Research Methods Flashcards
1
Q
Independant variable
A
the variable the experiment manipulates/changes.
2
Q
Dependant variable
A
the variable you measure.
3
Q
Extraneous / Confounding variables
A
the variables the experimenters try to control / keep the same.
extraneous: affects both conditions.
confounding: affects only one condition.
4
Q
Operationalisation
A
both IV & DV have to be measurable & you must operationalise.
- to test effects of the IV.
e.g. love can be measured by; time spent, heart rate, questionnaire, body language etc.
5
Q
Confounding variable
A
-when additional variable that is not the IV (e.g. temp), corrupts or impacts the original study for one condition.
-this means the experimenter may have tested something else with the influence of the CV.
6
Q
Extraneous variables
A
Can be situational or dispositional.
-situational: noise, time of day, lighting etc.
-dispositional/ppt: age, height, IQ, health, gender etc.
7
Q
Investigator effects
A
Behaviour of investigator may affect ppts & the DV.
E.g. investigator may be more positive with 1 group.
Acting in a more/less positive way is an alternative IV. (confounding variable).
researcher bias
8
Q
Order effects
A
Repeating a test impacting the performance.
9
Q
Aim
A
What the researcher is looking into, doesn’t need to point out variables.
E.g. to investigate whether caffeine makes people more talkative.
10
Q
Hypothesis
A
Statement which states precisely what the researcher believe to be true about the target population.
- a testable statement.
- must be operationalised (contain measures of the data).
11
Q
Null hypothesis
A
States that there will be no effect (if research doesn’t produce results to accept experimental hypothesis).
E.g. there will be no difference in the quantity of nightmares whether they watch a rom-com & horror.
12
Q
Directional hypothesis
A
Hypothesis predicts results of the effect of the variables.
Clear difference between 2 conditions.
E.g. people who drink caffeine become more talkative than those who don’t.
13
Q
Non-directional hypothesis
A
States that there is a relationship between the variables but doesn’t specify which way it will go.
E.g. people who drink caffeine differ in terms of talkativeness compared to people who don’t.
14
Q
Experimental method
A
Involves the manipulation of an IV to measure the effect on the DV. Experiments may be lab, field or quasi.
15
Q
Reliability
A
consistency.
When a piece of research can be repeated in order to get the same results.
16
Q
Validity
A
accuracy.
If you are measuring what you’re claiming to measure.
17
Q
Demand characteristics (extraneous)
A
Ppt acts in a certain way because they know/guessed aim of experiments.
-help-U/please-U effect.
-screw-U effect.
18
Q
Please-U effect
A
Act in a way they think is expected & over-perform to try & please the experimenter.
19
Q
Screw- U effect
A
Deliberately underperform to sabotage the results of the study.
20
Q
Ecological validity
A
-if the surroundings represents natural surroundings.
E.g. lab is unnatural (low ecological validity).
E.g. field is natural (high ecological validity).
21
Q
Mundane realism
A
-how realistic the task is to something they would do in their everyday life.
-realistic & unambiguous.
E.g. learning a list of words (lacks mundane realism).
22
Q
Historical validity
A
-if the results would be the same across all eras.
-if so, high historical validity, if not, low historical validity.
23
Q
Population validity
A
-if the results can be applied to different groups of people.
-high PV would be applicable to multiple people.
24
Q
Androcentricity & Gynocentricity
A
Andro: applying results of males to females.
Gyno: applying results of females to males.
25
Investigator effects
-any cues from investigator that could encourage certain behaviours in the pt.
-may lead to fulfilling of investigators expectations.
-it is **unknowing** on part of the investigator.
E.g. spending more time with ppts in 1 condition than the other.
26
Randomisation
Effects of EVs are minimised through this.
-order of things should be randomised.
-random allocation.
-order of tasks randomised.
E.g. names in hat, lollipops sticks.
27
Standardisation
Easily repeated to achieve the same results meaning it has a **high level of control**.
Requires standardised instructions.
28
Experimental designs
-independent group.
-repeated measures.
-matched pairs.
29
Independent groups design
-When 2 separate groups of people are allocated in 2 different conditions of the experiment.
-This means ppts experience only 1 level of the IV.
-Then performance of 2 groups is compared.
30
Repeated measures
-All ppts experience both conditions of the experiment.
-They experience both levels of the IV.
31
Matched pairs
-Ppts are paired together on variables relevant to experiment.
-Ppts with the similar qualities are paired together to attempt to control the confounding variables (ppt variables).
-Then performance of 2 groups is compared.
32
Strengths & Limitations of Independent groups design
Limitations:
-ppts who occupied the groups have different ppt variables which may reduce validity.
-more time consuming & less economical as more people needed for conditions.
Strengths:
-avoids order effects/practice effects as ppts take part in only 1 condition.
-avoids demand characteristics as ppts are less likely to guess aims.
33
Strengths & Limitations of Repeated measures
Strengths:
-ppt variables are more controlled as same are used **higher validity**.
-less time/money consuming.
Limitations:
-order effects **need to counterbalance**.
-practice effects.
-could guess aim of experiment.
34
Strengths & Limitations of Matched pairs
Strengths:
-ppt only take part in single condition so order effects and demand characteristics are less of a problem.
-reduces ppt variables.
Limitations:
-time consuming & economical especially if they use a pre-test.
35
Strengths & Limitations of Lab experiments
Strengths:
-high control so more likely that affect on DV is due to manipulation of IV. **easier to establish cause & effect**. High internal validity.
-easy to replicate.
Limitations:
-may lack generalisability as lab may be artificial. (Low external validity).
-demand characteristics.
-low mundane realism.
36
Strengths & Limitations of Field experiments
Strengths:
-higher mundane realism (high external validity).
Limitations:
-lack of control of CVs & EVs. Means cause & effect is more difficult to establish.
-ethical issues. If ppts are unaware they’re being studied they cannot consent.
37
Natural experiments
-Similar to lab or field as researcher measures effect of IV on DV.
-However, diff as researcher has no control over IV & cannot change it. (Someone or something else causes IV to change).
-E.g. before & after a natural disaster.
38
Strengths & Limitations of Natural experiments.
Strengths:
-provide opportunities for research (Rutter et al).
-high external validity as they involve study of real world issues.
Limitations:
-rare.
-ppts may not be randomly allocated to experimental conditions.
-may be conducted in a lab - lacks realism & caused demand characteristics.
39
Quasi experiments
-Have an IV that’s based on an existing difference between people.
-Not manipulated and can’t be changed.
-E.g. experimenting phobia and non phobia.
Like a natural experiment, DV could be naturally occurring or devised.
40
Strengths & Limitations of Quasi experiments
Strengths:
-carried out under controlled conditions & shares some strengths as lab.
Limitations:
-cannot randomly allocated ppts to conditions. (Confounding variables).
-IV isn’t deliberately changed so you can’t claim that IV caused observed change.
41
Population
-Refers to large group of individuals that a particular researcher is interested in studying.
-This is often called the **target population**
-It is impossible to include all members of TP, so researcher uses a **sample**.
-Ideally, sample is representative of TP & results can be generalised to TP.
-Most samples contain degree of bias.
42
Random sampling
All members of TP have an equal change of being selected.
Obtain full list and use random selected or put names in a hat.
43
Evaluation of Random sampling
:) unbiased so confounding/extraneous variables are equally distributed. Increases internal validity.
:) easy.
:( difficult & time consuming.
:( unrepresentative.
:( selected ppts may refuse to participate.
44
Systematic sampling
When every nth member of TP is selected.
Produces a sampling frame (list of TP).
45
Evaluation of Systematic sampling
:) objective.
:( time consuming.
:( may refuse to take part.
46
Stratified sampling
Reflects the proportion of people in subgroups in the TP.
Researcher identifies diff strata in population & works out proportions.
E.g. 10% whites in TP = 10% whites in sample.
47
Evaluation of Stratified sampling
:) representative.
:) generalisable.
:( cannot reflect every way in which people are different.
:( time consuming.
48
Opportunity sampling
Researchers simply select anyone who’s available.
Ask whoever is around.
49
Evaluation of Opportunity sampling
:) convenience sampling.
:) less costly.
:( unrepresentative.
:( not generalisable.
:( researcher bias.
50
Volunteer sampling
Ppts select themselves to be part of research (self selection).
Possibly through advert/poster.
51
Evaluation of Volunteer sampling
:) easy & minimal effort.
:( volunteer bias (could attract certain profile of person).
:( may want to impress researcher.
:( time consuming.
52
Ethical issues
Ethical issues arise when a conflict or dilemma arises between ppts rigjt & researchers needs to gain valuable & meaningful findings.
53
British Psychological Society (BPS)
-BPS has a code of ethics including a set of ethical guidelines.
-All psychological studies carried out must adhere to these rules.
-Psychologists must need valid justify able reasons to break ethical guidelines.
-Implemented by ethics committees.
54
Informed consent
-Informing ppts about aims of research and what their data will be used for, as well as the **right to withdraw**.
-Ppts must make informed judgement about their participation without feeling coerced.
-Researchers may feel informed consent makes their study unnaturalistic & meaningless.
55
Dealing with Informed consent
Ppts must be issued with consent form with relevant info.
Ppts under 16 require parental consent.
Other ways of gathering consent:
- presumptive consent: ask a similar group of ppts if the study is acceptable.
- prior general consent: ppts guve consent to take part in a no. of studies including one involving deception.
- retrospective consent: ppts asked for consent during debrief, after partaking in study.
56
Deception
Deliberately misleading or withholding info from ppts.
This means lack of proper informed consent.
Despite this, there’s occasions where it’s acceptable as it may affect their behaviour.
57
Protection from harm
Ppts shouldn’t be placed in anymroe harm than within their daily lives.
They must be protected from physical and psychological harm & emphasising right to withdraw.
58
Dealing with Deception & Protection from harm
Ppts just be given a full **debrief**. Ppts must be made aware of true aims and all details (existence of other groups).
Ppts should be told what their data will be used for & given the right to withhold data.
Research should provide contacts for counselling.
59
Privacy & Confidentiality
Ppts given right to control info about themselves - right of privacy, if invaded then confidentiality must be protected (under data protection act).
E.g. giving ppts numbers rather than revealing names.
60
Dealing with confidentiality
Maintain anonymity & refer to ppts through numbers when writing up investigation.
Could also use initials.
During briefing & debriefing tell ppts that their data is protected.
61
Cost benefit analysis
Responsibility of ethics committee (BPS) to weigh up costs & benefits of research proposals to decide whether a research study should go ahead.
Costs:
-harm for ppts.
-time consuming.
-flawed/biased conclusions.
Benefits:
-could pay ppts.
-might develop theories.
-ppts might learn something.
-funding allocated to certain places.
62
Punishment for unethical research
May be banned from practising as a psychologist. However it’s not considered a legal matter.
63
Qualitative data
-expressed through words.
-observations/interviews/notes from counselling session.
-take into account feelings & opinions.
-subjective.
**VALIDITY**
64
Evaluation of Qualitative data
:) richer data & broad.
:) greater external validity.
:) representative of complex human nature.
:( harder to analyse & draw conclusions.
:( has low reliability.
:( time consuming.
65
Quantitative data
-numerical data.
-scores/charts.
-objective data.
-surveys/experiments/scales.
66
Evaluation of Quantitative data
:) more reliable data.
:) easily replicated.
:) easy to analyse.
:( can produce narrow & over simplified info.
:( may fail to represent real life.
67
Self-report techniques
Answered by ppts themselves (interviews/questionnaires).
68
Questionnaires consist of?
-pre set list of written questions.
-ppts respond to questions.
-often used to find out how people think or feel.
-often used in experiments to measure the DV.
69
Open & Closed quedtions
Closed: fixed answer & no elaboration.
Open: more vague & opportunity to expand.
70
Types of Closed questions
1. Likert scales
2. Rating scales
3. Fixed-choice option
71
Likert scales
Where the respondent indicates their agreement (or otherwise) with a statement using a scale of usually five points.
Ranges from strongly agree to strongly disagree with each no. labelled.
72
Rating scales
A rating scale works in a similar way but gets respondents to identify a value that represents their strength of feeling about a particular topic.
E.g.
very entertaining 1 2 3 4 5 not entertaining at all.
73
Fixed-choice option
Includes a list of possible options & respondents are required to indicate through ticking or circling the ones that apply to them.
74
Strengths of questionnaires
-can collect data easily & quickly.
-cost effective.
-less intimidating (higher validity).
-easy to quantify.
-less demand characteristics.
-less social desirability - response bias.
-representative (lots of people).
-easy to compare (set questions).
75
Limitations of questionnaires
-may not be returned.
-ppts may not understand question (lower validity).
-low response rate.
-unreliable responses (methodological error).
-restricted answess.
-open questions are unclear & hard to analyse.
-biased sample (people who are likely to partake).
-require access (online).
-requires people to be able to read/write.
-language barrier.
76
Interviews
Face to face conversations where the interviewer asks questions.
-can be done over the online.
-1 to 1 basis.
-must follow an interview schedule.
3 types:
- unstructured
- structured
- semi-structured
77
Structured interview
-face to face questionnaire.
-script with pre-set questions
78
Strengths & Weaknesses of Structured interviews
-easy to analyse
-way to compare
-replicable
-reliable
-lacks depth
-unable to elaborate
79
Unstructured interview
-no set questions
-works like a conversation.
80
Strengths & Weaknesses of an Unstructured interview
-interviewer can build a rapport, so ppts feels more comfortable answering questions.
-responses can be followed up with questions to gain insight + receive unexpected info.
-flexible.
-better validity.
-hard to analyse.
-time consuming.
-inconsistent.
-hard to compare.
81
Semi-structured interview
-like a job interview.
-list of suggestions for questions, but the interviewer can go off course & ask for elaboration.
-follow ups.
82
Strengths & Weaknesses of an Unstructured interview
-easy to compare because same questions used
-interviewer can ask follow up questions for ppts to elaborate on ideas
- rapport can be built
-highly trained interviewer
83
Why are observations important?
-allow researchers to study what people do without having to ask them
-allows behaviours to be observed in natural or observed settings
-study complex interactions between variables
84
Naturalistic observations (advantages & disadvantages)
-In a real life setting i.e studying workers in a factory
-high external validity likely to show more natural behaviour, as its easier to generalise
-fewer demand characteristics
-lack of control = hard to replicate.
-unreliable.
-uncontrolled - confounding & extraneous variables.
85
Controlled observations (advantages & disadvantages)
Aspects of environment are controlled and some variables manipulated, to give ppts same experience, Often conducted in a lab (Ainsworth and Bandura).
-reliable.
-easy to compare.
-harder to apply to real life.
-social desirability/demand characteristics effect.
86
Overt observations (advantages & disadvantages)
When participants are aware that their behaviour is being observed & they’ve given informed consent. (Zimbardo)
-no ethical issues.
-risk of demand characteristics and social desirability bias.
-researcher bias.
87
Covert observations (advantages & disadvantages)
Where participants are unaware that they’re the focus of a study & their behaviour is being observed. (Ainsworth)
-less chance of demand characteristics.
-higher internal validity.
-ethical issues (deception & lack of informed consent).
88
Participant observations (advantages & disadvantages)
When the observer interacts with the group they are studying.
-higher external validity.
-too subjective.
-researcher bias.
89
Non participant observations (advantages & disadvantages)
When the researcher stays separate from the person they’re observing.
-objective.
-less validity.
90
Primary data & evaluation
Refers to the original data collected specifically for the purpose of the investigation by the researcher. First hand info gathered. (observation, questionnaire etc).
:) required info
:( requires time & effort
91
Secondary data & evaluation
Data that’s been collected by somebody other than the researcher. Exists before psychologist begins research (journal, statistics, books).
:) inexpensive
:) easily accessible
:( variation in accuracy & quality of the data.
:( challenges validity of conclusion.
92
Meta analysis & evaluation
Process of combining all findings from a number of studies on a particular topic. Aims to produce overall statistical conclusion. Uses secondary data.
:) larger & varied samples
:) generalisable to larger populations
:) increased validity
:( prone to publication bias
:( researcher may leave out insignificant info.
:( irrelevant & unrepresentative
93
What is the ‘measures of central tendency’?
‘Averages’ which give us information about the most typical values in a set of data.
94
What are descriptive statistics?
The use of graphs, tables and summary statistics to identify trends and analyse sets of data.
95
The mean (average)
-most sensitive of the measures of central tendency as it includes all of the scores/values in the data set within the calculation= most representative
-harder to calculate than other measures
-cannot be used for categorised data
-can be affected by extreme values
96
Categorised data
-can be divided into different categories but cannot be ordered/measured.
-can be nominal i.e hair colour, binary i.e has a pet or doesn’t and ordinal i.e ranking things 1st,2nd and 3rd.
97
The median
-easily identified (just find the middle of the 2 medians in an even number of values).
-easier to calculate than the mean
-unaffected by extremes.
-less representatives as it doesn’t not use all scores and ignores the actual highest/lowest values.
98
The mode
-very easy to calculate.
-data is often multi-modal so doesn’t show much.
-unrepresentative as it doesn’t use all scores.
-sometimes the only method that can be used.
99
Measures of dispersion
Based on the spread of scores: how far they vary from one another.
100
The range
-taking the lowest score from the highest and (usually) +1.
-adding 1 that allows for the fact that raw scores are usually rounded when recorded.
101
Range evaluation
-shows the overall spread of data in a set.
-easy to calculate.
-may not be representative of the data if there are extreme values at the top/bottom of the dataset.
102
Standard deviation
-calculate mean, subtract it from each score to get the deviation, square the deviation, find the total of the squared deviations, divide that by the number of scores in the set minus 1, find the square root of this.
-answer is how many values away scores lie from the mean.
103
Standard deviation evaluation
-provides an accurate measure of data spread as it takes all into account.
-provides useful information about how individual scores relate to each other and to the mean.
-gives a measure of the spread of the data as lower SD mean there was little variations between the scores.
-it is harder to calculate than the range.
104
Bar charts
-used when data is divided into categories (discrete data).
-difference in mean value can be easily seen.
-bars are separate to show different conditions.
-frequency is plotted on y axis and condition is plotted on x axis.
105
Histograms
-data is continuous not discrete(bars touch each other).
-x axis is equal sized intervals of a single categories.
-y axis represents the frequency within each interval.
106
Scattergrams
-depict associations between co variables.
-either co variable occupies the x axis and y axis and each point on the graph corresponds to the x and y position of the covariables.
107
Content analysis
An analysis on the content of something.
-can involve turning qualitative data to quantitative data.
-involves creating **coding systems** before analysing the material.
108
How to carry it out
1. Sampling - decide the material you’re sampling.
2. Pilot - familiarise with material.
3. Common themes - coding categories.
4. Tally - record number of occurrences of each category.
5. Compare - data & what it suggests & what to use.
109
Behavioural categories
-breaking up target behaviours into categories that are precisely defined, observable and measurable.
-made up of what the researcher sees+hears.
A: -can make data more structured and objective(if follows 3 criteria).
D: -must ensure all possible forms of behaviour are included in checklist, no ‘dustbin’ category to dump behaviours
-categories should be exclusive and not overlap.
110
Event sampling
-tally the number of times a behaviour occurs in a target individual/group
-A: -if behaviour is on list, always will be recorded
-helps catch behaviours that might be missed
D: -miss detailed behaviour if event is too complex
-need lots of observers.
111
Time sampling
-record behaviours within a pre-established time frame
A: more flexible are able to record more unexpected behaviours
D: can miss behaviour that is not in time frame--} unrepresentative of behaviour as a whole.
112
Content analysis: Cumberbatch & Gauntlett (2005)
-commissioned by OFCOM.
-looked at how smoking, alcohol + drug abuse was shown in TV shows watched by 10-15 year olds.
-looked at 246 programmes(76% soap operas) between Aug-Oct 2004 before
9pm watershed.
found that there were:
-1.2 drug related incidences per
hour(bad light).
-3.4 smoking incidences per
hour(neutral/negative light).
-12 drinking incidences per
hour(neutral/negative light).
-only 4% of shows tended not to show any of these(mostly game shows).
113
Strengths of Content analysis
-circumnavigates many ethical issues since data usually exists already publically.
-high external validity & may access data of a sensitive nature with author’s consent.
-flexible as it produces qualitative & quantitative data depending on research aims.
114
Limitations of Content analysis
-indirectly studied so communications produced are usually analysed outside the context where it occurred.
-researcher may attribute to opinions to the speaker that weren’t intended originally.
-however, most analysts acknowledge bias and reference they in their final report.
-subjective.
-lack of internal validity.
115
Correlations
-involves comparing data from same ppts.
-test relationship between 2 variables.
-alternative hypothesis = will be about
relationship not difference between conditions.
-have covariables(2 factors that
compare to each other).
116
Difference between Correlations & Experiments
-Correlations do not manipulate or have IV + DV whereas exps looks at effect of
IV on DV.
-difference between variables vs relationship/association between variables.
-correlational designs are studies i.e observations, self report and exp use experimental design i.e matched pairs, ind groups, repeated measures.
117
Correlations are NOT repeated measures
-comparing data from same ppts.
-at least 2 variables taken.
-**relationship** is tested between 2 scores.
-hypothesis focussed on RELATIONSHIP.
Alternative hypotheses.
-there will be a positive/negative correlation (directional).
-there will be a relationship (non-directional).
118
Types of correlations
Positive: co-variables increases with the other co-variable. Positive gradient.
Negative: as one co variables increases the other co variable decreases. Negative gradient.
Zero: no relationships between co variables.
119
Correlation coefficient
-shows us the numerical strength and direction of the relationship between co-variables as a number between
-1 (perfect negative correlation) and +1 (perfect positive correlation).
-If its higher than 0.8 it has a strong positive correlation.
-lower than -0.8 has a strong negative correlation.
120
Strengths of Correlations
-highlight potential causes and relationships between variables, which can be tested experimentally later --} may be unexpected, highlight pattern.
-little manipulation of data so it is easier and more economical to carry
out(secondary/existing data).
121
Weaknesses of Correlations
-correlation does not mean causation, even if a relationship exists(could be coincidence, unknown direction of causality).
-cannot establish cause and effect.
-unknown co variable/3rd variable may cause relationship.
-data is not operationalised so it may lack validity.
-may have inaccurate implications.
122
Case study
-in-depth, detailed investigations of one individual/group/event.
-usually conducted since individuals are rare/unique.
-used a range of RMs.
-could happen over extended time period.
-RM originated in clinical medicine (the case history ie. Patients personal history).
123
What is Triangulation
Use of different approaches to gather data to improve the credibility of the conclusions.
Increased validity of the case study.
124
Strength of Case studies: Rich & detailed qualitative data & high ecological validity
-more valid due to qualitative data.
-rich data=deeper insight, especially when presented atypical behaviour.
-less superficial than experiment & questionnaires lack mundane realism.
125
Strength of Case studies: Avoids practical/ethical issues
-allows for investigation of otherwise impractical/unethical situations.
-insight into subjective experiences.
126
Strength of Case studies: Triangulation
-helps generalise new hypotheses.
-developments of ideas draws together results from many areas.
127
Limitation of Case studies: Subjectivity bias causes low validity
-depends on who’s interpreting the data.
-scope for observer bias & subjective opinions if psychologist intrudes in the assessment of what data means.
-Freud criticised for distorting info to fit his own theories (Little Hans).
128
Limitation of Case studies: Generalisability
-based on one example so cannot generalise to other times, cultures, people places etc.
-unsure if conclusions can be applied elsewhere.
-unsure if investigation is representative.
129
Limitation of Case studies: Lack of replication & Time-consuming
-very unique & untypical behaviour.
-lacks **reliability**.
-time consuming to gather data from multiple sources (triangulation).
130
Pilot studies
-small scale.
-trial run.
-may include direct feedback from ppts.
-adjustments can be made.
-can save time/money in the long run.
