Resonance Assessment Test #1
First Semester
Instructions: Select the best answer for each question.
1. Select the electron movement that generates a resonance structure for this species.
CH3
H3C
CH3
H3C
N
H
H 2C
H
CH3
H
a.
N
CH3
H 2C
CH3
H
H 2C
b.
CH3
H 3C
N
H
H
CH3
H
H
H2 C
CH3
H 3C
N
H
CH3
CH3
H 3C
N
H
H 2C
c.
d.
2. Select an alternate Lewis structure for this species.
H
a.
H
H
C
C
C
H
H
H
O
H
C
C
C
H
H
H
C
C
H
O
c.
H
H C
C
C
H
H
H
H C
C
C
H
H
H
H
H
O
d.
H
H
-
3. Select the most stable Lewis structure for [NHCHCHCHCHCHO] .
4.
H
N
O
N
O
H
c.
H
d.
H
O
N
N
O
Select the best drawing for the resonance hybrid of this species.
O
O
H
O
H
H
I
a.
O
H
H
H C
H
b.
H
H
b.
a.
O
O
δ+
δ+
δ−
c.
O
δ−
δ−
H
δ−
O
b.
δ+
H
d.
H
O
δ+
δ+
H
δ+
Resonance Assessment Test #6
Second Semester
Instructions: Select the best answer for each question.
1.
Select the electron movement that generates a resonance structure for this species.
O
O
H
C
H
O
H
H
H
H
H
O
H
H
H
O
H
H
C
H
H
H
H
a.
2.
O
H
H
H
O
H
H
c.
O
O
O
a.
b.
c.
d.
Select the most stable Lewis structure for this species.
O H
H
H
H
a.
H
O H
C
H
H
H
H
H
b.
H
H
N
C
H
H
H
H
H
H
H
N
H
N
C
H
H
H
O H
O H
H
H
c.
H
H
N
C
H
H
H
H
H
d.
H
H
N
C
H
H
H
Select the best drawing for the resonance hybrid of this species.
O
δ−
δ+
δ−
O
O
c.
d.
δ+
δ−
a.
δ−
O
O
b.
C
H
d.
O
H
H
C
H
H
H
O
H
H
H
b.
O
4.
H
Select an alternate Lewis structure for this species.
O
3.
C
O
H
H
O
H
H
Difficulty and Discrimination Indexes
In order to present some psychometric properties of the tests, in terms of their validity and reliability, the
difficulty and discriminating indexes were computed for each item. The difficulty index (p) is a measure of the
proportion of examinees that answered the item correctly, and for a mastery model situation it is desirable that
students find the item “very easy”, in other words that 90 percent or more of the students answer the item
correctly. The discrimination index (d) is a measure of how well an item discriminates between the masters and
the non-masters; so positive discrimination indexes are desirable. When interpreting the value of a
discrimination index it is important to be aware of the relationship between an item’s difficulty index and its
discrimination index. Items that are either too simple or too difficult are not likely to be very discriminating.
Table 1. Difficulty, Discrimination Indexes and Mean Difficulty for Test Items
Task 1
Item
Task 2
Task 3
Task 4
p
d
Item
p
d
Item
p
d
Item
p
d
First Semester
Test 1
1
.850
.30
2
.906
.17
3
.901
.23
4
.972
.09
Test 2
1
.948
.11
2
.620
.56
3
.784
.28
4
.920
.19
Test 3
1
.827
.39
2
.577
.61
3
.892
.23
4
.887
.17
Test 4
1
.859
.28
2
.817
.39
3
.915
.16
4
.873
.19
Second Semester
Test 5
1
.897
.26
2
.879
.30
3
.685
.66
4
.867
.32
Test 6
1
.909
.20
2
.939
.08
3
.788
.40
4
.933
.10
Test 7
1
.836
.26
2
.848
.32
3
.655
.60
4
.133
.10
Mean
Difficulty
.874
.798
.797
.803
i.
Four short tests were given during the first semester (Tests 1- 4) and three during the second semester (Tests 5-7).
ii.
Each test had one question for each of the four tasks (Item 1 for Task 1, Item 2 for Task 2 and so on.
iii. A difficulty index (p) was calculated for each item using a frequency analysis. This index is a measure of how
easy or difficult the test item was for students.
iv. The difficulty index should be interpreted as a percent, where .85 means that 85% of students answered the item
correctly.
v.
If the p value is ≥ .90 the item was very easy, between .89 and .61 the item was easy, between .60 and .40 the item
was moderate, and less than .40 the item was difficult.
vi. The difference between the high ability (one third) and low ability (one third) approach was used to calculate the
discrimination index (d). Using these two opposite group scores, an index was calculated for each item using the
formula, d = (CH - CL) / .5 N.
vii. In norm-reference tests when the magnitude of the discriminating index is larger than .40 the item has very good
discrimination (VG), between .39 and .30 the item has good discrimination (GD), between .29 and .20 the item
should be revised (R), and less than .19 the item has no discrimination (ND). In criterion-referenced mastery tests,
as is this case, only positive discrimination indexes are desirable.
Multiple Regression Analysis
A multiple regression analysis was also performed where the predictor variables were the tasks and the
grades were the criterion variable. A stepwise regression selection process was used to identify the best
prediction model. Different models were considered as predictors of students’ final grades. In a stepwise
regression the predictor with the highest correlation (R2) with the criterion variable is entered first in the model.
Then it tests each of the remaining variables until it finds the highest R2. The overall R2 indicates the percentage
of the variance associated with the criterion variable. Based on this regression analysis, an ANOVA table was
generated for each semester.
For the first semester, the first model (one variable model) considered Task 1 to be the most highly
associated with higher grades (Table 2). In the second model (two variable model) Task 3 was added into the
model and a significant increment of 7.5% was observed in the proportion of variance, (R2). In the third model
(three variable model) a significant increment of 2.9% was observed in the proportion of variance. Second
semester data also associated Task 1 with the higher grades. The second model also added Task 3, with an
increment of 6.7% in the proportion of variance. The third model gave an increment of 3.5% in the proportion
of variance. (See Table 3)
Table 2. ANOVA Regression for the First Semester
Model
Task
1
Task
1,3
Task
Sum of
Degrees of
Mean
Squares
freedom (df)
Square
Regression
71.811
1
71.811
Residual
234.095
209
1.120
Total
305.905
210
Regression
95.914
2
47.957
Residual
209.992
208
1.010
Total
305.905
210
Regression
106.304
3
35.435
199.601
207
.964
305.905
210
1,2,3 Residual
Total
%
F
p
R2
%
64.113
.000
.485
48.5
-
47.502
.000
.560
56.0
7.5
36.748
.000
.589
58.9
2.9
increase
Statistical test (F value), probability level (p), the proportion of variance (R2), the percentage of variance (%), and the
percentage of incremental variance accounted for by the addition of predictors into the model. A probability level (p)
equal or less than .05 indicates a statistically significant incremental variance in each model tested.
Table 3. ANOVA Regression for the Second Semester
Model
Task
1
Task
1,3
Task
1,3,2
Regression
Sum of
Degrees of
Mean
Squares
freedom (df)
Square
23.313
1
23.313
Residual
206.126
160
1.288
Total
229.438
161
35.965
2
17.982
Residual
193.474
159
1.217
Total
229.438
161
42.565
3
14.188
Residual
186.873
158
1.183
Total
229.438
161
Regression
Regression
F
p
R2
%
%
increase
18.096
.000
.319
31.9
-
14.778
.000
.396
39.6
6.7
11.996
.000
.431
43.1
3.5
Statistical test (F value), probability level (p), the proportion of variance (R2), the percentage of variance (%), and
the
percentage of incremental variance accounted for by the addition of predictors into the model. A probability
level (p) equal or less than .05 indicates a statistically significant incremental variance in each model tested.