Technical Appendix
Navigating the Middle Grades:
Evidence from New York City
Michael J. Kieffer
April 2012
Navigating the Middle Grades:
A Descriptive Analysis of the Middle Grades in
New York City
Technical Appendix
Michael J. Kieffer
Teachers College, Columbia University
April 2012
© 2012 Research Alliance for New York City Schools. All rights reserved. You may make copies of and distribute this work for noncommercial educational and scholarly purposes. For any other uses, including the making of derivative works, permission must be
obtained from the Research Alliance for New York City Schools, unless fair use exceptions to copyright law apply.
CONTENTS
I.
Methods ....................................................................................................... 1
Sample......................................................................................................... 1
Measures ...................................................................................................... 1
II.
Data Analysis and Results .................................................................... 3
Descriptives ............................................................................................................. 3
Who’s on track to graduate and why? Predicting High School Graduation based on Grade Nine
Predictors ............................................................................................................... 4
What do students’ grade four-eight achievement and attendance trajectories look like? ........... 4
Does students’ grade four-eight achievement predict who’s on track in grade nine? ...............11
Does students’ grade four-eight attendance predict who’s on track in grade nine? .................14
Do particular demographic groups of students demonstrate middle-grades trajectories that are
associated with being off-track in grade nine? ................................................................19
Is middle grades performance equally predictive of later on-track status across ethnic and
language groups? ....................................................................................................29
Do these patterns hold across schools?
III.
.......................................................................32
Exploratory Analysis of School Characteristics .........................35
I.
METHODS
Sample
As noted in the main text, the analytic sample for the high school graduation analyses
was the cohort of New York City students who were first-time ninth graders in the 2005-2006
school year. The analytic sample for the middle grades analyses included four cohorts of
students who were first-time fourth graders between the 2000-2001 and 2003-2004 school years.
Our data cover the former cohort’s progress through high school graduation and the latter
cohort’s progress through grade nine. We identified first-time fourth graders by selected students
who were in grade four in the appropriate school year for their cohort, but were not in grade four
during the previous school year. We conduct the middle grades analyses primarily with the
entire population of students who ever appear in these four cohorts (N = 303,845), using fullinformation maximum likelihood to account for data missing due to attrition or other causes.
This sample thus included all students, including students classified as English language learners
and students with disabilities. Descriptive statistics on the sample are displayed in Table 1
below. We also checked results against an analyses using the subset of students with complete
data (n = 169, 953), i.e., those who do not enter or exit the district at any point between grades
four and nine, who progress through each grade annually, and who have complete data on the
variables of interest; results were largely similar when analyses were conducted with this subsample, so the results for the complete sample are reported here.
Measures
High School Graduation.
Students’ on-time graduation in the fourth year after they enrolled as first-time ninth
graders was drawn from the DOE’s Student Trackng System Dataset. Thus, graduation was
defined as graduating within four years, so this variable equaled 0 for students who graduated
later or completed a General Equivalency Diploma.
Performance in Grade Nine.
Measures of grade nine performance include credits earned over the course year, courses
failed over the course of the year, and grade point average across the year, each drawn from the
Course Detail Records file. Measures of grade nine performance also included the total
attendance rate, as a percent of days enrolled, drawn from the DOE’s Student Tracking System
Dataset. Measures also included a dummy variable for whether a Regents exam was attempted
and one for whether a Regents test was passed in grade nine.
Achievement in Grades four-eight.
Achievement in the areas of mathematics and reading/English-language arts was assessed
using the New York State tests, with scores drawn from the NY State ELA and Math Test Score
1
file. Because the scale for these tests changed over the years of administration and were not
vertically linked to be comparable across grade levels, scores were rescaled to be within-grade zscores, based on the district means and standard deviations. This approach is not ideal for
growth modeling because it subtracts out the normative trajectory for growth and assumes
homogeneity of variance across time. However, given the limitations of the scaling of the test
scores, it is more appropriate than using the original scaled scores. It also has benefits over using
proficiency levels, in that it preserves the continuous nature of achievement, as opposed to
arbitrarily dividing the distribution into discrete categories. Tests were taken annually, yielding
one score per year.
Attendance in Grades four-eight.
Attendance was measured as a percent of the days enrolled for each semester, yielding
two attendance rate values for each year. Attendance data were drawn from the DOE’s
Attendance System.
2
II.
DATA ANALYSES AND RESULTS
Descriptives
Table 1:
Means and standard deviations for achievement test scores, attendance along
with demographics for the analytic sample (N = 303,845)
Mean
Mathematics Achievement (within-grade zscores)
Reading Achievement (within-grade z-scores)
Attendance (Percent of Days Enrolled)
Race/ethnicity
Language Background
Grade 4
Grade 5
Grade 6
Grade 7
Grade 8
Grade 4
Grade 5
Grade 6
Grade 7
Grade 8
Fall, Grade 4
Spring, Grade 4
Fall, Grade 5
Spring, Grade 5
Fall, Grade 6
Spring, Grade 6
Fall, Grade 7
Spring, Grade 7
Fall, Grade 8
Spring, Grade 8
African-American
Asian
Hispanic
Native American
White
ELL in Grade 4
Language Minority
3
Standard
Deviation
-0.02
1.01
-0.06
1.03
-0.08
1.04
-0.10
1.06
-0.15
1.09
-0.50
1.02
-0.07
1.03
-0.09
1.04
-0.09
1.05
-0.12
1.06
94.00
7.53
92.91
8.25
93.06
11.35
92.14
9.06
92.03
12.58
91.05
10.91
91.39
12.47
89.64
12.64
90.75
12.95
87.08
13.88
Percentage of Sample
33.3%
12.3%
39.1%
0.4%
14.8%
9.4%
41.4%
Who’s on track to graduate and why? Predicting High School Graduation based
on Grade Nine Predictors
Logistic regression was used to determine whether the grade nine measures predicted ontime high school graduation. As shown in Table 1, credits earned, courses failed, GPA,
attendance rate, whether a Regents test was attempted, and whether a Regents test was passed all
predicted high school graduation. As mentioned in the main text, these predictors remain strong
when controlling for students’ grade-eight test scores and for fixed effects of high school. These
parameter estimates were used as relative weights for each measure in order to estimate a single
“on-track” indicator that summarizes these predictive relationships for each student. These
scores were then used in subsequent analyses.
Table 2:
Results of Logistic Regression Predicting On-time High School Graduation based
on Grade (G) 9 Predictors
Unstandardized Estimate
Wald χ2
Intercept
-7.12
1605.62***
G9 Credits Earned
0.31
3893.69***
G9 Courses Failed
-0.09
235.93***
G9 Grade Point Average
0.03
211.09***
G9 Annual Attendance Rate
0.03
675.88***
G9 Regents’ Test Attempted
0.33
88.77***
G9 Regents’ Test Passed
0.94
543.00***
*** p < .001
What do students’ grade four-eight achievement and attendance trajectories look
like?
To address the question concerning the nature of students’ growth trajectories in
achievement and attendance across grades four through eight, we fitted a series of piecewise
unconditional growth models using latent growth modeling in a structural equation modeling
(SEM) framework (Bollen & Curran, 2006). Piecewise models allow for nonlinear trajectories
in which students demonstrate different rates of growth during different specified periods. They
have the advantage of directly modeling true rates of growth (i.e., growth rates that are freed of
occasion-specific measurement error) for each theoretically important period for which sufficient
data points are available.
Figure 1 displays a path diagram for the hypothesized unconditional piecewise growth
model for attendance. As shown, students’ individual growth trajectories in attendance were
specified to have an initial (Fall, grade four) status and four slopes representing growth in four
distinct periods: Fall, grade four to Fall, grade five; Fall, grade five to Spring, grade six; Spring,
grade six to Spring, grade seven; and Spring, grade seven to Spring, grade eight. Each slope was
allowed to vary across children and the slopes were allowed to covary with one another and with
4
students’ initial (Fall, grade four) status. Inspection of empirical growth plots (Singer & Willett,
2003) suggested that this piecewise model was appropriate to compare the population average
growth trajectory as well as individual differences in the shape and elevation of students’ growth
trajectories. Fitting of various unconditional models also indicated that this model was superior
to other theoretically viable specifications. It is worth noting that the second period is longer due
to the number of measurement occasions; with ten occasions, a four-slope piecewise model is
only possible if one slope covers a longer period than the other three. Comparisons of alternate
models indicated that this particular piecewise model, with a longer second period, fitted the data
better than potential alternatives.
As shown in Table 3, fitting the unconditional piecewise growth model for attendance
provides insight into the average trajectory for attendance as well as individual variation around
that trajectory. As shown in the second row of Table 3, students’ initial status, on average, was
relatively high (approximately 94 percent of days enrolled) in the fall of grade four. As shown in
the third through sixth rows of Table 2, each slope was negative, indicating declines in
attendance on average, with the largest decline (a decline of 3 percent of days enrolled) occurring
between spring of grade seven and spring of grade eight. The variance components displayed in
the seventh through eleventh rows of Table 2 indicate that there was substantial variation in
students’ initial status and each rate of growth, with the largest variance occurring again between
spring of grade seven and spring of grade eight. Together, these two findings suggest that this
period involves not only the largest declines in attendance for all students but also the widest
variation in declines, with some students declining relative to other students to a much greater
extent than in previous periods.
In addition, this unconditional piecewise growth model provides insight into the
relationship between early levels and later rates of growth in attendance. Correlations between
students’ initial status and their rates of growth are displayed in the twelfth through fifteenth
rows of the right column titled “Selected Standardized Estimates.” As shown, initial status had a
moderately sized negative relationship with students’ rates of growth between fall, grade four
and fall, grade five, but only trivially sized relationships with students’ rates of growth during
later periods. This suggests that students’ levels of attendance prior to the middle grades
provides little information for predicting the extent to which they will maintain or decline in
attendance during the middle grades.
Figure 2 displays a path diagram for the hypothesized unconditional piecewise growth
model for mathematics achievement. As shown, students’ individual growth trajectories in
mathematics achievement were specified to have an initial (grade four) status and two slopes
representing growth in two distinct periods: grade four to grade six and grade six to grade eight.
Each slope was allowed to vary across children and the slopes were allowed to covary with one
another and with students’ initial (grade four) status. A parallel model with the same piecewise
specification was fitted to reading/English-language arts achievement. Inspection of empirical
growth plots suggested that this model was appropriate for both mathematics achievement and
reading/English-language arts achievement. Fitting of various unconditional models also
indicated that this model was superior to other theoretically viable specifications for both
achievement outcomes.
As shown in Table 4, fitting the unconditional piecewise growth model for mathematics
and reading/English-language arts achievement provides insight into the levels and relative
change in achievement for the average student in NYC schools. In interpreting these results, it is
5
important to recall that within-grade z-scores were used, in the absence of a more absolute
developmental scaled score, so change represents students’ relative movements within the rank
order rather than growth in a traditional sense. As shown in the second through fourth rows of
Table 4, estimates of initial status were close to the city average in grade four and average
change was minimal, as we would expect given that the within-grade z-score scale eliminates
average growth with increasing grade level. More interestingly, the variance components
displayed in the fifth through seventh rows indicate much wider variation in initial (grade four)
status (.86 within-grade SD) than in either rate of growth (.03 within-grade SD per year for both
Slope 1 and Slope 2 in mathematics; .02 for both Slope 1 and Slope 2 in reading/Englishlanguage arts). This suggests that there is substantial stability in the rank-order of students’
achievement levels. For instance, a student with a high rate of growth in mathematics relative to
the sample (i.e., 1 SD above the mean in Slope 1) would only change in the rank-order by 0.17
within-grade SD each year; a student with an analogously high rate of growth in reading/ELA
would only change by 0.14 within-grade SD.
These unconditional growth models of achievement also provide insight into the extent to
which early levels of achievement predict later rates of growth, as shown in the eighth through
eleventh rows and the fourth and sixth columns of Table 4. For both mathematics achievement,
students’ initial (grade four) status had a trivially sized relationship with students’ later rates of
growth (rs between -.01 and -.11). This should not be interpreted to mean that early levels do
not strongly predict later levels of achievement; in fact, the previous findings above concerning
stability of the rank-order suggests that they do. Rather, they suggest that growth trajectories are
largely parallel, with students who start substantially higher in grade four demonstrating growth
trajectories that neither increase nor decrease substantially than those of their peers.
6
Figure 1:
Path diagram for hypothesized piecewise linear growth model for attendance
7
Figure 2:
Path diagram for hypothesized piecewise linear growth model for mathematics
achievement
8
Table 3:
Selected Results for Unconditional Piecewise Growth Model for Attendance (N = 303,845)
Unstandardized Estimates
Fixed
Effects
Variance
Components
Initial (Fall, Grade 4) Status
93.95***
Slope 1 (Fall, Grade 4 – Fall, Grade 5)
Slope 2 (Fall, Grade 5 – Spring, Grade 6)
Slope 3 (Spring, Grade 6 – Spring, Grade 7)
Slope 4 (Spring, Grade 7 – Spring, Grade 8)
Initial Status
-1.32***
-0.67***
-1.28***
-3.01***
49.45***
Selected Standardized
Estimates
Slope 1
40.44***
Slope 2
17.60***
Slope 3
45.84***
Slope 4
88.49***
Covariances Initial Status with Slope 1
-16.86***
-.38
Initial Status with Slope 2
0.36**
.01
Initial Status with Slope 3
2.34***
.05
Initial Status with Slope 4
0.02
.00
Slope 1 with Slope 2
-9.20***
-.35
Slope 1 with Slope 3
-1.55***
-.04
Slope 1 with Slope 4
1.20***
.02
Slope 2 with Slope 3
1.76***
.06
Slope 2 with Slope 4
-2.12***
-.05
Slope 3 with Slope 4
-13.80***
-.22
Note: For the purposes of FIML, this model also included factors and indicates for mathematics and reading/ELA
achievement and Grade 9 ontrack indicator score, which were allowed to covary with the latent growth factors for
attendance.
** p < .01; *** p < .001
9
Table 4:
Selected Results for Unconditional Piecewise Growth Models for Mathematics and Reading
Achievement (N = 303,845)
Fixed
Effects
Mathematics Achievement
Unstandardized
Selected
Estimates
Standardized
Estimates
-0.02***
Reading/ELA Achievement
Unstandardized
Selected
Estimates
Standardized
Estimates
-0.05***
Initial (Grade 4)
Status
Slope 1 (Grade 4 –
-0.03***
-0.02***
Grade 6)
Slope 2 (Grade 6 –
-0.03***
-0.01***
Grade 8)
Variance
Initial Status
0.86***
0.81***
Components Slope 1
0.03***
0.02***
Slope 2
0.03***
0.02***
Covariances Initial Status with
-0.02***
-0.11
-0.01***
-.06
Slope 1
Initial Status with
-0.01***
-0.07
-0.001
-.01
Slope 2
Slope 1 with Slope 2
0.003***
0.12
-0.01***
-.29
Note: For the purposes of FIML, this model also included factors and indicates for attendance and Grade 9 ontrack
indicator score, which were allowed to covary with the latent growth factors for mathematics and reading/ELA
achievement.
*** p < .001
10
Does students’ grade four-eight achievement predict who’s on track in grade
nine?
Two SEM models were fitted to investigate whether variation in students’ levels and
rates of relative change in achievement predict their grade nine indicator score. As shown in the
path diagram in Figure 3, the grade nine ontrack indicator for students’ probability of on-time
high school graduation was regressed on the growth terms for the piecewise growth model for
mathematics achievement. A parallel model was fitted for reading/English-langauge arts
achievement predicting the grade nine ontrack indicator.
Table 5 displays the selected results of fitting this SEM model for mathematics
achievement. As shown, initial status in mathematics has a strong relationship with the grade
nine ontrack indicator. The two slopes in mathematics achievement also had moderate to large
relationships with the grade nine ontrack indicator, with the stronger relationship demonstrated
by the later growth term, representing growth between grade six and grade eight. Together, these
findings suggest that initial (grade four) status in achievement provides substantial information
for later probability of high school graduation, but also that the extent to which students change
during the middle grades also provides valuable information. In particular, growth during the
middle grades is substantially more predictive of later probability of high school graduation than
growth during the upper-elementary grades.
Table 6 displays the selected results of fitting a second, analogous SEM model for
reading/English-language arts achievement. As shown, initial status in reading/ELA has a strong
relationship with the grade nine on-track indicator. The two slopes in reading/ELA achievement
also had moderate relationships with the grade nine on-track indicator that were approximately
the same as each other. As with mathematics achievement, these findings suggest that grade four
levels of reading/ELA achievement provide substantial information to predict probability of later
high school graduation, but also that changes during the middle grades provide valuable
information. However, unlike mathematics achievement, changes during the middle grades were
similarly predictive of later graduation as change during the upper-elementary grades.
11
Figure 4:
Path diagram for hypothesized structural equation model in which latent growth
in mathematics achievement predicts Grade 9 indicator for the probability of ontime high school graduation
12
Table 5:
Selected Results from Structural Equation Model with Initial Status and Rates of Growth in
Mathematics Achievement Predicting Grade (G) 9 Ontrack Indicator Score (N = 303,845)
Paths
Unstandardized
Estimates
1.63***
Standardized Estimates
Math Initial (Grade 4) Status  G9 Ontrack
.51
Indicator
Math Slope 1 (Grade 4 –Grade 6)  G9 Ontrack
3.70***
.21
Indicator
Math Slope 2 (Grade 6 –Grade 8)  G9 Ontrack
6.62***
.39
Indicator
Note: Model also included variances and intercepts for mathematics achievement initial status, slope 1, and slope 2
as well as residual variances for Grade 9 ontrack indicator score. For FIML purposes, model also included latent
growth factors for reading/ELA and attendance which were allowed to covary with mathematics growth terms and
Grade 9 ontrack indicator.
*** p < .001
Table 6:
Selected Results from Structural Equation Model with Initial Status and Rates of Growth in
Reading/English-Language Arts (ELA) Achievement Predicting Grade (G) 9 Ontrack Indicator
Score (N = 303,845)
Paths
Unstandardized
Estimates
1.51***
Standardized
Estimates
.47
Reading/ELA Initial (Grade 4) Status  G9 Ontrack
Indicator
Reading/ELA Slope 1 (Grade 4 –Grade 6)  G9 Ontrack
5.79***
.28
Indicator
Reading/ELA Slope 2 (Grade 6 –Grade 8)  G9 Ontrack
6.79***
.33
Indicator
Note: Model also included variances and intercepts for reading/ELA achievement initial status, slope 1, and slope
2 as well as residual variances for Grade 9 ontrack indicator score. For FIML purposes, model also included latent
growth factors for mathematics and attendance which were allowed to covary with reading/ELA growth terms and
Grade 9 ontrack indicator.
*** p < .001
13
Does students’ grade four-eight attendance predict who’s on track in grade nine?
A SEM model analogous to that fitted for the question above was fitted to investigate the
extent to which levels and rates of growth in attendance predict students’ grade nine on-track
probability of later high school graduation. As shown in Figure 4, grade nine on-track indicator
score was regression on the intercept and four slope terms for the attendance growth model.
Table 6 displays selected results for fitting this SEM model. As shown, students’ initial status in
attendance had a strong relationship with their grade nine on-track indicator score and each of
the four slope terms also had a moderate relationship with the grade nine on-track indicator. As
with achievement, this finding indicates that students’ level of attendance in grade four provides
information about whether they will be on track in grade nine for ultimately graduating from
high school, but also that students’ growth or declines in attendance during the upper-elementary
and middle grades provide additional information about whether they will be on track in grade
nine. The magnitudes of the relationships between rates of growth and grade nine on-track
indicator are largely similar across the different periods studied.
To investigate the relative contributions of attendance and achievement during the middle
grades to grade nine on-track indicator score, an additional SEM model that included regression
paths between grade nine on-track indicator and the growth parameters for both attendance and
achievement was fitted. This hypothesized model is displayed in Figure 5. Due to the high
covariances among growth parameters for mathematics achievement and reading/ELA
achievement, these were not modeled separately. Instead, a simple composite for achievement
for each time point was estimated by averaging the z-scores for mathematics achievement and
reading/ELA achievement; these then served as the indicators for a piecewise latent growth
model for achievement as shown in Figure 5. Table 8 presents the results from fitting this
model. As shown in the rightmost column, the standardized regression paths indicated that
effects of both attendance and achievement initial levels and rates of growth remained robust
when accounting for both simultaneously. These estimates are somewhat smaller than those
presented in the attendance-only model in Table 7 and the achievement-only models in Tables 5
and 6, but remain non-trivial in magnitude. Moreover, the finding that growth in attendance and
achievement during the middle grades adds information beyond that provide by students’ initial
status in these predictors continues to hold when both predictors are accounted for
simultaneously.
14
Figure 4:
Path diagram for hypothesized structural equation model for latent growth in
attendance predicting Grade (G) 9 ontrack indicator for probability of high school
graduation.
15
Table 7:
Selected Results from Structural Equation Model with Initial Status and Rates of
Growth in Attendance Predicting Grade (G) 9 Ontrack Indicator Score (N =
303,845)
Paths
Unstandardized
Estimates
0.195***
Standardized
Estimates
.47
Attendance Initial (Fall, G4) Status  G9
Ontrack Indicator
Attendance Slope 1 (Fall, G4 –Fall, G5)  G9
0.175***
.38
Ontrack Indicator
Attendance Slope 2 (Fall, G5 –Spring, G6)  G9
0.241***
.34
Ontrack Indicator
Attendance Slope 3 (Spring, G6-Spring, G7)
0.137***
.31
G9 Ontrack Indicator
Attendance Slope 4 (Spring, G7-Spring, G8)
0.085***
.27
G9 Ontrack Indicator
Note: Model also included variances and intercepts for reading/ELA achievement initial status,
slope 1, and slope 2 as well as residual variances for Grade 9 ontrack indicator score. For FIML
purposes, model also included latent growth factors for mathematics and attendance which were
allowed to covary with reading/ELA growth terms and Grade 9 ontrack indicator.
*** p < .001
16
Figure 5:
Path diagram for hypothesized structural equation model in which latent growth
in achievement (simple composite of mathematics and reading/ELA achievement)
and attendance predicts Grade 9 on-track indicator
Note: Model also included measurement models for attendance as shown in Figure 1 and for
achievement analogous to the model shown in Figure 2. Att = Attendance; Ach = Achievement
Composite
17
Table 8:
Selected Results from Structural Equation Model with Initial Status and Rates of
Growth in Both Attendance and Achievement Predicting Grade (G) 9 Ontrack
Indicator Score (N = 303,805)
Paths
Unstandardized
Standardized
Estimates
Estimates
Attendance Initial (Fall, G4) Status  G9
0.13***
0.30
Ontrack Indicator
Attendance Slope 1 (Fall, G4 –Fall, G5)  G9
0.12***
0.26
Ontrack Indicator
Attendance Slope 2 (Fall, G5 –Spring, G6)  G9
0.16***
0.22
Ontrack Indicator
Attendance Slope 3 (Spring, G6-Spring, G7)
0.09***
0.21
G9 Ontrack Indicator
Attendance Slope 4 (Spring, G7-Spring, G8)
0.07***
0.21
G9 Ontrack Indicator
Achievement Composite Initial (G4) Status  G9
1.23***
0.37
Ontrack Indicator
Achievement Composite Slope 1 (G4-G6)  G9
3.42***
0.17
Ontrack Indicator
Achievement Composite Slope 2 (G6-G8)  G9
4.51***
0.22
Ontrack Indicator
Note: As shown in Figure 5, mnodel also included variances and intercepts for and covariances
among all latent growth terms as well as a residual variance for Grade 9 ontrack indicator score.
*** p < .001
18
Do particular demographic groups of students demonstrate middle-grades
trajectories that are associated with being off-track in grade nine?
Given the relationships between middle-grades trajectories (level and growth) in
attendance and achievement with the later grade nine on-track indicator, we next investigated
whether particular demographic characteristics including race/ethnicity and language background
predict students’ middle-grades trajectories. Table 9 presents descriptive statistics on
achievement and attendance by race/ethnicity group while Table 10 presents descriptive statistics
on achievement and attendance by language backgrounds.
Specifically, we fitted a series of SEM models in which demographic characteristics
predicted initial status and slopes for attendance and achievement. Figure 5 presents the SEM
model for ethnicity (represented as a series of dummy variables with White specified as the
reference category) predicting initial status and piecewise rates of growth in attendance, while
Figure 6 presents the analogous SEM model for each achievement outcome. Analogous models
were fitted for language background (represented as a series of two dummy variables for English
language learner designated in grade four and Language Minority, non-ELL, with native English
speakers specified as the reference category).
19
Table 9:
Means and standard deviations for achievement and attendance by race/ethnicity
AfricanAsian (n
Latino (n
Native
White
American = 37,329) = 118,911) American (n
(n =
(n =
= 1155)
44,871)
101,237)
Mathematics
Grade 4
-0.28
0.62
-0.21
-0.32 (0.98)
0.53
Achievement
(0.90)
(1.08)
(0.91)
(1.01)
(within-grade zGrade 5
-0.34
0.66
-0.24
-0.40 (1.08)
0.50
scores)
(0.94)
(1.04)
(0.93)
(0.96)
Grade 6
-0.37
0.70
-0.27
-0.40 (1.05)
0.42
(0.94)
(1.06)
(0.94)
(0.97)
Grade 7
-0.41
0.73
-0.29
-0.47 (1.15)
0.44
(0.96)
(1.04)
(0.94)
(0.98)
Grade 8
-0.46
0.88
-0.33
-0.49 (1.08)
0.34
(0.96)
(1.10)
(0.94)
(1.01)
Reading
Grade 4
-0.20
0.41
-0.30
-0.30 (0.96)
0.53
Achievement
(0.92)
(1.08)
(0.92)
(1.08)
(within-grade zGrade 5
-0.27
0.42
-0.29
-0.36 (0.99)
0.60
scores)
(0.94)
(1.01)
(0.95)
(1.06)
Grade 6
-0.28
0.47
-0.30
-0.36 (1.02)
0.48
(0.94)
(1.05)
(0.96)
(1.05)
Grade 7
-0.29
0.48
-0.20
-0.38 (1.02)
0.49
(0.95)
(1.03)
(0.98)
(1.05)
Grade 8
-0.33
0.51
-0.32
-0.41 (0.98)
0.44
(0.93)
(1.14)
(0.93)
(1.13)
Attendance (Percent Fall,
93.29
96.76
93.51
92.75 (8.95)
94.62
of Days Enrolled)
Grade 4
(8.55)
(5.21)
(7.49)
(6.01)
Spring,
91.90
96.40
92.43
91.61 (9.30)
93.63
Grade 4
(9.37)
(5.57)
(8.18)
(6.55)
Fall,
92.36
96.17
92.46
91.52
93.71
Grade 5
(12.03)
(9.19)
(11.58)
(13.18)
(10.04)
Spring,
91.12
95.84
91.68
90.14
92.76
Grade 5
(10.14)
(6.04)
(8.97)
(12.24)
(7.49)
Fall,
91.09
95.62
91.47
90.30
92.75
Grade 6
(13.39)
(9.96)
(12.48)
(13.23)
(12.14)
Spring,
89.91
95.58
90.38
88.43
91.93
Grade 6
(12.08)
(7.24)
(10.72)
(13.42)
(9.36)
Fall,
90.32
95.47
90.66
89.41
92.50
Grade 7
(13.38)
(9.55)
(12.51)
(13.56)
(11.22)
Spring,
88.31
94.95
88.67
86.88
91.04
Grade 7
(13.81)
(9.79)
(12.58)
(15.25)
(10.77)
Fall,
89.66
95.32
89.87
88.26
91.87
Grade 8
(14.00)
(9.05)
(13.14)
(15.20)
(11.40)
Spring,
86.00
92.38
85.99
84.38
88.16
Grade 8
(15.01)
(9.47)
(14.13)
(15.63)
(12.01)
20
Table 10:
Means and standard deviations for achievement and attendance by language
background
Mathematics
Achievement (withingrade z-scores)
Reading Achievement
(within-grade z-scores)
Attendance (Percent of
Days Enrolled)
Grade 4
Grade 5
Grade 6
Grade 7
Grade 8
Grade 4
Grade 5
Grade 6
Grade 7
Grade 8
Fall,
Grade 4
Spring,
Grade 4
Fall,
Grade 5
Spring,
Grade 5
Fall,
Grade 6
Spring,
Grade 6
Fall,
Grade 7
Spring,
Grade 7
Fall,
Grade 8
Spring,
Grade 8
Native
English
Speakers
(n = 177,868)
-0.04 (0.98)
-0.11 (1.00)
-0.15 (1.00)
-0.18 (1.03)
-0.25 (1.03)
0.00 (1.00)
-0.04 (1.03)
-0.07 (1.02)
-0.08 (1.03)
-0.12 (1.04)
93.41 (7.89)
Language Minority,
Non-ELL (n =
98,083)
ELLs in Grade
4 (n = 28,572)
0.20 (0.98)
0.19 (1.00)
0.18 (1.03)
0.17 (1.05)
0.15 (1.11)
0.07 (0.97)
0.08 (0.96)
0.08 (0.99)
0.09 (1.00)
0.08 (1.04)
95.07 (6.60)
-0.73 (0.99)
-0.65 (1.07)
-0.60 (1.08)
-0.58 (1.08)
-0.49 (1.06)
-1.01 (0.89)
-0.89 (0.97)
-0.86 (0.98)
-0.84 (1.04)
-0.77 (0.96)
93.98 (7.77)
92.03 (8.73)
94.33 (7.04)
93.50 (8.21)
92.50 (11.38)
94.13 (10.89)
92.78 (12.39)
91.27 (9.56)
93.59 (7.78)
92.74 (8.90)
91.37 (12.85)
93.29 (11.90)
91.89 (12.71)
90.12 (11.53)
92.65 (9.52)
91.56 (10.26)
90.66 (12.93)
92.78 (11.36)
91.22 (12.61)
88.63 (13.32)
91.45 (11.12)
89.87 (12.16)
89.99 (13.50
92.23 (11.67)
90.40 (13.09)
86.16 (14.52)
88.75 (12.48)
87.14 (13.68)
21
Figure 5:
Path diagram for hypothesized latent growth in attendance predicted by
racial/ethnic group
Note: Model also included measurement model for attendance as shown in Figure 1. F= Fall,
S = Spring, G = Grade
22
Figure 6:
Path diagram for latent growth in mathematics predicted by racial/ethnic group
Note: Model also included measurement model for achievement as shown in Figure 2.
G = Grade
23
Table 11 presents the results for attendance predicted by ethnic group while Table 12
presents the results for achievement. As shown in Table 11, Native American, Latino, and
African-American students all have notably lower initial (grade four ) status in attendance,
compared to White students, while Asian students have notably higher grade four status.
African-American students demonstrate steeper declines in each of the first three periods, but a
less steep decline in the last period, compared to White students. Latino and Native American
students demonstrate steeper declines during the middle two periods, spanning grade five to
grade seven. Asian students have less steep declines in each of the four periods. It is worth
noting that the residual variance for the final measurement occasion was set to 0 to avoid
convergence problems. As shown in Table 12, Native America, Latino, and African-American
students had substantially lower mathematics and reading/ELA achievement in grade four than
their White peers (nearly 1 SD in each case). These gaps persist through grade eight as shown by
the relatively trivial differences in rates of growth demonstrated by these three ethnic groups.
These results are also illustrated in Figures 5-7 in the main text.
Table 13 presents the results for attendance predicted by language background and Table
14 presents the results for achievement. As shown in Table 9, ELLs had slightly higher
attendance in grade four than their native English-speaking counterparts, and relatively similar
rates of growth. In contrast, language minority learners who were not designated as ELLs had
notably higher attendance rates in grade four and slightly less steep rates of decline, compared to
native English speakers. As with the models for attendance by ethnicity, the residual variance
for the final measurement occasion was set to 0 to avoid convergence problems in this model. In
contrast, ELLs’ achievement was much slower than their counterparts, as shown in Table 14. As
shown in the column marked Y-standardized estimates, the standardized difference between
ELLs and their native English-speaking peers was ¾ of a SD for mathematics achievement and
nearly 9/10ths of a SD for reading/ELA achievement in grade four. ELLs made notably
improvements over time, as indicated by their more positive rates of growth in both the grade
four-six and grade six-eight periods, but remain far below their peers as shown in Figures 9 and
10 in the main text. Language minority learners who were not designated as ELLs had somewhat
higher achievement in grade four in both mathematics and reading/ELA as well as slightly higher
rates of growth in both periods.
24
Table 11:
Selected Results for Piecewise Growth Model for Attendance Predicted by
Race/Ethnicity (N = 303,699)
Fixed Effects
Initial (Fall, Grade 4) Status
Slope 1 (Fall, Grade 4 – Fall, Grade 5)
Slope 2 (Fall, Grade 5 – Spring, Grade
6)
Slope 3 (Spring, Grade 6 – Spring,
Grade 7)
Slope 4 (Spring, Grade 7 – Spring,
Grade 8)
Variance
Components
Intercept (for
White)
Native American
Asian
Latino
African-American
Multiracial
Unknown
Intercept (for
White)
Native American
Asian
Latino
African-American
Multiracial
Unknown
Intercept (for
White)
Native American
Asian
Latino
African-American
Multiracial
Unknown
Intercept (for
White)
Native American
Asian
Latino
African-American
Multiracial
Unknown
Intercept (for
White)
Native American
Asian
Latino
African-American
Multiracial
Unknown
Initial Status
Unstandardized
Estimates
94.59***
Selected Y-Standardized
Estimates
-1.89***
2.18***
-1.12***
-1.36***
0.13
-2.40***
-1.38***
-0.27
0.31
-0.16
-0.19
0.02
-0.34
-0.10
0.64***
0.07
-0.14**
0.14
-1.45
-0.51***
-0.02
0.10
0.01
-0.02
0.02
-0.23
-0.79***
0.31***
-0.30***
-0.26***
-1.32*
0.81
-0.75***
-0.19
0.07
-0.07
-0.06
-0.32
0.20
-0.78*
0.31***
-0.89***
-0.82***
-0.83
-3.63*
-3.27***
-0.12
0.05
-0.13
-0.12
-0.12
-0.53
0.16
0.40***
0.10
0.38***
2.23*
2.50
48.74***
0.02
0.04
0.01
0.04
0.24
0.27
Slope 1
41.54***
Slope 2
17.38***
Slope 3
46.08***
Slope 4
89.01***
Note: Model also included residual covariances among the latent growth terms as in the unconditional growth model. * p < .05;
** p < .01; *** p < .001
25
Table 12:
Selected Results for Piecewise Growth Model for Mathematics and
Reading/English-language Arts Achievement Predicted by Race/Ethnicity (N =
303,699)
Fixed Effects
Initial
(G4)
Status
Slope 1
(G4 –
G6)
Slope 2
(G6 –
G8)
Intercept (for
White)
Reading/ELA (N = 294,863)
Unstandardized
Selected YEstimates
Standardized
Estimates
0.56***
Native
American
Asian
Latino
AfricanAmerican
Multiracial
Unknown
Intercept (for
White)
-0.86***
-0.94
-0.86***
-0.96
0.10***
-0.75***
-0.81***
0.10
-0.81
-0.88
-0.13***
-0.83***
-0.76***
-0.14
-0.93
-0.85
-0.34***
-0.47***
-0.04***
-0.37
-0.52
-0.25***
-0.44***
-0.02***
-0.28
-0.49
Native
American
Asian
Latino
AfricanAmerican
Multiracial
Unknown
Intercept (for
White)
0.01
0.07
0.004
0.03
0.08***
0.02***
0.003
0.51
0.15
0.02
0.05***
0.02***
-0.01***
0.33
0.17
-0.08
0.05
-0.001
-0.03***
0.32
-0.01
0.008
0.003
-0.02***
0.06
0.02
0.00
-0.001
0.01
0.04
0.09***
0.005*
-0.002
0.52
0.03
-0.01
0.05***
0.01***
0.01**
0.35
0.09
0.04
0.00
0.006
0.71***
0.04
-0.002
0.84***
0.07*
-0.06
0.68***
0.48
-0.39
Native
American
Asian
Latino
AfricanAmerican
Multiracial
Unknown
Variance
Components
Mathematics (N = 296,323)
Unstandardized
Selected YEstimates
Standardized
Estimates
0.54***
Initial
Status
Slope 1
0.03***
0.98***
0.02***
Slope 2
0.03***
0.97***
0.02***
Note: Model also included residual covariances among the latent growth terms as in the unconditional growth
model. * p < .05; ** p < .01; *** p < .001
26
Table 13:
Selected Results for Piecewise Growth Model for Attendance Predicted by
Language Background (N = 303,622)
Fixed Effects
Initial (Fall, G4)
Status
Slope 1 (Fall, G4 –
Fall, G5)
Slope 2 (Fall, G5 –
Spring, G6)
Slope 3 (Spring, G6
– Spring, G7)
Slope 4 (Spring, G7
– Spring, G8)
Variance
Components
Intercept (for
English-only)
Language Minority
non-ELL
English language
learner in G4
Intercept (for
English-only)
Language Minority
non-ELL
English language
learner in G4
Intercept (for
English-only)
Language Minority
non-ELL
English language
learner in G4
Intercept (for
English-only)
Language Minority
non-ELL
English language
learner in G4
Intercept (for
English-only)
Language Minority
non-ELL
English language
learner in G4
Initial Status
Unstandardized
Estimates
93.35***
Selected YStandardized Estimates
1.70***
0.24
0.63***
0.09
-1.52***
0.44***
0.07
0.60***
0.09
-0.72***
0.11***
0.03
-0.03
-0.01
-1.44***
0.32***
0.05
-0.20***
-0.03
-3.03***
-0.07
-0.01
-0.03
-0.00
49.52***
Slope 1
41.60***
Slope 2
17.34***
Slope 3
46.19***
Slope 4
89.01***
Note: Model also included residual covariances among the latent growth terms as in the unconditional growth
model. * p < .05; ** p < .01; *** p < .001
27
Table 14:
Selected Results for Piecewise Growth Model for Achievement Predicted by
Language Background
Fixed
Effects
Initial
(G4)
Status
Slope 1
(G4 –
G6)
Slope 2
(G6 –
G8)
Intercept (for
English-only)
Language
Minority nonELL
English
language
learner in G4
Intercept (for
English-only)
Language
Minority nonELL
English
language
learner in G4
Intercept (for
English-only)
Language
Minority nonELL
English
language
learner in G4
Mathematics (N = 292,250)
Unstandardized
Selected YEstimates
Standardized
Estimates
-0.03***
Reading/ELA (N = 294,791)
Unstandardized
Selected YEstimates
Standardized
Estimates
0.01***
0.24***
0.26
0.08***
0.27
-0.67***
-0.73
-0.95***
-0.87
-0.04***
-0.03***
0.04***
0.26
0.04***
0.14
0.12***
0.73
0.10***
0.45
-0.04***
-0.02***
0.03***
0.19
0.02***
0.14
0.09***
0.57
0.06***
0.45
Variance
Components
Initial
0.78***
0.93
0.74***
0.91
Status
Slope 1
0.03***
0.95
0.02***
0.96
Slope 2
0.03***
0.97
0.02***
0.98
Note: Model also included residual covariances among the latent growth terms as in the unconditional growth
model. *** p < .001
28
Is middle grades performance equally predictive of later on-track status across
ethnic and language groups?
To investigate whether middle-grades performance is equally predictive of being on-track
in grade nine for ultimate high school graduation, we fitted the model displayed in Figure 5
separately to sub-samples comprises of each ethnic group and of each language group.
To investigate whether middle-grades performance is equally predictive of being on-track
in grade nine across the different ethnic and language groups, we fitted the model displayed in
Figure 5 separately to sub-samples comprises of each ethnic group and of each language group.
As shown by the standardized estimates in Table 15, the relative predictive power of attendance
and achievement levels and rates of growth appeared to be largely similar across ethnic groups,
with some exceptions. For African-Americans, both initial status and Slope 1 in attendance had
notably larger effects on grade nine “on-track” indicator score than for other groups. In addition,
Slope 3 for White students was notably less predictive of grade nine “on-track” indicator score
than for other groups. As shown in Table 16, predictive relationships were also largely similar
across language backgrounds, with perhaps only one exception. Attendance Slope 3 was less
predictive for language minority students not designated as ELLs compared to other groups.
29
Table 15:
Selected Results from Structural Equation Models with Initial Status and Rates of Growth in Both
Attendance and Achievement Predicting Grade (G) 9 Ontrack Indicator Score, Fitted Separately
for Each Ethnic Group
Paths
White (n =
44, 871)
.29***
Standardized Estimates
Asian (n =
African-American (n
37, 327)
= 101,237)
.26***
.37***
Latino (n =
118, 898)
.31***
Attendance Initial (Fall, G4) Status  G9
Ontrack Indicator
Attendance Slope 1 (Fall, G4 –Fall, G5)
.27***
.23***
.32***
.27***
 G9 Ontrack Indicator
Attendance Slope 2 (Fall, G5 –Spring,
.30***
.23***
.31***
.26***
G6)  G9 Ontrack Indicator
Attendance Slope 3 (Spring, G6-Spring,
.03
.16***
.23***
.16***
G7) G9 Ontrack Indicator
Attendance Slope 4 (Spring, G7-Spring,
.39***
.35***
.28***
.31***
G8) G9 Ontrack Indicator
Achievement Composite Initial (G4)
.31***
.30***
.35***
.33***
Status  G9 Ontrack Indicator
Achievement Composite Slope 1 (G4-G6)
.11***
.12***
.17***
.18***
 G9 Ontrack Indicator
Achievement Composite Slope 2 (G6-G8)
.17***
.19***
.19***
.21***
 G9 Ontrack Indicator
Note: As shown in Figure 5, mnodel also included variances and intercepts for and covariances among all latent
growth terms as well as a residual variance for Grade 9 ontrack indicator score. *** p < .001
30
Table 16:
Selected Results from Structural Equation Models with Initial Status and Rates of Growth in Both
Attendance and Achievement Predicting Grade (G) 9 Ontrack Indicator Score, Fitted Separately
for Each Ethnic Group
Native
English
Speakers
(n =
177,842)
.33***
Standardized
Estimates
English Language
Learners in G4
(n = 28, 567)
Language
Minority,
Non-ELL
(n = 98, 074)
Attendance Initial (Fall, G4) Status  G9 Ontrack
.30***
.29***
Indicator
Attendance Slope 1 (Fall, G4 –Fall, G5)  G9 Ontrack
.29***
.29***
.24***
Indicator
Attendance Slope 2 (Fall, G5 –Spring, G6)  G9
.28***
.25***
.30***
Ontrack Indicator
Attendance Slope 3 (Spring, G6-Spring, G7) G9
.21***
.20***
.03
Ontrack Indicator
Attendance Slope 4 (Spring, G7-Spring, G8) G9
.28***
.27***
.38***
Ontrack Indicator
Achievement Composite Initial (G4) Status  G9
.37***
.32***
.31***
Ontrack Indicator
Achievement Composite Slope 1 (G4-G6)  G9
.17***
.18***
.14***
Ontrack Indicator
Achievement Composite Slope 2 (G6-G8)  G9
.18***
.21***
.19***
Ontrack Indicator
Note: As shown in Figure 5, mnodel also included variances and intercepts for and covariances among all latent
growth terms as well as a residual variance for Grade 9 ontrack indicator score. *** p < .001
31
Do these patterns hold across schools?
To investigate the extent to which these patterns hold across different school or are
specific to certain schools, we used multilevel SEM modeling to partition the variance in the
growth terms for attendance and achievement into within-school and between-school
components. This approach provides insight into the extent to which middle schools influence
these outcomes. In particular, we fitted models for individual growth nested within grade six
school, including only those indicators for attendance and achievement between grade six and
grade eight. Figure 7 presents the path diagram for the hypothesized two-level model, with both
within-school and between-school components. Table 13 presents selected results from fitting
this model. As shown by the interclass correlations (which represent the proportion of total
variance that is between schools) for attendance in Table 17, only very small amounts of the
variance in attendance initial (grade six) status (five percent) and slopes (two-three percent) are
associated with differences between schools. As shown by the interclass correlations for
achievement, larger but still relatively small percentages of the variance in achievement initial
(grade six) status (27 percent) and slopes (10 percent) are associated with differences between
schools. This is evidence to suggest that individual student differences within schools are much
larger than differences between schools and that this is particularly the case for attendance.
32
Figure 7:
Path diagram for hypothesized multilevel latent growth model for attendance and
achievement as nested within grade six school
Note: Model also included measurement models for attendance as shown in Figure 1 and
for achievement analogous to the model shown in Figure 2. Att = Attendance; Ach =
Achievement Composite; G = Grade
33
Table 17:
Selected Results for 3-Level Piecewise Growth Model for Unconditional
Attendance Growth Nested within Grade 6 School (N = 266,450)
Fixed Effects
Variance Components
Attendance Initial (Fall, G6) Status
Attendance Slope 3 (Fall, G6 – Spring, G7)
Attendance Slope 4 (Spring, G7 – Spring, G8)
Achievement Initial (G6) Status
Achievement Slope 2 (G6 –G8)
Attendance Initial Status
Attendance Slope 3
Attendance Slope 4
Achievement Initial Status
Achievement Slope 2
Note: Model also included covariances among the latent growth terms.
34
Intercept
Intercept
Intercept
Intercept
Intercept
Within-school
Between-school
Interclass Correlation
Within-school
Between-school
Interclass Correlation
Within-school
Between-school
Interclass Correlation
Within-school
Between-school
Interclass Correlation
Within-school
Between-school
Interclass Correlation
Estimates
91.98***
-1.23***
-2.39***
-0.09***
-0.02***
115.35***
6.06***
.05
34.51***
0.66***
.02
35.93***
1.22***
.03
0.62***
0.22***
.27
0.03***
0.003***
.10
III.
EXPLORATORY ANALYSIS OF SCHOOL CHARACTERISTICS
To explore whether these patterns of achievement and attendance growth differ by
observable school characteristics, we conducted some additional descriptive analyses.
Specifically, we identified which school students were enrolled in for grade six. We then
divided the schools into four quartiles based on three dimensions: 1) their students’ average
growth rate in achievement between grade six and grade eight, 2) their students’ average growth
rate in attendance between Fall, grade six and Spring, grade seven (i.e., attendance slope three in
the original attendance growth models), and 3) their students’ average growth rate in attendance
between Spring, grade seven and Spring grade eight (i.e., attendance slope four). We then
estimated descriptive statistics (means and standard deviations) for several publically-available
variables for school characteristics by achievement and attendance growth quartile. School-level
variables were measured in the year in which the sampled students were in grade six; in most
cases, this means that we had four values for the school-level variables corresponding to the four
cohorts, which were then averaged. It is worth noting that this approach characterizes schools
based not on their students’ levels of achievement and attendance (as might typically be done for
accountability purposes), but rather on how their students change in achievement and attendance
during the middle grades.
Table 18 and 19 present the results of these exploratory analyses, with Table 18
presenting results for teacher characterized and Table 19 presenting results for student
characteristics aggregated to the school level. When one compares the four quartiles in Table 18,
it appears that the measured teacher characteristics, including experience and “highly qualified”
status, were roughly the same across schools with substantially different achievement and
attendance growth. In contrast, demographic variables do appear to be somewhat different
across the schools, consistent with our previous student-level analyses of demographic
predictors, as shown in Table 19. For instance, consistent with our previous results, schools
which demonstrated higher average achievement growth in the middle grades had lower
proportions of African-American students, compared with schools which demonstrated lower
average achievement growth. Similarly, schools which demonstrated higher average attendance
growth during the Fall, grade six to Spring, grade seven period had lower proportions of AfricanAmerican and Latino students, compared to schools which demonstrated lower average
attendance growth. As described in the main text, schools with higher rates of achievement and
attendance growth also tended to have much lower concentrations of students receiving free
lunch. These exploratory analyses are not meant to support causal inferences about the school
characteristics that matter most, but are rather intended to spur additional research into what
schools can do to support more positive achievement and attendance trajectories during the
middle grades.
35
Table 18:
Means for Teacher Characteristics for Schools Classified in the First through
Fourth Quartile, based on Achievement and Attendance Growth
Percent of teachers
with more than 2
years in this school
Percent of teachers
with more than 5 years
experience anywhere
School Average
G6 - G8
Achievement
Growth
Quartile 1
Quartile 2
Quartile 3
Quartile 4
67.31
63.96
66.63
64.19
56.26
53.79
55.16
53.09
Percent of core
classes taught by
highly qualified
teachers
88.38
85.61
88.64
87.59
School Average
F,G6 - S,G7
Attendance Growth
Quartile 1
Quartile 2
Quartile 3
Quartile 4
65.48
64.58
66.39
65.65
54.93
54.23
55.33
53.76
85.28
86.02
87.99
91.19
School Average
S,G7 - S, G8
Attendance Growth
Quartile 1
Quartile 2
Quartile 3
Quartile 4
66.61
65.83
64.69
65.95
54.40
55.54
53.49
55.63
88.28
87.48
87.11
87.57
Table 19:
Means for Student Characteristics for Schools Classified in the First through
Fourth Quartile, based on Achievement and Attendance Growth
Enrollment
Total
School Average
G6 - G8
Achievement
Growth
Quartile 1
Quartile 2
Quartile 3
Quartile 4
695
689
738
804
Percent of
students
receiving
free lunch
72.30
75.01
67.70
64.21
School Average
F,G6 - S,G7
Attendance
Growth
Quartile 1
Quartile 2
Quartile 3
Quartile 4
654
676
782
790
84.38
78.91
67.37
46.73
46.49
42.39
31.80
19.91
46.50
43.48
37.96
25.75
15.45
14.80
14.77
12.91
24.72
19.00
13.32
10.20
School Average
S,G7 - S, G8
Attendance
Growth
Quartile 1
Quartile 2
Quartile 3
Quartile 4
898
703
671
715
71.21
67.74
70.15
70.78
29.21
34.56
35.82
40.81
38.57
37.74
39.75
37.58
15.70
14.40
14.77
13.56
13.89
19.80
18.18
12.30
36
Percent of
students who
are AfricanAmerican
40.97
42.45
31.89
23.22
Percent of
students
who are
Latino
37.78
37.81
39.76
38.80
Percent of
students
who are
ELLs
11.90
14.49
15.13
16.95
Percent of
students in
special
education
18.09
20.86
14.96
12.28
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