factor in the environment that can affect a participants performance.
Participant variable
A characteristics of a participants in a study that could affect the results of the experiment, even though its not the main focus of the study.
Investigator variable
The experimenter unconsciously conveys to participants how they should behave.
Demand characteristics
Cues that may indicate the purpose of an experiment to participants, which can lead them to change their behaviour.
Pilot study
A small-scale, preliminary (done ahead of time) study that is conducted before a larger more comprehensive study.
Null hypothesis
A statement that there is no relationship between two variables being studied, and that any differences observed are due to chance.
How do you write a hypothesis?
Phrase it using an if - then format.
E.g. if I water a plant every day, then it will grow.
Predict a relationship between an independent and dependent variable. It is a statement about the expected outcome of a study.
What are the steps to writing an operationalisedhypothesis?
...
Single-blind trials
Participants are unaware of which study group they are in but researchers know.
Double- blind trials
Neither participants nor researchers know what group they are in to prevent bias.
Placebo
An inactive substance or other intervention that looks the same as and is given the same way as an active drug or treatment being tested.
Randomisation
The process of assigning participants to treatment and control groups, assuming that each participants has an equal chance of being assigned to any group.
Standardisation
Keeping everything the same for all participants so that the investigation is fair.
Opportunity sampling (pros and cons)
a sampling technique that involves selecting participants for a study based on their availability and willingness to take part.
E.g. ask members of the population of interest if they would participate in your research.
+ Quick and easy to access a sample
-Sample may not be representative of the target population
-Findings may not be generalisable
Random sampling (pros and cons)
A type of probability sampling in which the researcher randomly selects a subset (a part of a larger group) of participants from a population.
E.g. lottery or random number method.
+Lack of bias
+Simple and quick to select sample
-The sample may not truly reflect the target market