making variables detailed and specific so they can be measured
5
New cards
Aim
general statement that the researcher intends to investigate, developed from research
6
New cards
Hypothesis
statement made at start of study (directional or non-directional)
7
New cards
Directional hypothesis
difference/relationship between two conditions is made clear (which DIRECTION)
8
New cards
Non-directional hypothesis
no previous research to suggest a direction the experiment will go in, 'there will be a difference' (two tailed)
9
New cards
Correlational hypothesis
relationships between two co-variables (can be directional or non-directional) predict positive or negative correlation
10
New cards
Null hypothesis
states there will be no significant difference between the two groups
11
New cards
Extraneous variables
any variables that could interfere with DV identified where possible at start of a study & measures put in place to minimise influence (age of participants, lighting, etc)
12
New cards
Confounding variable
variable from outside the study and may impact the result (personality of participants, time of day, etc)
13
New cards
Types of extraneous variables
situational (environment), participant (individual differences), investigator effects (influence from researcher), demand characteristics (deliberate change of behaviour)
14
New cards
Demand characteristics
how participants change their behaviour in regards to the study and info they pick up from it - may try to work out the aim of the stufy and act accordingly to how they think will support it (please-U effect) or how to cause issues (screw-U effect)
15
New cards
Investigator effects
behaviour of investigator may influence ppts and skew results, includes things such as body language and smiling, can influence those in the study to act differently from how they may have originally, unwanted influence
16
New cards
Randomisation
use of chance to reduce researcher's influence on design of investigation; can include randomly generating the order of words for a memory test or the order of conditions could be randomised Key aspect is counter balancing - attempts to control order effects; half ppts take part in condition A then B and half B then A (REMEMBER ABBA)
17
New cards
Lab experiments
highly controlled environments, researcher manipulates IV and records effect on DV (Milgram obedience study)
18
New cards
Strengths of lab experiments
- High control over extraneous variable so you can conclude that the IV has had an affect on DV
- Replication is possible due to high level of control so results can be checked for reliability
19
New cards
Limitations of lab experiments
- Participants aware they're being tested which risks demand characteristics being observed
- Artificial environment so lacks generalisability, low mundane realism (doesn't reflect real life)
20
New cards
Field experiments
IV manipulated in everyday setting to see effect on DV
21
New cards
Strengths of field experiments
- Higher mundane realism because natural environment
- Behaviour is likely to be more authentic (less likely to be demand characteristics)
22
New cards
Limitations of field experiments
- Less control over extraneous variables
- Difficult to replicate (low reliability)
- Can be time consuming and costly
- ethical issues if participants unaware they're being studied
23
New cards
Natural experiments
Researcher takes advantage of pre-existing IV
'Natural' as IV would have changed even if researcher hadn't have studied it
24
New cards
Strengths of natural experiments
- Provides opportunities for research that may otherwise have not have occurred due to practical/ethical issues
- High external validity because includes study of real life
25
New cards
Limitations of natural experiments
- Naturally occurring events are rare so can't be generalised
- Pt's can't be randomly allocated so may be confounding variables
26
New cards
Quasi experiments
No control over allocation of pt's to conditions or the IV
IV could be based on existing difference between people, no one has manipulated the variable it just exists
27
New cards
Strengths of quasi experiments
- IV is a naturally occurring difference between people meaning changes in the DV may have more realism than if the IV was artificially created
- Pt's are likely to be aware they're being studied making consent easier to gain and so ethical issues
28
New cards
Limitations of quasi experiments
- Can only be used when naturally occurring differences between people can be easily identified (difficult to set up)
- Task used to further data for the DV may be unrealistic meaning the data has little mundane realism
- No random allocation leading to confounding variables
29
New cards
Independent groups/measures
involves different groups doing each condition (e.g. condition 1 consists of pt's A & B - condition 2 consists of pt's C & D)
30
New cards
Evaluation of independent groups/measures
Strengths: - Pt's are less likely to guess the aim - Easy to set up and not time consuming
Limitations: - Individual differences may influence results
31
New cards
Repeated measures
Involves all participants experiences both (all) conditions, done to prevent order effects
32
New cards
Evaluation of repeated measures
Strengths: - By using all the same pt's in all conditions, there are no individual differences to act as confounding variables
Limitations: - By doing the experiment more than once in different conditions, the participants may be affected by order affects
33
New cards
Counterbalancing
order efefcts can be overcome by this. ppts split into 2 smaller goups, half ppts do condition A then condition B while the other half do condition B followed by condition A (ABBA) This means effects of doing one condition after another in a repeated measures design with be counteracted
34
New cards
standardisation
try to ensure all participants experience research process in same way
35
New cards
random allocation
independent groups design - reduces potential of bias in groups
36
New cards
randomisation
researchers may randomise parts of the procedure to remove any bias by making it all 'due to chance' e.g. ppts may do the conditions in a random order by having a computer decide what order to present material to them
37
New cards
pilot studies
small scale trial run of experiment, allows you to check that design or method wil work, useful as practice to check for timings + instructions and to identify any issues Changes can be made to ensure that the study runs smoothly and fewer errors are likely to occur in the procedure
38
New cards
matched pairs
different groups in each condition but the groups are matched on key factors
39
New cards
Random sampling
all members of target population have equal chance of being picked , randomly chosen through computer programme or picking names out of a hat
40
New cards
evaluation of random sampling
strengths - free from researcher bias as cannot choose people to 'help' their hypothesis Limitations - difficult and time consuming because have to obtain info on every person in T.P., unlikely to reflect number of each member in society (unrepresentative), people may refuse to take part leading to a need for another randomiser
41
New cards
systematic sampling
every nth number of ppts is chosen to take part, sample frame is established (e.g. alphabetical order) and system is applied
42
New cards
evaluation of systematic sampling
strengths - avoids bias as researcher isn't directly choosing ppts, more representative than random sampling as more unlikely to end up with unbalanced sample Limitations - still may not reflect correct quantities of each group in sample realistically, time consuming
43
New cards
Stratified sampling
target population broken down into it's key demographic components and ppts are selected from each strata according to it's relative size in the population
44
New cards
evaluation of stratified sampling
strengths - avoids researcher bias because calculated based on realistic results from target or general population, sample is representative because designed to accurately reflect composition of poulation so generalisation of findings is able to occur Limitations - identified strata cannot reflect each person's different beliefs and feelings so complete representation is not possible, extrememly time consuming
45
New cards
Opportunity sample
anyone willing and available to participate is selected as difficult to obtain representative sample of T.P
46
New cards
Evaluation of opportunity sampling
strengths - convenient, saves time and money limitations - creates bias as sample is unrepresentative as population drawn from neraby (very specific) cannot be generalised to rest of population, researcher bias as complete control over who partcipates
47
New cards
Volunteer sampling
ppts select selves to be part of sample. can be collected via adverts or a common room notice board, for example
48
New cards
evaluation of volunteer sampling
strengths - easy to do and requires less time + minimal input from researcher as ppts come to them limitations - volunteer bias as may attract a certain 'profile' or person affecting generalisation of findings and leading to possible demand characteristics
49
New cards
What is reliability?
extent to which research is consistent, if research is repeated in same way with same findings results are said to be reliable
50
New cards
How can we improve reliability?
increasing objectivity will increase consistency over time and between people. such operationalised variables are essential. standardising procedures with ppts is another way to ensure reliability internally + externally as ppts have consistent experience and study can be replicated to test reliability of results
51
New cards
What is validity?
extent to which the research actually measures and tests what it claims to
52
New cards
What is face validity?
whether a test, scale or measures appears on the 'face of it' to measure what it's supposed to measure
53
New cards
How can we improve external validity?
improved by developing realistic tests and using natural settings for studies rather than artificial tests in highly controlled conditions
54
New cards
What is internal validity?
extent to which the results of the study are due to the tested variable rather than extraneous or confounding variables
55
New cards
What is external validity?
extent to which results of study can be generalised to other people (population validity), other settings (ecological validity), and across time (temporal validity)
56
New cards
measures of central tendency
mean, median, mode
57
New cards
evaluation of mean
S: most sensitive measure as includes all reuslts making it more representative L: also means it's easily distorted by extreme values, cannot be used on nominal data, harder to calculate than other measures
58
New cards
evaluation of median
S: unaffected by extremes, easier to calculate than the mean L: less representative than mean as it doesn't use all scores
59
New cards
evaluation of mode
S: easy to calculate, unaffected by extreme values L: data is often multi-modal so meaningless, doesn't use all scores
60
New cards
measures of dispersion
range and standard deviation
61
New cards
evaluation of the range
S: shows overall spread of whole dataset, easy to calculate L: may not be representative of data if there's extreme values at the top and/or bottom of the dataset
62
New cards
Standard deviation
tells you the spread of data around the mean and allows you to see relationships between scores
63
New cards
Evaluation of Standard Deviation
S: provides representative measure of data spread, provides useful information about how individual scores relate to each other and to the mean, gives a measure of reliability of data as small standard deviations mean there was little variation in scores L: harder to calculate than the range
64
New cards
correlation
illustrates strength ofdirection of an association between two or more co-variables they're plotted on a scattergram where one co-variable forms the x axis and the other forms the y axis
65
New cards
evaluation of correlations
S: useful for preliminary research in order to provide precise and quantifiable measures of 2 related variables, can suggest ideas for further research, quick and economical. L: cannot show cause and effect, lack of experimental manipulation and control so extraneous variables are a variable, can be misued or misinterpreted
66
New cards
normal distributions
a symmetrical, bell-shaped curve meaning the mean, median and mode are all the same value and meet perfectly in the middle, most people located within the centre area with few people at either ends, tails never reach zero as extreme results are always a possiblity
67
New cards
right/positive skew
mean has the highest value then the median then mode as all different values
68
New cards
left/negative skew
mode has the highest value, then median, then mean so all values different
69
New cards
bar graph
consist of categorical data for comparison, categories go on x axis (bottom) and requency of occurrence on the y axis (side)
70
New cards
scattergrams
should be titled, one variable is plotted on the x axis, other on y axis, each axis should be labelled, for each pair of scores a dot/cross is placed on the graph where 2 scores meet, display correlational data
71
New cards
data tables
contain descriptive statistics such as measures of central tendency and dispersions. for some data, it may be limited to percentage calculations all collumns and rows need to be lablled and have an appropriate title
72
New cards
histograms
these are used when the x axis is showing continuous data (bars touching) while the y axis shows the frequency of occurence of that data
73
New cards
Pie chart
circular statistical graphic divided into sections to illustrate numerical proportion, arc length of each sections is proportional to the quanity it represents
74
New cards
pie chart calculation
(requency/total) * 360
75
New cards
frequency polygon
show info from frequency table, first have to find the midpoints of each class, mid point of a class is the point in middle of the class e.g. a class "10-19" the midpoint is 14.5 Plot midpoints and join them up with straight lines
76
New cards
Nominal data
represented in categories, discrete as one item can only appear in a category
77
New cards
Ordinal data
data that has order, usually includes rating scales, doesn't have equal or universal intervals between each measurement
78
New cards
interval data
based on numerical scales with equal intervals between each measurement
79
New cards
statistical testing
provides way of determing whether hypotheses should be accepted or rejected, tells us whether differences/relationships between variables are statistically significant or occurred by chance
80
New cards
Significance level for sign test
always 0.05 unless otherwise specified
81
New cards
how results are significant with sign test
calculated value has to be equal to or lower than critical value
82
New cards
statistical test for nominal data + independent groups
chi square
83
New cards
statistical test for nominal data and repeated measures
sign test
84
New cards
statistical test for ordinal data and independent groups
mann-whitney
85
New cards
statistical test for ordinal data and repeated measures
wilcoxon
86
New cards
statistical test for ordinal data and correlation (relationship)
spearman's rho
87
New cards
statistical test for interval data and independent groups
unrelated t-test
88
New cards
statistical test for interval data and repeated measures
related t-test
89
New cards
statistical test for interval data and correlation (relationship)
Pearson's R
90
New cards
evaluation of nominal data
S: most simple, easy L: only limited calculations can be made using it, superficial, measures of central tendency are innapropriate
91
New cards
evaluation of ordinal data
S: supports a more sophisticated analysis, enabling comparisons using measures of central tendency L: do not have standardised interval scale so cannot guage options (like interval data)
92
New cards
How can you evaluate interval data?
best type of data for analysis as it supports all types of calculations and is suited to the most powerful inferential tests
93
New cards
When will you be asked to design a study?
In Paper 2, you're often asked to do this. It can be: an experiment, correlation or observation and can be for up to 12 marks
94
New cards
How would you answer a design a study question?
- Only write what the bullet points ask for; quite literally set up your work like this: "BULLET 1:", "BULLET 2:".. etc - Make sure that your study is practical and replicable (e.g. use of standardised procedures, specificity, don't EVER say anything unethical, realistic samples)
95
New cards
Explain what is meant by probability
probability (p) is the likelihood that something will happen. It is expressed as a number between 0 and 1, where 0 means an event will definitely NOT happen and 1 means it definitely WILL.
96
New cards
Give an example/examples of probability
- the probability of getting tails when you toss a coin: P = 1/2 = 0.5 - probability of getting a 6 when you throw a 6-sided dice: P = 1/6 = 0.16666... or 0.17
97
New cards
What are significance levels?
they're assigned to establish the probability of results being due to chance. If this is low, then we can reject our null hypo and accept our experimental/alternative hypo
98
New cards
In Psychology, what is the significance level we are willing to accept? Why?
1 in 20 likelihood (or p is less than or equal to 0.05) This means we can be 95% confident that there is a true difference (experiment) or a relationship (correlation) between variables
99
New cards
Where might psychologists need to use stricter levels of significance?
In cases that have more dangerous implications such as in drug testing or in replications of studies where the aim is to verify original findings
100
New cards
Type 1 error
- wrongfully accept experimental hypothesis - Believe there is a difference or a relationship when there isn't - this is also known as a false positive or an error of optimism