Item analysis

Item analysis uses different statistical measures in order to determine any problems with the ‘items’ that make up a question in an exam, and hence the question itself. Are your questions working as they should?
The discrimination index (DI) measures how discriminating items in an exam are – i.e. how well an item can differentiate between good candidates and less able ones. For each item it is a measure based on the comparison of performance between stronger and weaker candidates in the exam as a whole.
Are you considering or do you already use point biserial to analyse items in your exams? Our view is that it is useless for all but the simplest of tests - read more to find out why.
The PCC measures the strength of the relationship between two variables - in an exam this would be between candidates’ item/question/scenario marks and their exam marks. The Pearson's Correlation attempts to draw a line of best fit through the data points plotted on a graph, and the coefficient evaluates how far away from the line of best fit these data points are.
The Horst Partial Knowledge Index or PKI (Maxinity terminology) is a statistic which can be used to help understand and consequently improve item reliability. It is particularly useful at identifying problem items, as a negative Horst PKI indicates that more candidates answered a question with a specific wrong answer (most popular distractor) than the correct answer.