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Claudia Cappa Statistics and Monitoring Section, UNICEF, NY Webinar Series on the Measurement of Child Protection Overview of methods to collect data on.

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Presentation on theme: "Claudia Cappa Statistics and Monitoring Section, UNICEF, NY Webinar Series on the Measurement of Child Protection Overview of methods to collect data on."— Presentation transcript:

1 Claudia Cappa Statistics and Monitoring Section, UNICEF, NY Webinar Series on the Measurement of Child Protection Overview of methods to collect data on children outside of family care

2 Preliminary considerations Review conducted in the context of the US Government Evidence Summit on COFC Definition of our target population Identification/enumeration Estimation of the population size Additional information on living conditions etc

3 Differences in Vulnerabilities Vulnerable Children Children in Institutions Child-headed Households Children in Detention Separated or Unaccompanied due to conflict or natural disaster Trafficked for Labor or Sexual Exploitation Street Children Armed Forces / Groups

4 Overall challenges Methodological and practical Isolated or hard to reach locations Live in conditions of illegality or secrecy Weakness of administrative data Stigma Ethical Consent Experience of trauma Follow-up Etc

5 Some specific challen ges Street children Main challenges: Lack of agreed operational definition and criteria for the identification of street children, intelligence gap and sampling issues Children living in institutions Main challenges: Many institutions are unregistered and many countries do not regularly collect/report data on children in institutional care

6 Methods Identified Time-Location Sampling Capture/Recapture Respondent Driven Sampling Household Surveys Establishment Surveys Institution-Based Surveys Administrative data on alternative care Neighborhood Method Child Protection Information Management System Child Labor Monitoring System Special Methods to Identify Children who Work

7 Criteria: reliability, validity and scope Reliability, i.e. extent to which the results of the methodology are consistent over time Validity, i.e. extent to which the methodology actually achieves the intended purposes Scope, i.e. extent to which the methodology can be used on a large scale and is generalizable to alternative settings

8 Time Location Sampling (TLS) Time-Location Sampling Capture/Recapture Respondent Driven Sampling Neighborhood Method Child Protection Information Management System Child Labor Monitoring System Household Surveys Establishment Surveys Institution-Based Surveys and Databases Special Methods to Identify Children who Work A probabilistic sampling strategy used to recruit members of a target population known to congregate at specific times in set venues Step one: ethnographic mapping exercise of venues, days and times through key informants (law enforcement officials, NGOs, service provides, members of the target population) Visit to the sites for verification and first enumeration Selection of samples (two stage process) Appropriate populations: Hard-to-reach, vulnerable, stigmatized, or hidden populations. May be useful with migrant or highly mobile populations.

9 Time Location Sampling (TLS) Time-Location Sampling Capture/Recapture Respondent Driven Sampling Neighborhood Method Child Protection Information Management System Child Labor Monitoring System Household Surveys Establishment Surveys Institution-Based Surveys and Databases Special Methods to Identify Children who Work Strengths Large and diverse sample When appropriate weights are used = Representativeness (compared to simple convenience sample) Limitations Intelligence Gap, i.e. difficulties in constructing a strong sampling frame Possible bias Access to venues Proportion of missing population

10 Capture/recapture sampling Time-Location Sampling Capture/Recapture Respondent Driven Sampling Neighborhood Method Child Protection Information Management System Child Labor Monitoring System Household Surveys Establishment Surveys Institution-Based Surveys and Databases Special Methods to Identify Children who Work Originally developed for animals Adapted to estimate size of human population groups that are mobile or have limited access to services for which no sampling frame is available Assumption: Group is closed (fixed size and composition) and the study area is complete Being capture does not change the likelihood of being captured in the future It is possible to identify individuals that have been captured previously Groups is homogenous and sources are independent All individuals have equal chances of appearing in each sample

11 Capture/recapture sampling Time-Location Sampling Capture/Recapture Respondent Driven Sampling Neighborhood Method Child Protection Information Management System Child Labor Monitoring System Household Surveys Establishment Surveys Institution-Based Surveys and Databases Special Methods to Identify Children who Work Strengths and limitations Less vulnerable to external manipulation Weak evidence to show that the methodology generates the same results every time it is used Used for street children (Brazil) Rely on highly skilled interviewers Weather and mobility issues Risk of over-estimation (tendency to avoid re-capture)

12 Respondent driven sampling (RDS) Time-Location Sampling Capture/Recapture Respondent Driven Sampling Neighborhood Method Child Protection Information Management System Child Labor Monitoring System Household Surveys Establishment Surveys Institution-Based Surveys and Databases Special Methods to Identify Children who Work Type of snowball sampling that aims to overcome the potential bias associated with traditional snowball sampling methods Used to recruit statistically representative samples of hard-to-reach groups by taking advantage of intragroup social connections to build a sample Focus not on size but on representativeness

13 Respondent driven sampling (RDS) Time-Location Sampling Capture/Recapture Respondent Driven Sampling Neighborhood Method Child Protection Information Management System Child Labor Monitoring System Household Surveys Establishment Surveys Institution-Based Surveys and Databases Special Methods to Identify Children who Work Useful to quickly recruit large numbers of people from hidden population Data collection can begin anywhere with a pool of eligible respondents (seeds and waves) Double reward: being interviewed and recruiting others Interviewers must ask the respondents to describe the relationship to the person who recruited him/her and how many people are known to be part of the population Information used to make indirect estimates about the social network connecting the population and the proportion of the population in different groups Good to capture children not in contact with services Provide information on possible bias

14 Household Surveys 400 clusters are selected with probability proportional to size, and about 20 households are selected and surveyed randomly from each cluster. Appropriate populations: child-headed households & children unrelated to head of household Strengths Global reach Standardized methodology Readily-accessible data Limitations Weaknesses in its ability to identify unrelated children in a household Time-Location Sampling Capture/Recapture Respondent Driven Sampling Neighborhood Method Child Protection Information Management System Child Labor Monitoring System Household Surveys Establishment Surveys Institution-Based Surveys and Databases Special Methods to Identify Children who Work

15 MICS4 Survey Design Workshop MICS Questionnaire for households

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17 Children without parental care: the case of Burundi Percentage of children aged 0-14 who are: MICS 2006

18 Database of institutions Time-Location Sampling Capture/Recapture Respondent Driven Sampling Neighborhood Method Child Protection Information Management System Child Labor Monitoring System Household Surveys Establishment/Hou sehold Surveys (Commercial Sexually Exploited Children) Databases of institutions Special Methods to Identify Children who Work

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20 Thank you Acknowledgements: Tom Pullum, James Orlando, Meredith Dank, Susan Gunn, Maury Mendenhall and Kate Riordan


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