Research Methods Flashcards
What do aims come from?
Theories
What are aims?
General statements that describe the purpose of the investigation
What is a hypothesis?
A clear, precise, testable statement at the start of the study that clearly describes the relationship between the variables of the theory
What makes a hypothesis directional?
If there is previous research on the subject that is being investigated that suggests a direction
What makes a hypothesis non-directional?
If there is no previous research on the subject being investigated, or if previous research does not suggest a direction
How do we write a non-directional hypothesis?
There will be a difference in DV between IV (experimental condition) and IV (control condition)
How do we write a directional hypothesis?
There will be an increase in DV between IV (experimental condition) and IV (control condition)
OR
There will be an decrease in DV between IV (experimental condition) and IV (control condition)
How do we write aims?
To investigate…
What is an experimental hypothesis?
A hypothesis that predicts some difference between the results that has not occurred due to chance
What is operationalisation?
Making concepts testable by making them scientific and quantifiable
e.g. “The number of…”
What is the IV?
The Independent Variable
This is what the experimenter manipulates
What is the DV?
The Dependent Variable
This is what the experimenter measures
What are Variables?
Anything that varies or changes in an investigation
What are Extraneous Variables?
Variables other than the IV which could potentially affect the DV if they are not controlled
What are Confounding Variables?
Variables other than the IV which may have affected the DV
They make it difficult to see what has caused changes to the IV, so it is hard to establish clear cause and effect
How many Experimental Methods are there?
4
How many Experimental Conditions are there?
2 levels
What are the 2 levels of Experimental conditions?
The control condition
The experimental condition
What are the types of experiment?
Lab Experiment
Field Experiment
Natural Experiment
Quasi Experiment
What is a Lab Experiment?
An Experimental Method that uses a controlled environment
The researcher manipulates the IV and records the effects on the DV
Evaluate Lab Experiments
Good - high control
- allows for replicability
- can be certain of cause and effect
- minimises extraneous variables
Bad - low mundane realism
- lacks generalisability
- artificial tasks may lead to artificial behaviour/demand characteristics
What is a Field Experiment?
An experimental method that uses a real world setting
The researcher manipulates the IV and records the effects on the DV
Evaluate Field Experiments
Good - higher mundane realism
- real setting
- high external validity
- lower demand characteristics
Bad - less control
- harder to find cause and effect
- hard to replicate
What is a Natural Experiment?
An Experimental Method where the IV is naturally occurring, and would have occurred even if the researcher wasn’t there
The researcher records the effects on the DV
Evaluate Natural Experiments
Good
- Creates opportunities for studies that might not have been possible (earthquakes, volcano eruptions)
- High ecological validity due to using natural problems and real world issues
Bad
- Rare to find a naturally occurring event
- Limits generalisation
- No control as IV exists already
- Participants may not be randomly allocated
What are Quasi Experiments?
An Experimental Method where the variables already exist, so the IV has not and cannot be determined by anyone
e.g. twins, adoption - cannot control who is a twin and who has been adopted
Evaluate Quasi Experiments
Good - controlled conditions
- good replicability
- can be certain of cause and effect
Bad
- Possible confounding variables as you cannot randomly allocate participants
- Cannot claim the IV has caused any observed change as it has not deliberately been changed by the researcher
What are 6 Research Issues?
Demand Characteristics
Confounding Variables
Extraneous Variables
Standardisation (lack of)
Investigator Effects
Randomisation
What are Demand Characteristics?
When participants try to work out what is going on in the experiment (the aim), and change their behaviour so it is no longer natural
They can show the ‘please you’ or ‘screw you’ effect where they deliberately please the experimenter or sabotage the experimenter
What are Investigator effects?
Unconscious actions or Unstandardised procedures that may influence the research outcome
What are Internal Extraneous Variables?
Participant Variables
They are the differences between participants such as age, gender or personality
What are External Extraneous Variables?
Situational Variables
They are features of the experimental situation such as noise, temperature and weather
How many Experimental Designs are there?
3
What are the Experimental Designs?
Independent Groups
Repeated Measures
Matched Pairs
What is Independent Groups Design?
An Experimental Design where two separate groups of participants experience two different conditions of the experiment
- all participants experience only one level of the IV
- one group would do the control condition and one would do the experimental condition
- performance of the 2 groups would be compared
Evaluate Independent Groups Design
Good
- Less chance of demand characteristics as participants only complete one so cannot figure out the aim
- No order effects as they have only done one
- No practice effects as they have only done one
- Can use random allocation
Bad
- Participant variables might make it difficult to establish cause and effect - there may be confounding variables
- Need a larger sample
- Takes longer/costs more
What is Repeated Measures Design?
An Experimental Design where all participants experience both conditions of the experiment
- Each participant completes one condition (either experimental or control)
- Participants then swap and complete the other condition afterwards
The scores from both conditions would be compared to see the differences
Evaluate Repeated Measures Design
Good
- Counterbalancing means that participant effects are minimised as every participant is completing every condition
- Fewer people are needed as they take part in both conditions
- Allows for Random Allocation
Bad
- Practice effects - participants may be used to the task, or may work out the aim if they complete it more than once
- Order effects - participants may be tired after the first condition, or may care less and not try as hard on the second condition
What is Matched Pairs Design?
An experimental design where participants are matched based on possible participant variables that may affect the DV
e.g. 2 people with glasses, 2 people with hearing aids, 2 people aged 22 etc.
- one person from the pair completes one condition and the other person completes the other
Evaluate Matched Pairs Design
Good
- Reduces Participant Variables by having similar people in each condition
- Avoids practice effects - demand characteristics are less likely
- Avoids order effects
Bad
- Participants can never be matched exactly, even if they were identical twins
- Matching might be time-consuming and expensive
What is a Population?
The large group of individuals a researcher is interested in studying
Also called the target population
What is a Sample?
The smaller group who take part in the research
How many sampling techniques are there?
5
What are the sampling techniques?
Volunteer Sampling
Random Sampling
Opportunity Sampling
Stratified Sampling
Systematic Sampling
What is Random Sampling?
Every member of the target population has an equal chance of being selected
e.g. names in a hat, random number generator
Evaluate Random Sampling
Good
- Should represent the target population
- Should eliminate a sampling bias
Bad
- Difficult to achieve (effort, money)
- Time consuming - some people might say no
What is Opportunity Sampling?
The researcher selects participants from whoever is available at the time
Evaluate Opportunity Sampling
Good
- Quick
- Easy
(cost effective)
Bad
- Might be biased
- Might not provide a representative sample
What is Volunteer Sampling?
Participants put themselves forward to be put in the sample
They self-select
Evaluate Volunteer Sampling
Good
- Easy to find people willing to participate (less time consuming)
Bad
- Might not provide a sample representative of the whole population
- Might not have enough participants
- Might suffer Volunteer Bias if they are too keen to participate (please you effect), or they might all be the same type of person
What is Stratified Sampling?
The target population is broken down into smaller groups, and these are then sampled from
The sample is a proportional representation of the target population
Evaluate Stratified Sampling
Good
- Avoids researcher/sampling bias
- Can generalise results as sample should be representative of the population
Bad
- Takes a lot of time
- Difficult to do, and some people might say no, so you’d have to start again
What is Systematic Sampling?
Every nth member of the target population is selected
e.g. every 8th house on the street
every 5th person on the register
Evaluate Systematic Sampling
Good
- Should provide a representative sample as it is a set selection across the whole target population
- Reduces researcher bias
Bad
- Difficult to achieve (time, effort, money)
- There might not be enough diversity to represent the whole population
What are Correlations?
The strength and direction of an association between co-variables (the relationship between them)
How are Correlations plotted?
Using a Scattergram
One Co-Variable is on the X axis, and one is on the Y axis
Each point on the graph is the X and Y position of each co-variable
What are the types of Correlation?
Positive correlation - as one variable increases so does the other
Negative correlation - as one variable increases, the other rises
No correlation - there is no pattern between the variables
Why can’t we assume a cause and effect from a Correlation?
Correlation does not equal causation
It simply points out the relationship between the variables
How can we work out the strength of a Correlation?
Using a Correlation Coefficient
Between 0 and +1 shows a Positive Correlation
Between -1 and 0 shows a Negative Correlation
For a Correlation to be strong, it must have a Coefficient of at least .8
(-0.8 or +0.8)
What do Descriptive Statistics Include?
Measures of Central Tendency
Measures of Dispersion
What are Measures of Central Tendency?
Measures that give us averages about the typical values in a set:
Mean
Median
Mode
What are Measures of Dispersion?
Measures that are based on the spread of scores and how they vary and differ from one another:
Range
Standard Deviation
What is the Mode?
The most frequent value
There can be 2 modes (bimodal)
There can be no mode
How do we calculate the Mode?
See which value is the most frequent
Evaluate the Mode
Good
- Easy to find
Bad
- May be more than one answer
- May not represent the whole results
What is the Median?
The Middle Value when results are placed in order
Used for Ordinal and Interval data
How do we calculate the Median?
Place results in order
Find the Middle Value
Evaluate the Median
Good
- Easy to calculate for small data sets
- Not affected by anomalies (outliers)
Bad
- Not a true representative of all data
- Can be difficult for larger data sets
What is the Mean?
The overall average of a data set
How do we calculate the Mean?
Add up all the results
Divide by the number of results there are
Evaluate the Mean
Good
- Uses all the data
Bad
- Can be distorted by outliers
What is the Range?
The largest value minus the smallest value
It tells us how spread out the data is
How do we calculate the Range?
Subtract the Smallest Value from the Largest Value
Evaluate the Range
Good
- Easy to calculate
Bad
- Affected by extreme data
What is Standard Deviation?
A measure of Dispersion that tells us how far the data is from the mean
Evaluate Standard Deviation
Good
- Accurate
Bad
- Difficult
How do we ensure good design of self-report techniques?
Avoid Jargon
Avoid Emotive Language
Avoid Leading Questions
Avoid Double-Barrelled Questions
Avoid Double Negatives
What is a Peer Review?
The assessment of scientific work by others who are specialists in the same field.
They should be objective and unknown by the Researcher.
What are the main aims of Peer Review?
1) Decide if a proposed research project should receive funding
2) Assess research for quality and accuracy by validating the quality of the hypotheses, methodology, statistical tests, and conclusions
3) To suggest amendments or improvements, or even suggest it is unsuitable for publication
Evaluate Peer Reviews
Good
- Spots mistakes and prevents invalid or inappropriate data from being published
Bad
- Some reviewers may use their anonymity to criticise rival researchers on purpose
- Burying groundbreaking research - reviewers might reject research that goes against mainstream theory or the status quo
- Publication bias - publishers may want research that will make good headlines to increase the readers, or they might want positive results
- Takes a long time
- Sometimes fraud can be missed if results are fabricated
- Bias - peer reviewers might know the person and dislike them or their university
- File Drawer Phenomenon - work might be left for too long
What is Reliability?
Consistency
- you get the same results every time you complete the research
How can we test Reliability?
1) Test Re-Test (inter-rater reliability)
- Carry out the research once
- Carry the same research on the same participants at a later date
- Calculate the correlation to check how similar the results are
- If the coefficient is 0.8 or above, it is reliable
2) Inter-Observer Reliability
- Carry out the research once
- Have a separate second researcher carry out the research
- Correlate the results
- If the coefficient is 0.8 or above, it is reliable
When do we need to improve Reliability?
If the Correlation Coefficient is less than .8
How can we improve Reliability?
Questionnaires
- remove some questions
- re-write some questions
- replace open questions with closed questions to reduce ambiguity
Interviews
- use the same interviewer each time or train all interviewers to standardise the procedure
- avoid leading questions
- use a structured interview for more control
Experiments
- use a lab experiment for more control
- ensure it is standardised for replicability
Observations
- operationalise behavioural categories
- train observers - standardise
What is Internal Validity?
Whether we are measuring what we set out to measure
- there are no confounding or extraneous variables that could have affected what we are measuring
What is External Validity?
How generalisable the findings are beyond the research setting
- e.g. to the real world (ecological validity), or across different eras (temporal validity)
What is Face Validity?
It looks like the experiment measures what it is supposed to be measuring
What is Concurrent Validity?
If the results from this study are close to the results from another already established study
What is Ecological Validity?
The extent to which findings can be generalised to other settings and situations
What is Temporal Validity?
The extent to which findings can be generalised to other historical times and eras
How can we assess Face Validity?
The researcher or another expert will look at the test and see if it seems to measure what it is supposed to be measuring
How can we assess Concurrent Validity?
Test your participants with your test
Test them again with an already established test
Collect Results and Correlate the scores
High concurrent validity will have a correlation coefficient of .8 or more
How can we Improve Validity?
Questionnaires
- put in a lie scale to test consistency of results
- make it anonymous to reduce social desirability
Experiments
- control extraneous variables by using a control group for comparisons
- standardise procedures
- use a double blind to reduce investigator effects
Observations
- use covert observations so behaviour is authentic
- ensure behavioural categories are not too broad or ambiguous
Aim to use quantitative methods
If using qualitative methods, use quotes or triangulation (different sources of the same information)
What are the 3 levels of data?
Nominal
Ordinal
Interval
What is Nominal Level Data used for?
Categories
Lowest and most basic data such as tally charts
Discrete data (1 item can only appear in 1 category)
What is Ordinal Level Data used for?
Ordered Data
The rank/place/rating
Lacks precision as it is subjective and there is not a set interval between each unit
e.g. 1st, 2nd, 3rd
What is Interval Level Data used for?
Based on Numerical Scales
Includes units of equal, precisely defined size
A standardised and operationalised unit of measurement
What Descriptive Statistics do we use for Nominal Data?
The Mode
It is the most often in all categories
What Descriptive Statistics do we use for Ordinal Data?
Median
Range
What Descriptive Statistics do we use for Interval Data?
Mean
Standard Deviation
It is the most precise measurement for the most precise level
How do we complete an Inferential Statistics Test?
1) Test of Difference or Test of Association?
2) What Experimental Design is used?
3) What is the Level of Measurement?
What is Insignificant Data?
In 95% of cases the results would have happened anyway
What is Significant Dats?
In 5% or less of cases the results would have happened anyway
This suggests the IV has caused the DV
What are the Features of Science?
Objectivity
Empirical Method
Replicability
Falsifiability
Theory Construction
Hypothesis Testing
Paradigms and Paradigm Shifts
Who theorised the Features of Science?
Karl Popper
What is Objectivity? (FOS)
Not allowing personal biases to affect our data
What is the Empirical Method? (FOS)
Approaches based on gathering evidence through direct observation and experience
What is Replicability? (FOS)
The extent to which procedures and findings can be repeated across different circumstances and contexts
Important to find validity and reliability
What is Falsifiability? (FOS)
Scientific theories should hold themselves up for hypothesis testing and the possibility of being disproven
This is why we always have a null hypothesis
Strong theories are those that have been repeatedly tested and not falsified
What is Theory Construction? (FOS)
Gathering evidence to form a theory - a set of general laws or principles that can explain behaviours
- could be a hunch that leads to experiments to develop a theory
What is Hypothesis Testing? (FOS)
Testing a theory through empirical methods
Can support and strengthen a theory
Can disprove a theory
What are Paradigms and Paradigm Shifts? (FOS)
Paradigm
- a set of shared beliefs and assumptions
- Kuhn suggested psychology has too many conflicting approaches to be a science
Paradigm Shift
- science progresses through scientific revolution
- researchers may begin to question the accepted paradigm, and this gathers pace until a paradigm shift occurs
- this is when contradictory evidence cannot be ignored, so there is a shift to the new belief/paradigm
What are the Sections of a Scientific Report?
Abstract
Introduction
Method
Results
Discussion
References
What is the Abstract? (SR)
A short summary
It includes all major elements of a report
- aims
- hypotheses
- method
- procedure
- results
- conclusion
It lets others know if they want to read it in full
What is the Introduction? (SR)
A Literature Review of the past research and theories that relate to their study
It progresses from general to specific and ends on the most relevant aims and hypotheses
What is the Method? (SR)
A section including
- Design
- Sample/Participants
- Apparatus/Materials
- Procedure
- Ethics
Should be detailed enough to allow replication
What are the Results? (SR)
Summarising key findings
- Descriptive Statistics
- Inferential Statistics
- Qualitative results and findings
Raw data goes in an appendix
What is the Discussion? (SR)
Summarising the results in verbal form
Discusses limitations
Suggests how they can be modified for future studies
Consider the wider implications
What are the References? (SR)
Adds any details of source materials
How are References written for Journal Articles?
Surname, Initials (Date) Title of article, Journal title, edition. Page numbers
How are References written for Books?
Author, Surnames and initials (Date) Title pf book, place of publication, publisher
What does Content Analysis do?
Turns Qualitative Data into Quantitative Data
What is the process of Content Analysis?
1) Coding
- Categorise the data into meaningful units (codes)
- These could be categories, themes, phrases, key words
2) Count
- Count how many times these codes occur
- Highlight the transcripts to count for occurrences
There is your quantitative data
What does Thematic Analysis do?
Leaves Qualitative Data as Qualitative Data but makes it easier to analyse
What is the process of Thematic Analysis?
1) Identify Themes
- identify key ideas that are recurrent
Could put these into broader categories
2) Directly quote these to illustrate each theme in the final report
How do you calculate the Percentage Something is of another Value?
Divide by the total value
Multiply by 100
How do you calculate Percentage change?
Change/Original x 100
How do you calculate Percentage Increase?
Calculate the Change
Find the change as a % of the original value
How do you calculate Percentage Decrease?
Calculate the Change
Find the change as a % of the original value
What is a Type 1 Error?
When the Null Hypothesis is rejected and the alternative is accepted when the Null Hypothesis is actually true
e.g. saying a fat man is pregnant
- He could not be pregnant
- As people have accepted he is pregnant although he could not be, the alternative hypothesis has been accepted when the null hypothesis is actually true
What is a Type 2 Error?
When the Alternative Hypothesis is Rejected and the Null Hypothesis is Accepted although the Alternative Hypothesis is actually true
e.g. saying a heavily pregnant woman is not pregnant
- She is pregnant
- As people have accepted she is not pregnant although she is, the null hypothesis has been accepted when the alternative hypothesis is actually true
Why do we test at the 5% level?
To try to get a balance between Type 1 and Type 2 errors to reduce the risk of both of them
