Types of Experiment
Laboratory - The IV is manipulated by the researcher and the experiment is carried out in a lab or other contrived setting away from the participants normal environment.
Field - The IV is manipulated by the researcher but the experiment is carried out using participants in their normal surroundings.
Quasi - Th IV is naturally occuring (e.g. cloudy day or medical conditions) not manipulated by the researcher.
Types of Experiment; pros and cons
Pros - Manipulate that the IV is affecting the DV - High controls produce easily replicable research
Cons - Low ecological validity due to artificial settings - Artificial setting does not reflect real life events
Pros - Behaviour is likely to be a more truthful refelction of real life actions - Ecological validity will be higher
Cons - Lack of controls means we cannot assume the IV is affecting the DV - May be ethical issues as participants may not be aware of the experiment
Pros - Life like - IV already set up - Can study the effect variables researchers cannot manipulate - High ecological validity
Cons - Lack of control over all variables - They can be difficult to replicate due to natural occurance
Repeated measures design - This involves using the same people in each condition of the experiment
Independent measures design - This involves using different people in each condition of the experiment
Matched groups design - The involves using different people in each condition but an attempt is made to make the participants as similar as possible based on certain key characteristics (any that may influence the findings). This is done by testing the individuals on the key characteristics, pairing them based on similar scores and then placing one member of each pair into each group.
Experimental design; pros and cons
Pros - Comparing each person with themselves, you can compare individual differences - Fewer participants
Cons - Can be influenced by order effects e.g. boredem - May work out the IV so may behave differently
Pros - Isn't affected by order effects - Less oppurtunity to work out the IV - Less time consuming than matched
Cons - Does not control participant variables effectively - Large sample is needed to ensure the IV causes the DV and not the individual differences
Pros - Avoids order effect and discovering IV - Controls variables better than independent
Cons - Is time consuming - Impossible to match 100% to aviod individual differences
Participants variables - Characteristics of the individual that may influence the results
E.g. age, intelligence, skill, experience and gender
How to control
- Have repeated or matched design groups to rule out differences
- Allocate participants to conditions on a random basis so participant variables are evenly distributed between conditions
Situatuional variables - Any feature of the research situation which influence behaviour
E.g. Order effects (doing the activity twice may create boredem or skill in the activity)
How to control
- Have repeated or matched designs to avoid repetition
- The experiment should be counter-balanced, where the participants are split into 2 groups. e.g Group A does condition 1 then 2. Group B does condition 2 then 1.
E.g. Environmental factors (Time of day, temperature etc)
How to control
- Impose controls that ensure there are as few differences as possible between the 2 conditions e.g. same room etc
E.g. Demand characteristics (Cues in an environment that communicate to participants what is expected opf them which may unconsciously affect the behaviour of participants)
How to control
- Do not tell participants the hypothesis of the investigation, leave it as a single blind
This is an intelligent guess as to what they are likely to discover based on previous research findings.
Types of Hyptheses
Alternatvie - This predicts how one variable (IV) is likely to affect another variable (DV)
e.g. There will be a significant difference in the number of balls successfully thrown into a bucket when completing the task with a resting heart rate or an increased heart rate.
Null - This predicts that one variable (IV) will not have an effect on another variable (DV)
e.g. There will be no significant difference in the number of balls successfully thrown into a bucket when completing the task with a resting heart rate or an increased heart rate.
Direction of Hypotheses
This predicts that the IV will have a significant effect on the DV, but it does not predict the direction this effect will go in.
e.g. Rainy weather will have a significant effect on peoples emotion
This predicts not only the IV will have a significant effect on the DV but also the direction this effect will go in
e.g. Rainy weather has a significant effect on peoples happiness
Mean - Dividing the sum of a set of quantities by the number of quantities set. E.g. 2+3+1 = 3.
Advantage - All data is used to find the answer.
Disadvantage - Very large / small numbers can distort data.
Median - The middle value of a set of values. E.g. 1,4,5,6,7 = 5.
Advantage - Very large / small numbers do not distort the data.
Disadvantage - Takes a long time to calculate from a large set of values.
Mode - The most occuring set of data in a group. E.g. 2,4,2,2,6 = 2.
Advantage - Can be used if the data is non numerical,
Disadvantage - May be more than one mode.
Measures of dispersion
Range - The difference between the lowest and highest score.
e.g. 1,3,5,6,9,11 = 11-1 = 10
Variance - The indication of how spread about the data is.
Calculation -Calculate the mean - Subtract mean from each score(d) -Square each d score (d2) - Calculate mean of the d2
Standard Deviation - A way of measuring how spread out the data is.
Calculation - Square root the variance (d2 / d2)