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Psychology - Experimental designs Cheat Sheet (DRAFT) by

Research methods - Experimental designs

This is a draft cheat sheet. It is a work in progress and is not finished yet.

experi­mental design

Indepe­ndent groups design
Repeated measures design
Matched pairs design
Advantages
Eliminate order effects such as practice and fatigue affects because partic­ipants are only taking part in one condition less less likely to guess the aim of the study and show demand charac­ter­istics
Eliminate partic­ipant variables
Eliminate order effects such as practice and fatigue affects because partic­ipants are only taking part in one condition less less likely to guess the aim of the study and show demand charac­ter­istics
Disadv­antages
-Resea­rchers cannot control the effects of partic­ipant variables
Partic­ipants are more likely to experience order effects such as practice and fatigue affects this means they are more likely to get the aim of the study and show demand charac­ter­istics this causes low internal validity
Resear­chers cannot control all partic­ipant variables
Improv­ements
-Resea­rchers can randomly allocate partic­ipants to conditions equally distribute partic­ipant variables
Resear­chers may use two different tests to reduce practise effects they may also use counte­rba­lancing which can be used to avoid demand charac­ter­istics
Resear­chers can conduct a pilot study to consider key variables that are important when matching

Order effects

there are two techniques resear­chers can use to reduce the problem of order effect when using a repeated measures design
Counte­rba­lancing
Counte­rba­lancing is when resear­chers alternate the order in which partic­ipants perform in different ciondi­tions of an experi­ment. for example group one does A and then B group two does B and then A this is to evenly distribute the impact of order affects across conditions however it does not eliminate them
random­isation
Random­isation is when material for each condition in an experiment is presented in a random order for example the same words are presented but in a different order for each partic­ipate

Random allocation

Random allocation uses a non-biased method to allocate partic­ipants to experi­mental condit­ions. for example the researcher will number the partic­ipants and put the numbers in a hat. the first number that is drawn is allocated to condition A the second number that is drawn is allocated to condition B the third person that is drawn is allocated to condition A again and so on. the researcher will continue to allocate partic­ipants until equal number of partic­ipants are in each condition. for example if there are 100 partic­ipants 50 partic­ipants would be in condition A and 50 partic­ipants will be in condition B

Key terms

Target population
The target population is the wider group of people from whom the sample is drawn
Sample
This sample is a smaller group of people selected from a larger population for the purposes of the study

Sampling techniques

The five types of sampling techniques
opport­unity sampling
An opport­unity sample is a sample of those from the target population who are most easily available at the time of the study
strengths
weaknesses
It is the easiest sampling method because the partic­ipants are already there so it takes less time to recruit them
The sample is biased because it is drawn from a small group of the population most studies okay out in univer­sities the partic­ipants will mainly consist of students therefore you cannot generalise the findings to the rest of the population
random sampling
A random sample is a sample of partic­ipants are selected using a random technique such as a name out of a hat so every member of the target population has an equal chance of being chosen
strengths
weaknesses
The sample is biased because each member of the target population has an equal chance of being chosen therefore the findings can be genera­lised to the rest of the population
It is time-c­ons­uming as you need to list all the partic­ipants and then contact those randomly selected
stratified sampling
Stratified sample is made by classi­fying the target population into subgroups based on the frequency in the population and then partic­ipants are selected randomly to propor­tio­nally represent the subgroup
strengths
weaknesses
The sample is likely to be more repres­ent­ative at the sample propor­tio­nately represent the subgroups in the target population the findings therefore can be genera­lised the rest of the population
The sampling method is time-c­ons­uming as you must identify the subgroups select partic­ipants randomly and then contact them
systematic sampling
A sample obtained by selecting every nth number from the target population this could be by using the random number button on a scientific calculator or a random number generator
strengths
weaknesses
The sample is unbiased because the research it uses an objective system therefore the findings can be genera­lised to the rest of the population
The sample may be biased unless you select a number using a random number generator and then select every nth number
volunteer sampling
a sample that is made up of those from the target population that offered to take part
strengths
weaknesses
It is an easy sampling method because the partic­ipants offered to take part so it takes less time to recruit them
The sample may be biased because partic­ipants are likely to be highly motivated have extra time on their hands or need money this leads to volunteer bias therefore the findings cannot be genera­lised to the rest of the population