Transcript Student Mobility, Attendance and Achievement

Student mobility,
attendance and student
achievement
(or disruptions and student achievement)
Looking at six years of
Queensland state schooling data
Research Forum – 29 February 2008
Queensland Department of Education
Training and the Arts
Performance Monitoring and Reporting Branch
Margo Bampton
Susan Daniel
Alistair Dempster
Roland Simons
with thanks to Andrea Findlay and Nicholas White
Section A – OUTLINE
Section B – STUDENT TRACKING ACCURACY
Section C – SOCIO ECONOMIC DATA
Section D – STUDENT DISRUPTION DATA
Section E – RESEARCH AIMS
Section F – SIMPLE RELATIONSHIPS
Section G – DETAILED ANALYSIS
Section A – OUTLINE
Section B – STUDENT TRACKING ACCURACY
Section C – SOCIO ECONOMIC DATA
Section D – STUDENT DISRUPTION DATA
Section E – RESEARCH AIMS
Section F – SIMPLE RELATIONSHIPS
Section G – DETAILED ANALYSIS
Section A. Outline
The story so far
• Enduring relationship Socio-Economic Position (SEP) 
achievement (i.e., Phillip Holmes Smith, Barry McGaw, Ken
Rowe, Sue Thompson, Lisa De Bortoli).
• SEP is multi-dimensional, difficult to decompose into
specific components
• Our data is amongst the most advanced nationally and
internationally.
• We have EQID (unique student identifier) which allows for:
• Over 6 years of longitudinal tracking
•
Approximately 40,000 students tracked longitudinally in this study.
Section A. Outline
Main Aim
Estimating:
Impact of disruption (attendance and
mobility) in Queensland Primary schooling.
Section A. Outline
Structure of our analysis
Socio Economic Position
Mobility Year 2 to Year 7
2001
Jan
2006
Year 7
Attendance Rate
Sem1 2006
Year 7
Test Results
August 2006
Section A. Outline
Structure of our analysis
Year 7
Attendance Rate
Sem1 2006
Mobility
Year 2 to Year 7
Socio Economic Position
Year 7
Test Performance
August 2006
Section A – OUTLINE
Section B – STUDENT TRACKING ACCURACY
Section C – SOCIO ECONOMIC DATA
Section D – STUDENT DISRUPTION DATA
Section E – RESEARCH AIMS
Section F – SIMPLE RELATIONSHIPS
Section G – DETAILED ANALYSIS
Section B. Student Tracking Accuracy
The Unique Student Identifier (EQID)
• EQID is key to many of our analyses
involving tracking students (attendance
and mobility)
• Questions:
– How reliable is EQID? (i.e. does the same
EQID represent the same student over time?)
Section B. Student Tracking Accuracy
The nature of student enrolments
• There are three enrolment collections per year – Feb, Aug, Nov.
• Using the EQID to track students from collection to collection, the
degree of ‘churn’ was investigated.
• Students who were in one collection and not in the previous
collection have been termed ‘New’ students.
• Students who were in one collection and not in the next collection
have been termed ‘Left’ students.
• Students who were in one collection and in the next collection have
been termed ‘Kept’ students.
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Left after
0%
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New
Left after
Section B. Student Tracking Accuracy
‘New’ and ‘Left’ students by collection
Not at the Previous Collection (New) and Not at the Next Collection (Left) 2006
20%
18%
16%
14%
12%
10%
8%
6%
4%
2%
F eb A ug N ov
F eb A ug Nov
F eb A ug Nov
F eb A ug Nov
F eb A ug Nov
F eb A ug N ov
F eb A ug N ov
F eb A ug N ov
F eb A ug Nov
F eb A ug Nov
F eb A ug N ov
F eb A ug Nov
01
02
03
04
05
06
07
08
09
10
11
12
'Left' Students
'New' Students
Section B. Student Tracking Accuracy
‘New’ and ‘Left’ students Feb-Nov
Percentage of ‘New’ & ‘Left’ Students 2001/02 – 2006/07
Year level of Students who have ‘Left’ or are ‘New’
between the February and November Collection Dates
Section B. Student Tracking Accuracy
Matching across the Year 7-8 transition
In this time of greatest “churn”, how reliable is the
EQID?
Q: Are the ‘new’ students really new or are they
‘left’ students with a different EQID?
Section B. Student Tracking Accuracy
Matching across the Year 7-8 transition
The 4 (non-EQID) Matching Criteria 1. surname
2. first name
3. birth date
4. gender
Section B. Student Tracking Accuracy
Non-EQID Matching across the Year 7-8 transition
Year 7 to 8
Transition
No of Non-EQID
Matches(*)
No of Year 7s
% non-EQID matches of all year
7s
Nov-01 – Feb02
229
38025
0.6%
Nov-02 – Feb 03
348
39396
0.9%
Nov-03 – Feb 04
252
39849
0.6%
Nov-04 – Feb 05
199
40655
0.5%
Nov-05 – Feb 06
304
40498
0.8%
Nov-06 – Feb 07
288
41112
0.7%
*Matched on last name, first name, gender and birth date and not on EQID
In all 6 years studied, less than 1% of students
could be matched across the year 7 to 8 transition
by non-EQID criteria.
Only a very small proportion of ‘left’ students might
possibly be still in EQ with a different EQID
Section B. Student Tracking Accuracy
Matching across the Year 7-8 transition
As the ‘kept’ students are the ones
we use for our analyses, how reliable
is the EQID within this group.
Q: Are students with the same EQID
really the same student?
Section B. Student Tracking Accuracy
Matching across the Year 7-8 transition
% of ‘kept’ students
with fewer than 4 of
the matching
criteria fulfilled
% of students with all 4
criteria fulfilled
Total ‘kept’
students
2001
6.4%
93.6%
30655
2002
7.9%
92.1%
32160
2003
8.4%
91.6%
32726
2004
8.2%
91.8%
33161
2005
8.6%
91.4%
33000
2006
8.8%
91.2%
33304
Year
(2919 students)
Section B. Student Tracking Accuracy
Matching across the Year 7-8 transition
A visual inspection of the data for these 8.8% (n=2919) of students revealed the following:
Visual inspection of students whose data fulfilled less than 4 of the
matching criteria
OK
Surname
Suspicious
Unlikely
Grand Total
2655
210
40
14
2919
91.0%
7.2%
1.4%
0.5%
100.0%
OK
Appear to be Correct – mis-spelling, reversal of first and last name, abbreviation eg Sue for Susan
Name
Only the last name is different – appears to be legitimate name change
Susp
Suspicious – Robert Smith and John Smith with all other details the same
Unlikely
Unlikely – Robert Smith and Susan Smith
Section B. Student Tracking Accuracy
Matching across the Year 7-8 transition
A visual inspection of the data for these 8.8% (n=2919) of students revealed the following:
Fewer than 4 criteria filled. (EQID
matched)
Appears
Family Possible
to be
Name
Different
same
Change? Student
student
Unlikely
to be
same
student
All 4 criteria
filled as well
as an EQID
match
Total
Year 7 students
2655
210
40
14
30385
33304
8.0%
0.6%
0.1%
0.0%
91.2%
100.0%
99.2%
More than 99% of students who match on EQID appear to be the same student.
Section A – OUTLINE
Section B – STUDENT TRACKING ACCURACY
Section C – SOCIO ECONOMIC DATA
Section D – STUDENT DISRUPTION DATA
Section E – RESEARCH AIMS
Section F – SIMPLE RELATIONSHIPS
Section G – DETAILED ANALYSIS
Section C. Socio Economic Data
Socio Economic Position (SEP)
• Parental Occupation and Parental Education
information is collected but incomplete.
• Postcode is also used for IRSED calculations at the
student and school levels
• Results indicate a small but clear and enduring
relationship with achievement
Section C. Socio Economic Data
Student Achievement by Student IRSED
Standardised Student Achievement by Student IRSED
900
Standardised Student Achievement Scores
850
800
Example gradient
yr 2003 grade 3 Reading
yr 2003 grade 3 Numeracy
yr 2003 grade 3 Writing
yr 2003 grade 5 Reading
750
yr 2003 grade 5 Numeracy
yr 2003 grade 5 Writing
700
yr 2003 grade 7 Reading
yr 2003 grade 7 Numeracy
yr 2003 grade 7 Writing
650
yr 2005 grade 3 Reading
yr 2005 grade 3 Numeracy
yr 2005 grade 3 Writing
600
yr 2005 grade 5 Reading
yr 2005 grade 5 Numeracy
550
yr 2005 grade 5 Writing
yr 2005 grade 7 Reading
yr 2005 grade 7 Numeracy
500
yr 2005 grade 7 Writing
450
400
Low
Mid-Low
Mid-High
Student IRSED
High
Section C. Socio Economic Data
Student Achievement by Parental Education
Standardised Student Achievement by Parental Education Level
900
850
grade 3 Reading
Standardised Student Achievement
800
grade 3 Numeracy
grade 3 Writing
grade 5 Reading
750
grade 5 Numeracy
grade 5 Writing
700
grade 7 Reading
grade 7 Numeracy
grade 7 Writing
650
grade 3 Reading
grade 3 Numeracy
grade 3 Writing
600
grade 5 Reading
grade 5 Numeracy
550
grade 5 Writing
grade 7 Reading
grade 7 Numeracy
500
grade 7 Writing
450
Example gradient
400
yr 9
yr 10
yr 11
yr 12
Parental Education Level
cert I - 4
Diploma
Bachelor
Section C. Socio Economic Data
Student Achievement by Parental Education
Parental_Occupation
Category 1. Senior management in large business organisation,
government administration and defense, and qualified professionals
Category 2. Other business managers, arts/media/sportspersons
and associate professionals
Category 3. Tradesmen/women, clerks and skilled office, sales and
service staff
Category 4. Machine operators, hospitality staff, assistants,
labourers and related workers
Category 5. Not in paid work in last 12 months
Please note: Highlighted categories were removed from the analyses
Section C. Socio Economic Data
Student Achievement by Parental Occupation
Standardised Student Achievement by Parental Occupation
900
Standardised Student Achievement Scores
850
yr 2003 grade 3 Reading
800
yr 2003 grade 3 Numeracy
yr 2003 grade 3 Writing
yr 2003 grade 5 Reading
750
yr 2003 grade 5 Numeracy
yr 2003 grade 5 Writing
700
yr 2003 grade 7 Reading
yr 2003 grade 7 Numeracy
yr 2003 grade 7 Writing
650
yr 2005 grade 3 Reading
yr 2005 grade 3 Numeracy
yr 2005 grade 3 Writing
600
yr 2005 grade 5 Reading
yr 2005 grade 5 Numeracy
550
yr 2005 grade 5 Writing
yr 2005 grade 7 Reading
yr 2005 grade 7 Numeracy
500
yr 2005 grade 7 Writing
450
Example gradient
400
Category 5
Category 4
Category 3
Parental Occupation
Category 2
Category 1
Section C. Socio Economic Data
• Given the similarity of the relationships we
decided to use IRSED calculations (based on
both student and school postcode) at both
student and school levels
Section A – OUTLINE
Section B – STUDENT TRACKING ACCURACY
Section C – SOCIO ECONOMIC DATA
Section D – STUDENT DISRUPTION DATA
Section E – RESEARCH AIMS
Section F – SIMPLE RELATIONSHIPS
Section G – DETAILED ANALYSIS
Section D. Student Disruption Data
• School enrolment >> mobility
[Since 2000 in primary schools]
• Student absence >> attendance
• [Sem 1 2006 in primary schools]
• Both student and school level data available
• We have the capacity to link this data to
student test achievement and
Socio-Economic Position (SEP) using EQID
Section D. Student Disruption Data
Mobility
• Student enrolment data includes:
• Each year Feb, Aug, & Nov enrolment data
collections
• We can track a student’s school location and
infer stability / mobility
• Student movements can be tracked within
Queensland state schooling only.
Section D. Student Disruption Data
Mobility
Transitions
- number of times the student had a change of school
code (may be under-reported - only three collections a
year)
- total number of schools the student has been
enrolled in (may also be under-reported)
Stability
- how many years has student been at current
school?
Section D. Student Disruption Data
Mobility
Timing
- number of transitions between Years 2 and 5
- number of transitions between Years 5 and 7
- did the student move within 2006?
- number of transitions at ‘natural breaks’ between years i.e.
between November and February
- number of ‘disruptive’ moves i.e. within a year
Section D. Student Disruption Data
Mobility
Distance
- was the most recent move under 10km?
- was the most recent move greater than 100km?
- distance of most recent move in km
Other
- did the student have a break from the Queensland state
system?
- did the student leave the school and return?
Section D. Student Disruption Data
Mobility
School Mobility
- percentage of students enrolled recently (Year 6 or Year 7)
- percentage of students enrolled early (Year 1 or Year 2)
- percentage of students with four or more transitions
Section D. Student Disruption Data
Mobility
students
in Year
7 2006,of enrolment
Proportion of Proportion
students inofYear
7 2006
by number
changes
(n=40181)
by number of enrolment
changes
since Year 2 (N=40181)
60%
Percentage
50%
40%
30%
20%
10%
0%
0
1
2
3
Number of changes
4
5+
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2
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2
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2
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2
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2
No 002
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2
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20
No 03
v
2
Fe 003
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2
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2
No 004
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2
Fe 004
b
2
Au 005
g
2
No 005
v
2
Fe 005
b
2
Au 006
g
20
06
Section D. Student Disruption Data
Mobility
Percentage of 2006 Year 7 cohort enrolled at 'final' school
100%
80%
60%
40%
20%
0%
Section D. Student Disruption Data
Mobility
Percentage of 2006 Year 7 cohort enrolled at 'final' school
100%
80%
Less than 50% of these
students were still at their
original school
60%
40%
20%
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2
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2
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2
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b
2
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g
20
06
0%
Section D. Student Disruption Data
Mobility
Percentage of 2006 Year 7 cohort enrolled at 'final' school
100%
80%
Over 20% of these
students had a new
school in Year 6 or 7
60%
40%
20%
Fe
b
2
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2
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v
2
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2
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2
No 002
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2
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2
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20
No 03
v
20
Fe 03
b
2
Au 004
g
2
No 004
v
2
Fe 004
b
2
Au 005
g
2
No 005
v
2
Fe 005
b
2
Au 006
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20
06
0%
Section D. Student Disruption Data
Mobility
% of All Students in Each Socio-Economic Group Based on
School IRSED (2006) by Number of Moves
% of all students
High
Mid-High
Mid-Low
Low
60%
55%
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
0
1
2
3
Number of School Moves
4
5+
Section D. Student Disruption Data
Mobility
Percentage of students showing SES of final and
original school
SES of school High
of origin
Mid-High
Mid-Low
Low
0%
20%
40%
60%
80%
100%
SES of final school
low
mid low
mid high
high
Based only on students who started and stayed in state schooling, and who changed schools
Section D. Student Disruption Data
Attendance
Student absence data includes:
• Recordings of full and half day absences for
each child
• We used full day absences
• This information is compared to total number
of possible days attended so we can calculate
attendance rate
Section D. Student Disruption Data
Attendance
Student Attendance
- total days absent
- attendance rate (days attended/days enrolled)
- number of episodes of absence
- maximum episode length
- average episode length
Section D. Student Disruption Data
Attendance
School Attendance
- centre level attendance rate (total student days
attended/total student days enrolled)
- total episodes for all students
- average number of episodes per student
- average episode length for all students
Section D. Student Disruption Data
Attendance
Student Absence for Sem 1 Yr 7 (2006)
All Students
15%
Students
10%
5%
0%
0
1 2
3
4 5
6
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21+
Total Days Absent
Section D. Student Disruption Data
Attendance
Student Attendance Rate for Sem 1 Yr 7 (2006)
All Students
15%
Students
10%
5%
0%
100% 99% 98% 97% 96% 95% 94% 93% 92% 91% 90% 89% 88% 87% 86% 85% 84% 83% 82% 81% 80% 80%+
Attendance Rate
Section D. Student Disruption Data
Attendance
Length of Each Episode of Absence in Days for Sem1 Yr 7 (2006)
0
1
2
3
4
5
6
7
8
9
10
>10
20000
40000
60000
80000
100000
120000
Section D. Student Disruption Data
Attendance
Average of Student Attendance by IRSED Sem 1 Yr 7 (2006)
All Students
Average Attendance
100%
95%
90%
85%
Low
Mid Low
Mid High
High
Section A – OUTLINE
Section B – STUDENT TRACKING ACCURACY
Section C – SOCIO ECONOMIC DATA
Section D – STUDENT DISRUPTION DATA
Section E – RESEARCH AIMS
Section F – SIMPLE RELATIONSHIPS
Section G – DETAILED ANALYSIS
Section E. Research Aims
Logically: Disruptions are a constant
challenge for education systems as they
interfere with the delivery of curriculum.
Research: Both mobility and absenteeism
have been linked with achievement declines
in research around the world.
Context: Is this relevant in Qld? A state
where children can travel very large
distances when moving schools.
Section E. Research Aims
Context - Attendance
• Student attendance rates will be reported from
the 2007 National Report on Schooling in
Australia (ANR), MCEETYA, to be released in
2008.
• Measure: The number of actual fulltime
equivalent ‘student days’ attended as a
percentage of the total number of possible
student days attended over the period
Section E. Research Aims
Context - Mobility
• “Between 1997 and 2000, 4 out of every 10 adults in
Queensland had moved at least once (Australian Bureau
of Statistics (ABS), 2000).
• Of these,72% had relocated within 20 kilometres and
49% within five kilometres of their previous home.
• Employment opportunities and changes in housing were
the main reasons cited for the moves. Seventy percent
of the moves were made by unemployed persons.
• Thirty eight percent of couples with children and 43% of
single parents with children moved during this period.”
Source: Student mobility - reasons, consequences and interventions
Dr. Reesa Sorin & Rosemary Iloste
JCU and EQ
Section E. Research Aims
The Study
• literacy and numeracy in Year 7 in 2006
(41,261 students)
• utilised the unique student identifier (USI)
• tracked over 38,000 primary school students
across a six year period, from year 2 to year 7.
• compared a series of mobility measures
• compared a series of attendance measures
Section E. Research Aims
The Study
Analysis in three phases
Phase 1 – student mobility
Phase 2 – student attendance
Phase 3 – combined
Section E. Research Aims
Data Sample
Phase 1: Mobility
- removed part timers, repeaters

40,181 students
Phase 2: Attendance
- semester 1 data collected in November

39,467 students
Phase 3: Attendance and Mobility
- required attendance and mobility data

38,381 students
Section E. Research Aims
The Problem with SEP
Socio-economic position is a generic
(multi-factorial) indicator of advantage or
disadvantage and as such reveals very
little about the ways in which some
students are differentially affected by
advantage/disadvantage.
Section A – OUTLINE
Section B – STUDENT TRACKING ACCURACY
Section C – SOCIO ECONOMIC DATA
Section D – STUDENT DISRUPTION DATA
Section E – RESEARCH AIMS
Section F – SIMPLE RELATIONSHIPS
Section G – DETAILED ANALYSIS
Section F. Simple Relationships
Many authors in the field have found…
Direct relationships between attendance/mobility
and achievement show a consistent negative
correlation
We can show this is true for Qld as well and also
true using attendance data
Section F. Simple Relationships
Mobility
Scale Score Averages for Yr 7 (2006) by Number of Moves
Reading
Writing
Numeracy
750
Scale Score
700
650
600
550
500
0
1
2
3
Number of Moves
4
5+
Section F. Simple Relationships
Attendance
Scale Score Averages for Yr 7 Results (2006) by Attendance Rate
Writing
Reading
Numeracy
750
Scale Score
700
650
600
550
100%
98%
96%
94%
92%
90%
88%
86%
84%
82%
Attendance Rate
80%
78%
76%
74%
72%
70%
Section F. Simple Relationships
The literature suggests that the relationship is
contingent….
in that it disappears when socio-economic
characteristics have been controlled for
We didn’t find this…..
Section F. Simple Relationships
Correlations (Student Level)
SEP (IRSED)
.29
Attendance Rate
Reading
Number of
school moves
Section F. Simple Relationships
Correlations (Student Level)
SEP (IRSED)
.16
.29
Attendance Rate
-.17
Number of
school moves
-.13
Reading
Section F. Simple Relationships
Correlations (Student Level)
SEP (IRSED)
.16
.29
Attendance Rate
.17
-.17
-.13
Reading
-.14
Number of
school moves
Section F. Simple Relationships
Correlations (Student Level)
If you square this
= .084 or 8.4%
SEP (IRSED)
.16
.29
Attendance Rate
.17
-.17
-.13
Reading
-.14
Number of
school moves
If you square this
= .018 or 1.8%
If you square this
= .028 or 2.8%
Section F. Simple Relationships
Correlations (Student Level)
SEP (IRSED)
Attendance Rate
Controlling for SEP
Partial Correlations
.13
Reading
-.14
-.11
Number of
school moves
If you square this
= .012 or 1.2%
If you square this
= .017 or 1.7%
Section A – OUTLINE
Section B – STUDENT TRACKING ACCURACY
Section C – SOCIO ECONOMIC DATA
Section D – STUDENT DISRUPTION DATA
Section E – RESEARCH AIMS
Section F – SIMPLE RELATIONSHIPS
Section G – DETAILED ANALYSIS
Section G. Detailed Analysis
What is a Regression?
• Tells us the amount of variance in one
variable (e.g., student reading results)
that can be explained by one or more
other variables (e.g., student
attendance and mobility)
Section G. Detailed Analysis
Regressions
• A series of linear regressions using
achievement on August 2006 Year 7 Tests as
the dependent variable were conducted.
• In the first series of regressions, the number
of school enrolment transitions was the only
explanatory variable included. The results
found that this measure of student mobility
has limited association with student
achievement:
Section G. Detailed Analysis
Regression entry order
Additional Variance Explained
Step1
# school transitions
2.3%
Student
READING
Section G. Detailed Analysis
Regression entry order
Additional Variance Explained
Step1
# school transitions
2.4%
Student
WRITING
Section G. Detailed Analysis
Regression entry order
Additional Variance Explained
Step1
# school transitions
2.9%
Student
NUMERACY
Section G. Detailed Analysis
Now we controlled for Socio Economic data and
find results are still significant
Section G. Detailed Analysis
Regression entry order
Additional Variance Explained
Step1
Socio-Economic
8.3%
Step2
# school transitions
1.8%
Student
READING
Section G. Detailed Analysis
Regression entry order
Additional Variance Explained
Step1
Socio-Economic
7.0%
Step2
# school transitions
1.3%
Student
WRITING
Section G. Detailed Analysis
Regression entry order
Additional Variance Explained
Step1
Socio-Economic
7.6%
Step2
# school transitions
1.4%
Student
NUMERACY
Section G. Detailed Analysis
Then we let computer choose the order of entry
(Stepwise entry) from a list of possible variables
Using Reading scores as a case in point
Section G. Detailed Analysis
Best Case model for Attendance
Additional Variance Explained
Step1
Socio-Economic
8.3%
Step2
Indigenous
2.0%
Step3
# episodes
of absence
1.5%
Student
READING
Total
12.1%
Section G. Detailed Analysis
Worst Case model for Attendance
Additional Variance Explained
Step1
Socio-Economic
8.3%
Step2
Indigenous
2.3%
Step3
Remote
0.1%
Student
READING
Step4
Gender
0.1%
Step5
Rural
0.1%
Step6
Avg episode length
Not significant
Total
10.6%
Section G. Detailed Analysis
Best Case model for Mobility
Additional Variance Explained
Step1
Socio-Economic
5.7%
Step2
Indigenous
2.9%
Step3
# school transitions
1.3%
Student
NUMERACY
Total
9.9%
Section G. Detailed Analysis
Worst Case model for Mobility
Additional Variance Explained
Step1
Socio-Economic
8.3%
Step2
Indigenous
2.3%
Step3
# episodes
of absence
1.5%
Step4
Remote
0.1%
Step5
Gender
0.1%
Step6
Rural
0.1%
Step7
Previous move
more than 100km
0.0%
Student
READING
Total
12.4%
Section G. Detailed Analysis
Using the best predictors of disruption we get the
following models of prediction of student test scores
Section G. Detailed Analysis
Typical Model
Additional Variance Explained
Step1
Socio-Economic
6.0%
Step2
Indigenous
2.8%
Step3
# episodes
of absence
1.6%
# school transitions
0.7%
Step4
Student
READING
Total
11.2%
Section G. Detailed Analysis
Typical Model
Additional Variance Explained
Step1
Socio-Economic
5.5%
Step2
Indigenous
2.6%
Step3
# episodes
of absence
1.7%
# school transitions
0.8%
Step4
Student
WRITING
Total
10.6%
Section G. Detailed Analysis
Typical Model
Additional Variance Explained
Step1
Socio-Economic
5.7%
Step2
Indigenous
3.3%
Step3
# episodes
of absence
2.3%
# school transitions
1.0%
Step4
Student
NUMERACY
Total
12.4%
Section G. Detailed Analysis
This is what we found at a school level
Section G. Detailed Analysis
Typical Model
Additional Variance Explained
Step1
Socio-Economic
35.0%
Step2
Indigenous
3.1%
Step3
Avg episodes
of absence
0.9%
percentage
since year 1/2
0.3%
Step4
School
READING
Total
39.2%
Section G. Detailed Analysis
Typical Model
Additional Variance Explained
Step1
Socio-Economic
32.5%
Step2
Indigenous
2.6%
Step3
percentage
since year 1/2
1.5%
Step4
# episodes
of absence
School
WRITING
0.3%
Total
36.9%
Section G. Detailed Analysis
Typical Model
Additional Variance Explained
Step1
Socio-Economic
28.1%
Step2
Indigenous
3.6%
Step3
# episodes
of absence
1.9%
Step4
percentage
since year 1/2
School
NUMERACY
0.8%
Total
34.2%
Section G. Detailed Analysis
Finally, in forcing a different order we could glean some
interpretation of the socio-economic variance
Section G. Detailed Analysis
Correlations (Student Level)
SEP (IRSED)
.16
Attendance Rate
-.17
-.13
Number of
school moves
We know from the correlations that there is
some overlap in SEP measures and disruption
Reading
Section G. Detailed Analysis
So if we force a different entry order in the regression we
can estimate this overlap in explanation
Section G. Detailed Analysis
Typical Model
Additional Variance Explained
Step1
Socio-Economic
35.0%
Step2
Avg episodes
of absence
1.9%
School
READING
Original Stepwise
Total
36.8%
Section G. Detailed Analysis
Typical Model
Additional Variance Explained
Step1
Avg episodes
of absence
10.4%
Step2
Socio-Economic
26.5%
School
READING
Forced Order
Total
36.8%
Section G. Detailed Analysis
So we can say that at a school level
Of the 35% explanation provided by SEP
That 8.5% (35.0% – 26.5%) could be explained by average
number of episodes of absence.
Or that 24.3% (8.5%/35%) of the SEP
– reading achievement
relationship (in Year 7 2006) can
be explained by a measure of
attendance at a school level
Section G. Detailed Analysis
Typical Model
Additional Variance Explained
Step1
Socio-Economic
28.1%
Step2
Avg episodes
of absence
3.3%
School
NUMERACY
Original Stepwise
Total
31.4%
Section G. Detailed Analysis
Typical Model
Additional Variance Explained
Step1
Avg episodes
of absence
11.8%
Step2
Socio-Economic
19.6%
School
NUMERACY
Forced Order
Total
31.4%
Section G. Detailed Analysis
So we can say that at a school level
Of the 28.1% explanation provided by SEP
That 8.5% (28.1% – 19.6%) could be explained by average
number of episodes of absence.
Or that 30.2% (8.5%/28.1%) of the
SEP – numeracy achievement
relationship (in Year 7 2006) can
be explained by a measure of
attendance at a school level
Section G. Detailed Analysis
We can present this in a more visually appealing way
Section G. Detailed Analysis
Of the SEP/Achievement Relationship …
School Level
Reading achievement
Section G. Detailed Analysis
Of the SEP/Achievement Relationship …
School Level
Reading achievement
Attendance
(24.3% Total)
Section G. Detailed Analysis
Of the SEP/Achievement Relationship …
School Level
Reading achievement
Attendance
(24.3% Total)
Mobility
(10.0% Total)
Overlap
Attendance-Mobility
(4.2%)
Section G. Detailed Analysis
Of the SEP/Achievement Relationship …
Student Level
Reading achievement
Section G. Detailed Analysis
Of the SEP/Achievement Relationship …
Student Level
Reading achievement
Attendance
(16.9% Total)
Section G. Detailed Analysis
Of the SEP/Achievement Relationship …
Student Level
Reading achievement
Attendance
(16.9% Total)
Mobility
(9.6% Total)
Overlap
Attendance-Mobility
(5.8%)
Section G. Detailed Analysis
Of the SEP/Achievement Relationship …
Overlap
Indigeneity-Attendance
(5.8%)
Attendance
Overlap
Attendance-Mobility
(5.8%)
(16.9% Total)
Indigeneity
(27.7% Total)
Mobility
(9.6% Total)
Overlap
Indigeneity- Mobility
(4.5%)
Student Level
Reading achievement
Section G. Detailed Analysis
Summary of findings
• The effects appear to be linear
• Results demonstrate that attendance/mobility
can explain significant proportions of the
SEP-achievement relationship (but not
everything)
• Indigeneity is a significant predictor
Section G. Detailed Analysis
There do appear to be small cohort effects for
school disruptions
Section G. Detailed Analysis
Of the SEP/Achievement Relationship …
Approximately 50% of the SEP – achievement
relationship can be explained by Indigeneity,
Attendance, and Mobility.
Section G. Detailed Analysis
Implications
• Value of USI for data integration
• Sophistication and integration of data emerging in Australia
(exemplified by Qld)
• There is some sense in decomposing the influence of SEP
on student achievement
• Prioritising effects for intervention - School disruptions
(Attendance before Mobility)
• Can build risk profiles for students
Other research
• http://www.soe.jcu.edu.au/Mobility_Web/
Contact us
[email protected]
[email protected]
[email protected]
[email protected]
The End