San Antonio’s
EASTSIDE PROMISE
NEIGHBORHOOD
A NEIGHBORHOOD PROFILE
1
Prepared by:
Christine Drennon, Ph.D.
Trinity University
with significant contributions by:
Dr. Laura McKiernan, CI-NOW;
Dr. John Orange, Trinity University;
Mr. Charlie Mitchell, Ph.D. student, The Johns Hopkins University.
With significant help from:
Mr. Don Arispe,
Ms. Rina Moreno,
Mr. Sean Henry,
and Ms. Alberta Harris.
With additional contributions from:
Ms. Jaclyn Bays,
Ms. Sheila Castle,
Ms. Erin Dunk,
Mr. David Nikaido,
All of Trinity University.
September, 2011
2
This report integrates the findings of ten months of data collection and analysis of the Eastside
Promise Neighborhood, in San Antonio, TX. The Eastside Promise Neighborhood is bounded by I-37 to
the west, Fort Sam Houston to the north; AT&T Center Parkway to the east and East Commerce Street
to the south. The Eastside Promise Neighborhood is approximately 3.5 square miles, and is home to
17,955 people.
The report is divided into 6 sections: the first is statistical presentation of the current state of
the neighborhood; the second is an inventory of the assets found in the Eastside Promise Neighborhood;
the following two sections are two different representations of the neighborhood – one given by
community members and the next a quantitative analysis of the academic performance of our students;
the next section is also quantitative, and includes the segmentation analysis and predictive statistics
modeling student academic performance through the elementary and middle school years. The final
section integrates the data from all of these data-gathering efforts with the best practices and evidence
documented in the academic and policy literature to analyze segments of time through a child’s
elementary and middle school years in an attempt to elucidate the complexity of life in the Eastside
Promise Neighborhood. It is here that the needs of community are articulated. A separate document
includes the best practices and evidence bases for the findings in this analysis.
3
SECTION I: A STATISTICAL SNAPSHOT OF THE PROMISE NEIGHBORHOOD
Section I is a quantitative ‘snapshot’ of the community that resides in the Eastside
Promise Neighborhood [EPN] (how many people live there, what they do, how much money
they make, and so on). The snapshot is intended to present a baseline from which to begin to
debate, discuss, and prepare an educational plan for the community, thus the focus will return
repeatedly to the educational system, and school-community relations. To help facilitate the
preparation of the educational plan, and to ensure that it is ‘data-driven’ and community-led,
two interpretations follow the snapshot: one from the community itself and the other a
quantitative interpretation of student achievement in the schools. The evidence base for each
of the indicators presented below is presented in an additional report.
Much of the following data is presented by census tract and/or by zip code; please refer
to the following two maps for reference:
4
Our Basic Demographics
Approximately 17,955 people live in the Eastside Promise Neighborhood1. The total population
and ethnic make-up of the population has changed in the past two decades; once the heart of
San Antonio’s African-American community, the area is now home to over 12,000 Hispanics,
who make up 67.5% of the population today. There was a 2.58% decrease in the population of
our area since 2000, although San Antonio grew by 16% in the past 10 years.
.
Total Population
African-American
Hispanic
Anglo
Other (including ‘of
two or more races’)
2000 population
18,431 (100%)
6,268 (34%)
10,351 (56.16%)
821
(4.45%)
991
(5.37%)
2009 population
17,955 (100%)
4,462 (24.85%)
12,127 (67.54%)
1,254 (6.98%)
112
(.6%)
Percent change
-2.58% change
-26.9%
+20.26%
+56.85%
1
All demographic information (except where noted) is taken from the American Community Survey 2005-2009
sample. www.census.org.
5
The distribution of races and ethnicities among the neighborhoods and among the school is not random;
the eastern neighborhoods are more heavily African-American – a reflection of the racially restrictive
covenants that were present in housing deeds in the more western neighborhoods and elsewhere in the
city, and which may have been enforced through the late 1940s. Residents of Harvard Place-Eastlawn,
the neighborhood association to the eastern section of our Promise Neighborhood, recall the days of
segregated housing, schools, and commercial establishments, noting that many settled in their homes in
the 1950s because “they were the only place we could buy a house”.
Like many inner-city neighborhoods in San Antonio, the population in the Promise Neighborhood has
aged, but a young generation is once again present as new families have moved into the neighborhood:
6
AGE
MALE
FEMALE
TOTAL
PROMISE
SAN
NEIGHBROHOOD ANTONIO
8.3%
Under 5 years
990
671
1,661
9.25%
5 to 9 years
702
747
1,449
8.07%
10 to 14 years
641
724
1,365
7.60%
15 to 19 years
529
609
1,450
8.08%
20 to 24 years
108
464
992
5.52%
25 to 29 years
493
575
1,068
5.95%
30 to 34 years
636
613
1,249
6.96%
35 to 39 years
485
519
1,004
5.59%
40 to 44 years
571
399
970
5.40%
45 to 49 years
611
694
1,305
7.27%
50 to 54 years
681
644
1,325
7.38%
55 to 59 years
533
434
967
5.39%
60 and 64 years
304
486
1,028
5.73%
65 and 69 years
84
206
375
2.09%
70 to 74 years
248
247
495
2.76%
75 to 79 years
177
287
464
2.58%
80 to 84 years
146
349
495
2.76%
64
8,003
229
8,897
293
17,955
1.63%
100.00%
85 years and over
TOTAL
7.5%
7.7%
7.3%
7.9%
15.2%
13.8%
12.7%
5.1%
4.1%
5.3%
3.7%
1.4%
8.3%
7.5%
7.7%
7.3%
7.9%
100.00%
5,925 people are less than 19 years old (32.9% of the population), a slight increase from the 2000 census
(31.4%). There is not an appreciable difference between males and females overall, but in the early
adult cohorts there is an appreciably larger number of females. Our young adult population dwindles in
comparison with the larger city and in comparison to other age cohorts in the Promise Neighborhood,
indicating that these young people are leaving the neighborhood for a variety of reasons, that may
include both financial independence and incarceration.
7
Median age
Male
Female
1102
53.1
50.1
57
1110
48.9
45.9
52.3
1301
33.4
32.3
34.5
1305
37
39.2
36.6
1306
25.8
19.9
34.1
1307
27.1
27.1
27.5
The median age in the neighborhood is 37.58, compared to 32.7 for the City of San Antonio – indicating
an aging population, which helps us understand the decreasing enrollment in our inner-city schools. The
youngest cohorts (less than 5 and 5-9) hold a larger percentage of the population than older ones do; as
they age, once empty schools may once again be full, making planning difficult but accurate projections
a necessity.
The median age in one census tract (1102) is 53.1 years; not far from here though, a much
younger population can be found, in census tract 1306 where the median age is 25.8. Statistics such as
these pose difficulties for community development (due to the different needs of such radically different
sub-populations) but also present opportunities due to the interesting diversity present in such a small
area. The older neighborhoods are also (not surprisingly) the more established neighborhoods where
the housing stock has been revitalized or maintained over the past 100 years, and where the mobility
rates are not as high as elsewhere.
The younger neighborhoods (to the east) are also the neighborhoods where the housing authority has
significant public housing multi-family development. There are public housing developments for the
8
elderly and disabled in the western census tracts, which may also be pushing the median age higher in
those tracts. The overwhelmingly lower age range in tract 1306 is a direct reflection of the presence of
the Wheatley Courts apartment complex (there are 171 grade-school age children living in the Wheatley
Courts).
Family and Household Structure
The total number of families in the Eastside Promise Neighborhood has decreased faster than the
decrease in overall population, indicating a shift in family2 and household3 structure in the past 10 years.
Total number of families
Total married-couple
families with children
Single parent, maleheaded families with
children
Single parent, femaleheaded families with
children
Children living with
grandparents or other
relatives
Number in 2000
4,214
1,056
Number in 2009
3819
775 (20.29%)
Percentage change
-10.34%
-36.25%
245
227
-7.9%
946 (22.4%)
983 (25.7%)
+3.7%
693
(386 with
grandparents)
In 2000 22.4% of our families with children were single-parented by a female householder; in 2009 that
figure jumped to 25.7% (compare to 9.9% for the city) – is the area is being left to single parents? There
are currently 36.25% fewer married couple families than there were in 2000 – a possible indication of
other additional stresses at work in the area – this idea will be explored in later sections.
2
A family consists of a householder and one or more other people living in the same household who are related to
the householder by birth, marriage, or adoption. All people in a household who are related to the householder are
regarded as members of his or her family. A family household may contain people not related to the householder,
but those people are not included as part of the householder’s family in tabulations.
3
A household includes all the people who occupy a housing unit. (People not living in households are classified as
living in group quarters.) A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single
room that is occupied (or if vacant, is intended for occupancy) as separate living quarters.
9
Total number of children
Total number of children
living with parent(s)
Total number of children
in married-couple
families
Total number of children
living in other than
married-couple families
Total number of children
in single parent, maleheaded families
Total number of children
in single parent, femaleheaded families
Children living with
grandparents or other
relatives
Children in foster care or
with unrelated adults
Children NOT living with
parents
Children in single-parent
households
Number in 2009
5,474
4,698
Percentage of children
100%
85.8%
2,048
37.4%
2,650
48.4%
444
8.1%
2,206
40.29%
693
12.65%
83
1.5%
776
14.1%
62.6%
The family structure in the Promise Neighborhood is complex. There are more single-parented children,
but possibly even more importantly, the structure of the family itself is more complicated, such that
census data may not capture the adult-child relations prevalent in the area. Families adapt to financial
hardship in a variety of ways, including adaptive family relationships. Aunts, uncles, and grandparents
play increasingly important roles as parents cope with increasing stress on the family. Over half of the
children in the Promise Neighborhood do not reside in traditional married-couple families; rather, the
majority are found in single-parent families, or are living with other relatives. The relationship between
family structure and academic success is a complicated one; at the most cursory level, the
preponderance of children in non-traditional families are considered ‘at-risk’ of academic failure, but the
family structure itself is probably an intermediary variable that indicates other stresses at work in the
community, including financial and social. Understanding the cause-effect relationship here is
important, as the ‘cause’ of academic failure is not necessarily the family structure, but the stresses that
produced that family structure; indeed, the family structure is probably a positive social and cultural
adaptation to extreme financial stress.
Family and Household Finances
Our families struggle financially. 2005-2009 American Community Survey data indicates that 60.1% of
the children in the Promise Neighborhood live below the federal poverty line (more than two-times the
10
number in the City of San Antonio (26.6%)4. The median household income for the five census tracts
was $19,766 in 2009 (compared to $43,087 for San Antonio)5.
Per capita
income
Median family
income
Percent
families in
poverty
Percent
female-headed
families in
poverty
Percent of
children in
poverty
Unemployment
rate
1102
1110
1301
1305
1306
1307
$10,661
$13,358
$12,918
$9,269
$7,482
$7,459
San
Antonio
$21,418
$15,286
$21,130
$36,875
$18,875
$16,015
$17,175
$51,540
28.8%
70.8%
27.4%
56%
76.1%
61.3%
14.8%
65.4%
89.9%
44.8%
72.4%
83.8%
96%
41.1%
35.3%
75.7%
27.4%
56.6%
74.7%
60.7%
26.6%
7.8%
11.8%
6.2%
7.6%
6.9%
16.8%
4.3%
Compared to the City of
San Antonio (itself a lowincome city compared to
other cities its size in the
U.S.), the Promise
Neighborhood census
tracts are mired in
poverty. When examined,
the statistics elucidate a
community in which a high
percentage of children are
living in poverty, many of
whom live with their
mothers only (see table
below). Our femaleheaded households exhibit
4
For a four person family (2 adults, 2 children) the poverty threshold is $17,083 (2009); for the three person family
(1 adult, 2 children), the poverty threshold is $17,083. Since most of our families are single-parented, I will use the
three-person threshold when calculating examples. 2011 federal poverty guidelines indicate for a family of three
the 100% poverty threshold is $18,530.
5
The Center for Public Policy Priorities in Austin Texas has calculated an alternative poverty index that is locally
st
specific and more in line with 21 century spending habits than the 1960s poverty index used by the federal
government. CPPPs calculation for a suitable, livable income level for a family of 4 in San Antonio is $37,000 (CPPP
2001, Making It: What it Really Takes to Live in Texas).
11
the highest levels of poverty of all of our families, reaching 96% in the neighborhood straddling I-35,
south of Fort Sam Houston; the new SAHA property, Sutton Oaks, is located in this census tract. Sutton
Oaks is a mixed-income development (30:30:30); the income figures quotes here are from the American
Community Survey (2003-2008) which includes the former SAHA development (Sutton Homes) which
was traditional public housing development (100% of units for 30% AMI). Thus the poverty figures
quoted here may change in the next few years as Sutton Oaks fills with new families. The families that
are represented in these statistics were offered vouchers through the Section 8 program; 89% have
chosen not to return. Wheatley Courts is located in the other tract showing exceptionally high poverty
rates; it is a traditional SAHA property which is home to 284 households ($5,284 average family income).
The census tracts further west in the Promise Neighborhood report significantly higher median family
incomes than those further from downtown.
A greater percentage of our children are living in poverty than ever before. 88% of our children
live below 200% poverty; 61% live at or below the federal poverty line, and 34% live in families or
households making less than 50% of the federal poverty standard.
Child Poverty Rates (0-17 years old) (assuming 5,439 children between 0-17 years old):
Under .50
.50-.74
.75-.99
1.00-1.24
1.25-1.49
1.50-1.74
1.75-2
(<$8,541)
($,8542-$12,641)
($12,812-$16,912)
($17,083-$21,182)
($21,183-$25,453)
($25,453-$29,724)
($29,725-$34,166)
<100% federal poverty limit
<200% federal poverty limit
ESTIMATED NUMER
1,852
898
521
596
321
378
235
PERCENTAGE
34%
17%
10%
11%
6%
7%
4%
3,271
4,801
60%
88%
The financial strains of joblessness and poverty both drive and are results of the social and personal
stresses that characterize our Promise Neighborhood. A vicious feedback loop exists between poverty,
lack of proper healthcare, lackluster academic performance, high attrition rates, and back to poverty.
Each of these indicators is detailed below.
Health Indicators
The physical and mental health of the population and the accessibility of health care are central
concerns of our Promise Neighborhood planning effort. There are two full-service clinics in the
proximity of the Promise Neighborhood and one mental health clinic (each is detailed more thoroughly
in the asset section of this report). We are using two measures of accessibility: geographic and financial,
i.e., what is the distance one must travel to obtain health care and once there, how much does it cost?
The economic or financial obstacles to healthcare may be more burdensome than the geographic ones,
given the convenience of two substantial clinics in the area and a comprehensive public transit system.
According to the American Community Survey, 16.8% of students in SAISD do not have health
insurance; in the Promise Neighborhood, survey data reports that approximately 24.4% of our students
fail to have any form of health insurance.
12
But geographic and financial obstacles may not be the only barriers to obtaining healthcare;
educational and/or social obstacles may also hinder access. Amongst the births to mothers residing in
our Promise Neighborhood, 12% in 2008 were to young mothers of less than 18 years old; 65.9% were
to single mothers (of any age). 38.7% of all mothers did not receive care in their first trimester, and
16.5% delivered early.
78202
Births
271
Births to Mothers age
37 (13%)
<18
Births to single mothers
178 (65%)
Low Birth Weight Births
35 (12%)
<2500 g
Estimated Premature
42 (15.4%)
<37 weeks
Births to Mothers
107 (39%)
Receiving Late or No
Prenatal Care >1st
trimester
Medicaid Funded Births
208 (76%)
Births Occurring within
64
24 months of Previous
Birth
Births to Mothers BMI 30 85 (31%)
and above before
Pregnancy
Births to Mothers with
163
Less than HS/GED
Infant Deaths
1
www.voicesforchildrenSA.org
78208
111
9 (8%)
San Antonio (2008)
26,940
74 (66%)
12 (10%)
46%
9%
21 (18%)
9%
41 (36%)
84 (75%)
24
51%
36 (32%)
27%
55
0
While the implications of these statistics on a child’s growth are not entirely clear, a pattern has
developed early of less than adequate attention to physical development of both mother and child.
When asked about diet, responses from parents in our neighborhood indicate that our children eat
more fruits and vegetables and eat fast food less than children in the rest of the city, but the current
obesity rate and other health indicators belie this survey data. A better indicator of healthcare
accessibility may be access to health insurance. Census data indicate that for those children living within
the jurisdiction of the San Antonio Independent School District [SAISD], 19.7% are uninsured. School
district surveys report that approximately 75% of our children are insured (albeit under-insured).
Census statistics for insurance coverage of youth in SAISD:
2008
2009
Under 18 years
76,016
82,233
No insurance
17,348
13,777
% uninsured
22.8%
16.8%
TOTAL
158,249
31,125
19.7%
13
SAISD statistics for insurance coverage of youth in the PN schools:
NUMBER OF STUDENTS ENROLLED IN VARIOUS INSURANCE PROGRAMS BY SCHOOL
Chip
Care Link
Medicaid Private Military TOTALS STUDENT POPULATION
PERCENTAGE
6
0
229
11
2
248
284 87.32%
8
2
307
16
0
333
499 66.73%
27
5
365
26
0
423
493 85.80%
29
4
220
18
3
274
413 66.34%
18
4
200
25
1
248
344 72.09%
75.66%
TYNAN EC
WASHINGTON EL
BOWDEN EL
PERSHING EL
WHEATLEY MS
Early attention to pre-natal and early childhood healthcare nurtures the cognitive development that is
taking place so quickly in each child’s early years (before they turn 5 years old). Thus attention to
healthcare will help us understand the preparation our little ones receive as they get ready for school.
Educational Attainment
The two demographic indicators explored above (economic and access to healthcare) impact the
academic success rates amongst our youth. In fact, levels of educational attainment in our adult
population provide insight into the poverty figures presented above.
1102
Number of
people
over 25
With less
than 9th
grade
education
Without a
high
school
diploma
With a
high
school
diploma
Associate’s
degree
Bachelor’s
degree
Higher
degree
1110
1301
1305
1306
1307
SAN
ANTONIO
807,449
608
1,735
2,554
2,238
2,696
1,207
28.61%
29.9%
16.2%
15.9%
22%
28.3%
10.5%
55.42%
57.06%
74.2%
66.44%
53.41%
47.47%
10.5%
44.58%
42.94%
25.8%
33.56%
46.59%
52.53%
89.5%
1.9%
5.3%
5.5%
3%
1.8%
9.6%
6.7%
3.2%
4.4%
4.1%
3.3%
2.8%
6.7%
15.0%
0.8%
4.1%
.9%
.9%
0
4%
8.4%
It is said that the greatest predictor of a child’s academic success in school is the educational attainment
level of their parent(s). Only one of our census tracts has a majority of adults holding a high school
14
diploma; the others vary between 50-75% of adults not having achieved their high school diploma.
While we cannot automatically assume that the children that struggle in school necessarily have parents
who failed to complete high school, the data encourages further research into this relationship. If the
relationship indeed exists among our Promise Neighborhood families, intervention at the adult
education level may be warranted, as role models in the family may be as or even more influential on a
student’s success than additional programming or support in the school itself can be.
Before looking at the academic success rates of our students, some of their basic social
characteristics are presented in the table below. These are intended to demonstrate the stresses that
many of our children work under in school, thus, together with the adult education levels in the
neighborhood, providing some insight into the levels of academic challenge amongst our children.
Bowden ES
Have limitedEnglish
proficiency
Receive free or
reduced lunch
Qualify for
Special
education
Pershing ES
Washington ES
27.4%
32.9%
18.8%
Wheatley
MS
27.0%
SAISD
98.8%
98.5%
99.6%
98.8%
92.6%
7.1%
5.8%
8.2%
25.6%
10.7%
18.2%
To summarize, our Promise Neighborhood schools have higher rates of students for whom English is not
their first language, and probably is not spoken at home; and our middle school students are more than
twice as likely to have been identified to receive special education instruction as other students in our
district. The entire district is comprised of students whose families struggle financially, and our Promise
Neighborhood schools are representative of that.
Taking the above information into account, we can now examine academic success rates. All
academic and educational indicators are explored more in section 4 of this report (including the
segmentation analysis). Academic preparedness may be measured beginning in kindergarten with the
Texas Primary Reading Inventory [TPRI] test. The TPRI is given three times during the kindergarten year
– at the beginning, middle, and end of the school year [BOY, MOY, and EOY, respectively] – to determine
readiness. Three tests are given that together comprise the TPRI. Kindergarteners are identified as
‘developed’ or ‘still developing’ on each test. In order to perform statistical testing on these results, we
have labeled ‘developed’ as ‘1’ and ‘still developing’ as ‘0’ for each of the three tests, and then added
the three scores together. A child who scored ‘developed’ on all three tests thus receives a ‘3’ in this
coding scheme; a child who scored ‘still developing’ receives a ‘0’. Ideally, all kindergarteners enter
kindergarten ready so that the school year may be spent preparing them to enter their elementary
school years. Unfortunately, many of our Promise Neighborhood children are not considered
‘kindergarten ready’ until they actually finish kindergarten. The valuable lessons of kindergarten thus
must be integrated with the more basic skills identified as age-appropriate for our 5-year olds.
As indicated in the charts below, at the beginning of kindergarten 69.3% of our students are
considered not kindergarten-ready; it will take them all of their kindergarten year to become kinderready, at which time they advance to 1st grade. It goes without saying the values of kindergarten are
partially lost on many of these children.
15
0
1
2
3
n=
TPRI_BOY
36%
33%
25%
6%
1212
TPRI_MOY
16%
33%
34%
17%
1268
TPRI_EOY
7%
18%
28%
47%
1428
47%
28%
TPRI_EOY
18%
7%
17%
34%
33%
TPRI_MOY
16%
6%
25%
TPRI_BOY
33%
36%
3
2
1
0
The impact of kinder-readiness on latter grade performance is examined in later sections of this report.
Beginning, the 3rd grade, the academic indicator used in the following statistical analyses is based on the
results of the Texas Assessment of Knowledge [TAKS]. Our children perform at a consistently lower rate
than other students in the school district and state.
16
3rd grade reading
4th grade reading
5th grade reading
6th grade reading
7th grade reading
8th grade reading
Percent
commended
19.7%
11.4%
12.4%
18.4%
8%
17.9%
Percent pass
(district)
62.8% (84%)
52.6% (77%)
61.3% (78%)
53.2% (77%)
57.1% (77%)
56.2% (86%)
Percent pass
(campus group6)
88%
78%
78%
73%
77%
83%
Percent fail to
pass
17.4%
35.9%
26.3%
28.3%
33.6%
25.8%
The Promise Neighborhood Advisory Board agreed to use ‘commended performance’ as the baseline
against which to measure student performance, as commended performance on the Texas standardized
tests is the best signal of college readiness, and is comparable with other states’ acceptable levels. It is
also an indication of performance on the next phase of Texas assessment instruments known as STARR.
The students in our Promise schools consistently score lower than comparable students in the school
district and across the state.
Reading
Math
Percent
commended
(all Promise
schools)
17.4%
17.5%
Percent
Percent
commended commended
(district)
(campus group)
Percent
commended
(state)
22%
16%
33%
29%
24%
28%
Past years’ results indicate that the students attending schools in the Promise Neighborhood achieve
commended scores far less often than students across the state or the district. When our Promise
Neighborhood schools are compared to comparable schools throughout Texas, a pattern emerges that
may help us understand the learning trajectories of our children. In the earliest grades tested (3rd, 4th)
our children perform a bit below the levels of comparable children elsewhere. By the 5th grade though,
they have become competitive with those children (although not with the majority of the state). This
evolution may indicate the extreme lack of preparedness of our very young students (kindergarten-3rd
6
Campus Group: Each campus is assigned to a unique comparison group of 40 other public schools (from
anywhere in the state), that closely matches that campus on six characteristics. Comparison groups are provided
so that schools can compare their performance to that of other schools with whom they are demographically
similar. Comparison groups are also used for determining the Comparable Improvement Gold Performance
Acknowledgments.
The demographic characteristics used to construct the campus comparison groups include those defined in statute
as well as others found to be statistically related to performance. They are:
 the percent of African American students enrolled for 2009-10;
 the percent of Hispanic students enrolled for 2009-10;
 the percent of White students enrolled for 2009-10;
 the percent of economically disadvantaged students enrolled for 2009-10;
 the percent of limited English proficient (LEP) students enrolled for 2009-10; and
 the percent of mobile students as determined from 2008-09 cumulative attendance.
Source: Texas Education Agency (http://www.tea.state.tx.us/); Glossary, AEIS 2009-1010.
17
grade), but their increasing achievement in the upper-elementary grades – a possible indication of a
greater need for early childhood educational interventions.
Looking ahead to student performance in the middle school years, there is a statistically
significant difference in reading TAKS grades between those who attend Wheatley Middle School and
those who attend the pre-K – 8th grades academies in the area; those attending the academies are more
likely to achieve a commended performance in the middle school years on the TAKS reading test than
those attending the traditional middle school (Wheatley).
There are at least three possible explanations for this disparity: K-8th academies provide a
consistent environment between the elementary and middle school years that the transition from 5th to
6th grade in the traditional progression from elementary to middle school does not; poorer-performing
students at the academies in the area may be moving to Wheatley in their middle school years; or
(possibly) the academies actually are more effective at preparing these students for higher levels of
academic achievement. There is a distinct possibility that students at risk of dropping out of school in
the later years, and coming from situations of poverty and insecurity at home, may respond well to the
smaller, more intimate scale of the academies. Finally, higher densities of poor performing students
may cause a negative contagion, the more dispersed they are the less of an impact the contagion may
have.
63%
62%
58%
36%
27%
23%
15%
6%
Target Middle
10%
Other SAISD Middle
Commended
Pass
The chart to the left indicates
that 6% of the students entering
Wheatley Middle School earned
commended performance on
their 5th grade TAKS test; 10% of
those attending other SAISD
middle schools did, and 15% of
those attending a non-SAISD
middle school achieved
commended performance on
their 5th grade TAKS – an
exemplary example of the selfselection that appears to happen
in the student body as our EPN
children age.
Non SAISD Middle
Fail
Educational data and analysis will be presented in greater depth in the following sections of this
report.
In addition to health care and school preparedness, the neighborhood itself also impacts the readiness
of our children to learn. The following section examines the housing stock in particular.
18
Housing Stock
The neighborhoods in the Eastside Promise Neighborhood were built beginning in the late 19th
century (those neighborhoods furthest to the west, bordering downtown) and throughout the 20th
century. They are predominately older neighborhoods, and the housing styles reflect an earlier building
tradition, with pier and beam foundations, tall, vertical windows, and a floor plan that once allowed the
air to move through an un-air conditioned home. Today, many of these houses are considered obsolete,
although young urban families value the architectural style and craftsmanship embodied in them.
Because of the high building standards with which many were constructed, they are worthy of
reinvestment, although rehabilitation costs are similar to new construction costs at the edge of town. In
the meantime, many have passed into renter-ship; 51.6% of homes are owner-occupied. There are 225
Section 8 properties in the neighborhood, and the public housing authority offers 555 subsidizedhousing units for families, elderly and the disabled. A real estate survey conducted in 2006 categorized
the housing stock in the area as having the “Lowest housing values. Highest foreclosure rate. Highest
rate of vacant parcels. Very low construction activity. Highest rate of code complaints” (MVA 2006).
It is recommended that American families budget between 25%-35% of their gross monthly income on
housing7. Thirty-six percent of families in rental units in the Promise Neighborhood pay less than 30% of
their monthly income on rent; it is estimated that 58.2% pay over 30% -- signaling a lack of affordable
housing in the neighborhood and offering a possible explanation for our higher than average mobility
rates.
7
The conventional 30 percent of household income that a household can devote to housing costs before the
household is said to be “burdened” evolved from the United States National Housing Act of 1937. The National
Housing Act of 1937 created the public housing program, a program that was designed to serve those “families in
the lowest income group.” By 1940, income limits gave way to the maximum rent standard in which rent could not
exceed 20 percent of income – in practice, the same as the predecessor income limit standard. The Housing Act of
1959 maintained maximum rents, but it also gave local public housing authorities more autonomy in establishing
them. By 1969, the escalation of rents by public housing authorities struggling to meet spiraling operation and
maintenance costs nearly nullified the purpose of the public housing program established in 1937 to serve the
nation’s neediest. To reverse this, the Brooke Amendment (1969) to the 1968 Housing and Urban Development
Act, established the rent threshold of 25 percent of family income; that is, a family would be required to pay onequarter of its income in rent. By 1981, this threshold had been raised to 30 percent, which today remains the rent
standard for most rental housing programs. Because the 30 percent rule was deemed a rule of thumb for the
amount of income that a family could spend and still have enough left over for other nondiscretionary spending, it
made its way to owner-occupied housing too. ( Who Can Afford To Live in a Home?: A look at data from the 2006
American Community Survey by Mary Schwartz and Ellen Wilson US Census Bureau).
19
1102
Total
number
of
housing
units
Total
number
occupied
Total
number
vacant
Of
occupied,
total
number
owned
Of
occupied,
total
number
rented
Median
rent
1110
1301
1305
1306
1307
SAN
ANTONIO
399
1002
1545
1708
1854
862
504,440
258
(64.6%)
812
(81.03%)
1183
(76.5%)
1356
(79.3%)
1652
(89.%)
751
(87.1%)
90%
141
(35.3%)
190
(18.9%)
362
(23.4%)
352
(20.6%)
202
(10.8%)
111
(12.8%)
10%
145
(28.3%)
345
(34.4%)
680
(44%)
679
(39.7%)
748
(40.3%)
315
(36.5%)
60.1%
113
(28.3%)
467
(46.6%)
503
(32.5%)
677
(39.6%)
904
(48.7%)
436
(50.5%)
39.9%
$503
$530
$651
$524
$590
$517
$730
Putting this story together, the table below details the population’s economic characteristics and the
state of the housing stock in an attempt to illustrate the housing need in the area (the pink areas in the
map are public parks).
20
The assumptions implicit in this analysis are, once again, the recommendation that families pay less
than 30% of their household income on housing per month. The published HUD area household median
income is $57,800. Census income increments do not match our area income subcategories, thus the
more conservative estimates are shown below:
21
<30%
AMI8
30-60%
AMI
60-100%
AMI9
Number of
households
making
<$14,999
(<30% ami)
Number of
rental units
less than
$500
Percent
need met
Number of
households
making
$15,000$34,999
(30 60%ami)
Number of
rental units
$500$1000
Percent
need met
Number of
households
making
$34,999$49,999
(60-85%
ami)
Number of
households
making
>80% AMI
Number of
rental units
>$1,000
1102
125
1110
292
1301
344
1305
555
1306
785
1307
332
52
187
159
304
359
181
41.6%
64%
46.2%
54.7%
45.7%
54.5%
52
314
300
582
587
286
53
265
318
292
477
230
100%
84.3%
100%
50.3%
81.2%
80.4%
61
101
138
80
144
71
20 (7.7%)
105
(12.9%)
401
(33.8%)
139
(10.2%)
136 (8.2%)
62 (8.2%)
69
44
19
According to the findings above, there is a lack of housing availability (rental) for those making less than
30% of area median income. In all census tracts in the Promise Neighborhood, there is simply not
enough housing available for these families, meaning that if they wanted to remain in the neighborhood
8
9
AMI – area median income
Most households in these income brackets own a home.
22
but for some reason needed to find a new home, most would be unable to do so. Only two of the
census tracts in the neighborhood have enough housing for those making 30-60% ami. This information,
in turn, may provide insight into the high mobility rates we document amongst our families.
Not surprisingly, there is a high rate of mobility through our Promise Neighborhood. Indeed our innercity neighborhoods have served as zones of transition for decades, as young families move through
them on their way to a more stable setting at the edge of town, where they begin to make their own
investments and improvements to our physical infrastructure. Our inner-city neighborhoods reflect this
mobility in the deteriorating condition of the homes. In addition to the physical impacts of high mobility
rates, the social implications are as profound, especially on our children. Children who move from
school to school (especially during a school year) are less likely to perform well on their standardized
tests than a student who has attended the same school for consecutive years (this relationship will be
detailed in the following sections of this report).
YEAR HOUSEHOLDER
MOVED INTO UNIT
Occupied housing units
Moved in 2005 or later
Moved in 2000 to 2004
Moved in 1990 to 1999
Moved in 1980 to 1989
Moved in 1970 to 1979
Moved in 1969 or earlier
1102
258
9.3%
22.5%
19.4%
12.0%
8.1%
28.7%
1110
812
19.3%
34.7%
24.8%
9.0%
6.2%
6.0%
1301
1305
1306
1307
1,183
22.1%
26.9%
24.2%
4.9%
7.9%
14.1%
1,356
34.5%
33.3%
13.1%
3.8%
4.3%
10.9%
1,652
31.8%
20.7%
22.2%
4.5%
3.2%
17.6%
751
38.5%
15.2%
16.9%
15.6%
4.5%
9.3%
SAN
ANTONIO
454,189
34.2%
25.9%
19.5%
8.1%
6.3%
6.0%
Mobility rates by School (compare to 28.1% for the school district):
Elementary School
Bowden
Pershing
Washington
Mobility Rate
25.7%
30.7%
32.4%
The neighborhoods further to the east (served by Washington) appear to be the most transitional; they
also have the highest number of rental homes.
The final concern about our housing stock is its age. The year a home was built indicates the
probability of either lead and/or asbestos in the construction materials. Interior lead-based paints were
phased out in the 1960s but not regulated until 1978; asbestos was commonly used in commercial
buildings for a plethora of functions until the 1970s when it too was regulated.
1102
Total housing units
Built 2005 or later
Built 2000 to 2004
Built 1990 to 1999
Built 1980 to 1989
Built 1970 to 1979
Built 1960 to 1969
Built 1950 to 1959
399
0.0%
3.5%
1.3%
2.8%
6.5%
15.5%
5.3%
1110
1,002
0.0%
1.3%
7.4%
4.3%
12.4%
6.5%
12.6%
1301
1305
1306
1307
1,545
1.7%
0.0%
1.2%
2.1%
11.1%
8.7%
12.0%
1,708
6.3%
20.8%
0.5%
16.1%
3.1%
12.6%
14.9%
1,854
0.0%
0.0%
3.8%
3.2%
9.1%
16.2%
26.1%
862
0.0%
1.0%
4.4%
22.4%
11.1%
6.1%
25.3%
SAN
ANTONIO
504,440
4.2%
10.9%
13.5%
18.5%
18.4%
11.6%
11.1%
23
Built 1940 to 1949
Built 1939 or earlier
4.8%
60.4%
15.7%
39.9%
10.0%
53.3%
8.4%
17.3%
22.4%
19.2%
14.6%
15.0%
5.4%
6.2%
The two census tracts furthest to the west (and closest to downtown) have the oldest houses, built well
before 1940. In addition, those census tracts border I-37, another source of lead contamination.
Section I is intended to present a picture of the current conditions of the Eastside Promise
Neighborhood, yet a statistical representation such as this one fails to capture the non-quantifiable
elements in the neighborhood, thus we offer our inventory of assets.
SECTION II: THE ASSET INVENTORY OF THE EASTSIDE PROMISE NEIGHBORHOOD
We begin this neighborhood profile not with a needs assessment, but with an asset inventory.
Adopting the philosophy of John McKnight and Jody Kretzmann in Buildings Communities From the
Inside Out: A Path toward Finding and Mobilizing a Community’s Assets (1993) the Eastside Promise
Neighborhood [EPN] Advisory Board sought to learn of the strengths of the neighborhood and
community, in addition to the needs often prioritized by more traditional methods of community
development. Asset-based community development theorizes that community (any community) is
24
built and rebuilt in a cooperative process linking citizens, government, and other institutions; in
communities that are already empowered, external support (in the form of resources and support)
focuses on capacities of the community while the unfulfilled needs within that capacity are identified
and addressed. In disempowered communities (poor neighborhoods for example) the support is based
on incapacities. By reversing this process and identifying the existent and nascent capacities in our
Promise Neighborhood community we will also find the unfulfilled needs and work to address them
together.
Kretzmann and McKnight identify five ‘levels’ or scales of asset: the personal (resident’s gifts),
the associational (clubs and groups people belong to but do not get paid by), institutional (non-profits,
service providers), physical (space, environment), and exchange (how and what we buy and sell). A
variety of methods were used to collect this asset data. Personal or individual assets were collected by
survey on April 30 at the Dignowity Hill/Bowden Elementary School Promise Neighborhood block party;
institutional assets were collected by survey conducted amongst all Promise Neighborhood board
members and snow-balled out to include all providers active in the Promise Neighborhood footprint.
Following this initial survey, a series of ‘provider panels’ was conducted the Promise Neighborhood
Advisory Board to learn more of the services available in and to the Eastside Promise Neighborhood.
Associational assets were discovered through conversations between advisory board members and
interviews with community members. Physical and commercial assets were collected via a
‘neighborhood walk’ on May 25 and June 1. 15 teams of 2 were deployed through the neighborhood,
each given approximately 700 parcels of land to inventory and describe. The categories of land use
included residential (and the condition of the property), vacant land, multi-family units, service provider,
commercial business, and church. All are mapped above (residential property is mapped in pale gray).
Personal Assets. Citizen involvement has been a key piece of the Eastside Promise Neighborhood
planning process; the advisory board is composed of citizens representing both associations and
institutions throughout the Promise Neighborhood. These community members are active in numerous
facets in their community and are aware of the assets and strengths they can offer to the community
and have been doing so for years. In addition, those not participating in the day to day activities of
Promise were surveyed in order to ‘inventory’ the personal assets of the members of the community. A
modified form of Kretzmann and McKnight’s personal asset inventory was used; people were asked:
 When you think about your skills, what three things do you do best?
 What are your passions, what do you care deeply about?
 Are there any skills you would like to teach?
 Are there any skills you would like to learn?
We asked about one’s passions after asking about one’s skills, to try and understand shared values in
the community that drive people to do what they do and to care about certain things. The table below
is a summary of the personal asset inventory done to date. Many identified ‘caretakers’ as a primary
skills, and ‘faith’ as a passion. It tells of a community with a deep faith base, where a citizen’s sense of
social responsibility may come from their faith. Knowing this, the churches emerge as key institutions
with whom to work to foster a revitalized promise neighborhood.
What are your SKILLS?
Caretaker (15)
Housekeeper (15)
Cook (16)
What are your PASSIONS?
My faith (9)
Service to others (19)
Problem solving (12)
What would you like to TEACH?
Decision-making (8)
Cooking (14)
Faith (6)
25
Medical assistant (4)
Roofer, mechanic, nurse, art,
dance, concrete, gardening,
computers
My family (6)
Reading, acting, talking
Mentoring (5)
Finance, mechanics, concrete,
hair, parenting, beading, math,
basketball
Associational Assets. Associational assets included in this report are largely churches and neighborhood
associations. The churches reach far deeper into the community than addressing issues of faith; many
are also refuges for people in need of basic services including food, and places of fellowship and
friendship. Because there is a direct link between these larger associations and the personal assets and
passions that neighbors offered, we identify this as a key strength on which to rebuild our Promise
Neighborhood.
Over 50 churches and ministries serve the Eastside Promise Neighborhood. The historic roots of the
EPN have been closely associated with the African-American community in San Antonio, and the faith
community in the area reflects that history. Despite the changing demographics (the area is no longer
predominately African-American, as explained in the previous section), many of the churches continue
to serve an African-American congregation. People commute long distances to attend services, so the
ministry of the churches is wide-spread. Yet while their congregations may be geographically dispersed,
their facilities are not, and a profound opportunity emerges as these churches begin to organize for the
improvement of the neighborhood.
26
There are four neighborhood associations that serve the Promise Neighborhood plus a
resident’s council at Wheatley Courts (under the purview of SAHA). Due to the nature of San Antonio
city government (our elected officials are largely voluntary), it is difficult for individual citizens to
navigate city government, especially citizens in need of fundamental services or in crisis. Our
neighborhood associations often act as liaison between residents and the city for this reason. The
neighborhood associations have neighborhood plans that are agreements between the citizens and the
city for future investments in infrastructure in the neighborhoods.
Institutional Assets. Institutional assets make up the most visible and formal part of the community’s
fabric. The combination of public and not-for-profit institutions in the Promise Neighborhood is rich.
The institutional assets I wish to highlight in this report include healthcare providers, early childcare
providers, out of school time programming providers, and educational providers. While it is fairly simple
to map their locations, the number of services provided by each is multi-dimensional and complex. The
facilities themselves are offices, meeting halls, and emergency shelters, while the offices make decisions
about services, funding, staffing, and out-reach – all interconnected, all assets that if understood,
supported, and promoted will serve as one of the foundational elements of community revitalization.
One of the challenges of capturing these institutions for local community development will be
the ‘direction’ of their responsiveness; while our associations are fully responsive to and responsible to
the neighborhood, institutional allegiances often lie outside the neighborhood, thus responsiveness may
27
be, first and foremost, to the central office rather than to the local community in which they are located
(e.g., the neighborhood, city-owned, park).
Healthcare providers.


Frank Bryant Health Center (FBHC) is a full-service family health care provider that addresses all
the basic health care needs of its patients, including medical, dental and behavioral health care
services. It lies just outside of the Promise Neighborhood, on East Commerce Street. There is an
on‐site pharmacy; lab and eligibility services are also offered. In addition, dental health
education, Mental/Behavioral Health, Pediatrics, Primary Care, and Women’s Health are
offered. Medicaid, Medicare, and other insurance policies are accepted. The dental and mental
health services at the FBHC are funded by Methodist Healthcare Ministries. The Frank Bryant
HC is a federally qualified health clinic run by Communicare.
The East San Antonio Medical Center (1954 E. Houston St.) houses several independent medical
clinics, including the E.T. Dixon Clinic, the South Texas Center for Pediatric Care, the Carol Clinic
for Family Centered Healthcare, and FIVE medical doctors with their own private practices. The
Dixon Clinic is owned by Methodist Healthcare Ministries. There are two family practice doctors
on staff, 4 social workers, 1 registered nurse, and one registered dietitian. The clinic serves the
most indigent patients with no means of payment, and thus does not accept patients with any
form of medical insurance, including Medicaid and Medicare (those with Medicaid or Medicare
are often referred to the FBHC. In addition to the Methodist Healthcare Ministries on this site,
28




there are also 5 additional doctors, a home health care business, and a diagnostic clinic. These
practionners do accept insured patients.
The Adult Mental Health Clinic at the Ella Austin Health Center (1920 Burnet) is a mental health
clinic run by The Center for Health Care Services. There are two (80% time) doctors on staff,
plus 5 licensed professional counselors. The clinic serves people with major depression,
schizophrenia, and bi-polar disorders. They charge a fee for service and accept Medicaid and
Medicare.
The East Pointe Medical Center (2011 E. Houston St) is a LapCorp Patient Services facility and
also performs some dialysis treatment.
The Eastside Christian Dental (2606 E. Houston St)
Davis Family Dentistry located at 210 Chestnut
Family Services
 The Ella Austin Community Center (1023 N. Pine) promotes limited short-term support in
periods of family crisis including emergency food, infant formula, clothing, information and
referral, rental and utility assistance, and income tax assistance (VITA); Early Child Development,
Parenting Classes, Senior Services, Individual & Family Services, Youth Development AfterSchool Program, Emergency Food & Utility Assistance.
 River City Area Center (414 N. Hackberry) is a long-term care nursing home. Most patient needs
are accommodated at the site, including medical and mental health treatment.
 Strong Foundation Ministries
 Salvation Army Dave Coy Center (226 Nolan)
 The Catholic Worker House (622 Nolan) provides homeless services to families and single
people. Food is also served at noon and dinner times.
Educational Service Providers:
29
Early Child Care [ECC] Educational Opportunities:
Licensed Pre-Schools
• Antioch Christian Academy (45)
• Ella Austin Child Center (169)
• Healy-Murphy (163)
• Miller Child Development Center (70)
• Wee Care Development Center (25)
• St. Paul’s Episcopal Montessori (80)
Registered Child-Care Home
• Maggie Sullivan (12)
• Sharon Thomas (12)
Pre-K and Head Start
• Bowden Elementary Pre-K (41)
• Pershing Elementary Pre-K (65)
• Tynan Head Start Center (121 from 78202, 78208)
30
Grade and Middle School Educational Opportunities:
•
•
•
•
•
•
Tynan Early Childhood Center (3-5 years old)
Elementary schools
• Bowden (preK-5)
• Pershing (preK-5)
• Washington (K-5)
Middle School
• Wheatley MS (6-8)
Private Schools
• Carver Academy
• Antioch Academy
Charter Schools
• City Center Health Careers
Alternative Schools
• Healy Murphy
Out of School Time [OST] Educational Opportunities:
•
•
•
•
•
•
•
Ella Austin Community Center (50)
San Antonio Youth Centers
• Antioch Missionary Baptist Community (233, but serves outside PN)
• St. Paul’s United Methodist Church (150, but serves outside the PN)
SA Youth Centers
• 150 at Pershing Elementary
All Stars
• 90 at Bowden Elementary
Healy Murphy
Youth Against Gang Activity
• Wheatley Middle School
HIS BridgeBuilders
There are approximately 1,661 children between 0-5 years old in the EPN. There are 227 seats in pre-K
and Head Start programs (combined); there are approximately 576 seats in registered and home
daycares in the EPN, although most of those seats are not taken by a child who lives in the EPN. Our
median household income is $20,892, making daycare a luxury for many families. While these numbers
were detailed and developed in the previous section of this report, the message at this point is clear:
while we appear to have many educational assets in our neighborhood, some are not available to our
children for financial reasons, but perhaps more importantly, there simply are not enough.
31
Commercial Assets in the Promise Neighborhood: There are 296 private businesses in the EPN. It is said
that economic community development requires that money generated in an area must remain there
for 3-4 transaction cycles in order to impact the local economy (and thus community) of that area. Thus
the question remains: despite a seemingly large number of employers, how much of the money
generated in the EPN remains there and for how long?
These are the assets identified in the past year in San Antonio’s Eastside Promise Neighborhood. It is
upon these assets that true community revitalization and development can begin.
SECTION III: THE COMMUNITY’S REPRESENTATION/INTERPRETATION OF THE NEIGHBORHOOD
The information presented in the two proceeding sections is (metaphorically) a map of the
neighborhood – what is there, how much, and where. In the following two sections two different
methods are used to weave the data together in an attempt to understand how individual and
families navigate their landscape (both literally and metaphorically). The first section documents the
community’s perceptions of their Promise Neighborhood. The second section is a segmentation analysis
of the academic performance of our elementary and middle school students. Together these two
interpretations will enable us to better understand daily life in the Promise Neighborhood, how our
32
families cope with the stresses posed in a challenging inner-city neighborhood, and how our children
mature and perform academically.
Community Engagement
Fifteen focus groups were held between February 1st and March 1st, 201110. Existent social and
associational groups were enlisted to participate in this effort; the effort was built on existent groups,
since people seem more willing to share ideas and discuss difficult topics amongst others with whom
they already have relationships, as opposed to building completely new relationships. Conversations
addressed questions about the neighborhood, schools, and families in the community. Each focus group
lasted approximately 2 hours and 5-8 people were included. The questions that guided the
conversations included:
Questions about the Schools in your community
 If you could design the ideal school, how would it work?
o What would be the same?
o What would be different?
 What are the greatest challenges our kids face when they are in elementary school, middle
school, and high school?
 This question has two parts:
o For your children’s future, please rank these from most important (#1) to least
important (#5):
 Spend time with family
 Get a job
 Get married
 Go to college
 Take care of family
 Other __________
o How are you preparing your child for that future?
 What problems does your child have at school?
 What stops our kids from completing high school?
Questions about the neighborhood and community




Either 1 or 2:
1. What brought you to this neighborhood? What keeps you here? If you want to leave, what
makes you want to leave?
2. Please tell us about the people your [trust, lean on, go to for help]. What are they like? How
did you get to know them?
Can you meet your family’s needs in this community? (examples: health, exercise, food,
businesses)? Where do you go for help? (let’s make a list)
o What needs CANNOT be met in this community? Where do people go? Can we make a
list?
10
Focus groups were conducted with: English-speaking and Spanish-speaking parents at Pershing Elementary;
English-speaking and Spanish-speaking parents at Bowden Elementary; English-speaking and Spanish-speaking
parents at Washington Elementary; English-speaking and Spanish-speaking parents at Tynan Early Childhood
Center; English-speaking parents at Wheatley Middle School; Harvard Place/Eastlawn Neighborhood Association;
Dignowity Hill Neighborhood Association.
33

If you could wave a magic wand, what changes would you make in your community?
Question that ties schools to community
 Name every single thing that kids need to go from Pre-K to high school graduation (make a list
on a flip chart).
o Who is responsible to provide these things (put on the flip chart)?
 What are we missing? What haven’t we asked???
Although all questions were open-ended, conversation was guided toward education and neighborhood
assets and concerns. From these two areas, we can begin to discern overall concerns about the quality
of life in the neighborhood, and resident’s interpretations of what works and what does not, and why.
Despite holding 15 different focus groups, at 15 different locations, with 15 different set of participants
(no one participated in more than one focus group), the same themes emerged again and again:
 Drugs are a major problem in the community and seems to affect many facets of life;
 Nearly every instance of some negative school experience involved miscommunication between
teachers/staff and parents.
Overwhelmingly, participants have a desire to implement the changes themselves.
Looking more closely at the themes that emerge from the conversations:
How Would You Change Your Neighborhood?
When participants were asked about what changes they would make to their community/neighborhood
(not their child’s school), several themes emerged: (presented in order of importance)
 Dealing with the rampant drug problem,
 lack of police presence in the community,
 issues with neighborhood blight,
 issues with school safety,
 more parks/ recreation,
 dealing with prostitutes and gangs,
 changing their neighborhood’s bad image,
 having a more diverse base of businesses within the community.
Dealing with the Rampant Drug Problem. The severe drug problem in the community was mentioned
by almost every focus group, and it seemed that participants thought the drug problem was their
community’s most pressing concern. Community members believe that the police seem to ignore the
problem and drug dealers /users seem ambivalent to law enforcement11:
C1: “Hmm. There’s drugs and prostitutes and all that kind of stuff around, you know, even with
the cops there. Of course they outnumber the cops right, but you would think that they’d be
scared? Seeing the cops there? But they are not scared they keep doing what they’re doing”.
Mod: “Why are they not scared? What’s your guess”?
C1: “I don’t know maybe it’s no biggie for them to go to jail. Many of them have been in and out
of jail. I mean that’s what I would think”.
11
Throughout these transcriptions, various forms of coding were used, so different individuals may have received
different coded identifiers to protect their personal identity. ‘Mod’ refers to the moderator or facilitator of the
focus group.
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C2: “But you know, on the corner of my street there’s always a drug dealer or a prostitute. We
run them out, but eventually after a time they come back. I tried to call the cops, and let them
know I just got, even if whether or not it’s true, I got solicited. And they drove by so they have a
reason to come back, but I don’t actually know if they ever do come out but I’ve never heard
anything or seen anything but they make me think that they have. So that to me [its].. big”.
Local businesses seem to help facilitate drug deals and help drug dealers hide from police detection:
P2: You know even at some of these stores [drug dealers] do transactions inside these little
stores…these little conscience [sic] stores they have…they go in there and you see the drug
dealer and they are doing the exchange in the back in the bare area. It is impossible for the
tellers not to be seeing…because they have those big mirrors what’s going on in the store.
In some cases, the drug problem seems to even affect the quality of life by preventing the use of some
public spaces:
11: “Yea we go to the park and there’s confrontations over there and there’s somebody with
drugs sitting out over there.”
11: “There’s always a man smoking a pipe right where the kids walk and so I call the cops to help
and they don’t even come! You know my kids are three and two and he would come over and I
would say get the hell outta here!!”
2: “We were walking through the woodchips at the park over there and there were needles in
the wood chips and I was like uh uh”.
3:“Well what makes it not work for me is pretty much the drugs and the ones who like to hang
out at those corner stores. You know the ones who hang out like it’s their second home or
something”
This quote may sum up the shared sentiments about the community’s drug problem:
M: What about…the drug problem in the community?
P2: It’s rampant I don’t know how they are going to curb it. They have been trying for so many
years. I don’t know if there is anything can be fixed unless the entire community…I mean the
entire community...not just some become involved. Everybody is afraid even the children…I
know I am. I don’t want anything to fall on the kids for something I said.
[This woman is afraid that if she was to inform the police about a particular drug dealer or user that she
would put her children and herself in danger (This is what she meant by “I don’t want anything to fall on
the kids for something I said”).]
Little Police Presence in the Community. After the drug problem, the lack of police presence is one of
the major concerns participants have. Police don’t seem to respond when the participants need them:
3: “In every situation where there’s something [going on] and I’m out there looking and [the
police are] just sitting there and I’m like what are you doing here?? You know the cops [don’t
come]. And I just think you know you’re here to protect and serve, not to judge. No one said
you’re perfect”.
C4: “What doesn’t work for me is that I feel like every time I call the police they never come”.
M: “Sometimes you can walk up and tell something to the [police officers who are] parked and
they don’t do anything”.
P2: “They don’t do nothing”.
P1: “They just sit there”.
According to community members, there is such little police presence; criminals know police officers
only patrol particular areas at particular times:
P1: “I agree with you Ms. (P2) I feel that in this community that there should be more officers
riding around at night [and] more than just [in] particular areas. You might want to commit a
crime and you know what time to do it”.
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Mod: “They are that predictable”?
P2: “Yeah the crack heads learn the schedule. And they move to another street. Or they give
signals from one street to the next street”.
P1: “They know when do go outside and when not to go outside. They know if they want to go
rob Ms. [M’s] house what time the police are sitting across the street”.
It is also important to analyze the themes that were not very prevalent: violent/destructive property
crime and whether personal safety is threatened. Very few participants reported destructive property or
violent crime; such things only came up 2 times: one participant’s home was robbed 4 times and there
was an arson related crime near another participant’s home. No participant ever spoke about murders
or gun violence in their community. No person ever expressed that they didn’t feel safe in their
neighborhood (the person whose home was broken into 4 times in the past now has several dogs to
protect her home, and says that no one would dare break into her home now). So the strong desire of
participants for more police enforcement doesn’t seem to be derived from violent crimes or for personal
safety reasons; the desire for more police enforcement seems to mostly stem from a desire to control the
drug problem.
Even though no participant expressed concern for their personal safety, participants did reveal
that many residents are fearful:
C4: “One of the things that doesn’t work for me, at least on my block, I know I’m the only one
that calls the police. My other neighbors are very, very fearful; they don’t want to get involved.
They just let things happen”.
M: “Do you have a neighborhood watch program there”?
C4: “Probably not but I don’t think anyone on my block would call”.
C2: “They wouldn’t [call] in [my neighborhood] either”.
Issues with Neighborhood Blight. Neighborhood blight is a theme that arose in almost every focus
group. Abandoned and decrepit housing seems to be ubiquitous in the neighborhood. There are also
problems with streets and sidewalks:
C2: “Where we live at, off of Rio Grande, it seems like every three months Rio Grande has a
cave in. And the city comes out, patches it up, and everybody in the neighborhood had learned
to read the streets. So if water is coming out the street be careful because it’s going to cave in.
So, I like that, I mean the streets might be a little tore up, but there’s no traffic”.
Large amounts of trash also seem to be present in the neighborhood:
P1: “There are people who leave trash when they leave and this is always a problem here. Lots
of trash outside of the houses. [I don’t know, never.. it’s always a problem in this place.. much
trash]”.
P2: “I’ve realized this as well. And also abandoned cars and always lots and lots of trash in the
street”.
Issues with School Safety. Although few participants mentioned being concerned for their personal
safety, many participants said they were concerned with the safety of their children. Bullying was the
focus of participant’s concern with school safety. Afterschool violence seems to be of particular concern.
Participants want some sort of after school patrol to protect their children:
P3 (via translator): “the parents come to drop off their children and then when they take
off…and the parent[s] [don’t know] that their [children are] getting beaten up from that point
to this point. You know? She said that there have been times that she’s walked this way and the
kids are taking off…they’re going down to another street or their beating up another kid.
Um…she said that that’s one of the things that she wants to change. To create more safety for
the children by working with the staff”.
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P1: “I wanna see more officers out once [the children] get past Washington. But once they get
past and up and in that side street Dawsen, that’s when the fighting [starts]. Kids want to start
jumping on other kids. We had an incident where a little girl was attacked by another little
student. Cuz they was outside the…. Perimeter [of the school]. And they say “oh I can do that
now because [adults/police] are over there”.
Traffic outside the schools was also a concern. Participants want speed bumps near schools to protect
children:
C1: “I live down the street from Wheatley and I saw a kid walk to and from school but cars are
just flying by. So I think that there needs to be a few speed bumps down the street maybe not
just that street but down Gevers”.
C2: “Yeah, and ya’ll talking about the inner street. When I come off of my street on Walters,
going over the Walters Street bridge is this big old flashing yellow lights – slow down – I call the
policeman every other morning saying, ‘ya’ll need to put a police officer on the corner of
Walters and Gable, I’ve seen a child almost get hit by the students on the way to St. Philips.’”
More Parks/Recreation. Many participants wanted more parks in their neighborhood so children could
have a place to play. Some participants also wanted some form of cheap recreation:
3: “CHEAPER recreations. Because they are fifty dollars! Fifty bucks for recreation? We didn’t
have to pay [that] when we were little. You know [to] get the little snack or dinner or lunch or
whatever, you gotta pay fifty dollars to get all that”!
Deal with the Prostitutes and Gangs. Although not nearly as prevalent as the drug issue, issues about
prostitutes and gang members did occasionally arise. Although issues with prostitution were not
mentioned very often, those who did mention it seemed to suggest prostitutes are ubiquitous. Gang
members also seemed to be a concern, again not mentioned very frequently.
Neighborhood Perception: Although not discussed very much, a desire to change the image of the
Eastside was expressed by participants in multiple focus groups:
S1: “I would change everybody’s perception on how they think the eastside is”.
Mod: “You would want to change people’s perceptions of how the Eastside is? What would you
want their perceptions to change to?”
S1: “Like. For them to think that this is the only side of town that’s really bad because it’s really
not”.
Student: “They always think that”.
Student: “It’s not that bad, the same stuff that’s going on over here is going on northside and
the Westside, the southside”.
More Businesses. Some participants were frustrated with the lack of business variety present in their
neighborhood:
C4: “Something like a cute little shop or a nice little place to eat with a little personality with a
little ambiance where you want to stay and hang out you’re not just there to sit and eat and
leave. I’m not even sure what other businesses, because I think that question kind of came up
too, but what are the kinds of businesses that actually would serve the community that the
community would feel like ‘I really want to be there where there was some sort of energy’”.
What Changes Would You Like to Make to Your Child’s School?
When asked about what changes they wanted to their child’s school, several themes emerged:
(listed in order of importance)
37





more parental involvement,
more engaged teachers,
more real life/ real world experiences for children,
a more hands on/real world based curriculum,
and physical improvements to their child’s school.
More Parental Involvement. Most participants wanted parents to be more involved in their child’s
school:
P1: “I notice…coming out to the games…that …our opponents…if we’re here their [side] is full of
the opponent’s families…you couldn’t count [the parents from our school] on your hands…and
that’s for the boys and the girls. It’s very few parents that come out. So…I agree that we need to
get you know more parent involvement and me and Mrs. Carole already spoke on that too.
Trying to get…to go out we’re going to start beating our feet to try to get more parents to come
out. Because she’s right. You have all these children, but you can’t even count the parents on
your hands”.
Some participants seemed to suggest that some parents only attend school functions when the schools
offer some service for free:
P3 (via translator): “They don’t come to those types of events. She says that she likes the way
the other principals put it…[parents] are not going to come unless you give them something.
That’s the way she sees it”.
P1: “If [parents] know its benefiting them they will fly-out…they will cut their soap operas off if
it’s benefiting them. I feel it’s for the kid. You should break your neck arm and a leg to be here
with your child”.
One participant offered an explanation as to why parents only seem to come to school events when
services are rendered for free:
Mod: “Do you think it is out of necessity or is it because they are like that. Are the parents more
worried about filling their needs…in the sense of resources?”
P2: “I think it could be both. But I think in some cases it is more of a necessity. Because a lot of
the parents they do receive food stamps…especially when it’s at the end of the month that’s
when you get a lot of people coming in. The stamps only last so long”.
By her own account, P2 did not become involved in her child’s education until later in her child’s
elementary school years. And as her quote above suggests, the reason she was not involved was
because she was working to provide for her family:
Mod: “what was keeping you from being involved at the elementary level”?
P2: “I was working working working and just not there I was there but just not there you know?”
Another participant suggested that parents simply don’t care:
P1: “A lot of parents…send they kids to school just to get them out of the house… [with an
attitude like] ‘he don’t have to be in my hair, put him off on somebody else’…but [ parents feel
as though their children are the school’s problem] because they [are at school] during the day.
When they come home then they're [the parent’s] problem”.
More Engaged Teachers. In general, many participants thought that teachers were not effectively
engaging their children:
C2: “Well there are some teachers that have the passion to make sure your baby get it right.
And then we have the ones that just sit there that just make you want to holler, because they
don’t care. They don’t have the passion, you see the kids they’re laying down they’re going to
sleep. You walk in the room you go to sleep, because there’s no passion there for the love of
teaching to make sure, I call it old school teachers, you know that have the passion that see C3
38
over there struggling and they stop and they take their time to make sure that if C3 still don’t
get it, C3 stays after school until he gets it right. These new teachers, “Hey you got it, you don’t
forget it I’m getting tired.”
Participants cited several reasons why teachers may not be engaging their students. Many teachers are
new and inexperienced:
C8: “My son is in eleventh grade and when I went to his open house, all the teachers were real
young, 25 years old. I went up to one and said, “who are you are you a student?” “Oh I’m
Damien’s teacher.”…Yes, more experience. My son is a straight A student every month, but he’s
saying the teacher is boring. That’s no good. Real young teachers, kids don’t really look up to
them”.
Participants seem to believe that some teachers are not able to effectively manage their classrooms or
particular students:
P2: “Some kids…are more distracting than others with their behavior…they have so many things
they have to do even before they start class that day. And sometimes the instructors that’s the
last thing they wanna have to deal with. And some of the instructors don’t teach as well as they
could or they are not as motivated to… impart the knowledge that they have. Sometimes it’s the
kids that decide to not to complete high school or graduate because of the instructors that they
have. I can say that some instructors are fed up with the kids who come into the class with
issues and [teachers] do not know how to properly address those issues”.
Sam Houston High School students mentioned that teachers don’t implement “hands on” lessons:
Student: “They should change the way they teach too. Ordinarily they just lecture and lecture”.
Mod: “How would you like them to teach”?
Student: “Hands on”.
Student: “More activities and stuff”.
Student: “More projects”.
More Real Life/ Real World Experiences. Quite a few participants wanted children to have some sort of
after school program or after school activities/opportunities. But, the focus of these opportunities
seemed to focus on giving children real world/real life experiences. Many participants wanted children
to have the opportunity to make money. Apparently the need for money can be a gateway to illegal
activity:
C1: “I think there should be some stuff. Only because if you stand outside you can see all these
kids do is get into trouble. Kids are trying to make money, not only to help themselves but to
help their families. And these drug people take advantage of that, because really that’s what
they’re doing, they’re taking advantage of these kids that need it, these families that need it,
because they think that’s the only way that they can make money”.
Some models for these types of programs are Sanyo (no longer in existence) and a Family Services
Program:
C2: “Okay, so when we were talking about kids, when I was growing up and I turned 13, and I
was in junior high school, there was a program called Sanyo. Sanyo had funds for kids to work
and when I was with Sanyo I first started off in recreation. Sanyo helped kids from 13-21, and
when you turned 16 you were able to work on base. That’s how I got my first base job, before I
turned 19 I was at E3. And I got it through Sanyo, they even gave you a chance to work after
school, 4 hours a day, Monday through Thursday, but it still gave us a chance to make money.
There weren’t any young kids, we weren’t on the streets trying to sell drugs and stuff because
we knew we had a Sanyo job which helped a lot of our families. And a lot of kids helped play
mortgage, CPS bill, plus buy school clothes for themselves and their siblings and their families”.
39
C1: “In response to the Sanyo thing, the city has a thing that they do every year, Family Service
participates in it as well, what we do is we hire students from ages 16-18 (it changes every year)
so this past year I think it was from 16-18. But I feel it shouldn’t be those students who get it
because when you’re 16, 17, or 18 you can get a job at McDonald’s or whatever. I think they
should get it at the middle school, from there on up. And keep it from 13-18 if that’s the case,
but start it there. Because that’s when kids really start to see the need to help their parents , a
need like, “Oh we want this but they can’t give it to us.” Or, “Oh we don’t have enough food.”
You know, they start really seeing that need. Those middle kids, they get left out”.
Even if those programs are not related to some sort of life skills, participants just wanted something to
keep children out of trouble. One student acknowledged that after school programs (like YAGA which
he/she is a part of) keep him/her out of trouble:
Mod: “So you want afterschool programs in the school”?
Student: “Yeah, so we can stay out of trouble, ya’ll keep me out of trouble. Ya’ll done keep me
out of trouble”.
A More Hands on Curriculum. The desire to have children experience real world/ real life experiences
manifests itself in a desire to implement a more “hands on”/ real world relevant curriculum. Students
and parents alike wanted a curriculum that featured more hands on activities:
C1: “I would definitely change the curriculum. I think kids need to have more hands-on learning,
instead of just sitting there and listening. I think they’d do better if they actually have their
hands on it, when they can feel it. I think that students should have a choice in what they want
to be in. It seems like they choose your elective for you”.
Some parents gave examples of the types of programs they would like to see; in particular this quote
indicates that this participant wants a career/jobs supplement to the curriculum:
C3: “In [an unidentified] High School they have the elective of the Explorer program for the
SAPD. The reason why I know this is because my daughter is now at central headquarters, she is
also doing the LOTC Explorer program. In two years she’ll be able to do the ride-along program
with them. But they’re already talking about making her the next chief of police”.
Interestingly, whenever school curriculum was mentioned, the focus was almost exclusively focused
upon a hands on/ real world based curriculum. There was only one instance of participants wanting the
curriculum to focus upon preparing students for college, and only one instance of a participant
complaining about the curriculum not imparting basic skills. In regards to the curriculum, here is the only
instance of a participant wanting schools to prepare students for college:
5: “There’s this one program where they do that and they follow kids through high school when
I was going to ___ they didn’t have it but now I’m going to ___and it’s a and they take them to
universities. So they learn about college. They only have it for the class of 2013 which is the class
under me but I think they should do that for all of us. Because I haven’t you know I had seen all
of the schools in San Antonio but they get to go out of the city and out of the state to see
schools and I think it’s very good”.
This is odd, considering most participants ranked college as the primary goal for their children or (in the
case of students) for themselves. Maybe participants believe schools are already doing a good job of
preparing students for college.
Physical Improvements. Many participants wanted immediate improvements to their child’s school:
3: “The technology a lot of the buildings like Wheatley is a very old building. It needs a lot of
work”.
4: “Even like the intercoms, everything. Everything is messed up over there”.
2: “Here they can’t do a playground; they don’t have the budget for it”.
40
Mod: “Any other ideas about things you would change or keep the same”.
Student: “The water fountains”.
Student: “They nasty, they taste like blood…they taste like crusty, that’s what I meant”.
Some participants wanted their children to have greater access to computer labs (both at Wheatley and
Sam Houston):
3: “You know that ridiculous there’s what? 500 kids at this school and they only have 10
computers for them to work on? And they only have certain designated times to be in the
library. There are a lot of things that these kids are not given the advantage [that] others are”.
A Need for Better Parent-Teacher Communication
One of the largest themes to emerge from the content analysis is the theme “negative feelings towards
or experiences of school”. When this theme was analyzed, nearly all instances of negative feelings or
experiences with school involved a lack of communication between parents and teachers.
Some participants complained that they do not know what their child is being taught in the
classroom. This seems to hurt the parent’s ability to help their children with their homework:
C4: “They keep teaching kids new strategies, so when you are a parent who is capable of
working with your kids, you say, ‘Oh, I know that. Ok let me help you.’ And you go and do it your
way and they’re like, ‘What? What is this it doesn’t make any sense? That’s not what the
teacher told me?’ And I’m like, ‘But it works.’ Then I have to go back to the school and tell the
teacher, ‘I don’t know what the heck you’re doing’”!
C5: “I had to stay home and work with Ashley [with her math homework]. When I was growing
up I was taught long division, but now they do it totally different. And she was so upset with me
and she came back and said, thanks Mom, I got a bad grade. So I went to her teacher and said,
‘how did she get it wrong?’ And she said, ‘well it’s not the answer that she got wrong it’s the
process’. I said ‘what the hell?’”
Not only do parents report not knowing what is being taught in the classroom, but also many report that
teachers do not effectively explain homework assignments to them or their children:
P1: “I think they aren’t good teachers, there aren’t explanations, because they say nothing
more, mountain of papers, no explanations, there aren’t open books”
P2: “But my sister [who is a student] doesn’t understand […] Because she doesn’t go or she’s
sick. She knows that this isn’t explained and she doesn’t understand the way that the teacher
explains that… she asks for an explanation but doesn’t get one”.
P3: “With my son it’s the same as the woman, because he doesn’t understand, and I tell him
that I’ll go talk with [the teacher] to explain… to teach him like this... I talk with [teacher] and say
it isn’t... because he doesn’t understand and so he’s crying”.
Parents seem to want more communication from teachers about the academic status of their children:
2: “I think for myself um I don’t get the signs like sometimes enough communication from her
and you know because I’ll ask like you know it how is my daughter doing and she’ll be like she’s
fine she’s doing great and I’m like ok can we elaborate a little more you know like shoot me an
email or something? Luckily she’s doing really well but I’d still like to have that communication
you know. The parent teacher conference and if I had that I think it would be better…Well at
least like 4… Like one every couple of months you know for my own personal assurance”.
8: “[The teacher and I] set down an agreement that at the beginning of the school year, if my
child has problem learning you will call me and let me know, but they didn’t. So my child has to
sit back in fifth grade to repeat fourth grade”.
Some participants wanted some positive feed-back from their teachers instead of a solely negative
dialogue:
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P1: “But I feel that [the school] should…you know… open their doors make the parents feel
welcome. Don’t call the parents every time with something negative. Maybe if [parents] get a
phone call saying something positive about their child they would might want to come out…you
know… something positive…every time [I] get a phone call it has to be negative. I feel that’s why
a lot of parents don’t want to come out because they getting this negative…And maybe the
parents would come out more if they get a positive…say well you son or you daughter is doing
this in the school maybe the parents would come out more”.
There also seems to be a miscommunication about school policies. One parent didn’t know why she
wasn’t contacted when her child sustained an injury and another parent received no help in navigating a
school’s registration process:
“And then here it’s like when my daughter fell and hit her head on the playground I didn’t even
know that she did that ok. I was just like can ya’ll explain to me how she got this mark on her
forehead? Well you know she fell and hit it on the swings ok well no body called to tell me that.
They didn’t call me or tell me anything I had to ask my own daughter what happened to her
head. Does that make any sense”?
2: “Well one change that I just thought of. Well I just have one daughter so all of this is new to
me and you know getting her here and getting the information that is required for her to be
here like registration I got it was like through word of mouth…Nothing was explained to me so I
think that I would change that you know when a parent comes for the first time this is your
registration form…I still don’t know why we need a physical I guess to me it’s like if my child isn’t
physically fit she’s not allowed to come to school why do we need blood work and why do we
need dental and stuff? What for you know I still don’t know to this day what for you know it’s
just something that is required. Explain you know”!
The lack of communication was not entirely the school’s fault. Many parents know their child’s needs
but didn’t seem to express those needs to teachers. This parent knows his/her child is shy and may not
be good with certain projects, but doesn’t seem to inform his/her child’s teacher about it:
9: Well you know my son is just shy. Like he doesn’t talk. He knows how to do the work but the
teacher says he doesn’t know how to do it. But then when he comes home he can do it. And
maybe you know they could have certain kids do certain projects if they don’t like to talk.
Because that’s where they’re missing because they don’t know if he’s really struggling or if he
just doesn’t like to talk you know what I mean. Some people just struggle in public speaking.
Complaints about teachers dominated a significant proportion of the discussion related to education.
Based upon assumptions, it seems that many parents knew their child had particular issues or needs but
did not express them to their teachers.
Our conclusion that the vast majority of the negative experiences of school stem from
communication problems is reinforced by analyzing all the instances of positive school experiences or
feelings; nearly all of the positive school experiences expressed were cases of good communication
between parents and teachers. Parents usually had good feelings towards teachers and staff that
communicate with them in a clear fashion:
P1: I love the counselors cuz they pull me out [and told me] what is wrong with Thomas (her
son). [They told me]Thomas is bored that’s the problem. He needs to be challenged because the
work is too easy for him. So they put him in AP and now he is an honor roll student as well as
being here at Wheatley. So the counselors work with real good with me helping both of my
children.
C1: My son is in first grade and he brought home some homework that I was like, what? What is
this? So his teacher is really good. I have his email address, his personal cell, his work phone. So I
call him whenever, “Mr. ___, I need to sit with you tomorrow and you need to help me go over
my son’s homework.”
42
The Parent Advocate Program at Tynan seems to be a program that effectively bridges the gap between
parents and school. The school can effectively communicate with parents via the Advocates and the
parents can readily express their needs to the Parent Advocates:
3: Parent Advocates….I met them through the school. At first I didn’t know I was like who are
these people? Well they need to come to your house. For what? I don’t like people coming into
my house what are they coming here for? And she was just like well they’re just here to tell you
how your child is doing and I’m like ok. And I didn’t know they had all that help for you you
know like one time I had a problem and you know when my momma passed away I needed help
and so she gave me a lot of different resources and stuff so they’re looking good at this school. I
think they should have that at middle school and high schools.
Who is Responsible for the Changes You Want in Your Community?
In regards to who is responsible for the changes the participants seek, there are two overall
themes: elected officials and personal responsibility. The participants showed a very strong sense of
personal responsibility; the vast majority of the conversation related to “who is responsible for the
changes in your community” centered squarely upon participants’ strong sense of personal
responsibility. But to fully understand why this is, analysis of the other theme is necessary.
There are three sub-themes under elected officials: city council, the school board, and
state/government in general. In regards to city council, City Councilwoman Ivy Taylor was mentioned
frequently as the person responsible for implementing many changes in the neighborhood. The school
board was seen as responsible for some of the school related changes, and, in general, participants
seemed to look to state/government in general for needs that could not be met by the school board.
But in regards to what elected officials actually accomplished, some participants seemed to be
frustrated with the slow pace of and/or lack of change in their communities:
C4: “You kind of want to go to an SAISD board meeting or something and make the board
members aware. So that they can do stuff.”
Mod: “Have you tried that or do you know anyone who has?”
C4: “No”.
C2: “I have. I went to the school district when I was on a long range community. I told Dr.
Duron. I have walked up and showed them the intersection, they know about it. It took, like
Walters and 35, the Sutton Homes almost 7 years to get a gate to protect their babies from the
cars as they walked to school… Because a child lost her life on that bridge. And it took almost 7
years”.
This frustrations seems to extend beyond elected officials:
M: “you have a small group of people that want to see change and want to make change. But
then they start the huge big process and by the time you even start the ball rolling it’s like forget
it. It’s too much work and it’s too much… you have to go through so many people to get
anything in your community. It’s crazy…it’s discouraging and it’s tiring. You feel like you are
battling up against a wall after wall after wall. Literally, people start falling off. They are like
forget it!”
This frustration with the lack of or slow pace of change facilitated by elected officials may be fueling a
desire among some to take matters into their own hands, which may explain why participants showed a
great sense of personal responsibility. But participants also expressed a strong desire to organize, which
may stem from the frustrations expressed in the previous quote. Here is one participant emphasizing
the themes of organization and personal responsibility:
C6: “Well what I’m saying is, I think we can develop our own thing and you know just be leaders
in the community. I’ve been saying just be a leader, and there aren’t other people that want to
be leaders until you’ve got a focus group…we can all as people who live in the community, then
43
we’re from different parts of the community, we make our presence known and make a leaders
forum. When we march we’re going to march together.
This sentiment even seems to be present with the students as exemplified in this quote by a YAGA
student:
Mod: Who is responsible for making these changes [to your community] ?.
Students (in unison): we are!
S1: They were just trying to close Sam Houston down. They should have seen how many people
packed up into that auditorium. Because them that’s why our school stayed open.
This question was not asked in the same form to all groups. One facilitator in particular encouraged
participants to specifically state who they believed to be responsible for specific things children need to
successfully complete school; almost all the responses centered around the responsibility of the
parents/families.
It is important to note the limitations of generalizing this sense of responsibility to the broader
community. The very act of participating in these focus groups indicates that participants are already
actively involved in their communities. This is bolstered by the fact that many of them are very involved
and active in their schools. The participants revealed that apathy runs very deep in their community:
M: “Things like this promise neighborhood, I’m going to tell you something. When we were
talking about what it is supposed to be or suppose to change. Some of the parents were like is
this something else they are promising and their saying that they are doing? I’m going to tell you
they said in Spanish that it is a load of crap”.
A: “Cuz it has been happening so many times before”.
M: “some people get tired…they think it’s (not) going to change and some people just give
up…and nobody cares”.
Participants seem to think that some parents are apathetic to the needs of their children:
C5: “I know a couple of home visits that we’ve done. There were some actually that went,
there’s one I thought it was the craziest one. You knock, where’s the child, and the child comes
out and it’s the only one that doesn’t look like he’s high. But you look into the living room, the
mother is thrown on the floor because she’s real high, and then the mother’s mother which is
the grandma, and she’s sitting in the chair and she’s high! And she’s like an old woman! You
know! And to me that was like, Oh my god no wonder this kid is not going to school and not
paying attention and doesn’t care because nobody at home cares.”
Mod: “How often do you think that happens?”
C5:
“In that area?!”
Mod: “Like 10%, 20%, 80%?”
Everyone: “80%”.
Mod: “I hear 80 here, I hear 30”.
Many participants also seem to think that most parents are not involved their child’s school/education;
it seems that the only way to get parents involved is to offer them an incentive:
M: “She says that in her case what she sees…parents right now…when you have an arts and
craft or when something is giving out to them when it is benefiting them. They don’t come for
the child’s education. They don’t come to those types of events. She says that she likes the way
the other principals put it…they are not going to come unless you give them something. That’s
the way she sees it”
P1: “And I agree with [P1] a lot of parents feel that…they send they kids to school just to get
them out of the house.
Everyone: [Murmurs of agreement]
P1: “[Send him to school] so he don’t have to be in my hair, put him off on somebody else”.
44
So it is clear that the participants will be a great resource in helping the project move forward, but, if the
participant’s characterization of the community at large is true, then pervasive apathy might be a
problem.
Finally, how well residents navigate the assets already present in the neighborhood may elucidate the
degree to which those associational, institutional, and commercial assets are integrated into the
community they reside in. Is their outreach – and their concern – local, or does it lie outside the
neighborhood (and if so, how can we encourage a reprioritization so they will engage and serve)?
Needs That Are/Can Be Met in the Community
When analyzed across groups, there are several types of organizations which participants use to
meet their needs: churches, healthcare providers, non-profit organizations, food/restaurants, and
stores/shops.
There are a total of seven churches that participants specifically stated they go to for help: St.
Patrick's Church, Antioch Missionary Baptist Church, Hodges Chapel, God's House, St. Gerard's, Mount
Sinai Baptist Church, and St. Stevens. The participants mostly use the churches for food, although there
are notable exceptions. Hodges Chapel has donated 5 cases of water bottles to Wheatley’s LOTC when
they were marching in a parade, and St. Stevens provides resources for house repairs. Of all the
churches, Antioch seems to be used by the community the most.
There are a total of nine healthcare providers that participants use to meet their healthcare
needs. But, unfortunately, participants only gave definite names or addresses for a few of them: Health
Care Ministries (probably referring to the Methodist Healthcare Ministries Dixon Clinic), Frank Bryant
Center, Ella Austin, some clinic on Rio Grande, Dr. Leo Edwards on 2011 East Houston St., the pharmacy
across the street from 2011 East Houston St., East Medical, Centro Med, and University Hospital. One
participant specifically mentioned that they use Ella Austin for some form of mental health services.
There are nine non-profit organizations the residents use to meet their needs: The Claude Black
Center, HIS Bridge Builders, United Way, Gathering Place, Carver Center, Food Shelter, Community
Assistance Ministries, the Salvation Army, and Melrose. Unfortunately, in many cases the participants
were not specific about what services they received from these organizations; participants mostly
reported receiving food and clothing from them.
Participants use stores/shops to fill their needs for various goods, services: AutoZone, Carb.
O'Reilly's, Finger Nail Shop, H.E.B, Fred Lloyd, Dollar General, Dollar Tree, Rent-A-Center, The Dollar
Store, gas stations, and the laundry mat. AutoZone, Carb, and O’Reilly’s are auto related goods or
services businesses. There also seems to be a desire to get cheap goods, as represented by Dollar
General, Dollar Tree, and The Dollar Store. In addition, participants use fast food places and restaurants
to fill their need for food: Angel's Restaurant, Burger King, Church's Chicken, Jack in the Box, King's, and
McDonald's.
There are several organizations or businesses that do not fall into broader categories: schools,
parks/recreation, and government services. Multiple participants reported that schools provide
information so parents can find necessary resources. Participants also cited using parks and pools near
their homes or schools for exercise.
If we look at the services or goods rendered from the most frequently visited places, food and
healthcare represent the largest categories. Food by far is the largest category. These are the
organizations/ businesses from which participant have sought or seek food: Sinai (church), St. Gerard's,
Angel's Restaurant, Burger King, Church's Chicken, Jack in the Box, King's (fast food chicken),
McDonald's, food shelters, H.E.B, Bridge Builders, God's House, and schools. Many participants report
that churches provide food to those who need it. Some fulfill their food needs at fast food places.
45
Healthcare was the other major category. Besides the healthcare providers that were listed previously,
some participants mentioned that they used parks and a pool to exercise.
Needs that Cannot be Met in the Community
Even though healthcare represented one of the largest categories of needs that can be met in
the community, oddly it is the largest category in regards to needs that cannot be met in the community
as well. Healthcare providers represented half of the places participants reported going to meet their
needs: Baptist Methodist, Southside Dental, Hospitals, Overnight clinics, Pediatricians, Santa Rosa, South
Cross, Southeast Baptist, Southwest General, a children's hospital on the Southside, and the Zarzamora
Mental Health Clinic. Unfortunately, participants merely mentioned whether they have visited these
places and, in many cases, did not provide what kinds of services they received.
Other than healthcare, food is the next largest category for unmet needs. Unfortunately, all the
places which participants say they meet this need are fast food places and restaurants: Chick-Fil-A, Papa
John's, Peter Piper's, Chuckie Cheese, Apple Bee's, Chili’s, Olive Garden, and Souper Salad. It is
important to note all of these places are from a couple of people in the Tynan focus group.
There are several organizations/ businesses which do not fit into broader categories: courthouse/legal
services, mechanics, and malls. There is one church, the Community Bible Church which provides vital
resources to Bridge Builders.
This analysis of the community’s interpretation of neighborhood and school conditions provides
tremendous insight into how people navigate the landscape painted in the first two sections of this
report (descriptive statistics and asset inventory), and why they feel that at times things do not work
properly. The next section is a statistical analysis of student performance, intended to provide another
interpretation of how our young people navigate their own landscape, and, in turn, how the landscape
or environment impacts our young people.
IV. A QUANTITATIVE REPRESENTATION AND ANALYSIS OF STUDENT ACHIEVEMENT
Community members and parents have strong feelings about how and why student’s succeed
(or fail to succeed) in school. Similarly, a statistical analysis presents an additional interpretation of the
interplay of various factors present that may influence student achievement. Together, these two
interpretations provide deep insight into a student’s daily life, the impact of their environment, and the
cumulative impact of succeeding years as each student progresses through school – to ultimately
graduate and begin their adult life, or leave before completing high school and try and enter the work
world, often ill-prepared.
Student-level data was obtained from the San Antonio Independent School District for all
students who attended school between 2003-2010 in Wheatley Middle School or any of its feeder
elementary schools (including Cameron Academy, Gates Academy, Hirsch Elementary, M.L. King
Elementary, Bowden Elementary, Miller Elementary, Pershing Elementary, Washington Elementary, and
W.W. White Elementary). 16,128 students are included in this data set12. The original variables that are
included in the data set are:
12
The number appears high, but it includes every student that registers in one of our Wheatley schools over the 7
years for which data has been collected. Due to the tremendous mobility in the school district, students enter in
grades other than kindergarten or first, making longitudinal analyses difficult, which explains why we included all
of the feeder schools, not only those in the promise footprint.
46
Identifying
information
Campus ID
Student ID
(scrambled)
Home address
Socio-economic
information
Ethnicity
Is economically
disadvantaged
Age
Sex
Testing data
TPRI scores
TAKS scores (all
tests, all grades)
absenteeism
Days absent
Withdrawn
Years in SAISD
Personal
information
Is special ed
Primary disability
Is gifted/talented
Is ‘at-risk’
Is retained
In addition, variables have been transformed for analysis and modeling purposes, so that the total
number of variables being analyzed is 324.
Three levels of analyses are presented below: descriptive statistics will provide an overview of
the variables collected; a segmentation analysis allows us to look more closely at student-level
performance and divide our student body into segments, sub-populations, or clusters that perform
similarly, and predictive modeling analyzes the relationship between significant variables that culminate
in various levels of student performance and may aid in understanding cause and effect relationships.
Descriptive Statistics:
a. student body
16,128 students have attended one or more of the schools in the
Wheatley Middle School feeder pattern between 2003 and 2010.
EE
37 Attendance through elementary school is fairly stable, with similar
th
PK
3972 class sizes at every level from kindergarten through 5 grade. Yet
th
th
note the drop in attendance from 5 to 6 grade – the year that
K
4910
most students should be making the transition to Wheatley Middle
st
1
4893
School. Trying to dissect this apparent loss of students, of the 4006
nd
2
4574 5th grade students in the database, 619 attended 6th grade at
rd
3
4273 Wheatley (19%), 527 went to another middle school in this feeder
th
4
4178 pattern (possibly one of the academies) and 2,110 are unaccounted
for 64%), meaning they did not attend 6th grade in any of the schools
th
5
4006
associated with the Wheatley Middle School feeder pattern. Only
th
6
2095
19% of the total possible 5th graders elected to attend their
th
7
2014 neighborhood middle school; 81% opted to attend 6th grade
th
th
8
1991 elsewhere. Data for the 2010-2011 6 grade class illustrates this
th
dispersal. Sixty-three 5 graders graduated from Bowden, 20
enrolled in Wheatley in the fall; 48 5th grades graduated from Pershing, 12 enrolled in Wheatley in the
fall; 65 5th graders graduated from Washington, 33 enrolled in Wheatley in the fall (additional schools
outside of the Promise Neighborhood also feed Wheatley). If our goal is to encourage neighborhood
stability Wheatley must be made a more attractive option to which to send one’s middle school age
children.
N
47
Ethnicity
Cumulative
Frequency
Valid
Asian
Valid Percent
Percent
49
.3
.3
.3
African American
5909
36.6
36.8
37.1
Hispanic
9543
59.2
59.5
96.6
19
.1
.1
96.8
519
3.2
3.2
100.0
16039
99.4
100.0
-99
74
.5
System
15
.1
Total
89
.6
16128
100.0
Native American
White
Total
Missing
Percent
Total
There is currently an Hispanic majority in these schools, yet African-Americans continue to make up over
35% of the student body, making this cluster of schools the most ethnically diverse in the school district.
As the Anglo population grows (see demographic information in section 1), this diversity will increase,
which will make these schools increasingly attractive to young urban families. Nearly ¼ (24.36%) of our
elementary age children are considered ‘English-language learners’ or have limited English proficiency;
they most probably speak Spanish at home, and speak solely Spanish all summer long.
Gender
Cumulative
Frequency
Valid
Missing
Total
Percent
Valid Percent
Percent
Female
7905
49.0
49.1
49.1
Male
8208
50.9
50.9
100.0
Total
16113
99.9
100.0
15
.1
16128
100.0
System
48
Economically Disadvantaged
Cumulative
Frequency
Valid
Yes
No
Total
Missing
Total
System
Percent
Valid Percent
Percent
15153
94.0
94.0
94.0
960
6.0
6.0
100.0
16113
99.9
100.0
15
.1
16128
100.0
The poverty levels
described for the
neighborhood are also
reflected in the student
level data; 94% of
students qualify for free
or reduced lunch – the
overwhelming majority
for income reasons.
Throughout the elementary school years, high performing students may be tested to receive ‘giftedtalented’ services [GT]. As the table indicates, an increasing number of students are identified through
the early years, until 5th grade, where 6% of the student body is labeled GT13. The interesting thing to
note here is the subsequent drop in percentage, as our 5th graders move into 6th grade – their middle
school years. Fewer students are labeled GT in our middle schools than were in our elementary schools,
possibly indicating these students are electing to attend middle school outside the cluster (i.e., the
absolute number of GT students in the area remains that same, but the percentage attending Wheatley
MS drops significantly).
Yes
No
n=
Gifted & Talented Grade KG
1%
99%
4910
Gifted & Talented Grade 1
1%
99%
4893
Gifted & Talented Grade 2
3%
97%
4574
Gifted & Talented Grade 3
4%
96%
4273
Gifted & Talented Grade 4
5%
95%
4178
Gifted & Talented Grade 5
6%
94%
4006
Gifted & Talented Grade 6
4%
96%
2095
Gifted & Talented Grade 7
5%
95%
2014
Gifted & Talented Grade 8
4%
96%
1991
Similarly, those with learning differences and difficulties are also identified throughout the elementary
years, so they may receive the special services they need to succeed in school. As with the GT data, the
percentage of students receiving special education increases through these years as students are
identified. Looking especially at the ‘learning disability’ category, the percentage of students identified
as learning disabled jumps from 11.1% of the student body in 5th grade to 14.8% in the 6th grade,
indicating that perhaps students with learning disabilities are remaining in the Wheatley cluster while, at
the same time, students who excel [GT] are opting out.
13
Once a child is identified ‘gifted and talented’ that identification remains until they graduate from high school or
leave the district (at which time they may be reassessed).
49
KG
Grade 1
Grade 2
Grade 3
Grade 4
Grade 5
Grade 6
Grade 7
Grade 8
T otal
Learning Disability
3.0%
4.2%
5.7%
7.6%
9.3%
11.1%
14.8%
15.1%
15.3%
6.2%
Speech Impairment
6.0%
6.0%
5.8%
5.2%
4.1%
3.8%
2.9%
2.0%
1.5%
3.8%
Emotional Disturbance
1.0%
1.5%
1.7%
2.0%
2.2%
2.5%
3.1%
3.8%
3.7%
1.9%
Other Health Impairment
1.1%
1.5%
1.5%
1.7%
2.2%
2.0%
2.7%
2.5%
2.2%
1.5%
Mental Retardation
0.8%
1.0%
1.1%
1.1%
1.5%
1.6%
2.3%
2.3%
2.5%
1.3%
Autism
0.2%
0.2%
0.1%
0.1%
0.3%
0.1%
0.1%
0.1%
0.1%
0.3%
Noncategorical Early Childhood
0.2%
0.1%
0.1%
0.1%
0.1%
0.1%
0.0%
0.0%
0.0%
0.2%
Visual Impairment
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.1%
Orthopedic Impairment
0.0%
0.1%
0.1%
0.1%
0.1%
0.1%
0.2%
0.3%
0.1%
0.1%
Auditory Impairment
0.1%
0.0%
0.1%
0.0%
0.1%
0.1%
0.0%
0.0%
0.1%
0.1%
T raumatic Brain Injury
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.0%
523
624
629
651
724
753
492
485
481
2345
(Count includes multiple responses)
Mobility – Of the 16,128 students in the database, not one student was present from kindergarten
through the 8th grade. 39 students (0.2%) were in the Wheatley cluster from 1st through 8th grade, and
134 (0.8%) attended Wheatley schools from 2nd through 8th grade – a true sign of the degree of mobility
of these eastside families.
Absenteeism:
Days Absent
N
Minimum
Maximum
Mean
Std. Deviation
Absent Days Grade EE
37
0
67
10.81
12.728
Absent Days Grade PK
3972
0
76
8.24
7.751
Absent Days Grade KG
4910
0
70
7.69
7.658
Absent Days Grade 1
4893
0
73
6.83
7.121
Absent Days Grade 2
4574
0
74
6.10
6.515
Absent Days Grade 3
4273
0
66
5.81
6.168
Absent Days Grade 4
4178
0
59
5.56
6.073
Absent Days Grade 5
4006
0
52
5.71
6.212
Absent Days Grade 6
2095
0
105
6.68
8.114
Absent Days Grade 7
2014
0
85
8.01
9.506
Absent Days Grade 8
1991
0
96
9.34
10.919
Valid N (list wise)
0
Elementary school-age children miss 5-6 days of school/year (the equivalent of one week of school);
absenteeism becomes a greater problem in the higher grades, when middle school students are missing
8-9 days of school/year – nearly the equivalent of two weeks of school. As will be shown in later
sections, absenteeism correlates highly with academic performance in some grades.
50
Age:
Average Age at Beginning of Year
Age range per
N
Minimum
Maximum
Mean
Std. Deviation
grade
Grade_EE_age
37
2.31
5.60
3.2415
.81936
3.0-3.99
Grade_PK_age
3982
3.00
5.00
4.3436
.47153
4.0-4.99
Grade_KG_age
4925
4.92
7.58
5.5369
.34469
5.0-5.99
Grade_01_age
4905
5.38
8.70
6.6385
.44087
6.0-6.99
Grade_02_age
4582
6.38
15.05
7.7289
.52728
7.0-7.99
Grade_03_age
4281
7.38
11.26
8.7935
.56073
8.0-8.99
Grade_04_age
4181
7.55
12.27
9.8184
.57198
9.0-9.99
Grade_05_age
4006
9.03
13.33
10.8480
.59219
10.0-10.99
Grade_06_age
2095
10.73
14.80
11.9135
.63897
11.0-11.99
Grade_07_age
2014
11.66
15.89
12.9087
.63122
12.0-12.99
Grade_08_age
1991
12.66
16.47
13.8830
.63688
13.0-13.99
Valid N (list wise)
0
In this dataset (age), two important trends emerge. The first concerns the increasing average (mean)
age of each class, and the second concerns the increasing standard deviation in age in each class. The
ages above are calculated on a decimal scale such that 10.0 denotes 10 years and 0 months while 10.9
equates to 10 and 9/10ths of a year. Thus 10.5 equals 10 years and 6 months old. Note that with each
successive year of school the average age in the cohort increases more than it should, i.e., the average
age in any grade should theoretically be X.5, yet we see ages creeping up probably due to the retention
rate in earlier grades.
In addition to the increasing average age in higher grades, the standard deviation of all ages in a
grade also increases. The result is that there is more age diversity in the higher grades than in the lower,
again possibly due to the compounding effects of retention. Increased age emerges as a causative
variable in the analyses described below.
b. Indicators of academic performance were collected to try and ascertain the academic performance of
individual students, with the ultimate goal of understanding the interplay between variables such as
absenteeism, attendance in pre-K, family mobility, and academic achievement.
1. measuring kindergarten-readiness. Kindergarten is not required in the State of Texas, but if a child is
registered for kindergarten, normal expectations of attendance apply. Although it is not required,
kindergarten has been shown to be overwhelmingly predictive of academic preparedness and success
throughout elementary school, which then is predictive of middle school accomplishment. In a
nationwide survey of kindergarten teacher’s perception of school readiness and behavioral skills of their
classes, 30% of the teacher’s report that the majority of children in their classes had problems following
directions and cooperating as part of a group14. The impact of this lack of preparedness (the inability to
14
Charlie page 3
51
work well with others, and the difficulty following directions) has been traced into high school; behavior
problems as early as 6 years old are correlated positively with lower competencies in math and reading
(after controlling for IQ) at 17 years old – evidence that students who fall behind in their very early years
(due to a lack of socialization skills) fail to catch up in the successive years.
The Texas Primary Reading Inventory [TPRI] test was chosen as the indicator of kindergarten
readiness (as it is the only diagnostic test given to our kindergarteners that will allow comparison). The
TPRI is given three times during the kindergarten year – at the beginning, middle, and end of the school
year [BOY, MOY, and EOY, respectively] – to determine readiness. Ideally, all kindergarteners enter
kindergarten ready so that the school year may be spent preparing them to enter their elementary
school years. Unfortunately, many of our Promise Neighborhood children are not considered
‘kindergarten ready’ until they actually finish kindergarten. The valuable lessons of kindergarten thus
must be integrated with the more basic skills identified as age-appropriate for our 5-year olds.
There are 3 TPRI tests we used (phonemic awareness, grapho-phonemic knowledge, and
listening-reading comprehension), for which each student is deemed ‘developed’ or ‘still developing’. A
‘1’ was assigned to ‘developed’ and a ‘0’ to ‘still developing’; the three test scores were then combined
such that a 3 indicates a student who is considered developed on all three tests, a 2 a student who is
developed in 2 test areas and still developing on 1 and so forth.
At the beginning of kindergarten 69.3% of our students are considered not kindergarten-ready);
it will take them all of their kindergarten year to become kinder-ready, at which time they advance to 1st
grade (even at the end of kindergarten, 53% of our children have not mastered all of the skills identified
to make one ‘kinder-ready). It goes without saying the values of kindergarten are partially lost on many
of these children.
0
1
2
3
n=
TPRI_BOY
36%
33%
25%
6%
1212
TPRI_MOY
16%
33%
34%
17%
1268
TPRI_EOY
7%
18%
28%
47%
1428
47%
28%
TPRI_EOY
18%
7%
17%
34%
33%
TPRI_MOY
16%
6%
25%
TPRI_BOY
33%
36%
3
2
1
0
52
2. measuring elementary school progress
Beginning in the 3rd grade, elementary school students are tested each year in reading and math
and later in social studies and science as well with the Texas Assessment of Knowledge [TAKS] test.
Commended
Performance
Passed
Fail to Pass
n=
TAKS Reading Grade 3
20%
63%
17%
3609
TAKS Reading Grade 4
12%
53%
35%
3349
TAKS Reading Grade 5
12%
61%
27%
3256
TAKS Reading Grade 6
18%
53%
29%
1555
TAKS Reading Grade 7
8%
58%
34%
1449
TAKS Reading Grade 8
18%
56%
26%
1415
The Eastside Promise Neighborhood Advisory Board has agreed to use ‘commended performance’ as the
benchmark for our students, as it is the better indicator of preparedness for college than the passinglevel of performance. The following table includes all of the years (2003-2010) for which data has been
collected, so individual years of extraordinarily high or low performance are not skewing the data. ‘Yes’
refers to passing the TAKS test, and ‘no’ to failing to pass.
Our 3rd graders perform at the same level as others in our district – signaling their ability to succeed
academically, yet that advantage fades in later grades, possibly as the pressures of a stressful
environment are felt by a child as they grow.
II. Segmentation Analysis:
The purpose of a segmentation analysis is to subdivide the population into clusters or subsets that
resemble one another, thus allowing further analysis to be done on each cluster. Because the primary
concern of the Promise Neighborhood for this data set is student academic achievement, clusters were
identified that group students with similar academic performance together. K-means clustering was
used for this analysis, and is a method of cluster analysis which aims to partition observations into k
clusters in which each observation belongs to the cluster with the nearest mean. For two reasons three
separate segmentation analyses were done: (1) the records for a significant majority of the students in
53
the data set are incomplete, thus by narrowing the time span in each cluster or segment more students
were included; and (2) logically, certain grades and certain traits belong together and different
predictive relations emerge. The three segments are K-3rd grade; 3rd-5th grade, and 6th-8th grade.
Kindergarten – 3rd Grade Segmentation Analysis
The first segmentation is of students in kindergarten through 3rd grade. 1,645 students (of the
16,128 (10%) students in the database) attended school in the Wheatley cluster from kindergarten
through 3rd grade (implying that 90% of students changed schools in to or out of the cluster during those
years), and thus are included in this segmentation analysis. To determine cluster membership,
attendance in pre-K, mobility, age in 3rd grade, retention, and 3rd grade TAKS reading and math score
results, absentee rates, gender, and ethnicity were included. Four clusters were identified: cluster
number 1 is populated with students who are academically successful. Clusters 2 and 3 perform well
enough to advance from year to year, but fail to excel. Their academic performance is often
inconsistent. Cluster 4 consists of students who are consistently poor achievers. Their failure rate on
the 3rd grade TAKS test is approximately 43% in reading and 71% in math. Note that age in the earliest
grades does not show a distinct pattern, but by 2nd grade and especially evident in 3rd grade, cluster 4 is
populated by older students (as much as 1 year older than the mean, and at least 6 months older than
the recommended age limit for the grade). Ethnicity and gender fail to play a significant role in
determining cluster membership.
Reading across, 8.2% of the 1,645 students comprise cluster #1. Their average age in kindergarten was
5.7 (slightly above 5 ½); their ages in 1st, 2nd, and 3rd grade were 6.7, 7.7, and 8.7 respectively; they
missed 4.1, 3.8, 3.1, and 3.1 days of school in K, 1st, 2nd, and 3rd grade, and finally they achieved a 1.66
TPRI score at the beginning of kindergarten, a 2.65 at the end, and 1.97 and a 1.87 on the 3rd grade math
and reading TAKS15.
A profile of a student in cluster 1 and cluster 4:
N=1,645
% achieving ‘commended’ on 3rd
grade reading test
% attended Pre-K
% have changed schools
Cluster 1 – highest performers
98%
Cluster 4 – lowest performers
3%
61%
10%
41%
40%
15
The TAKS test is coded such that a 2 indicates a commended score, a 1 indicates a passing score, and a 0
indicates failure to pass.
54
% missed 0-2 days schools
56%
29% (40% missed >6 days)
In order of importance, the variables that carry the most weight in determining one’s cluster
membership in grades K-3rd grade are:
 age in 3rd grade,
 followed by 3rd grade TAKS reading performance,
 rates of absenteeism,
 3rd grade math TAKS score,
 Mobility,
 attendance in pre-K.
Mobility and attendance in pre-K are dwarfed by the power of the other variables in predicting cluster
membership. Note that these variables are tightly intertwined – some may be truly causative (meaning
they can be identified as a cause of one’s academic performance) while others are symptoms of
something else at work in the child’s life (absenteeism, for example, may signal risks in the household
that prevent the child from getting to school daily).
3rd-5th grade Segmentation Analysis
1,136 students in our database attended Wheatley cluster schools from the 3rd-5th grades16. The
3rd-5th grade cluster analysis included reading and math standardized tests in all grades, plus age,
student mobility, absenteeism, gender, and ethnicity. Again, TAKS scores were re-coded such that fail to
pass is coded ‘0’, met standard is coded ‘1’, and commended performance is coded ‘2’.
The greatest predictors of cluster membership in the 3rd-5th grade segmentation are ‘age in 5th
grade’, followed by math and reading TAKS scores in the 4th grade, then in the 5th grade, then
absenteeism. Once again, we must distinguish between first and second order variables; first order
variables may be identified as direct causes of the outcome we are measuring; second order variables
are indirect causes and are often indicative of additional forces at work that are more difficult to
quantify and study. Age emerges in both this and the earlier segmentation as a critical variable in
determining cluster membership; age is the only indication in this statistical test that may signify
retention (increased ages are probably explained by repeating an earlier grade), thus the importance of
age may be signaling the importance of retention in an earlier grade – are these students who truly
struggle due to a learning disability or difference and thus their age signifies the struggle, or were they
retained, and then became overage and perhaps stigmatized socially for being older? Perhaps both
explain this situation, but each requires a very different form of intervention.
16
th
As indicated earlier in the report, there were 4,000 students in the sample who attended the 4 grade in the
rd th
Wheatley cluster, but only 1,136 who attended 3 -5 grade in the cluster, thus making them eligible for inclusion
in this segmentation analysis.
55
The profiles of a student in cluster 1 and 4:
N=2,079
% commended reading (all
grades)
% have changed schools
% have missed <5 days school
Average age in 5th grade
Cluster 1 – highest performers
70-77%
Cluster 4 – lowest performers
1% (43-67% fail in all grades)
13%
87%
10.67
34%
29% (40% missed > 6 days)
11.36
In order of importance, the variables that carry the most weight in determining one’s cluster
membership in grades 3-5 are:
 one’s age in 5th grade,
 4th grade math TAKS,
 4th grade reading TAKS,
 and mobility.
Again, age in 5th grade is indicative of retention, which is highly correlated with TAKS performance, thus
simply one’s age is not causal, but rather serves as a proxy for other forces at work.
5th - 8th Grade Segmentation Analysis
One of the most important things to note about the 5th-8th grade segmentation analysis is the
distribution of students between clusters 1-4. Recalling that this segmentation overlaps (5th grade) with
the previous one, note that cluster 4 in the previous analysis held 22% of the 1,336 students analyzed; in
the 5th-8th grade segmentation analysis, cluster 4 contains 38% of the 386 students analyzed. What’s
going on? We already know that we lose a high number of students from the Wheatley cluster to
other schools both in and outside SAISD in the transition from 5th to 6th grade; what this segmentation
population is telling us is that the students who remain in the cluster tend to be lower-performing,
thus cluster 4 holds a larger percentage of the population than it did before the transition to middle
school. In fact, cluster 4 is the largest cluster in this third segmentation analysis (48.1%), meaning that
the majority of our students are now low-performers. The student population at Wheatley Middle
School has a much higher percentage of students at-risk of very low performance than the elementary
feeder schools did. In addition, the most at-risk students are probably not even included in the
segmentation analysis because their data entries are not complete (and thus are automatically removed
during the analysis), thus the results presented are quite conservative in the estimation of numbers of
students who populate clusters 3 and 4.
The variables that are the greatest predictors of cluster membership in the 5th-8th grade segmentation
analysis are:
 reading TAKS performance in the 8th grade,
56



followed by math (7th grade),
absenteeism,
and math (8th grade).
The profile of a student in cluster 1 and 4:
N= 858
% commended reading (all
grades)
% have changed schools
% have missed <5 days school
Average age in 7th grade
Cluster 1 – highest performers
48-65%
Cluster 4 – lowest performers
0-8% (41-73% fail in all grades)
13%
100%
12.3
13%
49% (22% missed >11 days)
13.0
All of the statistical analyses tables are included in the appendices following this report.
III. PREDICTIVE MODELING:
The purpose of predictive modeling is to analyze the relationships between variables that combine to
predict a student’s academic achievement level. This analysis was broken into four different pieces
because at different stages in a student’s academic trajectory, different variables emerge as more and
less determinative of one’s achievement level. For instance, in 3rd grade, how well one performed in
kindergarten is predictive of 3rd grade reading levels, but age and mobility may be more predictive. By
5th grade, it appears that how well one performed in the previous years and the number of years in the
same school have become increasingly important in predicting performance. The four analyses are: PreK - K; K-3rd grade; 3rd-5th grade; 5th-8th grade.
Predicting the Impact of Pre-K attendance on Kindergarten readiness.
Consolidating the top and bottom TPRI performance groups (0-1) and (2-3) provides comparison groups
that aid in the interpretation of the influence of Pre-K attendance on KG readiness. The lack of
differentiation between TPRI score groups 2 and 3 clearly indicates that these two groups are very
similar, with high percentages attending Pre-K and only slight differences in performance. The average
profile of students with high TPRI performance is defined by Pre-K attendance, female gender, slightly
higher age than low performing students and fewer absences.
Attended PK
Gender
TPRI BOY
High
Performance
Yes
73%
No
27%
Female 59%
Male
41%
Grade KG
mean
Age
KG Absences range
n=
Low
Performance
57%
43%
46%
54%
Total
750
462
604
608
5.56
5.50
5.52
0 - 55
372
0 – 66
840
7.64
1212
57
Attendance in a pre-K or Head Start program does not automatically produce kindergarten-readiness
though; note that 57% of those that attended pre-K were not kinder-ready.
The model to compare high and low performance TPRI groups identifies age, Pre-K attendance
and gender, in descending order of importance, as significant predictor variables. Absences in KG are
only marginally significant as a negative influence on performance.
The positive influence of age would indicate that in kindergarten one’s age is probably best
interpreted as a measure of maturity. Slightly more mature students (i.e., older students) who attended
Pre-K are the highest performers in kindergarten. Among students who attended Pre-K, females
achieved higher TPRI-BOY scores than males. However, among students who did not attend Pre-K there
is no significant difference in TPRI-BOY performance between female and male students.
K-3rd: Influence of Pre-K on TPRI and 3rd grade Reading Test
There is a highly significant difference in TPRI results between students who attended Pre-K and
those that did not. The performance on the TPRI at the beginning and end of year clearly shows that
Pre-K attendees are better prepared for these tests, and thus for kindergarten and the beginning of
school.
There is a significant difference on 3rd grade reading test results between students who
attended Pre-K and those that did not, but not nearly as large as the difference we see on the TPRI
comparison. The performance on the 3rd grade reading test shows that Pre-K attendees are slightly
better prepared, but only by a small margin. This would seem to indicate that some of the advantage
gained from Pre-K may be lost by the time students reach 3rd grade.
The coefficients for the 3rd grade reading model show that the most influential independent
variable appears to be a negative influence with increasing age at grade 3 followed by a positive
influence for Pre-K attendance. Absences have a small but significant negative influence on the
probability of achieving a commended score. This does not link to KG readiness, but could show the
benefits of Pre-K attendance.
3rd-5th Grades: Predicting Academic Performance through the Elementary Years
The available factors that have significant relationships with 5th grade reading performance are
listed below in descending order of influence for prediction of commended results17:
 4th grade reading test results – categorical variable (levels: commended, passed, not passed)
 3rd grade reading test results – categorical variable (levels: commended, passed, not passed)
 Age in grade 5 – large negative coefficient, but low significance due to high variability
 Years in the same school – log transformation for positive nonlinear relationship
 Interaction between age and absence in 4th grade – log transformation for negative nonlinear
relationship.
5th Grade Reading Test Results:
Value
Years_Same_School
Reading Std Met Grade 5a2
Asymp. Sig.
(2-sided)
df
a
1
2
3
4
5
6
253.944
10
.000
8%
11%
13%
13%
14%
24%
T otal
12%
Pearson Chi-Square
Commended
Likelihood Ratio
261.984
10
.000
Yes
52%
59%
66%
69%
74%
68%
61%
Linear-by-Linear Association
218.698
1
.000
No
40%
30%
21%
18%
11%
8%
26%
N of Valid Cases
986
772
516
334
269
379
3256
a. 0 cells (.0%) have expected count less than 5.
n=
3256
4th Grade Reading Test Results:
17
Results of the statistical tests can be found in appendix X, table Y
58
Value
Years_Same_School
Reading Std Met Grade 4a2
Asymp. Sig.
(2-sided)
df
a
1
2
3
4
5
6
59.137
10
.000
6%
8%
12%
11%
10%
20%
T otal
12%
Pearson Chi-Square
Commended
Likelihood Ratio
58.079
10
.000
Yes
52%
50%
54%
54%
55%
57%
53%
Linear-by-Linear Association
42.147
1
.000
No
42%
42%
34%
35%
36%
23%
35%
N of Valid Cases
62
655
461
310
251
371
2110
a. 0 cells (.0%) have expected count less than 5.
n=
2110
3rd Grade Reading Test Results:
Value
Years_Same_School
1
Reading Std Met Grade 3a2
Commended
2
3
4
5
6
49.737 a
10
.000
15%
20%
19%
27%
T otal
19%
Pearson Chi-Square
16%
Likelihood Ratio
49.773
10
.000
40.415
1
.000
Yes
58%
64%
65%
67%
70%
64%
66%
Linear-by-Linear Association
No
42%
20%
20%
13%
11%
9%
15%
N of Valid Cases
12
121
455
310
242
363
1503
a. 2 cells (11.1%) have expected count less than 5.
n=
Asymp. Sig.
(2-sided)
df
1503
Relationship of Number of Years in Same School to Reading Test Results
The association between years in the same school and reading test results is highly significant
rd
for 3 , 4th and 5th grade. There is a significant linear relationship between years in the same school and
reading test performance. The differences are most noticeable between students who attend the same
school for 6 years and students who attend the same school for only one year.
The logistic regression results indicate that when we compare against a reference group of
students who attend a school for only the year of the test, a student who attends the same school for:
 6 years is 3.68 times more likely to achieve a commended score.
 5 years is 1.97 times more likely to achieve a commended score.
 2 years is 1.38 times more likely to achieve a commended score.
The high mobility rates of families in the Promise Neighborhood are thus an important predictor of a
student’s academic success, although, as mentioned previously, mobility is a second order variable, and
may be indicating additional stresses on the family and child that materialize as mobility (keeping in the
mind the high mobility rates of military families and the high academic performance of most of their
children).
Into Middle School
The only variables that provide a good model fit for predicting academic success in the middle
school years are reading test results for 5th, 6th and 7th grades and persistent 8th grade absences. The
most influential variable for the 8th grade model is the 5th grade reading test. There seems to be a
pattern of the 5th grade test being the most challenging, the 6th grade test being the least challenging
and the 7th grade test being more challenging than the 6th grade, but less challenging than the 5th
grade. Thus as our children progress through the grades, they seem to quickly fall into performance
patterns – such that success one year leads to success the next year (and vice versa). The interesting
question is why? Are classroom placements based on performance, such that the most successful
students from one year are placed together the following year, and create a high performing classroom
where achievement is expected, or is one’s performance one year a true predictor of performance the
next year, even without outside influences such as classroom placement?
The 5th, 6th and 7th grade test results and absences during the 8th grade have consistent
relationships with the 8th grade test results. The other variables (ethnicity, gender, years in same middle
school and age during the 8th grade) have inconsistent patterns in terms of relationships with 8th grade
reading test results, which causes problems for model development.
Finally, for the purpose of intervention, each grade was examined to determine the most
important variables (ordered below) that may aid in predicting passage and failure rates on the
standardized reading test given at the end of the year. In the table the variables impacting performance
59
are ordered (1 through 3 where ‘1’ has the highest impact on academic performance for that grade).
Where the ‘1’ is bolded and enlarged, that variable dwarfs all others in the analysis, such that others,
where included, may be unpredictable, and are not even included here if not statistically significant at
the .05 level.
3rd
Mobility
Increased
age
Advanced
absenteeism
(>4 days)
TAKS
reading
(previous
grade)
4th
5th
6th
3
3
2
4
1
2
2
1
1
7th
8th
?
2
2
1
1
1
SECTION V: Integrated Findings: Assets, Needs, Perspectives, and Evidence
The following section integrates the findings detailed above. In addition, for each life stage analyzed,
findings from the academic literature have been compiled to offer an objective perspective on the mix
of assets, needs, and findings reported here. The full literature review and evidence base are included
in an additional report. To ensure consistency throughout, the ‘life stages’ reported mirror those used
in our segmentation analysis.
60
NEED
~1,661 children 0-5 years;
69% not kinder-ready at
beginning of year;
by end of kindergarten
year 25% still ‘not ready’
0-5 years (infancy through kindergarten)
ASSETS
FOCUS GROUPS
REGRESSION ANALYSIS
803 potential spaces in
Keen awareness of
Attendance in Pre-K is
early childhood programs; importance of early
highly predictive of kinder6 licensed pre-schools
childhood education, but
readiness, although 57% of
3 registered child-care
concern for the expense;
those who attended pre-K
homes
Concern with parentwere NOT kinder-ready;
3 Pre-K and Head Start
teacher relationships and
age is most important
programs
lack of communication;
variable in predicting
1 Early Head Start
Parents identify drug
kinder-readiness
program;
problems in neighborhood (advanced age may signal
These seats could
as impacting their children advanced maturity in the
potentially serve 48% of
due to fear; parents would low grades);
our children
like more out-of-school
highest performers on
time programming to
TPRI attended pre-K.
protect their children from
crime; parents juggling
working and parenting,
which prevents them from
participating in school
events; parents of young
children have difficulty
navigating the school
district and don’t know
how to interface with
teachers; parentpartnership at Tynan
effective in supporting
parents, but needs to
reach more deeply
EVIDENCE BASE
Behavioral readiness
considered by many to be
a greater predictor of
kinder-readiness;
children living in poverty
are at greater risk for
behavioral problems;
Attendance in a Pre-K or
Head Start program shown
to have life-time influence;
significant cognitive
development happens
before formal schooling
actually even begins.
61
Integration of data sources: 0-5 years
One of the prevailing concerns for our EPN’s smallest children is the number who have not been
prepared to begin formal schooling by the time they reach 5 years old. Cognitively, significant
development has occurred by 5 years and, metaphorically, a ‘window’ is open in those years to begin
learning and to establish healthy learning habits that will last a lifetime. Unfortunately, for many of our
youngest, that window has not been opened and much of that age-appropriate learning is delayed until
elementary school. Academic preparedness is not the only trait identified though; behavioral readiness
has been identified in the literature as a significant problem, especially in poverty-stricken communities.
Young children often have not had the opportunity to interact with others, and thus are not ready to
learn in a group setting or interact with a teacher when they arrive in kindergarten for their first day of
school.
Given this lack of preparedness, we also sought to learn about the stresses at work on our youngest
families and their children. A lack of prenatal care and high childhood obesity rates do not necessarily
set up a cause-effect relationship with academic performance in the earliest grades, but may be
indicative of a lack of attention to physical development of both mother and child. Maybe due to a lack
of awareness or financial stress (most likely a combination of several variables like these), our young
mothers may not be receiving the care they need throughout pregnancy which, research shows, may
impact a child’s cognitive development even before birth. This lack of attention then pervades the
formative youngest years, when books should be read, pictures should be drawn, and colors should be
learned but are not.
The systemic nature of these variables and relationships can never be fully understood, but must be
appreciated in order to be addressed properly. If we can begin to understand some of these actions as
adaptations to an environment characterized by high rates of poverty, elevated crime rates, low
educational attainment amongst adults, and tense school-community relationships (as detailed in our
focus groups) then the most rational adaptations to this environment may in fact be some of the
behaviors we have seen this past year: complicated family structures, delayed schooling, and a lack of
attention to physical development. If this is the case, then more attention to the prevailing
environment may eliminate some of the elements to which people adapt. The mechanism here is
different: do we adjust the environment, or do we adjust the assets?
62
rd
NEED
ASSETS
~1,449 children 5-9
years;
rd
62.8% pass the 3
grade reading TAKS
(compared to 84% for
district);
19.7% pass at
commended level
compared to 22% at
district; <75% of
elementary-age
students have health
insurance (~66% at
Pershing and
Washington); 26%
limited English
proficiency in
elementary schools;
99% eligible for
free/reduced lunch;
retention rates by
st
school: 1 grade 14.1,
nd
8.5, 6.5 (8.4); 2 grade
rd
12.1, 0.0, 12.7 (4.6), 3
grade 12.0, 9.8, 7.0
(5.5) – beginnings of
st
overage; 14.22% 1
graders over-age,
nd
22.43% 2 graders
Programs in schools:
[differ by school –
see asset charts];
Family-SchoolCommunity
Partnership [FSCP];
Out-of-School
Programming
available through
schools;
Programming also at
Ella Austin CC,
Antioch MBC, St.
Paul’s’ UMC; HIS
Bridgebuilders;
Dignowity Hill NA
Education Leadership
partnership at
Bowden; Frank
Bryant Health Care;
Dixon Clinic
Years 5-9 (K-3 grade)
FOCUS GROUPS
SEGMENTATION
ANALYSIS
rd
Parent concern with: parent- K-3 segmentation
teacher relationships and
included: attendance
level of parent engagement;
in pre-K, mobility, age,
rd
parents of elementary-age
retention, 3 grade
children express fear over
reading and math
bullying and fighting in and
TAKS, absenteeism,
out of school (often fighting
gender, ethnicity.
starts on the way home);
Segment 1 8.2%,
parents would like to see
segment 4 19.0%
more parks for recreation for segments 2 and 3
their kids – a place to play;
72.7%; in order of
parents believe teachers do
importance, age in
rd
rd
not engage their children;
3 , 3 reading,
rd
want children to have after
absenteeism, 3
school
math, mobility, pre-K
activities/opportunities;
parents identify curriculum
as problem, not real-world
focused; parent-teacher
communication ineffective;
parents do not understand
homework and are
humiliated by teachers;
miscommunication about
school policies; difficulty
navigating school
registration process
REGRESSION ANALYSIS
EVIDENCE BASE
Age and mobility more
rd
predictive of 3 grade
performance than
kindergarten TPRI; preK attendees only
slightly better
rd
performers in 3 grade;
rd
by 3 grade increased
age has become a
negative influence;
significant relationship
between years in same
school and
commended
performance (not the
mobility but the
reasons for mobility)
Level of parental
academic
achievement
indicative of level of
student;
Mobility leads to loss
of social capital, which
impacts academic
performance ;
Parental expectations
and parenting style
more important than
attendance at school
functions
63
Integration of data sources: K-3rd grade
By the time our children reach elementary school, parents have interfaced with the neighborhood
school, and are often experiencing frustration, humiliation, and defeat. They speak of their own inability
to help their children do well in school, due in part, they believe, to a lack of communication between
school and family. The Family-School-Community Partnership was identified repeatedly in our focus
groups as a site of empowerment though – the parent room itself, but also the networks that are
building point to a revolutionary shift in family-school relationships, where the families will actually play
a significant role in the future of our neighborhood schools, as they become empowered and more selfconfident.
Children are beginning to differentiate academically in their early elementary years. While some
differentiation may be due to innate cognitive abilities, much more is probably due to early preparation
for learning, including attendance in an early childhood program, where they learn with others
interactively. While our children who attended a pre-kindergarten program in one of our neighborhood
schools tended to be better prepared for kindergarten (‘kinder-ready’), still the majority were deemed
‘still developing’ in the core subjects identified by the TPRI assessment instrument. In turn, how well a
student performed on the TPRI was predictive of how well they perform on their 3rd grade TAKS test – a
flawed but consistent benchmark by which to measure their progression. Again though, second order
variables are the greatest predictors of 3rd grade performance: age in 3rd grade and mobility emerge as
the greatest predictor so academic success by the 3rd grade, yet both point to more complicated
relationships in which the child may be embedded. Advanced age signals a late start into school or
retention in the early grades. Why would a child start school late or be retained din the earliest grades?
And are they related? Early retention is often due to academic preparedness – which is masked by
behavioral readiness. Young children act out when frustrated, and if they do not possess the social skills
needed to address their frustration more productively. Repeated bouts will often result in being labeled
and held back in the early grades because they are deemed ‘not ready’. At that point, age – which in
kindergarten had a positive correlation with performance – now becomes a negative influence in later
grades. But the more fundamental questions remain: why were they not ready? Again, we return to
early childhood preparation and socialization for the formal, institutionalized school setting.
Unfortunately, once one begins the educational trajectory, prior performance (we will see this even
more pronounced in the later grades) becomes the greatest predictor of success. Is this simply due to
cognitive ability or do we change a child’s environment to fit what we perceive as their stage or level of
learning, thus creating a feedback loop that is impossible to escape? For instance, if a child fails to
perform well in 2nd grade, are they placed in a classroom of others who failed to perform well in 2nd
grade, and, subsequently, almost expected not to perform well in subsequent years? Or do we allow
our youngest school children to begin each new school year with the highest expectations each year?
64
NEED
ASSET
th
~59.9% pass the 3,4,5
grade reading TAKS
(compared to 80% for
district);
~14.5% pass at
commended level; TAKS
scores show steady
progression downward
with advancing grades;
rd
over-age: 29.46% 3
th
graders, 32.48% 4
th
graders, 35.29% 5
rd
graders. Retention: 3
grade 9.6% (5.5 district),
th
4 grade 4.7% (2.0
th
district), 5 grade 5.7
(4.3 district); special
education 7% compared
to 10.7% district.
Underutilized special
ed?; <75% of
elementary-age students
have health insurance
(~66% at Pershing and
Washington); 26%
limited English
proficiency in
elementary schools; 99%
eligible for free/reduced
lunch;
Programs in
schools: [differ by
school – see
sheet]; FSCP; Outof-School
Programming
available through
schools;
Programming also
at Ella Austin CC,
Antioch MBC, St.
Paul’s’ UMC; HIS
Bridgebuilders;
Dignowity Hill NA
Education
Leadership
partnership at
Bowden; Frank
Bryant Health
Care; Dixon Clinic;
Years 8-10 (3rd-5th grade)
FOCUS GROUPS
SEGMENTATION
ANALYSIS
Parent concern with:
1,136 students;
parent-teacher
segments based on
relationships and level of
reading and math
parent engagement;
all grades, age,
levels of bullying; parents
mobility,
of elementary-age
absenteeism,
children express fear over
gender, ethnicity.
bullying and fighting in
7.2% in segment 1,
and out of school (often
69.6% in 2 and 3,
fighting starts on the way
home); parents would like 23.2% in 4. Age in
to see more parks for
5th grade greatest
recreation for their kids –
predictor of
a place to play; parents
segment –
believe teachers do not
indicates earlier
engage their children;
retention. Then
want children to have
math and reading
after school
in 4th grade, then
activities/opportunities;
5th then mobility.
parents identify
REGRESSION ANALYSIS
EVIDENCE BASE
Years in school
predictive of TAKS
performance in 5th
grade; most significant
predictors of 5th grade
performance are 4th
grade performance, 3rd
grader performance,
age in 5th grade, years
in the same school, and
possibly absenteeism,
but interrelated.
Positive relationship
between physical
activity and academic
performance 6-18
years old; in some
findings it has been
reported that
positive impacts of
Head Start are erased
by 3rd grade, while
other research claims
that Head Start
advantages last a
lifetime.
curriculum as problem,
not real-world focused;
parent-teacher
communication
ineffective; parents do
not understand
homework and are
humiliated by teachers;
miscommunication about
school policies; difficulty
navigating school
registration process;
65
Integration of data sources: K-5th grade
By the time our EPN children are in their later elementary years, their prior year’s performance is the
greatest predictor of how well they will do in any given school year. In turn, recall that the greatest
predictors of their performance in the earliest grades was how well prepared they were to enter
kindergarten; in 5th and 6th grade that preparedness continues to haunt them. In these later grades,
parents begin to express concern over the school curriculum – both their own lack of understanding of
the subject matter being taught and their perceived understanding that its relevancy to their children
may be questionable. Few identify the subject matter itself as irrelevant, but the teaching styles make
the subject matter irrelevant. Lecture classes and a ‘banking method’ of pedagogy (in which teachers
deposit knowledge in young minds) continue to dominate classrooms, yet, as parents describe, children
learn through doing, and become alienated and bored otherwise.
While our schools are required by both state law and national expectations to teach a given curriculum,
there are additional outlets to alleviate some of the tensions caused by lack of engagement in the
classroom. Out-of-school-time [OST] programming has been identified in the literature and policy
circles as a potentially untapped resource, especially in our inner-cities. Largely unregulated, OST
programs have been used in other states (namely Rhode Island) to effectively supplement their state
curriculum while building community pride at the same time. In 2010 the San Antonio Area Foundation
requested a study be done to map the current OST providers and inventory the various programs
available in different parts of our city. Not surprisingly, there is a disparity of academically-engaged
programming across the urban landscape – with our EPN falling into an OST “dessert” (to borrow a
popular metaphor). While some programming exists in the EPN much of it serves children coming from
schools other than our own, or has a cost associated with it that our families cannot afford.
Effective OST may address the perceptions of irrelevancy in the established school curriculum by
integrating core subjects with hand-on experiments and by integrating the arts into the learning
environment. Unlike many large cities, much of the OST in San Antonio is publically-funded (i.e.,
Challenge program that serves about 10% of our inner-city youth), and thus accessible to our poorest
families. If those programs can be expanded and/or focused into parts of the city where the risk of
dropping out is greatest, then OST may be effectively integrated into the school day thus augmenting
the state curriculum while providing the learning environment we know (both thorough experience and
academic research) to be the most effective for this age group.
66
NEED
ASSET
~1,365 children 10-14
years;
~55% pass the 6,7,8th
grade reading TAKS
(compared to 80% for
district);
~12% pass at
commended level;
those attending
academies in area far
out-perform those
who attend Wheatley
– two explanations;
72% of Wheatley
students have health
insurance; 48% of
students in Wheatleyfeeder elementaries
DO NOT attend
Wheatley; in 2010,
25.6% of Wheatley
students receiving
special ed. services
(10.7% for district);
2007-2010, 35.29% 5th
graders overage, 44.35
of 6th graders (shows
loss to academies);
42.25% 7th graders;
38.47% 8th graders
Out-of-School
Programming
available through
schools;
Programming also at
Ella Austin CC,
Antioch MBC, St.
Paul’s’ MC;
City Year at Wheatley
MS this year;
Extensive faith
community; YAGA at
Wheatley; Ft. Sam
Houston adopt-aschool; City Hall
mentors; CPS
mentors; SA Fighting
Back mentors; CIS;
Big Broths Big Sisters;
Family-SchoolCommunity
Partnership program
[FSCP]
Years 10-14 (5th-8th grade)
FOCUS GROUPS
SEGMENTATION
ANALYSIS
th
Parent concern with:
5-8 grade
lack of alignment
segmentation analysis
between real-world
includes: age in 5th,
experiences and needs
6th, 7th, 8th grade,
and curriculum;
reading and math
perception that kids
TAKS scores 5-8th
drop out due to
grade, days absent 5thperceived irrelevancy;
th
parents concerned with 8 grade, gender,
ethnicity, mobility,
safety walking to
years in same grade;
school; parental
involvement by middle
segment 1 holds 6.9%,
school diminished; kids segment 4 48.1%,
beginning to try and
segments 2 and 3
make money partly to
45.2%; greatest
help family – drawn
predictors of segment
into drug trade; hands
are in order: reading
on curriculum;
th
technology at Wheatley TAKS 8th grade, math
TAKS 7 grade,
dated; parent-teacher
th
communication lacking absenteeism, math 8
grade. By middle
–parents not aware of
what’s happening;
school, performance
parents want
in previous grades
teachers/schools to
highest predictor;
‘open their doors’;
consistent absences in
8th grade also
influential;
Lose significant # of
students between 5th 6th grade
REGRESSION
ANALYSIS
Most influential
variable for
predicting 8th grade
performance in
reading is 5th grade
performance.
Advanced
absenteeism also
important in 8th
grader performance.
EVIDENCE BASE
Positive relationship
between physical
activity and academic
performance 6-18
years old; OST used
in some states to
augment standard
curriculum;
extracurricular
activities such as
music and chess have
been found to have
an impact on
quantitative
reasoning skill
development;
67
Integration of data sources: 5th – 8th grade
The most startling finding in this age and grade segment is the sheer number of students choosing to
attend middle school elsewhere. Our three elementary schools – Bowden, Pershing, and Washington –
feed to Wheatley Middle School, yet approximately 52% of our 5th graders attend Wheatley. Instead,
48% are choosing other middle schools in the district, or even moving their children out of the district to
attend middle school. Most importantly, the children electing to attend schools other than Wheatley
are not a random sample of our elementary children; instead, they are the higher performers who
achieved higher scores consistently through elementary school, and who tended to miss fewer days and
had not been retained in earlier grades. The result of this sorting is that the incoming 6th grade class to
Wheatley tends to have a higher percentage of children identified as ‘at-risk’ (this is most clear when
examining the number of children in the four segments of our segment analysis – in the 3rd-5th grade
segmentation analysis 23.2% were in segment 4 (the lowest performers), but the 5th-8th grade segment,
48.1% are). Once at Wheatley, the school is now dominated by students who are struggling and failing
to engage in their learning environments. At some point (‘tipping-point’ majority or minority) that
attitude becomes a culture, and it pervades the entire school. These are the students most at risk of
dropping out in high school, or even before reaching high school.
The value of economic and racial diversity in our schools has been debated for years in the academic
literature. Should we continue the attempts to integrate our public schools, despite the resistance of
the past 40 years, or do we allow schools to re-segregate while providing truly equal resources to all?
The Wheatley experience may argue for a determined effort to integrate. If a school culture actually
materializes and ‘feeds back’ to impact all students, then we need to understand the role of the
classroom environment on teaching and learning, and possibly work to create a heterogeneous
classroom in which strong and struggling learners work together. This study may begin to provide
evidence that we must look more closely at the actual distribution of children between schools and try
and work toward the greatest diversity in any student body.
SECTION VI: CONCLUDING REMARKS:
So how do we understand and integrate all of this? Traditional models of community
development began with a needs assessment, which is followed by an asset inventory, from which the
‘gap’ (needs – asset = gap) is then calculated. Once the gap is identified, it can be filled, and community
development will commence. The ‘gap’ is filled with individual parts that combine to produce a ‘whole’
-- be it a neighborhood, a school, or a well-functioning family. Housing, plus schools, plus services, plus
a well-functioning economy add up to a ‘whole’. This tradition of speaking of wholes as composed of
individual parts stems from the industrial revolution and Newtonian science. But the model has failed to
work; has it failed to work because we haven’t sufficiently filled the gap, or has it failed to work because
the model is flawed (a linear approach to a non-linear reality)? I believe that the underlying
assumptions on which the model is built are deeply flawed, and that new community development
68
models that draw from new ideas in the physical, social, and management sciences may prove
transformative with new insights into how to proceed in a new and different way.
The underlying assumption of the needs, assets, gaps model is that of a linear system in which parts
add up to produce wholes in a fairly predictive manner. Yet experience has shown that parts fail to align
in a predictable way – implying nonlinear relationships with interdependent parts. Knowing this, we
must look beyond linear systems theory for our model, and look to nonlinear or complex systems theory
in which non-linearity and interdependence are assumed. We need an alternative model for
conceptualizing effective change – one that recognizes a series of systems and subsystems at work in
our Promise Neighborhood (educational, social, institutional, commercial systems, and so on). In fact,
the Promise Neighborhood initiative mandates that we reconceptualize community development along
these lines. Two questions thus emerge:


How do we encourage these subsystems to operate as a whole rather than as a series of
independent parts?
Looking more closely at the social system, how has it evolved and adapted to the environment it
perceives and how might we guide it to a new equilibrium?
Question 1 – there are two methods we might use to encourage the subsystems to articulate with one
another – case management and structural coupling. With a case management model systems
communicate via representatives and people who occupy multiple roles in various systems, and thus act
as the communicator between systems. Individuals are thus mediators between the systems. The main
practical problem with this solution however is that networks of persons cannot be introduced by
decisions and if done so they are unlikely to produce stabilized effects and often instead tend to form
systems of their own – thus adding an additional level of complexity for us to navigate. The second
mode of articulation is called structural coupling. Structural coupling is a relationship between systems
with each belonging to the others environment. It involves a system making it own complexity available
for the constitution of another system and vice versa. Each system can then take the others for granted
and concentrate on its own tasks. In organizational theory a system such as this is called
functional/cross-functional, in which teams are created of people serving or playing different functions
toward the same goal. Traditionally, teams are functionally specific; this alternative form of
organization exposes those working toward the same goal to the others – and their ways of thinking and
approaching the situation – working toward that goal, thus causing all to realize the greater
environment in which they work. How do we encourage this sort of articulation in our Promise
Neighborhood? How do we get each system to see the others as part of their environment, and thus
influential in day to day affairs? By exposing the various subsystems to one another regularly: parents
shadow a teacher for a day, teachers shadow a parent for a day, parents shadow a child, teacher
shadow an administrator, administrators shadow a teacher, and so on. Issues such as those
encountered in each of the subsystems fail to become vital until they become personal.
The second concern is for community members. How do individuals navigate this landscape?
How do they do it now? Research has shown that in complex systems such as this one, there is a logic
that orchestrates the emergence of novel structures – self-organizing structures – that allow adaptation
69
to the environment and that make the system extraordinarily flexible and robust against perturbations
from outside conditions. This type of self-organization is inherently sustainable – yet its existence is not
and cannot be achieved through central management; it is an order that only can be maintained
through self-organization. The local community has self-organized to take care of its many needs. It
picks and chooses from the institutional landscape, creating interesting combinations of resources that
enable people to make it through the day. Community members form a system of their own – the social
system. The function of their social system is to make sense of the environment by selectively
transforming the problems posed by the environment, such that problems are not solved but
reformulated, simplified, so that the social system can deal with them. In this way, the system builds up
its own internal complexity by self-organizing against the pressures of a complicated environment.
So how do our families respond to, accommodate, or self organize in response to an
environment of 8 am – 3 pm schooling for one child, 9 am – 4 pm for another, and day care hours for
another? How do young men respond to an environment that lacks jobs for unskilled workers? How
does the family structure we see in the Promise Neighborhood accommodate the stresses of poverty,
incarceration, and teenage pregnancy?
And how does all of this apply to an inner-city neighborhood with struggling schools? The
environment has been described in section one of this report; households make about $20,000/year;
few adults graduated from high school and their children replicate that pattern; there is a lack of jobs for
unskilled workers; many of our babies are born to very young mothers. So what are the self-organized
systems that have emerged in response to this environment? A network of family, friends, and
neighbors that cares for the youngest children; an informal economy in which those without skills or a
diploma function; a family structure that goes well beyond the nuclear members to care for our
children. These are the self-organized systems that are inherently sustainable. Are they always positive
adaptations? No, most certainly not. An informal economy dominated by the drug trade and
prostitution rings may be an adaptation to the lack of jobs and skills, but it is not a sustainable
adaptation on which to build a future. In-home childcare by friends, family, and neighbors, on the other
hand, may be understood as an adaptation to expensive early childhood education programs, inflexible
working hours, and institutionalized care and is, with support, highly sustainable.
If we adopt this model of community development, what are our options? There are two: if the
self-organized system is working yet struggling (if it is a positive adaptation to the environment), then
bolster it in any and every possible way; if it is not, then change the environment that it is a response to
so that a new system or adaptation may emerge. Sustainable change must be self-organized; it cannot
be introduced and enforced from the outside. Self-organizing systems allow for adaptation to the
prevailing environment. For instance, the complicated family structure found in our EPN detailed above
may be understood as a positive adaptation to a difficult environment. Drug use, high poverty rates,
and high rates of incarceration place tremendous stress on families; the family structure we see may be
the local adaptation to these stresses. How can it be supported and strengthened? Increased childcare,
better adult education opportunities, flexible school administrators would each alleviate some of the
burden on these families. In contrast, the high crime rates – especially drug use and prostitution – may
also be understood as adaptations to some of the same stresses, although in this case – negative or mal70
adaptations. Here, alternatives to this informal economy must be found – although they need not
necessarily be formalized.
71
APPENDIX I. STATISTICAL ANALYSIS
Segmentation Analysis: Kindergarten – 3rd Grade:
Segmentation KG - 3rd
Grade
1
2
3
4
Total
9%
43%
31%
17%
1110
Attended PK
Yes
61%
53%
56%
41%
584
Mobile Elementary School
Yes
10%
21%
24%
40%
268
Grade_03_age (mean)
8.71
8.53
8.49
9.46
8.69
Grade KG Yrs
2+ Years
0%
0%
0%
6%
12
Grade 1 Yrs
2+ Years
3%
0%
0%
32%
66
Grade 2 Yrs
2+ Years
0%
0%
0%
21%
40
Grade 3 Yrs
2+ Years
0%
0%
0%
29%
55
Reading Std Met Grade 3a2
Commended
98%
23%
12%
3%
251
Yes
2%
71%
79%
54%
716
No
0%
7%
9%
43%
143
Commended
83%
14%
6%
1%
171
Yes
17%
58%
52%
28%
526
No
0%
28%
42%
71%
413
0 - 2 Days
47%
46%
14%
29%
369
3 - 5 Days
26%
18%
28%
26%
258
6 - 10 Days
23%
13%
41%
18%
262
11 - 20 Days
4%
17%
15%
18%
171
Over 20 Days
0%
5%
3%
8%
50
0 - 2 Days
46%
54%
21%
31%
433
3 - 5 Days
32%
12%
42%
21%
275
6 - 10 Days
19%
11%
32%
27%
233
11 - 20 Days
3%
18%
4%
13%
131
Math Std Met Grade 3a2
Grade KG Days Absent
Grade 1 Days Absent
Grade 2 Days Absent
Grade 3 Days Absent
Gender
Over 20 Days
0%
5%
0%
8%
38
0 - 2 Days
58%
57%
20%
36%
466
3 - 5 Days
27%
10%
42%
22%
263
6 - 10 Days
15%
11%
32%
25%
222
11 - 20 Days
0%
20%
6%
13%
138
Over 20 Days
0%
2%
0%
5%
21
0 - 2 Days
56%
53%
21%
32%
443
3 - 5 Days
31%
14%
37%
24%
271
6 - 10 Days
13%
12%
33%
25%
234
11 - 20 Days
0%
17%
7%
16%
135
Over 20 Days
0%
4%
1%
4%
27
Female
55%
53%
48%
42%
553
Male
45%
47%
52%
58%
557
72
Ethnicity
Hispanic
66%
55%
62%
67%
666
African American
34%
44%
36%
32%
424
White
0%
1%
1%
1%
11
Asian
0%
0%
1%
0%
3
Native American
0%
0%
0%
0%
0
Likelihood Ratio tests18 for each variable (segmentation analysis K-3rd grade):
Likelihood Ratio Tests
Model Fitting Criteria
Effect
Intercept
Likelihood Ratio Tests
-2 Log Likelihood of Reduced Model
Chi-Square
df
Sig.
995.015a
.000
0
.
ReadingMetStd_3a2
1170.306
175.291
6
.000
MathMetStd_3a2
1110.495
115.480
6
.000
Age_Grade_03
1588.209
593.194
12
.000
Days_Absent_01
1147.714
152.699
12
.000
Days_Absent_02
1153.515
158.500
12
.000
Days_Absent_03
1102.850
107.835
12
.000
996.840
1.825
3
.609
1000.782
5.767
3
.124
Attended_PK
Mobile_Elementary
The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by
omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0.
Segmentation Analysis 3rd-5th Grade:
Segmentation 3rd - 5th
Grades
Mobile Elementary School
Yes
Grade_05_age (mean)
1
2
3
4
Total
10%
46%
22%
22%
1336
13%
15%
19%
34%
263
10.67
10.61
10.59
11.36
10.78
Grade 3 Yrs
2+ Years
0%
1%
0%
20%
63
Grade 4 Yrs
2+ Years
0%
0%
0%
5%
14
Grade 5 Yrs
2+ Years
0%
0%
0%
24%
71
18
The likelihood ratio tests provide the best insight into which variables are most predictive of cluster
membership. The Chi-square statistic (higher numbers) and the level of significance (closer to 0) allow us to
prioritize the variables. Due to tremendous interdependence between the variable though, prioritizing one
variable above another is not statistically sound, although their relative importance may be weighed.
73
Reading Std Met Grade 3a2
Reading Std Met Grade 4a2
Reading Std Met Grade 5a2
Math Std Met Grade 3a2
Math Std Met Grade 4a2
Math Std Met Grade 5a2
Grade 3 Days Absent
Grade 4 Days Absent
Grade 5 Days Absent
Gender
Ethnicity
Commended
77%
27%
2%
1%
273
Yes
23%
73%
82%
62%
902
No
0%
1%
16%
37%
161
Commended
73%
15%
0%
1%
187
Yes
27%
79%
35%
33%
723
No
0%
6%
65%
67%
426
Commended
70%
20%
1%
1%
218
Yes
30%
80%
78%
56%
929
No
0%
0%
21%
43%
189
Commended
69%
11%
0%
0%
154
Yes
31%
74%
28%
18%
634
No
0%
15%
71%
82%
548
Commended
86%
14%
1%
1%
200
Yes
14%
79%
29%
27%
671
No
0%
7%
70%
72%
465
Commended
93%
22%
1%
1%
261
Yes
7%
76%
70%
42%
809
No
0%
2%
29%
57%
266
0 - 2 Days
58%
45%
43%
25%
550
3 - 5 Days
29%
22%
37%
22%
349
6 - 10 Days
9%
22%
19%
30%
293
11 - 20 Days
3%
10%
1%
17%
118
Over 20 Days
0%
1%
0%
6%
26
0 - 2 Days
63%
49%
48%
23%
591
3 - 5 Days
24%
19%
36%
21%
313
6 - 10 Days
13%
22%
16%
28%
282
11 - 20 Days
1%
9%
0%
20%
118
Over 20 Days
0%
1%
0%
8%
32
0 - 2 Days
62%
42%
51%
23%
557
3 - 5 Days
18%
22%
32%
17%
304
6 - 10 Days
18%
22%
15%
23%
272
11 - 20 Days
2%
11%
3%
31%
172
Over 20 Days
0%
2%
0%
6%
31
Female
61%
54%
52%
42%
686
Male
39%
46%
48%
58%
650
Hispanic
67%
60%
47%
56%
757
African American
28%
39%
51%
42%
549
White
2%
1%
1%
2%
20
Asian
2%
0%
1%
0%
6
Native American
0%
0%
0%
0%
0
74
Likelihood Ratio Tests of variables (3rd-5th grade segmentation analysis):
Likelihood Ratio Tests
Model Fitting Criteria
Effect
Intercept
Likelihood Ratio Tests
-2 Log Likelihood of Reduced Model
Chi-Square
df
Sig.
1016.921a
.000
0
.
ReadingMetStd_4a2
1219.866
202.945
6
.000
ReadingMetStd_5a2
1121.268
104.348
6
.000
MathMetStd_4a2
1250.193
233.273
6
.000
MathMetStd_5a2
1160.467
143.546
6
.000
Mobile_Elementary
1030.402
13.481
3
.004
Age_Grade_05
1300.096
283.176
12
.000
Days_Absent_04
1100.481
83.561
12
.000
Days_Absent_05
1068.603
51.682
12
.000
The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by
omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0.
Segmentation Analysis 6th-8th Grade:
Segmentation 5th - 8th
Grades
Mobile Middle School
Yes
Grade_08_age (mean)
1
2
3
4
Total
10%
18%
34%
38%
386
13%
15%
11%
13%
48
13.49
13.81
13.59
14.05
13.79
Grade 5 Yrs
2+ Years
0%
3%
2%
10%
19
Grade 6 Yrs
2+ Years
0%
0%
0%
4%
6
Grade 7 Yrs
2+ Years
0%
0%
0%
4%
6
Grade 8 Yrs
2+ Years
0%
1%
0%
5%
8
Reading Std Met Grade 5a2
Commended
48%
19%
1%
0%
33
Yes
53%
75%
80%
27%
218
No
0%
6%
19%
73%
135
Commended
65%
38%
17%
8%
86
Yes
35%
59%
77%
46%
223
Reading Std Met Grade 6a2
75
Reading Std Met Grade 7a2
Reading Std Met Grade 8a2
Math Std Met Grade 5a2
Math Std Met Grade 6a2
Math Std Met Grade 7a2
Math Std Met Grade 8a2
Grade 5 Days Absent
Grade 6 Days Absent
Grade 7 Days Absent
Grade 8 Days Absent
Gender
Ethnicity
No
0%
3%
6%
46%
77
Commended
53%
24%
2%
0%
39
Yes
48%
76%
89%
41%
248
No
0%
0%
10%
59%
99
Commended
85%
71%
17%
0%
104
Yes
15%
29%
83%
64%
229
No
0%
0%
1%
36%
53
Commended
55%
15%
2%
0%
34
Yes
45%
74%
82%
20%
205
No
0%
12%
17%
80%
147
Commended
68%
16%
6%
1%
47
Yes
33%
69%
70%
22%
185
No
0%
15%
23%
77%
154
Commended
45%
3%
0%
0%
20
Yes
55%
78%
80%
12%
198
No
0%
19%
20%
88%
168
Commended
48%
3%
4%
0%
26
Yes
53%
87%
82%
32%
235
No
0%
10%
14%
68%
125
0 - 2 Days
65%
12%
65%
39%
177
3 - 5 Days
28%
26%
23%
23%
92
6 - 10 Days
5%
28%
11%
25%
73
11 - 20 Days
3%
31%
1%
12%
40
Over 20 Days
0%
3%
0%
1%
4
0 - 2 Days
73%
1%
57%
38%
160
3 - 5 Days
15%
16%
35%
25%
100
6 - 10 Days
13%
44%
8%
24%
81
11 - 20 Days
0%
32%
0%
12%
40
Over 20 Days
0%
6%
0%
1%
5
0 - 2 Days
70%
3%
52%
27%
137
3 - 5 Days
25%
19%
34%
22%
100
6 - 10 Days
5%
41%
14%
29%
91
11 - 20 Days
0%
24%
0%
17%
41
Over 20 Days
0%
13%
0%
5%
17
0 - 2 Days
68%
4%
52%
17%
123
3 - 5 Days
20%
16%
25%
22%
84
6 - 10 Days
10%
31%
17%
27%
87
11 - 20 Days
6%
23%
67
0%
37%
Over 20 Days
3%
12%
0%
11%
25
Female
58%
43%
56%
49%
198
Male
43%
57%
44%
51%
188
Hispanic
63%
69%
65%
72%
262
76
African American
28%
29%
34%
28%
116
White
8%
1%
2%
0%
6
Asian
3%
0%
0%
0%
1
Native American
0%
0%
0%
0%
0
Likelihood Ratio Tests of variables (5th – 8th grade segmentation analysis):
Likelihood Ratio Tests
Model Fitting Criteria
Effect
Intercept
Likelihood Ratio Tests
-2 Log Likelihood of Reduced Model
252.486a
Chi-Square
.000
Age_Grade_08
273.009
MathMetStd_7a2
MathMetStd_8a2
df
Sig.
0
.
20.523
12
.058
334.850
82.364
6
.000
287.611
35.125
6
.000
ReadingMetStd_7a2
287.975
35.490
6
.000
ReadingMetStd_8a2
339.069
86.583
6
.000
Days_Absent_07
311.445
58.959
12
.000
Days_Absent_08
282.802
30.316
12
.003
The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by
omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0.
REGRESSION ANALYSIS of TPRI scores at the beginning of the school year:
a
TPRI BOY
B
High
Intercept
-5.309
Perform
ance
Grade_KG_a
.702
ge
absence_KG .016
[Attended_P
.681
K=1]
Std.
Error
Exp(
B)
95%
Confidence
Interval for
Exp(B)
Lower Upper
Bound Bound
Wald df
22.07
1
2
12.46
1
8
Sig.
.000
2.019 1.367
2.981
.009
3.004 1
.083
.984
1.002
.139
23.93
1
9
.000
1.976 1.504
1.130
.199
.000
.966
2.595
77
[Attended_P
K=2]
[Gender=1]
0b
.
.
.561
.129
[Gender=2]
0b
.
0
.
.
18.78
1
4
.000
1.752 1.359
2.257
.
.
.
.
0
.
.
.
Dependent variables: age in kindergarten, number of absences in kindergarten, attended pre-K, and
gender.
Analysis of relationship between attendance in Pre-K and TPRI performance and absenteeism; note that
for those who did not attend pre-K, age and days absent become much more heavily weighted:
Attended PK
Yes
Ordinal by
Ordinal
No
Somers'
d
Ordinal by
Ordinal
Somers'
d
Value
.007
.006
.008
Approx.
Sig.
.807
.807
.807
-.158
.000
TPRI_BOY Dependent
-.134
.000
Absent Days Grade KG
Dependent
-.193
.000
Symmetric
TPRI_BOY Dependent
Absent Days Grade KG
Dependent
Symmetric
Analysis of relationship between attendance in Pre-K, TPRI performance, and age in kindergarten:
Attended PK
Yes
Ordinal by
Ordinal
No
Somers'
d
Ordinal by
Ordinal
Somers'
d
Symmetric
TPRI_BOY Dependent
Grade_KG_age Dependent
Symmetric
Value
.080
.068
.096
.117
Approx.
Sig.
.003
.003
.003
.000
TPRI_BOY Dependent
.097
.000
Grade_KG_age Dependent
.146
.000
Exp(B)
95% Confidence
Interval for Exp(B)
Multivariate model of TRPI-BOY scores:
TPRI_BOYa
B
Std.
Error
Wald
df
Sig.
78
1
2
3
Lower
Bound
Upper
Bound
.980
1.752
1.134
.
1.167
.
.962
1.143
.858
.
.886
.
.999
2.687
1.500
.
1.537
.
.972
2.794
2.007
.
1.912
.
.951
1.742
1.453
.
1.410
.
.994
4.481
2.771
.
2.593
.
Intercept
absence_KG
Grade_KG_age
[Attended_PK=1]
[Attended_PK=2]
[Gender=1]
[Gender=2]
Intercept
absence_KG
Grade_KG_age
[Attended_PK=1]
[Attended_PK=2]
[Gender=1]
[Gender=2]
Intercept
-3.180
-.020
.561
.126
0
.154
0
-6.617
-.028
1.027
.697
0
.648
0
-6.847
1.220
.009
.218
.143
.
.141
.
1.363
.011
.241
.165
.
.156
.
2.321
6.793
4.475
6.615
.781
.
1.201
.
23.575
6.399
18.166
17.891
.
17.373
.
8.700
1
1
1
1
0
1
0
1
1
1
1
0
1
0
1
.009
.034
.010
.377
.
.273
.
.000
.011
.000
.000
.
.000
.
.003
absence_KG
-.014
.018
.593
1
.441
.986
.951
1.022
Grade_KG_age
.766
.410
3.493
1
.062
2.151
.963
4.804
[Attended_PK=1] .936
.299
9.821
1
.002
2.549
1.420
4.577
[Attended_PK=2] 0
.
.
0
.
.
.
.
[Gender=1]
.578
.262
4.870
1
.027
1.783
1.067
2.980
[Gender=2]
0
.
.
0
.
.
.
.
Reversing:
TPRI_BOYa
0
1
Intercept
[Attended_PK=1]
[Attended_PK=2]
[Gender=1]
[Gender=2]
absence_KG
Grade_KG_age
Intercept
[Attended_PK=1]
[Attended_PK=2]
B
6.847
-.936
0
-.578
0
.014
-.766
3.667
-.810
0
Std.
Error
2.321
.299
.
.262
.
.018
.410
2.322
.300
.
Wald
8.700
9.821
.
4.870
.
.593
3.493
2.493
7.273
.
df
1
1
0
1
0
1
1
1
1
0
Sig.
.003
.002
.
.027
.
.441
.062
.114
.007
.
Exp(B)
95% Confidence
Interval for Exp(B)
Lower
Upper
Bound Bound
.392
.
.561
.
1.014
.465
.218
.
.336
.
.978
.208
.704
.
.937
.
1.051
1.038
.445
.
.247
.
.802
.
79
2
[Gender=1]
[Gender=2]
absence_KG
Grade_KG_age
Intercept
-.424
0
-.006
-.205
.230
.263
.
.019
.409
2.375
2.600
.
.096
.251
.009
1
0
1
1
1
.107
.
.756
.616
.923
.654
.
.994
.815
.391
.
.958
.365
1.096
.
1.031
1.817
[Attended_PK=1] -.239
.310
.595
1
.440
.787
.429
1.445
[Attended_PK=2] 0
.
.
0
.
.
.
.
[Gender=1]
.070
.269
.067
1
.795
1.072
.633
1.818
[Gender=2]
0
.
.
0
.
.
.
.
absence_KG
-.014
.019
.530
1
.467
.986
.949
1.024
Grade_KG_age
.261
.418
.391
1
.532
1.299
.573
2.945
The following table displays the results of a logistic regression model designed to predict commended
5th grade reading test performance, based on the independent variables listed above:
Parameter Estimates
5th Grade Reading T est a
B
95% Confidence Interval for Exp(B)
Lower Bound
Upper Bound
-1.219
Std. Error
5.667
.046
1
.830
3rd grade reading=commended
2.599
.751
11.972
1
.001
13.456
3.086
58.665
3rd grade reading=pass
1.164
.741
2.470
1
.116
3.202
.750
13.671
0b
.
.
0
.
.
.
.
4th grade reading=commended
3.202
.503
40.550
1
.000
24.584
9.176
65.870
4th grade reading=pass
17.333
Commended Intercept
3rd grade reading=no
Wald
df
Sig.
Exp(B)
1.922
.475
16.388
1
.000
6.835
2.695
4th grade reading=no
0b
.
.
0
.
.
.
.
ln_Yrs_same_school
.736
.305
5.798
1
.016
2.087
1.147
3.797
-.090
.049
3.422
1
.064
.914
.830
1.005
-2.034
2.312
.773
1
.379
.131
.001
12.162
ln_absence_Grade4 * ln_Grade4_age
ln_Grade5_age
a. T he reference category is: Not Commended.
b. T his parameter is set to zero because it is redundant.
The 3rd and 4th grade reading test results are treated as categorical variables. Each level of the
categorical variables is treated as a separate predictor variable, with ‘not passing’ as the reference level.
The commended results level is the most influential predictor from the 3rd and 4th grade reading tests.
The Years in Same School variable is treated as a scalar metric variable. The log transformation increased
the coefficient for the ‘years in same school’ variable and accounts for the nonlinear relationship with
performance on the 5th grade test, meaning that each additional year in school has an exponentially
higher level of influence on test performance. The 4th grade appears to a challenging year for testing.
The number of days absent from school and age-in-grade seem to be highly correlated and are not
significant as separate variables, but they are marginally significant as an interaction variable. The
interaction variable for absence and age has a nonlinear negative association with 5th grade test
performance. The 4th grade appears to be the most significant point in time for this relationship.
Statistics for Variables Included in the Model
80
N
Statistics
Reading Std Met Reading Std Met Years_Same_Scho
Grade 3a2
Grade 4a2
ol
Grade_04_age
1155
1155
1155
1155
Valid
Missing
Mean
Absent Days
Grade 4
Grade_05_age
1155
1155
0
0
0
0
0
0
1.0545
.8000
4.17
9.7261
5.79
10.7843
1.00
1.00
3
9.79
1
10.04 a
.56437
.64682
1.304
.52602
5.692
.57055
Variance
.319
.418
1.700
.277
32.394
.326
Minimum
.00
.00
1
8.38
1
9.38
Maximum
2.00
2.00
6
12.02
59
13.33
Mode
Std. Deviation
Percentiles
25
1.0000
.0000
3.00
9.3233
2.00
10.3479
50
1.0000
1.0000
4.00
9.6548
4.00
10.7041
75
1.0000
1.0000
5.00
9.9534
8.00
11.0219
a. Multiple modes exist. T he smallest value is shown
Regression analysis of 8th grade reading test performance:
Parameter Estimates
Reading Std Met Grade 8a2a
Yes
Commended
95% Confidence Interval
for Exp(B)
Intercept
B
-.277
Std. Error
.302
Wald
.838
absence_8
-.043
.019
ReadingMetStd_5a2
2.378
ReadingMetstd_6a2
ReadingMetStd_7a2
Exp(B)
Lower
Bound
Upper
Bound
1
Sig.
.360
5.240
1
.022
.958
.923
.994
.583
16.654
1
.000
10.780
3.441
33.771
1.131
.368
9.437
1
.002
3.099
1.506
6.378
1.594
.439
13.198
1
.000
4.926
2.084
11.642
-6.045
.753
64.447
1
.000
absence_8
-.052
.024
4.706
1
.030
.949
.906
.995
ReadingMetStd_5a2
4.346
.728
35.596
1
.000
77.154
18.507
321.645
ReadingMetstd_6a2
1.848
.477
14.986
1
.000
6.345
2.490
16.170
ReadingMetStd_7a2
3.789
.659
33.040
1
.000
44.214
12.147
160.938
Intercept
df
81
Variables in the Equation
95% C.I.for EXP(B)
B
Step 1 a
Mobile_Elementary(1)
S.E.
.370
Wald
.094
Age_Grade_03
df
Sig.
Exp(B)
15.508
1
.000
35.727
4
.000
Lower
Upper
1.447
1.204
1.740
Age_Grade_03(1)
-.032
.141
.050
1
.822
.969
.734
1.278
Age_Grade_03(2)
-.271
.148
3.346
1
.067
.763
.570
1.020
Age_Grade_03(3)
.043
.141
.091
1
.763
1.044
.791
1.377
Age_Grade_03(4)
.525
.135
15.077
1
.000
1.690
1.297
2.203
18.730
4
.001
Days_Absent_03(1)
.035
.122
.084
1
.773
1.036
.815
1.317
Days_Absent_03(2)
.061
.123
.242
1
.623
1.062
.835
1.352
Days_Absent_03(3)
.400
.136
8.628
1
.003
1.492
1.142
1.948
Days_Absent_03(4)
.712
.217
10.790
1
.001
2.037
1.332
3.115
-1.856
.121
234.122
1
.000
.156
Days_Absent_03
Constant
Table X: Predicting the risk of 3rd grade TAKS reading failure
The exponentiated B [Exp(B)] value is the greatest predictor of importance in these analyses. In order of
decreasing importance, absenteeism, age, and mobility are the greatest predictors of the possibility of
failing the reading TAKS test. With each variable, the relationship is positive, thus as absenteeism
increases, so too does the possibility of failure.
Variables in the Equation
95% C.I.for EXP(B)
B
Step 1 a
Mobile_Elementary(1)
S.E.
.306
Wald
.117
Age_Grade_04
df
Sig.
Exp(B)
6.832
1
.009
5.974
4
.201
Lower
Upper
1.358
1.080
1.708
Age_Grade_04(1)
-.116
.147
.622
1
.430
.891
.668
1.188
Age_Grade_04(2)
-.190
.148
1.634
1
.201
.827
.619
1.106
Age_Grade_04(3)
-.170
.152
1.257
1
.262
.844
.627
1.136
Age_Grade_04(4)
.156
.161
.940
1
.332
1.169
.853
1.602
8.025
4
.091
Days_Absent_04
Days_Absent_04(1)
.282
.127
4.887
1
.027
1.325
1.032
1.702
Days_Absent_04(2)
.230
.130
3.109
1
.078
1.258
.975
1.624
Days_Absent_04(3)
.358
.160
5.008
1
.025
1.431
1.046
1.958
Days_Absent_04(4)
.076
.304
.062
1
.803
1.079
.595
1.956
1.863
.148
158.968
1
.000
6.445
4.824
8.610
-1.129
.122
85.823
1
.000
.323
Reading3(1)
Constant
Table X: Predicting the risk of 4th grade TAKS reading failure
In order of decreasing importance, reading results from the previous year, followed by absenteeism and
mobility predict failure of the 4th grade reading test.
82
Variables in the Equation
95% C.I.for EXP(B)
B
Step 1 a
Mobile_Elementary(1)
S.E.
Wald
.067
.158
Age_Grade_05(1)
-.235
.192
Age_Grade_05(2)
-.273
Age_Grade_05(3)
Age_Grade_05(4)
df
Sig.
Exp(B)
.179
1
.673
6.390
4
.172
1.505
1
.187
2.130
1
-.071
.191
.139
.164
.193
Age_Grade_05
Days_Absent_05
Lower
Upper
1.069
.784
1.458
.220
.790
.542
1.151
.144
.761
.527
1.098
1
.710
.931
.640
1.355
.723
1
.395
1.179
.807
1.722
7.638
4
.106
Days_Absent_05(1)
.223
.158
2.004
1
.157
1.250
.918
1.703
Days_Absent_05(2)
-.012
.171
.005
1
.944
.988
.707
1.381
Days_Absent_05(3)
-.050
.199
.062
1
.803
.952
.644
1.405
Days_Absent_05(4)
.806
.356
5.119
1
.024
2.239
1.114
4.502
2.440
.133
338.716
1
.000
11.474
8.848
14.878
-2.629
.175
226.240
1
.000
.072
Reading4(1)
Constant
Table X: Predicting the risk of 5th grade TAKS reading failure
By the 5th grade, performance on the previous year’s TAKS reading test dwarfs all other variables in
importance of prediction. The only other variable that may even be significant in prediction is
absenteeism.
Variables in the Equation
95% C.I.for EXP(B)
B
Step 1 a
Mobile_MiddleSchool(1)
S.E.
Wald
-.772
.383
Age_Grade_06(1)
.406
.272
Age_Grade_06(2)
-.103
Age_Grade_06(3)
Age_Grade_06(4)
df
Sig.
Exp(B)
4.074
1
.044
5.238
4
.264
2.231
1
.292
.124
1
.320
.282
1.288
.363
.279
Age_Grade_06
Days_Absent_06
Lower
Upper
.462
.218
.978
.135
1.502
.881
2.560
.725
.902
.509
1.599
1
.256
1.378
.792
2.396
1.694
1
.193
1.438
.832
2.485
2.082
4
.721
Days_Absent_06(1)
.138
.233
.349
1
.554
1.148
.726
1.814
Days_Absent_06(2)
.040
.242
.028
1
.868
1.041
.647
1.674
Days_Absent_06(3)
.358
.271
1.746
1
.186
1.431
.841
2.435
Days_Absent_06(4)
.321
.516
.385
1
.535
1.378
.501
3.791
2.147
.182
139.984
1
.000
8.563
6.000
12.222
-2.524
.251
101.001
1
.000
.080
Reading5(1)
Constant
Table X: Predicting the risk of 6th grade TAKS reading failure
By the time our student enters 6th grade, and transitions to middle school, performance on the previous
year’s test, and mobility are the only reliable predictors of possible failure.
83
Variables in the Equation
95% C.I.for EXP(B)
B
Step 1 a
Mobile_MiddleSchool(1)
S.E.
Wald
.286
.312
Age_Grade_07(1)
-.181
.271
Age_Grade_07(2)
-.335
Age_Grade_07(3)
Age_Grade_07(4)
df
Sig.
Exp(B)
.839
1
.360
18.501
4
.001
.449
1
.280
1.433
1
.276
.279
.974
.792
.281
Age_Grade_07
Days_Absent_07
Lower
Upper
1.331
.722
2.451
.503
.834
.490
1.418
.231
.716
.414
1.238
1
.324
1.318
.762
2.279
7.925
1
.005
2.208
1.272
3.834
7.627
4
.106
Days_Absent_07(1)
-.428
.389
1.212
1
.271
.652
.304
1.396
Days_Absent_07(2)
-.352
.392
.805
1
.370
.704
.326
1.517
Days_Absent_07(3)
-.351
.393
.799
1
.371
.704
.326
1.520
Days_Absent_07(4)
.260
.406
.408
1
.523
1.296
.585
2.873
2.809
.189
220.417
1
.000
16.588
11.449
24.034
-1.628
.399
16.625
1
.000
.196
Reading6(1)
Constant
Table X: Predicting the risk of 7th grade TAKS reading failure
Seventh and eighth grade results are similar to 6th grade, although one’s age in 7th grade becomes more
significant than mobility while mobility is marginally significant in predicting 8th grade performance (well
behind reading in 7th grade and advanced absenteeism).
Variables in the Equation
95% C.I.for EXP(B)
B
Step 1 a
Mobile_MiddleSchool(1)
S.E.
-.843
Wald
.457
Age_Grade_08
df
Sig.
Exp(B)
3.410
1
.065
2.211
4
.697
Lower
Upper
.430
.176
1.053
Age_Grade_08(1)
.181
.309
.341
1
.559
1.198
.653
2.196
Age_Grade_08(2)
.261
.323
.654
1
.419
1.299
.689
2.447
Age_Grade_08(3)
-.122
.328
.139
1
.709
.885
.465
1.684
Age_Grade_08(4)
-.105
.315
.901
.485
1.671
Days_Absent_08
.110
1
.740
11.321
4
.023
Days_Absent_08(1)
.258
.293
.775
1
.379
1.294
.729
2.297
Days_Absent_08(2)
-.171
.287
.353
1
.552
.843
.480
1.481
Days_Absent_08(3)
.229
.310
.546
1
.460
1.257
.685
2.307
Days_Absent_08(4)
.999
.357
7.846
1
.005
2.715
1.350
5.460
3.040
.231
173.480
1
.000
20.906
13.298
32.864
-3.135
.312
100.760
1
.000
.044
Reading7(1)
Constant
Table X: Predicting the risk of 8th grade TAKS reading failure
84