RESEARCH METHODS 1

experimental method

involves the manipulation of an independent variable to measure the effect on the dependent variable (may be laboratory, field, natural, quasi)

aim

a general statement of what the researcher intends to investigate, the purpose of the study. are developed from theories and develop from reading about other similar research

hypothesis

a clear, precise, testable statement that states the relationship between the variables to be investigated - stated at the outset of any study (state operationalised variables and the relationship between them)

directional hypothesis

states the direction of the difference of relationship (used when there has already been a range of research carried out which relates to the aims of the investigation and suggests a particular outcome)

non-directional hypothesis

does not state the direction of the difference or relationship (used when there is no previous research or research is contradictory)

independent variable

some aspect of the experimental situation that is manipulated by the researcher - or changes naturally - so the effect on the DV can be measured

dependent variable

the variable that is measured by the researcher - any effect on the DV should be caused by the change in the IV

variables

any ‘thing’ that can vary or change within an investigation - variables are generally used in experiments to determine if changes in one thing result in changes to another

operationalisation

clearly defining variables in terms of how they can be measured

extraneous variables

any variable, other than the IV that may affect the DV if it not controlled - nuisance variables that don’t vary systematically with the IV - relatively easy to control/doesn’t confound

confounding variables

a kind of EV but they vary systematically with the IV, therefore we can’t tell if any change in the DV is due to the IV or CV

what is an example of an extraneous variable?

age of participants, lighting in the lab

what is an example of a confounding variable?

an event that happened to a particular group in the experiment (seeing prince william) or the time of day

demand characteristics

any cue from the researcher or research situation that may be interpreted by the participants as revealing the purpose of an investigation → may lead to a participant changing their behaviour within the research situation

investigator effects

any effect of the investigator’s behaviour (conscious/unconscious) on the research outcome (the DV). this may include everything from the design of the study to the selection/interaction with the participants

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 exactly the same formalised procedures and instructions for all participants in a research study

please-u effect

participants act in a way they think the researcher wants them to

screw-u effect

participants intentionally underperform to sabotage the study’s results

what is the purpose of standardisation?

to eliminate non-standardised instructions as being possible extraneous variables

independent groups design

two separate groups of participants experience two different conditions of the experiment, the performance of the two groups would then be compared

repeated measures design

all participants experience both conditions of the experiment, the scores from both conditions would be compared to see if there was a difference

matched pairs design

participants are paired together on a variable that has been found to affect the DV, then one member of each pair does one condition, the other does another

strength of an independent groups design

there are no order effects and participants are less likely to guess the aims of the study (no demand characteristics)

limitation of an independent groups design

participants in different groups aren’t the same in terms of participant variables (if there is a mean difference between groups on the DV this may be due to participant variables) and reduce validity of findings

less economical than repeated measures as twice as many participants are needed to produce equivalent data from a repeated measures design - increases time/money

random allocation

an attempt to control for participant variables in an independent groups design which ensures that each participant has the same chance of being in one condition as any other - attempts to evenly distribute participant characteristics across the condition of the experiment using random techniques

strength of repeated measures design

participant variables are controlled and fewer participants are needed so less time is spent recruiting

limitation of repeated measures design

order effects:

* each participant has to do at least two tasks and the order of the tasks may be significant
* repeating two tasks could create boredom or fatigue that might cause deterioration in the performance on the second task
* participants may improve through the effect of practice
* more likely to work out the aims of the study = demand characteristics

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counterbalancing

an attempt to control for the effects of order in a repeated measures design: half the participants experience the conditions in one order, and the other half in the opposite order

strength of matched pairs design

no order effects and demand characteristics are less of a problem

limitations of matched pairs design

matching may be time-consuming and expensive (less economical)

difficult to know which variables are appropriate to match participants on and participants can never be matched exactly

laboratory experiment

an experiment that takes place in a controlled environment within which the researcher manipulates the IV and records the effect on the DV, whilst maintaining strict control of extraneous variables

field experiment

an experiment that takes place in a natural setting within which the researcher manipulates the IV and records the effect on the DV

natural experiment

an experiment where the change in the IV is not brought about by the researcher but would have happened if the researcher wasn’t there - the researcher records the effect on a DV they have decided on (e.g. studying reactions to earthquakes)

quasi-experiment

an experiment whereby the IV has not been determined by the researcher, instead it naturally exists. based on an existing difference between people (age/gender). DV may be naturally occurring or devised by the experimenter

strength of laboratory experiments

* high control over CVs and EVs (high internal validity as we can be sure any effect on the DV is the result of manipulation of the IV - demonstrating cause and effect)
* replication is more possible due to high level of control (ensures new EVs aren’t introduced and see if findings are valid)

limitation of laboratory experiments

* lack generalisability (artificial and unlike everyday life - low external validity)
* participants are aware they are being tested in a lab = demand characteristics
* tasks might not represent everyday experience (mundane realism)

strength of field experiments

* high mundane realism - environment is more natural
* produce valid and authentic
* participants may be unaware they are being studies (high external validity)

limitation of field experiments

* loss of control of CVs and EVs
* cause and effect between IV and DV is more difficult to establish
* precise replication is often not possible
* ethical issues → can’t consent and is an invasion of privacy

strength of natural experiments

* provide opportunities for research that otherwise might not be undertaken for practical or ethical reasons (orphan studies)
* high external validity - involve study of real-world issues and problems as they happen

limitations of natural experiments

* naturally occurring event may be rare - reducing opportunity for research (limit scope for generalising to similar situations)
* participants aren’t randomly allocated to experimental conditions (in independent groups) → researcher is less sure whether the IV affected the DV
* romanian orphans: IV = adopted early/late. BUT there were lots of differences - late adoptees are less sociable meaning they are less appealing to prospective parents
* research = conducted in a lab and lack realism/demand characteristics

strength of quasi-experiments

* carried out under controlled conditions and share strengths of a lab experiment → replication

limitations of quasi-experiments

* can’t randomly allocate participants to conditions so there may be CVs
* IV is not deliberately changed by the researcher so we can’t claim that the IV caused any observed change

random sampling

a sophisticated form of sampling in which all members of the target population have an equal chance of being selected

selecting a random sample

1. obtain a complete list of all members of the target population
2. all the names on the list are assigned a number
3. actual sample is selected through the use of some lottery method (computer randomiser/picking numbers from a hat

systematic sample

when every *n*th number of the target population is selected

selecting a systematic sample

1. a sampling frame is produced → a list of people in the target population organised into an order
2. a sampling system is nominated (every 3rd, 6th or 8th person, etc)
3. begin from a randomly determined start
4. researcher works through sampling frame until the sample is complete

stratified sample

a sophisticated form of sampling in which the composition of the sample reflects the proportions of people in certain subgroups (strata) within the target population/wider population

selecting a stratified sample

1. researcher identifies the different strata that make up the population
2. proportions needed for the sample to be representative are worked out
3. participants that make up each stratum are selected using random sampling

opportunity sample

select anyone who happens to be willing and available

selecting an opportunity sample

1. researcher asks whoever is around at the time of their study

volunteer sample

participants select themselves to be part of the sample

selecting a volunteer sample

researcher may place an advert in a newspaper or on a common room noticeboard - willing participants may raise their hands when the researcher asks

evaluating random sampling

\+ unbiased → CVs and EVs should be equally divided enhancing internal validity

\- difficult and time-consuming to conduct (complete list of target population)

\- sample may still be unrepresentative

\- selected participants may refuse to take part

evaluating systematic sampling

\+ objective → researcher has no influence over who is chosen

\- time consuming and participants may refuse to take part

evaluating stratified sampling

\+ produces a representative sample → reflects the composition of the population

\+ generalisation of findings is possible

\- identified strata cannot reflect all the ways that people are different (complete representation of target is not possible)

\- time consuming to identify strata

opportunity sample

\+ convenient → less costly in terms of time and money (no need to get a list of population and no need to divide population)

\- sample is unrepresentative of target population so can’t be generalised

\- researcher has control over selection of participants and may avoid people (researcher bias)

evaluating volunteer sampling

\+ easy and requires minimal input from researcher - less time consuming

\+ researcher ends up with participants that are more engaged

\- volunteer bias: may attract a certain ‘profile’ of person that might be curious and more likely to try and please the researcher

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qualitative data

data that is expressed in words and is non-numerical (written accounts of thoughts/opinions or account of a researchers observation - transcripts) → collected through interviews/unstructured observation

quantitative data

data that is expressed numerically → collected in the form of individual scores (data open to being analysed and can be converted into graphs)

primary data

original data that has been collected specifically for the purpose of the investigation by the researcher, arrives from participants themselves and is gathered by conducting an experiment/Q/interview/observation

secondary data

data that has been collected by someone other than the person who is conducting the research (exists before and significance is known) → ‘desk research’ (includes data located in journal articles, books or websites)

evaluating qualitative data

\+ offers a researcher more richness of detail

\+ much broader in scope and gives participant the opportunity to more fully report thoughts

\+ greater external validity (meaningful insight)

\- hard to analyse → patterns and comparisons are hard to identify

\- conclusions rely on subjective interpretations

evaluating quantitative data

\+ simple to analyse → comparisons can be easily drawn, can draw graphs

\+ more objective and less open to bias

\- narrower in meaning, fails to represent ‘real life’

\- low external validity

evaluating primary data

\+ fits the job, authentic

\+ designed in a way that targets information researcher requires

\- expensive → requires time and effort (planning, preparation and resources)

evaluating secondary data

\+ inexpensive and easily accessed → minimal effort

\+ desired info already exists so no need for primary data

\- may be variation in the quality and accuracy of secondary data

\- data might be outdated or incomplete

\- content of data might not match objectives → challenges validity

meta-analysis

the process of combining the findings from a number of studies on a particular topic, the aim is to produce an overall statistical conclusion of a difference/relationship between variables based on a range of studies

evaluating meta-analysis

\+ larger, more varied sample that can be generalised across larger populations, increasing validity

\- prone to publication bias → researcher may not select all relevant studies, leaving out studies with negative results

\- conclusions may be biased as they only represent some of the relevant data

descriptive statistics

the use of graphs, tables and summary statistics to identify trends and analyse sets of data

measures of central tendency

the general term for any measure of the average value in a set of data

mean

the arithmetic average calculated by adding up all the values in a set of data and dividing by the number of values

median

the central value in a set of data when values are arranged from lowest to highest

mode

the most frequently occurring value in a set of data

evaluating the mean

\+ most sensitive measure of central tendency

\+ includes all values so it is more representative of data as a whole

\+ good for **interval** data

\- easily distorted by extreme values

evaluating the median

\+ isn’t affected by extreme scores

\+ easy to calculate

\+ good for **ordinal** data

\- actual values of lower and higher numbers are ignored

\- doesn’t use all the data - less representative

evaluating the mode

\+ easy to calculate

\+ good for **nominal** data

\- not useful when there are lots of modes

\- not representative of whole data set

measures of dispersion

the general term for any measure of the spread or variation in a set of scores

range

a simple calculation of the dispersion in a set of scores which is worked out by subtracting the lowest score from the highest score and **adding 1** as a mathematical correction

standard deviation

a sophisticated measure of dispersion in a set of scores. tells us by how much, on average, each score deviates from the mean

evaluation of the range

\+ easy to calculate

\- unrepresentative of the data set as whole

\- is influenced by outliers

\- doesn’t indicate whether numbers are closely grouped or spread out

what does a high standard deviation indicate?

there is a wide dispersion within a data set → not all participants were affected by the IV in the same way because the data is widely spread

what does a low standard deviation indicate?

the data is tightly clustered around the mean, implies that all participants responded in a similar way

evaluating standard deviation

\+ more precise of dispersion → includes all values

\- it can be distorted by extreme values and they may not be revealed

\- difficult to calculate

ethical issues

arise when a conflict exists between the rights of participants in research studies and the goals of research to produce authentic, valid and worthwhile data

informed consent

* making participants aware of the aims of the research, the procedures, their rights (and right to withdraw) and what their data will be used for → participants can then make an informed judgement whether to take part without being coerced
* informed consent may make the study meaningless because participants may respond to demand characteristics and not be natural

deception

* deliberately misleading or withholding information from participants at any stage of the investigation
* participants who haven’t received adequate information or been lied to can’t have given informed consent
* deception can only be justified if it doesn’t cause undue stress or if ppts could guess aim of investigation.

protection from harm

* participants must be protected from physical and psychological harm
* participants should be reminded they have a right to withdraw

privacy and confidentiality

* right of privacy: ppts right to control information about themselves - extends to the area the study took place (institutions/locations are not names)
* confidentiality: the right to have any personal data protected

how to deal with informed consent?

* consent letter detailing relevant information- assuming participant agrees this is signed. under 16s = parental consent
* prior general consent: ppts give permission to take part in many studies whereby one of them involves deception
* presumptive consent: researcher gathers opinions from a group like the ppts in the study but doesn’t inform actual participants
* retrospective: when ppts are asked for consent after they have participated in the study

how to deal with deception and protection from harm?

* debriefing: ppts made aware of true aims of investigation and details they weren’t aware of
* told what their data will be used for and the right to withdraw/withhold data
* reassure ppts their behaviour was normal or provide counselling

how to deal with confidentiality?

* personal details must be protected
* record no personal details and maintain anonymity using numbers or initials
* reminding ppts their data won’t be shared

BPS code of ethics

a quasi-legal document produced by the British Psychological Society that instructs psychologists in the UK about what behaviour is and isn’t acceptable when dealing with participants. is build around 4 major principles: respect, competence, responsibility and integrity

cost-benefit analysis

should be done before a study is carried out

* it done by the ethics committee whereby the pros and cons of the study are weighed up to determine whether the study will be ethical

pilot study

a small-scale version of an investigation which is done before the real investigation is undertaken → carried out to allow potential problems of the study to be identified and the procedure to be modified to deal with these (allows money and time to be saved in the long run)

single-blind procedure

researchers don’t tell the participants if they are being given a test treatment or a control treatment

done to ensure ppts don’t bias the results by acting in ways they think they should act - avoids demand characteristics

double-blind procedure

* neither the participants or researcher is aware of the aims of the investigation/who is receiving a particular treatment (utilised to prevent bias in research results)


* useful for preventing bias due to demand characteristics or the placebo effect
* reduces investigator effects - can’t influence ppt bhv

control condition

sets a baseline whereby results from the experimental condition can be compared to results from this one. if there is a significantly greater change in the experimental group compared to the control then the researcher can conclude the cause of effect was the IV

naturalistic observation

watching and recording behaviour in the setting within which it would normally occur

controlled observation

watching and recording behaviour within a structured environment where variables can be managed

covert observation

participants’ behaviour is watched and recorded without their knowledge or consent