The r correlation and the r-pbis are equivalent for a 2-category item.

The r correlation indexes the linear relationship between item score and total score and assumes that the item responses for an item form a continuous variable. The PearsonвЂ™s r correlation is the product-moment correlation between the item responses (as numeric values) and total score. The biserial correlation is not recommended for traits where the score distribution is known to be non-normal (e.g., pathology). The biserial makes the stricter assumption that the score distribution is normal. Since the biserial is an estimate of PearsonвЂ™s r it will be larger in absolute magnitude than the corresponding point-biserial. The biserial correlation is computed between the item and total score as if the item was a continuous measure of the trait. The biserial correlation is also a measure of the discrimination, or differentiating strength, of the item. The maximum item-total correlation bound is almost always 1.0, because it is typically desired that the r-pbis be as high as possible. If your sample size is small, it could possibly be reduced. This is typically a small positive number, like 0.10 or 0.20. The minimum item-total correlation bound represents the lowest discrimination you are willing to accept. A point-biserial of 0.0 provides no differentiation between low-scoring and high-scoring examinees, essentially random вЂњnoise.вЂќ A negative point-biserial is indicative of a very poor item, because then the high-ability examinees are answering incorrectly, while the low examinees are answering it correctly. A good item is able to differentiate between examinees of high and low ability, and will have a higher point-biserial, but rarely above 0.50. The Pearson point-biserial correlation ( r-pbis) is a measure of the discrimination, or differentiating strength, of the item. The item point-biserial (r-pbis) correlation. Item Discrimination Multiple Choice Items This is important as all items of a certain type (e.g., 3-category) might be flagged. The number of categories for the items must be considered when setting the bounds of the minimum/maximum values. The maximum item mean bound represents what you consider the cut point for the item mean being too high. The minimum item mean bound represents what you consider the cut point for the item mean being too low. A good rating scale item will have an item mean close to ВЅ of the maximum, as this means that on average, examinees are not endorsing categories near the extremes of the continuum. The interpretation of the item mean depends on the type of item (rating scale or partial credit). The range of the item mean is dependent on the number of categories and whether the item responses begin at 0.

The item mean is the average of the item responses converted to numeric values across all examinees. In fact, items with a P of 0.95 or higher typically have very poor point-biserial correlations. The primary consideration here is that if an item is so easy that nearly everyone gets it correct, it is not providing much information about the examinees. The maximum P value represents the cut point for what you consider to be an item that is too easy. The minimum should take into account the possibility of guessing if the item is multiple-choice with four options, there is a 25% chance of randomly guessing the answer, so the minimum should probably not be 0.20. For a test where we expect examinees to do poorly, the minimum might be lowered to 0.4 or even 0.3. For a relatively easy test, you might specify 0.50 as a minimum, which means that 50% of the examinees have answered the item correctly. The minimum P value bound represents what you consider the cut point for an item being too difficult. A high value means that the item is easy, and a low value means that the item is difficult. The P value is the proportion of examinees that answered an item correctly (or in the keyed direction). Item Difficulty The P value (Multiple Choice) The following are some definitions of, and considerations for, item statistics. This drives the ultimate goal of improving reliability and validity. At a higher level, the use of Iteman output has two steps: first, to identify which items perform poorly, and secondly to diagnose the problems present in those items.

Point biserial vs point measure