Research Methods Flashcards
(30 cards)
What is related data
Comparison of two sets of scores from the SAME partcipants
What is independent data?
Comparison of two sets of scores from DIFFERENT participants
What is nominal, ordinal and interval/ratio data?
Nominal - data collected in categories that can’t be placed on a scale eg those that conformed and those that didn’t
Ordinal - more about order, A ordinal variable, is one where the order matters but not the difference between values.
Interval - remember–interval scales not only tell us about order, but also about the value between each item
Ratio - can never be a zero. Eg height not difference in height.
If it is a test of correlation, what is the test?
Spearmans
Sample tests
Independent. Related
Nominal. Chi squared Sign test
Ordinal. Mann Whitney. Wilcoxon
Interval/ratio T test. T test
A difference is significant if it is unlikely to be due to chance ( if the null hypothesis were true)
A difference that would occur by chance less than 5 % is…
…deemed significant
What is an alternative hypothesis?
Predicts a difference
Instead of referring to percentages statisticians refer to p.values - % = p values.
So, if a difference would occur by chance less than 5% of the time then…
If a difference would occur by chance less than 1% of the time then…
Whether a difference is significant depends on what 2 things..
If the difference is significant we do what?
…it is significant by p>0.05 - the chance of making a type 1 error is 5%
…it is significant by p>0.05
Sample size
Size of the difference
We reject the null hypothesis and accept the alternative hypothesis
What is difference 1 tailed or 2 tailed hypothesis?
1 tailed = When the theory predicts that the difference will be in a particular direction - Used when previous research suggests a direction
2 tailed = non directional - different theories make different predictions - used when previous research is contradictory or there is no previous research.
Type 1 error..
Type 2 error..
False positive
Claiming a difference is significant when it’s not
More likely when lenient
False negative
Claiming a difference is not significant when it is
Likely when stringent
1) Spearman’s: When using Spearman’s, you are testing for a correlation between two sets of scores - how does it work?
2) Wilcoxon: when using Wilcoxons test you have 2 sets of scores from the same participants.
When using Spearman’s you are testing for a correlation between two sets of scores, The calculated value is the correlation coefficient (i.e., a value between -1 and +1) On the table below the p. values and levels of significance are given across the top and N (the number of pairs of scores) down the left hand side.
In the case of this test, the calculated / observed value has to be equal to or more than the critical value (ignoring whether the correlation is positive or negative).
WILCOXON:
When using Wilcoxon’s you have 2 sets of scores from the same. On the table below the p. values and levels of significance are given across the top and N (the number of pairs of scores) down the left hand. In the case of this test, the calculated / observed value has to be equal to or less than the critical value.
Mann - Whitney: When using a Mann-Whitney test you have scores from two separate groups of participants so there may be unequal numbers.
You will be given a table of either critical values of 1 tailed pr 2 tailed tests at p<0.05 depending on what you need. On the table, the left hand column and the top row indicate the number of participants in each condition. The critical value can be calculated by reading down and across from N= how many ppts. If the calculated value is lower than the critical value, so the difference is significant, we can reject the null hypothesis.
Chi-sqaured: The data used for chi-squared test is in the form of frequencies in catorgaries. Imagine an observational study of drivers in which you counted how many males and females wore seat belts or not.
When using chi-squared, one needs to know the degrees of freedom. The question to ask is; given that you know the column and row totals in the table of data, how many of the actual frequencies do you need to know to work out the rest?
You are given the calculated/observed value. You are also given a table of degrees of freedom down the first row and level of significance are across the top. Using the standard level of significance (0.05) we can read down to find the critical value for a 2-tailed test, (always two tailed!!!) and along for the DF (degree of freedom) which you are also given. - The observed/calculated value has to be equal to or higher than the critical value.
Ranking data..
If a subjective scale is used, then the data is at the ordinal level…
What is qualitative data?
What is quantitative data?
Methods of qualitative data?
What are two options in qualitative data analysis?
- Qualitative data comes in non-numerical form e.g. a description of a clinical case.
- Quantitative data comes in the form of numbers i.e. you count or measure something.
- Interviews - (especially un-structured involving open questions) and observations (especially unstructured and participant observation)
- 1) Converting qualitative data into quantitative data, often using in interviews. - Dunbar & Waynforth analysed the content of personal ads to identify whether males and females offered or asked for different qualities in romantic partners. The original advertisements were the qualitative data.
2) Extracting information without conversion into quantitative data. Freud gave a detailed account of a single patient in his study of Little Hans.
What is an advantage and disadvantage of using qualitative data?
1) ADVANTAGE: The main advantage claimed for qualitative data is that it has a greater validity than quantitative data. The central idea is that qualitative data is more true to life. hence ‘real’, than quantitative data. e.g. In a study of attachment interviews using open questions would enable ppts to talk in their own words about their experiences, so aspects of their experience that the researcher would be unable to anticipate.
2) A disadvantage of qualitative data is subjectivity and bias e.g. Rosenhan - his students reported an overwhelming sense of dehumanisation, severe invasion of privacy and boredom while hospitalized however the problem here is that Rosenhan and his colleagues were already critics of the psychiatric system, which might have made them biased in their observations. With no systematic data collection, they could have ‘cherry- picked’ the worse behaviour, interpreted the staff’s actions in negative ways and ignored the stresses that the staff were under. A different researcher could do the same observation and arrive at a completely different account of what life was like in such a hospital.
Two methods of analysing qualitative data?
1) Content analysis - The data analysis is the actual conversion of the qualitative data into the categories. In other studies this might involve scoring e.g. the amount of stress in different jobs from diaries. This requires you to identify, in advance, the categories / variables which get counted. Content analysis is used when you have a hypothesis to test, i.e., in an area of research where there is a clear theory to test and measurable variables identified
2) Thematic analysis - The aim of thematic analysis is to identify patterns/ themes within qualitative data and is used to analyse data from bodies of text, such as interviews, newspapers, articles etc.
This is often used when it is not obvious, in advance, what categories could be used in a content analysis.
This identifies categories, but does not count instances within categories.
It identifies themes, which may be used in subsequent research.
Thematic analysis is used in less well researched areas, where it is hard to know in advance what theories to test and which variables to measure
Theoretical and Inductive analysis..
What is the difference?
In theoretical analysis a theory guides the analysis e.g. in Dunbar’s content analysis his evolutionary theory of sex differences tells us the categories. In inductive analysis a theory only emerges from the data after analysis.
What is the defintions of the following words?
1) Reliability
2) Validity
3) Test re-test reliability
4) Spit half reliability
5) Inter-rater observer reliability
6) Intra-rater observer reliability
1) Consistency or sameness of a method, measure of researcher.
2) Truth/ accuracy of a method or measure
3) Similarity of two sets of scores taken on different occasions.
4) Similarity of two sets of scores from dfferent halves of a quesitonnaire.
5) Similarity of ratings made by different observers
6) Similarity of ratings made by the same observer.
What is the defintions of the following words?
1) Experimental realism
2) Ecological validity
3) Population validity
4) Standardisation
5) Randomisation
6) Correlation
1) The extent to which ppts are engaged in and take seriously the experimental task
2) The extent to which the results of a study generalise to other contexts.
3) Extent to which the results of a study generalise to other people, apart from the sample used.
4) Process of making everything the same in a study in some relevant way.
5) Alternative to standardisation for controlling extraneous variables in a study.
6) Statistical measure often used to test reliability of a measure
What is the defintions of the following words?
1) Face validity
2) Concurrent validity
3) Predicitive validity
1) Extent to which a way of measuring something looks valid.
2) Extent to which a score is similar to a score on another test that is known to be valid.
3) Extent to which a score predicts future behaviour.
Validity is to do with truth…
1) What is External validity?
2) What is Internal validity?
3) What are the threats to validity?
1) External validity:
- Ecological validity, Population validity, Temporal validity.
2) Internal validity:
- Operationalisation ( does the the study validly measure/manipulate the variables?)
- Control - are extraneous variables controlled?
- Experiemental validity.
4) -Threats:
- Demand characteristics - participants who know they are in a study may guess what the study is about and change their behaviour.
- Social desirability effects - participants may be behave/ respond in socially acceptable ways, either to look good to the research or to measure themselves.
- Order effects - what particpants experience earlier in the study may affect their behaviour later in the study e.g. fatigue.
- Hawthorne effect - ppts who know hey are in a study may try harder than they would in everyday life.
How to improve external validity?
One way to improve external validity is by repeating or replicating the study. This could be done with a completely different set of participants to test whether the first set of results has population validity. If the study was a lab experiment, the same hypothesis could be tested in a different environment such as a field experiment: This would test the experimental/ecological validity of the first experiment. A final way of improving the e validity, would be to repeat the study years later which would test the temporal validity of the first study.
How to improve internal validity?
Internal validity refers to how accurately a test or measuring instrument measures what it says it measures
Internal validity can be improved by ensuring that extraneous variables are controlled. The aim of this is to ensure that there are no differences between conditions apart from the independent variable. This allows researchers to test the cause and effect relationship.
Ideally, studies should also have experimental validity i.e. realism. In Loftus study, ppts may not have acted as natural as they would of in a real-life situation.
The best way to improve validity is in advance i.e. by conducting a pilot study in which problems, such as extraneous variables, can be identified and eliminated.
Concurrent validity involves assessing how closely the scores on the happiness questionnaire match a different measurement of happiness obtained from the same participants, for example from family/teacher reports
Content validity involves asking experts in the field to check the content of the questionnaire to see how accurately it measures happiness
Face validity is less rigorous and involves looking at the questions to see if they are genuinely asking about happiness