Through using this website, you have learned about, referred to, and evaluated research studies. These research studies are generally presented to the scientific community as a journal article. Most journal articles follow a standard format. This is similar to the way you may have written up experiments in other sciences.
Both qualitative and quantitative data are forms of empirical data- information which has been gathered from research observations.
Primary data â€“ information observed or collected directly from first-hand experience. Data that has been collected by the researcher for the study currently being undertaken, specifically relating to the aims and/or hypothesis of the study. Examples of primary data are the results of an experiment, answers from a questionnaire etc.
When you carry out a psychological experiment, you end up with a great deal of RAW DATA, usually in the form of 2 sets of scores â€“ one for each condition. The two sets of scores need to be compared to see if there is a noticeable difference between them. Often, you need to summarise this data so that you can easily see if your study has been successful.
In order to summarise a set of scores, a measure of central tendency is important, but on its own it is not enough. A measure of central tendency (such as the mean) doesnâ€™t tell us a great deal about the â€˜spreadâ€™ of scores in a data set (i.e. is the data made up of numbers that are similar or different?)
Percentages are descriptive statistics that show the rate/number/amount of something within every 100 (per cent). E.g. 5% means 5/100. Percentages can be plotted on a pie chart (whole pie = 100, segments represent a proportion of this 100%).
The difference between correlational analysis and experiments is that two variables are measured (two DVs â€“ known as co-variables). Correlational analysis requires quantitative data (in the form of numbers). For example, it could be used to measure the relationship between revision and exam.
Classic bell shaped curve, it is the predicted distribution when using equally likely sets of results. For example, if a light bulb has a lifetime of 100 hours we would expect some bulbs to last a little longer than 100 hours and some to last a little less.
A non-parametric test used for experiments where the data is at least nominal and repeated measures has been used.
Statistical analysis, like the sign test, produces an observed value, which is compared to a critical value (on a table of values) in order to determine whether a set of results are significant to a specific level.
We have all heard the phrase â€˜statistical testsâ€™ â€“ for example in a newspaper report that claims â€˜statistical tests show that women are better at reading maps than menâ€™. If we wanted to know if women are better at reading maps than men we could not possibly test all the men and all the women in the world, so we just test a small group of men and a small group of women. If we find the sample of women are indeed better with maps than the sample of men, then we infer that the same is true for all men and all women. However, it isnâ€™t quite as simple as that because we can only make such inferences using statistical (or inferential) tests. All statistical tests though are based on the idea of probability. So, before we start to look at the different statistical tests, we need to understand the role that probability plays in statistical testing as no test to guarantee human behaviour 100%.