research methods Flashcards
1
Q
negative skew
A
the mean is on the left side of the median and mode so the tail is on the left.
this shows that a large amount of data falls above the mean score
2
Q
positive skew
A
the mean is on the right side of the median and mode therefore the tail end is at the right side.
this shows that a large amount of data falls below the mean score.
3
Q
skewed distributions
A
scores are clustered to one side of the mean.
4
Q
distribution curves
A
(plot the frequency)
data can be distributed in different ways, either normal distributions or skewed distributions.
5
Q
normal distribution
A
displays frequency data in a symmetrical bell shape pattern.
the mean ,median and mode are all located at the highest peak and the dispersion of scores around both sides of the average is consistent and expressed In standard deviation.
6
Q
why do the tail end on normal distributions never touch the x axis
A
because extreme scores are always theoretically possible.
7
Q
pie charts
A
used with discrete data.
each segment of circle represents a proportion of scores.
8
Q
line graphs
A
also illustrate continuous data and use points connected by lines to show how something changes in value.
dv is plotted on y axis and iv plotted on x axis
9
Q
histograms
A
illustrate the distribution /frequency of data items -continuous scores.
frequency on y axis and equal size intervals on x axis.
10
Q
scattergram
A
used to show a relationship between two variables.
one co variable on x axis, one co variable on the y axis
a line of best fit may be drawn to estsblish the strength of relationship.
11
Q
bar chart
A
used to make comparisons between scores and are used with different groups /categories of data (discrete data)
12
Q
graphs
A
provide visual representation of a set of data that allows us to see the patterns in an east to understand way
13
Q
tables
A
show a summary of the raw scoresconvverted to descriptive statistics.
14
Q
small standard deviation
A
data points tend to be close to the mean pot the set
15
Q
large standard deviation
A
data points are spread out over wider range of values
16
Q
positive of standard deviation
A
sensitive and precise measure of dispersion as all values are take into consideration when calculating it.
17
Q
negative of standard deviation
A
doesn’t tell you full range of the data and it can be affected by extreme scores to give a skewed picture
18
Q
standard deviation defiition
A
statistical measure of variation in a set of data and describe how much, on average, all values differ from the mean.
19
Q
range
A
the difference between the highest and lowest values
20
Q
positive of range
A
easiest measure of dispersion to calculate
21
Q
negative of range
A
only takes into account most extreme scores which makes it unrepresentative of the data set as a whole.
22
Q
measures of dispersion
A
range - basic measure
standard deviation-sensitive measure
23
Q
advantages of the mode
A
easiest measure to calculate and unnfacected by extreme values
its the only measure you could calculate when data is in categories eg nominal.
24
Q
negative of mode
A
crude measure and can be unrepresentative in small data sets
becomes less useful when there are several modes in a data set
25
what's the mode
value that occurs most often. can be used with all levels of measurement.
26
positive of the median
the median is not affected by extreme values and is therefore unuseful when the mean is not appropriate
easier to calculate than the mean
27
negative of the median
not as sensitive as the mean because it does not include all of the data scores or values in the set.
28
what's a median
the central halfway value
asending order
29
positives of the mean
most sensitive measure of central tendency as it includes all of the scores in the data set and is therefore the most representative measure
30
negatives of mean
easily distorted by extreme values which may make it unrepresentative of the data set overall
31
mean definition
statistical average of a set of data.
32
measures of central tendencies
mean mode and median
33
descriptive data types
measure of central tendencies- info about the typical value
measures of dispersion - info about how spread out the values are
34
levels of measurement
nominal-attributes only named (WEAKEST)
ordinal- attributes can be ordered
interval- distance is meaningful
35
nominal data
categorical (eye colour, marital status)
frequency count for distinct categories where something can only belong to one separate category.
most basic and least informative data.
36
ordinal data-
categorical
numbers can be ordered in some way eg scale of 1-10 where 1- unattractive and 10- most attractive
37
interval data
scale- objective measure
measurements taken from a numerical scale where each unit is the same size and the gap between each unit is fixed and equal. eg length in cmheight
weight
time
income
38
positive of meta analysis
includes greater statistical power and more ability to generalise the findings to a wider population. considered to be evidenced based.
39
negative of meta analysis
meta analysis can be a difficult and time consuming in searching for the appropriate studies to examine.
meta analysis also require complex statistical skills and techniques.
40
what's meta analysis
researcher combines the findings from a number of previously published studies dealing with the same research question and produces a statistic to represent an average and common overall effect.
41
secondary data
data thats collected bro there people and already exists.
42
primary data
collected by researcher first hand and gathered directly from participants themselves.
43
pros of primary data
AUTHENTIC as its collected first hand from the participants themselves and so is specifically targeted to meet researches needs
44
cons of primary data
TIME CONSUMING to collect investigations require planning and preparation
45
pros of secondary data
EASILY accessible and requires minimal effort to colleg=ct ads it already exists.
46
cons of secondary data
may not meet researchers NEEDS and could be lacking in valuable info or could be out of date.
47
quantitive data
numerical data
48
qualitative data
non numerical data
49
pros of quantitive data
OBJECTIVE -free from bias- and capable of being MATHEMATICALLY ANALYSED easily allowing comparisons to be made.
50
cons of quantitive data
fails to consider participants feelings and emotions and lacks insight into the reasons behind human behaviour.
51
pros of qualitative data
IN MORE DETAIL and broader scope, allowing people to develop thoughts.
data gives MEANINGFUL INSIGHT and therefore high in EXTERNAL VALIDITY.
52
cons of qualitative data
difficult to analyse statistically so that comparisons are hard to make.
conclusions are based on subjective interpretations.
53
disadvantages of correlations
only establish a relationship between variables.
variables aren't being manipulated so we can't state wether one variable has caused the effect on the other variable - there could be other extraneous variables that have an impact on the relationship.
therefore can't establish cause and effect from correlational studies compared to the experimental method.
54
advantages of correlation
allow the study of variables which can't be manipulated
this is bc correlation do not require manipulation of behaviour and are used used when it may be unethical and impractical to manipulate variables artificially in experiments.
therefore don't break ethical guidelines.
55
advantages of correlations
useful in PRELIMINARY tool for further research.
as correlations are relatively quick and economical too conduct as they often use forms of secondary data. they can assess patterns of variables before as researcher commits to more length and time consuming research methods.
correlations can form the basis of a starting point for Reuther experimental research
56
correlation co efficient
more accurate way to indicate the strength of a correlation. always number between -1 and 1.
-1 being perfect neg and + one being perfect pos
57
how can correlations be shown
pictorially as a scattergram
numerically as a correlation coefficient
58
correlation key features
measures the relationship between 2 variables
direction of relationship can be positive or negative
strength can be strong moderate or weak
correlation can be represented as correlation co effiecient
correlation can be presented on scattergram
used to measure reliability or concurrent validity
59
positive correlation
as one variable score increases so does the other variable.
60
negative correlation
as one variable score increases, scores on the other variable decrease
61
zero correlation
there is no relationship between the two variables
62
directional hypothesis- correlation
there will be a -positive or negative- relationship between variable one and two
63
non directional hypothesis- correlation
there will be a relationship between variable 1 and 2
64
key features of experiments
investigate differences between conditions
manipulation and measurement of variables
test a hypothesis
establish cause and effect
65
purpose of an aim
identify variables the study is investigating
explain the outline and purpose of the study
66
experimental hypothesis
prediction of expected outcome of experiment . precise and testable statement
67
null hypothesis
what researcher is trying to disprove
68
operanlisation
describing variables in terms of how they will be precisely manipulated and measured.
69
non directional hypothesis
doesn't specify the expected direction of the results
70
types of experiments
lab field quasi natural
71
lab experiments
tightly controlled artificial environment
experimenter deliberately manipulates iv
experimenter manipulates dv
attempt to control/minimalise other extraneous variables
use standardised proceedures
72
field experiments
conducted in tightly controlled natural real world environment
experimenter deliberately manipulates the iv
measures the dv
there's minimal control over evs
73
natural and quasi
any setting
iv is naturally occurring
quasi - iv is pre existing characteristic eg age and gender
natural - the iv is an event/experience
dv is measured
experimenter has very very little control over evs
74
internal validity
the extent to which there is confidence in the iv causing effect on dv.
75
reliability
extent to which experiment can be repeated to check consistency off the results
76
mundane realism
tasks in experiment are representative of tasks completed in everyday life
77
demand characteristics
behaviour from ps that may be unnatural and affect how they perform on tasks
78
ecological validity
experimental setting represents real life situations
79
standardisation
procedures and conditions being controlled and kept the same across all conditions allowing for replication.
80
advantages to lab experiments
81
Advantages of lab experiments
High internal validity
The iv is the only thing that’s manipulated and other Evs are controlled. It’s therefore more likely that the iv os directly responsible for any changes of the dv.
82
Advantages of lab experiments
High reliability
This is because procedures in lab experiments are standardised
This means that the experiment can be replicated to check the results are consistent .
83
Disadvantages of lab experiments
Low ecological validity and mundane realism
Bc the setting is artificial so unlikely to represent a real life situation (eco validity)
Participantas are often asked to complete artificial tasks which they would not do in everyday life.
This means we can’t generalise the results as beyond the lab to real life.
84
Disadvantages to lab experiments
High demand characteristics
Participants are aware they’re taking part in an experiment and may picku on cues (demand characteristics) that reveal the aim.
This can lead to [articipants changing their behaviour in order to meet the experimenters expectations
This means participants behaviours unnatural and doesn’t reflect their true behaviour.
85
Advantages of field experiments
High mundane realism and ecological validity
They’re conducted in natural environments and participants complete everyday tasks
This means that behaviour is more likely to be natural and can therefore be generalised to real life
86
Advantage of field experiments
Low demand characteristics
Participants are unaware they’re taking art in an experiment therefore wont figure out the aim and alter their characteristics.
This means participant behaviour will be natural and reflect their true behaviour.
87
Disadvantages of field experiments
I field experiments have low internal validity
This is as its difficult to control EVS which might affect results due to the natural situation so we can’t say the iv is directly responsible for any changes in the dv
Therefore CAUSE and EFFECT can be established easily
88
Disadvantages of field experiments
Low standardisation
Conditions In field experiments are natural so they’re harder to Standadise and kept the same
This means that other researchers can’t replicate the exact same field experiments to check results are consistent.
89
Advantages of natural and quasi experiments
They can be used to study sensitive research questions
This is bc they allow for the investigation of variables that would otherwise be harmful or impossible to deliberately manipulate.
This means it is practical and ethical to conduct natural/quasi experiments in there circumstances.
90
Advantages of natural/quasi exp
They have high mundane realism and ecological validity
This is if they’re conducted in natural environments or if participants are given everyday tasks
This means behaviours are more likely to be natural and can therefore be generalised to real life
91
Disadvantages of natural/quasi experiments
Low internal validity
The iv is not manipulated directly and there is minimal control over EVS. Which can affect results
This means it is more difficult to establish cause and effect
92
Disadvantages of natural/quasi experiments
Low replicability
This it bc iv is naturally occurring and sometimes a one off event and procedures may not all be standardised
This means that other researchers can not replicate the exact same experiment to check the results are consistent.
93
What’s an EV
Varable that MAY effect the dv if not controlled. They’re “2nusance variables” that make it difficult to detect if iv has had an effect.
94
What’s a confounding variable
Type of EV. It is one that systematically changes alongside the iv. Therefore acts as an iv. This means is possible that it has caused change to the dv
95
Types of EVS
Participant variables
Situational variables
96
What’s situational varible
Features of the environment that may effect participants behaviour
97
Eg of situational variables
Noise temperature odours lighting
98
Control over situational varibles?
STANDADISE ALL PROCEEDURES
Ensure ps experience same condition.
Hold facors consistent and including these details in the standardised proceedures /instructions for all experiments to follow
This will eliminate the effect of such variables as there will be minimal variation in these factors across conditions
99
What are participant variables
Individual differences betweeen participants and ways in which they vary from each other
100
Examples of participant variables
Sex
Gender
Ethnicity
Experience
101
Control participant variables
RANDOMISATION of participant condition \randomly allocate participants to one condition so there’s no bias regarding which participants go in each condition. They should have a 50/50%chance of getting either condition.
Use a number generation or pick number out of hat to do this.
Therefore individual differences will be distributed evenly across conditions so they don’t alter systematically with the iv
102
Demand characteristics
Environmental clues to what the investigation is about, causing ps to alter their natural behaviour
103
Participant reactivity
Participants conform to what they believe the researcher expects and act overly pleasing to behave in ways they’re thing are socially desirable. Alternatively ps will try “ruin” the results by defying expectations and performing in an antagonist way.
104
How can demand characteristics be resolved/controlled
Using deception - not revealing there true aim and hypothesis of the study
Using double blind proceedures- neither the participants or researchers are aware of the condition the ps have been assigned too
105
Investigator effects
Any (uninterntional or unconscious) unwanted influence of the researchers behaviours/characteristic on the participant dats outcome
106
Non verbal cues
Eg raised eyebrows
May encourage /discourage perforance
107
Physical characteristics and mannerisms - investigator effects
Smiling
May courage/discourage performance
108
Expectancy effect
Researchers pre existing knowledge of aim and hypothesis may reveal unconscious cues to participants that effects their behaviour or any other features of the researcher may also effect participants behaviour.
109
Bias in interpretation- investigator effects
Researcher can interpreted them results of the data in a subjective way if they feel their view is correct. Thus is less of a problem if the data consists in an objective way- time
110
How can investigator effects be controlled
Using same researcher- all participants interact with the same individual
Using double blind proceedures- neither ps or researcher knows which conditions ps have been assigned too
111
Experimental designs
Repeated measures
Matched pairs
Individual groups
112
Experimental design- definition
How researchers allocate and organise participants to the condition of an iv in an experiment
113
Repeated measure design
Involves using same people in each condition of the iv and comparing like for like
Every participant does both conditions
114
What should researches do when using repeated measures
Cancel order effects via counter balcencing
115
What’s counterbalancing
Half participants undergo condition a first then condition b, then other half of participants do b first then a.
This ensures that any order effects are distributed between the conditions so the conditions of iv are equally effected
116
Adavantes of RMD
Partivipant variables are controlled for
This is bc the same participants take part in both conditions so there are no individual differences in factors like age and gender
This means demand variable is not effected by participant variables so any difference in conditions is LIKELY TO BE DOWN TO THE IV
117
Disadvantages of RMD
Demand characteristics ate likely to be experienced
This is bc participants take part In both conditions so become aware of hat is being manipulated
This means the DV IS LIKELY TO BE EFFECTED BY PARTICIPANTS WORKIING OUT THE AIM OF THE STUDY AND CHANGING THEIR BEHAVIOUR rather than the iv
118
Disadvantages of RMD
Order effects are likely to be experienced
Bc participants take part in both conditions so performance on the task which is completed the second time is likely to be either improved through practice or worsened through boredom.
This means the dv is likely to be affected by these factors
119
Independent group design
Involves using different people in each condition of the iv and compares each groups performance
Each p is allocated to a different condition of the iv
120
Advantages of IGD
There’s less chance of demand characteristics
This is bc articipants only tasks part in one condition and so are unaware of what’s being manipulated
This means the dv is unlikely to be affected by participants working out the aim of the study and changing their behaviour, so any difference between conditions is likely to be down to the iv
121
Advantages of IGD
Less chance of order effects
Bc ps only take part in one condition of the experiment
The dv is unlikely to be affected by tiredness /boredom/practice so any dif between conditions I s likely down to the iv
122
Disadvantages of IGD
Participant variables aren’t controlled for
There are individual differences between participants bc different ps take part in each condition so there will be “participant variablility” in factors like age and gender
This means dv is likely to be effected by participant variables and may explain why one group may perform better in one condition than the other
123
Matched pairs
Using dif participants in each condition of the iv, but participants in one condition are “matched “ with ps in the other condition on important key variables that are relevant to the investigation.
124
Advantages too MP
Less chance of demand characteristics participant varibles and order effects
This is bc ps only take part in one condition. Therefore they have less chance of guessing the aim of the study and change their behaviour so their behaviour isn’t altered as a result of practice.\/ boredom
Also bc participants are similar there are fewer difference between the groups
125
Disadvantages to matched pairs
Matched pairs are extremely difficult to achieve
This is bc it’s a very lengthy process to match participants. This can become a research study in itself and be very expensive and time consuming.
Thiis means matched pairs are rarely used in psychological research as its less practical.
126
What’s target population
Group of Pete who the researchers want to generalise their results too
127
What’s a sample
Small number of people taken from target population who participate. In the investigation
128
Sampling bias
If sample is selected over or under certain groups that compose the target population.
To avoid sampling bias the sample should be as large as possible.
129
Sampling techniques
Random sampling
Opportunity sampling
Volunteer sampling
Systematic sampling
Stratified sampling
130
What’s stratified sampling
Identifying groups called STRATA that exist in target population.
Calculating the PROPORTIONS of individuals needed from each strata to represent the overall target population
Once the proportions have been calculated another sampling technique will be used (random) to obtain the selected number of participants from each strata.
Those selected will be contacted and invited to participate.
131
Advantages o stratified sampling
The sample is very representative of the target population
This is bc the researcher has no bias and influence over which participants are being selected and all subgroups are represented.
This means the finding from the study can be generalised back to the target population
132
Disadvantages to stratified sampling
The sample is vey time consuming and inconvenient to use.
This is bc it requires full list of the population and having awareness of which strata each individual=dual belongs too, which may not be available info.
This means that the sample are difficult to use in psychological research bc they’re time consuming
133
What’s systematic sampling
This involves devising a sampling frame (list of people in the target population)
And a system being nominated to select every nth person.
Those selected will be contacted and invited to participate
134
Advantage of systematic sampling
The sample is likely to be representative of the target population
This is bc the researcher has no control over which ps are being selected so there’s minimal researcher bias
This means the finding from the study can be generalised back to the target population
135
Disadvantages if systematic sampling
It’s still possible that the sample will be unrepresentative of the target population
This is bc the processes of selecting ps may conside with a hidden trait in the population
This means that sometimes it’s difficult to generalise these findings back to the target population
136
Volunteer sampling
Self selected
Involves advertising the study and providing contact details so the individual can respond if they wish to participate.
137
Advantage of volunteer sampling
More convenient than random samples
Bc They’re less time consuming than random samples bc individuals approach the researcher themselves- dresearcher doesn’t have to seek them out
This means volunteer samples are often used in psychological research.
138
Disadvantages of volunteer samples
Unlikely to be representative of target pop
The sample is likely to be bias as volunteers have similar characteristics
This means it’s difficulty to generalise the findings back to the target population
139
Opportunity sample
Approaching and inviting those available at the time and place the researcher is looking fkf
140
Advantages of opportunity sampling
More convincing to use then random sampling
Bc they’re less time consuming to obtain as psychologists can use anybody willing at the time so there is no need to gather info about tho whole target population
This means they’re often used as they save effort
141
Disadvantages of opportunity sampling
Unlikely to be representative of the target population
The sample is bias as ps other share similar characteristics given they’re selected from one place at one time
This means it’s difficult to generalise back the findings back to target population
Also, the researcher is in control over selection of ps and therefore likely to be bias
142
Random sampling
Every ps in target population has equal chance of being selected through a lottery system with no bias from the psychologist. Those selected are then contacted and invited to participate. Those selected are then contracted and invited to participate.
Eg Imputting all those in target population to a computer and allocates to the amount of numbers
143
Advantages of random sampling
Likely to be represented in target population
Everyone in target population has the same and equal chance of being selected without bias from the researcher
This means the findings from study can be generalised back to the target population
144
Disadvantage of random sampling
More time consuming and incomnvineitn compared to volunteer sampling
This is bc the researcher has to obtain information ant everyone in that target population to give each one an equal chance of being selected- this is often Impractical if target population is large
This means random sampling is difficult to achieve in psychological research because it is time consuming
Also random sampling may still be unrepresentative in practice p.
