Research Methods Flashcards
1
Q
Define ‘experimental method’
A
Manipulation of IV to measure effect on DV.
Experiments may be laboratory, field, natural or quasi.
2
Q
What is the ‘independent variable (IV)’?
A
Some aspect of the experimental situation that’s manipulated by researcher/changes naturally - so effect on the DV can be measured.
3
Q
What is the ‘dependent variable (DV)’?
A
Variable measured by researcher. Any effect on DV should be caused by the change in IV.
4
Q
Define ‘aim’
A
A general statement of what the researcher intends to investigate; the purpose of the study.
5
Q
Define ‘hypothesis’
A
A clear, precise, testable statement that states relationship between variables to be investigated. Stated at outset of study.
6
Q
Define ‘directional hypothesis’
A
States the direction of the difference or relationship.
7
Q
Define ‘non-directional hypothesis’
A
Does not state the direction.
8
Q
Define ‘variables’
A
Any ‘thing’ can vary/change within investigation. Variables generally used to determine if changes in one thing result in changes to another.
9
Q
Define ‘operationalisation’
A
Clearly defining variables in terms of how they can be measured.
10
Q
What is meant by an ‘extraneous variable (EV)’?
A
Any variable, other than IV, that may have an effect on DV if not controlled. EVs essentially nuisance variables - don’t vary systematically with IV.
11
Q
What is meant by a ‘Confounding variable (CV)’?
A
Any variable, other than IV, may affect DV so can’t be sure of true source of changes to DV. Confounding variables vary systematically with IV.
12
Q
Define ‘demand characteristics’.
A
Any cue from researcher/situation that may be interpreted by participants as revealing purpose of investigation. May lead to participant changing behaviour.
13
Q
Define ‘investigator effects’.
A
Any effect of the investigator’s behaviour (conscious/unconscious) on DV. May include everything from design of study to selection of, and interaction with, participants during the research process.
14
Q
Define ‘randomisation’ and give an example.
A
Use of chance to control for effects of bias when designing materials and deciding order of conditions.
E.G. using random allocation- in an independent groups design with 4 conditions you might randomly allocate your selected participants into each of the groups.
15
Q
Define ‘standardisation’
A
Using exactly same formalised procedures and instructions for all participants in research study.
16
Q
Define ‘experimental design’
A
Different ways testing participants can be organised in relation to experimental conditions.
17
Q
What is ‘independent groups design’
A
Participants are allocated to different groups where each group represents one experimental condition.
18
Q
What is ‘repeated measures design’?
A
All participants take part in all conditions of the experiment.
19
Q
What is ‘matched pairs design’?
A
Pairs of participants are first matched on some variable(s) that may affect the DV. Then one number of the pair is assigned to Condition A and the other to Conditions B.
20
Q
Define ‘random allocation’.
A
Attempt to control for participation variables in independent groups design. Ensures each participant has same chance of being in one condition as any other.
21
Q
Define ‘counterbalancing’.
A
Attempt to control effects of order in repeated measures: half experience conditions in one order, and the other half in opposite order.
22
Q
What is a ‘laboratory (lab) experiment’?
A
Controlled environment within which the researcher manipulates the IV and records the effect on DV, whilst maintaining strict control of extraneous variables.
23
Q
What is a ‘field experiment’?
A
Takes place in a natural setting within which the researcher manipulates the IV and records the effect on the DV.
24
Q
What is a ‘natural experiment’?
A
An experiment where the change in the IV is not brought about by the researcher but would have happened even if the researcher had not been there. The researcher records the effect on the DV.
25
What is a 'quasi-experiment'?
Study that's almost an experiment but lacks key ingredients. IV not been determined by anyone (researcher/any other person) - the 'variables' simply exist, (old or young) . Strictly speaking - not an experiment.
26
Strengths of Independent Group Design.
(+) Order effects not problem - participants only experience one condition.
(+) Less likely guess aim as only experience one condition and don't see any manipulation.
27
Limitations of Independent Group Design.
(-) Participants who occupy different groups not same. If researcher finds difference between groups on DV it may be due to individual differences (participant variables) rather than IV.
(-) Design less economical than repeated measures as each participant contributes single result only. Twice as many participants needed to produce equivalent data to that used in repeated measures.
28
Strengths of Repeated Measures Design.
(+) Participant variables are controlled as you compare each participants score in one condition their score in another.
(-) Fewer participants are needed because they take part in more than one condition which means the study is more economical.
29
Limitations of Repeated Measures Design.
(-) As each participant has to do at least two tasks then the order of these tasks may be significant (i.e. there are order effects e.g. tiredness). Order acts as a confounding variable.
(-) More likely people will work out the aim of the study when they experience all conditions of the experiment. For this reason demand characteristics tend to be more of a feature of repeated measures than independent groups.
30
Strengths of Matched Pairs Design.
(+) An attempt to reduce participant variables as all participants are matched on important variables.
(+) Participants only take part in a single condition so order effects are less of a problem.
(+) Participants are less likely to guess the aim of the study (demand characteristics) because they only take part in a single condition and see no manipulation.
31
Limitations of Matched Pairs Design.
(-) Participants can never be matched exactly, even when identical twins are used there will still be some participant variables.
(-) Matching is time-consuming, expensive and requires a larger sample size which makes it less economical than other designs.
32
Strengths of lab experiments.
(+) High control of extraneous variables so researcher can ensure any effect on DV likely to be the result of the IV. (More certain about cause and effect- internal validity).
(+) Replication more possible than in other types due to the level of control Ensures new extraneous variables not introduced when repeating experiment.
33
Limitations of lab experiments.
(-) Lack of generalisability- the lab artificial and not like everyday life. In an unfamiliar context participants may behave in unusual ways so their behaviour cannot always be generalised beyond the research setting (low external validity).
(-) Participants are usually aware they are being tested which may give rise to unnatural behaviour or demand characteristics.
(-) Lab based tasks may not represent real life experience which means they will lack mundane realism.
34
Strengths of field experiments.
(+) Higher mundane realism than lab experiments because the environment is more natural. This means they produce behaviour that is more valid and authentic.
(+) Higher external validity, especially if participants are not aware they are being studied.
35
Limitations of field experiments.
(-) Loss of control of extraneous variables could mean cause and effect between the IV and DV in field studies may be more difficult to establish and precise replication is not possible.
(-) Ethical issues- if participants are unaware they are being studied they cannot consent to being studied which means research may invade privacy.
36
Strengths of natural experiments.
(+) Provide opportunities for research that may not otherwise be undertaken for practical or ethical reasons, such as the studies of institutionalised Romanian Orphans.
(+) High external validity because they involve the study of real-life issues and problems as they happen, such as the effects of a natural disaster on stress levels.
37
Limitations of natural experiments.
(-) Naturally occurring events happen rarely, reducing the opportunities for research. This may also limit the scope for generalising findings to other situations.
(-) Participants may not be randomly allocated to experimental conditions. This means the researcher may be less sure whether the IV affected the DV.
38
Strengths of quasi- experiments.
(+) Carried out in controlled conditions so have higher internal validity.
(+) Replication is possible because of the high level of control.
39
Limitations of quasi-experiments.
(-) Participants may not be randomly allocated to experimental conditions. This means the researcher may be less sure whether the IV affected the DV.
(-) Confounding variables are a problem with this design e.g. if comparing older with younger participants then memory would always have an effect on the DV.
40
Define 'population'.
A group of people who are the focus of the researcher's interest, from which a smaller sample is drawn
41
Define 'sample'.
A group of people who take part in a research investigation. The sample is drawn from a (target) population and is presumed to be representative of that population i.e. it stands 'fairly' for the population being studied.
42
What are 'sampling techniques'?
The method used to select people from the population.
43
What is meant by the term 'bias' in the context of sampling?
Certain groups may be over/under-represented selected sample selected. E.g., too many younger people or of one ethnic origin.
Limits extent to which generalisations can be made.
44
Define 'generalisation'.
The extent to which findings and conclusions from a particular investigation can be broadly applied to the population. This is made possible if the sample of participants is representative of the population.
45
What is a random sampling?
Every person in target population has equal chance of being selected.
Lottery method. Number randomly generated (hat/computer).
46
Strengths of random sampling.
(+) Free from researcher bias. The researcher has no influence over who is selected which prevents them from choosing people who may support their hypothesis.
(+) Produces a representative sample as each member of the target population has an equal chance of being selected.
47
Limitations of random sampling.
(-) Difficult and time consuming to conduct. A complete list of the target population may be extremely difficult to obtain.
(-) Could still end up with an unrepresentative sample.
(-) Selected participants may still refuse to take part which means you end up with more of a volunteer sample.
48
What is systematic sampling?
Participants selected using set patter. Every nth person from list of target population.
49
Strengths of systematic sampling.
(+) Avoids researcher bias, once the system for selection has been established the researcher has no influence over who is chosen.
(+) Fairly representative, it would be possible but quite unlucky to get an all male-sample through this method.
50
Limitations of systematic sampling.
(-) Selected participants may still refuse to take part which means you end up with more of a volunteer sample.
51
What is stratified sampling?
Participants selected according to their frequency in the target population.
Subgroups ('strata') identified. Relative percentages of the subgroups in the population are the calculated and reflected in the sample.
52
Strengths of stratified sampling.
(+) Avoids researcher bias, once the target population has been sub-divided into strata, the participants that make up the numbers are randomly selected and beyond the influence of the researcher.
(+) A highly representative sample because it is designed to accurately reflect the composition of the population. This means that generalisation of findings becomes possible.
53
Limitations of stratified sampling.
(-) Stratification is not perfect, the identified strata cannot reflect all the ways that people are different, so complete representation of the target population is not possible,
(-) Selected participants may still refuse to take part which means you end up with more of a volunteer sample.
54
What is opportunity sampling?
Those simply available i.e. ones nearest/easiest to obtain.
| Ask people nearby - students in class, people who walk past you in shopping centre.
55
Strengths of opportunity sampling.
(+) Convenient- this method saves the researcher a good deal of time and effort and is much less costly in terms of time and money than other sampling techniques.
56
Limitations of opportunity sampling.
(-) Unrepresentative of the target population as it is drawn from a very specific area such as one street in a town so findings cannot be generalised to the target population.
(-) Researcher bias is high as they have complete control over the selection of participants so they may select people who will support their hypothesis.
57
What is a volunteer sampling?
Participants select themselves for the research. Advertise. Place an advert in a newspaper/noticeboard and participants come to you.
58
Strengths of volunteer sampling.
(+) Requires minimal input from the researcher and so is less time-consuming than other forms of sampling.
59
Limitations of volunteer sampling.
(-) Volunteer bias is a problem. Asking for volunteers may attract a certain type of person, that is, one who is helpful, keen and curious. This may affect how far findings can be generalised.
60
What are 'ethical issues' in Psychology?
Arise when conflict exists between rights of participants and goals of research to produce authentic, valid and worthwhile data.
61
What is the BPS code of ethics?
A quasi-legal document produced by the British Psychological Society (BPS) - instructs psychologists in UK about what behaviour is/isn't acceptable when dealing with participants. Built around four major principles: respect, competence, responsibility and integrity.
62
What is informed consent?
Participants should be able to make informed judgement about taking part.
Should be made aware of aims, procedure, their rights and also what their data will be used for.
63
How to deal with informed consent?
Consent letter/form detailing all relevant information that may affect their decision to take part. Agreement - signed. Under 16 require parental consent
64
What is protection from harm?
Participants should be at no more risk than everyday life.
Protected from both physical and psychological harm.
Includes embarrassment, feeling inadequate or stressed.
65
How to deal with protection from harm and deception?
Give full debrief. Made aware of true aims and any details not supplied during study (other groups/conditions).
Also told what data will be used for and must be given right to withhold/withdraw. Important if give retrospective consent.
Reassurance of normal/typical behaviour and if severe embarrassment/stress should be given counselling.
66
What is deception?
Deliberately misleading/withholding info.
| If done so participants can't give fully informed consent. May be justified if doesn't cause distress..
67
What is confidentiality and privacy?
Participants have right to control info about themselves.
If privacy invaded, confidentiality should be respected like names and other personal details anonymous under Data Protection Act. Extends to where it takes place like locations aren't named.
68
How to deal with confidentiality?
If personal details held, must be protected. However, more usual to simply record no details (maintain anonymity). Refer to participants using numbers/initials like in case studies.
Also standard practice during briefing and debriefing reminded data will be protected throughout process.
69
What is meant by 'pilot study' and why is it useful?
A small-scale version of an investigation that takes place before the real investigation is conducted. The aim is to check that procedures, materials, measuring scales, etc., work and to allow the researcher to maker changes or modifications if necessary.
70
How are pilot studies important for self-report methods?
E.g., questionnaires and interviews - helpful to try out questions and remove/reword those that are ambiguous/confusing.
71
How are pilot studies important for observational studies?
Way of checking coding systems before real investigation.
| May be important part of training observers.
72
What is a single-blind procedure?
Details like conditions participants are in kept.
Won't know if in a condition if there's another condition.
Attempt to control confounding effects of demand characteristics.
73
What is a double-blind procedure?
Neither participants nor researcher who conducts study aware of aims.
Important for drug trials.
Treatments administered by someone independent of investigation and doesn't which drug is real/placebo.
74
Define 'naturalistic observation'.
Watching and recording behaviour in the setting within which it would normally occur.
75
Define 'controlled observation'.
Watching and recording behaviour within a structured environment, i.e. one where some variables are managed.
76
Define 'covert observation'.
Participants' behaviour is watched and recorded without their knowledge and consent.
77
Define 'overt observation'.
Participants' behaviour is watched and recorded with their knowledge and consent.
78
Define 'participant observation'.
The researcher becomes a member of the group whose behaviour he/she is watching and recording.
79
Define 'non-participation observation'.
The researcher remains outside of the group whose behaviour he/she is watching and recording.
80
Evaluation (strengths and limitations) of naturalistic observations.
(+) High external validity as the findings can be generalised to everyday life because the behaviour is studied within the environment in would normally occur.
(-) Lack of control makes replication of the observation difficult.
(-) Extraneous variables due to lack of control make it more difficult to judge any pattern of behaviour.
81
Evaluation (strengths and limitations) of controlled observations.
(+) Extraneous variables less of a factor due to high control, so observation can be easily repeated.
(-) Produce findings that can't be readily applied to real-life settings as conducted in artificial setting.
82
Evaluation (strengths and limitations) of covert observations.
(+) Don't know being watched so removes problem of participant reactivity and ensures observed behaviour is natural. Increases validity.
(-) Ethics may be questioned as people may not wish to have their behaviours notes down. E.g. shopping is a public behaviour but the amount people spend on their shopping is highly personal.
83
Evaluation (strengths and limitations) of overt observations.
(+) More ethically acceptable because the participants are aware they are being observed.
(-) Participant reactivity is a problem as participants knowledge of being watched may significantly influence their behaviour.
84
Evaluation (strengths and limitations) of participant observations.
(+) The researcher can experience the situation as participants do; giving them increased insight into the lives of participants. This may increase validity of the findings.
(-) The researcher may come to identify too strongly with those they are studying and lose objectivity. Also known as 'going native' when the line between being a researcher and being a participant becomes blurred.
85
Evaluation (strengths and limitations) of non-participant observations.
(+) Allow the researcher to maintain an objective distance from their participants so there is less danger of them 'going native'.
(-) Researchers may lose the valuable insight to be gained in participant observation as they are too far removed from the people and behaviour they are studying.
86
What are 'behavioural categories'?
When target behaviour broken up into observable and measurable components.
Similar to operationalisation.
Target behaviour like affection could be observed if see hugging, smiling, holding hands etc.
No inference should be made
87
Define 'event sampling'.
A target behaviour or event is first established then the researcher records this event every time it occurs.
88
Define 'time sampling'.
A target individual or group is first established then the researcher records their behaviour in a fixed time frame, say, every 60 seconds.
89
What is an unstructured observation?
Researcher writes down everything they see.
Tends to produce accounts rich in detail. May be appropriate when observations are small in scale and involve few participants (therapy/counselling)
90
What is a structured observation?
Simplify target behaviours e.g. 'aggression' - specific acts (pushing, yelling etc).
Allow quantifying of observations using pre-determined list of behaviours and sampling methods.
91
Evaluation (strengths and limitations) of structured design observations.
(+) Use of behavioural categories makes recording of data easier and more systematic.
(+) Likely to produce quantitative data means analysing and comparing the behaviour more straightforward.
(-) No opportunity to record relevant data that lies outside of the set behavioural categories.
92
Evaluation (strengths and limitations) of unstructured design observations.
(+) Benefit from richness and depth of detail in data collected.
(-) Greater risk of observer bias as no objective behaviour categories.
(-) Produce qualitative data which is difficult to record and analyse.
93
Evaluation for behavioural categories.
Categories have to be structured and objective.
Should be observable, measurable and self-evident.
All possible forms of target behaviour. No 'dustbin category'.
Should be exclusive and not overlap.
- difficult to differentiate between 'smiling' and 'grinning'.
94
Evaluation for sampling methods.
Event - useful when target behaviour/event happens infrequently and could be missed if time sampling used.
However if event complex, observer may overlook important details.
Time - effective in reducing number of observations that have to be made.
However, those instances when behaviour sampled may not be representative of observation as a whole.
95
What is a 'self-report technique' in Psychology?
Any method in which a person is asked to state or explain their own feelings, opinions, behaviours and/or experiences related to a given topic.
96
Define 'interview'.
A 'live' encounter (face-to-face/ on phone) where interviewer ask set of questions to assess an interviewee's thoughts and/or experiences.
Three types: structured interview, unstructured interview, or semi-structured interview
97
What is an structured interview?
Pre-determined set of questions that are fixed in an order. Like a questionnaire but conducted face to face in real time
98
What is an unstructured interview?
Works a lot like a conversation. No set questions. General aim that certain topic will be discussed and interaction ten to be free-flowing.
Interviewee encouraged to expand answer as prompted.
99
What is a semi-structured interview?
List of questions that have been worked out in advance but interviewers also free to ask follow-up questions where they feel appropriate.
100
Strengths of structured interviews.
(+) Straightforward and easy to replicate due to their standardised format. This format also reduces differences between interviewers.
101
Limitations of structured interviews.
(-) Not possible, deviate from topic/ elaborate their points so may be a source of frustration for some participants.
(-) Risk of social desirability bias from participants more than with questionnaires as the interviewer is present.
102
Strengths of unstructured interviews.
(+) Much more flexibility so the interviewers can follow up on points as they arise and is much more likely to gain an insight into the worldview of the interviewee.
103
Limitations of unstructured interviews.
(-) Difficult to analyse data, the researcher may have to sift through much irrelevant information and drawing conclusions may be difficult.
(-) Risk of social desirability bias from participants more than with questionnaires as the interviewer is present.
104
Define 'questionnaire'.
A set of written questions (sometimes referred to as 'items') used to assess a person's thoughts and/or experiences
105
Define 'open questions' and give an example.
Questions for which there is no fixed choice of response and respondents can answer in any way they wish; for example, why did you take up smoking?
106
Define 'closed questions' and give an example.
Questions for which there is a fixed choice of responses determined by the question setter; for example, do you smoke? (yes/no)
107
Strengths of questionnaires.
(+) Cost effective- they can gather large amounts of data quickly because they can be distributed to large numbers of people.
(+) Can also be completed without the researcher being there (e.g. postal questionnaire) which reduces effort involved.
(+) Data is easy to analyse (particularly if the questionnaire uses fixed closed questions) so the data lends itself to statistical analysis.
108
Limitations of questionnaires.
(-) Responses may not always be truthful. Respondents may be keen to present themselves in a positive light which may influence their answers. This is called social desirability bias.
(-) Often produce response bias which is where respondents tend to reply in a similar way, for instance, always ticking yes.
(-) If questions are ambiguous the participants do not have an option of checking their understanding with a researcher which means they may interpret questions incorrectly.
109
Three ways to design questionnaires.
Likert Scale
Rating Scales
Fixed choice option
110
What is a likert scale in a questionnaire?
Respondents indicate their agreement (or otherwise)
111
What is a rating scale in a questionnaire?
Works similar to likert scale but gets respondents to identify a value that represents their strength about a particular topic.
112
What is a fixed choice option?
Includes list of possible options and respondents are required to indicate those that apply to them.
113
How are interviews designed?
Interview schedule - list of intended Qs.
Standardised - reduce interview bias. Typically take notes/ recorded & then analysed later.
Usually, participant and interviewer but group interviews may be appropriate in clinical settings.
One-to-one = quiet room (increases likelihood of opening up)
Start with neutral Qs to make participant feel comfortable/relaxed.
Also reminded that answer will be treated in the strictest confidence.
114
5 things to avoid when writing questions for and interview/questionnaire?
```
Overuse of jargon
Emotive language
Leading Questions
Double-barrelled
Double negatives
```
115
What is overuse of jargon?
Jargon - technical terms that are only familiar to those within a specialised feel/area.
116
What is emotive language?
If attitudes towards particular topic is clear from way in which Q is phrased.
Need to be neutral.
117
What is a leading question?
Guides respondent to particular answer.
E.g. when did you last driver over the speed limit?
Assuming person has broken speed limit at some point.
118
What is a double-barrelled question?
Two questions in one. Issue is participants may agree with one half but not the other.
119
What is a double-negative question?
Difficult to decipher.
I am not unhappy with my job.
= I happy with my job.
120
What is meant by the term 'correlation' in Psychology?
A mathematical technique in which a researcher investigates an association between two variables, called co-variables
121
What is meant by the term 'co-variables'?
The variables investigated within a correlation, for example height and weight. They are not referred to as the independent and dependent variables because a correlation investigates the association between the variables, rather than trying to show a cause and effect relationship.
122
Define 'positive correlation'.
As one co-variable increases so does the other. For example, the number of people in a room and noise are positively correlated.
123
Define 'negative correlation'.
As one co-variable increases the other decreases. For example, the following two co-variables: number of people in room and amount of personal space are negatively correlated.
124
Define 'zero correlation'.
When there is no relationship between the co-variables. For example, the association between the number of people in a room in Manchester and the total daily rainfall in Peru.
125
Define 'qualitative data'.
Data that is expressed in words and non-numerical (although qualitative data may be converted to numbers for the purposes of analysis).
From interviews, extracts of diaries/notes
126
Define 'quantitative data'.
Data that can be counted, usually given as numbers.
| Scores of participants, number of recall in experiments
127
Define 'primary data'.
Information obtained first-hand by researcher for purposes of a study.
In psychology, such data is often gathered directly from participants as part of an experiment, self-report or observation.
128
Define 'secondary data'.
Information already collected by someone else so pre-dates current research project.
In psychology, might include work of other psychologists/ government statistics.
129
What are 'descriptive statistics'?
The use of graphs, tables and summary statistics to identify trends and analyse sets of data.
130
What are 'measures of central tendency'?
The general term for any measure of the average value in a set of data.
131
Define 'mean'.
The arithmetic average calculated by adding up all the values in a set of data and dividing by the number of values there are.
132
Define 'median'.
The central value in a set of data when values are arranged from lowest to highest.
133
Define 'mode'
The most frequently occurring value in a set of data.
134
Define 'measures of dispersion'.
General term for any measure of the spread/variation in a set of scores
135
Define 'range'
Calculation of spread of scores by taking lowest value from the highest value and (usually) adding 1 as a mathematical correction.
136
Define standard deviation
Sophisticated measure of dispersion in a set of scores. Tells us how much scores deviate from the mean.
137
What is a 'scattergram'?
A type of graph that represents the strength and direction of a relationship between co-variables in a correlational analysis.
138
What is a 'bar chart'?
A type of graph in which the frequency of each variable is represented by the height of the bars.
For discrete data - categories
139
What is a 'histogram'?
Bars touch each other for continuous data
140
What is a 'line graph'?
Continuous data and use points connected by lines to show something changes in value
141
What's the difference between correlations and experiments?
Experiment - manipulation of IV to measure effect on DV.
Correlation - no maniuplation of a variable so can't establish cause and effect between covariables.
Could be influence of 'variables' (intervening variables)
142
Strengths of correlations
(+) Useful preliminary tool for research. Assessing strength and direction of relationship allows precise and quantifiable measure of how two variables are related. Suggests future research ideas.
Starting point before committing to environmental study.
(+) Quick + economical. No need for controlled environment and no manipulation of variables.
143
Limitations of correlations
(-) Lack of experimental manipulation and control can only tell us how variables related but not why. Can't demonstrate cause and effect so don't which co-variables is causing the other to change.
(-) Untested variable may cause relationship (third variable problem)
(-) Misuse/misinterpreted. Causal sometimes seen as ''facts
144
Evaluate qualitative data.
(+) More richness of detail. More broader in scope and gives participant licence to develop thoughts and feelings.
(+) Greater external validity than quantitative - more meaningful insight into participant's worldview
(-) Difficult to analyse, summarise statistically so patterns and comparisons may be hard to identify.
(-)Conclusions rely on subjective interpretations of researcher so may be subject to bias particularly if there's preconceptions
145
Evaluate quantitative data.
(+) Simple to analyse so easy comparisons can be made.
(+) Numerical means more objective and less open to bias
(-) Much narrower in scope and meaning. So may fail to represent 'real-life'.
146
Evaluate primary data.
(+) Fits the job. Authentic data for purpose. E.g questionnaires and interviews can be designed in such a way they specifically target info researcher requires.
(-) Require time and effort. Conducting experiment requires los of planning, preparation and resources.
147
Evaluate secondary data.
(+) Inexpensive/easy to access so requires minimal effort. May find desired information that already exists so no need to conduct primary data collection.
(-) Substantial variation in quality and accuracy of secondary data. At first may seem valuable and promising but may be out-dated/incomplete.
(-) May not quite meet researcher's needs or objectives.
148
What is a 'normal distribution'?
Symmetrical spread of frequency data that forms bell-shaped pattern. Mean, median and mode all located at the highest peak.
149
What is a 'skewed distribution'?
Spread of frequency data that is not symmetrical, where data clusters to one end.
150
What is a 'positive skew'?
Type of distribution. Long tail on right side of peak and most of distribution concentrated on left. Mode - at peak
Median - next
Mean - dragged to right ( higher extreme scores)
151
What is a 'negative skew'?
Type of distribution. Long tail on left side of peak and most distribution concentrated on right.
Mode - at peak
Median - next
Mean - dragged to left (lower extreme scores)
152
What is peer review?
Assessment of scientific work by specialists in same field to ensure that any research intended for publication is of high quality.
Experts should be objective and unknown to author/researcher,
153
What is the economy?
State of a country/region in terms of production and consumption of goods and services.
154
What are the main aims of peer review?
Allocate research funding.
To validate quality and relevance of research. All elements are assessed: formulation of hypothesis. methodology chose, statistical tests used and conclusions drawn.
To suggest amendments/improvements.
- minor revisions thereby improve report or in extreme circumstances conclude work inappropriate for publication and be withdrawn
155
Evaluate peer review
Anonymity - keep anonymity for honest appraisal. However, minority may use anonymity to criticise rivals. More likely as competing for limited funding. So some journals favour open reviewing whereby reviewers name made public.
Publication bias - tendency to publish significant results. Could mean research that doesn't meet criteria is ignored/disregarded. Creates false impression of psychology.
Burying ground-breaking research - may suppress opposition views to maintain the status quo within particular field. Critical to contradicting views and favour match views.
Established scientists more likely chosen. But findings that match opinion likely passed over new, innovative ideas. So slows rate of change.
156
Give two examples of implications of psychological research for the economy
Attachment research into the role of the father. Father's provide same support. Mother can work so increase contribution to economy.
Development of treatments for mental illness.
Absence from work costs economy £15 billion and 1/3 due to mental disorders. Research means quicker diagnoses and treatments so can manage condition effectively and return to work.
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What is a case study?
An in-depth investigation, description and analysis of a single individuals group, institution or event.
Involves qualitative data. But over forms of experiments may produce quantitative data.
Take place over long period of time (longitudinal) may involve getting extra info from family, friends.
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Strengths of case studies.
(+) Offer rich, detailed insight so may shed light on very unusual and atypical form of behaviour. May preferred to superficial form that may be collected form an experiment or questionnaire.
(+) Contribute to understanding of normal functioning. E.g. HM demonstrated ' normal' memory processing
(+) May generate hypotheses for future or revision of entire theory.
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Limitations of case studies.
(-) Generalising of findings issue with small sample sizes.
(-) Information collected based on subjective selection and interpretation of researcher.
(-) Using personal accounts from participants, family and friends may be prone to inaccuracy and memory decay. Decreases validity
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What is content analysis?
Research technique enables indirect study of behaviour by examining communications that people produce e.g., texts, email, TV, film and other media
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Describe coding and quantitative data in content analysis.
Coding is the initial stage. Some data has to be analysed may be large amounts. Needs to categorise into meaningful units. E.g. counting number of times behaviour/word appears to produce quantitative data.
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What is thematic analysis?
• Inductive and qualitative approach to analysis that involves identifying implicit/explicitly ideas without the data. Themes will often emerge once the data has been coded.
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What do 'inductive' mean?
'Inductive' as specific content drives the theme un-like typical content analysis where a set of pre-determined categories
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How does thematic analysis work in terms of obtaining qualitative data?
Any idea, explicit or implicit that is recurrent. Likely to be more descriptive.
Themes in broader categories. Researcher may collect new set of data to test validity of themes and categories.
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Strengths of content analysis.
Can circumnavigate many normal ethical issues. Things to study may already exist in public domain. Don't need to obtain permission.
Can produce both qualitative and quantitative data.
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Limitations of content analysis.
People tend to be studied indirectly so outside of context. Researcher may attribute opiniones to speaker that weren't originally intended.
To be fair, analysts clear about own biases influence research process and make reference to these in final report. However may still suffer from lack of objectivity, especially when descriptive form employed.
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What is reliability?
How consistent findings from investigation or measuring device are. Device - desirable if produces consistent results every time it's used.
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What is test-retest?
Assessing same person on two separate occasions.
Reliable if same/similar results.
Must be enough time that can't remember answers but not so longs views haven't changed.
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What is inter-rater observability?
Extent which there's agreement between two/more observers involved in observations of a behaviour. Correlate observations.
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How to improve reliability in questionnaires?
Should exceed +0.80. Re-write, e.g less ambiguous questions, Closed instead of open.
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How to improve reliability in interviews?
Same interviewer or same training so not leading. Structured interviews better.
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How to improve reliability in experiments?
Lab as strict control e.g. instructions received same.
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How to improve reliability in observations?
Behavioural categories properly operationalised and measurable and self-evident.
Categories shouldn't overlap (hugging = cuddling).
If not, different observers make own judgement so differing/inconsistent records.
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What is validity?
Extent to which observed effect is genuine. Does it measure what it intended to (internal validity) and can it be generalised beyond research setting (external validity)?
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What is internal validity?
Whether observed effect due to IV manipulation. Major threat is demand characteristics.
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What is ecological validity?
Extent to which findings can be generalised to other settings/situations.
Form of external validity.
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What is temporal validity?
Extent to which findings from a research study can be generalised to other historical times and eras. Form of external validity
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Assessment of validity?
Face validity - whether test, scale or measure measures what it intends to.
Concurrent validity - results obtained same to those of an established test.
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How to improve reliability in experimental research?
Control group - so able to see effect of DV on IV/
Standardise procedures.
Single/double blind trial - reduce demand characteristics and investigator effects.
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How to improve reliability in questionnaire?
Lie scale - assess consistency of a respondent's response and control effect of social desirability bias.
Could also ensure anonymity
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How to improve reliability in observations?
Covert
| Well operationalised behavioural categories
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How to improve reliability in qualitative methods?
Though to have higher ecological validity than quantitative due to depth and detail.
Triangulations - number of different evidence sources.
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What is the abstract?
First section. 150-200 words. Major elements (aims, hypotheses, method procedure, results and conclusions).
Can read before any further examination.
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What is the introduction?
Literature review of relevant theories, concepts and studies related to study. Broad then becomes specific.
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What is the method?
Description of what researcher did like design, sample, apparatus, material, procedure and ethics.
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What is the results?
What researcher found, including descriptive and inferential stats. Raw data like calc are put in appendix.
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What is the discussion?
Summarise results/findings verbally. Discuss in context of evidence in intro. Mindful of limitations. Wider implications
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What is referencing?
List of sources referred to (journals, books, websites).
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What is a type 1 error?
False positive - incorrectly accept alternative and reject null hypothesis when the results AREN'T significant
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What is a type 2 error?
False negative - incorrectly accept null and reject alternative hypothesis when the results ARE significant.
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How does a type 1 error arise?
When they significance level not strict enough e.g (10%/0.1)
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How does a type 2 error arise?
When level of significance too stringent e.g (1%, 0.01)
