Experiment

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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.

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Types of Experiment; pros and cons

Lab 

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

Field 

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

Quasi 

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

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Experimental design

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.

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Experimental design; pros and cons

Repeated 

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

Independent 

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

Matched 

Pros - Avoids order effect and discovering IV - Controls variables better than independent

Cons - Is time consuming - Impossible to match 100% to aviod individual differences 

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Controls

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.

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Controls

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

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Hypotheses

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.

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Direction of Hypotheses

Two-tailed 

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

One-tailed 

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

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Analysing data

MeanDividing 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.

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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)

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