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Oppurtunity: A sample obtained by selecting people who are convenient, willing and available to take part.

Random: Involves every member of the target population having the cahnce of being selected e.g. pulling names out of a hat.

VoluteerPeople will volunteer to be a part of your study or sample. e.g. after seeing an advert they may put themselves forward to be a part.

Systematic: Systematic sampling involves having a specific system or plan for picking out participants to take part. e.g. having a list in front of you and choosing every 3rd person to take part.

Stratified: Stratified sampling uses the percentage of people found in its target population in its sample and then selects people using random sampling.

Quota: Quota sampling, like stratified sampling, uses the percentage of people found in its target population for its sample but uses oppurtunity sampling, unlike stratified sampling.

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Advantages of sampling methods.

Oppurtunity: Quick and conventient method as using people who are willing and available to take part in your study.

Random: Sample will be unbiased so may be representative of the target population meaning any results from the study will be ablee to be generalised to the rest of the population.

Volunteer: Quick and convenient as people are putting themselves forward to take part so they will be available.

Systematic: Quick and convenient as there is a simple system to be able to choose which participants will be used.

Stratified: This sampling technique is more representative than the others as it uses the percentage of the target populations in its samples whereas the others don't. Also, this method will be less biased as they use random sampling.

Quota: Same advantages as the stratified sampling (above)

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Disadvantages of sampling methods.

Oppurtunity: Unrepresentative sample obtained because prehaps only selecting people who look approachable, therefore cannot be generalised to te wider population so much.

Random: To ensure every member of the target population has an equal chance of being selected it will be a very time consuming task e.g. having to make sure every persons name is written out to go in a hat.

Volunteer: May be unrepresentative sample as may only be certain people in the right place at the right time to see the avert or if it was advertised in a paper it would be a certain type of person that would buy that.

Systematic: Unrepresentative of the population which you are trying to test as not everyone has an equal chance of being selected if there is a certain system to choose participants.

Stratified: Time consuming to work out percentages of populations to then get a sample from to then select participants that will be used.

Quota: The same as Stratified (above)

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Analysing data: Qualitative data.

Content analysis: Method used for investigating media sources using catagories and coding systems. i.e. qualitative data can be turned into quantitative data. High in ecological validity because it is data taken from a 'real event'. But may be open to bias as the researcher will see what they want to see.

Catagorisation: Process of combining the coding system into larger examples of behaviour. e.g. Coding of hitting may be under the group of aggressive behaviour.

Coding system: The process of labelling qualitative data items. Each of the codes will represent a small, meaningful event.

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Analysing data: Quantitative data.

Mean: Add numbers together and divide by number of numbers used.

+All scores taken into account and quick and easy to do

-Results distorted by outliers and could provide a score not in the results.

Median: Scores aranged in size order and the middle value is selected.

+Extreme values dont effect scores

-Uses all scores but not their actual values and is time consuming

Mode: The number that most frequently appears in the score set.

+Not distorted by outliers, quick and easy and produces number actually in the scores.

-May not be a mode in the results and mode may also not tell us anything

Range: Measure of distribution, take smallest from largest number.

+/- Easy to calculate but is distorted by outliers.

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A hypothesis is a clear, testable statement that predicts a difference or a relationship between variables.

(Written in the future tense, are operationalised and include the word significantly)

Alternative hypothesis: States there is a significant difference/relationship between 2 variables.

Null hypothesis: This states there will be no significant difference/relationship between 3 variables.

Directional: Hypothesis predicts the specific direction that the results will be in.

Non-directional: Hypothesis predicts there will be a difference but does not predict the direction that it will be different in.

Operationalised: If hypothesis is to be testable then the variables must be meausreable. so we must operationalise the IV, DV and the Co-Variables.

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