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A form of learning through association.  Ivan Pavlov, whilst experimenting on dogs noticed that if a bell was rung at the same time as the dogs were fed, they would salivate merely at the sound of a bell.   This idea has been applied to humans to explain how certain behaviours are learned. 

See also behaviourism

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.

Don't let yourself fall into the trap of believing that when there is a strong correlation between two variables that one of the variables causes the other. Association does not mean causation. For example, there is almost certainly a very high positive correlation between the length of people’s right arm and the length of their left arm. But the length of a persons left arm did not determine the length of their right arm. They are both determined by other factors i.e. genetics, diet etc.

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.
Correlation coefficient can be calculated in a number of ways such as with a Spearman Rho.

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. However, they cannot demonstrate a cause and effect.

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D

Statistics are a method of summarising and analysing data for the purpose of drawing conclusions about the data.  

Carrying out psychological research often involves collecting a lot of data.   As psychologists therefore we need to have knowledge of statistics so that we can make conclusions about our data.

We can make a distinction between descriptive and inferential statistics.   Descriptive statistics simply offer us a way to describe a summary of our data. 

Inferential statistics go a step further and allow us to make a conclusion related to our hypothesis.
 

Descriptive statistics give us a way to summarise and describe our data but do not allow us to make a conclusion related to our hypothesis.

When carrying out a test of difference (activity C) there are two main ways of summarising the data using descriptive statistics.   The first way is to carry out of measure of central tendency (mean, median or mode) for each of the two conditions. 

The mean is calculated by adding all the scores together in each condition and then dividing by the number of scores.  This is a useful statistic as it takes all of the scores into account but can be misleading if there are extreme values.  For example if the scores on a memory test were 2, 4, 5, 6, 7, 42, the mean would be 10 which is not typical or representative of the data.

The median is calculated by finding the mid point in on ordered list.   The median is calculated by placing all the values of one condition in order and finding the mid- point.  This is a more useful measure than the mean when there are extreme values. 

The mode is the most common value in a set of values. 

When carrying out correlational analysis the data is summarised by presenting the data in a scattergram.   It is important that the scattergram has a title and both axes are labelled.   From the scattergram we may be able to say whether there is a strong positive correlation, a weak positive correlation, no correlation, a weak negative correlation or a strong negative correlation but we can not make a conclusion about the hypothesis.

Determinists therefore believe that is possible to predict behaviour by identifying the cause of behaviour.

Although most psychologists believe in some form of determinism, many argue that hard determinism is too extreme.  They argue that humans do not always act involuntary and have some control over their behaviour.  This argument is known as soft determinism.

A further argument is that humans have free will.  The argument is that we have the freedom to act as we want at all times.  Psychologists who support the idea of free will, believe that the determinist argument is de-humanising as it treats people as if they were machines.

However, much of the research you will come across whilst studying psychology does not support the view that behaviour is unpredictable.  It is possible to identify behaviour patterns which, to some extent, do seem predictable. 

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There are three main types of experiment - laboratory experiments, field experiments and quasi (natural) experiments.

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An experimental design is a set of procedures used to control the influence of participant variables so that we can investigate the possible effects of the independent variable on the dependent variable.

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An experimental design is a set of procedures used to control the influence of participant variables so that we can investigate the possible effects of the independent variable on the dependent variable.

An independent measures design consists of using different participants for each condition of the experiment.  If two groups in an experiment consist of different individuals then this is an independent measures design. 

This type of design has an advantage resulting from the different participants used in each condition - there is no problem with order effects

The most serious disadvantage of independent measures designs is the potential for error resulting from individual differences between the groups of participants taking part in the different conditions.  Also an independent groups design may represent an uneconomic use of those participants, since twice as many participants are needed to obtain the same amount of data as would be required in a two-condition repeated measures design.

Other core studies look for the explanation of behaviour as a result of the situation a person is in.  For example, behaviour could be described as resulting from group pressure, the environment and so on.

Statistics are a method of summarising and analysing data for the purpose of drawing conclusions about the data.  

Carrying out psychological research often involves collecting a lot of data.   As psychologists therefore we need to have knowledge of statistics so that we can make conclusions about our data.

We can make a distinction between descriptive and inferential statistics.   Descriptive statistics simply offer us a way to describe a summary of our data. 

Inferential statistics go a step further and allow us to make a conclusion related to our hypothesis.

As the name suggests inferential statistics attempt to make an inference about our data.  That is, which hypothesis offers the best explanation for our results?

When we carry out a test of difference (activity C) we have two hypotheses.  A null hypothesis which states that the results will be due to chance, and the experimental (alternate) hypothesis, which predicts that the results are due to the manipulation of the independent variable. 

To assess the probability that the results are due to chance an inferential statistical test is used.   Inferential statistics tell us whether the difference between two sets of scores is significant or due to chance.  It is an academic convention that in psychology we accept the null hypothesis as the best explanation for out results unless there is a 5% probability (or less) of the results being due to chance.

5% probability is expressed as p<0.05 and if we find that the null hypothesis can be rejected we can be 95% confident of the conclusions.

When carrying out a test of difference (activity C) if the design is an independent measures design the appropriate inferential statistical test to use is the Mann Whitney U test.  

 When carrying out a test of difference (activity C) if the design is a repeated measures design the appropriate inferential statistical test to use is the Wilcoxon signed ranks test.  

To use this as a spread sheet go to www.holah.karoo.net/stats.htm

Whichever test is used a value is calculated which is called the observed value.  The value then has to be compared with the critical value to determine whether the null hypothesis can be rejected and at what value

When we carry out a test of correlation we have two hypotheses.  A null hypothesis which states that the results will be due to chance, and the correlational hypothesis, which predicts that there is a correlation or relationship between the two variables 

To assess the probability that the results are due to chance an inferential statistical test is used.   Inferential statistics tell us whether the relationship between two sets of scores is significant or due to chance.  It is an academic convention that in psychology we accept the null hypothesis as the best explanation for out results unless there is a 5% probability (or less) of the results being due to chance.

5% probability is expressed as p<0.05 and if we find that the null hypothesis can be rejected it we can be 95% confident of the conclusions.

When carrying out a test of correlation a Spearman Rho is used.  

Using a Spearman’s Rho a value is calculated which is called the observed value.  The value then has to be compared with the critical value to determine whether the null hypothesis can be rejected and at what value.

The average IQ is 100 and the scores are standardised so that about 64% of the population have a score between 85 and 115. 

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The variable which is being manipulated by the researcher is called the independent variable and the dependent variable is the change in behaviour measured by the researcher.

All other variables which might affect the results and therefore give us a false set of results are called confounding variables (also referred to as random variables).

By changing one variable (the IV) while measuring another (the DV) while we control all others, as far as possible, then the experimental method allows us to draw conclusions with far more certainty than any non-experimental method. If the IV is the only thing that is changed then it must be responsible for any change in the dependent variable.

Laboratory experiments allow for precise control of variables. The purpose of control is to enable the experimenter to isolate the one key variable which has been selected (the IV), in order to observe its effect on some other variable (the DV); control is intended to allow us to conclude that it is the IV, and nothing else, which is influencing the DV.

However it must also be noted that it is not possible to completely control all variables. There may be other variables at work which the experimenter is unaware of.

It is argued that laboratory experiments allow us to make statements about cause and effect, because unlike non-experimental methods they involve the deliberate manipulation of one variable, while trying to keep all other variables constant. Sometimes the independent variable (IV) is thought of as the cause and the dependent variable (DV) as the effect.

Furthermore experiments can be replicated. The experimental method consists of standardised procedures and measures which allow it to be easily repeated.

However such experiments are not typical of real life situations. These types of experiments are conducted in laboratories - strange and contrived environments in which people are asked to perform unusual or even bizarre tasks. The artificiality of the lab, together with the 'unnatural' things that the subjects may be asked to do, jointly produces a distortion of behaviour. Therefore it should be difficult to generalise findings from experiments because they are not ecologically valid (true to real life).

A further difficulty with the experimental method is demand characteristics. Demand characteristics are all the cues which convey to the participant the purpose of the experiment. A psychology experiment is a social situation in which neither the participants or the experimenters are passive, inanimate objects but are active, thinking human beings.

Another major problem with the experimental method concerns ethics. For example, experiments nearly always involve deceiving participants to some extent and it is important to recognise that there are very many areas of human life which cannot be studied using the experimental method because it would be simply too unethical to do so.

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A matched pairs design is a type of experimental design.

An experimental design is a set of procedures used to control the influence of participant variables so that we can investigate the possible effects of the independent variable on the dependent variable.

A matched pairs design consists of using different participants for each condition of the experiment but participant variables are controlled by matching pairs of variables on a key variable. 

In order to get the pairing precise enough, it is common to get one group of participants together and then look round for partners for everyone.  Participants can be matched on variables which are considered to be relevant to the experiment in question.  For example, pairs of participants might be matched for their scores from intelligence or personality tests.

Although this design combines the key benefits of both an independent and repeated measures design, achieving matched pairs of participants is a difficult and time consuming task which may be too costly to undertake.  Successful use of a matched pairs design is heavily dependent on the use of reliable and valid procedures for pre-testing participants to obtain matched the pairs.

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Observational studies therefore tend to be high in ecological validity as there is no intervention and if the observer remains undetected the method avoids problems with experimenter effects.  

On the other hand observational studies are difficult to replicate. 

There are a number of different types of observational studies including non-participant and participant observations, undisclosed observations and structured and unstructured observations.

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 simple 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 you friends may have different qualities to people in general. 

A further problem with opportunity sampling is that participants may decline to take part and your sampling technique may turn into a self selected sample.

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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.

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In some circumstances, psychologists can take advantage of a natural situation in order to carry out an investigation in circumstances which they cannot themselves manipulate.  For example, an all boy’s schools grades may be compared with a mixed schools grades.  The effects on the participants of being in single sex or mixed sex schools could be compared.

This is not a true experiment because the psychologist is unable to manipulate or control all variables.  For this reason it is referred to as a quasi-experiment.  It is possible, though to compare two groups, the equivalent of an experimental and a control group.  It has the advantage that the participants are unaware that they are taking part in an investigation and it is certainly not as artificial as a laboratory setting.

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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.

 Physiological reductionism, for example, argues that all behaviour and experiences can be explained (or reduced to) by biological factors such as hormones or the nervous system

Whereas genetic reductionism reduces all causes of behaviour to genetic inheritance. 

Social reductionism argues that all behaviour and experiences can be explained simply by the affect of groups on the individual.

The criticism of reductionist arguments is that they are too simplistic because they ignore the complexities of human behaviour and experience.  Behaviour often has a number of different causes and to reduce the possible explanations to one level can only provide a limited understanding.  

However, an advantage of the reductionist views is that by breaking down a phenomena to its constituent parts it may be possible to understand the whole.   This type of single mindedness has lead to some great discoveries in psychology as it has in the 'natural' sciences.

A repeated measures design is a type of experimental design.

An experimental design is a set of procedures used to control the influence of participant variables so that we can investigate the possible effects of the independent variable on the dependent variable.

A repeated measures design consists of testing the same individuals on two or more conditions.

The key advantage of the repeated measures design is that individual differences between participants are removed as a potential confounding variable. Also the repeated measures design requires fewer participants, since data for all conditions derive from the same group of participants.

The design also has its disadvantages.  The range of potential uses is smaller than for the independent groups design.  For example, it is not always possible to test the same participants twice.  

There is also a potential disadvantage resulting from order effects, although these order effects can be minimised.  Order effects occur when people behave differently because of the order in which the conditions are performed.  For example, the participant’s performance may be enhanced because of a practice effect, or performance may be reduced because of a boredom or fatigue effect.

Order effects act as a confounding variable but can be reduced by using counterbalancing.  If there are two conditions in an experiment the first participant can do the first condition first and the second condition second.  The second participant can do the second condition first and the first condition second and so on.  Therefore any order effects should be randomised.

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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 sampling technique is used in a number of the core studies, for example Milgram (1963).

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.

A snap shot study is a study carried out over a very short period of time such as hours and days.   In comparison a longitudinal study is carried out over a longer period of time such as weeks, months or years. 

Snap shot studies are obviously quicker and cheaper to carry out than longitudinal approaches and rarely suffer from attrition.  However they only provide us with a ‘snapshot’ of human behaviour and experience and therefore are not so useful when investigating development.

Snowball sampling can be used if your population is not easy to contact.  For example if you were interested in studying students who take illegal drugs you may ask a participant who fits your target population to tell their friends about the study and ask them to get in touch with the researcher and so on.  

Statistics are a method of summarising and analysing data for the purpose of drawing conclusions about the data.  

Carrying out psychological research often involves collecting a lot of data.   As psychologists therefore we need to have knowledge of statistics so that we can make conclusions about our data.

We can make a distinction between descriptive and inferential statistics.   Descriptive statistics simply offer us a way to describe a summary of our data. 

Inferential statistics go a step further and allow us to make a conclusion related to our hypothesis.

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, 1^st or 2^nd 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.

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Experiments attempt to manipulate one variable, the independent variable, and measure the changes to the dependent variable.

The experimenter attempts to control for extraneous variables (noise or time of day) which could effect the dependent variable.   If an extraneous variable does have an effect on the dependent variable we call this a confounding variable. 

Correlational analysis attempts to measure the relationship between two independent variables (or co-variables).  

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