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{\plain \f1 A QUESTION OF PRIORITIES:\par
}{\plain \f1 \par
}\pard \qc\sl0 
{\plain \f1 ATHLETIC BUDGETS AND ACADEMIC PERFORMANCE\par
}\pard \sl0 
{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}\pard \qc\sl0 
{\plain \f1 Kenneth J. Meier\par
}\pard \sl0 
{\plain \f1 \par
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{\plain \f1 Texas A&M University\par
}\pard \sl0 
{\plain \f1 \par
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{\plain \f1 Scott Robinson\par
}\pard \sl0 
{\plain \f1 \par
}\pard \qc\sl0 
{\plain \f1 Texas A&M University\par
}\pard \sl0 
{\plain \f1 \par
}\pard \qc\sl0 
{\plain \f1 J. L. Polinard\par
}\pard \sl0 
{\plain \f1 \par
}\pard \qc\sl0 
{\plain \f1 University of Texas-Pan American\par
}\pard \sl0 
{\plain \f1 \par
}\pard \qc\sl0 
{\plain \f1 Robert D. Wrinkle\par
}\pard \sl0 
{\plain \f1 \par
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{\plain \f1 University of Texas-Pan American\par
}\pard \sl0 
{\plain \f1 \par
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}\pard \qc\sl0 
{\plain \f1 The Texas Educational Excellence Project\par
}{\plain \f1 \par
}{\plain \f1 January 2000\par
}\pard \sl0 
{\plain \f1 \par
}{\plain \f1 A joint project of the Department of Political Science and the George Bush School of Government and 
Public Service, Texas A&M University\par
}{\plain \f1 Visit the TEEP website at http://people.tamu.edu/~kmeier/teep/ \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}{\plain \f1 \par
}\pard\page 
\pard \qc\sl0 
{\plain \f1 A Question of Priorities:\par
}{\plain \f1 \par
}{\plain \f1 Athletic Budgets and Athletic Performance\par
}\pard \sl0 
{\plain \f1 \par
}{\plain \f1 \par
}\pard \qc\sl480 
{\plain \f1 Abstract\par
}\pard \sl480 
{\plain \f1 \par
}{\plain \i\f1 Objective}{\plain \f1 .  Many organization theories suggest that divergent goals can hamper an organization\'92s 
pursuit of its primary mission.  This study analyzes the effect of the pursuit of divergent goals on 
American public schools.  }{\plain \i\f1 Methods}{\plain \f1 .  Using an educational production function, this paper assesses the 
relationships between athletic budgets and various aggregate measures of academic performance.  
}{\plain \i\f1 Results}{\plain \f1 .  Controlling for various known components of academic performance, athletic budgets have a 
significant negative relationship with academic performance.  }{\plain \i\f1 Conclusions}{\plain \f1 .  Schools that devote a large 
amount of resources to athletic budgets have lower levels of academic achievement.  A focus on 
athletics seems to institutionalize goals that conflict with the schools\'92 academic missions.  \par
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\pard \qc\sl480 
{\plain \f1 }{\plain \b\f2 }{\plain \f2 }{\plain \b\f1 A Question of Priorities:\par
}{\plain \b\f1  Athletic Budgets and Academic Performance}{\plain \f1 \par
}\pard \sl480 
{\plain \f1 \tab American schools are busy places.  They do much more than provide the basics of a K-12 
education for their students.  They are a source of civic pride, a major agent of political socialization, 
and the location for an almost unlimited number of extra-curricular activities.  Chief among these extra-curricular activities, for many schools, is the athletic program. A person might be excused if, upon 
visiting a school, he or she concluded that the primary objective of the school system was to field an 
athletic team (Coleman 1961).  Clearly, the American public educational system is an example of an 
organization that pursues multiple and often divergent goals (See Tyack 1985).  How schools integrate 
academics and athletics, therefore, can tell a great deal about how organizations deal with multiple goals 
(Simon 1947; Rainey 1997; Meier, Wrinkle and Polinard 1999).  \par
}{\plain \f1 \tab Extra-curricular activities have a long and storied justification in education.  Some have even 
argued that battles have been won on athletic playing fields where character and teamwork are taught. 
More narrowly, extra-curricular activities within the school system are perceived to have a positive 
academic impact on students.  A considerable body of research indicates that student participation in 
such extra-curricular activities as athletics minimizes delinquency (Landers and Landers 1978), mitigates 
dropouts (McNeal 1995) and has a positive effect on student achievement (Otto and Alwin 1977, 
Rehberg and Schafer 1968, Spreitzer and Pugh 1973).  This positive impact has long been a 
justification for the expenditure of a great deal of time and public money by school districts, students, 
and communities.  In fact, the emphasis in many schools resembles a mania over certain sports 
(Bissinger 1991).  }{\plain \f1 \par
}{\plain \f1 \tab Even if such participation has a positive impact for the participating students, however, the 
impact of athletics on academic performance of the }{\plain \ul\f1 entire}{\plain \f1  school remains an open question.  Students 
not on athletic teams may eschew studies for pep rallies and games.  Nonathletes may resent attention 
paid to athletics and become alienated or worse (e.g., Littleton Colorado).  If devotion to athletics 
competes with a school\'92s traditional academic mission, then athletic programs might have a negative 
impact on the academic skills of the school as a whole.\par
}{\plain \f1 \tab The question, theoretically, is how schools deal with the potential goal conflict. If athletics 
competes with traditional education missions of the schools, how do these organizations balance 
athletics and academics?  What, if any, are the academic implications of attention to athletics?  \par
}{\plain \f1 \tab Organizations that simultaneously pursue divergent goals are likely to be ineffective and may fail 
to attain either goal (Downs 1967; Rainey 1997).  Because schools are open systems responsive to 
environmental demands (Thompson 1967), multiple demands can often cause an organization to over-stretch its resources. The existence of multiple goals (athletics/academics) suggests the possibility of 
goal conflict within the organization and raises the possibility of tradeoffs--focusing on one goal while 
ignoring or downgrading others. \par
}{\plain \f1 \tab Goal conflict can endanger achievement of traditional organizational goals in two ways.  First, 
the pursuit of conflicting goals can force an organization to divert resources from traditional uses, in this 
case from academic programs to athletic programs.  Second, the inclusion of multiple organizational 
goals can inculcate conflicting values.  Establishing organizational values is a primary purpose of 
organizational leadership (Barnard 1938).  Organization leaders influence the values of organizational 
participants by shaping (institutionalizing) norms, structures, and processes within the organization 
(Selznick 1957; March and Olsen 1989).  Institutionalizing athletics may signal students that athletics 
are as important as or more important than academic achievement.  This symbolic devaluation of 
academics among students may make the education mission of the school more difficult to attain. \par
}{\plain \f1 \tab This research addresses whether athletics and academics are divergent goals and what effect 
divergence has on an organization.  Do expenditures on athletic budgets within a school district have an 
impact, either positive or negative, on over-all academic performance of that district?  \par
}\pard \sl480 
{\plain \b\f1 \tab Data and Methods}{\plain \f1 \par
}\pard \sl480 
{\plain \f1  \tab Our data come from Texas school districts covering the years 1997-1998.  Texas is an 
excellent environment to study the tradeoffs between athletics and educational attainment.  Texas is well 
known as a state which is fanatical about athletics in general and football in particular (Bissinger 1991; 
Gent 1973; Jenkins 1972).  Many school districts scheme and plot ways to keep from adding 
additional high schools and thus "diluting the talent" among multiple football teams.  High school students 
are sometimes \'93red shirted;\'94{\super \chftn {\footnote \pard \sa240 
{\plain \super\f1 \chftn }{}{\plain      }{\plain \super }{\plain \super\f1 }{\plain \f1 Red shirting is the practice of having a student repeat a year in school so that is he able to 
mature for another year and thus have a physical advantage in high school football.  Because high 
school rules prohibit such activities, a student is usually red shirted by repeating eight grade.}}}
}{\plain \f1  and spring, and now unofficial summer, football practices are virtually 
universal. }{\plain \i\f1 Friday Night Lights}{\plain \f1  describes the Texas football hysteria well.  To cite a well-known 
phrase, in some Texas school districts it is important to have a school that the football team can be 
proud of.\par
}{\plain \f1 \tab Our analysis includes all districts with a minimum of 1,000 students.  Data for 1997 and 1998 
exist for athletic budgets.   The number of cases will vary because not all schools report the full array of 
performance indicators used, but even this restriction leaves approximately 700 cases for analysis.\par
}{\plain \b\f1 Dependent Variables\par
}{\plain \f1 \tab Athletic programs could affect several dimensions of school performance.  The first, and most 
obvious, dependent variable is student attendance.  Attendance is an indirect performance measure 
simply because students who are not in class cannot learn.  Our measure is the mean percentage of 
students who attend class on any given day. \par
}{\plain \f1 \tab The second performance indicator taps basic skills.  Similar to many other states, Texas uses 
annual standardized tests as a means of measuring achievement.  Currently, Texas requires students in 
several grades to take the Texas Assessment of Academic Skills [TAAS].  The percentage of students 
who pass these tests in each school district is our measure of basic skills attainment.\par
}{\plain \f1 \tab In addition to basic skills and attendance, we include a third group of indicators to measure 
upper-level academic performance.  We include average scores for the district on the SAT and ACT 
exams.  Students who are considering going on to college take one or both of these examinations.  As 
such, these scores, unlike the TAAS, should reflect the upper end of student achievement in the district. 
We also use the percent of students who take the two exams as an indicator of students with college 
aspirations.  As the final indicator of quality education, we include the percentage of students who score 
above 1000 on the SAT or its equivalent on the ACT.\par
}{\plain \b\f1 Independent Variables}{\plain \f1 \par
}{\plain \f1 \tab The traditional method of examining research questions of this sort is with an education 
production function (Burtless 1996; Lockwood and McLean 1997). We follow this tradition and 
construct a regression that includes measures of environmental constraints, resources applied to the 
process, and organization policies designed to improve student performance. \par
}{\plain \f1 \tab As our basic research question involves the impact of athletics upon performance, we use the 
per student athletic budget of the districts as our primary independent variable.  This variable (mean 
$132, standard deviation 62) is highly skewed by some districts that spend a great deal on athletics, so 
it is subjected to a log transformation.  Our first hypothesis is that the relationship between athletic 
expenditures and student performance is positive (Otto and Alwin 1977, Rehberg and Schafer 1968, 
Spreitzer and Pugh 1973).  This hypothesis assumes that the individual level positive effect translates 
into a district-wide effect.  The alternative hypothesis, based on the idea of goal conflict, is that the 
relationship will be negative.\par
}{\plain \f1 \tab Our other independent variables are a host of control variables designed to ensure that the 
relationship, if any, between athletic budgets and student performance is not spurious.  Chief among 
these are the percentage of black and Latino students in the school.  Minority students do less well than 
non-minority students on measures of academic performance (Jencks and Phillips 1998; Meier and 
Stewart 1991). Controlling for the percentage of minority students in the school district adjusts for this 
fact.\par
}{\plain \f1 \tab Poverty must also be included as a control variable. As many commentators have noted, 
poverty is a serious constraint on students\'92 ability to learn. It not only means students lack access to 
learning tools in the home (e.g., computers), but it often correlates with other major problems that affect 
learning (e.g., unstable families, Necochea and Cune 1996; Fuller, et. al. 1996).  Our measure of 
poverty is the percentage of students from low income families, measured as eligibility for free or 
reduced price school lunches.  We expect relationships to performance will be negative.\par
}{\plain \f1 \tab Controlling for total per student expenditures will ensure that any relationship between athletic 
budgets and academic performance is not an artifact of an ample district budget.  The conventional 
wisdom, expressed by Hanushek (1996), contends that the relationship between money and student 
outcomes is not consistently positive.  This finding has been challenged recently by several studies 
(Hedges and Greenwald 1996; Lockwood and McLean 1997; Murray 1995; and Evans, Murray, and 
Schwab 1997). \par
}{\plain \f1 \tab Our precise expenditure variables are state aid and instructional funds per pupil.  State aid is 
meant to compensate for an inadequate local tax base.  Instructional funds are used to tap the focus on 
academics, rather than spending for buildings or other uses.  We expect both relationships to be 
positive.\par
}{\plain \f1 \tab Successful schools produce successful students and controlling for this learning environment is 
important.  We do so by using a number of measures: class size, gifted class enrollments, attendance 
(when it is not a dependent variable), and three teacher variables--salary, percent of non-certified 
teachers and average experience of teachers.\par
}{\plain \f1 \tab Class size, the number of students per teacher in the district, should be positively related to 
student performance (see Hedges and Greenwald 1996; Hanushek 1996, 54) as should gifted class 
enrollments (percent of students in) and attendance.  We expect positive correlations for the average 
teacher salary (Hanushek and Pace 1995) and experienced teachers (average number of years 
teaching) and negative relationships for the percentage of noncertified teachers.\par
}\pard \qc\sl480 
{\plain \f1 }{\plain \b\f1 Findings}{\plain \f1 \par
}\pard \sl480 
{\plain \f1 \pard\page \tab The results for class attendance are found in Table 1.{\super \chftn {\footnote \pard \sa240 
{\plain \super\f1 \chftn }{}{\plain      }{\plain \super }{\plain \f1 An examination of the residuals from ordinary least squares indicated that the errors were 
hetroscedastic, or nonconstant.  As a result, OLS estimates are not robust (Berry and Feldman 
1985,77).  To overcome these problems and provide estimates that are more robust, we apply iterative 
weighted least squares (Krasker 1988, Rubin 1983).  Specifically, we used the sine estimates approach 
of David Andrews (1974, 523), which generates coefficients that are "resistant to gross deviations of a 
small number of points and relatively efficient over a broad range of distributions."  When the data meet 
the error assumptions of ordinary least squares, this technique produces estimates identical to the 
ordinary least squares estimates.}}}
}{\plain \f1   Because our concern is the relationship 
between athletic budgets and student performance rather than estimating a full blown education 
production function, we will not discuss independent variables other than athletic expenditures.{\super \chftn {\footnote \pard \sa240 
{\plain \super\f1 \chftn }{}{\plain \f1 }{\plain \f1 The independent variables are highly collinear; as a result some may not be significant and 
others might be incorrectly signed.  Since our concern is having sufficient controls rather than precise 
estimates of each coefficient, this should not be a problem.  }}}
}{\plain \f1   They 
are included only as controls to make sure that any relationship between athletic expenditures and 
performance is not spurious.  While athletic expenditures are positively correlated with student 
attendance, the relationship does not meet traditional levels of statistical significance. The results are 
most consistent with the null hypothesis of no relationship between athletic budgets and overall class 
attendance.\par
}\pard \qc\sl480 
{\plain \f1 [Table 1 about here]\par
}\pard \sl480 
{\plain \f1 \tab Even without affecting class attendance, athletics can influence student performance on basic 
skills exams.  Students, for example, must pass classes to participate in athletics. Table 2 reveals that 
the relationship between athletic expenditures and student performance on the TAAS is }{\plain \ul\f1 negative}{\plain \f1 .  
Although the relationship is not large, it supports the position of those who argue that athletics detract 
from the basic academic mission of the public schools. All other things being equal, athletic budgets can 
have a maximum impact of approximately 4 percentage points on the TAAS.{\super \chftn {\footnote \pard \sa240 
{\plain \super\f1 \chftn }{}{\plain \f1 }{\plain \f1 The maximum impact is calculated by using the change in the dependent variable given the full 
range of the independent variable.  }}}
}{\plain \f1   While this is not an 
exceptionally large impact, the TAAS measures basic skills and is the performance indicator least likely 
to be affected by a focus on athletics.  After all, students who fail the TAAS are not likely to be eligible 
to participate in athletic activities.\par
}\pard \qc\sl480 
{\plain \f1 [Table 2 About Here]\par
}\pard \sl480 
{\plain \f1 \tab Table 3 examines the relationships between college entrance exam scores and athletic budgets.  
In both cases a strong negative relationship exists between athletic budgets and student performance on 
SAT and ACT exams.  All other things being equal, athletic budgets can have a maximum impact of 45 
points on the SAT or 1.2 points on the ACT.  These are substantively large impacts.  Table 4 extends 
this analysis to the percent of students taking these tests (and thus aspiring to attend college) and the 
percentage of students meeting the SAT 1000+ criterion.  Again in both cases, school districts with 
larger athletic budgets also have student bodies that are less likely to take college admissions tests and 
less likely to score highly on these tests.  The maximum impact is a 15 percentage point reduction in 
taking the test and a 17 percentage point reduction in exceeding the 1000+ standard.  The percentage 
of students taking the test is important since test scores often drop as an increased number of students 
take the exam.  Schools with large athletic budgets not only have fewer students taking college entrance 
exams, but those students who take the exams score substantially lower on them.  All four relationships 
in tables 3 and 4 are consistent with the argument that athletics is negatively linked to student 
performance rather than positively linked.\par
}\pard \qc\sl480 
{\plain \f1 [Tables 3 and 4 About here]\par
}{\plain \b\f1 Conclusions}{\plain \f1 \par
}\pard \sl480 
{\plain \f1 \tab The existing literature on the impact of athletics on student performance has concentrated on 
individual level analyses.  This level of analysis has found a positive relationship between athletics and 
student performance.  When the analysis is moved to the district level, however, a different finding 
results.  The results of the analysis above clearly indicate that over-all student performance is reduced 
by expenditures on athletics.  This finding has been consistent through every performance measure 
tested\'96-TAAS, SAT, ACT, aspirations (percent taking the test) and quality on SAT/ACT exams.  We 
found no evidence of a relationship between athletic budgets and student attendance.\par
}{\plain \f1 \tab These findings in combination with the individual level findings suggest that athletics while 
positive for the individuals who participate may have negative consequences for those who do not.  
Only such negative externalities would generate negative relationships at the district level if the 
relationship at the individual level remained positive.\par
}{\plain \f1 \tab In short, a concentration of school districts on athletics appears to undermine an essential goal 
of the organization.  Coleman (1961) suggested that the impressionistic view of American schools was 
that athletics were primary and academics secondary.  These results corroborate his impression.  
Where school districts spend more on athletics, academic performance is lower.\par
}{\plain \f1 \tab Our findings here cover only a single state and a state with a fanatical devotion to high school 
athletics.  Without additional research in other educational environments, however, we cannot be sure 
that these results will be corroborated.  Even limited, the findings are supported by basic organization 
theory and, thus, suggest additional research on the impact of athletic expenditures on the overall 
academic performance of a school district is merited.\par
}\pard \qc 
\pard\page {\plain \b\f3 TABLE 1: The Impact of Athletics on Student Attendance:\par
}{\plain \b\f3 \par
}{\plain \b\f3 Dependent Variable = Average Daily Attendance\par
}\pard 
{\plain \b\f3 \par
}{\plain \b\f3 \par
}{\plain \b\ul\f3 Variable             Coefficient             T-score}{\plain \ul\f3 \par
}{\plain \f3 \par
}{\plain \f3 Athletic Expenditures\par
}{\plain \f3     (logged)             .0289                1.28\par
}{\plain \f3 \par
}{\plain \ul\f3 Control Variables}{\plain \f3 \par
}{\plain \f3 \par
}{\plain \f3 Percent Black           -.0095                5.10\par
}{\plain \f3 \par
}{\plain \f3 Percent Latino          -.0006                 .42\par
}{\plain \f3 \par
}{\plain \f3 Low Income              -.0169                8.47  \par
}{\plain \f3 \par
}{\plain \f3 Gifted                  -.0047                 .80                     
                               \par
}{\plain \f3 Teacher Salary K         .0275                1.87  \par
}{\plain \f3 \par
}{\plain \f3 Class size              -.0898                4.42 }{\plain \f3 \par
}{\plain \f3 \par
}{\plain \f3 Teacher Non\par
}{\plain \f3 Certification           -.0059                 .85 \par
}{\plain \f3 \par
}{\plain \f3 Teacher Experience      -.0385                2.69\par
}{\plain \f3 \par
}{\plain \f3 State Aid                .0034                3.37\par
}{\plain \f3 \par
}{\plain \f3 Instructional Funds (k)  .2050                2.27 \par
}{\plain \f3 \par
}{\plain \f3 1998 Dummy              -.0234                 .62\par
}{\plain \f3 \par
}{\plain \ul\f3                                                      }{\plain \f3 \par
}{\plain \f3 \par
}{\plain \f3 R-Square          = .37\par
}{\plain \f3 \par
}{\plain \f3 Adjusted R-Square = .36\par
}{\plain \f3 \par
}{\plain \f3 F=                34.68\par
}{\plain \f3 \par
}{\plain \f3 Standard error      .39\par
}{\plain \f3 \par
}{\plain \f3 N                729\par
}{\plain \f3 \par
}{\plain \f3 Iterative reweighted least squares estimates using Andrews Sine, one 
iteration.\par
}{\plain \par
}\pard \qc 
\pard\page {\plain \b TABLE 2: The Impact of Athletics on Student Performance:\par
}{\plain \b Basic Skills\par
}{\plain \b \par
}{\plain \b Dependent Variable = TAAS Scores\par
}\pard 
{\plain \b }{\plain \par
}{\plain \par
}{\plain \ul Variable             Coefficient             T-score\par
}{\plain \par
}{\plain Athletic Expenditures\par
}{\plain     (logged)            -.4159                2.26\par
}{\plain \par
}{\plain \ul Control Variables}{\plain \par
}{\plain \par
}{\plain Percent Black           -.2086               14.67\par
}{\plain \par
}{\plain Percent Latino          -.0926                8.21\par
}{\plain \par
}{\plain Low Income              -.1625               10.28  \par
}{\plain \par
}{\plain Gifted                   .1616                3.34 \par
}{\plain \par
}{\plain Attendance              2.2909               11.13 \par
}{\plain \par
}{\plain Teacher Salary K         .3375                2.77  \par
}{\plain \par
}{\plain Class size              -.1300                 .81 }{\plain \f3 \par
}{\plain \f3 \par
}{\plain \f3 Teacher Non\par
}{\plain \f3 Certification           -.1859                3.54 \par
}{\plain \f3 \par
}{\plain \f3 Teacher Experience       .1927                1.75\par
}{\plain \f3 \par
}{\plain \f3 State Aid               -.0169                2.10 \par
}{\plain \f3 \par
}{\plain \f3 Instructional Funds (k) -.2560                 .35 \par
}{\plain \f3 \par
}{\plain \f3 1998 Dummy              3.9561               13.28\par
}{\plain \ul\f3                                                      }{\plain \f3 \par
}{\plain \f3 \par
}{\plain \f3 R-Square          = .80\par
}{\plain \f3 \par
}{\plain \f3 Adjusted R-Square = .79\par
}{\plain \f3 \par
}{\plain \f3 F=               217.18\par
}{\plain \f3 \par
}{\plain \f3 Standard error     3.02\par
}{\plain \f3 \par
}{\plain \f3 N                729\par
}{\plain \f3 \par
}{\plain \f3 Iterative reweighted least squares estimates using Andrews Sine, one 
iteration.\par
}\pard \qc 
\pard\page {\plain \b\f3 TABLE 3: The Impact of Athletics on Student Performance:\par
}{\plain \b\f3 College Board Scores\par
}{\plain \b\f3 \par
}{\plain \b\f3 Dependent Variable = \par
}\pard 
{\plain \b\f3                          ACT Scores        SAT Scores\par
}{\plain \b\ul\f3 Variable                 Slope     t       Slope     t      }{\plain \ul\f3 \par
}{\plain \f3 \par
}{\plain \f3 Athletic Expenditures\par
}{\plain \f3     (logged)            -.1214    3.03    -5.1129   2.70\par
}{\plain \f3 \par
}{\plain \ul\f3 Control Variables}{\plain \f3 \par
}{\plain \f3 \par
}{\plain \f3 Percent Black           -.0223    7.37     -.4758   3.22\par
}{\plain \f3 \par
}{\plain \f3 Percent Latino          -.0160    6.95     -.0706    .61\par
}{\plain \f3 \par
}{\plain \f3 Low Income              -.0324   10.12    -1.6183   9.94\par
}{\plain \f3 \par
}{\plain \f3 Gifted                  -.0047     .48     2.8996   6.40\par
}{\plain \f3 \par
}{\plain \f3 Attendance               .0467    1.19     2.2874   1.17\par
}{\plain \f3 \par
}{\plain \f3 Teacher Salary K        -.0029     .12      .6462    .54\par
}{\plain \f3 \par
}{\plain \f3 Class size              -.0671    2.10    -6.4090   4.06\par
}{\plain \f3 \par
}{\plain \f3 Teacher Non\par
}{\plain \f3 Certification           -.0414    3.99    -2.5256   4.68\par
}{\plain \f3 \par
}{\plain \f3 Teacher Experience       .0209     .97     2.3509   2.18\par
}{\plain \f3 \par
}{\plain \f3 State Aid               -.0095    5.66     -.5179   6.28\par
}{\plain \f3 \par
}{\plain \f3 Instructional Funds (k) -.0680     .45     -.0208   2.88\par
}{\plain \f3 \par
}{\plain \f3 1998 Dummy              -.0145     .24     2.9797   1.01\par
}{\plain \ul\f3                                                      }{\plain \f3 \par
}{\plain \f3 \par
}{\plain \f3 R-Square          =      .80                .62\par
}{\plain \f3 \par
}{\plain \f3 Adjusted R-Square =      .79                .61\par
}{\plain \f3 \par
}{\plain \f3 F                 =   146.15              85.39\par
}{\plain \f3 \par
}{\plain \f3 Standard error           .62              29.22\par
}{\plain \f3 \par
}{\plain \f3 N                     722                692\par
}{\plain \f3 \par
}{\plain \f3 Iterative reweighted least squares estimates using Andrews Sine, one 
iteration.\par
}\pard\page 
\pard \qc 
{\plain \f3 }{\plain \b\f3 TABLE 4: The Impact of Athletics on Student Aspirations:\par
}{\plain \b\f3 Going to College and Above Criteria\par
}{\plain \b\f3 \par
}{\plain \b\f3 Dependent Variable = \par
}\pard 
{\plain \b\f3                       Taking the Test     Above Criteria\par
}{\plain \b\ul\f3 Variable                 Slope     t       Slope     t      }{\plain \ul\f3 \par
}{\plain \f3 \par
}{\plain \f3 Athletic Expenditures\par
}{\plain \f3     (logged)           -1.5317    3.62    -1.7624   5.54\par
}{\plain \f3 \par
}{\plain \ul\f3 Control Variables}{\plain \f3 \par
}{\plain \f3 \par
}{\plain \f3 Percent Black           -.0345    1.14     -.0339   1.60\par
}{\plain \f3 \par
}{\plain \f3 Percent Latino           .0633    2.55     -.0154    .95\par
}{\plain \f3 \par
}{\plain \f3 Low Income              -.3109    9.14     -.2898  12.68\par
}{\plain \f3 \par
}{\plain \f3 Gifted                   .5688    5.70      .4095   6.22\par
}{\plain \f3 \par
}{\plain \f3 Attendance              3.1180    7.59      .4167   1.52\par
}{\plain \f3 \par
}{\plain \f3 Teacher Salary K         .2626    1.02     -.2232   1.29\par
}{\plain \f3 \par
}{\plain \f3 Class size              -.6282    1.86      .0508    .23}{\plain \f3 \par
}{\plain \f3 \par
}{\plain \f3 Teacher Non\par
}{\plain \f3 Certification           -.0417     .39     -.3638   5.23\par
}{\plain \f3 \par
}{\plain \f3 Teacher Experience       .5474    2.40      .2070   1.37\par
}{\plain \f3 \par
}{\plain \f3 State Aid                .0139    5.66     -.0868   7.33\par
}{\plain \f3 \par
}{\plain \f3 Instructional Funds (k) 3.0920    1.96     -.0090    .01\par
}{\plain \f3 \par
}{\plain \f3 1998 Dummy             -1.6946    2.67      .8130   1.96\par
}{\plain \ul\f3                                                      }{\plain \f3 \par
}{\plain \f3 \par
}{\plain \f3 R-Square          =      .44                .72\par
}{\plain \f3 \par
}{\plain \f3 Adjusted R-Square =      .43                .71\par
}{\plain \f3 \par
}{\plain \f3 F                 =    42.01             139.89\par
}{\plain \f3 \par
}{\plain \f3 Standard error          6.46               4.26\par
}{\plain \f3 \par
}{\plain \f3 N                     728                729\par
}{\plain \f3 \par
}{\plain \f3 Iterative reweighted least squares estimates using Andrews Sine, one 
iteration.\par
}\pard\page 
\pard \qc 
{\plain \f1 }{\plain \b\f1 References}{\plain \f1 \par
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{\plain \f1 \par
}\pard \fi-360\li360\tx0\tx360\tx720\tx1080\tx1440\tx1800\tx2160\tx2520\tx2880\tx3240\tx3600\tx3960\tx4320\tx4680\tx5040\tx5400\tx5760\tx6120\tx6480 
{\plain \f1 Spreitzer, Elmer and Meredith Pugh. 1973.  "Interscholastic Athletics and Educational Expectations," 
}{\plain \i\f1 Sociology of Education}{\plain \f1  45 (Spring): 171-182.\par
}\pard \tx0\tx360\tx720\tx1080\tx1440\tx1800\tx2160\tx2520\tx2880\tx3240\tx3600\tx3960\tx4320\tx4680\tx5040\tx5400\tx5760\tx6120\tx6480
{\plain \f1 \par
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{\plain \f1 Thompson, James D.  1967.  }{\plain \i\f1 Organizations in Action}{\plain \f1 .  New York: McGraw-Hill.\par
}\pard \tx0\tx360\tx720\tx1080\tx1440\tx1800\tx2160\tx2520\tx2880\tx3240\tx3600\tx3960\tx4320\tx4680\tx5040\tx5400\tx5760\tx6120\tx6480
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{\plain \f1 Wenglinsky, Harold.  1997.  }{\plain \i\f1 How Education Expenditures Improve Student Performance and 
How They Don't}{\plain \f1 .  Princeton: Educational Testing Service.\par
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