Psychometric properties of early childhood development assessment tools in low- and middle-income countries: a...

Valid and reliable measurement of early childhood development (ECD) is critical for monitoring and evaluating ECD-related policies and programmes. Although ECD tools developed in high-income countries may be applicable to low- and middle-income countries (LMICs), directly applying them in LMICs can be problematic without psychometric evidence for new cultures and contexts. Our objective was to systematically appraise available evidence on the psychometric properties of tools used to measure ECD in LMIC.

A systematic review following the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines.

MEDLINE, Embase, PubMed, PsycInfo, SciELO and BVS were searched from inception to February 2025.

We included studies that examined the reliability, validity, and measurement invariance of tools assessing ECD in children 0-6 years of age living in LMICs.

Each study was independently screened by two researchers and data extracted by one randomly assigned researcher. Risk of bias was assessed using a checklist developed by the study team assessing bias due to training/administration, selective reporting and missing data. Results were synthesised narratively by country, location, age group at assessment and developmental domain.

A total of 160 articles covering 117 tools met inclusion criteria. Most reported psychometric properties were internal consistency reliability (n=117, 64%), concurrent validity (n=81, 45%), convergent validity (n=74, 41%), test-retest reliability (n=73, 40%) and structural validity (n=72, 40%). Measurement invariance was least commonly reported (n=16, 9%). Most articles came from Brazil, China, India and South Africa. Most psychometric evidence was from urban (n=92, 51%) or urban-rural (n=41, 23%) contexts. Study samples focused on children aged 6-17.9 or 48-59.9 months. The most assessed developmental domains were language (n=111, 61%), motor (n=104, 57%) and cognitive (n=82, 45%). Bias due to missing data was most common.

Psychometric evidence is fragmented, limited and heterogeneous. More rigorous psychometric analyses, especially on measurement invariance, are needed to establish the quality and accuracy of ECD tools for use in LMICs.

CRD42022372305.