Expenditures and Achievement
Running header: EXPENDITURES AND ACHIEVEMENT
Expenditures and District Characteristics on Student Achievement in North Carolina
Jamila Jones Kennedy
George Mason University, Fairfax, Virginia
1
Expenditures and Achievement
2
Background and Purpose of the Study
There has been much debate in the literature on the impact of geographic location
on student achievement. Many educators, state board of education members, legislators,
and the general public believe that students from smaller and rural schools receive an
education that is inferior to that of students from larger urban or suburban schools.
Several studies have not found any significant differences between the two groups. In
research completed in the state of New York, Monk and Haller (1986) found that students
from smaller (often rural) schools achieved as well as students from larger schools.
Moreover, in one New Mexico study, which looked at factors affecting performance of
selected high school students, those attending schools in rural areas performed as well as
those in urban locales (Ward and Murray, 1985). Other scholars have found, however,
that rural-urban differences do exist. One study in Kansas found that the ACT scores of
rural students were two points lower than scores of urban students in each of the
categories on the ACT (Downey, 1980). Another examination of student performance in
Hawaii public schools found sub-standard achievement to be a pattern in rural areas
(McCleery, 1979).
Education spending has also been a hot topic of debate for many years. Taxpayers
often believe that schools receive too much funding and therefore, do not want more of
their tax dollars going towards education spending. Teachers and schools, on the other
hand, often claim that current funds are insufficient to finance necessary school
programs. Public education is a public good financed primarily by state and local
governments. Economic theory views education as an important input to the production
function. In fact, many empirical studies have shown that education provides positive
Expenditures and Achievement
3
returns to society as more education leads to higher productivity and wages (Angrist &
Krueger, 1991; Ashenfelter & Krueger, 1994; Card, 1995). Thus, because of these gains,
the government invests billions of dollars each year in education.
Through the years, it has become a common belief that increasing school funding
will lead to increased student academic achievement. Studies that do look at the impact of
education spending on student academic achievement have found varying results. After
reviewing the current literature, Picus and Robillard (2000) concluded that a clear link
between school spending and student academic achievement fails to exist. In a study of
schools in Austin, Texas, authors Murnane and Levy (1996) also concluded that the
availability of extra resources does not equal greater student achievement. However, after
reviewing 35 years of production function research, Verstegen and King (1998)
concluded that “resource inputs can and do make a difference in students’ educational
outcomes.”
Nyhan and Alkadry (1999) also attempted to answer the question of whether
school funding affects student achievement. The study averaged math, reading, and
writing standardized test scores to create one dependent variable, which was then
regressed on expenditures per student, the percent of students in poverty, and the percent
of minority students. No conclusive results were made. But does funding lead to higher
student performance or is this a misconception so easily believed by the public? Are there
disadvantages for urban or rural students?
The purpose of this paper is to explore the relationships between instructional
expenditures, student achievement, and demographics of school districts in North
Carolina. Specifically, this paper addresses the following research questions: (1) Do total
Expenditures and Achievement
4
expenditures (instruction, student services, per-pupil allocations, administrative,
operations, transportation, business, and other) predict average math scores on the
Scholastic Aptitude Test (SAT) for school districts in North Carolina?, (2) Do North
Carolina school districts' expenditures (current expenditures on instruction and teacher
salaries) and demographics (enrollment, size, poverty, fte, ratio) predict their status on
meeting Adequate Yearly Progress (AYP) proficiency growth in mathematics?, (3) Are
there differences in current expenditure on instruction by type of district (local or charter)
and geographic location (urban, rural, city, town, suburb)?, and (4) Are there differences
among school districts located in various geographic locations (urban, rural, city, town,
suburb) on current expenditures on instruction, student services, teacher salaries, and
other general expenses?
Method
This paper analyzes the effects of certain school district-related expenditures and various
demographics on student achievement in mathematics in school districts in North
Carolina. This paper will focus specifically on the use of the following statistical
procedures—multiple regression, logistic regression, two-factor ANOVA and
MANOVA.
Sample/ Data Description
The sample used in this paper consists of data on school districts in North
Carolina. These data were accessed via the internet and downloaded into spreadsheets for
Expenditures and Achievement
5
analysis. For the purposes of this paper, all data were obtained at the district level for the
2007 fiscal year (or the 2007-08 academic year).
Specifically, district expenditure data were obtained from the U.S. Department of
Education, National Center for Education Statistics, Common Core of Data (CCD), Local
Education Agency (School District) Finance Survey", 2007-08. These data have also
been released as the F-33 survey, which is conducted by the U.S. Census Bureau on
behalf of the National Center for Education Statistics. The primary purpose of the Local
Education Agency (School District) Finance Survey (F-33) is to provide revenue and
expenditure data for all school districts in the United States.
Demographic and school characteristics data were obtained from the U.S.
Department of Education, National Center for Education Statistics, Common Core of
Data (CCD), Local Education Agency (School District) Universe Survey", 2007-08. Its
purpose is to provide basic information about all education agencies and the students for
whose education the agencies are responsible. These data were accessed via the
Elementary/Secondary Information System (ELSi), an NCES web application that allows
users to view public and private school data and create custom tables and charts using
data from the Common Core of Data (CCD).
Additionally, data on poverty level were obtained from the US Census Bureau's
Small Area Income and Poverty Estimates (SAIPE), 2007. SAIPE produces estimates of
median household income for states and counties, and poverty for states, counties, and
school districts. These estimates are based on statistical models that use decennial census
data, household surveys, administrative data, and population estimates.
Expenditures and Achievement
6
Student achievement data were accessed from the North Carolina Department of
Public Instruction's (DPI) website. DPI is the agency charged with implementing the
State's public school laws and the State Board of Education's policies and procedures
governing pre-kindergarten through 12th grade public education. Specifically, the
analyses in this paper utilize average mathematics scores on the SAT Reasoning Test and
data on whether school districts met its Adequate Yearly Progress (AYP) proficiency
growth goal.
Variables
We used a number of variables related to district expenditures in the analyses. The
expenditure amounts are derived from current spending totals and the school district’s fall
membership data. Expenditures do not include spending for non-elementary and
-secondary programs (community service, adult education), or spending by a school
district for students not included in its fall membership counts.
Total Current Spending for Instructional Activities (TCURINST). This
variable is operationalized as the money spent per district for the 2007-2008 school year
on instructional related activities. Instructional expenditures include costs such as
textbooks, classroom supplies, and technological resources.
Total Current Spending for Support Services (TCURSSVC). This variable
includes school district expenditures on academic enrichment programs, after-school
programs, transportation, instructional staff support (e.g., teacher aides), and the
operation and maintenance of the school facility. Within this category, and also used in
the analyses, are INST (spending on instructional services), PUPL (per pupil allocation,
Expenditures and Achievement
GADMN (general administrative expenditures), SADMN (school administrative
expenditures), OPMAN (spending on operations and maintenance), TRANS (spending
on student transportation), and BUSOTH (other business expenses).
Total Current Spending for Other Elementary and Secondary Services
(TCUROTH). This variable includes all other expenditures related to elementary and
secondary districts not accounted for by the above variables. Teacher Salaries (TSAL).
This variable only includes expenditures for base salaries. Benefits and other
compensation were excluded from this analysis. .
We also used additional variables to describe certain characteristics and
achievement information for school districts in North Carolina. Geographic Location
(GEOG). These data were taken from the Common Core of Data Local Education
Agency Universe Survey. This data is a measure of a district’s location relative to
populous areas, and is a composite of the school locale codes, weighted by school
population, associated with the schools in the district’s jurisdiction. There are five
categories (city, urban, town, rural, and suburb).1 Type of District (TYPE). This
variable describes whether or not a school district is a local district (comprised of public
elementary and secondary schools) or a charter district (comprised only of public
elementary and secondary charter schools). Private schools were excluded from this
analysis. Poverty (PVTY). This variable includes the number of students aged 5 - 17
whose households are in poverty. Enrollment (ENRL). This variable describes the
number of students enrolled in each district for the fall portion of the 2007 school year.
Size of District (SIZE). We transformed the enrollment variable, ENRL, to create this
1
None of the school districts in the data were classified as urban. therefore, the analysis in this paper only
analyzes four categories - rural, city, town, and suburb.
7
Expenditures and Achievement
8
variable. For purposes of this paper, all school districts with total student enrollment of
20,000 or more, were classified as "large" districts. All other districts were classified as
"other." Mathematics Achievement (SAT, AYP). In this paper, we use two measures of
student achievement. The SAT Reasoning Test (formerly the Scholastic Aptitude Test or
Scholastic Assessment Test) is a standardized test for college admissions in the United
States. The test is intended to assess a student's readiness for college. Adequate Yearly
Progress, or AYP, is a measurement defined by the United States federal No Child Left
Behind Act that allows the U.S. Department of Education to determine how every public
school and district in the country is performing academically according to results on
standardized assessments. These assessments allow State Education Agencies to develop
target starting goals for AYP. After those are developed, states must increase student
achievement in gradual increments each school year.
Analysis
To explore whether school district predict mathematics achievement, we employ
multiple regression with dependent variable, average math scores on the Scholastic
Aptitude Test (SAT), and predictors, total expenditures on instructional activities
(TCURINST), expenditures on student services (TCURSVC), per-pupil expenditures
(PUPIL), and expenditures on instructional services (INST), general administration
(GADMN), school-related administration (SADMN), operations and maintenance
(OPMAN), business and other services (BUSOTH), and all other expenses (CUROTH).
To determine whether school districts' expenditures and demographics predict their status
on meeting Adequate Yearly Progress (AYP) proficiency growth in mathematics, we
Expenditures and Achievement
9
employ logistic regression with dependent variable, AYP = 1 (met AYP) or AYP = 0 (did
not meet AYP) and independent variables, expenditures on instructional services (INST),
teacher salaries (TSAL), enrollment (ENRL), large vs. other districts (SIZE), number of
students in poverty (PVTY), number of full time teachers (FTE), and student: teacher
ratio (RATIO). To determine if there are differences in current expenditure on
instruction by type of district (local or charter) and geographic location (urban, rural, city,
town, suburb), a two-factor ANOVA is employed with dependent variable, total
expenditures on instructional activities (TCURINST), factor A, geographic location
(GEOG - rural, city, town, suburb) and factor B, type of district (local or charter). We
will also compute effect sizes for the data in order to estimate the practical significance of
any effects. To determine if there are differences among school districts located in
various geographic locations on current expenditures on instruction, student services,
teacher salaries, and other general expenses, we use MANOVA with dependent variable,
geographic location (GEOG - rural, city, town, suburb) and independent variables, total
expenditures on instructional activities (TCURINST), student services (TCURSVC),
teacher salaries (TSAL), and all other expenditures (TCUROTH).
Results
The results of the analyses are discussed below.
Multiple Regression
The results from the multiple regression show that the prediction of SAT mathematics
test scores from current expenditures on instruction, current expenditures on student
Expenditures and Achievement
10
services, per-pupil expenditures, INST, GADMN, SADMN, OPMAN, BUSOTH, and
CUROTH is statistically significant, F(9, 108) = 3.34, p = .001. Further, R2 = .218
indicates that 21.8 percent of the differences in average SAT mathematics test scores are
explained by the differences in the various types of school district expenditures. There is
a statistically significant unique contribution to the prediction of average SAT
mathematics test scores for GADMN (p = .002), and TRANS (p = .010). The multiple
regression equation for predicting average SAT mathematics test scores is
SAT = - 0.000016 (GADMN) – 0.000009 (TRANS) + 494.61.The interpretation of the
regression coefficients indicates that the predicted average SAT mathematics score
decreases by 0.000016 when general administrative expenditures increase by one dollar,
holding other variables constant. Similarly, the predicted average SAT mathematics score
decreases by 0.000009 when expenditures on student transportation increase by one
dollar, holding other variables constant.
As noted previously, each predictor GADMN and TRANS, are statistically
significant at the .05 level. This indicates that each predictor has its own unique
contribution to the explanation of the variance in average SAT mathematics test scores.
The magnitude of the unique explanatory contribution for each predictor is indicated by
the squared value of its part correlation with average SAT mathematics test scores.
Specifically, 6.97 percent of the variance in average SAT mathematics scores is uniquely
accounted for by the variance in GADMN. Likewise, 4.97 percent of the variance in
average SAT mathematics scores is uniquely accounted for by the variance in TRANS.
For these data, GADMN is relatively more important than TRANS for the prediction of
average SAT mathematics test scores.
Expenditures and Achievement
11
In this analyses, we defined a restricted model by removing the 7 predictors that
were not statistically significant, thus obtaining a restricted model with two predictors:
GADMN and TRANS. The results show that the two predictors in the restricted model
(model 1: TRANS, GADMN) do not account for a statistically significant amount of the
variance in SAT math scores, R2 = .024, F(2, 115) = 1.43, p = .243. The change in R2
from Model 1 to Model 2 is statistically significant, R2change = .193, F(7, 108) = 3.81, p =
.001. This indicates that CUROTH, BUSOTH, OPMAN, INST, PUPIL, and CURINST
account for a statistically significant proportion of the variance in SAT math scores over
and above the proportion accounted for by the independent variables in the restricted
model. Since the restricted model and the full model differ in how much variance in SAT
math scores they account for, it was better to use the full model. See enclosures I and II
for the results of the multiple regression analysis.
Logistic Regression
The results in the Omnibus Tests of Model Coefficients indicate that the
prediction of a school district's status on meeting its AYP proficiency goal from current
expenditures on instruction and teacher salaries, as well as district characteristics such as
enrollment, size, poverty level, number of full time teachers and pupil: teacher ratio, is
statistically significant, χ2(7) = 29.94, p < .001.
The results of the Hosmer and Lemeshow goodness-of-fit test show that the chisquare statistic is non-significant, x2(8) = 7.70, p = .46, thus indicating a good fit for the
logistic regression model. The Nagelkerke R2 value is .311, indicating a relatively high
explanatory effect in the prediction of meeting AYP from all 7 predictors together.
Expenditures and Achievement
12
The descriptive information provided in the Classification Table indicates a relatively
good hit rate (74.8%). The sensitivity in this prediction is also relatively high,
60/(60+19)=75.9%, whereas the specificity is somewhat lower, 26/(26+10)=72.2.
Further, the false positive rate is 19/(26+19)=.42 or 42% and the false negative rate is
10/(10+60)=.14 or 14%.
Results from the Wald test indicate that there is one stat sig regression coefficient:
B5 = -0.002 (for students in poverty). The value of the odds ratio for poverty, Exp(B) =
0.998 indicates that the odds of a school district meeting AYP proficiency status
decreases by a factor of .998 (or about 1 time) when the number of students in poverty
increases by one unit, when controlling for all other predictors. Table 1 provides data for
the analysis of school districts' status on meeting AYP. See enclosure III for results of
the logistic regression analysis.
Two-Factor ANOVA
The means and standard deviations for total current expenditures on instruction by
type of district and geographic location are given in enclosure IV. The results from the
Levene’s test indicate that the homogeneity of variance assumption is not met, F(7, 199)
= 40.686, p = .000. Nonetheless, we present the results here. The ANOVA F-test shows
that there is a statistically significant main effect for TYPE, F(1, 199) = 98.10, p = .000,
pη2 = .33, a statistically significant main effect for GEOG, F(3, 199) = 30.71, p = .000,
pη2 = .32, and a statistically significant interaction between TYPE and GEOG, F(3, 199)
= 30.81, p = .000, pη2 = .32, at the .05 level of significance. The partial eta squared (pη2)
measure of effect size for TYPE (.33) indicates that 33 percent of school districts'
Expenditures and Achievement
13
expenditures on instruction is accounted for by gender differences, controlling for the
effects of GEOG and the interaction between TYPE and GEOG. Likewise, the value of
pη2 for GEOG (.32) shows that 32 percent of the differences in school districts'
expenditures on instruction are accounted for by differences among the geographic
locations, controlling for the effects of TYPE and the interaction between TYPE and
GEOG. Also, pη2 = .32 for the interaction effect size shows that the interaction between
TYPE and GEOG accounts for 32 percent of the differences in school districts'
expenditures on instruction, controlling for the effects of TYPE and GEOG. These
results are presented in table 2.
Given the statistically significant main effects for TYPE and GEOG, the Tukey
post hoc method of multiple comparisons was used to determine which groups differ in
average SAT mathematics test scores. The results indicate that, at the .05 level, there is a
statistically significant difference between school districts located in cities and school
districts located in towns (p = .013), but not between rural and city districts (p = .084),
rural and town districts (p = .514), rural and suburb districts (p = .967), city and suburb
districts (p = .703), and town and suburb groups (p = .545). Specifically, the results
provided by the 95 percent confidence interval for the difference between the means of
the two groups indicate that spending on instruction for school districts located in cities
was higher than spending on instruction for school districts located in towns, by a
difference of at least $5,851,745 but no more than $69,383,711 (see table 3). See
enclosure IV for the results of the two-factor ANOVA and Tukey post hoc tests.
Expenditures and Achievement
14
MANOVA
The results from the MANOVA analysis are as follows. Wilk's lambda (Λ = .92)
is statistically significant. Therefore, we can conclude that there is a statistically
significant difference between at least two groups on some linear combination of the
dependent variable. The results from the Eigenvalues table show that that the first linear
discriminant function (LDF1) accounts for 85.5 percent of the total variance for the set of
dependent variables across the groups. The remaining 14.5 percent is accounted for by
the second linear discriminant function (LDF2). The results from the Wilk's Lambda table
indicate that LDF1 is statistically significant, Λ = .92, χ2(6) = 16.37, p = .012. The second
linear discriminate functions, LDF2, is not statistically significant, Λ = .99, χ2(2) = 2.44,
p = .295. The Structure Matrix shows that the dependent variables, TCUROTH,
TCURINST, TCURSVC, correlate with LDF1.
The table Standardized Canonical Discriminant Function Coefficients shows that
INST, TCURSVC and TCUROTH define LDF1 but comparison of their standardized
coefficients shows that the meaning of LDF1 is defined primarily by TCURINST and
TCURSVC because their standardized coefficients , (-5.040 and 7.353, respectively) are
much higher than that for TCUROTH (-1.886). Given that TCURINST and TCURSVC
include expenditures for instruction and student support services, we label LDF1 as
student learning. Therefore, one dimension--student learning--emerged as a linear
combination of the dependent variables that provided the best separation of districts by
geographic location. See enclosure V for the results of the MANOVA analysis.
Expenditures and Achievement
15
Discussion
The issues surrounding efforts to assess the achievement of students are by no
means simple. To really assess school districts' impact on students, comparisons must be
made among students who are matched by origin, background, and access to information
before any meaningful conclusions about achievement can be rendered. Further, an
examination of the extent to which certain school districts have less total access to
educational information would be important to understand the differences in student
achievement in school districts nationwide.
The findings of the analyses may suggest that the district aid formula is successful
in creating equity across the state in how funding impacts achievement. Additionally, this
suggests the school finance funding formula constructs parity in the amount a district is
able to receive in funding per year and thus spend on student instruction. Further, the
results also imply that in North Carolina there is more to student achievement than the
amounts a district spends per pupil. Clearly, poverty plays a role in student achievement,
according to these findings.
Perhaps it is not the dollar amounts that make a difference but how the funds are
used. Possible future research on this issue could examine whether an association exists
between how a district spends it money and its impact on achievement levels. Further,
other kinds of funding, such as family and school resources, could also be examined to
determine if there is a relationship with student outcomes.
Expenditures and Achievement
16
References
Angrist, J. & Krueger, A. (1991). Does compulsory school attendance affect schooling &
earnings? Quarterly Journal of Economics, 106(4), 979-1014.
Ashenfelter, O. & Krueger, A. (1994). Estimates of the economic return to schooling
from a new sample of twins. American Economic Review, 84(5), 1157-1173.
Card, D. (1995). Earnings, schooling & ability revisited. Research in Labor Economics,
14, 23-48.
Dimitrov, D. (2008). Quantitative research in education. Oceanside, NY: Whittier
Publications, Inc.
Downey, R. (1980). Higher education and rural youth. Paper presented at the Kansas
State University Rural and Small Schools Conference, Auburn, AL.
McCleery, M. (1979). Stranger in paradise: Process and product in a district office.
Washington, D.C.: National Institute of Education.
Monk, D. & Haller, E. (1986). Organizational alternatives for small rural schools. Cornell
University: New York State College of Agriculture and Life Sciences, New York.
Murnane, R. J., & Levy, F. (1996). Evidence from fifteen schools in Austin, Texas. In G.
Burtless (Ed.), Does money matter? The effect of school resources on student
achievement and adult success (pp. 93-96). Washington, D.C.: Brookings
Institution Press.
Nyhan, R. C., & Alkadry, M. G. (1999). The impact of school resources on student
achievement test scores. Journal of Education Finance, 25(2), 211-227.
Expenditures and Achievement
17
Picus, L. & Robillard, E. (2000). The collection and use of student level data:
implications for school finance research. Educational Considerations, 28(1), 2631.
Verstegen, D. A., & King, R. A. (1998). The relationship between school spending and
student achievement: A review and analysis of 35 years of production function
research. Journal of Education Finance, 24(2), 243-262.
Ward, A. & Murray, L. (1985). Factors affecting performance of New Mexico high
school students. Paper presented at the Meeting of the Rocky Mountain
Educational Research Association, Las Cruces, NM.
Expenditures and Achievement
18
Tables
Table 1: Logistic Regression Analysis of School Districts' Status on Meeting AYP as a
Function of Expenditures and District Characteristics
________________________________________________________________________
95% Confidence Interval
for Odds Ratio
______________________
Variables
B
Wald χ2
Odds Ratio
Lower
Upper
________________________________________________________________________
INST
.00
.065
1.00
1.00
1.00
TSAL
.00
.258
1.00
1.00
1.00
ENRL
.00
1.770
1.00
1.00
1.00
SIZE
-1.286
1.080
.276
.024
3.125
PVTY
-.002
12.56*
.998
.996
.999
FTE
-.014
1.818
.986
.967
1.006
RATIO
-.389
1.661
.678
.375
1.224
(Constant)
6.049
2.142
_______________________________________________________________________
Note: Wald (df = 1).
*p < .01.
Table 2: Analysis of Variance for Total Expenditures on Instruction
_______________________________________________________________________
Source
df
F
pη2
p
_______________________________________________________________________
District type (TYPE)
1
98.10
0.33
.000
Geographic location (GEOG)
3
30.71
0.32
.000
TYPE X GEOG
3
30.81
0.32
.000
199
(3.016E15)
S within group error
_______________________________________________________________________
Note: The value enclosed in parentheses is the mean square error (MSw). S = subjects.
Expenditures and Achievement
19
Table 3: Multiple Comparisons for Total Expenditures on Instruction Among Geographic
Locations
Geographic Location
ΔM
95% CI for ΔM
SEΔM
Rural - City
-22873379.00
9609749.423
-47771001.02
2024243.01
Rural - Town
14744349.27
1.069E7
-12960550.80
42449249.33
Rural - Suburb
-6445558.67
1.399E7
-42691984.80
29800867.46
City - Town
37617728.27*
1.226E7
5851745.07
69383711.46
City - Suburb
16427820.33
1.522E7
-23009720.26
55865360.92
Town - Suburb
-21189907.94
1.593E7
-62457201.45
20077385.57
_____________________________________________________________________________________
Note: ΔM = Mean difference. SEΔM = Standard error of ΔM.
Expenditures and Achievement
Enclosures
Enclosure I: SPSS Output for Multiple Regression - Full Model
Variables Entered/Removedb
Model
d
1
Variables
Variables
Entered
Removed
CUROTH,
Method
. Enter
i
GADMN,
m
BUSOTH,
e
OPMAN,
n
TRANS, INST,
s
PUPIL,
i
CURINST,
o
SADMNa
n
0
a. Tolerance = .000 limits reached.
b. Dependent Variable: SAT
Model Summary
Model
R
d
1
.466a
R Square
Adjusted R
Std. Error of the
Square
Estimate
.218
.152
32.479
i
m
e
n
s
i
o
n
0
a. Predictors: (Constant), CUROTH, GADMN, BUSOTH, OPMAN,
TRANS, INST, PUPIL, CURINST, SADMN
20
Expenditures and Achievement
ANOVAb
Model
1
Sum of Squares
Regression
df
Mean Square
31689.122
9
3521.014
Residual
113929.971
108
1054.907
Total
145619.093
117
F
Sig.
3.338
.001a
a. Predictors: (Constant), CUROTH, GADMN, BUSOTH, OPMAN, TRANS, INST, PUPIL,
CURINST, SADMN
b. Dependent Variable: SAT
Coefficientsa
Model
Standardized
Unstandardized Coefficients
B
1
Std. Error
(Constant)
494.611
5.713
CURINST
6.490E-7
.000
PUPIL
3.003E-6
INST
Coefficients
Beta
t
Sig.
86.569
.000
1.711
1.427
.156
.000
.783
.805
.422
-6.809E-6
.000
-1.074
-1.710
.090
GADMN
-1.605E-5
.000
-.676
-3.107
.002
SADMN
5.962E-6
.000
1.722
1.065
.289
OPMAN
1.251E-6
.000
.392
.500
.618
TRANS
-7.862E-6
.000
-1.706
-2.622
.010
BUSOTH
-2.333E-6
.000
-.354
-.640
.523
CUROTH
-3.913E-6
.000
-.787
-1.515
.133
a. Dependent Variable: SAT
21
Expenditures and Achievement
Coefficientsa
Model
Correlations
Zero-order
1
Partial
Part
(Constant)
CURINST
.195
.136
.121
PUPIL
.190
.077
.069
INST
.157
-.162
-.146
GADMN
.089
-.286
-.264
SADMN
.185
.102
.091
OPMAN
.203
.048
.043
TRANS
.148
-.245
-.223
BUSOTH
.155
-.061
-.054
CUROTH
.162
-.144
-.129
a. Dependent Variable: SAT
Excluded Variablesb
Model
Collinearity
Beta In
1
CURSVC
t
.a
Sig.
.
.
Partial
Statistics
Correlation
Tolerance
.
.000
a. Predictors in the Model: (Constant), CUROTH, GADMN, BUSOTH, OPMAN, TRANS, INST,
PUPIL, CURINST, SADMN
b. Dependent Variable: SAT
22
Expenditures and Achievement
23
Enclosure II: SPSS Output for Multiple Regression - Comparison with Restricted Model
Variables Entered/Removedc
Model
d
1
Variables
Entered
Removed
TRANS,
Method
. Enter
GADMNa
i
m
Variables
2
e
n
s
i
o
CUROTH,
. Enter
BUSOTH,
OPMAN, INST,
PUPIL,
CURINST,
SADMNb
n
0
a. All requested variables entered.
b. Tolerance = .000 limits reached.
c. Dependent Variable: SAT
Model Summary
Model
Change Statistics
R
d
i
R Square
Adjusted R
Std. Error of the
R Square
Square
Estimate
Change
F Change
df1
df2
Sig. F Change
1
.156a
.024
.007
35.150
.024
1.431
2
115
.243
2
.466b
.218
.152
32.479
.193
3.812
7
108
.001
m
e
n
s
i
o
n
0
a. Predictors: (Constant), TRANS, GADMN
b. Predictors: (Constant), TRANS, GADMN, CUROTH, BUSOTH, OPMAN, INST, PUPIL, CURINST, SADMN
Expenditures and Achievement
24
ANOVAc
Model
1
2
Sum of Squares
Regression
df
Mean Square
3536.696
2
1768.348
Residual
142082.397
115
1235.499
Total
145619.093
117
31689.122
9
3521.014
Residual
113929.971
108
1054.907
Total
145619.093
117
Regression
F
Sig.
1.431
.243a
3.338
.001b
a. Predictors: (Constant), TRANS, GADMN
b. Predictors: (Constant), TRANS, GADMN, CUROTH, BUSOTH, OPMAN, INST, PUPIL,
CURINST, SADMN
c. Dependent Variable: SAT
Coefficientsa
Model
Standardized
Unstandardized Coefficients
B
1
2
(Constant)
Std. Error
491.231
4.976
GADMN
-1.860E-6
.000
TRANS
9.705E-7
.000
494.611
5.713
GADMN
-1.605E-5
.000
TRANS
-7.862E-6
CURINST
PUPIL
Coefficients
Beta
Correlations
t
Sig.
Zero-order
Partial
Part
98.719
.000
-.078
-.516
.607
.089
-.048
-.048
.211
1.389
.168
.148
.128
.128
86.569
.000
-.676
-3.107
.002
.089
-.286
-.264
.000
-1.706
-2.622
.010
.148
-.245
-.223
6.490E-7
.000
1.711
1.427
.156
.195
.136
.121
3.003E-6
.000
.783
.805
.422
.190
.077
.069
-6.809E-6
.000
-1.074
-1.710
.090
.157
-.162
-.146
SADMN
5.962E-6
.000
1.722
1.065
.289
.185
.102
.091
OPMAN
1.251E-6
.000
.392
.500
.618
.203
.048
.043
BUSOTH
-2.333E-6
.000
-.354
-.640
.523
.155
-.061
-.054
CUROTH
-3.913E-6
.000
-.787
-1.515
.133
.162
-.144
-.129
(Constant)
INST
a. Dependent Variable: SAT
Expenditures and Achievement
Excluded Variablesc
Model
Collinearity
Beta In
1
Sig.
Statistics
Correlation
Tolerance
CURINST
1.680a
3.442
.001
.307
.033
CURSVC
1.971a
2.979
.004
.269
.018
PUPIL
1.585a
3.201
.002
.287
.032
.282a
.789
.432
.074
.067
SADMN
2.052a
3.431
.001
.306
.022
OPMAN
1.332a
3.318
.001
.297
.048
BUSOTH
.222a
.461
.646
.043
.037
CUROTH
.219a
.786
.433
.073
.109
CURSVC
.b
.
.
.
.000
INST
2
t
Partial
a. Predictors in the Model: (Constant), TRANS, GADMN
b. Predictors in the Model: (Constant), TRANS, GADMN, CUROTH, BUSOTH, OPMAN, INST,
PUPIL, CURINST, SADMN
c. Dependent Variable: SAT
25
Expenditures and Achievement
Enclosure III: SPSS Output for Logistic Regression
Logistic Regression
Case Processing Summary
Unweighted
Casesa
Selected Cases
N
Included in Analysis
Missing Cases
Total
Unselected Cases
Total
Percent
115
100.0
0
.0
115
100.0
0
.0
115
100.0
a. If weight is in effect, see classification table for the total number of
cases.
Dependent Variable Encoding
Original Value
dim
ens
Internal Value
Not Met AYP
0
Met AYP
1
ion
0
Block 0: Beginning Block
Classification Tablea,b
Observed
Predicted
AYP
Not Met AYP
Step 0
AYP
Percentage
Met AYP
Correct
Not Met AYP
0
45
.0
Met AYP
0
70
100.0
Overall Percentage
a. Constant is included in the model.
b. The cut value is .500
60.9
26
Expenditures and Achievement
Variables in the Equation
B
Step 0
Constant
S.E.
.442
Wald
.191
df
5.347
1
Variables not in the Equationa
Score
Step 0
Variables
df
Sig.
INST
.051
1
.822
TSAL
.067
1
.795
ENRL
.080
1
.778
SIZE
.085
1
.771
PVTY
1.146
1
.284
FTE
.081
1
.776
RATIO
.965
1
.326
a. Residual Chi-Squares are not computed because of redundancies.
Block 1: Method = Enter
Omnibus Tests of Model Coefficients
Chi-square
Step 1
df
Sig.
Step
29.943
7
.000
Block
29.943
7
.000
Model
29.943
7
.000
Model Summary
Step
-2 Log likelihood
1
124.003a
Cox & Snell R
Nagelkerke R
Square
Square
.229
a. Estimation terminated at iteration number 6 because
parameter estimates changed by less than .001.
.311
Sig.
.021
Exp(B)
1.556
27
Expenditures and Achievement
Hosmer and Lemeshow Test
Step
Chi-square
1
df
7.701
Sig.
8
.463
Contingency Table for Hosmer and Lemeshow Test
AYP = Not Met AYP
Observed
Step 1
AYP = Met AYP
Expected
Observed
Expected
Total
1
8
10.143
4
1.857
12
2
10
7.762
2
4.238
12
3
8
6.403
4
5.597
12
4
4
5.254
8
6.746
12
5
4
4.678
8
7.322
12
6
5
3.797
7
8.203
12
7
2
3.025
10
8.975
12
8
2
2.332
10
9.668
12
9
2
1.489
10
10.511
12
10
0
.118
7
6.882
7
Classification Tablea
Observed
Predicted
AYP
Not Met AYP
Step 1
AYP
Percentage
Met AYP
Correct
Not Met AYP
26
19
57.8
Met AYP
10
60
85.7
Overall Percentage
a. The cut value is .500
74.8
28
Expenditures and Achievement
Variables in the Equation
B
Step
1a
S.E.
Wald
df
Sig.
INST
.000
.000
.065
1
.798
1.000
TSAL
.000
.000
.258
1
.611
1.000
ENRL
.001
.000
1.770
1
.183
1.001
SIZE
-1.286
1.238
1.080
1
.299
.276
PVTY
-.002
.001
12.559
1
.000
.998
FTE
-.014
.010
1.818
1
.178
.986
RATIO
-.389
.302
1.661
1
.198
.678
Constant
6.049
4.133
2.142
1
.143
423.836
a. Variable(s) entered on step 1: INST, TSAL, ENRL, SIZE, PVTY, FTE, RATIO.
Variables in the Equation
95% C.I.for EXP(B)
Lower
Step 1a
Exp(B)
Upper
INST
1.000
1.000
TSAL
1.000
1.000
ENRL
1.000
1.002
SIZE
.024
3.125
PVTY
.996
.999
FTE
.967
1.006
RATIO
.375
1.224
Constant
a. Variable(s) entered on step 1: INST, TSAL,
ENRL, SIZE, PVTY, FTE, RATIO.
29
Expenditures and Achievement
Enclosure IV: SPSS Output for ANOVA
Between-Subjects Factors
Value Label
TYPE
GEOG
N
0
charter
92
1
local
115
2
rural
107
3
city
47
4
town
35
5
suburb
18
Descriptive Statistics
Dependent Variable:TCURINST
TYPE
GEOG
charter
rural
1366200.00
847591.818
30
city
1179270.27
653408.952
37
town
1340687.50
920167.138
16
suburb
1649777.78
1357105.447
9
Total
1314326.09
847866.594
92
rural
42656337.66
3.552E7
77
2.49E8
2.312E8
10
town
28962368.42
1.949E7
19
suburb
73400666.67
4.509E7
9
Total
60761643.48
9.371E7
115
rural
31079663.55
3.538E7
107
city
53953042.55
1.449E8
47
town
16335314.29
1.991E7
35
suburb
37525222.22
4.817E7
18
Total
34340613.53
7.574E7
207
local
city
Total
Mean
Std. Deviation
N
30
Expenditures and Achievement
31
Levene's Test of Equality of Error Variancesa
Dependent Variable:TCURINST
F
df1
40.686
df2
7
Sig.
199
.000
Tests the null hypothesis that the error variance
of the dependent variable is equal across groups.
a. Design: Intercept + TYPE + GEOG + TYPE *
GEOG
Tests of Between-Subjects Effects
Dependent Variable:TCURINST
Source
Type III Sum of
Squares
Partial Eta
df
Mean Square
F
Sig.
Squared
Corrected Model
5.817E17
7
8.310E16
27.555
.000
.492
Intercept
3.129E17
1
3.129E17
103.770
.000
.343
TYPE
2.958E17
1
2.958E17
98.102
.000
.330
GEOG
2.778E17
3
9.260E16
30.705
.000
.316
TYPE * GEOG
2.788E17
3
9.292E16
30.813
.000
.317
Error
6.001E17
199
3.016E15
Total
1.426E18
207
Corrected Total
1.182E18
206
a. R Squared = .492 (Adjusted R Squared = .474)
Expenditures and Achievement
32
Post Hoc Tests
GEOG
Multiple Comparisons
TCURINST
Tukey HSD
(I) GEOG
(J) GEOG
Difference (I-J)
rural
dimensio
95% Confidence Interval
Mean
Std. Error
Sig.
Lower Bound
Upper Bound
city
-22873379.00
9609749.423
.084
-47771001.02
2024243.01
town
14744349.27
1.069E7
.514
-12960550.80
42449249.33
suburb
-6445558.67
1.399E7
.967
-42691984.80
29800867.46
rural
22873379.00
9609749.423
.084
-2024243.01
47771001.02
town
37617728.27*
1.226E7
.013
5851745.07
69383711.46
suburb
16427820.33
1.522E7
.703
-23009720.26
55865360.92
-14744349.27
1.069E7
.514
-42449249.33
12960550.80
-3.76E7
1.226E7
.013
-69383711.46
-5851745.07
-21189907.94
1.593E7
.545
-62457201.45
20077385.57
6445558.67
1.399E7
.967
-29800867.46
42691984.80
city
-16427820.33
1.522E7
.703
-55865360.92
23009720.26
town
21189907.94
1.593E7
.545
-20077385.57
62457201.45
n3
di
city
m
dimensio
e
n3
n
si
town
rural
dimensio
o
city
n3
suburb
n
2
suburb
rural
dimensio
n3
Based on observed means.
The error term is Mean Square(Error) = 3015678513561469.000.
*. The mean difference is significant at the .05 level.
Homogeneous Subsets
TCURINST
Tukey
HSDa,b,c
GEOG
Subset
N
1
2
town
35
16335314.29
rural
107
31079663.55
31079663.55
suburb
18
37525222.22
37525222.22
city
47
Sig.
53953042.55
.375
.306
Expenditures and Achievement
Means for groups in homogeneous subsets are
displayed.
Based on observed means.
The error term is Mean Square(Error) =
3015678513561469.000.
a. Uses Harmonic Mean Sample Size = 34.859.
b. The group sizes are unequal. The harmonic mean of
the group sizes is used. Type I error levels are not
guaranteed.
c. Alpha = .05.
Profile Plots
33
Expenditures and Achievement
Enclosure V: SPSS Output for MANOVA
Analysis 1
Stepwise Statistics
Variables Entered/Removeda,b,c,d
Step
Wilks' Lambda
Entered
Statistic
df1
df2
df3
1
TCURSVC
.969
1
2
186.000
2
TCURINST
.927
2
2
186.000
3
TCUROTH
.915
3
2
186.000
At each step, the variable that minimizes the overall Wilks' Lambda is
entered.
a. Maximum number of steps is 8.
b. Minimum partial F to enter is 0.
c. Maximum partial F to remove is 0.
d. F level, tolerance, or VIN insufficient for further computation.
Variables Entered/Removeda,b,c,d
Step
Wilks' Lambda
Exact F
Statistic
df1
df2
Sig.
1
3.006
2
186.000
.052
2
3.555
4
370.000
.007
3
2.774
6
368.000
.012
At each step, the variable that minimizes the overall Wilks'
Lambda is entered.
a. Maximum number of steps is 8.
b. Minimum partial F to enter is 0.
c. Maximum partial F to remove is 0.
d. F level, tolerance, or VIN insufficient for further
computation.
34
Expenditures and Achievement
Variables in the Analysis
Step
Tolerance
F to Remove
Wilks' Lambda
1
TCURSVC
1.000
3.006
2
TCURSVC
.009
4.580
.973
TCURINST
.009
4.124
.969
TCURSVC
.009
3.574
.951
TCURINST
.007
1.261
.928
TCUROTH
.047
1.208
.927
3
Variables Not in the Analysis
Step
0
1
2
3
Tolerance
Min. Tolerance
F to Enter
Wilks' Lambda
TCURINST
1.000
1.000
2.556
.973
TCURSVC
1.000
1.000
3.006
.969
TCUROTH
1.000
1.000
1.911
.980
TSAL
1.000
1.000
2.602
.973
TCURINST
.009
.009
4.124
.927
TCUROTH
.064
.064
4.068
.928
TSAL
.009
.009
3.400
.934
TCUROTH
.047
.007
1.208
.915
TSAL
.000
.000
3.400
.934
TSAL
.000
.000
3.400
.934
Wilks' Lambda
Step
Number of
Variables
Lambda
df1
df2
df3
1
1
.969
1
2
186
2
2
.927
2
2
186
3
3
.915
3
2
186
35
Expenditures and Achievement
Wilks' Lambda
Step
Exact F
Statistic
df1
df2
Sig.
1
3.006
2
186.000
.052
2
3.555
4
370.000
.007
3
2.774
6
368.000
.012
Pairwise Group Comparisonsa,b,c
Step
GEOG
1
rural
rural
F
Sig.
city
town
2
rural
F
rural
town
.050
.393
.050
.023
F
.732
5.264
Sig.
.393
.023
F
F
F
1.232
.003
.294
3.196
.003
.043
1.232
3.196
.294
.043
F
F
5.966
5.966
Sig.
city
.732
Sig.
Sig.
3
3.901
5.264
Sig.
town
town
3.901
Sig.
city
city
4.778
.993
.003
.397
4.778
2.238
Sig.
.003
.085
F
.993
2.238
Sig.
.397
.085
a. 1, 186 degrees of freedom for step 1.
b. 2, 185 degrees of freedom for step 2.
c. 3, 184 degrees of freedom for step 3.
36
Expenditures and Achievement
Summary of Canonical Discriminant Functions
Eigenvalues
Function
Canonical
Eigenvalue
% of Variance
Cumulative %
Correlation
1
.078a
85.5
85.5
.269
2
.013a
14.5
100.0
.115
dimension0
a. First 2 canonical discriminant functions were used in the analysis.
Wilks' Lambda
Test of Function(s)
Wilks' Lambda
Chi-square
df
Sig.
1 through 2
.915
16.368
6
.012
2
.987
2.443
2
.295
dimension0
Standardized Canonical
Discriminant Function Coefficients
Function
1
2
TCURINST
-5.040
3.264
TCURSVC
7.383
-3.578
TCUROTH
-1.886
1.209
Structure Matrix
Function
1
2
TCUROTH
.339
.932*
TCURINST
.468
.883*
TSALa
.477
.877*
TCURSVC
.542
.841*
37
Expenditures and Achievement
Pooled within-groups correlations
between discriminating variables and
standardized canonical discriminant
functions
Variables ordered by absolute size of
correlation within function.
*. Largest absolute correlation between
each variable and any discriminant
function
a. This variable not used in the
analysis.
Functions at Group Centroids
GEOG
Function
1
d
i
m
2
rural
-.195
.060
city
.471
.042
town
-.038
-.239
e
n
s
i
o
n
0
Unstandardized canonical
discriminant functions evaluated
at group means
38
Expenditures and Achievement
Classification Statistics
Classification Processing Summary
Processed
Excluded
207
Missing or out-of-range
0
group codes
At least one missing
0
discriminating variable
Used in Output
207
Prior Probabilities for Groups
GEOG
Cases Used in Analysis
Prior
d
i
m
e
n
s
i
o
n
0
Unweighted
Weighted
rural
.333
107
107.000
city
.333
47
47.000
town
.333
35
35.000
Total
1.000
189
189.000
39
Expenditures and Achievement
T-TEST GROUPS = GEOG(1, 2)
/VARIABLES = Y1 TO Y4.
40
Expenditures and Achievement
T-Test
Group Statistics
GEOG
TCURINST
d
i
1
rural
N
Mean
Std. Deviation
Std. Error Mean
0a
.
.
.
107
31079663.55
3.538E7
3420789.931
0a
.
.
.
107
15341205.61
1.611E7
1557844.675
0a
.
.
.
107
2796112.15
3154302.211
304937.905
0a
.
.
.
m
e
n
s
i
o
n
1
TCURSVC
d
i
1
rural
m
e
n
s
i
o
n
1
TCUROTH
d
i
1
rural
m
e
n
s
i
o
n
1
TSAL
d
1
41
Expenditures and Achievement
i
rural
107
22705205.61
2.584E7
2498358.948
m
e
n
s
i
o
n
1
a. t cannot be computed because at least one of the groups is empty.
T-TEST GROUPS = GEOG(1, 3)
/VARIABLES = Y1 TO Y4.
T-Test
Group Statistics
GEOG
TCURINST
d
i
N
Mean
Std. Deviation
Std. Error Mean
1
0a
.
.
.
city
47
53953042.55
1.449E8
2.113E7
1
0a
.
.
.
m
e
n
s
i
o
n
1
TCURSVC
d
42
Expenditures and Achievement
i
city
47
29556744.68
7.852E7
1.145E7
1
0a
.
.
.
city
47
4046234.04
1.091E7
1591415.427
1
0a
.
.
.
city
47
40070255.32
1.080E8
1.575E7
m
e
n
s
i
o
n
1
TCUROTH
d
i
m
e
n
s
i
o
n
1
TSAL
d
i
m
e
n
s
i
o
n
1
a. t cannot be computed because at least one of the groups is empty.
T-TEST GROUPS = GEOG(1, 4)
/VARIABLES = Y1 TO Y4.
T-Test
Group Statistics
43
Expenditures and Achievement
GEOG
TCURINST
d
i
N
Mean
Std. Deviation
Std. Error Mean
1
0a
.
.
.
town
35
16335314.29
1.991E7
3365340.806
1
0a
.
.
.
town
35
8487542.86
1.016E7
1718010.158
1
0a
.
.
.
town
35
1447314.29
1791190.596
302766.471
1
0a
.
.
.
m
e
n
s
i
o
n
1
TCURSVC
d
i
m
e
n
s
i
o
n
1
TCUROTH
d
i
m
e
n
s
i
o
n
1
TSAL
d
44
Expenditures and Achievement
i
town
35
11951971.43
1.474E7
2491982.419
m
e
n
s
i
o
n
1
a. t cannot be computed because at least one of the groups is empty.
T-TEST GROUPS = GEOG(2, 3)
/VARIABLES = Y1 TO Y4.
T-Test
Group Statistics
GEOG
TCURINST
d
i
N
Mean
Std. Deviation
Std. Error Mean
rural
107
31079663.55
3.538E7
3420789.931
city
47
53953042.55
1.449E8
2.113E7
rural
107
15341205.61
1.611E7
1557844.675
m
e
n
s
i
o
n
1
TCURSVC
d
45
Expenditures and Achievement
i
city
47
29556744.68
7.852E7
1.145E7
rural
107
2796112.15
3154302.211
304937.905
city
47
4046234.04
1.091E7
1591415.427
rural
107
22705205.61
2.584E7
2498358.948
city
47
40070255.32
1.080E8
1.575E7
46
m
e
n
s
i
o
n
1
TCUROTH
d
i
m
e
n
s
i
o
n
1
TSAL
d
i
m
e
n
s
i
o
n
1
Independent Samples Test
Levene's Test for Equality of
Variances
F
TCURINST
Equal variances assumed
t-test for Equality of Means
Sig.
27.283
t
.000
Equal variances not
df
-1.538
152
-1.069
48.428
-1.796
152
assumed
TCURSVC
Equal variances assumed
32.638
.000
Expenditures and Achievement
Equal variances not
47
-1.230
47.711
-1.090
152
-.772
49.411
-1.570
152
-1.089
48.330
assumed
TCUROTH
Equal variances assumed
21.921
.000
Equal variances not
assumed
TSAL
Equal variances assumed
27.547
.000
Equal variances not
assumed
Independent Samples Test
t-test for Equality of Means
95%
Confidence
Interval of the
Sig. (2-tailed)
TCURINST
Mean
Std. Error
Difference
Difference
Difference
Lower
Equal variances assumed
.126
-2.287E7
1.487E7
-5.226E7
Equal variances not
.291
-2.287E7
2.141E7
-6.590E7
Equal variances assumed
.075
-1.422E7
7917094.342
-2.986E7
Equal variances not
.225
-1.422E7
1.156E7
-3.746E7
Equal variances assumed
.277
-1250121.893
1146989.003
-3516221.102
Equal variances not
.444
-1250121.893
1620367.300
-4505686.956
Equal variances assumed
.119
-1.737E7
1.106E7
-3.922E7
Equal variances not
.282
-1.737E7
1.595E7
-4.943E7
assumed
TCURSVC
assumed
TCUROTH
assumed
TSAL
assumed
Independent Samples Test
t-test for
Equality of
Means
95%
Confidence
Interval of the
Difference
Upper
Expenditures and Achievement
TCURINST
Equal variances assumed
6512786.149
Equal variances not
2.016E7
assumed
TCURSVC
Equal variances assumed
1426216.143
Equal variances not
9028274.745
assumed
TCUROTH
Equal variances assumed
1015977.315
Equal variances not
2005443.170
assumed
TSAL
Equal variances assumed
4487804.840
Equal variances not
1.470E7
assumed
T-TEST GROUPS = GEOG(2, 4)
/VARIABLES = Y1 TO Y4.
T-Test
Group Statistics
GEOG
TCURINST
d
i
N
Mean
Std. Deviation
Std. Error Mean
rural
107
31079663.55
3.538E7
3420789.931
town
35
16335314.29
1.991E7
3365340.806
rural
107
15341205.61
1.611E7
1557844.675
m
e
n
s
i
o
n
1
TCURSVC
d
48
Expenditures and Achievement
i
town
35
8487542.86
1.016E7
1718010.158
rural
107
2796112.15
3154302.211
304937.905
town
35
1447314.29
1791190.596
302766.471
rural
107
22705205.61
2.584E7
2498358.948
town
35
11951971.43
1.474E7
2491982.419
49
m
e
n
s
i
o
n
1
TCUROTH
d
i
m
e
n
s
i
o
n
1
TSAL
d
i
m
e
n
s
i
o
n
1
Independent Samples Test
Levene's Test for Equality of
Variances
F
TCURINST
Equal variances assumed
t-test for Equality of Means
Sig.
8.090
t
.005
Equal variances not
df
2.343
140
3.073
104.703
2.364
140
assumed
TCURSVC
Equal variances assumed
6.571
.011
Expenditures and Achievement
Equal variances not
50
2.955
92.779
2.403
140
3.139
103.729
2.337
140
3.047
103.242
assumed
TCUROTH
Equal variances assumed
8.059
.005
Equal variances not
assumed
TSAL
Equal variances assumed
8.065
.005
Equal variances not
assumed
Independent Samples Test
t-test for Equality of Means
95%
Confidence
Interval of the
Sig. (2-tailed)
TCURINST
Mean
Std. Error
Difference
Difference
Difference
Lower
Equal variances assumed
.021
1.474E7
6292551.508
2303636.901
Equal variances not
.003
1.474E7
4798679.244
5229140.470
Equal variances assumed
.019
6853662.750
2899354.415
1121483.319
Equal variances not
.004
6853662.750
2319146.165
2248153.575
Equal variances assumed
.018
1348797.864
561413.737
238852.746
Equal variances not
.002
1348797.864
429714.628
496631.388
Equal variances assumed
.021
1.075E7
4601655.750
1655513.563
Equal variances not
.003
1.075E7
3528707.101
3755070.971
assumed
TCURSVC
assumed
TCUROTH
assumed
TSAL
assumed
Independent Samples Test
t-test for
Equality of
Means
95%
Confidence
Interval of the
Difference
Upper
Expenditures and Achievement
TCURINST
Equal variances assumed
2.719E7
Equal variances not
2.426E7
assumed
TCURSVC
Equal variances assumed
1.259E7
Equal variances not
1.146E7
assumed
TCUROTH
Equal variances assumed
2458742.982
Equal variances not
2200964.340
assumed
TSAL
Equal variances assumed
1.985E7
Equal variances not
1.775E7
assumed
T-TEST GROUPS = GEOG(3, 4)
/VARIABLES = Y1 TO Y4.
T-Test
Group Statistics
GEOG
TCURINST
d
i
N
Mean
Std. Deviation
Std. Error Mean
city
47
53953042.55
1.449E8
2.113E7
town
35
16335314.29
1.991E7
3365340.806
city
47
29556744.68
7.852E7
1.145E7
m
e
n
s
i
o
n
1
TCURSVC
d
51
Expenditures and Achievement
i
town
35
8487542.86
1.016E7
1718010.158
city
47
4046234.04
1.091E7
1591415.427
town
35
1447314.29
1791190.596
302766.471
city
47
40070255.32
1.080E8
1.575E7
town
35
11951971.43
1.474E7
2491982.419
52
m
e
n
s
i
o
n
1
TCUROTH
d
i
m
e
n
s
i
o
n
1
TSAL
d
i
m
e
n
s
i
o
n
1
Independent Samples Test
Levene's Test for Equality of
Variances
F
TCURINST
Equal variances assumed
t-test for Equality of Means
Sig.
13.525
t
.000
Equal variances not
df
1.523
80
1.758
48.321
1.575
80
assumed
TCURSVC
Equal variances assumed
14.222
.000
Expenditures and Achievement
Equal variances not
53
1.819
48.060
1.393
80
1.604
49.303
1.527
80
1.763
48.290
assumed
TCUROTH
Equal variances assumed
12.247
.001
Equal variances not
assumed
TSAL
Equal variances assumed
13.488
.000
Equal variances not
assumed
Independent Samples Test
t-test for Equality of Means
95%
Confidence
Interval of the
Sig. (2-tailed)
TCURINST
Mean
Std. Error
Difference
Difference
Difference
Lower
Equal variances assumed
.132
3.762E7
2.470E7
-1.153E7
Equal variances not
.085
3.762E7
2.140E7
-5397637.784
Equal variances assumed
.119
2.107E7
1.338E7
-5548588.428
Equal variances not
.075
2.107E7
1.158E7
-2215804.472
Equal variances assumed
.167
2598919.757
1865408.434
-1113361.334
Equal variances not
.115
2598919.757
1619960.060
-656006.187
Equal variances assumed
.131
2.812E7
1.841E7
-8516315.469
Equal variances not
.084
2.812E7
1.595E7
-3942946.300
assumed
TCURSVC
assumed
TCUROTH
assumed
TSAL
assumed
Expenditures and Achievement
Independent Samples Test
t-test for
Equality of
Means
95%
Confidence
Interval of the
Difference
Upper
TCURINST
Equal variances assumed
8.677E7
Equal variances not
8.063E7
assumed
TCURSVC
Equal variances assumed
4.769E7
Equal variances not
4.435E7
assumed
TCUROTH
Equal variances assumed
6311200.848
Equal variances not
5853845.701
assumed
TSAL
Equal variances assumed
6.475E7
Equal variances not
6.018E7
assumed
54