Research Methods

Aim

General statement to describe what the study intends to investigate

Variables

Any object, characteristic or event that varies in some way within the experiment

Independent Variable

The variable chosen by the investigator to be manipulated

Dependent Variable

The variable that is chosen by the investigator to be measured

Operationalisation

The act of putting the independent and dependent variables into practise by making them measureable

Extraneous Variables

Any variable that may randomly affect the dependent variable if left uncontrolled

Demand Characteristics

When participants percieve the demands of the study and consequently alter their natural behaviour accordingly

Experimenter Effects

When some characteristic of the experimenter distracts participants or causes them to behave unnaturally

Hypothesis

Precise testable statement that predicts the findings of an experiment

Null Hypothesis

Predicts there will be no difference or correlation between the conditions

Null Hypothesis (Difference)

There will be no difference between (condition a) and (condition b)

Null Hypothesis (Correlation)

There will be no correlation between (condition a) and (condition b)

Directional Hypothesis

Predics the direction of the results (how the DV is affected)

Directional Hypothesis (Difference)

Participants in (condition a) will be (iv) more/less than in (condition b)

Directional Hypothesis (Correlation)

There will be a positive/negative correlation between (condition a) and (condition b)

Nondirectional Hypothesis

Does not predict how, but suggests the DV will be affected

Nondirectional Hypothesis (Difference)

There will be a difference between participants in (a) and (b)

Nondirectional Hypothesis (Correlation)

There will be a correlation between (a) and (b)

Laboratory Experiment

• Controlled enviornment

• Extraneous variables can be contolled

• IV is directly manipulated

• Participants can be randomly allocated

Laboratory Experiment Strengths

• Easily controlled variables

• Easy to replicate

• Easy to establish cause and effect

Laboratory Experiment Weaknesses

• Low mundane realism

• High demand characteristics

Field Experiments

• Carried out in a natural enviornment

• Extraneous variables can not be controlled

• IV is directly manipulated

• Participants can be randomly allocated

Field Experiment Strengths

• High mundane realism

• Low demand characteristics

Field Experiment Weaknesses

• Can not control extraneous variables

• Lack of informed consent

Natural Experiments

• Either natural or controlled enviornment

• Extraneous variables may be controlled

• Naturally occouring event

• Participants can not be randomly allocated

Natural Experiment Strengths

• Ethical research

• High external validity

Natural Experiment Weaknesses

• Difficulty with cause and effect

• Lack of research opportunities

Quasi experiment

• Can be natural or controlled enviornment

• Extraneous variables may be controlled

• Naturally occouring trait

• Participants can not be randomly allocated

Quasi Experiment Strengths

Can be same as any other type of experiment

Quasi Experiment Weaknesses

Can be like any other experiment

Repeated Measures

Participants experience all conditions of an experiment

Repeated Measures Strengths

• Individual differences less likely

• Fewer participants required

Repeated Measures Weaknesses

• Order effects

• Demand characteristics

Independent groups

Completely different participants are used in each condition

Independent Groups Strengths

• No order effects

• Fewer demand characteristics

Independent Groups Weaknesses

• Individual differences

• More participants required

Matched Pairs

Different but similar groups will be used in each condition

Matched Pairs Strengths

• No order effects

• Fewer demand characteristics

• Individual difference less likely

Matched Pairs Weaknesses

• Time consuming to match

• No identical participants

Random Allocation of Participants

• Minimising experimenter bias

• Minimises extraneous variables as everyone has equal chance of either condition

Standardisation

• Minimises extraneous variables

• Standardising enviornments and instructions

Control groups

• Baseline for comparison to increase accuracy and validity

Single Blind Technique

• Minimises demand characteristics

• Participants are not aware of which condition they are placed in

Double Blind Technique

• Minimises experimenter bias

• Neither participant nor experimenter know the aims of the experiment

Counterbalancing (ABBA)

• Balances order effects

• Data is analysed as a whole

Randomisation (AB or BA)

• Coin flip to decide which condition comes first

• Data is analysed as a whole

Pilot Studies

• Small scale trial

• See any extraneous variables, save time and money, get participant feedback

Random Sampling

• Randomly selecting participants in an unbiased way

• Each member of the target population has equal chance of selection

Stratified Sampling

• Identifying relevant characteristics and selecting a reflecting ratio

• Participants randomly drawn from lists until required ratio is met

Systematic Sampling

• Selecting every nth participant from a list

Opportunity sampling

• Participants take part based on availability

• Researcher asks if they want to take part

Volunteer Sampling

• Participants self-select or volunteer to take part

• Researcher advertises and selects those who respond

Random Sampling Strengths

• More likely to be representative

• Low experimenter bias

Random Sampling Weaknesses

• can be unrepresentative

• difficult and impractical

Stratified Sampling Strengths

• More likely to be representative

• Low experimenter bias

Stratified Sampling Weaknesses

• Can be unrepresentative

• Impractical and time consuming

Systematic Sampling Strengths

• More likely to be representative

• No experimenter bias

Systematic Sampling Weaknesses

• Can be unrepresentative

• Impractical

Opportunity Sampling Strengths

• Practical

• Easy to gain informed consent

Opportunity Sampling Weaknesses

• Likely to be unrepresentative

• High experimenter bias

Volunteer Sampling Strengths

• Practical

• Willing participants

Volunteer Sampling Weaknesses

• Low population validity

• High volunteer bias

Mean

Arithmetic average

Mean Strengths

• Most sensitive/representative measure

Mean Weaknesses

• Can give peculiar outcomes

• Easily distorted by extreme scores

Median

Central score from a rank ordered list

Median Strengths

• Less distorted by extremes

Median Weaknesses

• Less sensitive

Mode

Most common score

Mode Strengths

• Not distorted by extreme scores

Mode Weaknesses

• Less sensitive

• May not be appropriate for small data sets

Range

Shows the spread of scores

Range Strengths

• Very easy to calculate

Range Weaknesses

• Less sensitive

• Easily distorted by extreme scores

Standard Deviation

Calculated from scores distance from mean

Standard Deviation Strengths

• Most precise measure of dispersion

Standard Deviation Weaknesses

• Hard to calculate

Distribution

Type of histogram that shows the frequency of a characteristic

Normal Distribution

• Majority of scores clustered around the mean

• Data is roughly symmetrical

• Mean, median and mode are same/similar

• Data forms a normal distribution curve

Normal Distribution

Positively Skewed Distribution

• Cluster of scores on the lower end of the data set

• Curve has a tail on the right

• Mean is higher than the median and mode

• Most scores are below the mean

Positively Skewed Distributions

Negatively Skewed Distributions

• Cluster of scores on the higher end of the data set

• Curve has a tail on the right

• Mean is lower than the median and mode

• Most scores are above the mean

Negatively Skewed Distributions