Research Methods Flashcards
1
Q
Aims
A
Outline research topic
“To investigate”
2
Q
Alternative hypothesis
A
A prediction
Directional or non-directional
3
Q
Directional hypothesis
A
States which way they predict the results will go
Eg boys will score higher on the maths test than girls
4
Q
Non directional hypothesis
A
States there will be a difference but not what that difference will be
Eg. “There will be a difference in maths test scores between boys and girls”
5
Q
Null hypothesis
A
This hypothesis is accepted if the results of the experiment are not significant
States that there will be no or any difference is down to chance
6
Q
Independent variable
A
The thing that is changed or manipulated
Eg the groups or the conditions
7
Q
Dependent variable
A
The thing that is measured
8
Q
Operationalised
A
Explaining how the variables could be manipulated/measured
9
Q
Correlational hypothesis
A
There is not an IV or DV
there are co-variables, 2 things that are measured and compared for a relationship
10
Q
How many types of sampling and their names
A
5 Systematic Stratified Opportunist Volunteer Random
11
Q
Random sampling
A
Every participant has an equal chance of being selected
12
Q
Opportunity sampling
A
Asking people who are around at the time to take part
13
Q
Volunteer sampling
A
Researcher advertises The study and people who see the advert may get in contact and volunteer
14
Q
Systematic sampling
A
Selecting every nth name from a list
15
Q
Stratified sampling
A
Selecting people from every portion of your population in the same proportion
16
Q
Strengths of random sampling
A
> free from researcher bias
16
Q
Weakness of random sampling
A
> Difficult and time consuming
> unrepresentative
18
Q
Strength of volunteer sampling
A
Easy to do
Less time consuming
19
Q
Weakness of volunteer sampling
A
> tend to get similar people taking part (volunteer bias)
> cannot generalise results
20
Q
Strengths of opportunist sampling
A
> Less time consuming
> And less costly in money
21
Q
Weaknesses of opportunist sampling
A
> Unrepresentative- usually end up with the same sort of people
Researcher bias
22
Q
Strength of systematic sampling
A
> avoids researcher bias
> usually fairly representative
23
Q
Weakness of systematic sampling
A
> can be unrepresentative
24
Q
Strength of stratified sampling
A
Clear representation of each portion of population
25
Laboratory experiment
> Artificially controlled environment
> Independent variable is manipulated
> Participants are randomly assigned to conditions
26
Weakness of stratified sampling
Difficult and time consuming
27
Strengths of the laboratory experiment
> Higher internal validity due to control,over extraneous variables
> easy to replicate
28
Weakness of laboratory experiment
> Artificial environment - less generalisable
| > Low external validity as it may bring out demand characteristics
29
Field experiment
> Natural environment
| > Independent variable manipulated (true experiment)
30
Strengths of field experiment
> high external validity - authentic behaviour
| > better external validity - more realistic due to natural environment
31
Weakness of field experiment
> Ethical issues - invasion of privacy as no consent is given
> difficult to support hypothesis due to extraneous variables (affects internal validity)
32
Natural experiment
> Natural or controlled setting
| > independent variable is not manipulated - unplanned and has occurred because of a naturally occurring event
33
Strengths of natural experiment
Ethical as it doesn’t cause he event
34
Weakness of natural experiments
Not replicable because natural occurring event is rare
35
Quasi experiment
> Natural or controlled setting
> independent variable not manipulated- based on existing difference between people eg. Age gender personality
> planned manipulation of naturally occurring independent variable
36
Strengths of a Quasi experiment
Depends on laboratory or field experiment would have the streets of those experiments
Eg lab - high internal validity
Eg field - high external validity due to natural environment it’s more realistic
37
Weakness of a quasi experiment
> confounding variables could impact data
38
What are self report methods
Questionnaires and interviews
| It is when participants report their own thoughts and feelings about a particular matter
39
Open questions
| What data does it give
Participant can give any answer they wish
| Qualitative data
40
Closed questions
| What data does it give
There are a set number of responses which participants select from
Quantitative data
41
3 types of closed questions
Fixed choice option
Likert scale
Rating scales
42
Fixed choice option
Includes a list of possible options and respondents are required to indicate those that apply to them
eg. Age bracket
43
Likert scales
The respondent indicates their agreement with the statement using a scale from strongly agree to strongly disagree
44
Rating scales
Participants selected value that represents the strength of feeling about a particular topic e.g. 1 to 5
45
Strengths of questionnaires
> Close questions produce quantitative data which is easier to analyse
> Open questions produce qualitative data which can provide unexpected answers and rich detail allowing researchers to gain new insights
> Respondents may feel more able to reveal personal information in the questionnaire
46
Weaknesses of questionnaires
> Social desirability bias may cause respondents to deliberately answer in a way which is socially acceptable
> Leading questions may cause respondents to answer in a particular way
> Only certain types of people fill-in and return questionnaires therefore there may be a sample bias
47
What are interviewes
Mostly face-to-face questions though some may be conducted over the phone
48
Structured interviews
> Contain standardise preset questions
> often a computer is used e.g. CAPI
> Sometimes limited responses due to predetermined answers
49
Semistructured interviews
> Some preset questions
> preset questions can be asked in any order
> researcher can veer from preset questions
> usually open ended
50
Unstructured interviews
> It is a conversation
> Obtained very detailed data
> Questions emerge during interview
51
Strengths of structured interviews
> Easy to analyse his answers are more predictable
| > Can be easily repeated because the questions are standardised (more replicable)
52
Weakness of structured interviews
> The answers the participants give me be restricted by the question that is asked
> Different questions may be interpreted in different ways by different participants
53
Strengths of unstructured interviews
> More detailed information can be obtained from each respondent
> information can be accessed that might not be revealed by using predetermined questions
54
Weaknesses of unstructured interviews
> The interviewers own opinions may influence the interviewee this is interviewer bias
> More difficult to analyse the data as there will be lots more of each respondent may have been asked different questions
55
Naturalistic observation
Research method carried out in a naturalistic setting in which the investigator does not interfere in any way just observes the behaviour in question
56
Controlled observation
Observation of behaviour under controlled conditions
57
Over observation
The participants are aware they are being observed
58
Covert observation
The participants are not aware that they are being observed
59
Structured observation
Researcher determines precisely what behaviours are to be observed and uses a standardised checklist to record the frequency in which they are observed within a specific time frame
60
Unstructured observation
The observer recalls all relevant behaviour but has no system
61
Participant observation
The researcher gets involved with the participant activity so they can experience it for themselves
62
Nonparticipant observation
The observer remain separate from the participants to maintain objectivity
63
Strengths of naturalistic observation
Truthfinding is due to no demand characteristics
| Good external validity
64
Weaknesses of naturalistic observation
Poor internal validity due to no control
| Small scale so one off
65
Strengths of controlled obervation
Good internal validity due to control of extraneous variables
66
Weakness of controlled observation
Introduces demand characteristics
| Less generalisable to real life - lowers external (ecologically) valid
67
Strengths of overt observation
Informed consent - ethical
68
Weakness of overt observation
Hawthorne effect acting different due to knowing you’re being watched
69
Strengths of covert observations
Authentic behaviour
70
Weaknesses of covert observation
Unethical as there is no informed consent
71
Strengths of structured observation
Easily repeated - replicable
| Good internal validity - standardisation
72
Weaknesses of structured observations
Restrictive - may see other behaviours but won’t be able to use them because they are not on the checklist
73
Strengths of Unstructured observations
Unrestrictive - record all behaviour including unexpected behaviours
74
Weaknesses of unstructured observations
Harder to replicate as there is no standardisation
75
Strengths of participant observations
High validity - better insights
76
Weakness of participant observations
Less objective - going native - lose insight of what your doing
Hard to collect data
77
Strengths of non participant observations
More objective
78
Weaknesses of non participant observations
Thoughts and feelings lost
79
When conducting and observation what may a psychologist use
Behavioural categories
80
Behavioural categories
They are used in structured observations as a checklist
| The target behaviour is broken down into behavioural categories and then operationalised
81
3 sampling methods and which observations are they used in
Unstructured - continuous recording
| Structured - event sampling and time sampling
82
Continuous recording
- all instances of target behaviour is recorded
83
Event sampling
Counting the number of times in a particular behaviour occurs in q target individual or group doesn’t take account of the time just to tally
84
Time sampling
Recording behaviour within a pre-established timeframe e.g. take note what a target individual is doing every 30 seconds of summer time interval
85
Strengths of behavioural categories
Data collections is more structured and objective
86
Weakness of behavioural categories
Categories are hard to define always exclusive and some overlap
87
Strengths of events sampling
It is useful when target behaviour or event happens frequently as it could be missed in time sampling
88
Weaknesses of the event sampling
Observer may overlook important details if the specified events is too complex
89
Strength of time sampling
It is effective in reducing the number of observations that have to be made
90
Weakness of times sampling
It may be on representative of the observation as a whole when behaviour is sampled
91
What’s a correlation
When two things are measured in order to identify if there is a relationship between a single numerical values produced that is used to describe the relationship this is called a correlation co efficient
92
Positive correlation
Both are those increase together
| As one covariable increases so does the other covariable
93
Negative correlation
As one covariable increases the other decreases
94
No Correlation
No relationship between variables
95
Curvilinear correlation
The relationship is predictive although it is not linear but curved
96
Intervening variable
A verbal that comes between two other variables which is used to explain the associations between those two variables
97
Continuous variable
Variable that can take on any value within a certain range
98
What is the correlation coefficient
Number between zero and one
Tells us how strong the correlation is the nearest to one the stronger the correlation
It has a sign in front of the number which tells us whether the correlation is positive or negative
99
What does the researcher do in correlations
Any correlation there is no manipulation of the two variables therefore it is not possible to determine cause and effect
100
What are the three types of experimental design
Independent groups
matched participants
repeated measures
101
Independent groups
Separate group of participants for each condition of the independent variable
E.g. independent variable music/new music equals one group has music playing in the other group has no music
102
Matched participants
For a group of participants for each condition of the independent variable that they are fitted to certain characteristics
E.g. independent variable gender equal separate groups of males and females but will make sure the groups are matched for age or income
103
Repeated measures
Every participant completes all conditions
E.g. one group has music playing in the same group does another test without music
Cannot be used if the independent variable is gender or age – (IV is quasi- pre-existing difference)
104
Advantages of independent groups
> Order effects are avoided
| > No demand characteristics
105
Disadvantages of independent groups
> Individual differences may occur - the people in the groups are different people therefore there may be differences
106
Advantages of repeated measures
> No individual differences
107
Disadvantages of repeated measures
> Demand characteristics may appear
| > Order effects
108
Advantages of matched pairs
> Control for individual differences is better
| > no order effects or demand characteristics as participants are only taking part in 1 condition
109
Pilot studies
Used to test deign and test measures used
Can be used to test reliability (retest)
Used to identify extraneous variables
And to ensure all the ethical issues have been dealt with
110
Ethics
The consideration of what is acceptable or right behaviour in the pursuit of a personal or scientific goal
111
What is the 5 ethical issues
```
Informed consent
Right to withdraw
Deception
Confidentiality
Protection of participants
```
112
Internal validity
What is it
What effects interval validity
Are we measuring what we set out to measure (accuracy)
| Extraneous variables and confounding variables
113
Extraneous variables
‘Nuisance’ variables that do not vary systematically with the iv and can often be controlled before the experiment begins
114
Confounding variables
Variables that do vary systematically with the iv so we cannot be sure what caused the change in dv
115
Participant variables
Any individual difference between people taking part that may interfere with the outcome of the investigation
116
External validity
Can we generalise it
Population validity - is our sample representative
Ecological validity - is the environment accurate to real life
(Temporal)Validity over time - is it still accurate to today’s society
117
What effects external validity
Demand characteristics
| Investigator effects
118
Demand characteristics
Participants may guess the aims of the research and then may act in a way they think is expected of them
Difficult to control
119
Investigator effects
Unwanted influence of the researcher on the experiment
| This may be unconscious behaviour such as smiling more with one condition than the other
120
Situational variables
Any aspect of the experimental environment that may interfere with th outcome of the investigation
121
Hawthorne effect
Added attention of being in a study affects participant behaviour
122
Social desirability bias
When participants try to look good by answering/behaving in a socially acceptable way
123
Interviewer bias
The interviewer affects the responses of the interviewee
124
Greenspoon effect
When the interviewer makes affirmative noises eg mhhhmm
125
Experimenter bias
When the experimenter effects the results eg through their body language, facial expressions etc
126
Participant reactivity
Social desirability bias
Demand characteristics
Hawthorne effects
127
Investigator effects
Experimenter bias
Interviewer bias
Greenspoon effect
128
Randomisation (control for experimenter bias)
Using chance at every available opportunity
129
Counterbalancing (control of order effects)
Half of the participants do one condition the other half does the other condition then they swap
130
Single blind design (control for demand characteristics)
Use of deception to mislead participants
131
Standardisation (control for experimenter bias)
Keeping everything the same for every participant
132
Double blind design (control for demand characteristics and experimenter bias)
When both the researcher and the participant don’t know the aims of the study
133
Reliability
Consistency
If they did the rest on another day would they get the same result
Is there standardisation of procedures and instructions
134
Inter-rater reliability
Used in observation
Are the observers all scoring the same way
Testing for consistency
135
Checking for reliability
Test, retest = reliability
Conduct the test again and see if you get the same result
Conduct a spearmans rho test comparing scores
Testing for correlation
136
Improving reliability
Observers familiarise themselves with behavioural categories
Conduct a small scale pilot study
Compare the data observers have gotten by calculating a correlation co-efficient
Operationalise variables if needed
Repeat
137
Purpose of peer review
> To validate the quality and relevance of research
> To suggest amendments or improvements
> Allocation of research funding
138
Process of peer review
Validity
Significance
Originality
Method
Design
Report can be accepted, amendments suggest or rejected
Final reports submitted to panel and assessed for publication
139
Evaluation strengths of peer review
Anonymity
Keeps a check on dishonest psychologists
Essential so that high quality researches produced
140
Evaluation- weaknesses of peer review
```
Publication bias
Burying ground-breaking research
Expensive
Time consuming
Subjective
Preserving status quo
```
141
Qualitative data
Expressed in words, non numerical
142
Quantitative data
Expressed numerically rather than in words
143
Primary data
First hand from participants, collected specifically for the purpose of the research
144
Secondary data
Data collected by someone other than the person doing the research
145
Strengths of qualitative data
> Detailed so more realistic as it gives a better insight
| > Greater external validity
146
Weaknesses of qualitative
> Hard to analyse data as it is opinion based
> Subjective to interpretation
> Harder to spot patterns and trends
> Research bias – if research has preconceptions about what they’re going to find
147
Strengths of quantitative data
Simple to analyse measure of central tendency in
Objective in this open to bias
Easy to represent e.g. graphs
148
Weaknesses of quantitative data
Narrow in scope information may fail to represent real life
| May miss important information
149
Strengths of primary data
Authentic as it is obtained from participants themselves
| Target what you want to find
150
Weaknesses of primary data
Time and effort consuming - requires planning and preparation resources
151
Strengths of secondary data
Easier to use less time-consuming
Cheaper it’s already done
Peer reviewed = valid
Use data otherwise inaccessible
152
Weaknesses of secondary data
Historically bias out of date
Subjective
Might not be valid in methodology
153
What are measures of central tendency
They are descriptive statistics that find the average they can be the mean the mode or the median
154
What is measures of dispersion
They find the spread/variety of the data
Range
Standard deviation – calculate how far scores deviate from the mean
155
Streaks of the mean
Uses all of the dates are in the calculation so it’s more representative of the data as a whole
156
Weaknesses of the mean
Is affected by extreme values
157
Strengths of the mode
Can be used with qualitative data
| Not affected by extreme values
158
Weaknesses of the mode
Isn’t always in mind maybe more than one
| Could measure that is a representative of the data as a whole
159
Strengths of median.
Not affected by extreme values can be used when data is not interval
160
Weaknesses of the median
Median may not even be in the data set if even amount of numbers
Isn’t representative
161
Strengths of the range
Includes all data pieces
162
Weaknesses of the range
Only takes into account the two most extreme values on representative of the day as a whole
163
Strengths of standard deviation
All of the data is included in the calculation shows more representative of the data set
164
Weaknesses of standard deviation
Affected by extreme values
| It’s complicated and time-consuming
165
Bar graph
Used for data in discrete categories
The bars are separated by a gap to show they are not continuous
It should plot the total or mean or percentage of scores for each group
The DV goes on the Y axes and the IV goes on the X axes
166
Histogram
Used for continuous frequency data
The bars are touching to show its continuous
X axes is made up of equal size intervals of a single category the Y axis represents frequency
Sometimes the frequency polygon is drawn by joining the midpoint at the top of the bar
167
Line graph
Used for continuous frequency data
X axis is made up of equal size intervals of a single category the Y axis represents frequency
Useful for comparing two sets of frequency data on the graph
168
Scatter graph
Use for correlation or data
Co-variables go on the axis
The dots and what dreams are sometimes a line of best fit is drawn
169
Normal distribution
Bell shaped curve
Mean mode and median all lie at the midpoint
No scores occur around during middle with fewer being plastered as they occur above and below the mean
The tiles of the curve never touch the horizontal axis as more extreme scores are always theoretically possible
170
Positive distribution
Positive Skews is when longtail is on the positive side of the peak
No scores for below the mean
Mean mode and median I’m not in the same place – the main gates go to the right because it is affected by extreme values
171
Negative distribution
And negative skews when the longtail was on the negative side of the peak
Most scores fall above the mean
The mean mode and median are not in the same place
172
Inferential statistics
Draw conclusions about our data
Tell us whether our results are significant enough that we can generalise with any certainty
Based around probability – assess the probability that results could just be down to chance if there is a low probability of this then we can generalise
173
Significance
If a test shows the results are significant we can accept our alternative hypothesis if they are not significant we accept that I’m null hypothesis
A test is significant if it meets the level of probability we have chosen
P< 0.05 or p< 0.01
174
First 3 Stages of the sign test
> Record each pair of data working out the difference
> Record a plus for a positive difference and the minus for a negative difference zero for no difference
> Add up all the plasters and all the minuses and select the smaller value
175
Second 3 stages of the sign test
The smaller value is the calculated value, S
Determine if it is a one-tailed or to two tailed test
State the hypothesis
176
Last 3 stages of the sign test
Check the result is in the right direction
Find the critical value of S with a table of critical values
If the calculated value is equal to or less than the critical value the result is significant
