1. Dale Ulrich, University of Michigan
Youngsik Park and Weimo Zhu, University of Illinois at Urbana-Champaign
Dale Ulrich, University of Michigan
Finding Unexpected Response Patterns in TGMD-2 Using Social Network Analysis
ILLINOIS
KINESMETRICS
BACKGROUND/NEED
RESULTS
Agreement of clustered groups between the two datasets
Contingency coefficient = .787, Kappa = .522
Distribution of ages by the clustered groups
Overall, ages are related to clustered groups.
Between two different occasion data, there were no age difference among the clustered groups.
Characteristics of clustered groups
According to ability level
Three to four groups could be found, high, (mid-high), low, (very low).
According to subtests (motor and objective skill test)
Two motor skill oriented groups were found.
The one (group 2) from high correct score groups and the other (group 6) from low correct score groups.
One objective skill oriented group was found.
Group 5 showed higher scores in objective skills than group 4 which showed higher group mean score than group 5.
Group 3 indicated a little motor dominant response pattern and group 1 indicated a little objective dominant response pattern.
Group 4 did not show any explicit response pattern in the subtest level.
According to 12 skill tests and 48 subskill items
Group 2 and 6
Group 1 and 2 showed overall high ability level across all skill tests.
A major difference between two groups was found in the response patterns for overhand throw (objective) skill test.
The correct score of group 1 is .867 and that of group 2 is .35.
Similar answering patterns were detected in all four subskill items of overhand throw skill test.
Another difference between two groups was that group 1 showed much higher mean correct scores in three most difficult items among motor skill test.
Group 3
In most of skill tests, this group was around the mid-high range in their ability.
Much lower correct scores than expected were detected in two difficult items - one from gallop skill test and one from strike skill test.
Group 4
A little lower mean scores were indicated in two skill tests such as jump and dribble and their subskill items.
TGMD – 2 ( Ulrich, 2000) is a well-known standardized test to assess gross motor skills for children age 3 to 11. The test consists of 12 skills – six skills each for two subtests, locomotor and object control. It is assumed that students who have similar summary scores would respond in a similar manner across all items. In practice, participants do not follow expected patterns. In aptitude assessment, those unexpected patterns have been used to identify invalid response patterns, such as cheating or guessing (Meijer & Sijtsma, 1995, 2001), and in personality domain, they were used for broader purposes such as detecting social desirability (Zickar & Drasgow, 1996) or person-fluctuation (Ferrando, 2004). These unexpected patterns, which may provide rich information for instruction or intervention, have been basically ignored in motor assessment.
Mean Scores
Item
Clustered Groups
Item name
 Item code 1 2 3 4 5 6 7
Total
Run
item.1.1.1
0.66
0.96
0.84
0.80
0.51
0.98
0.77
0.86
item.1.1.2
1.00
0.92
0.94
item.1.1.3
0.87
0.89
item.1.1.4
0.91
0.82
0.28
0.95
0.70
0.85
Gallop
item.1.2.5
0.32
0.62
0.50
0.30
0.04
0.27
0.47
item.1.2.6
0.59
0.90
0.68
0.13
0.93
0.63
item.1.2.7
0.75
0.43
0.72
0.88
item.1.2.8
0.48
0.64
0.09
0.52
0.78
Hop
item.1.3.9
0.36
0.39
0.00
0.33
item
0.60
0.06
0.73
item
0.38
0.55
item
0.61
item
0.45
0.34
0.79
Leap
item
0.74
0.15
0.67
item
0.76
0.83
item
0.58
0.25
0.42
Horizontal
jump
item
0.81
0.41
item
0.44
0.19
item
item
Slide
item
0.56
0.71
item
0.97
item
item
0.57
0.11
Striking
a stationary
ball
item
item
0.65
0.54
0.69
item
item
0.35
item
Stationary
dribble
item
0.02
item
0.29
0.20
item
item
Catch
item
item
0.26
item
0.17
Kick
item
0.49
item
item
item
Overhand
throw
item
0.46
item
0.22
item
item
Roll
item
item
0.31
0.53
item
item
PURPOSE
In this study, the children who showed unexpected response patterns in TGMD-2 were detected and the characteristics of the patterns were described using social network analysis approach.
METHOD
About Participants
Number of participants: N = 1143
Data were collected two times (occasions) from the same subjects.
Male (571, 50%), female (572, 50%), age 3 to 10 years old 
About Items
Number of items was 48 and the test consists of two subtests – locomotor and object control.
Each subtest consists of six skill tests and each skill test have three to five subskill items. 
Analysis Procedure
The raw data were pre-processed using the social network approach.
After pre-processing , the data were fitted to regression lines
Finding clustered groups
After fitting, the dissimilarity measures were calculated using the residuals and clustered groups were found using dissimilarity measures.
Explaining the clustered groups
Using clustered groups, descriptive statistics (mean and standard deviation) for each item were calculated.
Cross Validation
For cross validation, the two occasional datasets were processed in the same way.
The agreement of the classification by the clustered groups was calculated between the two datasets.
CONCLUSIONS
Frequency of the clustered groups
Occasion 1: 398, 44, 129, 210, 44, 64, 53
Occasion 2: 412, 57, 186, 119, 37, 66, 65
Group 3 and 4 was divided well in each data set. But the half of the members of the two groups are classified into the other group in different datasets. (group 3  group 4 or group 4  group 3).
Social network analysis is a useful approach in identifying unexpected response patterns in motor assessment, which in turn can be used to help design appropriate instruction and assessment.