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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

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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

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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

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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

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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.

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Section A. Outline Main Aim Estimating: Impact of disruption (attendance and mobility) in Queensland Primary schooling.

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Section A. Outline Structure of our analysis Year 7 Test Results August 2006 Year 7 Attendance Rate Sem Mobility Year 2 to Year Socio Economic Position Jan 2006

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Section A. Outline Structure of our analysis Year 7 Test Performance August 2006 Year 7 Attendance Rate Sem Mobility Year 2 to Year 7 Socio Economic Position

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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

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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 Unique Student Identifier (EQID)

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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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Not at the Previous Collection (New) and Not at the Next Collection (Left) 2006 Section B. Student Tracking Accuracy ‘New’ and ‘Left’ students by collection 'Left' Students 'New' Students

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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 ‘New’ and ‘Left’ students Feb-Nov

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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?

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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

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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 – Feb % Nov-02 – Feb % Nov-03 – Feb % Nov-04 – Feb % Nov-05 – Feb % Nov-06 – Feb % * 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

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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?

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Year % of ‘kept’ students with fewer than 4 of the matching criteria fulfilled % of students with all 4 criteria fulfilled Total ‘kept’ students %93.6% %92.1% %91.6% %91.8% %91.4% %91.2%33304 (2919 students) Section B. Student Tracking Accuracy Matching across the Year 7-8 transition

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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 OKSurnameSuspiciousUnlikelyGrand Total %7.2%1.4%0.5%100.0% OKAppear to be Correct – mis-spelling, reversal of first and last name, abbreviation eg Sue for Susan NameOnly the last name is different – appears to be legitimate name change SuspSuspicious – Robert Smith and John Smith with all other details the same UnlikelyUnlikely – Robert Smith and Susan Smith Section B. Student Tracking Accuracy Matching across the Year 7-8 transition

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Fewer than 4 criteria filled. (EQID matched) All 4 criteria filled as well as an EQID match Total Year 7 students Appears to be same student Family Name Change? Possible Different Student Unlikely to be same student %0.6%0.1%0.0%91.2%100.0% More than 99% of students who match on EQID appear to be the same student. 99.2% A visual inspection of the data for these 8.8% (n=2919) of students revealed the following: Section B. Student Tracking Accuracy Matching across the Year 7-8 transition

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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

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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

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Example gradient Section C. Socio Economic Data Student Achievement by Student IRSED

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Example gradient Section C. Socio Economic Data Student Achievement by Parental Education

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Please note: Highlighted categories were removed from the analyses Section C. Socio Economic Data Student Achievement by Parental Education

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Example gradient Section C. Socio Economic Data Student Achievement by Parental Occupation

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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 C. Socio Economic Data

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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

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School enrolment >> mobility [Since 2000 in primary schools] Student absence >> attendance [Sem 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

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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

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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

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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

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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

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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

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Proportion of students in Year , by number of enrolment changes since Year 2 (N=40181)

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Section D. Student Disruption Data Mobility

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Less than 50% of these students were still at their original school Feb 2001 Section D. Student Disruption Data Mobility

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Over 20% of these students had a new school in Year 6 or 7 Nov 2004

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Section D. Student Disruption Data Mobility

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Based only on students who started and stayed in state schooling, and who changed schools Section D. Student Disruption Data Mobility

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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

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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

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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

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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

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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

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Student attendance rates will be reported from the 2007 National Report on Schooling in Australia (ANR), MCEETYA, to be released in 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 - Attendance

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“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 Context - Mobility

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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

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Analysis in three phases Phase 1 – student mobility Phase 2 – student attendance Phase 3 – combined Section E. Research Aims The Study

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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 Data Sample

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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 E. Research Aims The Problem with SEP

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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

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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

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Section F. Simple Relationships Mobility

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Section F. Simple Relationships Attendance

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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

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Attendance Rate Number of school moves Reading.29 SEP (IRSED) Section F. Simple Relationships Correlations (Student Level)

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Attendance Rate Number of school moves Reading.29 SEP (IRSED) -.17 Section F. Simple Relationships Correlations (Student Level)

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Attendance Rate Number of school moves Reading SEP (IRSED) -.17 Section F. Simple Relationships Correlations (Student Level)

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Attendance Rate Number of school moves Reading SEP (IRSED) -.17 If you square this =.028 or 2.8% If you square this =.084 or 8.4% If you square this =.018 or 1.8% Section F. Simple Relationships Correlations (Student Level)

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SEP (IRSED) Controlling for SEP Partial Correlations Attendance Rate Number of school moves Reading If you square this =.017 or 1.7% If you square this =.012 or 1.2% Section F. Simple Relationships Correlations (Student Level)

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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

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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 What is a Regression?

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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 Regressions

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# school transitions Step1 2.3% Student READING Additional Variance Explained Section G. Detailed Analysis Regression entry order

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# school transitions Step1 2.4% Student WRITING Additional Variance Explained

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# school transitions Step1 2.9% Student NUMERACY Additional Variance Explained Section G. Detailed Analysis Regression entry order

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N ow we controlled for Socio Economic data and find results are still significant Section G. Detailed Analysis

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Socio-Economic Step1 # school transitions Step2 8.3% 1.8% Student READING Additional Variance Explained Section G. Detailed Analysis Regression entry order

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Socio-Economic Step1 # school transitions Step2 7.0% 1.3% Student WRITING Additional Variance Explained Section G. Detailed Analysis Regression entry order

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Socio-Economic Step1 # school transitions Step2 7.6% 1.4% Student NUMERACY Additional Variance Explained Section G. Detailed Analysis Regression entry order

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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

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Socio-Economic Step1 Indigenous Step2 # episodes of absence Step3 8.3% 2.0% 1.5% Student READING Total 12.1% Additional Variance Explained Section G. Detailed Analysis Best Case model for Attendance

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Socio-Economic Step1 Indigenous Step2 Remote Step3 Gender Step4 8.3% 2.3% 0.1% Student READING Rural Step5 Step6 0.1% Total 10.6% Avg episode length Not significant Additional Variance Explained Section G. Detailed Analysis Worst Case model for Attendance

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Socio-Economic Step1 Indigenous Step2 # school transitions Step3 5.7% 2.9% 1.3% Student NUMERACY Total 9.9% Additional Variance Explained Section G. Detailed Analysis Best Case model for Mobility

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Socio-Economic Step1 Indigenous Step2 # episodes of absence Step3 Remote Step4 8.3% 2.3% 1.5% 0.1% Student READING Gender Step5 Rural Step6 0.1% Total 12.4% Previous move more than 100km Step7 0.0% Additional Variance Explained Section G. Detailed Analysis Worst Case model for Mobility

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Using the best predictors of disruption we get the following models of prediction of student test scores Section G. Detailed Analysis

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Socio-Economic Step1 Indigenous Step2 # episodes of absence Step3 # school transitions Step4 6.0% 2.8% 1.6% 0.7% Student READING Total 11.2% Additional Variance Explained Section G. Detailed Analysis Typical Model

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Socio-Economic Step1 Indigenous Step2 # episodes of absence Step3 # school transitions Step4 5.5% 2.6% 1.7% 0.8% Student WRITING Total 10.6% Additional Variance Explained Section G. Detailed Analysis Typical Model

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Socio-Economic Step1 Indigenous Step2 # episodes of absence Step3 # school transitions Step4 5.7% 3.3% 2.3% 1.0% Student NUMERACY Total 12.4% Additional Variance Explained Section G. Detailed Analysis Typical Model

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This is what we found at a school level Section G. Detailed Analysis

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Socio-Economic Step1 Indigenous Step2 Avg episodes of absence Step3 percentage since year 1/2 Step4 35.0% 3.1% 0.9% 0.3% School READING Total 39.2% Additional Variance Explained Section G. Detailed Analysis Typical Model

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Socio-Economic Step1 Indigenous Step2 percentage since year 1/2 Step3 # episodes of absence Step4 32.5% 2.6% 1.5% 0.3% School WRITING Total 36.9% Additional Variance Explained Section G. Detailed Analysis Typical Model

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Socio-Economic Step1 Indigenous Step2 # episodes of absence Step3 percentage since year 1/2 Step4 28.1% 3.6% 1.9% 0.8% School NUMERACY Total 34.2% Additional Variance Explained Section G. Detailed Analysis Typical Model

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Finally, in forcing a different order we could glean some interpretation of the socio-economic variance Section G. Detailed Analysis

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Attendance Rate Number of school moves Reading SEP (IRSED) -.17 We know from the correlations that there is some overlap in SEP measures and disruption Section G. Detailed Analysis Correlations (Student Level)

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So if we force a different entry order in the regression we can estimate this overlap in explanation Section G. Detailed Analysis

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Socio-Economic Step1 Avg episodes of absence Step2 35.0% 1.9% School READING Total 36.8% Original Stepwise Additional Variance Explained Section G. Detailed Analysis Typical Model

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Avg episodes of absence Step1 Socio-Economic Step2 10.4% 26.5% School READING Total 36.8% Forced Order Additional Variance Explained Section G. Detailed Analysis Typical Model

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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 ) can be explained by a measure of attendance at a school level Section G. Detailed Analysis

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Socio-Economic Step1 Avg episodes of absence Step2 28.1% 3.3% School NUMERACY Total 31.4% Original Stepwise Additional Variance Explained Section G. Detailed Analysis Typical Model

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Avg episodes of absence Step1 Socio-Economic Step2 11.8% 19.6% School NUMERACY Additional Variance Explained Total 31.4% Forced Order Section G. Detailed Analysis Typical Model

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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 ) can be explained by a measure of attendance at a school level Section G. Detailed Analysis

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We can present this in a more visually appealing way Section G. Detailed Analysis

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School Level Reading achievement Section G. Detailed Analysis Of the SEP/Achievement Relationship …

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Attendance (24.3% Total) School Level Reading achievement Section G. Detailed Analysis Of the SEP/Achievement Relationship …

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Mobility (10.0% Total) Overlap Attendance-Mobility (4.2%) Attendance (24.3% Total) School Level Reading achievement Section G. Detailed Analysis Of the SEP/Achievement Relationship …

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Student Level Reading achievement Section G. Detailed Analysis Of the SEP/Achievement Relationship …

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Attendance (16.9% Total) Student Level Reading achievement Section G. Detailed Analysis Of the SEP/Achievement Relationship …

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Attendance (16.9% Total) Mobility (9.6% Total) Overlap Attendance-Mobility (5.8%) Student Level Reading achievement Section G. Detailed Analysis Of the SEP/Achievement Relationship …

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Attendance (16.9% Total) Mobility (9.6% Total) Indigeneity (27.7% Total) Overlap Indigeneity-Attendance (5.8%) Overlap Indigeneity- Mobility (4.5%) Overlap Attendance-Mobility (5.8%) Student Level Reading achievement Section G. Detailed Analysis Of the SEP/Achievement Relationship …

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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 Summary of findings

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There do appear to be small cohort effects for school disruptions Section G. Detailed Analysis

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Approximately 50% of the SEP – achievement relationship can be explained by Indigeneity, Attendance, and Mobility. Section G. Detailed Analysis Of the SEP/Achievement Relationship …

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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 Section G. Detailed Analysis Implications

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Other research

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Contact us

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The End

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