research methods

Directional hypothesis

Predictions the direction of difference between conditions

Used when there is previous research that suggests the findings will go in a particular direction

Non-directional hypothesis

Does NOT predict the direction of difference between conditions

Used when there is no previous research or past research that is contradictory

Null hypothesis

A statement predicting that there will be no difference in the results between the conditions

Used alongside alternative hypothesis in the case results dont go as planned

Independent variable

Factor that is directly manipulated/changed by a researcher in order to test its effect on another variable

Dependent variable

Variable that is measured by the researcher - any effect on the DV should be caused by the IV

Extraneous (nuisance) variables

All other factors that may vary in and experimental setting or between participants and affect the DV e.g tiredness, noise, temperature, mood

Confounding variables

Variable that varies systematically with the IV and thus confounds the results e.g order effect, demand characteristics, time of day

Usually and extraneous variable that hasn’t been controlled turns into a confounding variable

experiment

Scientific procedure undertaken to make a discovery to test hypothesis or demonstrate fact

Quasi experiment

Type of research design that aims to establish cause and effect relations

MRI

Magnetic resonance imaging

Non invasive brain scanning technique

Pet scan

Positron emission tomography

Brain imaging technique to see brain functions using metabolic activity

CAT scan

Computerised axel tomography

Medical imaging technique that uses x-rays

Ethics

Moral principles and standards that guide the conduct of researchers

Target population

Group of individuals that a researcher wants to study and to whom they intend on generalising

Sample

Smaller, manageable group of individuals selected from a larger target population

Random

Chance based processes used to ensure unbiased research design

Variable

Characteristic, factor or attribute that can change or vary measurement for research

Hypothesis

Statement that can be tested

Experimental design

Framework that guides experiment conditions

Counterbalancing

Experimental technique used in repeated measure designed to minimise variables

Reliability

Consistency and stability of a measurement or study’s results over time

Validity

Extent to which a test or research claims to measure while accurately representing the truth

Observation

Research method that involves watching and recording of behaviours, actions and responses

Bias

Tendency, inclination or prejudice toward or against someone or something

Inter-rater reliability

Measures the degree of agreement between two or more independent observers when studying the same thing

Sampling technique

Specific method a researcher uses to make a small group form target population

Open question

Interrogative statement that prompts a detailed qualitative response rather than yes or no answer

Closed question

Offers a limited set of responses such as ‘yes-no’, multiple choice, scale

Social desirability

Tendency to respond to questions or behave in a way that is seen favourable to others instead of true opinions

Ecological validity

How applicable research findings are to real world situations

Generalisation

Tendency to respond in the same way to different yet similar stimuli

Correlation

Statistical measure of the relationship between two variables

Demand characteristics

Clues within a research experiment that suggest its true purpose to participants therefore changing there opinions and responses

Representative

Subgroup of a larger population whose characteristics accurately mirror large population

Investigator effects

When a researches characteristics or behaviour influence the outcome of the study

Confederates

Individual who pretends to be a real participant in study but is actually part of experiment design they work to display specific behaviours from the participants

Realism

Emphasises the faithful and consistent depiction of internal human experiences, thoughts, feelings and emotions

Pilot study

Small-scale preliminary trial run of planned research project or method to test feasibility and identify potential problems

Independent measures design

Different participants are used in each condition of the experiment

Repeated measures design

The same participants take part in both conditions of the experiment

Matched pairs design

Pairs of participants are matched in terms of key variables such as age

One member of each pair is then placed in the two different conditions

Strengths of repeated measures design

• can control effect of participant variables

• Less participants needed for greater yield of data

Weaknesses of repeated measures design

• Order of conditions may effect performance - due to practice effect - boredom effect

• by second time they may guess the purpose of the experiment which may effect the behaviour

Strengths of independent measures design

• You can see if there is a difference of order of conditions

• They only do it once so no time to form opinions/change behaviour

Independent measures design weaknesses

• The researcher cannot control the effect of the participant variables - cofounding variables

• Independent group designs need more participants to end up with the same amount of data

Matched pairs design strengths

Counterbalances participant variables

Matched pairs design weaknesses

• Time consuming and difficult to match pairs - large group needed

• Not possible to control for all variables

Strengths of lab setting

• can establish cause and effect relationships as extraneous variables can be controlled

• Have standardised procedures and are able to be replicated by other researchers

• The means it has a high reliability

Lab setting

Controlled artificial setting - to ensure only the IV is being manipulated and that nothing else could be altering the DV

Weaknesses of lab setting experiments

• often have low ecological validity as they often take place under artificial conditions

• Participants will nearly always be aware that they are in a laboratory experiment

• Therefore, there behaviour might change (demand characteristics)

• Could also be researcher bias

Field setting

Natural environment often the participants own

Still manipulating IV and measure DV

Strengths of field setting

• higher mundane realism as the experimental situation is less artificial than a lab study

• May lead to higher ecological validity

• Reduce demand characteristics as the aims of the study may be less apparent - leading to more valid behaviour

• The experimenter can control the IV to measure the DV

• Therefore, cause and effect relationships can be discovered

Field setting weaknesses

• Harder to control extraneous and confounding variables because the experimenter does not have complete control over the environment

• This may reduce the internal validity of the experiment

• May be harder to replicate than lab students leading to issues either reliability

• May raise ethical issues

Strengths of Quasi/natural experiment

Allow things to be studied ethically and naturally such as real problems, tsunamis etc

Weaknesses of quasi/natural experiment

• because the IV has not been directly manipulated you cannot draw define cause and effect

• Cant control participant/extraneous variables

• Limited by conditions

• Replication can difficult or impossible

• Low generality

Random sampling

Every member of the target population has an equal chance of being selected eg. Picking names out a hat

Opportunity sampling

Selecting those who are around and available at the time e.g asking friends or class mates

Self-selecting sampling

Produced by asking or advertising for volunteers - the participants select themselves

Systematic sampling

Using a pre-determined system to select participants i.e. selecting every nth person e.g. every 10th or 20th person//

Stratified sampling

Participants are selected according to their frequency in the population sub groups are identified eg yr 7,8,9 and participants are obtained randomly from each strata in proportion to their occurrence in the target population

Quota sampling

This method is the same as stratified except that selection from the strata is done by another method such as opportunity sampling

Snowball sampling

This is where current participants recruit further participants form people they know. The sample ‘grows’ like a snowball

Strengths of random sampling

No bias from researcher

Weaknesses of random sampling

Time consuming

Could get bias sample withut realsiing

Strengths of opportunity sampling

Easiest, least time consuming

Weaknesses of opportunity sampling

Bias because sample is drawn from small target population

Ethical weakness

Strengths of self-selecting sampling

Gives access to a variety of participants - sample is more representative and less bias

Participants interested

Weaknesses of self-selecting sampling

Bias - volunteer bias may not have enough participants

Systematic sampling strengths

Unbaised participants selected using objective system

Systematic sampling weaknesses

Not truly unbaised/random unless you select a number using random methods and start with this person

Also may be biased withot realising

Strengths of stratified sampling

All subgroups represented

Likely to be more representative than other methods due to proportionality

Weaknesses of stratified sampling

Participants from subgroup may not be representative difficult to administer

Time consuming to identify subgroups

Strengths of quota sampling

We can guarantee that all subgroups in target population

Weakness of quota sampling

More difficult to administer

Participants opportunistically selected from each sub group

Strengths of snowball sampling

Enables researcher to locate groups of people who are different to access like drug addicts

Snowball sampling weaknesses

Not likely to be good cross-section because friends of friends

What does peter parker cried when charles darwin died

Protection

Privacy

Confidentiality

Withdraw

Consent

Debriefing

Deception

Protection of participants - in experiments

Risk should be no more than pps expect in everyday life

Physical and psychological harm

Pp’s should leave study unchanged from how they entered

Privacy - in experiments

Pp’s right to privacy must be respected

Invasions of privacy may effect well being and raised confidentiality issues

Respect social and cultural issues

Confidentiality in experiments

All data should be confidential

Pp’s should,ne anonymous unless prior informed consent

Right to withdraw - in experiments

Can withdraw anytime, during and after

Informed consent - in experiments

Pps must be told about anything that might reasonably affect their willingness to participate

Debriefing - in experiments

After the study the researcher must explain the nature of the study and should insure pp is back to normal

Ensure no harm has occurrd

Obtain feedback

Deception in experiments

Should be avoided at all costs especially where it would raise other issues

Would pp’s participate if they knew?

Ethical guidelines

Bps - regularly update ethical ‘guidelines’

‘Code of ethics and conduct’ bps 2009

Tells psychologists what behaviour is acceptable and give guidelines on how to deal with ethical dilemmas

Evaluation of ethical guidelines

‘Rules and sanctions’ - general as impossible to cover everything

Closed discussions about right and wring

Evaluation of the right to withdraw

Pp’s may feel they shouldnt withdraw as to not spoil the experiment

In many oarticipants are paid or rewarded in some way and may not feel able to withdraw

Evaluation of debriefing

Tries to address the balance where harm may of been done pp’s may feel cheated or embarrassed by their behaviour - debriefs ade a partial solution

Types of observations

Participant observation

Non-participant

Participant observations

Observations made by someone who is also participating in the activity being observed

Non-participant observation

Observations made by someone who is not participants in the activity being observed

Unstructured observations

Research records all relevant behaviour but has no system

Problems

there may be too much to record

Behaviours recorded are often those most visible or eye-catching but maybe not necessarily be important or relevant behaviour

Structured observations

Observational techniques, like all research aim to be objective and rigorous for this reason it is preferable to use structured observations. The two main ways to structure observations are using behavioural categories and sampling procedures

Time sampling

Recording behaviours in a given time frame

Event sampling

Counting the number of times a certain behavioural event occurs

Observer bias

The observer may only record data or interpret behaviour so that it fits in with their aim and hypothesis

This reduces validity

Can be resolved by having more than one observer

Inter-rater reliability

They watch the same observations to see if they see the same behaviour

If they do they have high inter-rater reliability

Pilot studies can be carried out to identify any problems before carrying out

If more than 80% agreement then it has inter-observer reliability

Social desirability bias

Participants in research behave in such a way as to show themselves in the best light rather than behaving in their normal way

This reduces validity

Questionnaire

Set of written questions designed to collect information about a topic or topics

Closed questions (questionnaire)

These have fixed response options and so only allow certain responses

Usually multiple choice or numerical rating