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Of course, there is also the a parameter from item response theory. So if 85% of top examinees got it right and 60% of low examinees got it right, the index would be 0.25. There is also the top/bottom coefficient, where the sample is split into a highly performing group and a lowly performing group based on total score, the P value calculated for each, and those subtracted.
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There is the cousin of the point-biserial, the biserial. There are two other indices commonly used in classical test theory. Are there other indices of item discrimination? Some constructs are easier to measure than others, which makes item discrimination higher. But, these can vary with sample size and other considerations. Besides that, a minimal-quality item might have a point-biserial of 0.10, a good item of about 0.20, and strong items 0.30 or higher. Well, most importantly consider the points above about near-zero and negative values. This item is very good, as it has a very high point-biserial for the correct answer and strongly negative point-biserials for the incorrect answers (which means the not-so-smart students are selecting them). The image below is example output from Iteman, where Rpbis is the point-biserial.
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This is an important step in item analysis.
#Point biserial vs point measure software
Of course, psychometric software like Iteman will also do it for you, and many more important things besides (e.g., the point-biserial for each of the incorrect options!). Since it is a Pearson correlation, you can easily calculate it with the CORREL function in Excel or similar software. If you calculated a correlation, it would be around 0.10. If you fit a regression line, it would have a positive slope. The scores are definitely higher for the Correct group. There are 10 examinees that got the item wrong, and 10 that got it correct. As such, it is sometimes called an item-total correlation.Ĭonsider the example below. The point-biserial coefficient is a Pearson correlation between scores on the item (usually 0=wrong and 1=correct) and the total score on the test. This is a total red flag it means that good students are getting the item wrong and poor students are getting it right, which almost always means that there is incorrect content or the item is miskeyed. If the reverse is the case, then the discrimination will be negative. If this isn’t the case, and the item is essentially producing random data, then it has no discrimination. If a math item on Fractions is good, then students with good knowledge of fractions will tend to get it correct, while students with poor knowledge will get it wrong. It means that it is differentiating between examinees, which is entirely the reason that an assessment item exists.
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While the word “discrimination” has a negative connotation, it is actually a really good thing for an item to have. Item discrimination is a psychometric concept regarding the quality of a test item, and the point-biserial coefficient is one of several indices for this concept. Lecture Notes – Online Course in Psychometrics and Assessment.TestAssembler (Automated Form Assembly).FastTest – Item Banking & Online Testing.80 into the Power (1-beta err prob) box, unless researchers want to change the power according to the current empirical or clinical context. Leave the alpha value at 0.05, unless researchers want to change the alpha value according to the current empirical or clinical context.Ĩ. Select Two if researchers are unsure whether the association/correlation will be positive or negative.Įnter ".01" into the Coefficient of determination p2 box if researchers believe there will be a small treatment effect.Įnter ".09" into the Coefficient of determination p2 box if researchers believe there will be a moderate treatment effect.Įnter ".25" into the Coefficient of determination p2 box if researchers believe there will be a large treatment effect.ħ. In the Tail(s) drop-down menu, select One if researchers have a definitive and literature-based reason for believing that the association/correlation that the correlation travels in a certain direction (either positive or negative). Under the Type of power analysis drop-down menu, select A priori: Compute required sample size - given alpha, power, and effect size.ĥ. Under the Statistical test drop-down menu, select Correlation: Point biserial model.Ĥ. Under the Test family drop-down menu, select t tests.ģ.