Experiments look for the effect that manipulated variables (independent variables, or IVs) have on measured variables (dependent variables, or DVs), i.e. causal effects.
Laboratory experiments pay particular attention to eliminating the effects of other, extraneous variables, by controlling them (i.e. removing or keeping them constant) in an artificial environment. This makes it more likely for researchers to find a causal effect, having confidence that no variables other than changes in an IV can affect a resulting DV. Laboratory experiments are the most heavily controlled form of experimental research.
Participants can also be randomly allocated to experimental conditions, to avoid experimenter bias (i.e. the experimenter cannot be accused of choosing who will be in each experimental condition, which could affect the results).
Evaluation of laboratory experiments:
- High control over extraneous variables means that they cannot confound the results, so a ‘cause and effect’ relationship between the IV and DV is often assumed.
- Results of laboratory experiments tend to be reliable, as the conditions created (and thus results produced) can be replicated.
- Variables can be measured accurately with the tools made available in a laboratory setting, which may otherwise be impossible for experiments conducted ‘in the field’ (field experiments).
- Data collected may lack ecological validity, as the artificial nature of laboratory experiments can cast doubt over whether the results reflect the nature of real life scenarios.
- There is a high risk of demand characteristics, i.e. participants may alter their behaviour based on their interpretation of the purpose of the experiment.
- There is also a risk of experimenter bias, e.g. researchers’ expectations may affect how they interact with participants (affecting participants’ behaviour), or alter their interpretation of the results.
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