Here are some exam style questions.
Here is a tick off what you need to know sheet for correlations.
A matching coefficient quiz.
Correlation coefficient can be calculated in a number of ways such as with a Spearman Rho. However during the first year you will not need to know how the correlation coefficient is calculated.
Another matching quiz.
A multichoice quiz.
And a true or false quiz.
You may have noticed that the usefulness of correlational analysis can be affected by how the variables are measured. For example is average GCSE score a valid measure of intelligence?
A cloze hypothesis quiz.
We can make a distinction between descriptive and inferential statistics. Descriptive statistics simply offer us a way to describe a summary of our data whereas inferential statistics go a step further and allow us to make a conclusion related to our hypothesis. For this AS course we only need to know about descriptive statistics.
If you are asked to draw a scattergraph in the psychological investigations exam, ensure that it has a title, that both axes are labelled and contain the appropriate units. You should not draw a ?line of best fit?. It doesn't matter which co-variable is on the x or y axis.
Scattergraphs are a very useful type of type of descriptive statistic because they can show at a glance the correlation between co-variables. You may be asked in the psychological investigations exam what type of relationship is shown in a scattergraph.
Correlation for Psychological Investigations
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 correlation0 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
Hypotheses for correlational analysis
When carrying out correlational analysis it is expected that the researcher will start with a hypothesis.
A hypothesis is a testable, predictive statement. The hypothesis will state what the researcher expects to find out. For example, there will be a significant positive correlation between average GCSE scores and performance on a memory test.
It is important that the two variables are clearly stated in the hypothesis.
When a hypothesis predicts the expected direction of the results it is referred to as a one-tailed hypothesis. For example the hypothesis above is stating that there will be a significant positive correlation between average GCSE scores and performance on a memory test. Note that a one tailed hypothesis can also predict a negative correlation.
When a hypothesis does not predict the expected direction of the results it is referred to as a two-tailed hypothesis. For example a two tailed hypothesis might be that there will there will be a significant correlation between average GCSE scores and performance on a memory test.
The hypothesis that states the expected results is called the alternate (or correlational) hypothesis as it is alternative to the null hypothesis. When conducting a correlation it is important that we have an alternate hypothesis and a null hypothesis. The null hypothesis is not the opposite of the alternate hypothesis it is a statement of no relationship. A null hypothesis might be that there will there will be no significant correlation between average GCSE scores and performance on a memory test.
The reason we have a null hypothesis is that the statistical tests that we use are designed to test the null hypothesis.
Correlational analysis always involves quantitative data.
Carrying out a correlation often involves analysing a lot of data. As psychologists therefore we need to have knowledge of statistics so that we can make conclusions about our data.
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 correlational analysis the data is summarised by presenting the data in a scattergram (or scattergraph). 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.
Evaluation of Correlational Analysis
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.