Field Test Analysis Report: SAS Macro and Item/Distractor/DIF Analyses Prepared by Yi-Hsin Chen,...

Field Test Analysis Report:SAS Macro and

Item/Distractor/DIF Analyses

Prepared by Yi-Hsin Chen, Chunhua Cao, and Stephanie

GreenCollege of Education at USFPresented at the meeting of

the Central Florida Assessment Collaborative (CFAC)

May 20th, 2014, Orlando Florida

Agenda of This Presentation

SAS macro for CTT test/item analysis, IRT 2PL model, and Mantel-Haenszel differential item functioning (DIF) analysis

Introduction of statistical concepts for test/item development

Item Analyses: CTT and IRT Distractor Analysis DIF Analysis

SAS macro for test/item, 2PL, DIF analyses

SAS Macro Outputs

A SAS macro developed for this project

There are six excel outputs Test score statistics Frequencies of options for each item Item analysis statistics Distractor analysis DIF 2PL item parameter

Available upon request at [email protected]

Test Score Statistics

Frequencies of Options

Item Analysis Statistics

Distractor Analysis

DIF Analysis

Statistical Concepts

of Test Scores

Sample size

N: Sample size

85, 60, 70, 44, 59, 89, 99, 79, . , 100

N=10

USED_N: Sample size used for analysis without missing data one missing data USED_N = 9

Central Tendency

MEAN: Arithmetic average Most frequently reported measure of

central tendency Sum of scores divided by number of

scores

1005

10095105100110

N

XX

Test Statistics: Central Tendency

MEDIAN (Q2): the score at the 50th percentile half of the examinees score above

median, and half score below median110

105

100

95

90

Median = 100

110

105

100

95

95

90

Median = 95+100 / 2 = 97.5

Percentiles

Percentile is considered when we consider the percentage of scores that fall below a given point

They are very useful for interpreting an individual student’s performance

Q1: The score is at the 25th percentile Q1 = 10, indicating 25 percent of the

students’ scores below 10 points

Q3: The score is at the 75th percentile

Variability

Range Subtract lowest score (Minimum) from

highest score (Maximum) This is a rough measure of variability

High score = 90

Low score = 50

Range = ? (40)

High score = 100

Low score = 50

Range = ? (50)

High score = 90

Low score = 30

Range = ? (60)

Variability

Standard Deviation (SD): an average points that deviates from

the mean score A measure of the amount of variability

in examinees’ total scores Large SD = large variability

(heterogeneity) Small SD = small variability

(homogeneity) (scores cluster closer to the mean)

VariabilityDeviation Scores Squared

100-92= 8 82 = 64

96-92 = 4 42 = 16

94-92 = 2 22 = 4

92-92 = 0 02 = 0

90-92 = -2 (-2)2 = 4

80-92 = -12 (-12)2 = 144

232 = (X-Mean)2

SD = (X-Mean)2 = 232 =

N 6

Scores

100

96

94

92

90

80

Mean = 92 6.22

Skewness and Kurtosis

SKEWNESS: a measure to tell the shape of the score

distribution, such as positive or negative skewness or symmetry

KURTOSIS: a measure of the "peakedness" of

the score distribution

Skewness

Skewnessa roughly negatively skewed distribution (bar

chart)

0

1

2

3

4

5

6

42 48 52 56 61 62 63 67 71 72 73 74 78 80 82 91

Fre

qu

en

cy

Score

Skewness

Skewness

a roughly positively skewed distribution (bar chart)

0

2

4

6

8

10

12

14

16

18

20

Fre

qu

en

cy

Score

Kurtosis

Different kurtosis values

K = 0

K < 0

K > 0

Reliability: Cronbach’s Alpha

A measure of the test reliability, indicating the internal consistency of the test

Sample dependent Different samples may obtain

different reliability with the same test

Ranges from 0 to 1 0.7 and above: good internal

consistency

Standard Error of Measurement

SEM (Standard Error of Measurement)

SEM = STD *

A higher reliable test can cause smaller SEM

Statistical Concepts

of Item Analysis

Item Analysis

Why care? – Item analysis helps you identify

problems with your items (or scoring)

These problems can be corrected, resulting in a better test, and better measurement

Item Analysis

When is it useful? – Item analysis is most useful when you are

developing a bank, or pool, of items that you will continue to use

It can be used when evaluating standardized tests

It is also a useful tool, anytime students have complained about an item

It can be used to identify mis-keyed items

Item Difficulty (p-value)

Item difficulty (proportion correct): the proportion of examinees tested

that answered the item correctly

# of students who responded correctly

total # of students who responded

p =

Ncorrect

Ntotal

p =

Item Difficulty (p-value)

p can range from 0 to 1.0 A rough level of item difficulty (p)

.80 and above moderately easy to very easy (mastery)

.80 - .30 moderate

.30 and below moderately difficult to very difficult

Item Discrimination

Discrimination can be computed using correlation This shows the relationship between

a single item and the total test

It is expected that students with high scores answer the item correctly

rpb = (point-biserial) correlationbetween item score and total

score

Item Discrimination

Corrected point-biserial correlation: A statistic similar to point-biserial

correlations The score of the individual item is

taken out of the total score so that the contribution of the item itself is removed from the correlation This statistic is more accurate to

represent item discrimination

Item Discrimination

Two ability groups (upper and lower) approach Median score is used to divide the students into

two groups Discrimination coefficient (D-value) =

percentage correct in the upper group – percentage correct in the lower group

Ranges from -1 to 1 An item with higher and positive D-value

indicates a good discriminating item An item with a negative D-value suggests that

the lower achieving group did better on an item than the higher achieving group, indicating a poor item

Item Discrimination

A rough scale of item discrimination (D) D can range from -1 to 1

.30 and above moderate to high discrimination

0 - .30 little to no discrimination

0 and below negative discrimination (unwanted)

Item Difficulty and Discrimination

Relationship between item difficulty and discrimination

there can be little discrimination: if nearly everyone gets the item right, or if nearly everyone gets the item wrong

there can be maximum discrimination: if about half the people got the item

right,and about half got the item wrong

Item Difficulty and Discrimination

Relationship between item difficulty and potentialdiscrimination

0 .5 1.0Item Difficulty

M

ax D

iscr

i mi n

a tio

n0

.51 .

0

Alpha If an Item Deleted

“The Alpha If Deleted” shows what would happen to the internal consistency when the item is deleted When the test_alpha_deleted coefficient goes

up, compared with the original test-alpha, it indicates that without the deleted item, the test can be more reliable (that item can be removed from the test)

When the test_alpha_deleted coefficient goes down, it means that deleting that item is not a good thing and also indicates that item is a good item

Statistical Concepts of Distractor Analysis

Distractor Analysis

used to determine which distractors students find attractive consider the proportion of (total)

students choosing each option compare the number of examinees

selecting each option in the High and Low groups, or

a* b c d

Total .78 .11 .03 .08

Example:Proportion of total examinees selecting each option

Selecting upper and lower groups

Upper and Lower groups are needed: to hand-compute D-values, and for distractor analysis when

comparing numbers of examinees

To select Upper and Lower groups: arrange the tests by total score separate out the tests for each group

top half becomes Upper group, and bottom half becomes Lower group

Upper and Lower groups are needed: to hand-compute D-values, and for distractor analysis when

comparing number of examinees

To select Upper and Lower groups: Upper group: top half (50%) or top

33% Lower group: bottom half (50%) or

bottom 33%

Selecting upper and lower groups

1. The capital of Switzerland isa) Bern.b) Zurich.c) Lucerne.d) Geneva.

Numbers in the High and Low groups who selected each option

Example 1: distractor analysis

a* b c d

Upper 13 0 1 1

Lower 1 3 2 9

2. The most important part of test planning is creating:

a) sound instruction.b) a test blueprint.c) an item analysis plan.d) the grading curve.



a b* c d

Upper 1 8 1 0

Lower 2 8 0 0

3. Which type of essay item contains the most explicit instructions to students?

a) extended responseb) fixed responsec) explicit responsed) restricted response

a b c* d

Upper 3 1 2 14

Lower 4 1 7 8



Statistical Concepts of 2PL IRT model Analysis

Two-Parameter Logistic Model

)](exp[1

)](exp[),,|1(

iji

ijiiijijXP

47

Alpha represents item discrimination The value is positive

Beta represents item difficulty with the mean of 0 and the SD of 1 Items with the negative values = easy items Items with the positive values = hard items

Statistical Concepts of DIF Analysis

49

Differential Item Functioning

A major concern regarding using the psychological measures is that these measures may “work differently” or be either “for or against” a particular group of examinees (e.g., gender or ethnicity)

When a test item unfairly favors one group over another, it can be said to show differential item functioning or DIF

50

Uniform or consistent DIF

51

Non-uniform or crossing DIF

52

Mantel Haenszel chi-square

0 1 Total

Reference Bt At NRt

Focal Dt Ct NFt

Total M0t M1t Tt

1

1

1

1L

t t

tt

L

t t

tt

MH

T

CBT

DA

subscript t = individual raw score

53

Mantel Haenszel chi-square

Controlling for the observed score, we want to see if the proportion correct for the focal group is equal to that for the reference group on an item

The MH statistic consists of a series of 2x2 contingency tables MH = 1 : No DIF MH < 1: DIF and favor the focal group

(dummy=0) if p < .05 MH > 1: DIF and favor the reference group

(dummy=1) if p < .05

Field Test Analyses

Test Statistics for Three Subjects

STATISTIC AnatomyN 269

USED_N 269MEAN 12.364

STD 3.337MIN 4Q1 10

MEDIAN 12Q3 15

MAX 21SKEWNESS -0.102KURTOSIS -0.447

ALPHA 0.533SEM 2.281

STATISTIC PrecalculusN 210


STD 2.978MIN 2Q1 8

MEDIAN 10Q3 11

MAX 20SKEWNESS 0.378KURTOSIS 0.679


STATISTIC Phy-SciN 183


STD 4.141MIN 4Q1 10

MEDIAN 13Q3 16

MAX 25SKEWNESS 0.088KURTOSIS -0.658


Item difficulty

Physical Science(31 items)

0-0.10 (1 item) 22

0.11-0.20 (2 items) 11, 28

0.21-0.30 (5 items) 16, 27, 9, 6, 20

0.31-0.70 (12 items) 30, 18, 12, 25, 31, 15, 2, 19, 24, 13, 26, 29, 21, 10, 23, 8, 7, 4, 3,

17, 5, 14

0.71-0.80 (1 item) 1

0.81-0.90 0 items

0.90-1.00 0 items

Item difficulty

Anatomy(27 items)

0-0.1 (0 items)

0.1-0.2 (2 items) 13, 2

0.2-0.3 (6 items) 16, 8, 27, 3, 10, 20

0.3-0.7 (14 items) 17, 4, 9, 14, 11, 5, 18, 26, 25, 7, 15, 22, 21,

24

0.7-0.8 (3 items) 12, 19, 1

0.8-0.9 (2 items) 23, 6

0.9-1.0 (0 items)

Item DifficultyItem

difficultyPre-calculus(21 items)

0-0.1 (0 Items)

0.1-0.2 (2 Items) 19, 3

0.2-0.3 (1 Item) 14, 1

0.3-0.7 (14 Items) 21, 10, 11, 18, 12, 20, 8, 16, 17, 15, 14, 6,

13, 2 0.7-0.8 (3 Items) 7, 5, 9

0.8-0.9 (0 Items)

0.9-1.0 (0 Items)

Value Physical Science31 items

Negative Value

(6 items) 11, 22, 20, 12, 31, 1

0-0.10 (2 items) 21, 5

0.10-0.20 (8 items) 23, 19, 6, 28, 25, 18, 10, 16

0.20-0.30 (6 items) 8, 3, 15, 2, 30, 27

Above 0.30

(9 items) 13, 7, 17, 24, 29, 9, 14, 26, 4

Value Pre-calculus27 items

Negative Value

(3 items) 3, 17, 13

0-0.10 (6 items) 16, 9, 20, 10, 27, 2

0.11-0.20 (9 items) 15, 4, 11, 26, 18, 12, 5,

14, 80.21-0.30 (9 items) 7, 1,

25, 22, 23, 21, 24, 6, 19

Above 0.30

0 items

Item Discrimination (Corrected point-biserial correlation)


Negative Value

(1 Item) 19

0-0.10 (2 Items) 11, 3

0.10-0.20 (13 Items) 9, 10, 2, 18, 8, 5, 17, 21, 1,

13, 14, 4, 120.20-0.30 (5 Items) 16, 15, 7,

6, 20

Above 0.30

(0 Items)

Value Number of items

Negative Value

(2 items) 11, 22

0-0.10 (4 items) 20, 1, 12, 31

0.11-0.20 (8 items) 28, 21, 23, 5, 19, 25, 6, 16

0.21-0.30 (6 items) 10, 3, 8, 27, 18, 9

Above 0.30 (10 items) 15, 30, 2, 17, 13, 24, 14, 7, 29, 26, 4

Value Anatomy27 items

Negative Value

(0 items)

0-0.10 (5 items) 13, 3, 16, 17,2

0.11-0.20 (7 items) 27, 9, 10, 20, 12, 6, 26

0.21-0.30 (11 items) 8, 4, 11, 1, 15, 18, 23, 15, 5, 7

Above 0.30

(5 items) 25, 24, 19, 22, 21

Item Discrimination(Two-Group Approach)


Negative Value

(0 Items)

0-0.10 (3 Items) 19, 3, 18

0.10-0.20 (7 Items) 21, 14, 9, 17, 10, 2, 11

0.20-0.30 (7 Items) 1, 5, 12, 13, 8, 7, 16

Above 0.30

(4 Items) 15, 20, 4, 6

Alpha Difference

Physical Science31 items

Negative Value

(8 items) 11, 20, 12, 31, 22, 1, 21, 5

0-0.005 (6 items) 23, 19, 6, 28, 25, 18

0.006-0.01 (3 items) 10, 16, 8

Above 0.01 (14 items) 3, 15, 2, 27, 30, 13, 7, 17, 24, 9, 29,

14, 26, 4

Alpha Difference

Anatomy27 items

Negative Value

(7 items) 3, 17, 9, 16, 13, 20, 10

0-0.005 (3 items) 27, 2, 15

0.005-0.01

(4 items) 4, 11, 26, 18

Above 0.01

(13 items) 12, 5, 14, 8, 1, 7, 25, 23, 6, 22, 21, 19,

24

Alpha Difference(Alpha and Alpha When deleted)

Alpha Difference

Pre-Calculus21 items

Negative Value

(2 Items) 19, 11

0-0.005 (3 Items) 3, 9, 10

0.005-0.01 (5 Items) 2, 8, 18, 5, 17

Above 0.01

(14 Items) 21, 1, 13, 14, 4, 12, 16, 7, 15,

6, 20

Item Analysis Summary

The test with reliability (alpha) less than .5 needs to be worried

Too hard item (e.g., p-value < 0.1 or 0.2) or/and too easy (e.g., p-value close to 1) items may be revisited

Revisiting Items with a negative value of discrimination is warranted, especially for the two-group item discrimination

Items with negative alpha difference between the original test alpha and the test alpha when deleted are not good, either

DIF Results: Precalculus

Girls = 0Boys = 1

Favor boys

DIF Results: Precalculus

Girls = 0Boys = 1

Favor girls

DIF Results: Anatomy

Girls = 0Boys = 1

Favor boys


Girls = 0Boys = 1

Favor girls


Girls = 0Boys = 1

Favor boys


Girls = 0Boys = 1

Favor girls

DIF Results: Physical Science

Girls = 0Boys = 1

Favor boys

DIF Results: Physical Science

Girls = 0Boys = 1

Favor girls

Distractor Analysis:Typical Problems and Solutions

Precalculus: Item 29

FrequencyRow Pct

Table of groupB by r19

groupBr19

A B C* D TotalLOWER GROUP 14

27.4521

41.1811

21.575

9.8051

UPPER GROUP 4741.23

3228.07

2723.68

87.02

114

Total 61 53 38 13 165

Frequency Missing = 76


The item is a hard item (p = 0.18)

Precalculus: Item 3


FrequencyRow Pct


groupBr3


32.2629

46.779

14.524

6.4562

UPPER GROUP 4030.30

5642.42

3022.73

64.55

132

Total 60 85 39 10 194


Precalculus: Item 3


Precalculus: Item 1


FrequencyRow Pct


groupBr1

A B C D* TotalLOWER GROUP 14

24.1427

46.555

8.6212

20.6958

UPPER GROUP 2317.83

4937.98

86.20

4937.98

129

Total 37 76 13 61 187


Precalculus: Item 1




groupBr14

- A B C D* TotalLOWER GROUP 5

5.568

8.893

3.3357

63.3317

18.8990

UPPER GROUP 54.55

43.64

43.64

5953.64

3834.55

110

Total 10 12 7 116 55 200Frequency Missing = 10


The item is challenging (p = 0.26)

Option C may be the potential key

Or students have a misconception on this item

Precalculus: Good Item


Discriminating well


groupBr21

- A B* C D TotalLOWER GROUP 33

33.3318

18.1823

23.2310

10.1015

15.1599

UPPER GROUP 2320.72

1614.41

4338.74

1816.22

119.91

111

Total 56 34 66 28 26 210

Precalculus: Good Item


Discriminating well

However, this item shows DIF and favors girls

Summary for Precalculus

Some items need to revisit: Items: 19, 3, 1, and 14

Develop some easy items (p=.70-.90)

Two DIF items Items 4 and 21

Anatomy: Hard Item


Not discriminating well


groupB

r3


18.5224

22.2225

23.1539

36.11108

UPPER GROUP 74.38

2817.50

4830.00

7748.13

160

Total 27 52 73 116 268


Anatomy: Hard Item


Not discriminating well

Anatomy: Potential Miskey

The item may have a miskey of Option D

The possible correct key is Option A (Majority of the upper group chose this option)


groupBr16

A B C D* TotalLOWER GROUP

5248.15

2725.00

109.26

1917.59

108

UPPER GROUP 9257.50

1610.00

127.50

4025.00

160

Total 144 43 22 59 268


Anatomy: Potential Miskey

The item may have a miskey of Option D


Or there is a misconception on this item

Anatomy: Good Item


groupBr25

A B C D* TotalLOWER GROUP

1311.93

3027.52

3229.36

3431.19

109

UPPER GROUP 85.03

2213.84

3119.50

9861.64

159

Total 21 52 63 132 268


The item has moderate difficulty level(p = 0.491)

Discriminating well

Anatomy: Good Item


Discriminating well

Summary for Anatomy

The p-value of the items look good, with half of the items being moderate difficult, almost one quarter of them being easy, and almost one quarter being difficulty

No negative discrimination items using the two-group approach (a good sign)

The test alpha is low (0.533) DIF: Items 14, 19 (favoring boys) and

items15, 22, 26 (favoring girls)

Physical Science: Item too hard



groupBr28

- A B C D* TotalLOWER GROUP

22.33

1922.09

3743.02

1922.09

910.47

86

UPPER GROUP

2020.62

44.12

4445.36

88.25

2121.65

97

Total 22 23 81 27 30 183

Physical Science: Item too hard


Physical Science: Potential Miskey

The item may have a miskey of Option C



groupBr11


40.7026

30.2317

19.778

9.3086

UPPER GROUP 6668.04

1111.34

1111.34

99.28

97

Total 101 37 28 17 183

Physical Science: Potential Miskey

Physical Science: Good Item


Discriminating well


groupBr27

A B C D* TotalLOWER GROUP 13

15.1230

34.8834

39.539

10.4786

UPPER GROUP 99.28

1919.59

3334.02

3637.11

97

Total 22 49 67 45 183

Physical Science: Good Item

Summary for Physical Science

Some items need to revisit: Items: 6, 11, 12, 22

Potential miskey item: 11 Develop some easy items

(p=.70-.85) DIF: Items 3 and 4 (favoring boys)

and Item 7 (favoring girls)

Field Test Analysis Report: SAS Macro and Item/Distractor/DIF Analyses Prepared by Yi-Hsin Chen,...

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