1
Q
Content Analysis (CA)
A
- Is a type of observational technique which involves studying people indirectly thru qualitative data
- Data can be placed into categories + counted (quantitative) or can be analysed in themes (qualitative)
2
Q
Qualitative and quantitative
A
- Qualitative data collected in a range of formats can be used e.g. video or audio recordings (or the interview transcripts), written responses (such as those provided to an open question in a questionnaire) or children’s drawings
- CA helps to classify responses in a way that is systematic which can then allow clear conclusions to be drawn
3
Q
Coding
A
- Is an important step in conducting CA + involves the researcher developing categories for the data to be classified
- Qualitative data can be extensive so coding is helpful in reaching conclusions about the data
- These categories provide a framework to convert the qualitative data to quantitative data which can then be used for further statistical analysis
- It is important for researchers to have their research questions formulated so they know exactly what their CA will focus on
- Researchers must familiarise themselves with the data before conducting any analysis so that they are confident that their coding system is appropriate for the task
4
Q
When is content analysis is useful?
A
- CA is helpful when conducting research that would otherwise be considered unethical
- Any data already released into the public domain is available for analysis e.g. newspaper articles meaning that explicit consent is not required
- For material that is of a sensitive nature like experience of domestic violence, participants can write a report of their experience which can be used in analysis
- This allows high quality data to be collected, even in difficult circumstances
5
Q
Content analysis involves design decisions:
A
- Sampling method = how material should be sampled e.g. time or event sampling
- Recording data = should data be transcribed or recorded (video camera) + should data be collected by an individual researcher or by a team
- Analysing and representing data = how should the material be categorised or coded to summarise it? + should the no of times something is mentioned be calculated or described using themes?
6
Q
Example of content analysis:
A
A researcher is interested in investigating prejudice + discrimination in the media towards refugees. To do this, they will follow the following procedures:
- The researcher will select a newspaper article relating to refugees
- They will read through the text, highlighting important points of reference + annotating the margins with comments
- Using the comments made, the researcher will categorise each excerpt according to what it contains e.g. evidence of prejudice, discriminatory language + positive regards towards refugees
- This process will be repeated for each newspaper article of interest identified by the researcher at the outset
- Once all steps above are completed for each newspaper article, the categories which emerged through the process of analysing the content are reviewed to decide if any need refining, merging or subdividing
- With the well‐defined (operationalised) behavioural categories, the researcher returns to the original articles + tallies the occurrence of each ‘behaviour’ accordingly.
- The qualitative data has now undergone analysis to produce quantitative data which can undergo further analysis such as statistical testing, descriptive statistics + producing graphs or tables
7
Q
Advantage of Content Analysis (1)
A
CA tends to have high ecological validity because it is based on observations of what people actually do e.g. real communications, such as recent newspapers or the books that people read
8
Q
Advantage of Content Analysis (2)
A
When sources can be accessed by others e.g. videos of people giving speeches, the CA can be replicated + therefore the observations can be tested for reliability
9
Q
Disadvantage of Content Analysis (1)
A
Researchers can still be biased when putting the data into categories which reduces the reliability + validity of the data because diff researchers may interpret the meaning of the categories differently
10
Q
Disadvantage of Content Analysis (2)
A
- Cultural differences may contribute to inconsistent interpretation of behaviour coding since language may be translated + therefore interpreted differently by someone of a different nationality
- As a result, the validity of findings from a CA can be questioned since it may not have been measuring what it intended to with accuracy
11
Q
Thematic Analysis (TA)
A
- Is a technique that helps identify themes throughout qualitative data
- A theme is an idea or a notion + can be explicit (such as stating that you feel depressed) or implicit (using the metaphor of a black cloud for feeling depressed)
- TA will produce further qualitative data but this will be much more refined
12
Q
Example of Thematic Analysis:
A
The researcher reviewing the articles for evidence of prejudice or discrimination against refugees, the following procedures would be followed:
1.Carry out steps 1–3 as if conducting a CA
2. After the researcher must decide if any of the categories identified can be linked in any way such as ‘stereotypical views’, ‘economic prejudice’ or perhaps ‘positive experiences for refugees’.
3. Once the themes are successfully identified, they can then be used in shorthand to identify all aspects of the data that fit with each theme
e.g. every time the researcher identifies an example within the data of a positive experience for the refugee, they might write ‘PER’ (positive experience for refugees) alongside it so that they are able to quickly re‐identify this theme in subsequent analysis of the data
4. Once all the steps above are completed, the themes which emerged will be critically reviewed to decide their relevance
5. This process will be repeated for each newspaper article of interest identified by the researcher at the outset.
6. Qualitative comparisons are drawn between major and minor themes of the analysis
13
Q
Advantage of Thematic Analysis
A
- High ecological validity
- Much of the analysis that takes place within these research methods are basing their conclusions on observations of real‐life behaviour + written and visual communications - E.g. analysis can take place on books people have read or programmes that people have watched on TV
- Since records of these qualitative sources remain, replication of the content/thematic analysis can be conducted
- If results were found to be consistent on re‐analysis then they would be said to be reliable
14
Q
Weakness of Thematic Analysis
A
- There is the possibility that TA can produce findings that are very subjective
- E.g. the researcher may interpret some things said in an interview in a completely diff manner from how they were intended due to their own preconceptions, judgements or biases
15
Q
Case Studies
A
- The purpose of a case study is to provide a detailed analysis of an individual, establishment or real‐life event
- A case study does not refer to the way in which the research was conducted as case studies can use experimental or non‐experimental methods to collect data
- E.g. a researcher may want to interview the participants, provide a questionnaire to their family or friends + even conduct a memory test under controlled conditions to provide a rich and detailed overview of human behaviour
16
Q
When are case studies used?
A
- Case studies are often used where there is a rare behaviour being investigated which does not arise often to conduct a larger study
- A case study allows data to be collected + analysed on something that psychologists have very little understanding of
- And can therefore be the starting point for further, more in‐depth research
17
Q
Example of famous case studies:
A
- HM
- Phineas Gage
- Little Albert
- Little Hans
- 9/11
- London Riots
18
Q
Advantage of Case Studies (1)
A
- It offers the opportunity to unveil rich, detailed info about a situation
- These unique insights can often be overlooked in situations where there is only the manipulation of 1 variable to measure its effect on another
19
Q
Advantage of Case Studies (2)
A
- Case studies can be used in circumstances which would not be ethical to examine experimentally
- E.g. the case study of Genie (Rymer, 1993) allowed researchers to understand the long‐term effects of failure to form an attachment which they could not do with a human participant unless it naturally occurred
20
Q
Disadvantage of Case Studies (1)
A
- There are methodological issues associated with the use of case studies
- By only studying 1 individual, an isolated event or a small group of people it is difficult to generalise any findings to the wider population since results are likely to be so unique
- Therefore this creates issues with external validity as psychologists are unable to conclude with confidence that anyone beyond the ‘case’ will behave in the same way under similar circumstances
- Thus lowering population validity
21
Q
Disadvantage of Case Studies (2)
A
- An issue when qualitative methods are used is that the researcher’s own subjectivity may pose a problem
- In the case study of Little Hans, Freud developed an entire theory based around what he observed
- There was no scientific or experimental evidence to support his suggestions from his case study
- This means that a major problem with his research is that we can’t be sure that he objectively reported his findings
- Consequently, a major limitation with case studies is that research bias + subjectivity can interfere with the validity of the findings/conclusion
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Q
Reliability
A
- is a measure of consistency
- If the results are not consistent then the measure is not reliable
- If researchers are using a questionnaire to measure levels of depression they want to ensure that the measure is consistent between participants + over time
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Q
Test-Retest Reliability
A
- The same person or group of people are asked to undertake the research measure e.g. questionnaire on different occasions
- The same group of participants are being studied twice so researchers need to be aware of any potential demand characteristics
- If the same measure is given twice in 1 day, there is a strong chance participants will be able to recall the responses they gave in the first test, + so psychologists could be testing their memory rather than the reliability of their measure
- Also ensure there is not too much time between each test
- If psychologists are testing a measure of depression + question the participants a year apart, it is possible they may have recovered + so they give completely different responses rather than that the questionnaire is not reliable
- After the measure has been completed on two separate occasions, the 2 scores are then correlated
- If the correlation is shown to be significant, then the measure is said to have good reliability
- Perfect correlation is 1 + so the closer the score is to this, the stronger the reliability of the measure
- But a correlation of over +0.8 is also perfectly acceptable + seen as a good indication of reliability
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Q
Inter-Observer Reliability
A
- Also known as inter-rater reliability
- Refers to the extent to which 2 or more observers are observing + recording behaviour in a consistent way
- A useful way of ensuring reliability in situations where there is a risk of subjectivity
- If a psychologist was making a diagnosis for a mental health condition it would be a good idea for someone else to also make a diagnosis to check that they are both in agreement
- In psychology studies where behavioural categories are being applied, inter‐observer reliability is also important to ensure the categories are being used in the correct manner
- Psychologists would observe the same situation or event separately + then their observations (or scores) would be correlated to see whether they are suitably similar
- If the correlation coefficient of the 2 observers is more than +0.8 then this means the reliability is strong
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Example of Inter-Observer Reliability
- Ainsworth’s Strange Situation
- During the controlled observation, her research team were looking for instances of separation anxiety, proximity seeking, exploration + stranger anxiety across the 8 episodes of the methodology (operationalised behavioural categories)
- Ainsworth found 94% agreement between observers + when inter‐observer reliability is assumed to a high degree, the findings are considered more meaningful
- If reliability is found to be poor, there are different ways in which it can be rectified depending on the type of measure being used
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Improving Reliability: Interviews
- Ensuring the same interviewer is conducting all interviews will help reduce researcher bias as there is the potential for variation in the way questions are asked which can lead to different responses
- Some researchers may ask questions that are leading or are open to interpretation
- If the same interviewer can't be used throughout the interviewing process then training should be provided to limit the potential bias
- Changing the interview from unstructured to structured will limit researcher bias
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Improving Reliability: Questionnaires
- Identify which questions that are having the biggest impact on the reliability + adjust them as necessary
- If they are important items that must remain in the questionnaire then rewriting them in a way that reduces them incorrectly interpreted may be enough
- E.g. if the item in question is an open question, it may be possible to change it into a closed question reducing possible responses + thereby limiting potential ambiguity
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Improving Reliability: Experiments
- Lab experiments are often referred to as having high reliability due to the high level of control over the independent variables, which makes them easier to replicate by following the standardised procedures
- To improve reliability within experiments researchers might try to take more control over extraneous variables, helping to further the potential for them to become confounding
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Improving Reliability: Observations
- Observations can lack objectivity as they are relying on the researcher’s interpretations of a situation
- If behavioural categories are being used, it is important that the researcher is applying them accurately + not being subjective in their interpretations
- One way would be to operationalise the behavioural categories
- This means that the categories need to be clear + specific on what constitutes the behaviour in question
- There should be no overlap between categories leaving no need for personal interpretation of the meaning
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Validity
Refers to whether a measuring instrument or study measures what it claims to measure (whether something is true or legitimate)
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Internal validity
Is a measure of whether results obtained are solely affected by changes in the variable being manipulated (IV) in a cause + effect relationship
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External validity
Is a measure of whether data can be generalised to other situations outside of the research environment
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Ecological validity
- Type of external validity
- Refers to the extent to which psychologists can apply their findings to other settings (everyday life)
- Lab experiments lack ecological validity
- Due to the artificial setting of a lab, it is difficult to generalise the findings to a more natural situation since behaviour may be very different as a result
- Exam Hint: If you are suggesting that results are low in ecological validity as evaluation, make sure you justify this point with specific examples relating to that individual study. Avoid writing sentences which could be ‘copy and pasted’ into another essay as this means you have not tied the commentary closely to the question at hand
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Temporal Validity
- Form of external validity
- Refers to the extent to which research findings can be applied across time
- E.g. Asch’s research into conformity is said to lack temporal validity because the study was conducted in a conformist era + thus the findings might not be as applicable in today’s society
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Population validity
- Form of external validity
- Refers to the extent to which the research can be applied to different groups of people apart from the group that were used in the study
- E.g. Asch’s study was carried out on males but could the study also be applied to females
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The validity of a psychological test or experiment can be assessed in 2 ways:
1. Face validity
2. Concurrent validity
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Face validity
- Does the test appear to measure what it says it measures?
- If there is a questionnaire that is designed to measure depression, do the items look like they are going to represent what it is like to have depression?
- If not it is not likely to have face validity
- A test of face validity is most likely to be conducted by a specialist in the given area (a clinical psychologist, doctor or other mental health specialist) familiar with the assessment of depression
- If the specialist believes that the instrument or measure is valid, this is often seen as a good indication of validity
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Concurrent validity
- This is where the performance of the test in question is compared to a test that is already recognised + trusted within the same field
- If psychologists are wanting to introduce a new measure of depression they might compare their results to the data obtained from a similar measure such as Beck’s depression inventory
- As both measures are looking to do the same thing it would be expected for participants to score relatively similarly on each questionnaire
- Statistically, a correlation of +0.80 or higher would assume that there is high concurrent validity
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Improving Validity: Experiments
1 - A **control group** is used in a lab experiment which allows psychologists to see whether the IV influences the DV
- If researchers are testing the efficacy of a new anti‐depressant drug they will often have an experimental group (receive the true medication) + a control group (receive a placebo)
- In this case, using a control group would allow a comparison to see whether the medication was truly effective thus giving greater confidence in the validity of the research
2 - Research also includes **single‐blind or double‐blind procedures** to improve validity
- This ensures that the knowledge of the conditions does not result in demand characteristics by participants or investigator effects from the direct or indirect behaviour of the experimenter
3 - Use **standardised instructions** (giving all participants the same instructions in exactly the same formats)
- Participants receive identical info + psychologists can minimise investigator effects
- In this way participants are less likely to have a different interpretation of what they are required to do whilst the researcher is at less risk of giving a higher level of info to some participants compared to others
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Improving Validity: Questionnaires
1 - Researchers will include a lie scale to check the consistency of participants’ responses
- One way this can be done is by having 2 items that are asking the same thing but in opposite ways
- E.g. on a scale measuring depression imagine that each item asks participants to rate from 1-5 with 1 = completely disagree + 5 = completely agree
- There might be 1 item in the scale that says, ‘I generally sleep well at night’ + another that says, ‘my sleeping has become worse’
- A participant can't respond to both items honestly with a rating of with 5 because they contradict each other
- Such items are then used to check the validity of an individual participant’s scores
2 - Ensure that participants know that their responses are going to be kept anonymous because by remaining unidentifiable, participants are less likely to give answers that are socially desirable
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Improving Validity: Observations
1 - Making sure that the researchers have minimal impact on the behaviour that they are observing
- One way to do this is to conduct a covert observation (researcher is not seen)
- Increases the likelihood that the behaviour observed is natural as participants will not be acting in a way that they see as correct or desirable for the sake of the study
2 - Use of behavioural categories
- Researchers will tick off behaviours when they are seen which helps to improve validity by reducing the chance of researcher subjectivity
- Ensuring that the categories are clearly defined + do not overlap would also further improve validity in observations
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Improving Validity: Qualitative vs Quantitative
- Research that employs qualitative methodology as opposed to quantitative methodology has higher ecological validity due to the depth of data that is collected thru the use of case studies or interviews
- However validity can be lowered because analysis is more subjective + open to the investigator’s interpretation
- To strengthen the validity here, there are several things that can be done:
- Using direct quotes from participants can help to improve validity as it provides evidence that what was being inferred from the data is accurate
- Also, by collecting data from a variety of sources e.g. having data that has come from interviews, observations + written reports from participants = process called triangulation
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Science
- Is a systematic approach to creating knowledge
- The fact that it is systematic + controlled means that we can rely on it and predict and control the world
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Psychology
The scientific study of the mind and behaviour
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Key features of Science
1) Objectivity
2) Empirical Methods
3) Replicability
4) Falsifiability
5) Theory Construction
6) Hypothesis Testing
7) Paradigm
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Objectivity
- Defined as dealing with facts in a way that is unaffected by beliefs, opinions, feelings or expectations
- Researchers should not let their personal opinions or biases interfere or affect the outcome of the research
- A high level of objectivity increases other researcher’s confidence that the results are accurate + can be replicated
- Objectivity is the basis of the empirical method
- A key feature of science is the ability for researchers to remain objective meaning that they must not let their personal opinions, judgements or biases interfere with the data
- Lab experiments are the most objective method because of the high level of control that is exerted over the variables
- On the other hand, a natural experiment, cannot exert control over the manipulation of IVs + viewed as less objective
- The observational + content analysis methods can fall victim to objectivity issues since the behavioural categories assigned are at the personal discretion of the investigator
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Empirical Methods
- An empirical method involves the use of objective, quantitative observation in a systematically controlled, replicable situation to test or refine a theory
- E.g. an experiment
- Empirical methods refer to the idea that knowledge is gained from direct experiences in an objective, systematic + controlled manner to produce quantitative data
- It suggests that we cannot create knowledge based on belief alone + therefore any theory will need to be empirically tested + verified to be considered scientific
- Adopting an empirical approach reduces the opportunity for researchers to make unfounded claims about phenomena based on subjective opinion
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Replicability
- The extent to which the findings of research can be repeated in different contexts + circumstances
- Refers to when research is carried out again in the future + whether similar findings can be found
- Replicability relies on the findings being consistent over time + it can help validate findings because we can be certain that if the study were to be repeated, the same findings would be gained
- This is very important in psych especially when sample sizes are small
- If we cannot repeat the findings gained from research then this indicates that we should not be using these results to inform policy or theories in psych
- Replicability tends to be greatest when the research method of a lab experiment has been used + replicability tends to be lowest when the experimenter has failed to manipulate the IV properly e.g. observations
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Replicability also serves the purpose of:
A) Guarding against scientific fraud
B) Researchers can check to see if results gained were a one off fluke possibly caused by extraneous/confounding variables
C) If research findings can be repeated, we would say that the findings are reliable
d) Replicability can also indicate that research findings are valid
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Falsifiability
- Defined by Popper (1934) as the notion that scientific theories can potentially be disproved by evidence + it refers to proving a hypothesis wrong
- Popper (1969) stated that genuine scientific theories should be tested + theories or ideas can be falsified
- This occurs when other research or theories have failed to support it or have severely contradicted it + therefore we might assume that the research or idea is false or incorrect
- Researchers will form theories that can be proven incorrect via experimental testing
- Popper stated that even when a scientific principle had been successfully tested and repeated it did not mean the principle was true, it simply meant that it had not been proven to be false (yet)
- Popper said that, “good sciences” such as Bio and Phys have theories which are constantly challenged but the theories are not usually proven to be incorrect or false because they are strong
- Pseudoscience disciplines produce theories that cannot be falsified easily e.g. Freud
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An example of falsifiability
- An example within psych which causes conflict in the scientific community for its lack of falsifiability is the psychodynamic approach
- A central principle of this approach is the notion of the Oedipus complex (occurs for boys during childhood where they must resolve an unconscious sexual desire for the opposite‐sex parent to develop the final element of their psyche: the superego)
- If a male individual disagrees that he has gone through this stage of psychosexual development, psychodynamic theorists would counter this with the belief that they were in denial (a defence mechanism) which is another part of the theory
- This makes it harder to disprove the theory because any disagreement can be explained by the approach
- Popper argued that if falsification cannot be achieved + the theory cannot have derived from a true scientific discipline which should instead be regarded as a pseudoscience
- Therefore, the psychodynamic approach casts doubt on the scientific rigour of psych when considered as a whole
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Theory
- A theory is a collection of general principles that explain observations + facts
- It is a framework/explanation for describing a phenomenon
- It may be based on observations about the world or on empirical evidence
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Theory Construction
- Theories are constructed via hypothesis testing + re-testing which is part of the scientific process
- Theories are constructed based on the results of a range of work conducted by many different researchers
- Scientific theory must be testable and falsifiable
- An example is Freud’s theory that focuses on the id, ego and superego which is unfalsifiable as the theory cannot really be tested properly + is regarded as non-scientific
- Researchers cannot really say that Freud’s theory is false as it cannot be tested properly in the first place
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Deductive Reasoning
- Involves firstly having a theory + then devising a hypothesis
- Researchers then test this theory using empirical methods such as experiments/observations
- Once the theory has been tested conclusions are drawn from the data
- Popper devised the Hypothetico-deductive model suggesting that theories/laws about the world should come first + then hypothesis should be generated + tested to see if the theory/law is correct
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Stages in Deductive Reasoning
1) Observation
2) Propose Theory
3) Testable hypothesis
4) Conduct a study to test hypothesis
5) Draw conclusions
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Inductive Reasoning
- A researcher observes instances of natural phenomenon or has observed some aspect of behaviour that then leads the researcher to come up with a hypothesis
- The hypothesis is then tested + conclusions are drawn from the research
- From the conclusions a theory is then generated about the area being investigated
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Stages in Inductive Reasoning
1) Observation
2) Testable hypothesis
3) Conduct a study to test hypothesis
4) Draw conclusions
5) Propose theory
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Hypothesis Testing
- Is an important feature of science as this is how theories are developed + modified
- A good theory should generate testable predictions (hypotheses) + if research fails to support the hypotheses then this suggests that the theory needs to be modified in some way
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Paradigm
Is a shared set of assumptions + agreed methods that are found within scientific disciplines
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Kuhn (1962)
- He suggested that what distinguishes scientific + non-scientific disciplines is the presence of paradigms
- Social sciences like psych lack a universal acceptance of paradigms + that is why psychology might be viewed as a pre- science
rather than a science
- Natural sciences like Bio and Phys have a number of principles at their core e.g. the theory of evolution
- Psych however, has too many internal disagreements + conflicting approaches to qualify as a science and is a pre-science
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Paradigm Shift
- Kuhn stated that a paradigm shift is when the result of a scientific revolution occurs
- A significant change in the dominant unifying theory of a scientific discipline occurs + causes a paradigm shift
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Paradigm shift occurs in 2 stages:
1) One theory remains dominant within a scientific discipline
- Some researchers might question the accepted paradigm + might have contradictory research that disagrees with the main paradigm
- Counter evidence might start to accumulate against the main paradigm critics might begin to gain popularity + eventually the counter evidence becomes hard to ignore
- The present paradigm might then be overthrown due to the emergence of a new one - This is an example of a paradigm shift.
2) An established science makes rapid progress + a scientific revolution occurs due to the paradigm shift
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Example of a paradigm shift
- The work of Copernicus in the sixteenth century
- The paradigm used to be that people thought that the Earth was at the centre of the universe but Copernicus was responsible for a paradigm shift
- He found that the sun is at the centre of the universe
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Writing Psychological Reports
1) Write the report in the **third person** e.g. A study was carried out ….. rather than, I carried out a study
2) The report should be clear so that exact replication would be possible
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Stages in writing a psychological report
1) Title
2) Abstract
3) Introduction to aims/hypothesis
4) Method section
5) Results section
6) Discussion section
7) Reference section
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1 - Title
- Provide a clear focus of the study + should involve the key variables that you are investigating
- It should not be too vague or too specific e.g. An investigation to study the relationship between health and stress levels
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2 - Abstract
- 150-200 words long
- This is usually written once the whole report has been completed because it involves a summary of main concepts
- It provides a clear + concise summary of the entire investigation so the readers can gain an overview of the piece of research + see if it is worth reading the whole report
- Info is provided such as: aims, experimental hypothesis (1 or 2 tailed), null hypothesis, research methods and procedures, experimental design, sample used (number, age, setting) and sampling method, brief account of findings, including statistical tests, results, levels of significance + conclusions of the study
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3 - Introduction to aims/hypothesis
- This is about justifying the need for conducting research
- It is important to think about research that already exists within the same field of psych
- Researchers try to identify if there a gap in existing research or if previous research created new questions that need to be answered
- This should include previous research (a review of related research) + a clear rationale about why you wish to study this topic area
- There should be a general discussion of the research topic + this should become more
focused towards the end of the intro section until you arrive at research that is more specific to your actual question
- From this info an aim can be stated
- In some cases a hypothesis can also be established that will be one/two tailed + you might want to state the null hypothesis too
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4 - Method Section
- This states how the investigation was carried out + it should be precise so that the study can be replicated
- Good idea to display the method using bullet points to aid clarity
- There are 5 subsections
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A) Design
i) The experimental design (independent measures, matched participants or repeated measures) + the reasoning why this particular design has been used
ii) The research method needs to be selected with justification
iii) The IV and DV need to be stated (+ confounding ones)
iv) Controls e.g. if counterbalancing was used or random allocation etc
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B) Sample
i) Details of your sample e.g. no of males + females, age, background, where did you get them from
ii) Explain the sampling method used + why
How were the sample accessed? Where did the sample originate from e.g. place?
- Participants must remain anonymous + do not use any names
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C) Apparatus/Materials
Make a concise list of materials that are required to carry out the research
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D) Procedure
- Bullet pointed steps (or written as a report) that need to be carried to conduct the research which must be written in sufficient depth + detail for easy replication
- Clear info must be presented from the start to the end of the research
- Briefing, standardized instructions + debriefing must be included
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E) Ethics
- Consider any ethical issues that arose within the stud + how they were addressed
- E.g. if participants were being deceived about the true aims of the study then it is important for the researcher to explain that there was an issue with informed consent but that this was dealt with using a debrief at the end of the study
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5 - Results Section
The results gained from the research:
1. Descriptive statistics - tables, charts, graphs, + raw data
- Central tendency such as the mode, mean and median should be stated + measures of dispersion (the range and standard deviation)
2. Inferential statistics - what statistical test has been used +why
- Significance levels + calculated values need to be reported
3. If qualitative data has been collected, categories and themes should be described with examples
4. State whether the experimental/null hypothesis is accepted/rejected
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6 - Discussion Section
The researcher will interpret the results of the study using 4 key areas:
A) Summary of results: the results are reported in a brief format + some explanation given about what they mean
B) Relationship to background research: the results of the study are discussed in relation to the research reported in the intro section + other related research
C) Limitations of methodology and modifications: criticisms may be made about the methods used in the study + modifications/improvements suggested
D) Implications and suggestions: the implication of the results for psychological theory + real life applications and suggestions can be made for future research
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7 - Reference Section
- The full details of any journals or books that have been mentioned in the report must be provided in the reference section
- Journal referencing
- Author’s name(s), date of publication, title of article, journal title, volume (issue number), page numbers
- E.g. Smith, M. (1991) “Effects of time of day and personality on intelligence and test scores” Personality and Individual Differences, 12(11). Pages 112-119
- Book referencing:
- Author’s name(s) (in alphabetical order), date of publication, title of book, place of publication, publisher
- E.g. Flanagan, C. and Berry, D. (2016) A Level Psychology. Cheltenham: Illuminate Publishing
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Correlations
- Are a technique for analysing the strength of the relationship between 2 quantitative variables known as co-variables
- The data for a correlation is usually obtained from a non-experimental source such as a survey
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Types of correlation
- Positive correlation: as 1 variable increases the other variable increases as well
- Negative correlation: as 1 variable increases the other variable decreases
- No correlation: there is no relationships between the 2 variables at all (0)
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Correlation Coefficient
- The strength of a correlation is between -1 and 1
- 0 = no correlation
- -1 = strong negative correlation
- +1 = strong positive correlation
- The strength of the correlation is known as the correlation coefficient
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Advantage of Correlations (1)
This technique allows psychologists to establish the strength of the relationship between 2 variables + measure it precisely
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Advantage of Correlations (2)
This technique also allows researchers to investigate things that could not be manipulated experimentally for ethical or practical reasons
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Advantage of Correlations (3)
Once a correlation has been conducted predictions can be made about 1 of the variables based on what is known about the other variable
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Disadvantage of Correlations (1)
- Correlational analysis cannot demonstrate cause and effect
- We cannot tell which variable influences the other
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Disadvantage of Correlations (2)
- Even if there is a correlation between 2 variables it may be that the variables are not actually related but that there is a third unknown variable which influences both (confounding variable)
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Disadvantage of Correlations (3)
- Correlations can only measure linear relationships + does not detect curvilinear relationships
- This is when there is a positive relationship up to a certain point but after that the relationship becomes negative or vice versa
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Level of Statistical Significance
- The level at which the decision is made to reject the null hypothesis in favour of the experimental hypothesis
- It states how sure we can be that the IV is having an effect on the DV + is not due to chance
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Chance
Something has no real cause it just happens e.g. by chance you are feeling happy today, there is no real cause that you can identify
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What are significance levels?
- From the results gained from our experiment (for both the control + experimental conditions) we look for whether a real difference exists between the 2 sets of data + how certain we are that there is a real difference
- If the 2 sets of data are very similar then a statistical test might indicate that chocolate makes no real difference to mood + we might accept the null hypothesis
- However, if there is a probability that there is a real difference between the 2 conditions (and this can be proved by conducting statistical tests) then we would accept the experimental hypothesis + reject the null hypothesis
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Probability
- Is a numerical measure that determines whether our results are due to chance or whether there is a real difference that exists between the experimental + control conditions (therefore we can accept the experimental hypothesis)
- If a real difference exists (calculated statistically) we can say that results are significant + the null hypothesis can be rejected (+ we would accept the experimental hypothesis)
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Which significance levels should you use?
- The standard level of significance is p<0.05 (5% level)
- 5% level of significance is mainly used because:
- It is not too strict or too lenient but is a middle, fair value of significance
- It minimises the chances of making a Type 1 or a Type 2 error
· It means that there is a 5% level of probability that results are due to chance/fluke therefore 95% certainty that our results are showing a real difference between control and experimental conditions
- If the level of significance is achieved then the probability of results being due to chance is 5% or less
- Expressed as p = 0.05 or p<0.05
- 5% significance levels are usually used when there is a directional 1 tailed hypothesis that has been clearly stated in the research
- Sometimes a 10% level of significance is selected + often used when we allow a 10% margin of error + we would be 90% certain that our results are really showing a significant difference
- Sometimes a very strict level of significance is selected at 1%
- This is often used when research findings are critical + very important e.g. when testing the effect of drugs on humans, we must make sure that results are not due to fluke but that a real difference occurs between the experimental and control conditions + that is why we set a stricter significance level
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Type 1 Error
- Would occur where we might reject the null hypothesis + accept the alternative hypothesis instead
- However, the results for the study are really due to chance + are not statistically significant
- So we have made a mistake
- We should have accepted the null hypothesis instea
- Also known as a false positive
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Type 2 Error
- Would occur where a real difference in the data is overlooked as it is wrongly accepted as being not significant, accepting the null hypothesis in error
- A false negative
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Descriptive Statistics
- Descriptive statistics can give summaries of data that we have collected from our research e.g. tables, bar charts, scattergrams, histograms, pie charts
- And an indication of what the statistical analysis might reveal about our results
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Level of Measurement
- Are used to try to categorise our data into 1 of 3 types so that we can correctly select the most appropriate statistical test to analyse our results
- 3 types are: nominal, ordinal + interval level
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Nominal Data
- Can be referred to as categorical data
- If a researcher was interested to know if more students doing A‐level psychology went to a school or a college, the data would be categorised as either ‘school’ or ‘college’ (2 distinct categories)
- If the data is nominal then each participant will only appear in 1 category = called discrete data
- It is not possible to be studying both at school + at college for the same qualification
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Ordinal Data
- Data is ordered in some way + the intervals between the data are not equal
- This is used to rank data where the values assigned have no meaning beyond the purpose of stating where one score appeared in relation to others
- E.g. if people were asked to rate their preference of local restaurants with 1 = least fav + 10 = fav, a researcher would be able to generate a list of restaurants from this data based upon the average ratings for each
- However, they wouldn’t be able to say for sure that the difference between the restaurants ranked in 1st and 2nd place was equal to the difference between the ones rated as 8th and 9th (maybe it was a very close call between those rated as 1st and 2nd but there was a much bigger difference between 8th and 9th)
- With ordinal data, that level of detail cannot be told
- It is only possible tell where they lie in relation to each other in an orderly fashion
- Ordinal data often appears in psych when researchers are investigating a non‐physical entity such as attitudes
- This is subjective as the ranks that are available will be interpreted as different by each participant
- It can therefore be regarded as lacking certainty due to the lack of objective insight into this type of data
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Interval Data
- It is like ordinal data as data also is ordered in some way
- However, the intervals between each value are equal in measurement
- This type is more objective + scientific in nature as a result
- E.g. temperature, time, heart rate, blood pressure, ruler measures
- The difference between 3 and 4 degrees is the same as the difference between 35 and 36 degrees
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Strength of nominal data
- Is easily generated from closed questions on a questionnaire or interview
- This can often be generated quickly + can be tested in a timely manner of reliability
- The mode is the measure of central tendency which can be applied to nominal data
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Limitation of nominal data
- Since there is no scale of reference for nominal data (which is simply categorical) it is not possible for the data to express its true complexity + can therefore appear overly simplistic
- There is no measure of dispersion which can be applied to nominal data
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Strength of ordinal data
Provides more detail than nominal data as the scores are ordered in a linear fashion e.g. from highest to lowest
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Limitation of ordinal data
- The intervals between scores aren't of equal value
- This means that an average (mean) can't be used as a measure of central tendency
- The median is most often used to overcome this limitation
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Strength of interval data
- It is considered more informative than nominal + ordinal
- The gaps between the scores are of equal value + therefore more reliable
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Limitation of interval data
- In some instances, the intervals are arbitrary
- E.g. 100 degrees is not twice as a warm as 50 degrees
- We can only say that the difference between 10 and 20 is the same as between 30 and 40 degrees
Psychology
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