Psychology- Research Methods

Experimental method

involves the manipulation of an independent variable to have an effect on the dependent variable, which can be measured and stated in the results

Aim

a general statement of what the researcher intends to investigate, the purpose of the study

alternative hypothesis

a testable statement that will predict what you will find, clearly stating the relationship between the variables to be investigated

directional hypothesis

a prediction where the researcher states the direction of difference or relationship between variables

non-directional hypothesis

a prediction where the researcher does not state the direction of difference or relationship between the variables

null hypothesis

a testable statement that predicts there will be no relationship between the variables to be investigated

Independent Variable

the aspect of the experiment that the researcher manipulates (or changes naturally) so the effect of the DV can me measured

Dependent Variable

the variable the researcher measures

operationalisation

clearly defining variables in terms of how they can be measured- should be specific and measurable

Extraneous variables

any other variable, that is not the IV, that may affect the DV if not controlled. They do not vary systematically with the IV

Confounding variables

any other variable, other than the IV, that affects the DV. does change systematically with the IV- so we can’t tell if the DV is changed by the IV or CV

Demand characteristics

cues from the researcher/ situation that may be interpreted by participants as revealing the aim of the experiment- participants may change their behaviour to help (Please-U effect) or hinder (Screw-U effect) the experiment

investigator effect

any effect of the investigators behaviour (conscious or unconscious) on the DV measured

Randomisation

the use of chance methods to control for the effects of bias when designing materials and deciding the order of experimental conditions

standardisation

using the exact same formalised procedures and instructions for all participants in a study

reliablity

a measure of consistency

Test- retest reliability

compare test scores over time to see if they are similar- if they are similar= high test-retest reliability

inter observer reliability

compare data from more than one researcher- if they are similar= high inter observer reliability

validity

legitimacy (accuracy) of the data collected

Face validity

whether a test actually measures what it claims to meaure

concurrent validity

whether a test agrees with other pre-existing tests that measure the same concept- gauged by correlating measures against each other

ecological validity

whether data from a test is generalisable to the real world, based on where the research is conducted and the tasks involved

temporal validity

whether results from a test hold true over time

experimental designs

the different ways participants are arranged in different experimental conditions

Independent groups

The participants only perform in one condition of the independent variable- one group of participants do condition A and a second group do condition B

strengths of independent groups

• no order effects

• participants are less likely to guess the aims of the study so fewer demand characteristics

weaknesses of independent groups

• there will be differences between participants, which can cause changes to the DV

• you need more participants to gather the same amount of data- so more expensive

random allocation can be used to solve this

repeated measures

the same participants take part in all conditions of the experiment

strengths of repeated measures

• no individual differences between participants

• fewer participants needed, so less time consuming and expensive

weaknesses of repeated measures

• order effects- knowledge from one condition may affect performances in the next

can be solved by counterbalancing- where half the participants do conditions in one order and the other half do it in the opposite order

matched pairs

pairs of participants are first matched of some variable that may affect the DV. Then one member of the pair is assigned to condition A and the other to condition B

strengths of matched pairs

• controls for individual differences

• no order effects

• demand characteristics are less of an issue

weaknesses of matched pairs

• can be time consuming and expensive

• a large pool of participants needed

• can be difficult to know which variables are appropriate for the participants to be matched

laboratory experiment

an experiment that takes place in a controlled environment- not necessarily in a lab and participant will go to researcher

strengths of lab experiment

• high control of variables so can be more confident that IV leads to DV

• can be replicated to check for consistency of findings

weaknesses of lab experiment

• bias from researcher can lead to more demand characteristics

• in an artificial environment, so participants may show more unnatural behaviour which leads to lower ecological validity

field experiment

an experiment in which the environment is familiar to the participant- researcher will go to the participant

strengths of field experiment

• familiar environment can lead to more natural behaviour- higher ecological validity

• less likely to show demand characteristics

weaknesses of field experiment

• less control over extraneous variables

• harder to replicate to check consistency

natural experiment

an experiment where there is no manipulation of the IV by the researcher- the IV is naturally occurring. Generally an external variable (experience). Participants have not been randomly allocated to conditions

strengths of natural experiment

• useful when unethical to manipulate IVs

• high ecological validity- real life experiences

weaknesses of natural experiment

• lack of control of EVs- reduces validity

• can’t ethically replicate to check reliability of findings, could be anomalous

quasi experiment

an experiment where the IV is not changed by the researcher but already naturally exists. participants cannot be randomly allocated to conditions

strengths of quasi experiment

• useful when unable to manipulate the IV

• real life experiences

weaknesses of quasi experiments

• lack of control over EVs- reduces validity

overt observations

participants know they are being watched, with consent

strengths of overt observations

more ethical, as informed consent is given

weaknesses of overt observations

• more likely to show unnatural behaviour as participants know they are being watched

• more likely to show demand characteristics which reduces validity

covert observations

the participants are unaware they are being watched- without consent

strengths of covert observations

• natural behaviour recorded so higher validity

• less likely to show demand characteristics

weaknesses of covert observations

• ethical issues as no informed consent given

• risks of invading participants privacy

participant observations

when the researcher is part of the group that is being observed

strengths of participant observations

• can be more insightful, which increases validity of data

weaknesses of participant observations

• behaviour may change from participants if they know they are being watched

• researcher may lose objectivity as ay start to identify strongly with participants and lose objectivity

non-participant observations

when the researcher is not part of the group and observes from a distance

strengths of non-participant observations

• researcher can be more objective

• can be easier to gather data

weaknesses of non-participant observations

• open to observer bias for examples of stereotypes

• researchers may lose some insight

• participants may be more likely to show demand characteristics

controlled observations

watching and recording behaviour in a a structured and artificial environment- participants come to be observed

strengths of controlled observations

• researcher is able to focus on a particular aspect of behaviour

• more control over extraneous and confounding variables

• easy to replicate

weaknesses of controlled observations

• more likely to be observing unnatural behaviour, as it takes place in an unfamiliar environment

• low ecologial validity

• demand characteristics presented

naturalistic observations

watching and recording behaviour in the setting it would normally take place- that is familiar to the participants. researcher goes to participants

strengths of naturalistic observations

• high validity

weaknesses of naturalistic observations

• low ecological validity if participants know they are being watched

• replication can be difficult

• uncontrolled confounding variables are presented

event sampling

involves counting the number of times a behaviour is carried out by the target group or individuals you are watching

strengths of event sampling

• increases validity as more data

• good for infrequent behaviours that are likely to be missed if time sampling was used

weaknesses of event sampling

• observer may overlook important details if in a chaotic environment hich can reduce validity

• can be difficult to judge start and end of a behaviour

time sampling

recording of a behaviour at set time intervals or within a timeframe

strengths of time sampling

• reduces number of observations, so easier for observer and more efficient

weaknesses of time sampling

• only a small amount of data collected so easy to miss data, which can be unrepresentative of the observation as a whole

unstructured observations

consists of continuous recording where the researcher writes everything they see during the observation

strengths of unstructured observations

more detailed, richer information- more accurate

weaknesses of unstructured observations

• produces qualitative data, which is more difficult to record and analyse

• more observer bias- lowers validity

• not replicable- hard to check consistency, so not replicable

structured observations

where the researcher quantifies what they are researching using a predetermined list of behaviours and sampling

strengths of structured observations

• easier as more systematic

• quatitative data is easier to analyse and compare

• less risk of observer bias

weaknesses of structured observations

• lacks depth of detail

• hard to achieve high inter observer reliability as filing the predetermined lists is subjective

case studies

a study of a unique individual person, a small group, an institution or an event. is idographic not nomothetic- about the individual. often longitudinal- happens over a long period of time

strengths of case studies

• ability to find out how to support others, in similar situations and forms basis for future research

• allows for research on situations where it would be unethical to manipulate variables

• can get really rich, detailed data

weaknesses of case studies

• could be unethical is someone has been in a bad/traumatic experience- do they have the capacity to consent

• observer may start to lose objectivity-which could lead to bias- reduces validity

• lacks generalizability

• unlikely to get similar case to check reliability

• take a long time and can be expensive

content analysis

type of observational method- participants are studies indirectly via the communication they may have produced. the aim is to summarise and describe the content in a systematic way, so overall conclusions can be drawn

coding and quantitative data

coding categories (meaningful units) are created and the number of times a word or phrase is used is counted. collects quantitative data

thematic and qualitative data

researcher reads/watches the source material several times to identify recurrent themes. themes can be combined to create overarching themes

strengths of content analysis

• ethical- no direct contact with participant, so no issues with consent

• reliability- coding and quantitative data- can repeat to check consistency of findings

weaknesses of content analysis

• researcher- interpretation can lead to subjective bias- reduces validity

• could be observer bias but can be eliminated by inter-observer reliability

Pilot study

• small scale version of an investigation

• aims to check the procedures identify problems and make any necessary changes so the true research is as valid as possible

• this saves time and money in the long run

single bind procedure

• the researcher knows what conditions the participant has been given, but the participant does not

• this reduces demand characteristics

double blind procedure

• neither the participants nor the experimenter knows who is receiving a particular treatment

• this reduces demand characteristics, researcher bias and investigator effects

Mean

the arithmetic average- add up all the values then divide by N (the number of values)

strengths of the Mean

• most mathematically accurate method of all the values- as it makes use of all the values

• good for interval data

disadvantages of the mean

• is influenced by outliers so can be unrepresentative

• not always a true score someone got

• sometimes does not make sense within the context of the data

median

arrange data from the lowest to highest then find the central value

strengths of median

• not affected by extreme scores

• easy to calculate with a small data set

• good for ordinal data

weaknesses of the median

• does not use all the data

• does not show outliers

mode

the most frequently occurring value in a set of data

strengths of the mode

• useful for nominal data- data in categories

• is a score someone actually got

weaknesses of the mode

• doesn’t represent the whole data set

measures of central tendency

refers to any measure which calculates an average value within a set of data

measures of dispersion

refers to any measure that calculates the variation in a set of data

range

the difference between the highest score and the lowest score

strengths of the range

• easy to calculate

weaknesses of the range

• affected by extreme values

• does not use all data