1. * The challenge of measuring hidden dropout in Israel Edna Shimoni Haim Portnoy Central Bureau of Statistics Twinning Activity B1 November 4-5, 2013

2. * A definition of hidden dropout * The need for this measure * Proposed methods * Proposed variables

3. * There in no universally accepted definition * …and therefore no standardized way to measure hidden dropout

4. * The phenomenon of a student's disengagement from school and learning, while formally enrolled * Multi-dimensional: behavioral, emotional and cognitive * It is closely related to lack of motivation and poor attitude towards school

5. Students' affective responses to school includes two domains: * Academic identification: poor academic achievement and school disaffection * Interpersonal relationship: social isolation and victimization

6. In Israel, its symptoms have been characterized to include: * Irregular school attendance * Poor academic achievements * Feelings of alienation towards the school learning process * Behavioral and social problems at school

7. * In 2007 the Compulsory Education Law was extended to encompass age 17 * Secondary schools are expected to retain students and prevent dropout * We may get an increase of hidden dropout, as schools may formally retain those who would otherwise be overt dropouts

8. * Hidden school disengagement may be an early stage of school dropout * In order to measure hidden dropout we need a set of easy-to-interpret, accurate and robust indicators * We need a measure of disengaged children at school, and to be able to identify those children while they are enrolled as a preventive action

9. * Since hidden dropout may be a first stage of overt dropout, one could aim at predicting the odds of dropping out of formal schooling. The objective could be a regression model to predict overt dropout * Derive a multidimensional measure of hidden dropout using the variables found to predict overt dropout

10. * For such an exercise to take place, it is imperative to identify the predictors and their availability * Derive a multidimensional measure of hidden dropout using the variables found to predict overt dropout * No subjective variables (emotional, cognitive) are available for ICBS

11. Socio-demographic variables * Gender * Parents' education and income * Number of siblings * Mother's marital status * Country of birth and parents' country of birth * Ethnicity (Jews/Arabs) * Type of locality * For girls: Teenage marriage, childbirth, pregnancy termination

12. School variables * Track of education (Vocational/General) * Special/Regular education, accommodations for examinations * Truant officer's records, Advancement of Youth at Risk records * Schools' supervision (General/Religious/Ultra- Orthodox) * Achievements at the national GEMS examinations * Average class size at school * Frequent school transitions

13. * Criminal files, juvenile delinquency care and in welfare residential care * Quality of education * Family stability * Parental health * Student absenteeism * Student discipline events at school * Student academic achievement throughout the school year From MANBAS – School Management System School climate? (from GEMS)

14. * These could be viewed as either indicators of hidden dropout, or predictors of overt dropout * But not all hidden dropout eventually ends up in actual dropout, and many cases of hidden dropout remain hidden * There can also be reasons for a student to drop out of school, without showing previous symptoms of hidden dropout * This may suggest that indicators of hidden dropout do not necessarily overlap with predictors of overt dropout

15. * Another way to identify hidden dropout could be identifying a proxy measure such as not taking the final Matriculation examinations * Interpreted as a result of disengagement, accounting for hidden dropouts who managed to stay in school to the end of 12 th grade * We could compare the characteristics of these students to those of overt dropouts, or build a model to predict this measure in itself. This could be a complementary measure to the ones proposed

16. * Hidden dropout remains a challenge as for operationalization and methodology * Only some of the candidate variables are currently available * Measuring hidden dropout vs. a model for predicting actual dropout – two separate tasks?

17. Thank You!