The Item Analysis output consists of four parts: A summary of test statistics, a test Item 1 in the sample output shows an example of this type of positive linear. Here are the procedures for the calculations involved in item analysis with data for an example item. For our example, imagine a classroom of 25 students who took a test which included the item below. The asterisk indicates that B is the correct answer. For each group, calculate a difficulty index for the item. Item analysis= The examination of individual items Item difficulty index= The proportion of test takers . Another example – item difficulty of and item.

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Interpreting the results of item analysis Item analysis example our example, the item had a difficulty index of. This means that sixty-four percent of students knew the answer. If a teacher believes that.

Another interpretation might be that the item was too difficult or confusing or invalid, in which case the teacher can replace or modify the item, perhaps using information from the item's discrimination index or analysis of response options.

item analysis example

## Assessment/Quality Test Construction/Teacher Tools/Item Analysis | Special Connections

The discrimination index for the item was. The formula for the discrimination index is such that if more item analysis example in the high scoring group chose the correct answer than did students in the low scoring group, the number will be positive.

At a minimum, item analysis example, one would hope for a positive value, as that would indicate that knowledge resulted in the correct answer. The greater the positive value the closer it is to 1.

If the discrimination index is negative, that means that item analysis example some reason students who scored low on the test were more likely to get the answer correct. This is a strange situation which suggests poor validity for an item.

The analysis of response options shows that those who missed the item were about equally likely to choose answer A and answer C. No students chose answer D.

Answer option D does not act as a distractor. This test should not contribute heavily to the course grade, and it needs revision. This is the general form of the more commonly reported KR and can be applied to tests composed of items with different numbers of item analysis example given for different response alternatives.

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When coefficient alpha is applied to tests in which each item has only one correct answer and all correct answers are worth the item analysis example number of points, the resulting coefficient is identical to KR Further discussion of test reliability can be found in J.

Standard Error of Measurement The standard error of item analysis example is directly related to the reliability of the test. Whereas the reliability of a test always varies between 0.

For example, multiplying all test scores by a constant will multiply the standard error of measurement by that same constant, but will leave the reliability coefficient unchanged. A general rule of thumb to predict the amount of change which can be expected in individual test scores is to multiply the standard error of measurement by 1.

## Understanding Item Analyses | Office of Educational Assessment

The smaller the standard error of measurement, the more accurate the measurement provided by the test. Further discussion of the standard error of measurement can be found in J.

Such statistics must always be interpreted in the context of the type of test item analysis example and the individuals being tested. Lehmann provide the following set of cautions in using item analysis results Measurement item analysis example Evaluation in Education and Psychology.

Holt, Rinehart and Winston, Item analysis data are not synonymous item analysis example item validity. An external criterion is required to accurately judge the validity of test items. By using the internal criterion of total test score, item analyses reflect internal consistency of items rather than validity.