Variables and hypotheses Flashcards
What are aims
General statement made by the researcher which says what they plan on investigating and the purpose of the study
Hypotheses
A hypothesis is a precise statement that is testable and clearly states the levels of the variables being investigated. They aren’t predictions they are just statements of fact that the researcher will accept or reject after the experiment.
Directional hypothesis/ one tailed hypothesis
States the direction of the relationship that will take place between the variables
General example- the more sleep a participant has the better their memory performance
Non directional hypothesis/ two tailed hypothesis
Doesn’t state the direction of the relationship between the variables
General example- the difference in the amount of hours of sleep a participant has will have an effect on their memory performance, which will be shown by the difference in the memory test scores of the participants.
Independent variable
This is the aspect of the experiment that is manipulated by the experimenter or naturally changes and affects the dependent variable which is then measured
Dependent variable
The aspect of the experiment that is caused to changed by the independent variable and is measured by the researcher
Operationalising variables
When the researcher clearly defines the variables in a form of how they are being measured, this means the variables should be defined and measurable.
General example: the participants that got more than 4 hours of sleep will score better on the memory test shown through their scored compared to those who got less than 4 hours of sleep.
‘Amount of sleep’ (the IV) could be operationalised as ‘4 hours of sleep’.
‘Memory ability’ (the DV) could be operationalised as ‘the score on a test of memory’ or ‘the number of words successfully recalled’.
When is a directional hypothesis used
When previous research suggests that the findings of a study will go in a particular direction
Extraneous variables
Refers to any variable other than the IV that effects the measurement of the DV. Could cause an error as it could show a relationship between the IV and DV that isn’t actually there
Examples are the lighting in the lab or the age of participants - these variables do not confound the results of a study but just make them harder to detect.
Confounding variables
Variables other than the IV that changes systematically between the different levels of the IV (conditions). This means that as you change the IV, you also change the confounding variable. This hides the IV’s actual effect because the influence of the confounding variable will be measured as well as the influence of the IV
general example: if you are measuring how exercise affects recalling of memory, there could be group 1 doing star jumps and group 2 standing still but the participants in group 1 might get tired and therefore exhaustion is the confounding variable as it could impact the results and it is not in both conditions of the experiment.
Null hypothesis (H0h)
States that there is not change in the measurement of the DV as a result of the manipulation of the IV
Alternative hypothesis (H1)
Alternative to the null, suggests that there is a change in the measurement of the DV as a result of the manipulation of the IV
Demand characteristics and how they can be reduced
Any clues that participants could respond to in an experiment where they could try and guess the sim or intended outcome of the experiment and therefore change their behaviour. Can cause the participant to act differently in the experiment from how they usually would. Causes participant reactivity which is when people don’t behave naturally because they know they’re being studied.
Demand characteristics can be reduced by not revealing the aim of the study to the participants, or by using an independent measures design so that participants only take part in one of the experimental conditions.
Please U effect and Screw U effect
PLEASE U - When participants act in a way that they think the researcher wants them to
SCREW U- Intentionally act in a way that could sabotage the results
Unnatural behaviour effects the validity of the results so therefore demand characteristics can be a problem in research
Investigator effects and how they can be reduced
Any unwanted influence from the researcher’s behaviour on the DV
Investigator effects be reduced by standardised instructions and double blind procedures
Randomisation
Use of chance to reduce the effects of bias from investigator effects
Things like assigning participants to tasks and selecting samples of participants should all be done randomly
Standardisation
Where the experience of the experiment is kept as identical as possible for every participant by using the same formalised procedures and instructions. Reduces the effect of extraneous and confounding variables (non standardised instructions could be an extraneous variable).
Situational variables
Factors of an environment that could effect a participant’s performance. Temperature, noise etc
Participant variables
Any characteristic or aspect of a participant and their background that could impact their performance. Age, religion, gender.
Experimental design
How participants are allocated to different experimental conditions
Independent groups
The participants only perform in one condition of the independent variable so there are different participants in each condition
Advantages of independent groups design
No order effects (effects that come from a participant completing two different tasks) for example, after participating in the first condition the participant could either perform better due to having experience or perform worse due to being tired or bored
Participants less likely to guess the aims of the experiment (reduces demand characteristics)
Disadvantages of independent groups design
Twice as many people needed to gather the same amount of data
No control over participant variables so participants might have different characteristics that could therefore impact the results (gender, age, intelligence). This could be solved by random allocation so each participant has the same chance of being in each condition of the independent variable
Repeated measures
Same participants take part in each condition of the independent variable
Advantages of repeated measures
There aren’t any participant variables between the two conditions because the same participants are used
Fewer participants needed than for independent groups design
Disadvantages of repeated measures
Order effects will be presented (effects that come from a participant performing in different tasks) could perform better or worse after the first condition. This could be solved by counterbalancing, half of the participants do conditions in one order and other half do them in the opposite order
Participants could guess the aims of the experiment
Matched pairs
Participants are matched based on qualities (participant variables) that have been found to effect the deep dent variable, one member of the pair does one condition and the other does the other condition
Advantages of matched pairs
Order effects and demand characteristics are less likely to have impact
Participant variables reduced
Disadvantages of matched pairs
Participants can never be matched perfectly, could still be some participant variables
Most time consuming and expensive type of design
Laboratory experiments
Take place in a highly controlled environment
IV is manipulated to and the effect on the DV is measures, effects of other variables are minimised as much as possible
Strengths if lab experiments
Highly controlled- extraneous variables are controlled which means it is most likely the IV that impacts the DV which increases the internal validity of the experiment
