analyzing and using test item data

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ANALYZING AND USING TEST ITEM DATA

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Page 1: Analyzing and using test item data

ANALYZING AND USING TEST ITEM DATA

Page 2: Analyzing and using test item data

Purposes and Elements of Item Analysis

To select the best available items for the final form of the test.

To identify structural or content defects in the items.

To detect learning difficulties of the class as a whole

To identify the areas of weaknesses of students in need of remediation.

Page 3: Analyzing and using test item data

Three Elements in an Item Analysis

1. Examination of the difficulty level of the items,

2. Determination of the discriminating power of each item, and

3. Examination of the effectiveness of distractors in a multiple choice or matching items.

Page 4: Analyzing and using test item data

The difficulty level of an item is known as index of difficulty.

Index of difficulty is the percentage of students answering correctly each item in the test

Index of discrimination refers to the percentage of high-scoring individuals responding correctly versus the number of low-scoring individuals responding correctly to an item.

This numeric index indicates how effectively an item differentiates between the students who did well and those who did poorly on the test.

Page 5: Analyzing and using test item data

Preparing Data for Item Analysis

1. Arrange test scores from highest to lowest.

2. Get one-third of the papers from the highest scores and the other third from the lowest scores.

3. Record separately the number of times each alternative was chosen by the students in both groups.

Page 6: Analyzing and using test item data

4. Add the number of correct answers to each item made by the combined upper and lower groups.

5. Compute the index of difficulty for each item, following the formula:IDF = (NRC/TS)100

where IDF = index of difficulty NRC = number of students

responding correctly to an item

TS = total number of students in the upper and lower groups

Page 7: Analyzing and using test item data

6. Compute the index of discrimination, based on the formula:IDN = (CU –CL)

NSGwhere IDN = index of discrimination

CU = number of correct responses of the upper group

CL = number of correct responses of the lower group

NSG = number of students per group

Page 8: Analyzing and using test item data

Using Information about Index of Difficulty

The difficulty index of a test item tells a teacher about the comprehension of or performance on material or task contained in an item.

Page 9: Analyzing and using test item data

Item Group Answers

A B C D

Total No. of

Correct Answers

Difficulty Index

H – L Discrimination Index

1H 20

L 20

3 14 2 1

10 7 3 021 52.5 7 0.35

2H 20

L 20

0 0 18 2

0 3 9 827 67.5 9 0.45

3H 20

L 20

3 8 4 4

10 2 4 410 25.0 6 0.30

4H 20

L 20

3 3 4 10

2 4 10 414 35.0 6 0.30

5H 20

L 20

15 2 2 1

1 10 4 516 40.0 14 0.70

Page 10: Analyzing and using test item data

For an item to be considered a good item, its difficulty index should be 50%. An item with 50% difficulty index is neither easy nor difficult.

If an item has a difficulty index of 67.5%, this means that it is 67.5% easy and 32.5% difficult.

Information on the index of difficulty of an item can help a teacher decide whether a test should be revised, retained or modified.

Page 11: Analyzing and using test item data

Interpretation of the Difficulty Index

Range Difficulty Level

20 & below21 – 4041 – 6061 – 80

81 & above

Very DifficultDifficultAverage

EasyVery Easy

Page 12: Analyzing and using test item data

Using Information about Index of DiscriminationThe index of discrimination tells a teacher the

degree to which a test item differentiates the high achievers from the low achievers in his class. A test item may have positive or negative discriminating power.

An item has a positive discriminating power when more students from the upper group got the right answer than those from the lower group

When more students from the lower group got the correct answer on an item than those from the upper group, the item has a negative discriminating power.

Page 13: Analyzing and using test item data

There are instances when an item has zero discriminating power – when equal number of students from upper and lower group got the right answer to a test item.

In the given example, item 5 has the highest discriminating power. This means that it can differentiate high and low achievers.

Page 14: Analyzing and using test item data

Interpretation of the Index of Discrimination

Range Verbal Description

.40 & above.30 – .39.20 – .29.09 – .19

Very Good ItemGood ItemFair ItemPoor Item

Page 15: Analyzing and using test item data

When should a test item be rejected? Retained? Modified or revised?

A test item can be retained when its level of difficulty is average and discriminating power is positive.

It has to rejected when it is either easy/very easy or difficult/very difficult and its discriminating power is negative or zero.

An item can be modified when its difficulty level is average and its discrimination index is negative.

Page 16: Analyzing and using test item data

Examining Distractor Effectiveness

An ideal item is one that all students in the upper group answer correctly and all students in the lower group answer wrongly. And the responses of the lower group have to be evenly distributed among the incorrect alternatives.

Page 17: Analyzing and using test item data

Developing an Item Data File Encourage teachers to undertake an item

analysis as often as practical

Allowing for accumulated data to be used to make item analysis more reliable

Providing for a wider choice of item format and objectives

Facilitating the revision of items

Facilitating the physical construction and reproduction of the test

Accumulating a large pool of items as to allow for some items to be shared with the students for study purposes.

Page 18: Analyzing and using test item data

Limitations of Item Analysis

It cannot be used for essay items. Teachers must be cautious about

what damage may be due to the table of specifications when items not meeting the criteria are deleted from the test. These items are to be rewritten or replaced.