Research Methods: Strengths + Weaknesses Flashcards
1
Q
Strengths of Repeated Measures Design
A
individual differences are removed compared to individual groups
2
Q
Weaknesses of Repeated Measures Design + how to deal with them
A
- order effects: order of conditions could affect performance: short break, two different tests, counter balancing: ensures each condition is tested first or second in equal amounts
- demand characteristics: ps guess aim of study and alter behaviour: single blind study: ps dont know true aim
3
Q
Strengths of Independent Group Design
A
no order effects
4
Q
Weaknesses of Independent Groups Design + how to deal with them
A
- no control of ps variables
- need twice as many ps
- randomly allocate ps to conditions to ensure theyre equivalent
- match ps
5
Q
Strengths of Matched Pairs Design
A
- controls some p variables
- ps wont guess aim
- no order effects
6
Q
Weaknesses of Matched Pairs Design + how to deal with them
A
- difficult and time consuming
- may be too costly
- may not control all variables
- restrict number of varibales to be matched on
- pilot study could show key variables that need to be matched
7
Q
Strengths of Random Sampling
A
unbiased: all members of target population have equal chance of being selected
8
Q
Weaknesses of Random Sampling
A
need to have a list of everyone in population and then contact those selected: takes time
9
Q
Strengths of Opportunity Sampling
A
- easiest method
- reduces costs and time taken to find ps
10
Q
Weaknesses of Opportunity Sampling
A
drawing sample from such a small part of population will prodcue bias
11
Q
Strengths of Stratified Sampling
A
most representative of all methods as sample is proportionate to subgroups and random
12
Q
Weaknesses of Stratified Sampling
A
very time consuming to identify subgroups and randomly select ps and then contact them
13
Q
Strengths of Systematic Sampling
A
unbiased as ps are selected using an objective system
14
Q
Weaknesses of Systematic Sampling
A
not truly unbiased/random unless you start with a random method for selecting the first ps and then select every nth person
15
Q
Strengths of Volunteer Sampling
A
wide variety of people which may make sample more representative
16
Q
Weaknesses of Volunteer Sampling
A
sample may be biased towards people who are motivated/confident or need money resluting in a volunteer bias
17
Q
Strengths of Lab Experiment
A
- easy to replicate: increases external validity
- good control over IV and DV: less extraneous variables
- easy to establish cause + effect
18
Q
Weaknesses of Lab Experiment
A
- materials may lack mundane realism
- researchers cant be sure they are behaving naturally as there may be demand charcateristics
19
Q
Strengths of Field Experiment
A
- higher mundane realism
- ps dont know they are being studied so no demand charcteristics
- real world study so more ecological validity and mundane realism
20
Q
Weaknesses of Field Experiment
A
- may be time consuming and unpredictable
- less control over extraneous variables so hard to establish cause + effect
21
Q
Strengths of Natural Experiment
A
- ps dont know they are being studied so no demand characteristics
- great for research that may be unethical to conduct
- its a real world study so more ecological validity and mundane realism
22
Q
Weaknesses of Natural Experiment
A
- random allocation not possible, therefore there may be confounding variables that can threaten internal validity
- as we are not manipulating the IV it makes it hard to establish cause + effect
- less control over extraneous variables so hard to establish cause + effect
23
Q
Strengths of Quasi Experiment
A
- allows comparison between types of people
- good control over IV and DV, so less extraneous variables
24
Q
Weaknesses of Quasi Experiment
A
- random allocation not possible, therefore there may be confounding variables that can theaten internal validity
- low in ecological validity
- researchers cant be sure they are behaving naturally as there may be demand charcteristics
25
Strengths of Controlled Observations
- less risk of extraneous variables: increases ability to interpret findings
- richer and more complete info is obtained
26
Weaknesses of Controlled Observation
- artificial situations can influence behaviour
- artificially makes it hard to generalise findings: lacks ecological validity
- investigator effects + demand characteristics from ps knowing theyre being observed
27
Strengths of Naturalistic Observations
- removes problems from demand characteristics or evaluation apprehension
- provide richer + fuller info
- ecological validity
- may work better with children and animals
28
Weaknesses of Naturalistic Observations
- no control: extraneous variables
- often aware of observation
- problems of replication
- ethical issues if ps dont know behaviour is being observed
29
Strengths of Covert Observations
- reduces risk of altering behaviour: valid
30
Weaknesses of Covert Observations
- ethical problem of deceit
- doesnt allow observer to ask more qs
- observer cant take notes openly
31
Strengths of Overt Observations
- avoids ethical problem
- can ask more qs
- can take notes openly
- can use interview methods to check insights
32
Weaknesses of Overt Observations
- group may refuse researcher permission to observe: may prevent them from seeing everything
- hawthorne effect: behave differently, undermines validity
33
Strengths of Participant Observations
- high in ecological validity: real life settings
- long lasting: detailed + rich info
- in some its difficult for useful research to be carried out if havent joined group
- easy to interpret ps behaviour as developed deep understanding
34
Weaknesses of Participant Observations
- ethical problems: deceiving
- could change + distort behaviour of group members: investigator effects
- issues of accuracy + objectivity: write after, become involved: bias
35
Strengths of Non Participant Observations
- high ecological validity under naturalistic
- avoids investigator effects
- observations less distorted as researcher is detached
36
Weaknesses of Non Participant Observations
- harder to make detailed observations
- detachment makes it difficult to interpret behaviour
- ps may become suspicious
37
Strengths of Questionnaires
- cost effective: large amount of data collected quickly
- researcher doesnt need to be present
- may be willing to share more info
38
Weaknesses of Questionnaires
- social desirability bias: respondents may not be truthful: loss of validity
- can take a long time to design
- only a certain type may respond
- interpret qs in different ways
- cant clarify qs
39
Strengths of Unstructured Interviews
- more flexibility than a structured interview as interviewer can follow up points as they arise: richness of data increases validity
- easier to develop rapport
40
Weaknesses of Unstructed Interviews
- training needed
- analysis of results/data not simple and due to there being potentially so much info its hard to draw conclusions
- may be an element of social desirability bias
41
Strengths of Structured Interviews
- straightforward to replicate due to standardised format
- reduces differences between interviewers
- easy to compare
- fairly quick
42
Weaknesses of Structured Interviews
- not possible for interviewers to deviate off topic or explore interesting points
- stops interviewees from being able to elaborate which can be frustrating
- limits richness of data: can decrease validity
43
Strengths of Primary Data
- control the researcher has over data
- clearly fits aims and hypothesis of study
44
Weaknesses of Primary Data
- very lengthy and therefore expensive process: recruiting participants, conducting study, analysing data
45
Strengths of Secondary Data
- simpler and cheaper
- plays important role in psychological research including review studies, meta analyses and correlational studies
46
Weaknesses of Secondary Data
- variation in quality + accuracy of data: could be outdates or incomplete
- may not exactly match the researchers requirements
47
Strengths of Correlations
- can allow us to study naturally occuring variables
- can measure things we cant experimentally due to ethical issues
- can suggest patterns that then lead to experiments
48
Weaknesses of Correlations
- can tell us how variables are related but not why
- correlation doesnt equal causation (there is no cause and effect relationship as its not experimental)
- may overlook an important intervening variable
- bidirectional ambiguity: unclear which direction correlation is going in
49
Strengths of Mean
- it takes all scores into account so its the most sensitive measure
50
Weaknesses of Mean
- can give a peculiar measure that cannot represent reality
- easily distorted by extreme scores making it unrepresentative
51
Strengths of Median
- unaffected by extreme scores in one direction
- more representative than the mean, especially with small data sets
52
Weaknesses of Median
- less representative when the data set is polarised
53
Strengths of Mode
- most useful for large data sets
- unaffected by extreme scores
54
Weaknesses of Mode
- unreliable for use with small data sets as small changes to the scores can result in it being multi-modal
55
Strengths of Range
- useful when median is being used as an average as the range used the top and middle set and median is the middle number
- easy to calculate
56
Weaknesses of Range
- easily distorted by extreme scores
- only uses 2 numbers no matter how large data set is so its a really basic indication of how data is spread
- doesnt give an indication of spread of data scpres as it just looks at highest and lowest scores
57
Strengths of Standard Deviation
- uses all scores in data set for calculation so its a more precise measure of the spread of data
58
Weaknesses of Standard Deviation
- more difficult to calculate than range
- may hide some extreme scores within data set
59
Strengths of Case Studies
- gives very rich detailed info about the case
- can be used to investigate rare behaviours
- can be used to investigate behaviours that would be unethical to manipulate
- converging evidence: increases validity
60
Weaknesses of Case Studies
- each case unique so not generalisable
- may have to recall past (retrospective recall) which could be distorted
- researcher bias could be an issue if they are looking for a particular thing
61
Strengths of Peer Review
- peers should be experts in the field so we know that we can trust their judgement
- journals are international which means that there will be widespread dissemination of the new research among peers
- published research provides benefits for the unis the researchers work for
- peer review ensures that only the best research gets published
- anonymity allows peers to be honest
- publication in peer reviewed journals can enhance the reputation of researchers
62
Weaknesses of Peer Review
- it isnt always possible to find an appropriate expert to review a research proposal or report
- peer review results in a preference for research that goes with existing theory rather than dissenting or uncoventional research
- academic journals are expensive to buy
- anonymity may mean peers abuse their position
- journals may only want to publish positive results to increase the standing of the journal
63
Strengths of Content Analyses
- high ecological validity: content is part of evryday, not artificial
- replicable: anyone can access same media and apply same behavioural categories
- few ethical issues: isn't any people in them as you're observing the media
64
Weaknesses of Content Analyses
- investigator bias: bring own prejudices and preconceptions very easily especially when defining operationalised codes
- culture bias: content tends to come from one culture, isn't representative
65
Strengths of Thematic Analyses
- flexibility: only notice themes as they emerge
- allows researchers own perspective: subjective nature
- few ethical issues: isn't any people in them
66
Weaknesses of Thematic Analyses
- can't use statistics: generates qualitative data
- subjectivity: may not spot themes they don't personally relate to
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