Research Methods Flashcards
theory
explanation for behaviour, tested using objective research methods
aim
general statement explaining the purpose of a study (to investigate)
independent variable
deliberately changed
dependent variable
what is being measured
operationalisation
making variables clearly defined and measured
hypothesis
- clear and precise testable statement
- states the relationship between the variables being investigated
alternative hypothesis
statement of a relationship between variables (there is a difference)
null hypothesis
statement of no relationship between variables (there is no difference)
cause and effect
the only thing that should cause a change in the DV is the IV
extraneous variables
- unwanted “extra variables” that may interfere with the relationship between the IV and DV
- can affect DV
what happens if extraneous variables aren’t controlled?
the researcher cannot truly know what caused the change in the DV
why are research procedures enforced?
its important to design studies in a systematic way in order to control possible extraneous variables
requirements for instructions to participants
- all participants must receive exactly the same information throughout an investigation (standardised)
- this ensure that what is said to participants doesn’t act as an EV
standardised procedures
- using the exact same methods and procedures for participants in a research study
- to control EVs
- only the IV should vary
randomisation
-using chance (eg.flipping a coin) to control effects of a bias when designing a study
demand characteristics
- characteristics of a study that may give away the purpose of the experiment
- participants might become aware of aim
- participants may change their behaviour to be seen in a better light/different way
experiments
looks at a measurable change in the DV caused by a change to the IV
(quantitative approach
lab experiment (type of experiment)
- experimenter has a high control over what happens
- takes place in a laboratory
evaluate the use of lab experiments
strengths:
-able to control the extraneous variables
weaknesses:
-behaviour in a lab is less “normal”-difficult to generalise
-participants may change their behavior (they’re aware that they’re being watched)
field experiment (type of experiment)
- takes place in a natural setting
- IV is manipulated by experimenter
evaluate the use of field experiments
strengths:
-more realistic than lab experiments (natural environment)
-can use standardised procedures (some control)
weaknesses:
-may lose control of EVs (difficult to show cause and effect)
-ethical issues (participants aren’t aware of the study
natural experiment (type of experiment)
- takes place in a natural setting
- IV isn’t changed by experimenter (naturally occurring)
evaluate the use of natural experiments
strengths:
-high validity (due to the real world variables)
-can standardise procedures (some control over EVs)
weaknesses:
-few opportunities to do this kind of research as behaviours may be rare
-may be EVs (due to the fact that participants aren’t randomly allocated to conditions)
experimental designs
different ways participants can be organised in relation to IVs/conditions of the experiment
order effects
EV arising from the order in which conditions are presented (in repeated measures)
independent groups (experimental design)
- different group of participants for each level of the IV (condition)
- control and experimental group
evaluate the use of independent groups
strengths:
-order effects aren’t a problem because participants only do the experiment once
weaknesses:
-different participants in each group, participant variables can act as an EV
participant variable
differing individual characteristics of participants
how do you deal with participant variables?
allocation: using chance or a systematic method to allocate participants to conditions, this way the researcher doesn’t influence who goes in each group and also makes participant variables even across the different conditions
repeated measures (experimental design)
all participants take part in all levels of the IV
evaluate the use of repeated measures
strengths:
-no participant variables
-fewer participants needed so its less expensive
weakness:
-order effects reduce validity (eg. practice effect: participants may do better the second time)
how do you deal with order effects?
counterbalancing: half the participants do the conditions in one order, other half do the opposite order
matched pairs
- participants tested on variables relevant to the study
- participants are matched, and one member of each pair goes into each condition
evaluate the use of matched pairs
strengths:
-no order effects
-fewer participant variables
weaknesses:
-takes time to match participants
-doesn’t control all participant variables
sample
subset of target population which aims to be representative of that population-aims to avoid bias
sampling method
system used to produce sample
target population
group that the researcher is interested in studying
purpose is to be able to generalise all findings/results to the target population
researcher bias
process where the scientists performing the research influence the results in order to to portray a certain outcome
random sampling
- each person has equal chance of selection
- numbers of target population in hat/number generator
evaluate the use of random sampling
strengths:
-no bias (because everyone has an equal chance of selection)
weaknesses:
-takes time (have to make a list of members of the target population)
opportunity sampling
selecting people that are available
evaluate the use of opportunity sampling
strengths:
-quick+cheap (participants are just there)
weaknesses:
-only represents the population from which it was drawn
-researcher bias
systematic sampling
selecting every nth person from a list of the target population
evaluate the use of systematic sampling
strengths:
-avoids most researcher bias
weaknesses:
-may end up with unrepresentative
stratified sampling
selecting participants in proportion to frequency in target population
evaluate the use of stratified sampling
strengths:
-very representative
weaknesses:
-very time-consuming to sort sub-groups
what is the issue with psychological studies?
conflict between participants’ rights and well-being and the need to gain valuable results
informed consent
participants must be told comprehensive information (nature, purpose and role) at the beginning and they can make an informed decision about whether or not they want to take part (consent)
right to withdraw
they should be told that they can leave the investigation at any time they wish
deception
participants shouldn’t be lied to or misled about aim
—mild deception can be justified (withholding info about the other group/condition)
privacy
participants have the right to control about themselves
confidentiality
- personal data must be protected and respected
- data collected belongs to that person
- personal details shouldn’t be accessible (usually anonymous)
protection from harm
- participants shouldn’t be placed at risk
- physical and psychological safety should always be protected
- not made to feel stressed or embarrassed
- right to withdraw
BPS guidelines
-code of conduct all professional psychologists in the UK need to follow
how to deal with informed consent
participants (or guardians) sign a form that tells them what is to be expected
how to deal with deception and protection from harm
- participants have a full debrief to explain true aims and other conditions ect
- participants are allowed to withhold data if unhappy with some aspects of study
- reduce stress (assure them it was typical behaviour)
- might be offered counseling)
how to deal with privacy and confidentiality
- participants must be anonymous (given numbers or referred to by initials)
- data must not be shared unless given consent
- participants should be reminded that data will be protected and remain confidential
interviews
- self report method
- face to face, real time contact and can also be done over phone or text
structured interviews
- reads list of questions
- can have prepared follow up questions
- follows exact script
unstructured interviews
- some questions prepared before
- new questions created depending on what the interviewee says
- much like a conversation
semi-structured interviews
- some questions decided before but follow up questions emerge at certain points
- “goes with the flow”
evaluate the use of interviews
strengths:
-produce a lot of info
-insight gained into thoughts and feelings
weaknesses:
-data can be difficult to analyse
-people may feel uncomfortable talking face to face
questionnaires
prepared list of questions which can be answered in writing, over the phone, internet ect
open questions
- more likely in an interview
- no fixed range of possible answers
- respondents are free to reply in any way they wish
- produces qualitative data
closed questions
- more likely in questionnaires
- fixed range of possible answers
- use rating scales, yes/no ect
- produces quantitative data
evaluate the use of questionnaires
strengths:
-can gather info from many people quickly
-questionnaires produce data that is easier to analyse than interviews (closed questions)-easier to make comparisons
weaknesses:
-social desirability bias (might not answer truthfully and give the answers they think is more appropriate/puts them in a better light—low validity
-may be unclear
-maybe be leading questions—lacks validity
leading questions
a question that prompts or encourages the desired answer
observation
a researcher watches or listens to participants and records data
natural observation
researcher records behaviour where it would normally occur
controlled observation
researcher manipulates aspects of the environment
covert observation
participants aren’t aware behaviour is being recorded
overt observation
told that they’re being recorded in advance
participant observation
researcher is part of the group being recorded
non-participant observation
researcher remains separate from the group that is being recorded
categories of behaviour
- systematic method of collecting data
- target behavior broken into separate observable changes
interobserver reliability
- observational studies should be carried out by more than one researcher because bias can be a problem
- a single researcher may overlook important details or only record data that fits expectations
- the group or pair should produce the same records of behavior
- they would watch at the same time, have the same categories of behaviour and correlate data
evaluate the use of observations
strengths:
-greater validity as it’s based of what people actually do (opposed to what they say they do which they could lie about)
-covert and natural observations show real life behaviour so it has high validity
weaknesses:
-ethical issues as researchers cant gain consent if they’re observing in a public place which invades their privacy
-observer bias:observers expectations affect validity as they may only record data that fits their expectations or may look over details
correlations
shows how things are linked together (associations)
co-variables
- analysis of qualitative (numerical) data
- continuous data
scatter diagrams
- a special graph used to plot correlation data
- one co-variable is on the x-axis and the other is on the y- axis
- a dot is placed where they meet
positive correlation
as one co-variable increases the other increases
negative correlation
as one co-variable increases the other decreases
zero correlation
no relationship between co-variables
evaluate the use of correlations
strengths:
-allows psychologists to carry out investigations on things that cant be experimented on
-high level of ecological validity as nothing is set up or manipulated
weaknesses:
-don’t show cause and effect
-no control over EVs so wrong conclusions may be drawn
case study
in-depth investigation of an individual, group, event or institution
case studies:qualitative method
- collect information about people’s experiences in words
- data in a case study may be describing past events or interviews (qualitative)
- some case studies may involve experimental testing that produces quantitative data (eg. intelligence tests which produce a numerical score) to see what the person can and cant do
case studies:longitudinal
- often carried out over a long period of time so we can see how behaviour changes
- may also collect retrospective case history
evaluate the use of case studies
strengths:
-triangulation:more than one method to collect data on the same topic
-naturally occurring
-high ecological validity
-rich and detailed qualitative data
weaknesses:
-lack of replication (every case is relatively unique)
-cant generalise findings (as it deals with only one person/group/event so you can never sure whether conclusions drawn from this case can be applied elsewhere)
-time consuming
-subjectivity bias causes low validity (findings are based on the psychologists opinion)
reliability
- if it can be repeated its reliable
- measure of consistency
reliability of quantitative methods
- tend to be more reliable
- lab experiments are controlled and easy to repeat exactly
- interviews/questionnaires: same person should answer the same questions in the same way- closed questions are likely to be more reliable
- observations:one or two observers (interobserver reliability) should produce the same observations if repeated
reliability of qualitative methods
- less reliable
- case studies and unstructured interviews are difficult to repeat in the same way
validity
relates to whether a result is a true reflection of “real world” behavior
validity of sampling methods
- the sample may not represent the target population
- high representativeness=stratified
- low representativeness=opportunity
validity of experimental designs
- repeated measures:order effects challenge validity (can be overcome by counterbalancing)
- independent groups:participant variables challenge validity (can be overcome by random allocation)
- matched pairs:overcome both problems (though it isn’t perfect)
validity of quantitative methods
- lab experiments: task, setting and participant awareness challenges validity=reduces naturalness and has high control
- field experiments:artificial tasks and lack of control of extraneous variables challenge validity=more natural
- methods for producing numerical data (eg questionnaires) lack validity as they reduce behavior to a score=therefore we get little to no insight into other aspects of their behavior
validity of qualitative methods
- case studies have greater validity as they give deeper insight into behavior, thoughts and the participant’s point of view
- difficult to analyse=reduces validity, findings are fairly subjective and the researcher’s own expectations influence the analysis
quantitative data
- quantities (numbers)
- can measure thoughts/feelings
evaluate the use of quantitative data
strengths: -easy to analyse an draw conclusions -straightforward to make comparisons -statistics are open to less interpretation=less chance of bias weaknesses: -lacks depth and detail -lacks validity -doesn't reflect real world complexity
qualitative data
- data in words
- can be turned into numbers by counting themes
evaluate the use of qualitative data
strengths:
-more depth and detail
-better insight (participant is free to fully express thought)
-more validity
weaknesses:
-difficult to analyse and summarise (a lot of material)
-difficult to draw conclusions
-conclusions may be based on the researcher’s own opinions=open to bias
primary data
- data that has been obtained first hand
- collected data matches the aim of the study (collected for the purpose of the research)
evaluate the use of primary data
strengths:
-suits the aims of the research=more useful
-authentic
weaknesses:
-takes time and effort to collect
-costly
-much easier and quicker to use that is already validated
secondary data
- second hand data
- from other studies or government statistics
- already exists
evaluate the use of secondary data
strengths: -easy and convenient to use -saves expenses -saves time -little effort weaknesses: -may not fit what the researcher is investigating -secondary data may be out-of-date, not quite complete or of poor quality=may waste time
descriptive statistics
express numbers in a way that shows the overall pattern
range
- spread of data
- arranges data in order and subtracts the lowest from the highest score
evaluate the use of ranges
strengths:
easy to calculate
weaknesses:
-can be distorted by extreme scores
mean
- mathematical average
- add up all the scores and divide by the number of scores
evaluate the use of means
strengths:
-uses all the data (most sensitive measure)
weaknesses:
-can be distorted by extreme values
median
- data put in order from lowest to highest
- middle value
evaluate the use of medians
strengths:
-not affected by extreme scores
weaknesses:
-less sensitive than the mean to variation in values
mode
most common score
evaluate the use of modes
strengths:
-very easy to calculate
weaknesses:
-can be unrepresentative
frequency tables
-frequency: number or times it occurs-frequency tables are a systematic way to organize data in rows and columns
frequency diagrams
- histogram: continuous categories, no spaces between bars
- bar chart: bars can be in any order
- normal distribution: symmetrical spread forms a bell shape with mean, median and made at peak
decimals
- any number written with a decimal
- position represents value
- eg. 0.2
fractions
- reduce to simplest form
- eg. 1/2
ratios
- way to express fractions
- 8:2=4.1
percentages
- fractions out of 100
- eg. 58%
finding the mean
add all scores and dividing by the number of scores
standard form
mathematical shorthand to represent very large or very small numbers
significant figures
numbers expressed to the required degree of accuracy eg.2 significant figure:
- 32,462=32,00
- 0.003256=0.0033
estimate results
rough calculation
