Using Test Item Analysis to Improve Students’ Assessment

1. Using Test Item Analysis to Improve Students’ Assessment Institutional Assessment starts with Classroom Assessment

2. Learning Objectives of This Session • 1. Explain difficulty index and discrimination index • 2. Calculate difficulty index and discrimination index • 3. Identify ineffective distracters • 4. Evaluate multiple-choice test items based on analysis results • 5. Apply table of specifications to improve content validity

3. Purpose of Item Analysis • 1. Ensure accurate measurement of knowledge or skill • 2. Enhance student learning • 3. Increase student engagement • 4. Avoid demoralizing students • 5. Increase confidence in drawing conclusions • Outcome achievement • Level of knowledge or skill mastery • Teaching effectiveness

4. Components of a multiple-choice item Test items used to measure the lowest level of cognitive taxonomy are (stem) • Analysis (distracter) • Application (distracter) • Knowledge (key) • Comprehension (distracter) The correct answer usually numbered as 1 The wrong answers usually numbered as 0

5. Two important indexes for Item Analysis • Item Difficulty Index • To tell how hard the item is • Item Discrimination Index • To tell how well the item to distinguish between high ability and low ability students

6. Item Difficulty Index • Is defined as the percentage or proportion of test takers who correctly answer the item. • For example, in a class of 30 students, if 20 students get the item correct and 10 are incorrect, the item difficulty index is 20/30 =0.67 • Range from 0 to 1

7. ITEM DIFFICULTY = NO. CORRECT / TOTAL Students Item1 Item2 Item3 Item4 Item5 Robert 1 1 1 1 1 Millie 1 0 1 1 1 Dean 1 0 0 1 1 Shenan 1 1 0 1 1 Cuny 1 1 1 1 1 Corky 1 0 1 1 1 Randy 1 1 0 1 1 Jeanne 1 1 0 0 1 Iliana 1 1 1 0 1 Lindsey 0 0 0 0 1 Item p= 0.9 0.6 0.5 0.7 1.0

8. Optimal P Values for Items with Varying Number of Options

9. Special Assessment Situations and Item Difficulty • Previously discussed item difficulty is most applicable to norm-referenced tests • For criterion-referenced tests or classroom tests, it is normal to have average p values as high as 0.9 because we expect most students to be successful • If a test were developed to select the upper 25%, it would be desirable to have items with p values that average 0.25 • In summary, although a mean p of 0.5 is optimal, item difficulty levels vary with purpose of a test.

10. Item Discrimination Index • Is defined as the difference of item difficulty between those who succeeded (called upper group or high-achievement group) and those who failed the test (called lower group or low-achievement group) • D = discrimination index (range from -1 to 1) • PU = difficulty index in the upper group • PL= difficulty index in the lower group • For example, Pu=0.8, PL=0.3, D=0.8-0.3=0.5

11. Guidelines for Evaluating D Values

13. Distracter Analysis • It allows you to examine how many students in the upper group and the lower group selected each option on a multiple-choice item • We expect distracters to be selected by more students in the lower group than students in the upper group. • An effective distracter must be selected by some students.

14. Distracter Analysis

15. Distracter Analysis

16. Building a Table of Specifications 1. Selecting content areas 2. Selecting learning outcomes to be tested 3. Determining the levels of objectives 4. Determining the question type 5. Determining the points for each question 6. Building a table

17. A Sample of Table of Specifications