{"id":3521,"date":"2026-04-01T19:09:01","date_gmt":"2026-04-01T19:09:01","guid":{"rendered":"https:\/\/data.unicef.org\/data-for-action\/?p=3521"},"modified":"2026-04-01T19:36:09","modified_gmt":"2026-04-01T19:36:09","slug":"measuring-learning-household-based-assessments","status":"publish","type":"post","link":"https:\/\/data.unicef.org\/data-for-action\/measuring-learning-household-based-assessments\/","title":{"rendered":"Measuring Learning Where Children Live: Household-Based Assessments from UNICEF and PAL Network\u00a0"},"content":{"rendered":"\n

A closer look at UNICEF’s Foundational Learning Skills (FLS) Module and PAL Network’s ICAN-ICAR household assessments.<\/em> <\/p>\n\n\n\n

This is the second post in our series exploring how we measure learning where children live, not just where they study.<\/em> <\/p>\n\n\n\n

The detective work of learning assessment <\/h4>\n\n\n\n

Imagine you are trying to understand how well children in a country can read. You could visit schools and test students\u00a0there,\u00a0but\u00a0you would\u00a0immediately\u00a0face two problems. First, what about the children who aren’t in school, or those who have never enrolled at all? Second, even among enrolled children, testing within a single grade tells you who in that grade is\u00a0learning,\u00a0but\u00a0not when in a child’s school journey most children\u00a0actually\u00a0acquire\u00a0foundational skills.\u00a0<\/p>\n\n\n\n

\u00a0Here’s\u00a0where household-based assessments differ fundamentally from school-based ones \u2014 and why this difference shapes everything that follows.\u00a0 Rather than drawing samples from school enrollment lists, they start from national census data. And instead of testing a single grade, they assess an entire age cohort,\u00a0going door to\u00a0door to reach all children of school age, whether enrolled,\u00a0frequently\u00a0absent or never attended at all. This single design choice changes everything about what the data can tell us.\u00a0\u00a0<\/p>\n\n\n\n

Since 2015, two publicly available household-based tools have been doing exactly this \u2014 UNICEF’s Foundational Learning Skills (FLS) module, administered in over 40 countries, and PAL Network’s ICAN-ICAR assessment, reaching 12 countries. Together they’ve assessed learning across more than 50 countries, reaching children that school-based assessments might not cover.  <\/p>\n\n\n\n

Sampling: A nationally representative foundation <\/h4>\n\n\n\n

Representativeness is a function of sampling design and random\u00a0selection, which both household-based and school-based assessments are designed to achieve. The key difference lies in the sampling frame:\u00a0unlike school-based evaluations that only reach students in the classroom,\u00a0household-based\u00a0assessments draw from a nationally representative pool – one that includes households\u00a0across the country.\u00a0\u00a0<\/p>\n\n\n\n

Both UNICEF\u2019s FLS in MICS and ICAN-ICAR are built on nationally representative sampling frameworks, typically derived from population census data\u00a0maintained\u00a0by National Statistical Offices. This foundation ensures that households are selected from across the country using a structured probability design. In practice, this means that each household\u00a0and individual\u00a0has a known chance of being included, allowing findings to reflect the population covered by the survey.\u00a0<\/p>\n\n\n\n

From this national frame, both tools use a\u00a0multi-stage\u00a0approach.\u00a0First, census enumeration areas (EAs) within geographic domains are randomly selected based on their population size. This is followed by\u00a0random\u00a0selection\u00a0of households within the selected EAs.\u00a0In MICS, the third stage is the\u00a0selection\u00a0of individuals within\u00a0the households.\u00a0For the FLS module, one child within the target age range is randomly selected.\u00a0This supports broad national coverage while keeping the survey manageable, given that MICS collects data across multiple\u00a0domains. ICAN-ICAR assesses all children within the eligible age range in each household, allowing for analysis within families.\u00a0<\/p>\n\n\n\n

These design choices also have implications for how results are interpreted. Differences in interpretation often relate to the sampling frame and to sample size. The sampling frame determines which population is covered, while sample size affects how precisely results can be estimated, including across regions or population groups. In both household- and school-based assessments, the use of probability-based multi-stage sampling reduces selection bias while producing a representative sample and allowing for inferences about the larger population. Precision is managed through proper stratification, adequate sample size and weighting.  <\/p>\n\n\n\n

Guardrails against bias <\/h4>\n\n\n\n

Household-based assessments build in several features to ensure results reflect all children, not just the easiest to reach.\u00a0The\u00a0first is callbacks. If a selected child\u00a0isn’t\u00a0home during the first visit, interviewers return multiple times if necessary. In MICS, at least three callback attempts are\u00a0required\u00a0before a case is considered incomplete. In school-based assessments,\u00a0a\u00a0child absent<\/a>\u00a0on test day is simply not included, and since absence is often linked to disadvantage, this can quietly skew results.\u00a0<\/p>\n\n\n\n

The second is population transparency. Because household surveys start from census data,\u00a0followed by a household listing exercise,\u00a0researchers know exactly who was\u00a0selected, making\u00a0it possible to\u00a0analyse\u00a0patterns in who\u00a0didn’t\u00a0participate\u00a0and assess whether non-response introduces bias.\u00a0<\/p>\n\n\n\n

The third is consent and assent. Both FLS and ICAN-ICAR require explicit consent from caregivers and assent from the child themselves – a procedural safeguard that also generates data on who declines and why, offering another window into potential non-response patterns. <\/p>\n\n\n\n

Complementary Implementation Models: National systems and community networks <\/h4>\n\n\n\n

The two tools differ in how they are implemented, reflecting their origins. MICS is implemented by National Statistical Offices, positioning it within official data systems. This supports standardization, comparability across countries, and alignment with national planning processes. ICAN-ICAR is implemented through networks of trained citizen volunteers. This approach builds community engagement and allows assessments to be carried out at scale while fostering local ownership of the findings. <\/p>\n\n\n\n

Interviewer training: Building consistency at scale <\/h4>\n\n\n\n

Training is central to how household surveys\u00a0maintain\u00a0consistency across large and diverse samples.\u00a0In MICS, interviewers are typically recruited through National Statistical Offices and undergo several weeks of structured training. Because MICS is a multi-topic survey, training is sequenced to build both technical understanding and practical skills. It often begins with classroom-based instruction using paper questionnaires, ensuring that interviewers understand the content and can continue data collection if digital tools fail. This is followed by supervised field practice in households. Only after this are modules introduced on computer-assisted platforms, along with\u00a0additional\u00a0field testing using the digital system.\u00a0<\/p>\n\n\n\n\n\n

Across both approaches, the objective is the same: to ensure that assessments are administered consistently, regardless of where they take place or who conducts them. <\/p>\n\n\n\n

Fieldwork monitoring: Ensuring data quality <\/h4>\n\n\n\n

Training is only one part of ensuring data quality. Monitoring during fieldwork plays an equally important role. <\/p>\n\n\n\n

In MICS, interviewers work\u00a0in small\u00a0teams under field supervisors\u00a0responsible\u00a0for\u00a0operational\u00a0logistics\u00a0and quality control. Team supervisors\u00a0assess\u00a0security conditions, arrange\u00a0travel\u00a0and coordinate\u00a0with local authorities.\u00a0They\u00a0monitor\u00a0interviewer\u00a0performance\u00a0through\u00a0direct\u00a0observations, random household revisits to\u00a0verify\u00a0information\u00a0or ensure that protocols have been\u00a0followed\u00a0and regular\u00a0feedback\u00a0sessions with interviewers. National Statistical Offices often deploy\u00a0additional\u00a0supervisory staff at regional or district levels to\u00a0observe\u00a0fieldwork and ensure that standards are\u00a0maintained\u00a0across locations.\u00a0Direct field supervision is\u00a0reinforced by daily synchronization of collected\u00a0data with the Central Office,\u00a0enabling\u00a0real-time\u00a0generation of\u00a0field check\u00a0tables, interviewer performance\u00a0metrics\u00a0and\u00a0data quality\u00a0dashboards. This\u00a0allows\u00a0national\u00a0survey\u00a0coordinators\u00a0to\u00a0flag issues for review or corrective action, providing the\u00a0immediate\u00a0feedback\u00a0needed to\u00a0field teams.\u00a0\u00a0<\/p>\n\n\n\n

In ICAN-ICAR, trained citizen volunteers typically work in pairs under the close supervision of Project Management Teams (PMTs) and District or County Coordinators (DCs\/CCs). Monitors conduct intensive field shadowing in at least five households per enumeration area, using a standardized Field Monitoring Checklist to directly assess enumerator performance and ensure strict adherence to standardized administration, sampling and ethical protocols. A critical feature of this process is independent scoring, where supervisors simultaneously record assessment results to calculate Inter-Rater Reliability (IRR), enabling on-the-spot corrections and identifying needs for targeted retraining early in the data collection process. For EAs not visited in person, supervisors maintain oversight through phone monitoring to track progress and provide real-time guidance to teams facing field challenges. This multi-layered approach is further strengthened by desk rechecks of all digital data synced via SurveyCTO and field rechecks, during which supervisors revisit sampled households to independently verify the accuracy and integrity of the collected information. If significant procedural errors or major data discrepancies are identified, monitors have the authority to order a complete resurvey of the community. <\/p>\n\n\n\n

These layers of oversight help ensure that data collection remains consistent and credible, even when conducted at scale. <\/p>\n\n\n\n

Language and context: Measuring learning fairly <\/h4>\n\n\n\n

Language is central to ensuring that assessments reflect what children actually know. <\/p>\n\n\n\n

Both FLS and ICAN-ICAR invest in adapting their tools to local contexts. The FLS is translated in collaboration with national authorities including the NSO and Ministry of Education while ICAN-ICAR is adapted by country partners in close partnership with subject matter and curriculum experts to reflect the languages of instruction largely used in the country.  <\/p>\n\n\n\n

These processes go beyond translation. They involve validation steps to ensure that assessment tasks are meaningful and comparable across contexts. <\/p>\n\n\n\n

Complementary approaches to understanding learning <\/h4>\n\n\n\n

Taken together, FLS and ICAN-ICAR illustrate how household-based approaches can generate different but complementary insights into children\u2019s learning.  Used alongside school-based assessments, these tools help build a more complete understanding of learning- one that reflects both how education systems function and how children experience learning in their daily lives. <\/p>\n\n\n\n

As countries continue to strengthen how learning is measured, the value lies not in choosing one approach over another, but in using these tools together to inform better policy and action. <\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

A closer look at UNICEF’s Foundational Learning Skills (FLS) Module and PAL Network’s ICAN-ICAR household assessments.  This is the second post in our series exploring how we measure learning where children live, not just where they study.  The detective work of learning assessment  Imagine you are trying to understand how well children in a country can read. 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