Research Methods Flashcards
1
Q
Define ‘experimental method’
A
Involves the manipulation of an independent variable to measure the effect on the dependent variable. Experiments may be laboratory, field, natural or quasi.
2
Q
Define ‘aim’
A
A general statement of what the researcher intends to investigate; the purpose of the study.
3
Q
Define ‘hypothesis’
A
A clear, precise, testable statement that sates the relationship between the variables to be investigates. Stated at the outset of any study.
4
Q
Define ‘directional hypothesis’
A
States the direction of the difference or relationship.
5
Q
Define ‘non-directional hypothesis’
A
Does not state the direction.
6
Q
Define ‘variables’
A
Any ‘thing’ that can vary or change within an investigation. Variables are generally used in experiments to determine if changes in one thing result in changes to another.
7
Q
What is the ‘independent variable (IV)’?
A
Some aspect of the experimental situation that is manipulated by the researcher - or changes naturally - so the effect on the DV can be measured.
8
Q
What is the ‘dependent variable (DV)’?
A
The variable that is measured by the researcher. Any effect on the DV should be caused by the change in the IV.
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 the independent variable (IV), that may have an effect on the dependent variable (DV) if it is not controlled. EVs are essentially nuisance variables that do not vary systematically with the IV.
11
Q
What is meant by a ‘Confounding variable (CV)’?
A
Any variable, other than the IV, that may have affected the DV so we cannot be sure of the true source of changes to the DV. Confounding variables vary systematically with the IV.
12
Q
Define ‘demand characteristics’.
A
Any cue from the researcher or from the research situation that may be interpreted by participants as revealing the purpose of the investigation. This may lead to a participant changing their behaviour within the research situation.
13
Q
Define ‘investigator effects’.
A
Any effect of the investigator’s behaviour (conscious or unconscious) on the research outcome (the DV). This may include everything from the design of the study to the selection of, and interaction with, participants during the research process.
14
Q
Define ‘randomisation’ and give an example.
A
The use of chance in order to control for the effects of bias when designing materials and deciding the 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 the same formalised procedures and instructions for all participants in a research study.
16
Q
Define ‘experimental design’
A
The different ways in which the testing of participants can be organised in relation to the 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
An attempt to control for participation variables in an independent groups design which ensures that each participant has the same chance of being in one condition as any other.
21
Q
Define ‘counterbalancing’.
A
An attempt to control for the effects of order in a repeated measures design: half the participants experience the conditions in one order, and the other half in the opposite order.
22
Q
What is a ‘laboratory (lab) experiment’?
A
An experiment that takes place in a controlled environment within which the researcher manipulates the IV and records the effect on the DV, whilst maintaining strict control of extraneous variables.
23
Q
What is a ‘field experiment’?
A
An experiment that 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'?
A study that is almost an experiment but lacks key ingredients. The IV has not been determined by anyone (the researcher or any other person) - the 'variables' simply exist, such as being old or young. Strictly speaking this is not an experiment.
26
Define 'population'.
A group of people who are the focus of the researcher's interest, from which a smaller sample is drawn.
27
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.
28
What are 'sampling techniques'?
The method used to select people from the population.
29
What is meant by the term 'bias' in Psychology?
In the context of sampling, when certain groups may be over or under-represented within the sample selected. For instance, there may be too many younger people or too many people of one ethnic origin in a sample. This limits the extent to which generalisations can be made to the target population.
30
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.
31
What are 'ethical issues' in Psychology?
These arise when a conflict exists between the rights of participants in research studies and the goals of research to produce authentic, valid and worthwhile data.
32
What is the BPS code of ethics?
A quasi-legal document produced by the British Psychological Society (BPS) that instructs psychologists in the UK about what behaviour is and is not acceptable when dealing with participants. It is built around four major principles: respect, competence responsibility and integrity.
33
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.
34
Define 'naturalistic observation'.
Watching and recording behaviour in the setting within which it would normally occur.
35
Define 'controlled observation'.
Watching and recording behaviour within a structured environment, i.e. one where some variables are managed.
36
Define 'covert observation'.
Participants' behaviour is watched and recorded without their knowledge and consent.
37
Define 'overt observation'.
Participants' behaviour is watched and recorded with their knowledge and consent.
38
Define 'participant observation'.
The researcher becomes a member of the group whose behaviour he/she is watching and recording.
39
Define 'non-participation observation'.
The researcher remains outside of the group whose behaviour he/she is watching and recording.
40
What are 'behavioural categories'?
When a target behaviour is broken up into components that are observable and measurable
41
Define 'event sampling'.
A target behaviour or event is first established then the researcher records this event every time it occurs.
42
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.,
43
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.
44
Define 'questionnaire'.
A set of written questions (sometimes referred to as 'items') used to assess a person's thoughts and/or experiences
45
Define 'interview'.
A 'live' encounter (face-to-face or on the phone) where one person (the interviewer) asks a set of questions to assess an interviewee's thoughts and/or experiences. The questions may be pre-set (as in a structured interview) or may develop as the interview goes along (unstructured interview).
46
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?
47
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)
48
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.
49
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.
50
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.
51
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.
52
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.
53
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).
54
Define 'quantitative data'.
Data that can be counted, usually given as numbers.
55
Define 'primary data'.
Information that has been obtained first-hand by the researcher for the purposes of a research project. In psychology, such data is often gathered directly from participants as part of an experiment, self-report or observation.
56
Define 'secondary data'.
Information that has already been collected by someone else and so pre-dates the current research project. In psychology, such data might include the work of other psychologists or government statistics.
57
Define 'meta-analysis'.
'Research about research', refers to the process of combining results from a number of studies on a particular topic to provide a overall view. This may involve a qualitative review of conclusions and/or a quantitative analysis of the results producing an effect size.
58
What are 'descriptive statistics'?
The use of graphs, tables and summary statistics to identify trends and analyse sets of data.
59
What are 'measures of central tendency'?
The general term for any measure of the average value in a set of data.
60
Define 'mean'.
The arithmetic average calculated by adding up all the values in a set of data ad dividing by the number of values there are.
61
Define 'median'.
The central value in a set of data when values are arranged from lowest to highest.
62
Define 'mode'
The most frequently occurring value in a set of data.
63
What is a 'scattergram'?
A type of graph that represents the strength and direction of a relationship between co-variables in a correlational analysis.
64
What is a 'bar chart'?
A type of graph in which the frequency of each variable is represented by the height of the bars.
65
What is 'statistical testing'?
Provides a way of determining whether hypotheses should be accepted or rejected. In psychology, they tell us whether differences or relationships between variables are statistically significant or have occurred by chance.
66
What is the 'sign test'?
A statistical test used to analyse the difference in scores between related items (e.g. the same participant tested twice).
67
Define 'peer review'
The assessment of scientific work by others who are specialists in the same field to ensure that any research intended for publication is of high quality.
68
Define 'economy'.
The state of a country or region in terms of the production and consumption of goods and services
69
Strengths of Independent Group Design.
(+) Order effects are not a problem because each participant only experiences one condition.
(+) Participants are less likely to guess the aim because they only experience one condition and do not see any manipulation.
70
Limitations of Independent Group Design.
(-) The participants who occupy the different groups are not the same. If a researcher finds a difference between groups on the DV it may be due to individual differences (participant variables) rather than the IV.
(-) Design is less economical than repeated measures as each participant contributes a single result only. Twice as many participants are needed to produce equivalent data to that used in repeated measures.
71
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.
72
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.
73
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.
74
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.
75
Strengths of lab experiments.
(+) High control over extraneous variables which means the researcher can ensure that any effect on the DV is likely to be the result of manipulation of the IV. (More certain about cause and effect- internal validity).
(+) Replication is more possible than in other types of experiment because of the level of control This ensures that new extraneous variables are not introduced when repeating the experiment.
76
Limitations of lab experiments.
(-) Lack of generalizability- the lab environment is 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.
77
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.
78
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.
79
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.
80
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.
81
Strengths of quasi- experiments.
(+) Carried out in controlled conditions so have higher internal validity.
(+) Replication is possible because of the high level of control.
82
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.
83
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.
84
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.
85
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.
86
Limitations of systematic sampling.
(-) Selected participants may still refuse to take part which means you end up with more of a volunteer sample.
87
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.
88
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.
89
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.
90
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.
91
Strengths of volunteer sampling.
(+) Requires minimal input from the researcher and so is less time-consuming than other forms of sampling.
92
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.
93
What are the five main ethical issues to consider in Psychology?
Informed Consent- making participants aware of the aims of the research, the procedure and their rights (including the right to withdraw).
Deception- participants should not be deliberately mislead and information should not be withheld from them at any stage in the investigation.
Protection from Psychological or Physical Harm- Participants should not be placed at nay more risk than they would be in their daily lives. This refers to physical harm or Psychological (e.g. embarrassment, stress and pressure).
Privacy- participants have the right to control information about themselves.
Confidentiality- participants personal data should be protected.
94
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.
95
Evaluation (strengths and limitations) of controlled observations.
(+) Extraneous variables are less of a factor sue to high control, this means the observation can be easily repeated.
(-) Produce findings that cannot be readily applied to real-life settings as the observation was conducted in an artificial setting.
96
Evaluation (strengths and limitations) of covert observations.
(+) Participants know they are being watched which removes the problem of participant reactivity and ensures observed behaviour is natural. This 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.
97
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.
98
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.
99
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.
100
Evaluation (strengths and limitations) of structured design observations.
(+) The use of behavioural categories makes the recording of data easier and more systematic.
(+) Likely to produce quantitative data which means analysing and comparing the behaviour observed between participants is more straightforward.
(-) No opportunity to record relevant data than lies outside of the set behavioural categories.
101
Evaluation (strengths and limitations) of unstructured design observations.
(+) Benefit from richness and depth of detail in data collected.
(-) Greater risk of observer bias as there are no objective behaviour categories.
(-) Produce qualitative data which is difficult to record and analyse.
102
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.
103
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.
104
Strengths of structured interviews.
(+) Straightforward and easy to replicate due to their standardised format. This format also reduces differences between interviewers.
105
Limitations of structured interviews.
(-) It is not possible, given the nature of a structured interview, for interviewers to deviate from the topic of elaborate their points and this 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.
106
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.
107
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.
108
Strengths of correlations.
(+) Useful preliminary tool for research. By assessing the strength and direction of a relationship, they provide a precise and quantifiable measure of how two variables are related. This may suggest ideas for future research.
(+) Often used as a starting point to assess possible patterns between variables before researchers commit to an experimental study.
(+) Relatively quick and economical to carry out. There is no need for a controlled environment and no manipulation of variables is required.
(+) Secondary data (data collected by others, e.g. government statistics) can be used which means correlations are less time consuming than experiments.
109
Limitations of correlations.
(-) Correlations cannot demonstrate cause and effect between variables and therefore we do not know which co-variables is causing the other to change or whether they are both the result of a third variable (the third variation problem).
(-) Can be misused or misinterpreted (particularly in the media) where they are presented as causal 'facts' when in reality they may not be.
110
Evaluation (strengths and limitations) of qualitative data.
(+) Offers the researcher much more richness of detail than quantitative data. It is a much broader scope and gives the participant/ respondent much more licence to develop their thoughts, feelings and opinions is a given subject.
(+) Greater external validity than quantitative data; it provides the researcher with a more meaningful insight into the participants worldview.
(-)Often difficult to analyse. It tends not to lend itself to being summarised statistically so that patterns and comparisons within and between data may be hard to identify.
(-) Conclusions often rely on the subjective interpretations of the researcher and may therefore be subject to bias. This is particularly relevant if the researcher has preconceptions about what they are expecting to find.
111
Evaluation (strengths and limitations) of quantitative data.
(+) Relatively simple to analyse therefore comparisons between groups can easily be drawn. Lends itself readily to statistical analysis.
(+) Data is more objective and less open to bias.
(-) Data is narrower in scope and meaning so may fail to represent 'real life'.
112
Evaluation (strengths and limitations) of primary data.
(+) Primary data is authentic data obtained from the participants themselves for the purpose of a particular investigation. This means the way data was collected specifically targeted the information required.
(-) Time and effort required by the researcher. For example, conducting an experiment requires considerable planning, preparation, resources and this is a limitation compared to using secondary data.
113
Evaluation (strengths and limitations) of secondary data.
(+) Inexpensive and easily accessed requiring minimal effort.
(+) When examining secondary data the researcher may find that the desired information already exists and so there is no need to conduct primary data collection.
(-) There may be substantial variation in the quality and accuracy of secondary data. Information might at first appear to be valuable and promising but on further investigation might be out-dated or incomplete.
114
What are the issues with the peer review process?
(-) A minority of reviewers may use their anonymity as a way of criticising rival researchers who they perceive as having crossed them in the past.
(-) Publication Bias- Research not seen as positive or headline grabbing may be ignored or disregarded. This could create a false impression of the current state of Psychology.
(-) Researchers tend to be especially critical of research that contradicts their own view and much more favourable to that which matches it.
(-)Established scientists are the ones more likely to be chosen as reviewers which means findings that chime with current opinion are more likely to be passed than innovative new research. Therefore peer review may slow down the rate of change within a particular scientific discipline.
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A Level Only
| What is a case study?
An in-depth investigation, description and analysis of a single individual, group, institution or event.
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| Strengths of Case Studies
(+) Insight into unusual cases e.g. HM may provide understanding of normal functioning.
(+) Ability to generate hypotheses for future studies.
(+) Able to research areas that may otherwise be unethical to research.
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| Limitations of Case Studies
(-) Generalisation is a problem and conclusions based on subjective interpretation of the researcher and very small samples.
(-) Personal accounts from family and friends may be prone to inaccuracy and memory decay.
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| What is a content analysis?
A research technique that enables the indirect study of behaviour by examining communications that people produce, for example, in texts, emails, TV, film and other media.
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| Strengths of Content Analysis
(+) Fewer ethical issues than other research methods as the data is secondary data that already exists. Thus no issues obtaining permission.
(+) High external validity- real world data.
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| Limitations of Content Analysis
(-)Information studied out of context, danger the researcher may attribute opinions and motivations to the speaker or writer that were not originally intended.
(-) Descriptive forms of analysis less objective.
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| Define Coding
The stage of a content analysis in which the communication to be studied is analysed by identifying each instance of the chosen categories (which may be words, sentences, phrases etc.)
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| Define Thematic Analysis
An inductive and qualitative approach to analysis that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded.
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| Define Reliability
Refers to how consistent the findings from one investigation or measuring device are. A measuring device is said to be reliable if it produces consistent results every time it is used.
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| What is Test-retest Reliability?
A method of assessing the reliability of a questionnaire or psychological test by assessing the same person on two separate occasions. This shows to what extent the tests (or other measure) produces the same answers i.e. is consistent or reliable.
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| What is Inter-observer Reliability?
The extent to which there is agreement between two or more observers involved in observations of a behaviour. This is measured by correlating the observations of two or more observers. A general rule is that if (total number of agreements)/ (total number of observations) is higher than .80 the data has high inter-rater reliability.
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| Define Validity
The extent to which an observed effect is genuine- does it measure what it is supposed to measure, and can it be generalised beyond the research setting within which it was found?
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| What is Face Validity?
A basic form of validity in which a measure is scrutinised to determine whether it appears to measure what it is supposed to measure- for instance, does a test of anxiety look like it measures anxiety?
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| What is Concurrent Validity?
The extent to which a psychological measure relates to an existing similar measure.
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| What is Ecological Validity?
The extent to which findings from a research study can be generalised to other settings and situations. A form of external validity.
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| What is Temporal Validity?
The extent to which findings from a research study can be generalised to other historical times and eras. A form of external validity.
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| What are statistical tests?
Used psychology to determine whether a significant difference or correlation exists (and consequently, whether the null hypothesis should be rejected or retained).
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What are the three levels of
measurement for data?
Quantitative data can be classified into levels of measurement such as nominal, ordinal or interval.
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| What is a Chi-Squared test and what conditions must be met to carry it out?
A test for an association (difference or correlation0 between two variables or conditions. Data should be nominal level using an unrelated design (independent groups).
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| What is a Mann-Whitney test and what conditions must be met to carry it out?
A test for a significant difference between two sets of scores. Data should be at least ordinal level using an unrelated design (independent groups).
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| What is a Wilcoxon test and what conditions must be met to carry it out?
A test for significant difference between two sets of scores. Data should be at least ordinal level using a related design (repeated measures).
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| What is a Spearman’s rho test and what conditions must be met to carry it out?
A test for correlation when data is at least ordinal level.
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| What is a Pearson’s r test and what conditions must be met to carry it out?
A parametric test for correlation when data is at interval level.
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| What is a Related t-test and what conditions must be met to carry it out?
A parametric test for difference between two sets or scores. Data must be interval with a related design (repeated measures or matched pairs).
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| What is a unrelated t- test and what conditions must be met to carry it out?
A parametric test for difference between two sets of scores. Data must be interval with an unrelated design (independent groups).
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(Comparing Approaches)
Some argue Psychology is not a science; what are the features of science?
* Paradigm- set of shared assumptions.
* Objectivity- all sources of personal bias are minimised to avoid distorting research.
* Empirical method- approaches based on gathering evidence through direct observation and experience.
* Replicability- extent to which scientific procedures and findings can be repeated.
* Falsifiability- a theory cannot be considered scientific unless it admits the possibility of bring proven false.
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(sections of a report)
What are the six key sections
of a psychological report
and what information can be
found in each section?
```
1. Abstract- The key details of the research report.
2. Introduction- A look at past research (theory and/or studies) on a similar topic. Includes the aims and hypothesis.
3. Method- A description of what the researcher(s) did, including design, sample, apparatus/ materials, procedure, ethics.
4. Results- A description of what the researcher(s) found, including descriptive and inferential statistics.
5. Discussion- A consideration of what results of a research study tell us in terms of psychological theory.
6. References- List of sources that are referred to or quoted in the article, e.g. journal articles, books or websites, and their full details.
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| Define Probability
A measure of the likelihood that a particular event will occur where 0 indicates statistical impossibility and 1 statistical certainty.
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| Define Significance
A statistical term that tells us how sure we are that a difference or correlation exists. A ‘significant’ result means that the researcher can reject the null hypothesis
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| What is the critical value?
When testing a hypothesis, the numerical boundary or cut-off point between acceptance and rejection of the null hypothesis.
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| What is a type 1 error?
The incorrect rejection of a true null hypothesis (error of the optimist- a false positive).
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| What is a type 2 error?
The failure to reject a false null hypothesis (error of the pessimist- false negative)
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| What is a correlation?
A mathematical technique in which a researcher investigates an association between two variables, called co-variables.
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| What is a correlation coefficient?
A number between -1 and +1 that represents the direction and strength of a relationship between co-variables.
