Reliability of Diagnosis

• A diagnosis can be considered reliable if more than 1 clinician gives the same diagnosis to the same individual and therefore offers the same treatment.
• Several studies have looked at the inter-rater reliability of DSM, with varying results.
• Brown et al (1996) found that there was a 67% agreement rate for major depression which is classed as good reliability.
• However, Beck et al (1961) found that agreement among clinicians was about the level of chance.
• They gave 2 psychiatrists 153 patients to diagnose, but the 2 only agreed 54% of the time, suggesting that diagnosis can be highly unreliable.
• In some cases the issue may come from the patient rather than clinician.
• Patients may vary in the detail and type of information they give, which can affect the diagnosis.

Social Norms Definition of Abnormality

Definition

• Every society sets standards according to which it expects its members to behave, these are called social norms..
• There are certain rules for appropriate conduct; explicit rules - clearly written, e.g. the law, implicit rules - being suggested but not actually expressed in a formal manner.
• anyone who goes against these conventional rules of conduct is seen as abnormal.
• According to this view, behaviour can't be considered abnormal as long as society accepts it.
• Socially normal behaviour is likely to be context and role specific, so what is seen as abnormal will vary between cultures as well as over time.

Evaluation

• A social norm definition of abnormality could be used to control those seen as not conforming to the social norms according to Szasz.
• Behaviour deemed as normal in one society may be seen as abnormal in another gaining someone a label unjustifiably.
• Social norms change over time; standards within society change from era to era which shows that the definition is not universal, for example, homosexuality.
• Definitions of abnormality can vary between the wider cultures and sub-groups, for example in Western society, there is a common assumption that the behaviour of the white population (wider culture) is the norm and that an deviation from this by another ethnic group (sub-group) indicates abnormality.

Statistical Definition of Abnormality

Definition

• The statistical definition of abnormality describes the 'norm' as something that is usual or typical.
• When behaviour is rare (statistically infrequent) it is regarded as abnormal.
• Some characteristics can be measured and the distribution of this characteristic can then be presented in a group known as the 'normal distribution curve'.
• Any normal characteristic occurs in the 95% of the population and any abnormal characteristic occurs in the 2.5% of the population on each side.
• The statistical definition uses an arbitrary cut-off point with no gradation.
• For example, DSM determines the presence of absence of a disorder on the basis of how many tick boxes can be checked. 

Evaluation

• The advantage of an arbitrary cut-off point is that it is objective, so subjective interpretation is not going to colour decisions; one person's opinion is not going to make the difference between whether someone is judged as abnormal or not, purely on the basis of their set of attitudes.
• Statistical abnormality can't differentiate between positive and negative abnormality, therefore someone who is considered a genius is seen as equally abnormal as someone who has a learning deficit.
• Statistical abnormality implies that infrequency is a key factor in deciding whether something is abnormal, yet some disorders, such as depression, are not that infrequent.
• This shows that a definition such as failure to function adequately may be more useful in defining abnormality.
• There may be political or social expediency in where the cut-off point is set as this can manipulate whether something is deemed abnormal, just as much as social norms definitions.
• A problem with the definition is that what appears rare in some cultures is common in others, for instance, depression is rarely reported in Asian cultures and mental illness is rarely diagnosed in China, in contrast to Western cultures such as the USA and UK.
• This definition relies on people being diagnosed with mental illness but some cultures don't seek help.
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Animal Studies

Description

Ethological Methods:

• This is where animals are studied in their natural environment, often through naturalistic observation, or by experimentation, where some aspect of the animal's environment is manipulated.

Laboratory Studies:

• This is where the animals are studied in an artificial environment that allows precise control and measurement over variables.
• Whereas ecological methods are designed to provide insight about animals, lab studies allow generalisations from animals to humans.

With Schizophrenia

• Animals are treated in various ways to see if certain factors induce schizophrenia-like symptoms.
• For example, Castner et al (1998) exposed pregnant animals to a dose of radiation to induce brain change in the foetuses.
• This showed that hallucinations and memory problems appeared after puberty in rats that had been irradiated as a foetus.
• For example, psychotic behaviour is elicited in rats by the administration of excessive dopamine to test the dopamine hypothesis.

Evaluation

Strengths

• Animals are easier to control than humans, which means it is easier to conduct experiments on them because experiments require the control of variables.
• Animals can be used for experimentation where ethical considerations would prevent the use of human participants.

Weaknesses

• Animal studies are often criticised for being anthropomorphic; this is the tendency to believe that an animal's behaviour is due to the same type of thinking as a human's, even if there is no real evidence to support this.
• There are problems of extrapolating from animals to humans as we are different species and may not react in the same way.
• It is difficult but necessary for observed behaviour to be interpreted correctly by researchers as animals can't explain how they feel, this means researchers must make assumptions about hallucinations based on brain wave patterns and behaviour, making it subjective and susceptible to researcher bias.
• We can't assess the suffering of animals accurately and it is morally wrong to inflict pain and distress on animals.

Twin Studies

Description

• Twin studies are used to see if behaviours are shared by those who are genetically similar.
• Psychologists measure concordance rates between twins.
• Concordance rates measure the percentage of twins who share a particular trait; if one of the twins has a particular trait, what percentage of the other twins also have this trait.
• Twin studies involve comparing MZ (monozygotic/identical) twins, who share 100% of their genes, and DZ (dizygotic/non-identical) twins, who share 50% of their genes.
• If concordance rates are high for MZ twins, this suggests a genetic cause for this trait.
• If concordance rates are low, this suggest environmental influence.
• It is assumed that all twins are brought up in the see environment as each other.
• this means that if concordance rates are higher for MZ twins than DZ twins, it suggests a genetic cause for a trait.
• If concordance rates are similar for MZ twins and DZ twins, this suggests an environmental cause for this trait.
• For MZ twins reared apart, a high concordance rate provides especially strong support for a genetic cause for a trait.

With Schizophrenia

• When an identical twin is diagnosed with schizophrenia, the other twin is studied to measure the frequency with which both of them get schizophrenia.
• MZ and DZ twins are both used to see how likely it is that if one twin has the disorder so will the other one.
• Concordance rates are compared to see if it gives evidence that the amount of genetic material shared indicates the likelihood of both suffering from schizophrenia.
• Twins are genetically tested to ensure they are identical.
• Researchers will use hospital records to identify the first individual of the pair to be diagnosed.

Evaluation

Strength

• There is no other way to study genetic influences so clearly, because no other humans share 100% of their DNA.
• Although the amount they share their DNA differs, both MZ and DZ twins share their environments, so there is a natural control over environmental effects.

Weaknesses

• One of the problems is that twins are relatively rare in the population, therefore the sample pool is not very large and any findings may not be representative of the development of non-twins.
• MZ twins share their DNA but even in the womb they may experience different environments, which may lead them to develop differently.
• MZ twins may be treated more alike than DZ twins because they are identical and share their gender too, so their environments may not be as controlled as might be thought.

Secondary Data

Describe

• Secondary data is a second hand analysis of pre-existing (primary) data.
• It may be analysed in a different way or used to answer a different question from that in the original research.
• Secondary data analysis uses data that researcher is interested in completing.
• Secondary data usually interprets, analyses, evaluates, explains or comments on primary data.
• Bachrach et al (1991) is an example of a study that uses secondary data.

Evaluate

Strengths

• It is cost and time effective as researchers don't need to incur expenses of data collection themselves.
• Less likely to be ethical issues such as informed consent, as the data are not collected from people directly and information is already in the public domain.
• It can provide a larger database than an individual researcher could hope to collect, so there might be detail.
• Can be from different sources, so there is a possibility of comparing data to check for reliability and validity.

Weaknesses

• Reliability and the validity of the primary data is unknown so the analysis may be flawed because of some of the original errors.
• There may be problems with the use of and interpretation of data as the data was initially collected to ask a different question.
• When the data is analysed to be presented as results, there may be some subjectivity. 
• The primary data may have been gathered some time before, so it's not in the relevant time period.

Primary Data

Describe

• Primary data is data that is you collect yourself using methods such as experiments, questionnaires and observations.
• The data can be both qualitative and quantitative.
• Milgram (1963) and Bandura, Ross & Ross (1961) are examples of studies that use primary data.

Evaluation

Strengths

• The data is being used for the purpose intended, so is likely to be relevant to the study compared to secondary data.
• Primary data can be qualitative and quantitative, allowing researchers to analyse results in various ways.
• Collecting primary data means that researchers are in contact with their participants and can be sensitive to any issues that arise, such as participants becoming distressed.
• The nature of the participants can be taken into account here, whereas with secondary data, there may be systematic bias in the data which the researcher is unaware of.
• Research which is carried out first-hand gains credibility and respect from others because it is based on original documents rather than on interpretations or opinions.
• Primary data is reliable because the researcher can replicate the procedure to check results, as they know the procedure and how the data was collected.
• Operationalisation is done with the research aim in mind, so there is likely to be validity with regard to the aim.
• Since primary data is taken directly from the population, it is one of the best types of data for research methods like surveys.

Weaknesses

• Primary data sets are often quite small so may be harder to draw conclusions from compared to a meta-analysis of secondary sources.
• There may be a danger of personal bias on the part of the researcher which could affect the data collection.
• Researchers may be subjective in the types of data they look for, in particular data that 'fits; the hypotheses they are trying to test.
• Primary data collection is more likely to exploit potential participants than secondary data.
• The data has to be gathered from scratch, which involves finding a large enough population (to make the sample generalisable); this makes it more costly and time consuming than secondary data.
• Primary data to the time, place and number of participants, whereas secondary data can come from different sources to give more range and detail.