Loading presentation...

Present Remotely

Send the link below via email or IM


Present to your audience

Start remote presentation

  • Invited audience members will follow you as you navigate and present
  • People invited to a presentation do not need a Prezi account
  • This link expires 10 minutes after you close the presentation
  • A maximum of 30 users can follow your presentation
  • Learn more about this feature in our knowledge base article

Do you really want to delete this prezi?

Neither you, nor the coeditors you shared it with will be able to recover it again.


AQA PSYA4: Research Methods

No description

Lauren O'Bryan

on 9 June 2014

Comments (0)

Please log in to add your comment.

Report abuse

Transcript of AQA PSYA4: Research Methods

AQA PSYA4: Research Methods
Application of Scientific Method
Data Analysis and Reporting
Designing Investigations
Validating new knowledge
Research methods and concepts
Reliability, validity and sampling
Ethical issues
Spearman's Rho
Chi Squared
Mann Whitney U Test
Wilcoxon T Test
Evaluation of Scientific Methods
Can psychology claim to be a science?
Are the goals of science appropriate for psychology?
Reductionism and Determinism
Scientific research is desirable
Psychology uses scientific method
Lack of objectivity and control
Nomothetic vs Idiographic
Scientific methods haven't worked
Qualitative research
Peer Review
Peer Review and the Internet
Finding an Expert
Publication Bias
Preserving Status Quo
Can't correct old research
Laboratory Experiments
Field Experiments
Natural Experiments
Experimental Design
Self-Reporting Methods
Observational Studies
Correlational Analysis
Case Studies
Other Research Methods
Moral Justification
Existing constraints
Ethical Issues
Code of Conduct
Dealing with Ethical Issues
Sampling Techniques
Experimental Research
Experimental Research
Observational Techniques
Observational Techniques
Self-Report Techniques
Self-Report Techniques
Opportunity Sample
Volunteer Sample
Random Sample
Stratified and Quota Samples
Snowball Sampling
Inferential Analysis, Probability and Significance
Descriptive and Inferential Statistics
Analysis and Interpretation of Qualitative data.
Allows psychologists to produce verifiable knowledge about behaviour that wasn't just commonsense or 'armchair psychology'.
Psychologists can generate models that can be falsefied conduct well controlled experiments to test these models.
It has been questioned if that just using a scientific method turns psychology into a science or not.
Miller (1983
Suggests using scientific method is just 'dressing up' psychology as a science.
While they may be using scientific tools that doesn't make it a science and that it is better described as a psuedoscience.
Hard to measure things objectively as object of study reacts to the researcher which can lead to problems such as expermenter bias or demand characteristics.
Heisenberg (1972)
Other sciences also face this problem.
Unable to measure a subatomic particle without the presence of the reasearchers and the measurement of it affecting it.
Called uncertainty princluple and similiar to an experimenter effect but in physics.
R.D. Laing
Claimed it was innapropriate to view a person experiencing distress as a chemical system gone wrong.
Treatment of individuals could only be successful if they were treated as such.
Science takes a more nomothetic approach, trying to find similarities and group people together.

Psychological appraches to treating mental illnesses have had a best moderate success suggesting that maybe the goals of science aren't always appropriate.
Even more qualitative methods are still scientific in their aim to be valid. they are made objective by being compared with others as a means of verification.
Reduces complex phenomena to simple variables to allow the study of causal relationships between them.
Deterministic in search of causal relationships because otherwise scientific research could not be used as a method of understanding behaviour.
Assessment of scientific work by other who are qualified in the field.

To ensure that the conduction and publishing of research is of a high standard.
Three main purposes:
Allocation of research funding
Publication of research in scientific jounals
Assessing the research rating of university departments.
With the increasing availability and fast pace of information on the internet it is hard to maintain quailty of information.
On many sites such as Wikipedia and Philica it is the reader that decides what is valid or not. Though scientific research should really only be peer reviewed by people with appropriate qualifications, with the use of the internet everyone is becoming able to add to the information flow - true or not.
Conducted in a laboratory or laboratory-like setting.
High in internal validity
Low in mundane realism
can be low in external validity
experimenter bias/demand characteristics.
High mundane realism
High external validity
Experimenter effects reduced
Demand characteristics
Lower internal validity
Ethical issues (deception)
Experiment conducted in a much more natural environment.
Experiment that takes advantage of existing IV's.
Correlational not causal.
Low demand characteristics
high in mundane realism
Time consuming
Sometimes little valid results produced
Hard to determine causation
Low internal validity
Independent Groups
Matched Pairs
Repeated Measures
Not always possible to find an appropriate expert to review a research proposal.
Smith (1999)
If an individual does not properly understand the material, this could lead to poor research being passed.
Anonymity usually practised so reviewers can be honest and objective.
Anonymity can lead to rivals settling scores or trying to bury others research though as social circumstances inevitably affect objectivity.
This has led to some journals practicing open reviewing to combat this.
Journals prefer to publish positive results, suggested that this is due to editors wanting to publish research with important implications.
Journals also tend to not publish replications of study, which suggests journals are as bad as newspapers for wanting eye-catching stories.
Leads to publication bias that in turn leads to misrepresentation of facts.
Richard Horton
"The mistake of course, is to have thought the peer review any more than a crude means of discovering the acceptablily - not the validity - of a new finding"
Science generally resistant to change and therefore likely to ignore any large shifts in opinion that research might create, prefering to instead ignore that research.
Brooks (2010)
Points to peer-reviewed research that was subsequently debunked but is still used in debate in parliment.
Once research has been published the results remain in the public view evem is they are later shown to be wrong or just poor research.
Different people in different conditions.
Participants matched to person in other condtion based on various criteria.
Criteria could be things such as age, gender or IQ.
Each participant tested under all conditions.
No order effects
Less demand characteristics
Hard to make comparison
More people needed
Individual differences reduced
Lower order effects
Time consuming
People never totally similar
Easy to compare
Fewer people needed
Increased demand characteristics
Need counterbalancing
There are other methods such as:
Content analysis: a kind of observational study
Cross-cultural research: comparng the effects of different cultural practices on behavior
Meta-Analysis: combines results of many studies to reach overall conclusions.
Detailed studies into a single individual, institution or event.
Generally longitudinal.
Uses information from a range of sources such as the person concerned and also from their family and friends.
Allows study of complex interaction between many factors.
Difficult to generalise from case studies.
Can be unreliable due to heavy use of recollection.
Correlation does not show causation.
Useful for identifing relationships between co-variables.
Other intervening variables could explain why the variables are linked.
May lack validity.
Open Questions
Closed Questions
Use sampling methods:
Time sampling
Event sampling
Naturalistic or controlled.
Observer bias possible.
Mututally exclusive behavioural categories to record instances of behaviour.
Questions developed as a response to previous answers.
Real-time, face-to-face.
Set of questions given to participants.
More repeatable
Provide qualitative data.
Rich insight
Harder to analyse
Easier to analyse
Provide qantitative data
People from target population that are most easily available.
Usually people who are geographically close.
100% attendance
Not representative
Sample bias
Participants volunteer to take part in study.
Immediate consent
Participants easy to get
Sample bias (volunteer bias)
Participants selected randomly from target population.
Potentially unbiased
Time consuming
Can be biased if people refuse participation
Sample bias reduced
Time consuming
May not get representative sample
Systematic Sample
Pick participants with a system
Identify a few suitable participants, ask them to point in direction of other possible candidates.
Useful when hard to find suitable participants (e.g. eating disorder studies)
Prone to bias
Strata identified within a population.
Predetermined number ofparticipants taken from each group in proportion to representation in target population.
Stratified : Done by random techniques
Quota : Done using opportunity sampling.
Representative sample
Ecological validity
Time consuming
Large sample needed
Opportunity sample can lead to bias
Informed Consent
Right to Withdraw
Protection from Harm
Giving the participant all the information they need to make an informed decision about whether or not to participate in the study.
Can cause demand charachteristics if participant too much.
Issues if study could cause harm or distress and they haven't given full consent.
Deliberately misinforming participant.
Stops participant being able to give informed consent.
Giving participants the right to leave the study if they feel uncomfortable.
Important if participant has been deceived.
Could create sample bias.
Covers all physical and psychological harm.
Important that no permanent damage caused.
Hard to guarantee that no harm will be caused.
Participant has right to keep personal data secret.
Can be hard as often publishing research can make the identity of participants obvious.
Sometimes hard not to invade participants privacy.
If behavior is being observed in own home situation, privacy becomes a big issues.
Standards of privacy, confidentiality and informed consent.
Respect for the dignity and worth of all persons.
Deception only acceptable when needed to protect the integrity of the research.
Maintain high standards.
Responsibility to protect from harm.
Responsibility to debrief participants to identify an unforeseen harm and arrange for any assistance if needed.
Should be honest and accurate.
Should bring instances of misconduct to the attention of the BPS.
Informed Consent
Right to Withdraw
Protection from Harm
Presumptive consent:
Group of people similar to participants asked if they would be okay with the study.
Used to presume if the participants would/wouldn't agree to participate.
Asked to sign document agreeing to participate.
Right to withdraw given.
Need for deception judged by ethics committee.
Debriefed at end of study.
Participant can withdraw information after debrief.
Can withdraw if they feel uncomfortable.
May feel unable to, especially if they have been paid for participation.
Researchers try to avoid harmful situations.
Try to keep risk of harm lower than that of day life.
Participants kept anonymous.
If participants need to be able to be distinguished from each other, alias or number used.
Observation only acceptable informed consent.
Allow participants private space as much as possible.
If psychologist deemed to have deviated from acceptable ethics, they may be disbarred from BPS and therefore unable to practice psychology.
Several reasons for using animals in psychological research:
Offer greater control and objectivity in studies

Can be used when experiments would not be viable for humans (e.g. Harlow's monkeys)

Enough physiology and evolutionary past in common to be able to draw conclusions about humans from animal testing.
Sentient Beings
Animal Rights
There is evidence that animals other than primates are sentient and yet they are still used in research.
Brain-damaged human individuals are not sentient, yet they would not be used in research without consent.
There is evidence to suggest animals can respond to pain even if they may not be able to feel it.
Peter Singer (1990)
Discrimination on basis of species is no different from ageism or racism.
Gray (1991)
We have no duty of care towards animals and so speciesism is not equivalent to human isms such as racism.
Singer's view is utilitarian (Greater good)
Reagan (1984)
Animals have right to be treated with respect and should not be used in research.
No circumstances under which animal research is acceptable.
Having rights is dependent upon having responsibilities in society, animals do not have responsibility and so it can be said they do not have rights.
Animals (Scientific Procedure Act:
Requires animal research only takes place in licensed facilities.
To acquire license, must meet criteria:
Research important enough to justify research.
Cannot be done using other methods.
Minimum numbers of animals used.
Discomfort or suffering kept to minimum.
3 R's:
Russel and Birch (1959)
When experiment repeated, all conditions must be the same or a change in result could be due to a changed result.
Two or more observers should get the same record.
Extent to which the observers agree is called inter-observer reliability.
Reliability of observations can be improved though training observers to use the coding system/behaviour checklist appropriately.
Internal Reliability: all questions should be measuring same thing.
External reliability: Measure of consistency.
Inter-interviewer reliability: Whether two interviewers produce the same outcome.
Split-half method: Compare performance on two halves of a questionnaire.
If test is assessing same thing in all questions then scores should have a close correlation between both halves.
Test-retest method: Given questionnaire and then given it again after an amount of time.
If measure is reliable outcome should be the same.
Lab experiments aren't always low in external validity and field experiments aren't always high in it.
Validity dependent more on how artificial and contrived an experiment is, also how aware the participant is of being studied.
Mundane Realism
Internal Validity
External Validity
How applicable an experiment is to the real world.
How far the results can be applied to the general population.
The more representative the sample it is, the more generalisable it is.
High in internal validity if the experiment measures what it intended to measure.
Affected by internal validity.
External validity deals with how applicable the results are to different times (historical validity), cultures (population validity) and other people and situations (ecological validity).
Observations cannot be valid if the coding system/behavioural checklist is flawed.
Observer bias: when what is observed is affected by expectations of observer.
Observational studies likely to have high ecological validity due to more natural behaviors - not always the case though.
Face Validity
Concurrent Validity
Does the test lok like it's measuring what it is intended to meaure?
Estabilished by comparing performance on new questionnaire with previously established test on same topic.
If performance has high correlation then this is evidence of high concurrent validity.
Inferential Tests
Measures of Central Tendency
Measures of Dispersion
Selecting the right test
Justifying your choice
Stating conclusions
Inferential tests allows researchers to work out the likelihood of something occurring.
There are different significance levels that psychologists can choose but usually a 5% significance level is chosen.
Significance levels are degrees of uncertainty.
A 5% significance level means there is a 5% chance of the results occurring even if there is no real association between the samples.
Help to draw conclusions about populations based on samples drawn from them.
Allows us to infer if a pattern is due to chance or not.
Observed and Critical Values
Observed Value
Critical Value
By using statistical tests we create a test statistic. The test statistic for any set of data is called the observed value as it is based on the observations made.
Critical values are the values compared against the observed value to determine if it is significant or not.
The critical value is the number the observed value must reach in order to be significant.
Finding the appropriate critical value:
Degrees of freedom (df)
One-tailed or two-tailed test
Significance level
In most cases this can be gained by looking at the number of participants in the study (N)
if directional hypothesis: One tailed
If non-directional hypothesis: Two-tailed
Usually P=0.05 (5%)
Observed value either needs to be more or less than this value. (It should be stated under each table which)
Choosing which statistical test
Depends on research design and level of measurement.
Justifying choice of test
Levels of Measurement
How to choose which test to use:
Nominal Data?
Independent Groups?
Spearman's Rho
Mann-Whitney U
Wilcoxon T
Identify level of measurement with reference to actual data.
State whether a test of correlation or difference is needed and justify this.
If a test of difference is used, state if it is independent groups or repeated measures and justify this.
Data separated into categories
e.g. Tall, Short
Data is ordered in some way
Difference between each item not always the same
e.g. Ordering people by height
Data measured using units of equal intervals
e.g. Counting correct answers
Calculated by adding all values and dividing by the number of values.
Can be unrepresentative if there are extreme values.
Not suitable for nominal data.
Middle value of an ordered list.
Not affected by extreme values.
Not all values reflected in the median.
Not suitable for nominal data.
Most common value.
Not useful when there are several modes.
Only measure suitable for categorised data.
Suitable for other data types too.
Standard Deviation
Difference between highest and lowest value.
Easy to calculate.
Affected by extreme values.
Spread of data around the mean.
More precise as all data taken into account but extreme values not expressed.
Bar Chart
Height of bar = frequency
Suitable for all levels of measurement.
Dot or cross shown for each pair of values.
Suitable for correlational data.
Bottom left to top right = positive correlation
Top left to bottom right = negative correlation
No pattern = No correlation
Are the data nominal, ordinal or interval?
Is a correlation involved or is there a difference between two data sets?
Is the design repeated measures or independent groups?
Test of correlation needed as hypothesis stated a correlation.
Data are ordinal or interval.
Therefore the appropriate test is Spearman's Rho.
Data has been put into categories and therefore is nominal.
The results are independent in each cell and the expected frequencies in each cell are greater than 4.
The appropriate test is therefore chi-squared.
A test of difference is required because the hypothesis states a difference.
The design was independent groups and the data was ordinal or interval.
Therefore Mann-Whitney is a suitable test.
A difference test is required as the hypothesis states a difference between the two conditions.
The design is repeated measures or matched pairs and the data scores are interval or ordinal data.
Therefore, a wilcoxon test is appropriate.
Key features of conclusions:
State observed value.
Say if this is greater or less than the critical value.
State whether the null hypothesis can be accepted or rejected.
For Spearman and Chi-Squared this means observed value must be greater than critical value to reject null.
For Mann-Whitney and Wilcoxon observed value must be lower than critcal value.
Restate hypothesis you are accepting.
Summarising qualitative data
An iterative process
General Principles
Validity and Reflexivity
1. Read and reread data, trying to understand meaning communicated and perspective of participants.
2. Break the data into meaningful units.
3. Assign label/code to each unit. These will form the basis of your categories.

Each unit may be given more than one code/label.
4. Combine the simpler codes into larger categories/themes.
5. Check can be made on categories by collecting new data and applying them to categories. If the new data fits then they represent the data appropriately.
6. Final report shoudl discuss and material to illustrate the themes.
7. Conclusions can be drawn, which may include new theories.
Reflexivity is the term used to describe the extent to which the process of research reflects a researcher's values and thoughts.
May be demonstrated using triangulation.
Compares results from a variet of different studies of the same thing/person.
The studies will likely have used different methodology.
If the results agree this supports their validity, if they disagree this can lead to further research to increase understanding.
Difficult to summarise, cannot use measures of central tendency or spread.
Must instead try to identify repeated themes.
Most qualitative research aims to be inductive, finding theories FROM the data.
Less commonly used is a deductive approach. Used in triangulation, previously existing categories are used to categorise new data.
The data must be gone through repeatedly which takes a lot of time.
The main intention is the find a way to order the data in a way that represents the participants' perspective.
Type 1
Type 2
Type 1 errors are false positives.
These occur when the significance level is too lenient.
Type 2 errors are false negatives.
They occur when the significance level is too stringent.
i.e. the null hypothesis is accepted even when there is a difference.
i.e. the null hypothesis is rejected even when there is no difference.
Step 1 : State alternative and null hypothesis
Step 2: Record data, rank each co-variable and calculate the difference.
Step 3: Find the observed value of rho (correlation coefficient)
Step 4: Find the critical value of rho
Step 5: State the conclusion
Null hypothesis:
There will be no correlation.
Alternative hypothesis:
There will be a correlation (non-directional)
There will be a correlation in this direction (directional)

Rank each variable separately from low to high.
If there are two or more of the same number then calculate the mean of the ranks that those numbers would have been given and this is the rank for all of them.
Can be useful to use a table for all the values.
Participant Number
Variable 1
Rank A
Variable 2
Rank B
Difference between A and B
Using the formula:
and the values already calculated in the table, calculate the value of rho.
If the hypothesis is directional, a one-tail test is used.
If the hypothesis is non-direction, a two-tailed test is used.
Use the table of critical values to find the critical value.
When comparing the observed value to the critical values only the number counts not the sign.
The sign just shows if the correlation is positive or negative.
If the observed value is greater than or equal to the critical value then the null hypothesis is rejected and the alternative hypothesis is accepted.
However, if the hypothesis states a positive correlation and a negative correlation is found then we still need to accept the null as it does not meet the criteria of the alternate.
Same the other way around.
Same the other way around.
Repeated measures design.
Independent groups design.
The expected frequencies for each shouldn't be less than 5, so for a 2x2 table this would mean no less than 20 participants.
Step 1: State alternative and null hypothesis
Step 2: Draw up contingency table
Step 3: Find observed value by comparing observed and expected frequencies for each cell.
Step 4: Add all values in final column
Step 5: Find the critical value of chi square
Step 6: State the conclusions
Null hypothesis: There will be no difference.
Alternative hypothesis:
There will be a difference (non-directional)
There will be a difference in this direction (directional)
Cell A
Cell B
Cell C
Cell D
Variable 1
Variable 2
A + B
A + C
B + D
C + D
A + B + C + D
The value of chi-squared is the sum of the values in the last column.
Calculate degrees of freedom:
(Number of rows - 1) x (Number of columns - 1)
In this case:
df = (2-1) x (2-1) = 1
Look up the value on critical values table, whichever value corresponds to the degrees of freedom (right hand column) and the significance level (top rows) is the critical value.
If observed value is less than critical value then null hypothesis accepted. If observed value is more than or equal to critical value then we reject the null hypothesis and accept the alternative.
If the direction is not in the direction that the hypothesis suggested though, the null must still be accepted and the alternate rejected.
Step 1: State alternative and null hypothesis
Step 2: Record the data in a table and allocate points.
Step 3: Find observed value of U
Step 4: Find the critical value of U
Step 5: State the conclusion
Null hypothesis: There will be no difference.
Alternative hypothesis:
There will be a difference (non-directional)
There will be a difference in this direction (directional)
To give points, take each score individually and compare it to all the other scores of the OTHER group.
Every time a score in the other group is higher than the score you're looking at: 1 point
Every time a score in the other group is the same as the score you're looking at: 0.5 points
The observed value of U is just the lowest score value.
In this case, that would be 5.5
N1 = Number of participants in group 1
N2 = Number of participants in group 2
The critical value tables for Mann-Whitney compare N1 against N2 so that's how you find the critical value.

Your N1 against your N2 in the table = the critical value for your test.
You need to pick the right table though, for one-tailed directional hypothesis) or two-tailed (non-directional hypothesis) and for the right significance level, usually 5% unless stated otherwise.
If the observed value is less than or equal to the critical value then the difference is significant and so the null hypothesis can be rejected and the alternative hypothesis accepted.
If using a directional hypothesis though, difference must be the direction stated in the hypothesis in order to reject the null, otherwise the null must still be accepted even if the difference is significant as it is significant in the wrong direction.
Step 1: State alternative and null hypothesis
Step 2: Record the data, calculate the differences between scores and rank
Step 3: Find the observed value of T
Step 4: Find the critical value of T
Step 5: State conclusion
Null hypothesis: There will be no difference.
Alternative hypothesis:
There will be a difference (non-directional)
There will be a difference in this direction (directional)
Work out the difference between the results and then rank from low to high.
If there are tied ranks then work out the mean of the ranks that would have been given and use that.
Ignore signs, -1 and 1 count as the same difference.
If the difference is zero, omit this result.
T = the sum of the ranks of the less frequent sign.
In this case, the less used sign is minus.
So T = 1
which is the rank of the only minus difference, -1.
N = 4 (as one result was omitted)
Look up critical value in tables against N and under the correct tail and significance level.
If observed value of T is less than or equal to the critical value then the difference is significant and the null hypothesis can be rejected and the alternative hypothesis is accepted.
If using a directional hypothesis in order to accept the alternate hypothesis and reject the null, the difference must be in the correct direction to match the hypothesis or the null must be accepted and the alternative rejected.
Full transcript