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The process of item analysis varies depending on the psychometric model. For example, classical test theory or the Rasch model call for different procedures. In all cases, however, the purpose of item analysis is to produce a relatively short list of items (that is, questions to be included in an interview or questionnaire) that constitute a ...
An important goal of item analysis is to identify and remove or revise items that are not good indicators of the underlying trait. [2] A small or negative item-correlation provides empirical evidence that the item is not measuring the same construct measured by the assessment. Exact values depend on the type of measure, but as a heuristic, a ...
Item analysis within the classical approach often relies on two statistics: the P-value (proportion) and the item-total correlation (point-biserial correlation coefficient). The P-value represents the proportion of examinees responding in the keyed direction, and is typically referred to as item difficulty .
The higher a person's ability relative to the difficulty of an item, the higher the probability of a correct response on that item. When a person's location on the latent trait is equal to the difficulty of the item, there is by definition a 0.5 probability of a correct response in the Rasch model.
Often discussed in tandem with KR-20, is Kuder–Richardson Formula 21 (KR-21). [4] KR-21 is a simplified version of KR-20, which can be used when the difficulty of all items on the test are known to be equal. Like KR-20, KR-21 was first set forth as the twenty-first formula discussed in Kuder and Richardson's 1937 paper.
Differential item functioning (DIF) is a statistical property of a test item that indicates how likely it is for individuals from distinct groups, possessing similar abilities, to respond differently to the item. It manifests when individuals from different groups, with comparable skill levels, do not have an equal likelihood of answering a ...
Item tree analysis (ITA) is a data analytical method which allows constructing a hierarchical structure on the items of a questionnaire or test from observed response patterns. Assume that we have a questionnaire with m items and that subjects can answer positive (1) or negative (0) to each of these items, i.e. the items are dichotomous .
In a survey, the proportions of people positively answering some different items can be expressed as percentages. As the total amount is identified as 100, the compositional vector of D components can be defined using only D − 1 components, assuming that the remaining component is the percentage needed for the whole vector to add to 100.