Research in Physical Education, Sport and Health
2015, Vol. 4, No. 2, pp.135-138
ISSN(Print):1857-8152; ISSN(Online):1857-8160
IMPACT OF SPECIFIC - MOTOR VARIABLES ON THE 100 METERS SPRINT
WITH SUDENTS FROM 14 YEARS
UDC: 796.012 -055.2-057.875
(Original scientific paper)
Astrit Iseni1, ZoranRadic2, AleksandarSimeonov2
1
Faculty of Physical Education - Tetovo, Macedonia
Ss. Cyril and Methodius University, Faculty of Physical Education, Skopje, Macedonia
2
Abstract
This research paper examines the impact of some specific-motor abilities on the success of running
sprint at 100 meters. The purpose of this research paper is to establish the connection between specificmotor skills as predictor system on running the 100 meters as criteria system.The survey was conducted
with 60 male persons aged 14 years ± 6 months, in primary school "NaimFrasheri" - Kumanovo. The study
used 17 variables of which 16 variables for assessment of specific engine compartment and 1 variable to
assess the running of 100 meters as motor ability. Variables assessment of specific motor skills are:
1.Taping hand (MTH), 2.Taping foot (MTF), 3.Taping with foot on wall (MTFW), 4. Raising the
body(trunk) in 30 seconds from lying on the back (MRBLB), 5. Raising the body (trunk) in 30 seconds
Swedish casing (MRBSC), 6. Push-ups (MPU) 7. Squat test (MSQT), 8. Sit and reach test (MSAR), 9.
Splitting up the legs (MSL), 10. Arcing with stick (MAS), 11. Eight by tilting (MET), 12. 10X5m Shuttle
(M10X5), 13. T- TEST (MTT), 14. Five jump from place (MFLJP), 15. Long jump from place (MLJP) and
16. triple jump from place (MTM), while variable to assess motor ability as a single criterion variable is:
17. Running 100m (TR100m). Based on the results of the regression analysis, where as predictor variables
were taken sixteen variables for assessment of specific engine compartment, and as criteria was taken only
one variable to assess the engine compartment, it can be concluded as follow: specificity motor variables
as predictor variables used in this paper have a statistically significant impact on running variables on 100
meters (MTP100m).The variables of the motor-specific capabilities (such prediction system), have a
significant statistical impact MRU100m variable, the level of significance Q = 0.000. It is also worth saying
that the whole prediction system has great influence on the variable MET, worth 0.419 and 0.002
significance level, where this value is a positive sign which means that the impact of the variable MET on
the variable MRU100m is positive. From this we can conclude that the better are the result in a test of
agility eight with bending, that better will be the result in running the 100 meters and vice versa. Also from
all prediction systems significant impact has the variable MLJP, worth -0.383 and significance level of
0.020, whereas this value is a negative, and the border of significant influence is variable MAS (arcing
with stick) with -.217 value and significance level of 0.059.
Key words: specific - motor abilities, running, students, correlation, regression analysis.
Introduction
Running short tracks was known before our era, where at that time the best description of these running
was given from the most famous poets Homer, in his legendary epics "Iliad and Odyssey" where he often
mentioned the fast runners. Sprint running was among the top olympic disciplines 776 BC, where they ran
into a course of (192.27 m), also running these courses are among the most attractive in athletics (Radic,
2006). Of all racing disciplines including: running short, medium and long run, sprint running are among
the first that should start with general training with the children, 10-12 years of age, and specialized
exercises 14-16 age (Bompa, 2000). Very few articles have focused on running sprint for athletes and
students of 14 years age. Some authors believe that functional and motor skills are among the most
important skills that are applied to success in sprint running (Homenkov 1977; Brown, Ferrigno & Santana
2000; Milanovic 2007).
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IMPACT OF SPECIFIC - MOTOR VARIABLES ON …
Methods
Sample of respondents
The sample population was derived from male students, aged 14 ± 6 months. The research was conducted
on 60 subjects in primary school "Naim Frasheri" - Kumanovo. The sample in this research is non-selective
regarding specific - motor abilities or running short routes. The results of this survey will be undertaken
only from respondents who regularly followed classes in physical education and had participated in all
motor tests.
Sample of variables
The study used 17 variables of which 16 for assessment of specific motor abilities or prediction parameters
and 1 variable to assess the running of 100 meters or as criteria variable.
Variables for assessment of specific motor skills with numbers:Tapping the hand (MTH), Tapping foot
(MTF), Tapping the foot of the wall (MTFW), Raise the trunk in 30 seconds. from lying on your back
(MRT30''), Raise the trunk in 30 seconds. swedish case (MRT30''SC), Push-ups (MPU), Squats (MSQU),
Sit and reach (MSR), Splits (MSP), Flash with sticks (MFS), Eights by tilting (MET), 10 x 5m Shuttle
run (M10X5), T-TEST (MTT), Five jump of place (MFJP), Long jump from place (MLJP), Three jump
of place (MTJP).
While variable to assess the running is with number: Running 100 meters (RU100m). Motor variables
are chosen as representative of motor sizes from the second row in research (Kurelič et al., 1975).
Method of processing data
In order to determine the impact between specific - motor variables as predictors system and variable
running the 100 meters as criteria the system was applied regression analysis or the method of analysis of
the impact and the relationship that belong to the multivariate analysis. The following tables shows the
results of basic statistical parameters or predictor variables and measures of central tendency and dispersion
for each indicator: 1. The minimum score, maximum score, mean, standard deviation shows the
intercorelacion and all motor-specific variables with variable running 100 meters and tables of regression
analysis. For the data processing is applied the statistical package SPSS 22.0.
Results
Table 1. Descriptive statistical parameters of the motor variables
МТН
МТР
МТНЗ
МПТЛГ30
МПТШС30
МЧУЧ
МСКЛ
МДПС
МШПА
МИП
МОСН
М10X5
МТТ
МПМ
МСДМ
МТМ
ТР100м
136
Min
20,00
21,00
17,00
10,00
8,00
4,00
2,00
7,70
137,00
53,00
16,20
134,00
7,05
575,00
115,00
340,00
123,00
Max
31,00
38,00
31,00
33,00
43,00
83,00
41,00
37,80
201,00
173,00
25,16
248,00
11,16
1167,00
235,00
618,00
204,00
Mean
25,70
29,22
24,63
23,22
30,87
26,35
16,35
21,71
170,49
101,05
20,06
212,08
8,45
827,30
169,40
477,92
154,65
SD
2,71
3,85
3,49
4,47
6,53
14,22
10,06
6,84
15,60
22,54
1,71
21,47
0,95
123,78
24,26
63,31
19,01
Skew
-0,05
0,08
-0,41
-0,19
-1,02
1,62
0,70
-0,05
-0,14
0,52
0,42
-0,65
0,63
0,27
-0,12
0,15
0,57
Kurt
-0,96
-0,53
-0,50
0,51
1,93
4,14
-0,36
-0,41
-0,57
0,47
0,72
1,42
0,29
-0,04
-0,04
-0,28
0,33
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A. Iseni1, et al.
Table 2. Intercorelacion of specific - motor variables
1
2
МТН
1
МТР
.612**
МТНЗ
МПТЛГ30
3
4
5
6
7
8
9
10
11
.358
.468**
1
.427
**
.244
.194
1
МПТШС30
.180
.158
.249
.162
1
МЧУЧ
.172
.229
.123
.172
.049
1
МСКЛ
.149
.228
.224
.343**
.199
.139
1
*
МДПС
.157
.231
.038
.273
-.068
-.046
.138
1
МШПА
.432**
.377**
.296*
.288*
-.128
-.050
.010
.318*
1
МИП
-.155
-.170
-.008
-.184
.015
-.141
-.128
-.147
-.054
1
МОСН
-.437**
-.351**
-.328*
-.247
.082
-.030
-.205
-.115
.404**
.300*
1
М10X5
-.286
*
-.193
-.184
-.092
*
.020
МТТ
-.263
*
*
*
-.267
-.104
-.237
-.195
.013
.004
МПМ
.556**
.362**
.456**
.383**
.046
.121
.142
МСДМ
.528**
.318*
.390**
.422**
.101
.076
МТМ
.499**
.297*
.484**
.355**
.080
.108
-.119
.427**
ТР100м
-.288
13
14
15
16
17
1
**
*
12
-.291
-.121
-.237
-.055
-.015
-.154
-.008
.351**
1
-.080
.335
**
.318*
.091
.118
.503**
.331**
.160
.236
.522**
-.218
.059
.164
.417**
.362**
.582**
.457**
.498**
-.166
-.028
-.052
-.279
-.248
.036
.508
**
-.210
-.223
-.180
.032
1
.
.422**
.431**
.450**
.368
**
1
.695**
1
.861**
.723**
1
.582**
.599**
.580**
Table 3. Regression analysis of variable TR100m - Odds (a)
B
МТН
МТР
МТНЗ
МПТЛГ30
МПТШС30
МЧУЧ
МСКЛ
МДПС
МШПА
МИП
МОСН
М10X5
МТТ
МПМ
МСДМ
МТМ
R=.789
,65
,81
-,99
-,11
-,39
,01
,23
-,11
,18
-,18
4,66
-,16
,90
-,04
-,30
-,01
Std. Err.
1,03
,69
,69
,50
,32
,14
,21
,30
,16
,09
1,44
,09
2,45
,03
,13
,07
R2=.622
Beta
,09
,16
-,18
-,03
-,14
,01
,12
-,04
,15
-,22
,42
-,18
,05
-,23
-,38
-,04
T
,64
1,17
-1,43
-,22
-1,24
,09
1,10
-,38
1,10
-1,94
3,23
-1,76
,37
-1,10
-2,41
-,16
Sig.
,53
,25
,16
,83
,22
,93
,28
,71
,28
,06
,00
,09
,72
,28
,02
,87
Q=.000
Discussion
In table 1, are shown the results of basic statistical parameters of specific motor variables such as:
minimum score, the maximum score, the mean as the main indicator, standard deviation as the main
indicator and main indicators of the shape of the curve distribution, the asymmetry of the curve or indicator
skewness, and the curvature of the curve or indicator kurtosis.
From Table 1, we can conclude that the values of all motor-specific variables had major differences
between the minimum and maximum results. Value standard deviations in 9 out of 17 tests (MCHUCH,
MSKL, MSHPA, MIP M10X5, MPM MSDM, MTM, TR100m) on a high level, and the results, which are
heterogeneous or results that have high variability while the specific engine test is at a low level, indicating
that discrimination is not satissfactory and that these results are homogeneous, or results that had low
variability.
The asymmetry of the curve is low in almost all variables, and some others with negative values which
means that the distribution is normal (below 0), while the rounded value of the curve for most variables is
below 2.75, so all these values are platikurtichen, meaning that the results are distributed from the mean,
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IMPACT OF SPECIFIC - MOTOR VARIABLES ON …
except variable MCHUCH that result is greater of 4.00, which means that this value is leptokurtichen
character.
Table 2 shows intercorelacioni ratios among all motor-specific variables and variable TR100m. From
the view of the correlation matrix between the motor-specific variables and variable TR100m we can see
that out of 136 possible correlation coefficients, 56 are statistically significant. Correlations with higher
values are shown between variables: MTM and MPM worth .861, MTM and MSDM with vrdnost .723
MSDM and MPM worth .695 and MTP and MTN, worth .612. Also most of the correlation coefficients are
not statistically significant, so we will not discuss them in detail.
Multiple correlation between the system of predictor variables and the criterion variable (TR100m) is
shown in Table 3 and its value is 0.789 (R = 0,789), or specified correlation explains the common variability
with about 62% (R2 = 0,622). The remaining 39% of the variability in explaining the criterion variable
(TR100m) can be attributed to other anthropological features that were not included in these surveys
(anthropometric, functional other motor tests, conativ, cognitive and social). From the displayed regression
analysis of the variable TR100m it is seen that between prediction system and criteria variable there Is no
statistically significant effect, it shows its worth of 0.000.
It is worth saying that the whole system of prediction the greatest single influence has the variable
MOSN (tab.3), worth 0.419 and 0.002 significance level, where this value is a positive sign before which
means that the impact of the variable on the variable MOSN is TR100m positive. From this we can conclude
that the better will be the result in a test of agility eight by tilting, the better the result will be running the
100 meters and back will be much lower value of eight test by tilting, the lower will be the result of running
100 meters. Also from the prediction system the most individually significant impact has the MSDM
variable with a value -0.383 and significance level 0,020, where this value is a negative, and the border of
significant influence is variable MIP (with Flash Palitsi) with -.217 value and level of 0.059 significance.
The values of other variables have an impact but not with statistical significance so you we will not discuss
them in detail.
Conclusion
Based on the results and performed analyzes one can conclude that:
Specifically - motor variables as predictor variables used in this paper, have a statistically significant impact
on the criteria variable running the 100 meters (TP100m). From these results we conclude that students of
this age who possess the motor skills as agility, explosive strength and speed in the legs, and also the
flexibility of hands will achieve better results in sprinter running of 100 meters where there are lso apparent
explosive force and speed of the upper and lower limbs.From here we can recommend to all coaches and
teachers engaged in the development of athletics or more specifically with sprint running as one of the most
attractive disciplines of running to practice these types of motor development tests of agility, explosive
strength and speed and flexibility in their plan of exercise programs, and also preferably at least twice a
year to realize measurements of motor skills and other antpological premises in order to see the current and
final status of students and achieve better results within the school sports, why not also in professional
sports.
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