Research method-A2

A correlation refers to a mathematical technique which measures the relationship or association between two continous variables(co-variables). These are plotted on a scattergram where each axis represents one of the variables being investigated. 

A correlation coefficient represents the strength of the correlation. Statistical tests of correlation produce a numerical value somewhere between -1 and +1. This is the correlation coefficient. This value tells us the strength of the relationship between the two variables. The closer the coefficient is 1(+1 or -1), the stronger the relationship between the co-variables. the closer to zero,the weaker the relationship is. 

• It also represents the direction of the correlation. 
• Value of +1 represents a perfect postive correlation
• Value of -1 represents a perfect negative correlation
• A correlation coefficient of +50 is as strong as -50
• They are caluctaed using an inferential test such as Pearson's or Spearman's.

Case studies are detailed, in-depth and longitudinal. It may also involve gathering data from family or friends of the individual as well as the person themselves.

Case studies often involve analysis of unusual individuals or events, such as a person with a rare disorder. Therefore, case studies are unusual and include typical cases.

Usually involve qualitative data as researchinteers will construct a case study of the individual or event concerned, perhaps using interviews, obervations or questionnaires.

A03

• Rich,detailed insight- such detail is likely to increase the validity of the data collected.
• Enables study of unusual behaviour-some cases can in fact help our understanding of 'normal' functioning.
• However, it is prone to researcher bias-conclusions are based on the subjective interpretation of the researcher which may reduce the validity of the study.
• PPs' accounts may be biased-personal accounts from pps and family may be prone to inaccuracy/memory decay.

Content analysis is a type of observational reserach. People are studied indirectly via the communications they have produced. This may include: spoken interaction, writtern forms and examples from the media.

Coding is the first stage of content analysis, it may produce quantitative data. 

A theme in content analysis refers to any idea that is recurrent, i.e it keeps 'cropping up'in the communication being studied. thus thematic analysis produces qualitative data.

AO3:

• Many ethical issues may not apply- the material to study eg TV may already be in the public domain.
• Content analysis is a flexible method- it can be adapted to produce both qualitative and quantitative data 
• However, communication is studied out of context-the reseacher may attribute motivations to the speaker or writer that were not intended.
• It may lack objectivity-such bias may threaten the validity of the findings and conclusions.

Reliability is a measure of consistency- if a particular measurement is repeated and the same result is obtained then that measurement is described as being reliable.

Assesing reliablity:

• Test-retest is when you test the same person twice.
• inter-observer is when observations are compared from different observers- 2 or more observers compare their data by conducting a pilot study to check that observers are applying behavioural categories in the same way. Observers should watch the same event, or sequence of events but should record their data independently.
• Reliability is measured using correlation. in test-retest and inter-observer reliability, the two sets of scores are correlated. The correlation coefficient should exceed +80 for reliability.

Improving reliability:

• in questionnaires, the researcher may replace some open questions(which can be misinterpreted) with closed, fixed choice alternatives which may be less ambiguous.
• in interviews, use the same interviewer each time, they must also be trained
• in experiments, use standardised procedures.
• in observations, behavioural categories should be measurable, categories should not overlap- therefore operationalisation of behaviourial categories is important.

Validity refers to whether an observed effect is genuine and represents what is actually 'out there' in the real world- is the result legitimate?

Data can be relaible but not valid. For instance a test that claims to measure intelligence may produce the same result everytime when the same people are tested but not measure what it is desined to measure.

Ecological validity refers to whether findings can be generalised from one setting to another. 

Temporal validity refers to whether the findings remain true over time. this means that findings should be consistent over time. eg Asch's study may lack temporal validity as it was conducted during a conformist era.

Assessing validity: 

Face validity refers to whether a test looks like it measures what it should. this is acheieved by simply 'eyeballing' the measuring instrument or by passing it to an expert to check.

Concurrent validity refers to whether findings are similar to those on a well-established test.

Statistical tests are used to see if results are due to chance. A statistical test is used to determine whether a difference or association/correlation found in a particular investigation is statistically significant(i.e whether the result could have occurred by chance or there is a real effect).

Choosing a statistical test:

• looking for a difference or  a correlation
• is experimental design related(repeated measures or matched pairs) or unrelated(independent groups).
• what is the level of measurement?

Statistical tests and their conditions for use :

• Nominal data-categories, each item can only appear in one category, there is no order.
• Ordinal data-placed in order, and intervals are subjective. Data is collected on a numerical, ordered scale but intervals are variable, so that a score of 8 is not twice as much as a score of 4. Ordinal data lacks precision as it is based on subjective opinion rather than objective measures.
• Interval data- units of equal size. interval data is based on numerical sacles that include units of equal, precisely defined size. it is better than ordinal data as more deatil is preserved as the scores are not converted to ranks.

If the statistical test is not significant the NULL HYPOTHESIS is accepted. this hypothesis states there is 'no difference' or 'no correlation' between the conditions. the statistical test thus determines which hypothesis is 'true' and which we accept and reject.

The null hypothesis is accepted or rejected at a particular level of probablity. Probablity is a measure of the likelihood that a particular event will occur, where 0 is a statisical impossibility and 1 a staistical certainty. 

Using statistical tests: the usual level of significance is 0.05(or 5%). this means the probability that the observed effect(the result) occured by chance is equal to or less than 5%

The calculated and critical values- to check for statistical significance the calculkated value (result of statitistical test)is compared with a critical calue in a table of critical values based on probabilities.

Using tables of critical values- to find the correct critical value, there are three criteria: hypothesis one-tailed or two-tailed, number of pps or degrees of freedom and level of significance.

Type 1 and type 11 errors: type 1 error is when the null hypothesis is rejected and the alternative hypothsius is accepted when the null hypotheis is 'true'. this is false positive. Type 11 error is when the null hypotheis is accepted but, in reality, the alternative hypothesis is 'true'. this is false negative.A type 1 error is  more likely to be made if the significance level is too linent(too high), whilst a type 11 error is more likely if the sigmificance level is too stringent(too low)- significant values may be missed in type 11 error.

An abstract is a summary of the study that includes all the major elements: the aims and hypotheses, method/procedure, results and conclusions.

The intro is a literature review. The research review should follow a logical progression-beginning broadly and becoming more specific until the aims and hypotheses are presented.

The method must be detailed enough for replication, it should inculde sufficient detail so thhat other reserachers sre able to replicate the study: this must include the design, the sample, apparatus, procedure and ethics.

Results must have descriptive and inferential statistics. Descriptive stats such as tables, graphs and charts, inferential stas include reference to the choice of statistical test, calculated and critical values, the level of significance and the final outcome.

Discussion is a summary, it can identify the relationship to previous research and must include limitations and implications. 

In a psychologuical investigation referencing holds thematic weight. it may include journal articles, books, websites etc.

• Paradigms and paradigm shifts- Kuhn(1962) said that what distinguishes scientific disciplines from non-scientific disciplines is a shared set of assumptions and methods-a paradigm. Kuhn argued that social sciences lack a unversally accepted paradigm and are best seen as 'pre science', unlike natural sciences. Paradigm shifts occur when there is a scientific revolution.
• Theory construction- a theory is a set of general laws or principles that have the ability to explain particular events or behaviours. testing a theory depends on being able to make clear and precise predictions on the basis of the theory. A hypothesis can then be tested using scientific methods to determine whether it will be supported or refuted.
• Falsifiability- Popper (1959) argued that the key criterion of a scientific theory is its falsifiability. Genuine scientific theories should hold themselves up for hypothesis testing and the possibility of being proved false.Popper distinguished between theories which can be challenged, and what he called 'pseudosciences' which couldn't be falsified.
• Replicability is testing the validity of research results- if a scientific theory is to be 'trusted', the findings from it must be shown to be repeatable across a number of different contexts.
• Objectivity is used to reduce bias in research- researchers must keep a 'critical distance' during research. they must not allow their personal opinions or biases to 'discolour' the data or influence the behaviour of pps. lab experiments tend to be more objective due to the greatest level of control.
• Empirical methods are direct experience,derived from the greek word 'empiricism'. EM emphasise the importance of data collection based on direct, sensory experience. experimental and observational method are examples of EM

In an experiment- A control group means that the reseracher is more confident that changes in the DV were due to the effect of the IV. Standardised procedures minimise the impact of pps reactivity and investigator effects.

In Questionnaires- lie scales control for the effects of social desirability bias and respondents are assured that all data sub,itted is confidential.

In observations- behavioual categories must be well-defined, thoroughly operationalised and not ambiguous or overlapping.

In qualitative research- interpretive validity demonstarted through the coherence of the reporting and the inclusion of direct quotes from pps. Also, triangulation involves using a number of different sources as evidence.