Attendance in Early Childhood Programs as a

Attendance in Early Childhood
Programs as a Key Facet of Dosage
Presenters
Hedy Chang, Attendance Works
Stacy Ehrlich
University of Chicago Consortium on Chicago School Research
Faith Connolly
Baltimore Education Research Consortium, Johns Hopkins University
Cheri Vogel, Mathematica Policy Research
Discussant
Amanda Bryans
Office of Head Start
Organization of This Session
Presentations
I. Building on the work on attendance in K-12

Measurement of attendance in k-12, implications for achievement, and understanding
roots of attendance issues as key starting points for consideration of attendance in ECE
II. Emerging research in ECE: Attendance patterns and child outcomes



A. Findings from Chicago Public Schools
B. Findings from Baltimore Public Schools
C. Findings from Baby FACES
III. Exploring the predictors of attendance in ECE



A. Findings from Chicago Public Schools
B. Findings from Baltimore Public Schools
C. Findings from Baby FACES
IV. What research is needed to pursue this set of issues further?
Discussant Comments
 Implications for practice and policy in early care and education
I.
BUILDING ON THE WORK ON
ATTENDANCE IN K-12
Hedy Chang
Attendance Works
Unpacking Attendance Terms
• The % of enrolled students who attend school each day.
It is used in some states for allocating funding.
Average Daily
Attendance
Truancy
Chronic
Absence
• Typically refers only to unexcused absences and is defined by
each state under No Child Left Behind. It signals the potential
need for legal intervention under state compulsory education
laws.
• Missing 10% or more of school for any reason -- excused,
unexcused, etc. It is an indication that a student is
academically at risk due to missing too much school
4
High Levels of Average Daily Attendance (ADA)
Can Mask Chronic Absence (CA)
CA in schools with 95% and 90% ADA
Chronic Absence For 6 Elementary Schools in
Oakland, CA with @ 95% ADA in 2012
Chronic Absence for 6 Schools in New
York City with 90% ADA in 2011-12
30%
30%
25%
25%
20%
20%
15%
10%
12%
13%
13%
15%
16%
26%
20%
20%
20%
A
B
C
21%
23%
15%
7%
10%
5%
5%
0%
0%
A
B
C
D
E
F
% Chronic Absence
98% ADA = little chronic absence
95% ADA = don’t know
93% ADA = significant chronic absence
D
E
F
% Chronic Absence
5
Sporadic – Not Just Consecutive – Absences Matter
New York City Schools (2008)
•
A 407 alert is issued when a student misses 10 consecutive days or 20 days over a 40
day period. It misses more sporadic absence.
•
1 out of 5 elementary school children were chronically absent.
Source: Nauer, K. et al, Strengthening Schools by Strengthening Families, Center for New York City Affairs
New School, Oct 2008
6
Students Chronically Absent in Kindergarten and
1st Grade are Much Less Likely to Read Proficiently
in 3rd Grade
Percent Students Scoring Proficient or Advanced on 3rd Grade ELA
Based on Attendance in Kindergarten and in 1st Grade
100%
80%
64%
60%
43%
41%
40%
17%
20%
0%
No attendance risks
No risk
Small risk
Moderate risk
High risk
Small attendance risks
Moderate attendance risks
High attendance risks
Missed less than 5% of school in K & 1st
Missed 5-9% of days in both K & 1st
Missed 5-9% of days in 1 year &10 % in 1 year
Missed 10% or more in K & 1st
Source: Applied Survey Research & Attendance Works (April 2011)
7
The Long-Term Impact of Chronic Kindergarten
Absence is Most Troubling for Poor Children
5th Grade Math and Reading performance by K attendance for children living In poverty.
Academic performance was lower even if attendance had improved in 3rd grade.
Average Academic Performance
52
50
48
46
Reading
Math
44
42
40
0-3.3% in K
3.3 - 6.6% in K
6.6-10.0% in K
>=10.0% in K
Absence Rate in Kindergarten
Source: ECLS-K data analyzed by National Center for Children in Poverty (NCCP)
Note: Average academic performance reflects results of direct cognitive assessments conducted for ECLS-K.
8
Multiple Years of Elementary Chronic Absence
= Worse Middle School Outcomes
Each year of chronic absence in elementary school is associated with a substantially
higher probability of chronic absence in 6th grade
18.0x
Increase in
probability of
6th grade
chronic
absence
Chronic absence in 1st
grade is also associated
with:
7.8x
5.9x
•
•
Lower 6th grade test
scores
Higher levels of
suspension
Years of Chronic Absence in Grades 1-5
Source: Oakland Unified School District SY 2006-2012, Analysis By Attendance Works
9
Suggested Framework For
Unpacking Absences
Myths
Absences are only a
problem if they are
unexcused
Sporadic versus
consecutive absences
aren’t a problem
Attendance only
matters in the older
grades
Barriers
Aversion
Child struggling
academically
Lack of access to
health care
Lack of engaging
instruction
Poor
transportation
Poor school climate
and ineffective school
discipline
No safe path to school
Parents had negative
school experience
10
Hypothesis: Going to School Every Day
Reflects When Families Have …
Hope
for a better future
+
Faith
that school will help their child succeed
+
Capacity
Resources, skills, knowledge needed to get to school
11
Recommended Site Level Practice
Associated with Reduced Chronic Absence
12
II. EMERGING RESEARCH IN EARLY CARE
AND EDUCATION
Attendance Patterns and
Child Outcomes
II. A. PRESCHOOL ATTENDANCE IN
CHICAGO PUBLIC SCHOOLS (CPS)
Stacy Ehrlich
University of Chicago
Consortium on Chicago School Research
Preschool programs and children included in Chicago
study
© CCSR
 Children enrolled in school-based preschool programs in
Chicago Public Schools
 Excluded Montessori and children in non-inclusive special
education programs
Programs:
• Serve 3- and 4-year-olds
• Most are ½-day
Students:
• 88% qualify for free and
reduced lunch
• 29.4% qualify for English
Language Learners (ELL)
services
50%
42%
40%
40%
30%
20%
10%
10%
4%
6%
0%
Latino
African
American
White
Asian
Data retrieved from Office of Early Childhood Education, CPS (updated
June, 2012): http://www.ecechicago.org/about/glance.html
Other
Percent preschool students in each absence
category
Preschool students have very high rates of chronic
absenteeism
50%
45%
45%
40%
36%
35%
30%
25%
20%
20%
14%
15%
12%
10%
5%
0%
Age 3
(PreK)
Age 4
(PreK)
Age 5
(K)
Age 6
(1st)
Age 7
(2nd)
Age
© CCSR
10%
Absence rate:
10% < 15%
15% < 20%
20%+
Age 8
(3rd)
16
17
Students with lower preschool attendance have
lower kindergarten readiness scores on all subtests
Not controlling for prior knowledge
3.5
3.0
*
**
Logits
2.5
2.0
1.5
***
**
** **
***
***
1.0
* **
** **
***
0.5
© CCSR
Math
Letter Recognition
Not Chronically Absent
Pre-Literacy
Social-Emotional
Development
Chronically Absent
* Indicates that scores are significantly different from scores of students who absent 0<3.3%, at p<.05 level; **p<.01; ***p<.001
20%+
15<20%
10<15%
6.6<10%
3.3<6.6%
0<3.3%
20%+
15<20%
10<15%
6.6<10%
3.3<6.6%
0<3.3%
20%+
15<20%
10<15%
6.6<10%
3.3<6.6%
0<3.3%
20%+
15<20%
10<15%
6.6<10%
3.3<6.6%
0<3.3%
0.0
18
In math and letter recognition, the relationship between
absences and outcomes is stronger for students with
lower prior skills than for those with higher prior skills
Math
4.0
4.0
Letter Recognition
3.5
3.5
3.5
3.0
3.0
3.0
2.5
2.5
2.5
2.0
2.0
2.0
1.5
1.5
1.5
1.0
1.0
1.0
0.5
0.5
0.5
0.0
0.0
25% 0%
0.0
© CCSR
0%
5%
10%
15%
Absence Rate
20%
5%
10% 15%
Absence Rate
Social-Emotional Development
4.0
20%
25%
0%
5%
10%
15%
Absence Rate
20%
Analyses control for prior preschool experience, race, gender, neighborhood poverty and social status, special education status, ELL status, and
program type. Missing data points represent values with fewer than 30 students.
25%
19
© CCSR
Roughly 1/3 of chronically absent 4-year-olds
continue to be chronically absent in kindergarten
Students who are chronically absent in preschool are
5 times more likely to be chronically absent in 2nd
grade than other students
20%
Percent CA in Second Grade
18%
16%
15.5%
14%
12%
10%
8%
6%
3.1%
4%
2%
0%
© CCSR
Chronically Absent in
Preschool
NOT Chrocnially Absent in
Preschool
20
Multiple years of chronic absenteeism puts students
at risk of needing academic intervention before 3rd
grade
Average second grade DIBELS Oral
Reading Fluency score
105
100
95
90
85
80
75
70
65
60
55
98.8
Risk for
intervention+
94.6***
88.9***
81.8***
72.9***
Not chronically
absent
(n=4,073)
© CCSR
21
Chr in PreK
(n=1,381)
Risk for substantial
intervention+
Chr in PreK + K Chr in PreK, K, Chr in PreK, K,
(n=423)
and 1st grade 1st, and 2nd
(n=255)
grade
(n=306)
* Indicates that scores are significantly different from scores of students who are never chronically absent, at p<.05 level; **p<.01; ***p<.001
+ In the DIBELS 6th Edition Assessment and Scoring Guide (Good & Kaminksi, 2002), these are labeled as “Some Risk,” indicating
the need for additional intervention and “At Risk,” indicating the need for substantial interventions.
II. B. ATTENDANCE IN
BALTIMORE CITY SCHOOLS’ PRE-KINDERGARTEN
AND KINDERGARGEN
Faith Connolly
Linda S. Olson
Baltimore Education Research Consortium (BERC)
Johns Hopkins University
Methodology – Data Source
Cohort 1-Enter PreK in 2006-07
• (n= 3,364) - 77% remained through school year
2010-11
Cohort 2-Enter K in 2007-08
• (n= 6,374), -81% were still enrolled 2010-11
Cohort 3-Enter PreK in 2008-09
• (n=4,057), 85% remained in 2010-11
Methodology – Regression Models
• Attendance – Average Daily Attendance
(ADA) and Chronic Absence (CA)
• Suspension
• Retention
• Later identification for Special Ed Services
• Grades 1 and 2 SAT10
• Grade 3 MSA (State Assessment)
Methodology – Regression Models
• Covariates - primary
• Gender/Race/Ethnicity
• Free/Reduced Price Meals
• Being overage
• MMSR
• Receipt of Special Education services in K
CA in Baltimore City
Grade
2010-11
2009-10
2008-09
2007-08
2006-07
Pre-K
26.5%
27.4%
19.5%
21.6%
21.7%
K
22.9%
22.5%
17.8%
19.4%
20.6%
Grade 1
21.0%
19.5%
15.6%
16.4%
18.7%
Grade 2
17.9%
18.2%
13.6%
14.5%
15.2%
Grade 3
17.6%
16.1%
12.2%
12.8%
14.4%
CA Patterns by Neighborhood
Pre K 2006-07
K in 2007-08
Both PreK and K
Later Chronic Absence
That Year
Only
Percent CA in PreK (2006-07)
PreK
(n=505)
36.4%
21.8%
20.2%
12.1% 9.5%
Once More
Twice More
Percent CA in K (2007-08)
K
(n=903)
29.5%
24.8%
24.1%
Three Times
More
21.6%
Four More
Times (only
PreK)
0%
25%
50%
75%
100%
CA in PreK & K and Attendance
• Significant predictor of ADA and CA in
later grades
• Two to 3 times more likely to be retained
before they reached third grade
• Lower achievement scores in reading and
math in G1 and G2, and lower math in G3
Head Start Outcomes
• Head Start graduates had highest rates of
attendance compared to all other groups
• By Grade 3 students had caught up to their
peers on state assessments
• More often identified to receive Spec Ed
services after K than peers independent of
their attendance.
II. C. ATTENDANCE IN CENTER-BASED
EARLY HEAD START CLASSROOMS AT AGES 1-2
Cheri Vogel, Pia Caronongan, Jaime
Thomas, and Kimberly Boller
Mathematica Policy Research
Acknowledgments
 OPRE, OHS, and ACF
 Amy Madigan, our Baby FACES Federal
Project Officer
 89 Early Head Start programs, their staff,
and nearly 1,000 families and children
What Is Baby FACES?
 Descriptive study of a nationally representative sample
of 89 Early Head Start programs

Followed newborns and 1-year-olds through their
experience in the program
 Rich data from multiple sources:
–
–
–
–
–
–
–
Direct in-home child assessments at ages 2 and 3
Weekly services offered and received (first time available)
Program director interviews
Parent interviews
Staff interviews (home visitors and teachers)
Staff-child reports
Classroom and home visit observations
Data Sources
 Direct in-home child assessments at age 3
– Language: PPVT-4, PLS-4 Auditory Comprehension
– Social Emotional: BRS Emotion Regulation; BITSEA
Problem and Competence
– Parent/child interaction: Parental Synchronicity (twobags)
 Weekly Family Service Tracking (FST)
– Teacher (or home visitor) completed weekly
• Number of center days scheduled (offered)
• Number of center days attended by children and percentage
taken up
• Reasons for missed days (due to program or due to family)
Methods
 Descriptive information: Multiple imputation to
account for missing data in FST reports
 Predicting age 3 outcomes from attendance over two
years (age 1 to 3)
– Continuous: total center days attended
– Binary: total days attended (240+ vs. <240 days)
• Equates to about 2.3 days per week or 50% of the amount
recommended by OHS
– Far lower standard than definition of CA in other presentations
– No analogous measure of average daily attendance
Things to Keep in Mind
 Early Head Start programs typically operate year
round (not on a school year)
– Some variability in summer closures
 Focusing on center-based today
– Early Head Start offers home-based and combinations of
service options in addition to center-based services
• About half of the Baby FACES sample was in the home-based
option
• Changes occur between options (although are relatively
uncommon)
 Infants/toddlers may face challenges to attend center
regularly (longer program year, illness, etc.)
Services Offered and Received Varies Seasonally
Number of Center Days Offered and Received per Week, Monthly Averages
Center days per week
5
4
3
Center days
offered
2
Center days
attended
1
0
Source: FST data July 2009-June 2010. Sample includes 1-year-old Cohort only.
Range of Early Head Start Attendance Policies
Number of Center Days Missed Before
Disenrollment
3–5 consecutive
6–10 consecutive
15–30 consecutive
3–5 days in one month
10 days in six months
20 days in nine months
5–10 days in a year
Sample Size
Weighted
Percentage of
Programs (Std.
Error)
19 (7.02)
33 (9.58)
21 (8.72)
11 (7.46)
3 (3.29)
3 (2.79)
10 (6.75)
30
Source: 2010 Program Director Interview. Sample restricted to programs that offered centerbased services and had an attendance policy
Center Attendance
 On average, children were offered over 4 center
days per week and attended over 3 days per week
– Total days offered was higher between ages 1-2 (225) vs.
ages 2-3 (194)
– Total days attended was also higher between ages 1-2
(190 days) vs. ages 2-3 (167 days)


Take-up rate was similar across ages/years at
around 85 percent.
Average days attended over 2 years is 261 days
(57% had 240 or more days)
Attendance Predicting Outcomes at Age 3
Comparing attending 240 days or more vs. fewer, age 1 to 3
Outcome
Multivariate Linear
Regression (coefficient)
PPVT-4
5.9 (p<.10)
PLS-4 (English)
9.1 (p<.05)
Sample size
190-219
• Some evidence for better language development at age 3,
although associations are modest, especially for the PPVT-4.
• Sensitivity tests were not significant.
• No associations with social emotional or parent-child
interaction outcomes.
III. EXPLORING FACTORS RELATED
TO ATTENDANCE IN EARLY CARE
AND EDUCATION
III. A. EXPLORING FACTORS RELATED TO
PRESCHOOL ATTENDANCE IN
CHICAGO PUBLIC SCHOOLS (CPS)
Stacy Ehrlich
University of Chicago
Consortium on Chicago School Research
Health, logistics, and family-related reasons account
for 80 percent of why preschool children miss school
Reasons for Absences
Sick
12%
Wellness Appointment
5%
Chronic Illness
3%
Transportation
10%
54%
Child Care
Family-related
3%
5%
Vacation
© CCSR
3%
Other
4%
Don't Know
Data source: Attendance logs
Note: "Other" includes school phobia, lack of sleep, religious observances, weather, safety issues, and a general other category.
43
African American and Latino children miss more
school due to being sick, and African American
families face more logistical obstacles
Reasons for Absences, by Race
18%
15.7%
16%
14%
12%
10.7%
10%
1.3%
8%
6.6%
6%
0.9%
4%
2%
0.9%
1.9%
Don't Know
1.0%
Other
2.0%
Vacation
0.6%
1.3%
0.6%
Family-related
0.6%
Transportation
Chronic Illness
0.6%
6.4%
7.5%
3.9%
© CCSR
Data source: Attendance Logs
Wellness Appointment
Sick
0%
White
(n=164)
Child Care
Latino
(n=507)
African American
(n=485)
44
45
Most parents believe regularly attending preschool is
important
 Almost 2/3 of parents believe that attending preschool
regularly matters
 These beliefs are related to children’s attendance in preschool
Attendance MATTERS, as much as
later years
7.5%
Attendance MATTERS, but less than
later years
10.7%
Attendance somewhat matters / doesn't matter
0%
2%
4%
6%
8%
© CCSR
Absence rate
Data source: Parent interviews
10%
13.2%
12%
14%
Schools with better climate also have higher
preschool attendance
 School safety: Teachers report little/no disorder in hallways, physical
conflict among students, vandalism, robbery or theft, and threats of
violence against teachers.
 Teacher-parent trust: Teachers and parents are partners in improving
student learning
 Parent involvement: Parents are active participants in their child's
schooling
 School commitment: Teachers are deeply committed to the school.
 Teacher collective responsibility: Teachers share a strong sense of
responsibility for student development, school improvement, and
professional growth
© CCSR
 Preschool inclusiveness: Preschool teachers report they feel a part
of the larger elementary school and work with kindergarten teachers
46
III. B. PREDICTING ATTENANCE IN PRE-K AND K
IN BALTIMORE PUBLIC SCHOOLS
Faith Connolly
Linda S. Olson
Baltimore Education Research Consortium (BERC)
Johns Hopkins University
Who is CA in PreK and K
Comparisons of CA students to higher
attenders found:
• No differences gender, race, or ethnicity
• More likely to receive FARMS
• More likely to receive special education
services
• More likely from some neighborhoods
III. C. PREDICTING ATTENDANCE IN CENTERBASED EARLY HEAD START CLASSROOMS
Cheri Vogel, Pia Caronongan, Jaime Thomas, and
Kimberly Boller
Mathematica Policy Research
Methods
 Predicting attendance: multi-level models
– Up to 2 years of data for each child/family
– Children nested within programs
– Models include child, family, staff, and program
characteristics as predictors
• Characteristics measured the previous spring predicts attendance
over the following year
Predicting Center Attendance
Child Predictors
Center Days
Take-Up
Race and Ethnicity (vs. white)
African American
ns
Hispanic
ns
Other
ns
Boys
3 pct pts
DLL
ns
Birth weight
ns
Excellent or very good
health
ns
Family Predictors
Center Days
Take-Up
Maternal Demographic Risks (vs.
lower risk)
Medium risk
ns
High risk
ns
Psychological Risks (vs. no risk)
One risk
ns
Two or more
risks
ns
Enrolled in
pregnancy
Left EHS before
eligibility ended
ns
15 pct pts
51
Predicting Center Attendance
Staff Predictors
Center Days
Take-Up
Race and Ethnicity (vs. white)
African American
ns
Hispanic
ns
Other
ns
Speaks language
other than English
ns
Has a BA or higher
5 pct pts
Years of experience
in EHS
Program Predictors
Multiple approach (vs.
single)
Center Days
Take-Up
ns
Population served:
Over 50% families
with unsafe
neighborhood/or
experience violence
ns
5 pct pts
ns
Over 50% families
with MH or SA
problems
Has a degree in EC
ns
Over 50% families
with multiple risks
ns
Has a CDA
ns
Fully implemented
ns
52
Takeaways
 Evidence that children take up offered services at
relatively high rates, despite frequent absences
 Weak positive associations between attendance
and language outcomes
 Teacher education related to higher attendance
 Those who leave the program participate at
lower levels while enrolled
– Reach out to families who are not engaged
53
IV. WHAT RESEARCH IS NEEDED TO
PURSUE THIS SET OF ISSUES
FURTHER AND SUPPORT PRACTICE?
Highest Priority Next Step for Research:
Hedy Chang
Attendance Works
Examining the relationship between early childhood
program quality and attendance
 Does ECE program quality predict attendance?
 Is program quality more important in predicting
attendance in ECE programs than in k-12?
 How should program quality be taken into account in
examining the linkages between attendance in early
childhood programs and later achievement?
55
Highest Priority Next Step for Research:
Stacy Ehrlich
University of Chicago Consortium on Chicago School Research
Working towards a shared understanding of how to
measure attendance accurately and in timely, useful ways
 Clear definitions to ensure that programs, schools, and
districts measure attendance in similar ways
 Setting up data systems that allow for accurate, ongoing
measurement of student-level absences
 Develop data tools that allow for easy-to-understand, on-time
data for use by practitioners and researchers
56
Highest Priority Next Step for Research:
Faith Connolly
Baltimore Education Research Consortium,
Johns Hopkins University
Understanding What Happens 0 to 5
 Understanding parent and family perspective on Head
Start, pK, and K enrollment and attendance
 More public reporting of attendance in public school
PreK and K
 Development of data systems that capture and link
early childhood data to pK-12 Data Systems
57
Highest Priority Next Step for Research:
Cheri Vogel
Mathematica Policy Research
Programs need supports to collect data and use it to
understand attendance patterns. Data are complex:
 Children may exit the program or change service options
(in EHS)
 Requires staff diligence, data systems that can record the
information, and some ability to manipulate the data
 We used multiple imputation because of evidence that
data were not missing at random (complex to implement)
58
DISCUSSANT COMMENTS:
IMPLICATIONS FOR PRACTICE AND POLICY IN
EARLY CARE AND EDUCATION
Amanda Bryans
Office of Head Start
QUESTION AND ANSWER
60
References and Resources
Connolly, F. & Olson, L.S. (2012). Early elementary performance and attendance in Baltimore city
schools’ pre-kindergarten and kindergarten. Baltimore: Baltimore Education Research Consortium.
http://baltimore-berc.org/pdfs/PreKKAttendanceFullReport.pdf
Ehrlich, S.B., Gwynne, J.A., Pareja, A.S., & Allensworth, E.M. with Moore, P., Jagesic, S. & Sorice, E.
(2014). Preschool attendance in Chicago public schools: Relationships with learning outcomes and
reasons for absences. Chicago: The University of Chicago Consortium on Chicago School Research.
http://ccsr.uchicago.edu/sites/default/files/publications/Pre-K%20Attendance%20Report_0.pdf
Vogel, C.A., Boller, K., Xue, Y., Blair, R., Aikens, N., Burwick, A., Shrago, Y., Carlton, B.L., Kalb, L,
Mendenko, L., Cannon, J., Harrington, S. and Stein, J. (2011). Learning as we go: A first snapshot of
Early Head Start programs, staff, families, and children. OPRE Report #2011-7, Washington, DC.
Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S.
Department of Health and Human Services.
http://www.mathematica-mpr.com/publications/PDFs/earlychildhood/learning_vol1.pdf
[Note that reports on children at ages 2 and 3 are forthcoming]
61
Contacts
• Hedy Chang, Attendance Works
[email protected]
• Faith Connolly, Baltimore Education Research
Consortium, Johns Hopkins University
[email protected]
• Stacy Ehrlich, University of Chicago Consortium on
Chicago School Research
[email protected]
• Cheri Vogel, Mathematica Policy Research
[email protected]
62