key terms

0.0(0)
studied byStudied by 0 people
learnLearn
examPractice Test
spaced repetitionSpaced Repetition
heart puzzleMatch
flashcardsFlashcards
Card Sorting

1/157

encourage image

There's no tags or description

Looks like no tags are added yet.

Study Analytics
Name
Mastery
Learn
Test
Matching
Spaced

No study sessions yet.

158 Terms

1
New cards
Ethics
Rules of conduct recognised as appropriate to a particular profession or way of life - set out by the BPS
2
New cards
Participants
Investigators must consider ethical implications and consequences on participants, potential benefits of research must outweigh potential risks
3
New cards
Consent
Researcher must obtain written consent where possible, full understanding of objectives and all risks that would stop them from wanting to participate, consider those who can’t consent for themselves e.g., children, mentally impaired
4
New cards
Deception
Researchers should avoid hiding the nature of their research because it could cause distress after the study, consult for colleagues (that have no status in study) and inform participants as soon as possible - if colleagues question ethical standards, the researcher should re-think procedure
5
New cards
Debriefing
After the experiment, participants should be informed of the true nature of the research, if not before, participants should leave in the state they arrived because anything that could cause harm has been removed, if support is needed, everyone should be treated equally, regardless of their performance
6
New cards
Withdrawal
Participants should be informed they have the right to withdraw at any point, data can be destroyed at any point at the request of the participants
7
New cards
Confidentiality
Any information provided by participant should be kept secret, unless agreed otherwise (Data Protection Act), any released data should not allow participants identities to be revealed and if it will participants should be warned before the study
8
New cards
Protection
Participants protected from physical or mental harm - they should not face anything they wouldn’t normally, follow-up measures if necessary
9
New cards
Observational research
Not all ethical guidelines can enforced but wellbeing and privacy should be respected, no observing in areas where they wouldn’t expect to be observed
10
New cards
Giving advice
Participants must be informed if physical/ psychological problems are found - researchers should only give advice if qualified
11
New cards
Hypothesis
Statement with what you believe to be true
12
New cards
Aim
Statement of what you are intending to investigate
13
New cards
Directional hypothesis
States the kind of difference (more/less) - one-tailed hypothesis
14
New cards
Non-directional hypothesis
States only there is a difference - two-tailed hypothesis
15
New cards
Experimental hypothesis
Hypothesis for an experiment
16
New cards
Research hypothesis
Hypothesis written for any other kind of study
17
New cards
Alternative hypothesis
Hypothesis alternative to null hypothesis
18
New cards
Null hypothesis
Hypothesis with no difference or relationship
19
New cards
Non-directional hypothesis for independent groups/ matched pairs
There will be a difference in (operationalised DV) between participants who (IV1) and participants who (IV2)
20
New cards
Directional hypothesis for independent groups/ matched pairs
There will be a more/ fewer/ lower/ faster/ slower/ increase/ decrease in (DV) between participants who (IV1) and participants who (IV2)
21
New cards
Null hypothesis for independent groups/ matched pairs
There will be no difference in (DV) between participants who (IV1) and participants who (IV2)
22
New cards
Non-directional hypothesis for repeated measures
There will be a difference in (operationalised DV) when participants experience (IV1) and when they (IV2)
23
New cards
Directional hypothesis for repeated measures
There will be a more/ fewer/ higher/ lower/ faster/ slower/ increase/ decrease in (DV) when participants experience (IV1) and when they (IV2)
24
New cards
Null hypothesis for repeated measures
There will be no difference in (DV) between when participants experience (IV1) and when they (IV2)
25
New cards
Non-directional hypothesis for correlation
There will be a relationship/ association/ correlation between CV1 and CV2
26
New cards
Directional hypothesis for correlation
There will be a (positive/ negative) relationship/ association/ correlation between CV1 and CV2
27
New cards
Null hypothesis for correlation
There will be no relationship/ association/ correlation between CV1 and CV2
28
New cards
Variable
Anything that can change or change something else in an experiment
29
New cards
Independent variable
Variable that is manipulated by the researcher - different levels of experimental conditions needed
30
New cards
Dependent variable
The effect that is measured in the experiment
31
New cards
Operationalisation
Defining variables in terms of how they can be measured
32
New cards
Extraneous variables
Variables that can affect DV if it is not controlled
33
New cards
Confounding variables
Any variables other than the IV that have affected the DV - we have to be confident what part of the results are IV or confounding variables
34
New cards
Demand characteristics
Change in participant behaviour due to thoughts not the IV - these should be minimised, or it can affect validity
35
New cards
Investigator effects
Change in participant is due to investigator effect - these effects can reduce validity
36
New cards
Randomisation
Minimise variable effects by randomly organising the experiment not selectively
37
New cards
Standardisation
Controls variables by keeping everything the same e.g. writing down instructions
38
New cards
Target population
The group of people the researcher is interested in
39
New cards
Sample
Group taken from target population intended to represent them
40
New cards
Representative sample
Obtain a sample that is un-biased so generalisations can be made - the larger the sample size the less chance of bias (15 participants is best)
41
New cards
Random sampling
Number each member of target population from a complete list, choose numbers using random selection methods, everyone has equal chance of being chosen
42
New cards
Pros of random sampling
Produces potentially unbiased sample, this means CVs/ EVs are controlled, enhances internal validity
43
New cards
Cons of random sampling
Difficult to obtain and time consuming, complete list of population is hard to get, also some participants may refuse to take part
44
New cards
Systematic sampling
Every nth number of the target population is selected starting at a random point
45
New cards
Pros of systematic sampling
Potentially unbiased, the first item is usually selected at random, objective method
46
New cards
Cons of systematic sampling
Difficult to carry out and time consuming, a complete list of the population is required, may as well use random sampling
47
New cards
Stratified sampling
The proportion of factions in target population should be accurately represented in the sample, randomly choose the correct proportion from each subgroup
48
New cards
Pros of stratified sampling
Representative sample that allows generalisation
49
New cards
Cons of stratified sampling
People within subgroup may differ and therefore sample could be biased
50
New cards
Opportunity sampling
Sample is whoever is in a certain place and is free at a certain time
51
New cards
Pros of opportunity sampling
Cheaper and less time consuming
52
New cards
Cons for opportunity sampling
Often produces a biased sample, researcher might approach certain people (researcher bias)
53
New cards
Volunteer sampling
Researcher puts out an advert and sample group are the people who respond
54
New cards
Pros of volunteer sampling
Easy to produce and less time consuming
55
New cards
Cons of volunteer sampling
Bias sample, volunteers are likely to be a certain type of person (volunteer bias)
56
New cards
Laboratory experiments
Takes place in a controlled environment where the researcher can manipulate the IV and control EV
57
New cards
Pros of lab experiments
High internal validity where the effect of the IV and DV is more certain because EVs and CVs can be controlled

Replication of experiment is possible, greater control means less chance of new EVs introduced and findings can be confirmed - supports validity
58
New cards
Cons of lab experiments
Artificial tasks so stops generalisability and low external validity

Demand characteristics can occur due to cues which means findings may be due to the cues not the IV - low internal validity
59
New cards
Field experiments
Takes place in a natural setting where the researcher can manipulate the IV and record the effect on the DV
60
New cards
Pros of field experiments
More natural environment, Ps are more comfortable, and behaviour is more authentic, results can be generalised

Participants are unaware of being studied, behave normally so generalise findings so increases external validity
61
New cards
Cons of field experiments
More difficult to control CVs/ EVs, observed changes in the DV may not be due to IV, more difficult to establish cause and effect

Important ethical issues, no informed consent, invasion of privacy
62
New cards
Quasi experiment
The IV cannot possibly be manipulated but does exist
63
New cards
Pros of quasi experiment
Practical/ ethical option, unethical to manipulate IV, only way causal research can be done

Greater external validity, involve real-world issues, findings are more relevant to real experiences
64
New cards
Cons of quasi experiment
Hard to claim IV effects DV

Can’t allocate participants so confounding variables may affect DV
65
New cards
Natural experiments
IV is naturally occurring and is not manipulated by the researcher
66
New cards
Pros of natural experiments
High control, replication is possible

Comparisons can be made between people, IV is a difference between people
67
New cards
Cons of natural experiments
Can’t allocate participants so confounding variables can affect DV

Causal relationships are not demonstrated, researcher does not manipulate the IV, cannot say for certain that any change in the DV was due to the IV
68
New cards
Independent groups design
Participants are split into groups where each group does one condition, there is no order effect (cannot use knowledge from condition 1 to influence condition 2), individual differences can influence findings, have to find double the participants - time consuming and expensive
69
New cards
Repeated measures design
Participants take part in all conditions of the study, individual differences are controlled because group is the same for all conditions, creates order effect
70
New cards
Counterbalancing
2 different groups, one does condition 1 first, one does condition 2 first - balances out order effects of both
71
New cards
Matched pairs design
Pairs assigned due to similarity in a variable that could affect DV - each person in the pair does a different condition, no order effects, less individual differences, matching participants is difficult, time consuming and expensive
72
New cards
Naturalistic observation
Takes place in the setting where the behaviour would naturally happen

Pros = high external validity, each to generalise

Cons = hard to replicate, many confounding/ extraneous variables
73
New cards
Controlled observation
Some variables are controlled and manipulated

Pro = less confounding/ extraneous variables

Con = harder to generalise and apply to everyday life (low external validity)
74
New cards
Covert observation
Participants are unaware they are being observed

Pro = higher internal validity and no demand characteristics

Con = ethical problems (consent)
75
New cards
Overt observation
Participants are aware they are being observed

Pro = more ethically sound and acceptable

Con = demand characteristics
76
New cards
Participant observation
Researcher joins target group to record observations

Pro = higher external validity and better insight into participants

Con = may become too invested and not objective
77
New cards
Non-participant observation
Researcher observes target group while staying separate

Pro = remains objective

Con = lose some valuable data due to less knowledge of participants
78
New cards
Factors affecting design behaviour
Importance of access to physical response - behaviour recorded in observation, thoughts are not

Type of people being studied - children or those iwth disabilities may only be able to be studied through observation
79
New cards
Event sampling
Make a record every time the behaviour occurs
80
New cards
Time sampling
Record when it happens in a time frame
81
New cards
Pilot studies
Trial run of an experiment with a few participants to check the investigation will run correctly

Pro = saves time and money in the future

Questionnaires/ interviews = check questions are effective, ensure number of questions and time period are correct

Observation = trial and train observers
82
New cards
Self-report techniques
Ways in which participants can express their views or opinions
83
New cards
Questionnaires
Collect quantitative and qualitative data

Closed questions = limited options and participants choose which is most relevant - quantitative data

Open questions = let the participant write down their opinion, not limited - qualitative data

Avoids jargon, emotive language, double negatives, leading questions, double-barrelled questions
84
New cards
Pro of questionnaires
Gathers lots of information quickly and cheaply

Researcher bias doesn’t have to be present

Data is easy to analyse
85
New cards
Con of questionnaires
Social desirability bias = change response for a specific answer

Response bias = same answer each time
86
New cards
Interviews
Collects qualitative data
87
New cards
Structured interviews
Pre-determined questions

Pro = easy to replicate, little difference between interviews so easy to compare

Con = more limited data as there is no deviation from set topic
88
New cards
Semi-structured interviews
Some set questions with un-set follow ups
89
New cards
Unstructured interviews
No set questions, more conversational

Pro = wider range of data, responses in real life more likely to be truthful

Con = more chance of interview bias, some irrelevant information that you will have to sift through
90
New cards
Correlation
Technique of data analysis, measures strength (number) and direction (+/-) of the relationship between 2+ variables (with numerical variables), information is presented on a scattergram, correlation doesn’t mean causation
91
New cards
Positive correlation
Greater than 0, +1.00 is perfect positive correlation, as one variable increases so does the other
92
New cards
Negative correlation
Less than 0, -1.00 is perfect negative correlation, as one variable increases the other variable decreases
93
New cards
Zero correlation
Numerical value of 0, no relationship/ correlation between variables
94
New cards
Curvilinear correlation
Positive to a point and then negative
95
New cards
Factors affecting design decisions for correlations
Type of data = variables must have a numerical value and exist over a range

Possibility of manipulation = likely to choose this analysis when variables can’t be manipulated

When a similarity is being assessed, e.g. twin study

Assess reliability = if there are 2 sets of results for one variable then the scores should correlate
96
New cards
Pros of correlation
Preliminary research tool = gives a starting point and helps to write a hypothesis

Can use stats from previous studies so is quick and easy to work out the correlation

Precise and quantifiable data for the relationship between 2 variables
97
New cards
Cons of correlations
Correlations can be misinterpreted

Only states there is a relationship, not why or which variable affects the other

Interviewing variable = something not being tested could cause the relationship between 2 variables
98
New cards
Directional correlational hypothesis
States whether relationship is positive or negative
99
New cards
Non-directional correlational hypothesis
Only states there is a relationship
100
New cards
Statistical tests
Tests used to determine if results are significant, significance level of 0.05 used in psychology, alternative hypothesis accepted if p < 0.05, from tests you will get a calculated value that you compare to critical value in a table, to get the critical value you need - significance level, number of participants, one-tailed or two-tailed, if test has an ‘R’ in the name calculated > critical