A CHI SQUARE APPROACH TO DETERMINE THE EFFECT OF STUDENTS' RESIDENCE ON ACADEMIC PERFORMANCE

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*Corresponding author: Email: fzokwonu_delsu@yahoo.com;
Original Research Article
Journal of Basic and Applied Research
International
15(4): 280-286, 2016
ISSN: 2395-3438 (P), ISSN: 2395-3446 (O)
International Knowledge Press
www.ikpress.org
A CHI SQUARE APPROACH TO DETERMINE THE EFFECT
OF STUDENTS’ RESIDENCE ON ACADEMIC
PERFORMANCE
F. Z. OKWONU
1*
1
Department of Mathematics and Computer Science, Delta State University, P.M.B.1, Abraka, Nigeria.
AUTHOR’S CONTRIBUTION
The sole author designed, analyzed and interpreted and prepared the manuscript.
Received: 2
nd
September 2015
Accepted: 6
th
October 2015
Published: 30
th
December 2015
__________________________________________________________________________________
ABSTRACT
This paper focused on residential effect on students’ academic performance. This discussion may twist or
elaborate on this concept towards asking questions such as; does students’ residential type impair or encourage
study? The contingency table, the chi square and the P-hat techniques are applied to answer the research
question based on hypothesis testing. The analysis based on the above methods revealed that students’ resident
has both positive and negative effect on students’ academic performance. The finding further indicated that
students within the university campus have reading appetite than students residing in mixed residential areas.
Although, further investigation revealed that reading culture is highly noticeable for both categories of students
during examination but on ordinary times, students on university hall of residence has reading appetite than their
counterpart off campus, the contrary is also true. On the other hand, this depends on individual priority and
motivation. The researchers in their view intuitively observed that students’ academic performance is individual
based but not perfectly on residential type but on life style, association and self-priority.
Keywords: Contingency table; chi square; p-hat; residential type; academic performance.
1. INTRODUCTION
Students’ academic performance is characterized with
different factors such as gender, age, social economic
status of parents, school mobility, daily study hours,
university residential halls and anxiety [1,2].
However, the quality of student and their performance
is vital to educators. Students are very important
component in any educational institutions and as such
their academic performance in either ways is worth
investigating. The performance of students in either
ways enhances the feature leaders and the economy
of a nation [3,4]. This paper is though specific on the
effect of students’ academic performance with respect
to residential type; however, other factors also affect
students in their academics. It was observed that
teaching technique, demographic factors, personal
perception and irregular class attendance are major
factors that affect students’ academic performance
[5-7]. It was observed that poor communication
skills, environmental factors, class size, finance,
extracurricular activities, marital status, and basic
learning facilities are factors that also affect students’
academically [4-7].

Okwonu; JOBARI, 15(4): 280-286, 2016
281
Research on students’ performance categorized as
excellent and poor are based on the grade point
average measurements [8]. Ali [3] observed that
“course assessment influence student academic
performance while other factors such as
demographic, active learning, class attendance, and
peer influence” influences student academic
performance positively. This researcher object to the
above claims by Ali, insisting that the above factors
have equal chances of positively or negatively
influencing students’ academic performance. Farooq
[9] observed that social economic status and parental
educational level also influences students’
performance academically. Some factors that affect
students’ academic achievement and attitude with
specific mention to web based education was
investigated and they concurred that traditional
teaching procedure may yield poor academic
performance and the study further posited that modern
learning techniques enhances students’ academic
performance [10], how? Sanders and Morrison
observed that web based learning procedure affect
students’ performance in either ways [11,12].
Ginsburg et al. [13] observed that factors such as
health status, physical and social environment and
students living condition, mobility have positive and
negative effects on students educational attainment. It
has been observed that change of residence has
negative and positive effect on educational
achievement of students’ [13-19]. Changing children
school also has positive and negative effect on the
child’s academic performance. Performance gimmick
is a major problem in students’ academic performance
when investigated and analyzed.
Residency relocation and school mobility has been
observed as a factor that may impair students’
performance [20]. Mlambo [21] identified and
analyzed learning preference, age, gender and entry
grades as certain factors that affect academic
performance of students’ characterized by high rate of
failure measured by grade point average. Although,
the study was particular in measuring relationships
and associations by pairing (learning preference and
gender, entry grade and gender, age and learning
preference, age and entry grade) using the Pearson chi
square. In summary, Mlambo observed that none of
the aforementioned factors “significantly affect
academic performance” [21]. Heinesen [22] posited
that class size is a contributing factor to poor
academic performance. It has been identified also that
parents, teachers and students contributions are
factors that may cause poor academic performance,
and further stated that the combination of the above
factors influences academic performance though
varying from one student to another, academic
institution to another and region to region [23].
Self-priority, parent income, parent educational
attainment, schooling history, class attendance and
student age influence students’ academic achievement
[24-28]. Research findings revealed that age and
gender has insignificant effect on student academic
performance and further analysis posited that place of
residence, schooling history and class attendance has
significant effect on academic performance [19].
Further presentation showed that place of residence
and previous educational history is associated.
Blimling [29] investigated the academic performance
of students residing in university hall and day
students. The discussion showed that students residing
in university hall of residence indicates little
difference in terms of academic performance over day
students in the United States. Further discussion
revealed that students residing in university hall of
residence performed better than day students [30].
Research also indicated that parent support help
students to perform better academically [31]. Ali et al.
[1] investigated the factors affecting “graduate
students academic performance considering tuition
fee trend, daily study hours, age, gender,
accommodation, and schooling”. The finding showed
that “daily study hours” contribute to graduate student
academic performance. Research conducted by
Adetunde and Asare [32] opined that no significant
difference between day and boarding school students
in terms of their academic performance in
mathematics. Research suggested that residential
school accommodation play vital role in the physical
well-being of the students and hence their academic
performance positively or negatively [2]. The
uniqueness of the findings of previous researchers is
that they all based their methods of hypothesis testing
on the use of the contingency table as prelude to the
computation of the chi square value. Similarly, these
procedures are adopted in this discussion.
This paper is coined to investigate the effect of
residential type (on and off campus resident/
accommodation) on students’ academic performance
in Delta State University, Abraka campus. To achieve
the objective of this study, the contingency table, chi
square and the proposed P-hat techniques are applied.
These techniques are applied to determine if
residential types have effect on students’ academic
performance positive or negatively or the effect is
equally likely. Basically, using the hypothesis
formulated, these techniques will aid us in analyzing
the outcomes and decision taken. The information
inferred from this process will be communicated to
decision makers for policy formulation with respect to
prioritizing the types of residential accommodation
suitable to students.

Okwonu; JOBARI, 15(4): 280-286, 2016
282
The reminder of this paper is organized as follows.
The contingency table, chi square and the P-hat
methods are described in Section Two while results
and discussions are presented in Section Three.
Conclusions follow in Section Four.
2. METHODS
In this section, we describe methods for computing
the chi square and P-hat values, respectively. This
process consists of the tabulation of the data set using
the contingency table. Basically, the observed values
are reported on the contingency table followed by
similar computation of the expected values and hence
based on the data set on the contingency table, the
formulas for the respective techniques are applied to
obtain the computed values. Analysis is done by
comparing the computed values with the table values
for decision purpose. The respective methods are
discussed as follows.
2.1 Contingency Table and Chi Square
The contingency table reveals the relationship
between two or more categorical variables or simply
put the process of displaying the distribution of
observations based on their values on two or more
variables [33]. It is applied to unravel the
effectiveness of a system under study [34,35] and it is
applied to conceptualize, organize and report data
[36]. Table 1, below describe the general form of
contingency table.
Table 1. General form of the 2x2 contingency table
Variable
B Row
total
Group 1 Q W Q+W
Variable
A Group 2 D F D+F
Column
total
V=Q+D
U=W+F
H=V+U
The information on the contingency table is analyzed
using the chi square statistic in order to infer if the
variables are statistically independent or associated
[37]. One basic assumption of the chi square is that
the expected value should be at least five or more
[38]. The expected frequency is computed based on
the following formula;
,
n m
nm
R C
EV
N
×
=
(2.1)
Where
nm
EV
denotes the expected frequency in the
nth
row and the
mth
column,
n
R
is the sum of the
nth
row,
m
C
denote the sum of the
mth
column
and
N
is the grand sum in the table. Based on the
values computed using Equation (2.1), and the
observed values, the chi square
2
( ),
χ
value is
computed based on the following formula;
2
21 1
(OV )
,
k l
nm nm
n m
nm
EV
EV
χ
= =
−
=
∑∑ (2.2)
where
OV
denotes the observed value with
( 1)( 1),
R C
− −
degrees of freedom. It is vital to
observe that a large chi square value implies some
large differences between the observed value and the
expected value [33]. The chi-squared statistic test is a
type of hypothesis testing procedure that give statistic
that is approximately distributed as the chi squared
distribution [35,39]. Although, the chi square value is
not bounded [40]. The chi square test based on the
contingency table implies test of independence [36].
2.2 P-hat
The P-hat technique like the chi square method deeply
relies on the information from the contingency table.
This technique is a statistical formulation designed
based on probability axioms. The value of the
comparative benchmark is obtained from the standard
normal probability table. It is a decision rule similar to
the chi square for test of independence. This
formulation is derived from the conventional chi
square and its decision rule is uniquely the same as
that of the chi square. The transformed observed value
is based on the initial observed value by using the sum
of the row value to divide the value in each cell. This
technique satisfies the axiomatic probability
assumption;
0 (X) 1,
P
≤ ≤
and
(X) 1.
P
=
∑
From the information in the row and column sum of
the transformed observed value, the expected value is
obtained. The mathematical formulation is described
below;
2
1 1 2
(T )
,
l k
nm nm
n m
nm
T
T
κ ξ
= =
∂ −
=∂
∑∑
l
(2.3)
1
,
m
nm l
n
n
C
T
R
=
∂ =
∑
(2.4)
( ) ( )
,
( )
n nm m nm
nm
nm
R T C T
TN T
∂ × ∂
=∂
l
(2.5)

Okwonu; JOBARI, 15(4): 280-286, 2016
283
where
nm
T
∂
denotes the observed value,
nm
T
l
is
the expected value,
n
is the value in each cell and
( (1 )), / , 20.1
p p p n N
ξ ε ε
= − = =
and the
decision benchmark is obtained as follows,
[
]
1 | z | ,
ζ θ
= −
and
z
is equal to the sum of
thediagonal of the transformed observed value from
the confusion matrix. The decision benchmark is
bounded between zero and one. The value of
ζ
is
obtained from the standard normal probability table.
The decision rule (DR) is described as follows;
,
,
.
,
Reject the null hypothesis
DR otherwise
κ ζ
κ ζ
>

=<

3. RESULTS AND DISCUSSION
This research is designed to investigate the effect of
students’ residence on their academic performance.
This among other factors and the influx of second,
third and fourth year students residing proportionately
on campus and off campus residential type is
considered in the present discussion. In this study,
1,250 students were scheduled for interview in other
to infer reasons while they prefer campus or outside
campus accommodations. In all, only 991 responded
with different reasons and 259 were indifferent with
their reasons. In both residential categories, 541
students out of the 650 proposed students were
interviewed, this figure consist of both male and
female for the on campus students and 450 were
equally interviewed out of the proposed 600 students
for the off campus resident. In general, about 541/650
representing 83% responded for the on campus
category and 450/600 representing 75% responded for
the off campus category. The reasons for this
variation is that the university hall of resident is
uniquely built, secure, inexpensive nature of
university hall of resident and student circle can easily
be predicted, the contrary is true for the off campus
resident because most of the apartments are scattered
in different quarters or areas and so. Table 2 contains
detailed information on the observed values and Table
3 contained the expected values.
Table 2. Observed values of students residing on
and off campus hostels
Male Female Column total
Campus
340
201
541
Off campus 293 157 450
Row
total
633
358
991
From the above information, 64% total male students
(54% on campus and 46% off campus) support
residing in both campus and off campus and 36%
total female students (56% on campus and 44% off
campus) support both categories.
Table 3. Expected values of students residing on
and off campus hostels
Male Female Column total
Campus
345.56
195.44
541
Off campus 287.44 162.56 450
Row total 633 358 991
In Table 3, since all the values are greater than 5, the
chi square procedure is suitable for this discussion.
The hypothesis is given below;
3.1 Hypothesis
0
:
H
Students residing in University hall of
residence perform better academically than
students residing outside the University hall
of residence,
1
:
H
Students residing in University hall of
residence do not perform better academically
than students residing outside the University
hall of residence.
The development of this hypothesis poses many
questions and answers. However, most of the students
argued that student academic performance does not
necessary depend on the residential type though
acknowledged that it has positive and negative
influence on academic performance. Some students
that have resided on campus and later relocated to off
campus strongly agreed that they have weak reading
appetite due to relaxation and other side attractions.
They further pointed out that their grade point average
as on campus students were better than their grade
point average as off campus students. But other
category of students opined that academic
performance depends on individual priority and peer
group influence and not necessary students’ type of
accommodation. They further stated that individual
motivation, parental status, reliance on parents
intervention, class lecture notes, resumption date and
time, finance, time spent on social media, lecturer’s
relationship with students during lecture hour, lecture
hour, lifestyle, lack of basic books and internet are
vital components that impede students’ academic
performance and pointed out that both categories of
students have merit and demerit on their residential
type as it affects academic performance. To back the
above claims, ten mixed resident students offered
their academic grade point average for six semesters.
The information in Table 4 showed that the academic
performance of students does not really depend on

Okwonu; JOBARI, 15(4): 280-286, 2016
284
accommodation type but the combination of
residential type, safe learning environment and self-
priority.
Next we obtain the degree of freedom, (R-1)(C-1)=
(2-1)(2-1)=1 at 5% level of significance, hence the
table chi square value is 3.84. This implies that the chi
square table value is greater than the computed chi
square value. The implication is that the alternate
hypothesis is not decline meaning that students’
academic performance does not necessary depends on
place of resident. From Table 1 and Equations (2.4-
2.5) we obtained the probability based observed
values and the expected values as follows;
Comparing the computed value with the table value,
we conclude that the alternate hypothesis cannot be
decline. Both techniques have similar conclusions.
Table 4. Academic performance of ten mixed resident students for six semesters
S/N 1
st
(on C) 2
nd
(on C) 1
st
(on F) 2
nd
(on F) 1
st
(on C) 2
nd
(on C)
1 2.62 2.97 2.53 2.36 2.62 2.77
2 3.19 3.50 3.37 2.90 3.53 3.56
3 1.58 1.57 1.09 0.96 1.15 1.49
4 1.95 1.57 1.81 2.64 2.29 2.23
5
3.05
2.48
3.66
3.14
2.41
3.59
6 1.69 1.75 2.18 1.95 1.76 1.63
7 3.49 3.73 3.16 3.08 3.55 3.73
8 2.51 2.20 2.94 2.71 2.99 3.08
9 2.68 2.52 2.67 2.95 3.49 3.90
10
2.49
3.22
3.56
3.98
3.64
4.03
On C: On campus; On F: Off campus
Table 5. Computed chi square value
OV
EV
D=(0V
-
EV)
D
2
X
2
=
D
2
/EV
340 345.56 -5.56 30.914 0.089
201
195.44
5.56
30.914
0.158
293 287.44 5.56 30.914 0.108
157 162.56 -5.56 30.914 0.190
991 991 0.00 0.546
Table 6. Probability based observed value (POV)
Male Female Row total
On campus 0.6285 0.3715 1
Off c
ampus
0.6511
0.3489
1
Column total 1.2796 0.7204 2
Table 7. Probability based expected value (PEV)
Male Female Row total
On campus
0.6398
0.3602
1
Off campus 0.6398 0.3602 1
Column total
1.2796
0.7204
2
Table 8. Computation of P-hat value
POV PEV PD=(POV-PEV) PD
2
Pd=PD
2
/POV
2
*( * )
p q
ε ζ
=
P-hat=Pd*
ε
0.6285 0.6398 -0.0113 0.000128 0.000324 5.0249 0.00163
0.3715
0.3603
0.0113
0.000128
0.000927
5.0249
0.00466
0.6511 0.6398 0.0113 0.000128 0.000302 5.0249 0.00152
0.3489 0.3603 -0.0113 0.000128 0.00105 5.0249 0.00528
2.0000 2.0000 0.0000 0.01309
P=497/991=0.502, q=1-P=0.498, based on the diagonal of Table 5, Z=0.9774, and the table value is
1 | | 1 | 0.9774 |
1 0.8365 0.1635.
tab
P Z
θ θ
= − = −
= − =

Okwonu; JOBARI, 15(4): 280-286, 2016
285
4. CONCLUSION
This paper focused on the effect of residential
accommodation type on students’ academic
performance. Different researchers have investigated
factors that affect students’ academic performance
based on social economic status of students’ parents,
age, sex, entry qualification and requirements, finance
and school mobility. In particular, as referenced
above, a research based on off campus and on campus
students’ performance in mathematics revealed that
there was little or no significant difference in their
grades. Hence, the researcher concluded that they
performed equally irrespective of the type of
accommodation. In this paper, the focus was on the
comparative academic performance of on and off
campus students of the Delta State University,
Abraka, based on their residential type. The use of the
contingency table, chi square statistic and the P-hat
proposed method revealed that residential type does
not necessary have effect on student academic
performance. The information obtained from both
categories of students showed that student academic
performance depended on individual priority,
motivation, parental and peer group influence.
COMPETING INTERESTS
Author has declared that no competing interests exist.
REFERENCES
1. Ali S, Haider Z, Munir F, Khan H, Ahmed A.
Factors contributing to the students academic
performance: A case study of Islamia
university sub-campus American Journal of
Educational Research. 2013;1(8):283-289.
2. Mokashi MV, Yadav VS, Khadi PB. Gender
difference on anxiety and academic
achievement among selected residential high
school children. Journal of Psychology. 2012;
3(2):107-111.
3. Ali N, Jusoff K, Ali S, Mokhtar N, Salamt AA.
The Factors influencing students’ performance
at universiti teknologi MARA kedah, Malaysia.
Canadian Research & Development Center of
Sciences and Cultures. 2009;3(4):81-90.
4. Mushtaq I, Khan SN. Factors affecting
students’ academic performance. Global
Journal of Management and Business
Research. 2012;12(9):17-22.
5. Crosnoe R, Johnson MK, Elder GH. School
size and the interpersonal side of education: An
examination of race / ethnicity and
organizational context. Social Science
Quarterly. 2004;85(5):1259-1274.
6. McCoy LP. Effect of demographic and
personal variables on achievement in eighth
grade algebra. Journal of Educational Research.
2005;98(3):131-135.
7. Roberts GA. The effect of extracurricular
activity participation in the relationship
between parent involvement and academic
performance in a sample of third grade children
Available:https://www.lib.utexas.edu/etd/d/200
7/ robertsg11186/robertsg 11186.pdf 2007
8. Hijazi ST, Naqvi SMMR. Factors affecting
students’ performance: A case of private
colleges. Bangladesh e-Journal of Sociology.
2006;3(1):1-10.
9. Farooq MS, Chaudhry AH, Shafiq M, Berhanu
G. Factors affecting students' quality academic
performance: A case of secondary school level.
Journal of Quality and Technology
Management. 2011;VII(III):01-14.
10. Erdogan Y, Bayram S, Deniz L. Factors that
influence academic achievement and attitudes
in web based education. International Journal
of Instructional Media. 2008;1(1):31-48.
11. Sanders DW, Morrison-Shetlar AI. Student
attitudes toward web-enhanced instruction in
an introductory biology course. Journal of
Research on Computing in Education. 2001;
33(3):251-262.
12. Alomyan H, Au W. Exploration of
instructional strategies and individual
difference within the context of web-based
learning. International Education Journal.
2004;4(4):86-92.
13. Ginsburg C, Richter LM, Fleisch B, Norris SA.
An analysis of associations between residential
and school mobility and educational outcomes
in South African urban children: The Birth to
Twenty cohort. International Journal of
Educational Development. 2010;1-10.
14. Wood D, Halfon N, Scarlata D, Newacheck P,
Nessim S. Impact of family relocation on
children’s growth, development, school
function, and behaviour. The Journal of the
American Medical Association. 1993;270(11):
1334-1338.
15. Simpson GA, Fowler MG. Geographic
mobility and children’s emotional / behavioral
adjustment and school functioning. Pediatrics.
1994;93(2):303-309.
16. Ginsburg C, Norris SA, Richter LM, Coplan
DB. Patterns of residential mobility amongst
children in Greater Johannesburg-Soweto,
South Africa: Observations from the Birth to
Twenty cohort. Urban Forum. 2009;20(4):
397-413.
17. Haveman R, Wolfe B, Spaulding J. Childhood
events and circumstances influencing high

Okwonu; JOBARI, 15(4): 280-286, 2016
286
school completion. Demography. 1991;28(1):
133-157.
18. Ingersoll GM, Scamman JP, Eckerling WD.
Geographic mobility and student achievement
in an urban setting. Educational Evaluation and
Policy Analysis. 1989;11(2):143-149.
19. Newman-Ford L, Lloyd S, Thomas S. An
investigation in the effects of gender, prior
academic achievement, place of residence, age
and attendance on first-year undergraduate
attainment. Journal of Applied Research in
Higher Education. 2009;1(1):13-28.
20. Heinlein LM, Shinn M. School mobility and
student achievement in an urban setting.
Psychology in the Schools. 2000;34(4):349-
357.
21. Mlambo V. An analysis of some factors
affecting student academic performance in an
introductory biochemistry course at the
University of the West Indies. Caribbean
Teaching Scholar. 2011;1(2):79-92.
22. Heinesen E. Estimating class-size effects using
within-school variation in subject-specific
classes. The Economic Journal. 2010.
120(545):737-760.
23. Diaz AL. Personal, family, and academic
factors affecting low achievement in secondary
schools. Electronic Journal of Research in
Educational Psychology and Psychopedagogy.
2003;1(1):43-66.
24. Aripin R, Mahmood Z, Rohaizad R, Yeop U,
Anuar M. Students’ learning styles and
academic performance. 22
nd
Annual SAS
Malaysia Forum, 15
th
July 2008, Kuala Lumpur
Convention Center, Kuala Lumpur, Malaysia;
2003.
25. Devadoss S, Foltz J. Evaluation of factors
influencing students attendance and
performance. American Journal of Agricultural
Economics. 1996;78(3):499-507.
26. Anderson G, Benjamin D. The determinants of
success in university introductory economics
courses. Ournal of Economic Education. 1994;
25(2):99-119.
27. Romer D. Do students go to class? Should
they? Journal of Economic Perspectives. 1993;
7(3):167-174.
28. Siegfried J, Fels R. Research on teaching
college economics: A survey. Journal of
Economic Literature. 1979;17(3):923-939.
29. Blimling GS. A meta-analysis of the influence
of college residence halls on academic
performance. Journal of College Student
Development. 1999;40:551-561.
30. Schrager RH. The impact of living group social
climate on student academic performance.
Research in Higher Education. 1986;25:
265-276.
31. Cutrona CE, Cole V, Colangelo N, Assouline
SG, Russell D. Perceived parental social
support and academic achievement: an
attachment theory perspective. Journal of
Personality and Social Psychology. 1994;66:
369-378.
32. Adetunde AI, Asare B. Comparative
performance of day and boarding students in
secondary school certificate mathematics
examinations: A case study of Kasena-Nankana
and Asuogyaman districts of Ghana Academia
Arena. 2009;1(4):7-10.
33. Michael RS. Chi-square as an index of
association; 2010.
Available:http://seamonkey.ed.asu.edu/~alex/c
omputer/sas/df.html
34. Albrecht C. Contingency table and the chi
square statistics: Interpreting computer
printouts and constructing tables; 2013.
Available:http://extension.usu.edu/evaluation/fi
les/uploads
35. Okwonu FZ. Analyzing the cause of vehicular
breakdown using contingency table: A
preliminary study on DBS/Core Area and
Okpanam, Asaba, Delta State, Nigeria. Asian
Journal of Mathematics and Computer
Research. 2015;4(3):164-168.
36. Ingersoll GM. Analysis of 2x2 contingency
tables in educational research and evaluation.
International Journal for Research in
Education. 2010;27:1-14.
37. Onchiri S. Conceptual model on application of
chi-square test in education and social sciences.
Educational Research and Reviews. 2013;
8(15):1231-1241.
38. Yates F. Contingency table involving small
numbers and the χ2 test. Supplement Journal
of the Royal Statistical Society. 1934;1(2):
217-235.
39. Howell DC. Chi square test- analysis of
contigency tables; 2012.
Available:http://www.uvm.edu/~dhowell/meth
ods7/Supplements/ChiSquareTests.pdf
40. Fleiss JL, Levin B, Paik MC, ed. Statisyical
methods for rates and proportions. 3
rd
ed. John
Wiley & Sons Inc: Hoboken, NJ; 2003.
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