-use before a test is administered helps assure that the test is a reliable and valid measure -use after the test has been administered makes it possible to build a bank of high-quality test items for use during future years -use after allows the teacher to remove errant questions that adversely affect the quality of classroom measures in the current year
-use before a test is administered helps assure that the test is a reliable and valid measure -use after the test has been administered makes it possible to build a bank of high-quality test items for use during future years -use after allows the teacher to remove errant questions that adversely affect the quality of classroom measures in the current year
#2knowledge of which incorrect options students select must be a clue to the nature of the misunderstanding, and thus prescriptive of appropriate remediation
#2knowledge of which incorrect options students select must be a clue to the nature of the misunderstanding, and thus prescriptive of appropriate remediation
#2knowledge of which incorrect options students select must be a clue to the nature of the misunderstanding, and thus prescriptive of appropriate remediation
Item Difficulty -easiest -each item should have been correctly answered by a little over half of the class
p=0.88
p=0.88
most scores clustered on high side unusual low scores spread out over the wide area of the distribution
Most children will score very high on these measures. Children with learning problems will be easily identified by their pattern of lower scores.
This test would be very difficult for most applicants.
Most common formula: right – (wrong/n-1) where n is the number of alternatives for an item
The more difficult the likelier students will guess an answer to the question.
p=0.88
p=0.88
-dependent on teacher’s grading philosophy or goal for testing -if well-constructed test designed w/ items of this difficulty level is administered, student scores should distribute as a normal Gaussian curve; distribution will have a mean score equal to the average difficulty level of test items -if to maximize differences between students, optimal diff. index is 0.50
problem for teachers who assign grades according to a rigid scale e.g. 60-69% range would be a D for this reason teachers often design test items to have difficulty indexes over 0.70
A is chosen by only 2; may be too obviously wrong. C is working well. B has drawn far too many students; should be examined to determine what caused this choice. Teacher should consider reteaching the topic and explaining why B is wrong. A and B need to be rewritten for the next edition of the test.
Valid: students who have higher scores know more about the subject than students with lower scores.
#2 Split the group at the median, top 50% and bottom 50%. If several students are at the median, allocate them randomly to one or the other group to get balanced groups.
p=0.70 D=.90-.50=.40
p=0.50 D=-0.20
#2 Scores in the middle will not be used #4 Percentage of students who got the item right #5 difference between number of pupils in the upper and lower groups who got the item right
p=0.70 D=.90-.50=.40
-use before a test is administered helps assure that the test is a reliable and valid measure -use after the test has been administered makes it possible to build a bank of high-quality test items for use during future years -use after allows the teacher to remove errant questions that adversely affect the quality of classroom measures in the current year
To answer this a pre-test and post-test must be given, and the results compared. An item by item comparison can be made by means of an item-response chart.
3.
Item Analysis the effort to improve individual questions after they are used process of examining answers to questions in order to assess the quality of individual test items and test itself
4.
Item Analysis the effort to improve individual questions after they are used process of examining answers to questions in order to assess the quality of those items and of the test
students didn’t understand the concept being tested
item could be badly constructed
26.
Distribution with Negative Skew picture from http://billkosloskymd.typepad.com
27.
Distribution with Negative Skew picture from http://billkosloskymd.typepad.com
p > 0.70
Useful in identifying students who are experiencing difficulty in learning the material.
28.
Distribution with Negative Skew picture from http://billkosloskymd.typepad.com
Diagnostic Testing
Used to identify learning problems experienced by a child.
29.
Distribution with Negative Skew picture from http://billkosloskymd.typepad.com
Diagnostic Testing
Made up of easy test items that cover core skill areas of a subject.
30.
Distribution with Positive Skew picture from http://www.ken-szulczyk.com/lessons/statistics/asymmetric_distribution_01.png
31.
Distribution with Positive Skew picture from http://www.ken-szulczyk.com/lessons/statistics/asymmetric_distribution_01.png
Out-of-Level Testing
Used to select the very best top students for special programs.
32.
Distribution with Positive Skew picture from http://www.ken-szulczyk.com/lessons/statistics/asymmetric_distribution_01.png
Out-of-Level Testing
The optimal level of item difficulty is the selection ratio .
33.
Out-of-Level Testing picture from http://www.ken-szulczyk.com/lessons/statistics/asymmetric_distribution_01.png
selection ratio:
number that will be selected
number of applicants
34.
Out-of-Level Testing picture from http://www.ken-szulczyk.com/lessons/statistics/asymmetric_distribution_01.png
Sample Problem:
A university summer program for junior high school students limits admission to 40 slots. 200 students are nominated by their high schools.
What should be the average difficulty level of the program’s admission test?
35.
Out-of-Level Testing picture from http://www.ken-szulczyk.com/lessons/statistics/asymmetric_distribution_01.png
40 admission slots
200 test takers
The test should have an average difficulty level of 0.20 (low difficulty index).
36.
Out-of-Level Testing picture from http://www.ken-szulczyk.com/lessons/statistics/asymmetric_distribution_01.png The average score would be only 20% plus a bit more for the guessing factor. The distribution of scores would show a positive skew. The best students would be evident at the upper end of the distribution.
37.
Out-of-Level Testing picture from http://www.ken-szulczyk.com/lessons/statistics/asymmetric_distribution_01.png The average score would be only 20% plus a bit more for the guessing factor. The distribution of scores would show a positive skew. The best students would be evident at the upper end of the distribution.
38.
Out-of-Level Testing picture from http://www.ken-szulczyk.com/lessons/statistics/asymmetric_distribution_01.png The average score would be only 20% plus a bit more for the guessing factor. The distribution of scores would show a positive skew. The best students would be evident at the upper end of the distribution.
46.
Problem When using a rigid grading scale, over half of the students will fail or get a D. picture from http://savingphilippinepupilsandparents.blogspot.com/
47.
Distracter Analysis a multiple choice item has a low difficulty index ( p < 0.30 ) examine the item’s distracters 1 2
48.
Distracter Analysis a multiple choice item has a low difficulty index ( p < 0.30 ) examine the item’s distracters 1 2
55.
Hogan’s Method N=40 ; Median=35 ; The test contained 50 items . * Indicates correct option. Item Group A B* C D 5 High 0 90 10 0 Low 10 50 30 10 Total 5 70 20 5 in %
56.
Sample Problem N=40 ; Median=35 ; The test contained 50 items . Item Group A* B C D 5 High 40 60 0 0 Low 60 30 0 10 Total 50 45 0 5
Used to correlate item scores with the scores of the whole test
A special case of the Pearson Product Moment Correlation, where one variable is binary (right vs. wrong), and the other is continuous (total raw test score)
Used to correlate item scores with the scores of the whole test
A special case of the Pearson Product Moment Correlation, where one variable is binary (right vs. wrong), and the other is continuous (total raw test score)
A negative point-biserial correlation means that the students who did well on the test missed that item, while those students who did poorly on the test got the item right.
D=1.00 has maximum positive discriminating power where all pupils in the upper group get the item right while all pupils in the lower group get the item wrong.
D=0.00 has no discriminating power where an equal number of pupils in the upper and lower groups get the item right.
D=1.00 has maximum positive discriminating power where all pupils in the upper group get the item right while all pupils in the lower group get the item wrong.
D=0.00 has no discriminating power where an equal number of pupils in the upper and lower groups get the item right.
D=1.00 has maximum positive discriminating power where all pupils in the upper group get the item right while all pupils in the lower group get the item wrong.
D=0.00 has no discriminating power where an equal number of pupils in the upper and lower groups get the item right.
71.
Sample Problem * Indicates correct option. Find the p and D . Item Group A B* C D 5 Upper 10 0 10 0 0 Lower 10 2 4 1 3
73.
Analysis of Criterion-Referenced Mastery Items
74.
Crucial Questions: To what extent did the test items measure the effects of instruction?
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Item Response Chart + means correct - means incorrect Items 1 2 3 4 5 Pretest (B) Posttest (A) B A B A B A B A B A Jim - + + + - - + - - + Dora - + + + - - + - + + Lois - + + + - - + - - + Diego - + + + - - + - - +
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