Experimental Methods- RESEARCH METHODS Flashcards
RESEARCH METHODS
What must experiments must have
Independent Variable (I.V)
Dependent Variable (D.V)
Independent Variable
Variable that changes
Operationalised Independent Variable
Make measurable
Dependent Variable
Variable you measure
Extraneous Variable
Variables we need to control
Confounding variables
Factors other than the independent variable that may cause a result.
Examples of Participant Variables (3)
Any characteristic or aspect of a participant’s background
- Age
- Eyesight
- Tolerance to caffeine
Examples of Situational Variables (3)
Environmental variables that alter participants’ behaviours
- Speed of throw
- Amount of coke
- Rain
Standardised Procedure
Keeping everything the same. Control with sensible design
Counterbalancing
- Combine results
- Compare Condition A with Condition B
- Order effects should cancel out
- Any difference that remains must be due to the IV
Repeated measures design
Using the same participants in each condition. Two sets of materials matched for difficulty.
Strength of Repeated measures design (2)
- Controls for individual differences
- Need fewer participants
Weakness of Repeated measures design (3)
- Each participant has to do at least 2 tasks
- Order of doing tasks is significant- there are order effects.
- More likely to work out the aim
Independent measures design
Using different participants in each condition.
Strength of Independent measures design (3)
- Order effects not a problem
- Less demand characteristics
- Less likely to work out the aim
Weakness of Independent measures design (2)
- Twice as many participants would be needed to produce equivalent data
- Increases time and money spent on recruiting participants
Matched pairs design
Using different but similar participants in each condition
Strength of Matched pairs design (2)
- Participants only take part in a single condition so order effects and demand characteristics are less of a problem
- Less likely to work out the aim
Weakness of Matched pairs design (3)
- Matching may be time- consuming and expensive, particularly if a pre- test is required
- Less economical
- Difficult to identify appropriate variables
Lab experiment
- Experimenter manipulates the IV
- Controlled conditions- situational variables are controlled
Strength of Lab experiment
- Internal validity is increased (can see cause and effect)- scientific
Weakness of Lab experiment
- Low ecological validity- (less we can apply our results to the real world)
Field experiment
- Experimenter manipulates the IV
- Conducted in the real world
Strength of Field experiment
- Better ecological validity
Weakness of Field experiment
- Lack of control (can no longer see cause and effect)
Natural experiment
- When the researcher measures the effect of an IV on a DV
Strength of a Natural experiment
- Have high external validity (as they involve the study of real- world issues/ problems)
Weakness of a Natural experiment
- May only happen very rarely, reducing opportunities for research
- Participants may not be randomly allocated to experimental conditions (ONLY independent group design)
Quasi experiment
- Have an IV based on an existing difference between people (e.g age/ gender) that no one has manipulated this variable, it simply exists.
Strength of a Quasi experiment
- Often carried out under controlled conditions e.g replication
Weakness of a Quasi experiment
- Cannot randomly allocate participants to conditions and therefore there may be confounding variables
Difference between an aim and hypothesis
- Aims are developed from theories and develop from reading about other similar research and is what you are investigating in a study
- A hypothesis is a precise statement which clearly states the relationship between the variables being investigated, e.g directional/ non- directional
Directional hypothesis
When you state what direction the results will be (e.g one group will perform better than the other group.) Previous research should suggest the direction of the results
Non- directional hypothesis
When you think there will be a difference- but not sure which one will do better. No previous research exists
Null hypothesis
There will be no difference- the IV will have no effect
Difference between population and sample
- Population is the entire group that you want to draw conclusions about.
- Sample is the specific group that you will collect data from.
Opportunity sample
Researchers select anyone who happens to be willing and available
Volunteer sample
When participants select themselves to be part of the sample
Random sample
All members of the target population have an equal chance of being selected:
1. Complete list of all members of target population.
2. All names/ members on the list are assigned a number
Systematic sample
Is when every nth member/ participant of the target population is selected
Stratified sample
Composition of the sample reflects the proportions of people in certain subgroups within the Target population OR Wider population
Descriptive statistics
Describing data (patterns, range)
What is central tendency?
Averages
Examples of measures of central tendency
Mean
Median
Mode
Mean
Strength- Most sophisticated measure because it uses all the data
Weakness- Sensitive to outliers/ extreme data
Median
Strength- Not affected by outliers/ extreme scores
Weakness- Not as sensitive as it doesn’t use all the data
Mode
Frequencies, most simple measure
What is measures of dispersion?
How spread out the data is
Examples of dispersion
Range spread
Standard deviation- how close is the data to the mean- variability of the data
External validity (generalisability)
Whether we can generalise results outside of study, e.g other groups of people
Internal validity (demand characteristics, bias)
Design of your study
Measuring reliability
If we did the study again would we get the same results
Improving reliability
Re- write behaviour categories- make more specific
Re- train observers
