Psychodynamic: How Science Works

For this section you need to be able to: a Describe and evaluate the case study as a research method used in psychology and as used in the psychodynamic approach. b Describe, assess and apply issues of reliability, validity, subjectivity, objectivity and generalisability in the analysis of qualitative data. c Evaluate Freud’s theory in terms of credibility (eg Masson, 1989). d Describe, assess and apply the terms ‘cross-sectional’ and ‘longitudinal’ as applied to research methods. e Describe, assess and apply issues of ethics and issues of credibility with regard to using personal data from methods such as case studies (e.g. should such data be in the public domain?). f Describe and evaluate the correlational method/design. g Identify, describe and apply a positive and a negative correlation, and a strength (eg +0.87) of correlation. h Identify, describe and apply different sampling techniques including random sampling, stratified sampling, volunteer and self-selected sampling, and opportunity sampling, including advantages and disadvantages of each technique.

A case study is a detailed study of an individual or small group of people.

Case studies are particularly useful in revealing the origins of abnormal behaviour.  In fact some forms of psychotherapy such as psychoanalysis rely on building up a long and detailed case history as an aid to understanding and helping the client.   For example, the case study of Little Hans was conducted to help Hans get over his fear of horses.  Data was gathered from his parents and from Hans (through his father) about Hans’ life and behaviour throughout his childhood.  Freud was able to analyse this data in order to find a common theme which he linked to the Oedipus complex.

Case studies allow us to look at people in situations which we could not possibly have engineered e.g. recovery from illness. Case studies usually provide an in-depth picture producing rich qualitative data and sometimes produce quantitative data too.  A major strength of case studies is that they often relate to real life.

It is possible to argue that case studies are often a valid method because they usually gather information from the person directly concern without interference from the researcher.

However case studies only relate to one individual (or a small group of people) and we cannot therefore generalise from the results.  We have no way of assessing how typical the individual is.  If the study is retrospective (if the individual is asked to look back over his/her life) then memory may not be accurate and indeed, people may deliberately mislead the researcher.  The data may therefore be unreliable.  It is difficult to control variables and the close relationship between researcher and participant may introduce bias. We can not usually make cause and effect statements and of course case studies are time consuming and therefore expensive.

Qualitative data are those which are concerned with describing meaning, rather than with drawing statistical inferences. What qualitative methods (e.g. case studies and interviews) lose on reliability they gain in terms of validity. They provide a more in depth and rich description.

Reliability refers to how consistent a study or measuring device is. A measurement is said to be reliable or consistent if the measurement can produce similar results if used again in similar circumstances.  Qualitative data is said to lack reliability as the methods used to collect qualitative data such as case studies and open ended questions can not be replicated in the same standardised way as more quantitative measures such as with closed questions.

Validity refers to whether a study measures or examines what it claims to measure or examine.  Qualitative data are often said to be high in validity because they provide a detailed description of a participants experiences and behaviour.

Subjectivity refers to looking at something from the perspective of an individual.  Qualitative measures such as case studies are said to be very subjective because the psychologist is interested in the individual’s feelings, opinions and so on.  Clinical case studies can also introduce extra subjectivity and therefore bias as the psychologist carrying out the case study may perceive the individuals behaviour from their own theoretical perspective, such as the psychodynamic perspective.

Objectivity refers to seeing what is really there unaltered by our own biases.  Qualitative data are said to be low in objectivity as they are providing opinions and experiences

A longitudinal approach is where a group of participants is followed up after a period of time.

Longitudinal studies are often found in the area of developmental psychology because they are ways of studying change over time. The longitudinal approach may suffer from attrition which refers to the loss of participants from a study. Reasons for attrition might include participants no longer wanting to take part in the study, moving away or losing contact. When attrition occurs psychologists have to question the representativeness of the remaining sample.

Longitudinal studies take a long time, which makes them more expensive and also requires patience because you have to wait a long time for the results.  Importantly once started the longitudinal design can not be modified and of course the study is impossible to replicate because of societal changes and the sample may be unrepresentative because of events specific to that generation.

A cross-sectional approach is where groups of individuals of different ages are compared at the same point in time. A major limitation of this approach is that it is difficult to match the relevant variables because we are not studying changes in the same person.

Correlation

Correlation refers to a measure of how strongly two or more variables are related to each other.

A positive correlation means that high values of one variable are associated with high values of the other. Or if you like, the variables increase together.

A negative correlation means that high values of one variable are associated with low values of the other. Or if you like, as one variable increases the other decreases. Note that like a positive correlation, a negative correlation still indicates that some kind of relationship exists.

If there is no correlation between two variables they are said to be uncorrelated.

A correlation coefficient refers to a number between -1 and +1 and states how strong a correlation is. If the number is close to +1 then there is a positive correlation. If the number is close to -1 then there is a negative correlation. If the number is close to 0 then the variables are uncorrelated.
+1.0 perfect positive correlation
+0.8 strong positive correlation
+0.5 moderate positive correlation
+0.3 weak positive correlation
+ 0.1 very weak positive correlation
0 no correlation

-0.1 very weak correlation
-0.3 weak negative correlation
-0.5 moderate negative correlation
-0.8 strong negative correlation
-1.0 perfect negative correlation

Correlations are very good for showing possible relationships between variables and some times are the only practical or ethical way of carrying out an investigation.

Researchers may use correlational analysis as a starting point in their research and if a relationship between variables is found they can then investigate this further – perhaps using experimentation to investigate if there is a causal relationship.

However correlational analysis cannot demonstrate a cause and effect relationship between variables. For example if we found a positive correlation between GCSE scores and attendance rates at school we cannot say that high attendance causes high achievement or that low attendance leads to low achievement. It is possible that low achievement is leading to low attendance, that low attendance is leading to low achievement or that another variable say illness is causing both low achievement and low attendance at school.

Sampling Techniques

One of the most important issues about any type of method is how representative of the population the results are.
The population is the group of people from whom the sample is drawn. For example if the sample of participants is taken from sixth form colleges in Hull, the findings of the study can only be applied to that group of people and not all sixth form students in the UK and certainly not all people in the world. Obviously it is not usually possible to test everyone in the target population so therefore psychologists use sampling techniques to choose people who are representative (typical) of the population as a whole.

Opportunity Sampling

Opportunity sampling is the sampling technique most used by psychology students. It consists of taking the sample from people who are available at the time the study is carried out and fit the criteria your are looking for. This may simply consist of choosing the first 20 students in your college canteen to fill in your questionnaire.

It is a popular sampling technique as it is easy in terms of time and therefore money. For example the researcher may use friends, family or colleagues. It can also be seen as adequate when investigating processes which are thought to work in similar ways for most individuals such as memory processes.

Sometimes, particularly with natural experiments opportunity sampling has to be used as the researcher has no control over who is studied. However, there are many weaknesses of opportunity sampling.

Opportunity sampling can produce a biased sample as it is easy for the researcher to choose people from their own social and cultural group. This sample would therefore not be representative of your target population as your friends may have different qualities to people in general.

A further problem with opportunity sampling is that participants may decline to take part and therefore the participants chosen may be an even more biased sample as those participants responding may be a particular type of person.

Self selected sampling

Self selected sampling (or volunteer sampling) consists of participants becoming part of a study because they volunteer when asked or in response to an advert. This technique, like opportunity sampling, is useful as it is quick and relatively easy to do. It can also reach a wide variety of participants.

However, the type of participants who volunteer may not be representative of the target population for a number of reasons. For example, they be more obedient, more motivated to take part in studies and so on.

Random Sampling

This is a sampling technique which is defined as a sample in which every member of the population has an equal chance of being chosen. This involves identifying everyone in the target population and then selecting the number of participants you need in a way that gives everyone in the population an equal chance of being picked. For example, you could put all of the names of the students at your college in a hat and pick out however many you need. Random sampling is the best technique for providing an unbiased representative sample of a target population.

However random sampling does have limitations. Random sampling can be very time consuming and is often impossible to carry out, particularly when you have a large target population, of say all students. For example if you do not have the names of all the people in your target population you would struggle to conduct a random sample.

If you ask people to volunteer for a study the sample is already not random as some people may be more or less likely to volunteer for things. Similarly if you decided to put out an advert for participants it would be almost impossible to guarantee that every member of your target population has an equal chance of viewing the advert.

Stratified Sampling

Stratified sampling involves classifying the population into categories and then choosing a sample which consists of participants from each category in the same proportions as they are in the population. For example, if you wanted to carry out a stratified sample of students from a sixth form college you might decide that important variables are sex, 1st or 2nd years, age, have a part-time job and so on. You could then identify how many participants there are in each of these categories and choose the same proportion of participants in these categories for your study.

The strength of stratified sampling is therefore that your sample should be representative of the population. However, stratified sampling can be very time consuming as the categories have to be identified and calculated. As with random sampling, if you do not have details of all the people in your target population you would struggle to conduct a stratified sample.

If the sample is not randomly selected from the categories it is then called a quota sample.