Research Methods Flashcards
Independent variable
Variable that’s changed by researcher or naturally
Dependent variable
Variable measured by researcher - effect caused by change in Independent Variable
Laboratory experiment
> Highly controlled environments
>Eg: Milgram’s experiment on obedience.
Laboratory experiment: CONS
> Lack generalisability - lab environment = artificial + not like everyday life - behaviour can’t be generalised (low external validity)
Participants aware they’re being tested - demand characteristics
Tasks might not represent real-life experience
Laboratory experiment: PROS
> High control over extraneous variables (ensure change in IV caused effect on DV)
Demonstrates cause + effect (high internal validity).
Replication more possible - high level of control
Important to check results are valid
Field experiment
> Natural/everyday setting
Researcher manipulates IV + records effect on DV
Eg: Holfing’s hospital study on obedience.
Field experiment: CONS
> Less control over extraneous variables - cause + effect = harder to establish + replication = often not possible
Ethical issues - don’t know they are being studied = no informed consent
Field experiment: PROS
> Higher mundane realism than lab - more natural
>Produce more valid + authentic behaviour (high external validity)
Natural experiment
> Pre-existing independent variable
>IV not brought about by researcher - would happen even if researcher wasn’t there
Natural experiment: CONS
> Naturally occurring event might happen rarely - reduced opportunities for research
Less scope for generalising findings
Participants not randomly allocated - researcher = less sure that IV affects DV.
Natural experiment: PROS
> Allows research to take place that might not be ethical otherwise.
High external validity - study of real-life issues + problems as they happen
Quasi experiment
> Almost an experiment but not quite
IV based on existing difference between people (e.g. age or gender)
No-one manipulates IV - just exists
Quasi experiments: CONS
> Cannot randomly allocate participants to conditions - may be confounding variables.
Quasi experiments: PROS
> Controlled conditions - same strengths as lab study
Experimental method: Aims
> Broader or less precise than hypothesis
Experimental method: Hypothesis
> Testable + predictive statement generated from theory
>Either predicted difference between IV + DV (experimental hypothesis) or predicted relationship between variables
Experimental method: Operationalising hypotheses
> Hypothesis should be operationalised
>Variables + how they will be measured must be clear
The alternate (experimental) hypothesis
> States expected effect of IV on outcome = statistically significant.
The null hypothesis
> States that there is no effect in a study.
Directional (one tailed) hypothesis
> Direction of predicted difference
E.g. teenagers will sleep for more hours/week than adults ages 20-40
Predicts nature of effect of IV on DV
Non-directional (two tailed) hypothesis
> Predicts difference between 2 conditions where direction difference will be
E.g. significant difference between teenagers + adults aged 20-40 in no. of hours they sleep/week
Predicts IV will have effect on DV but direction isn’t specified.
Design of experiments: Independent
> Different participants used in each condition
Each condition includes different group of participants
Random allocation - ensures each participant has equal chance of being assigned
Design of experiments: Independent PROS
> Avoids order effects (practice or fatigue) as people participate in one condition only.