metadata: authors: 0: author: Knauer,Heather Ashley
1: author: Kariger,Patricia Karol
2: author: Jakiela,Pamela
3: author: Ozier,Owen
4: author: Fernald,Lia C. H.
count: Kenya
docna: 0: docna: Multilingual Assessment of Early Child
            Development : Analyses from Repeated Observations of
            Children in Kenya
docty: Policy Research Working Paper
abstracts: cdata!: In many low- and middle-income
            countries, young children learn a mother tongue or
            indigenous language at home before entering the formal
            education system where they will need to understand and
            speak a country's official language(s). Thus,
            assessments of children before school age, conducted in a
            nation's official language, may not fully reflect a
            child's development, underscoring the importance of
            test translation and adaptation. To examine differences in
            vocabulary development by language of assessment, this study
            adapted and validated instruments to measure developmental
            outcomes, including expressive and receptive vocabulary.
            This study assessed 505 children ages 2 to 6 in rural
            communities in Western Kenya with comparable vocabulary
            tests in three languages: Luo (the local language or mother
            tongue), Swahili, and English (official languages) at two
            time points, five to six weeks apart, between September 2015
            and October 2016. Younger children responded to the
            expressive vocabulary measure exclusively in Luo much more
            frequently than did older children: 44–59 percent of those
            ages 2 to 4, compared to 20–21 percent of those ages 5 to 6.
            Baseline receptive vocabulary scores in Luo and Swahili were
            strongly associated with receptive vocabulary in English at
            follow-up, even after controlling for English vocabulary at
            baseline: a multivariate regression of follow-up English
            vocabulary on standardized measures of receptive vocabulary
            in all three languages yields an estimate, for Luo, of β =
            0.26, SE = 0.05, p < 0.001; and for Swahili, β = 0.10, SE
            = 0.05, p = 0.032. The study also found that parental Luo
            literacy at baseline was associated with child English
            vocabulary at follow-up, while parental English literacy at
            baseline was not: a multivariate regression on both
            measures, along with household controls, yielded, for Luo, β
            = 0.11, SE = 0.05, p = 0.045; the coefficient on English was
            not statistically significantly distinguishable from zero
            (p=0.18). The findings suggest that multilingual testing is
            essential to understanding the developmental environment and
            cognitive growth of multilingual children.
geo_reg_key: 517191
repnme: repnme: Multilingual Assessment of Early Child
            Development : Analyses from Repeated Observations of
            Children in Kenya
display_title: Multilingual Assessment of Early
            Child Development : Analyses from Repeated Observations of
            Children in Kenya
listing_relative_url: /research/2019/09/31440164/multilingual-assessment-early-child-development-analyses-repeated-observations-children-kenya
publishtoextweb_dt: 2019-09-27T00:00:00Z
no_of_pages: 83
author_keys: 0: author_key: 1319053
1: author_key: 947286
2: author_key: 1149895
3: author_key: 691398
model_extractions: Policy Research Working Paper 9028 Multilingual Assessment of Early Child Development Analyses from Repeated Observations of Children in Kenya _Heather A. Knauer Patricia Kariger Pamela Jakiela Owen Ozier Lia C. H. Fernald_ Development Economics Development Research Group September 2019
Policy Research Working Paper 9028

# **Abstract**

In many low- and middle-income countries, young children learn a mother tongue or indigenous language at home before entering the formal education system where they will need to understand and speak a country’s official language(s). Thus, assessments of children before school age, conducted in a nation’s official language, may not fully reflect a child’s development, underscoring the importance of test translation and adaptation. To examine differences in vocabulary development by language of assessment, this study adapted and validated instruments to measure developmental outcomes, including expressive and receptive vocabulary. This study assessed 505 children ages 2 to 6 in rural communities in Western Kenya with comparable vocabulary tests in three languages: Luo (the local language or mother tongue), Swahili, and English (official languages) at two time points, five to six weeks apart, between September 2015 and October 2016. Younger children responded to the expressive vocabulary measure exclusively in Luo much more frequently than did older children: 44–59 percent of those ages 2 to 4, compared to 20–21 percent of those ages 5 to 6. Baseline receptive vocabulary scores in Luo and Swahili were strongly associated with receptive vocabulary in English at follow-up, even after controlling for English vocabulary at baseline: a multivariate regression of follow-up English vocabulary on standardized measures of receptive vocabulary in all three languages yields an estimate, for Luo, of β = 0.26, SE = 0.05, p < 0.001; and for Swahili, β = 0.10, SE = 0.05, p = 0.032. The study also found that parental Luo literacy at baseline was associated with child English vocabulary at follow-up, while parental English literacy at baseline was not: a multivariate regression on both measures, along with household controls, yielded, for Luo, β = 0.11, SE = 0.05, p = 0.045; the coefficient on English was not statistically significantly distinguishable from zero (p=0.18). The findings suggest that multilingual testing is essential to understanding the developmental environment and cognitive growth of multilingual children.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at oozier@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
MULTILINGUAL CHILD ASSESSMENT

# **<mark>Multilingual Assessment of Early Child Development: Analyses from Repeated Observations of Children in Kenya</mark>**

Heather A. Knauer<sup>1*</sup> , PhD, Patricia Kariger<sup>1</sup> , PhD, Pamela Jakiela<sup>2</sup> , PhD, Owen Ozier<sup>3</sup> , PhD, Lia C. H. Fernald<sup>1</sup> , PhD 1University of California, Berkeley School of Public Health 2Center for Global Development 3The World Bank Development Research Group, Human Development Team

*Current institution: University of Michigan, School of Social Work

**Keywords** : school readiness, multilingual environments, language of instruction, BPVS, MDAT, PPVT

**JEL codes:** I21, I24, I25, I28, O15

Acknowledgments: We thank Sheyda Esnaashari, Saahil Karpe, Rohit Chhabra, and Emily Cook-Lundgren for research assistance throughout this project. We thank the staff of Innovations for Poverty Action (Kenya), specifically, Patricia Gitonga, Michael Meda, Jessica Jomo, and the field team they led during data collection for this project. We are especially grateful to the KLPS team, who generously shared their previously adapted instruments with us. This work was supported by The World Bank, Washington, DC (via three facilities: the Strategic Impact Evaluation Fund, the Early Learning Partnership, and the Research Support Budget). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily reflect the views of The World Bank, its board of executive directors, or the governments they represent. This article has been accepted for publication in _Developmental Science_ ; the accepted and final typeset version may be found through either of these links: https://onlinelibrary.wiley.com/doi/10.1111/desc.12875 ; https://doi.org/10.1111/desc.12875 .
MULTILINGUAL CHILD ASSESSMENT Up to 40% of children worldwide speak a mother tongue that is not used at school (Walter & Benson, 2012; Clegg & Simpson, 2016), which results in significant, negative consequences for hundreds of millions of children (Ball, 2011). For example, in a global study of fourth-grade students, children whose home language differed from the testing language were 10% less likely to achieve the most basic level of reading proficiency compared to students who spoke the testing language at home (UNESCO, 2018). Though mother tongue instruction is potentially complicated to implement in linguistically diverse environments, it may allow children to learn more and may better permit their parents to engage with teaching materials and monitor student performance (Konsonen, 2005; Benson, 2002; Lieberman, Posner, & Tsai, 2014). In the case of Kenya, 45% of mothers of school-aged children cannot read English at a second-grade reading level (Uwezo, 2015); in one study, 72% of parents reported not understanding how to interpret student-learning data (Lieberman et al., 2014). Thus, a country’s policy regarding language of instruction (LOI) can have significant implications for children’s development in ways that interact with poverty, parental literacy, ethnicity, and other risk factors faced by vulnerable children as they move through the formal education system.

# **Child Assessment in Multilingual Environments**

Child development assessments allow teachers to understand how and what children are learning, to diagnose learning differences or language disorders, and to benchmark achievement against national or international standards (Snilsveit et al., 2016; Armon-Lotem, de Jong, & Meir, 2015). Similarly, researchers and policymakers rely on child assessments to examine program effectiveness. In both academic and nonacademic settings, students are routinely tested in only one language, either the LOI or parents’ preferred language. It is challenging to assess 2
MULTILINGUAL CHILD ASSESSMENT child development, language disorders, and school readiness in such populations, both because these children develop linguistic skills in multiple languages simultaneously and because most widely used measures of child development have not been validated in local languages and lowand middle-income country (LMIC) contexts. <mark>Many assessments created and validated in U.S. or European samples do not demonstrate the same strong psychometric characteristics when applied in different settings (Fernald, Prado, Kariger, & Raikes, 2017). To capture the linguistic development of children in LMIC contexts, it is crucial to adapt, or develop, and subsequently validate assessments in children’s mother tongues (Prado et al., 2018).</mark> Child development assessments conducted in a single language may not fully reflect a multilingual child’s developmental outcomes and learning trajectory (Cummins, 1979; Cummins, 2001; <mark>Peña, Bedore, & Kester, 2015).</mark> Bilingual children’s conceptual vocabularies are similar in size to those of monolingual children; however, their vocabulary size in each language is smaller than that for monolingual children (Hammer et al., 2014; Bialystok et al., 2010). The amount of overlap in children’s vocabulary between the two languages may depend on how typologically related the two languages are (Hammer et al., 2014). Furthermore, bilingual children’s performance on language assessments in their second language may have more to do with exposure to the second language than knowledge transfer based on firstlanguage proficiency (Keller, Troesch, & Grob, 2015). For this reason, children may perform better on certain aspects of the tests, such as letter sounds, syllables, or reading fluency, when they are tested in the LOI as compared to their native language (Bialystok, <mark>Majumder, & Martin,</mark> 2003). Greater reading fluency or decoding skills in the LOI, however, do not necessarily indicate that children have greater reading comprehension in the LOI (Piper, Schroeder, & Trudell, 2016; Piper, Zuilkowski, & Ong’ele, 2016). For multilingual children, assessment of 3
MULTILINGUAL CHILD ASSESSMENT language and other domains of development should account for all the child’s languages (Pearson, Fernandez, & Oller, 1993). Furthermore, the assessment should ideally capture the complexity of the child’s language environment or the extent to which a child’s language is specific to a certain context (i.e., school, home, or community) (Toppelberg & Collins, 2010; Pearson et al., 1993). To date, most studies of bilingual or multilingual child language development have been conducted in high-income countries (Barac, <mark>Bialystok, Castro, & Sanchez,</mark> 2014), although a few studies have been conducted in Sub-Saharan Africa (e.g., Cockcroft, 2016; Demuth, 2003; Alcock & Alibhai, 2013; Alcock, Holding, Mung’ala-Odera, & Newton, 2008; Alcock, 2017; Potgieter & Southwood, 2016). Thus, limited data are available to help us understand young children’s verbal development in LMIC contexts.

# **Current Approaches to Child Assessment across Contexts**

<mark>There is an inherent tension between the desire to employ widely used, well-validated measures and the need to adapt items to local contexts. Assessments that are well validated in one context but not appropriately adapted for another may not maintain their properties (Peña, 2007) and may perform unreliably (Gibson, Jamulowicz, & Oller, 2017). This problem is particularly pronounced for tests designed and validated in high-income countries that, without thorough and careful adaptation, often generate items poorly suited to an LMIC context (Fernald et al., 2017; van de Vijver & Poortinga, 2005). Investigators generally have four approaches when using a measure in a new country or context: adoption (translation of an existing test without modification); adaptation (translation with careful modification of items, responses, and administration); expansion (adding items to an existing test to suit a particular cultural or linguistic context); or creation of new tests</mark> **<mark>(Figure 1</mark>** <mark>). These approaches have been used in the</mark> 4
MULTILINGUAL CHILD ASSESSMENT LMIC context (Weber, Fernald, Galasso, & Ratsifandrihamanana, 2015; He & van de Vijver, <mark>2012) and in higher income contexts, where the parallel design of assessments is necessary to simultaneously test children’s verbal development across multiple languages (Haman,</mark> <mark>Łuniewska, & Pomiechowska, 2015). When multiple tests are needed to comprehensively</mark> <mark>measure various capacities, a more diversified strategy may be to adopt some tests, adapt others, expand an existing test to include new test items, and create new tests that are internally valid for the context.</mark>

# **Language Policy in the African Context**

Over 2,149 mother tongue languages are spoken in Africa (Lewis, Simons, & Fenning, 2016), and more than a quarter of the African population speaks a native language that is not in official use in the educational system or by the government ( **Figure 2** ; Lewis et al., 2016). Despite UNESCO’s recent call for at least 6 years of mother tongue education (UNESCO, 2017), there are several reasons for resistance to mother tongue instruction. For example, parents and teachers sometimes believe that children who learn in the mother tongue language will fall behind those who learn in English (Jones, 2012; Trudell, 2007). In addition, linguistically appropriate teaching materials are not always available (Musau, 2003; Waithaka, 2017), and teachers may not be fluent in the local mother tongue (Trudell & Piper, 2014; Manyonyi et al., 2016). The misalignment between children’s first languages and those used in schools has important implications for the assessment of school readiness and learning outcomes: namely, children from linguistically marginalized families risk being underserved by the educational system.

5
MULTILINGUAL CHILD ASSESSMENT

# **<mark>Current Study</mark>**

Our study took place in the Luo-speaking region of western Kenya, a country with 68 spoken languages (Lewis et al., 2016). English and Swahili are the official languages (i.e., for all government proceedings and publications), but literacy rates in these languages, while perhaps relatively high within Sub-Saharan Africa, are still quite low. For example, only 55% of mothers of school-aged children, and about 51% of children aged 7–13 years can read English at a second-grade level (Uwezo, 2015). In our study area, only 31% of young primary school students are taught in Luo, while the rest are taught in either English or Swahili (Piper & Miksic, 2011).

The purpose of this study was to compile a set of child development assessments to evaluate <mark>the effects of a literacy promotion program on multilingual children’s development.</mark> Our first aim was to validate language assessments for children aged 2–6 years. Our second aim was to understand children’s performance on receptive vocabulary assessments in mother tongue (Luo) and official languages (English and Swahili), and the extent to which scores on each of these assessments were associated with children’s receptive and expressive vocabulary at a 5- to 6-week follow-up. <mark>We hypothesized that baseline scores in all languages, but especially English, would be significantly associated with child English receptive vocabulary at follow-up, as they all measure aspects of language skill.</mark> Our final aim was to examine the relationship between caregiver literacy––in both the mother tongue (Luo) and the LOI (English)––and child receptive and expressive vocabulary and to test whether the strength of the association between mother tongue and LOI literacy varied with caregiver literacy. We focused primarily on children’s English vocabulary at follow-up as an indicator for school readiness, as this is the LOI at higher grade levels and the de facto language of instruction for many young children in our study area.

6
MULTILINGUAL CHILD ASSESSMENT <mark>We hypothesized that caregiver literacy in both languages at baseline would be significantly associated with child receptive and expressive language at follow-up, and that the association between the baseline measure of child receptive vocabulary in Luo and English vocabulary at follow-up would be strongest among children whose parents had lower English literacy.</mark>

# **Methods**

# **Study design and sample description**

The measures described in this paper were developed for an ongoing cluster-randomized trial in Kenya’s Kisumu and Homa Bay counties that is designed to evaluate the effectiveness of a book distribution and parenting training program on child development (see trial registry: <u>https://doi.org/10.1186/ISRCTN68855267</u> and pilot results: Knauer, Jakiela, Ozier, Aboud, & Fernald, in press). Families with at least one child between the ages of 24 and 83 months were recruited from a set of nine primary school catchment areas in rural communities within two hours’ drive from Kisumu. A total of 357 primary caregivers (one per household) and 510 children were assessed during household visits (average 1.43 children per household); five child assessments were incomplete, resulting in an analysis sample size of 505. A total of 442 children were assessed at follow-up (5–6 weeks later), with 68 children lost due to relocation or difficulty in making contact.

# **Measures**

# **Overview of child assessments**

To develop our test battery, we used adoption, adaptation, expansion, and creation of new <mark>tests for different developmental domains. A</mark> ll assessments were translated to Swahili or Luo and then back-translated to English by a different team of translators (two for each language) who 7
MULTILINGUAL CHILD ASSESSMENT did not have access to the original measure. The first and second authors (HK and PK) then met with a group of translators and discussed each translation to ensure that words conveying the desired meaning were chosen over direct translation (in Swahili and Luo, several words were often possible depending on the intent of the item). The assessments were then pretested, and any additional study team concerns or discrepancies were addressed. Items for the vocabulary assessments were ordered by difficulty, as measured in a small pilot sample (between 30 and 61 respondents).

The assessors hired to administer the tests in the current study had university degrees, were from the study area, spoke Luo as their mother tongue, and were trained on the full battery of tests by the first and second study authors. On a subset of 48 children, two assessors double coded the baseline assessment to assess interrater reliability (IRR) for each of the assessments

# ( **Supplemental Table 1** ).

# **Receptive vocabulary**

We created receptive vocabulary assessments based on the British Picture Vocabulary Scale III (BPVS III) (Dunn, Dunn, & Styles, 2009), which includes 168 items for individuals 3– 16 years old (see details of translation and adaptation in **Appendix A** ). Knowledge of words is measured by asking the respondent to point to one of four pictures that corresponds to a word (object, person, or action) spoken by the assessor. The BPVS has been adapted for use in South Africa (Cockcroft, 2016) and Indonesia (Prado, Alcock, Muadz, Ullman, & Shankar, 2012) and is the British adaptation of the Peabody Picture Vocabulary Test (Dunn & Dunn, 1997), which has also been used in neighboring areas of Kenya (Ozier, 2018). As we wanted to capture young children’s knowledge of Luo, Swahili, and English words, we created three sets of 8
MULTILINGUAL CHILD ASSESSMENT nonoverlapping words of varying difficulty, with 27 Luo items, 32 Swahili items, and 34 English items. Administration ended when a child failed six of a set of eight items.

# **Expressive vocabulary**

We developed our own measure of expressive vocabulary after reviewing various expressive vocabulary tests and concluding that the stimulus words and/or pictures were not appropriate to the context (see details in **Appendix B)** . The assessment was a picture-naming task, in which children were presented with flash cards bearing a single illustrated stimulus item or object (noun) per card and were asked in the child’s preferred language, “What is this?” for each item. Children were not instructed as to which language to respond in, but responses in any language were accepted. We did not provide further instruction because code-switching during conversation is common in this area, and very young children may not be aware which language they are actually speaking for a given word. Thus, a child could respond to each item in the 20item test in English, Luo, or Swahili to score a pass for expressing the word verbally. Administration ended with three consecutive fails.

# **Other child-level assessments**

The Malawi Developmental Assessment Tool (MDAT) was created and validated for use in rural Malawi with children 1–84 months of age (Gladstone et al., 2010). It includes four 34item subscales (fine motor/perception, language/hearing, gross motor, social-personal), with many items adapted from existing Western tests (see details of our adaptation in **Appendix C** ). The MDAT is currently being used in various countries, including Mali, Sierra Leone, Rwanda, Burkina Faso, and Zimbabwe (M. Gladstone, personal communication, June 24, 2016). The western Kenya adaptation was initiated by the first, second, and fifth authors (HK, PK, and LCHF) for the Kenya Life Panel Survey (e.g., Baird, Hicks, Kremer, & Miguel, 2016), a 9
MULTILINGUAL CHILD ASSESSMENT longitudinal study that examines the intergenerational effects of health investments. We used the translations and piloting data from that study to further adapt and expand the language and fine motor/perception subtests of the MDAT for the current study. The final adapted language test had 26 items. <mark>To further reduce the overall length of the test, we created start and stop rules for three different age groups (24–35 months; 36–59 months; 60–71 months) based on pass rates during piloting.</mark>

# **Caregiver survey**

Data were gathered on household assets, housing quality, household size and composition, and the age and education level of primary caregivers. In addition, we assessed caregiver literacy by asking caregivers to read a simple, five-word (second-grade level) sentence in each language adapted from the Early Grade Reading Assessment (EGRA; <mark>Gove & Wetterberg, 2011).</mark> Caregivers who read more than one word incorrectly in all three languages were categorized as illiterate. Working memory in caregivers was assessed using a summary score of the forward and backward digit span test (Ozier, 2018; out of 20 possible), and mental health was measured using an adapted version of the Centers for Epidemiological StudiesDepression scale (CES-D; Radloff, 1977; scores range 0-60). Household support for learning was measured with a set of items drawn from the HOME Inventory, Family Care Indicators, and UNICEF MICS4 (Bradley, Corwyn, McAdoo, & Coll, 2001; Hamadani et al., 2010; Kariger et al., 2012).

# **Statistical Analysis**

10
MULTILINGUAL CHILD ASSESSMENT To address the first aim of validating our assessments by examining their psychometric properties, we measured: 1) the internal consistency of the measures using Cronbach’s alpha; 2) IRR using Cohen’s kappa, Krippendorff’s alpha, and percent agreement; 3) construct validity by examining the correlations between the measures; and 4) convergent validity by examining associations with known covariates in bivariate regressions. For our second aim, to better understand the relationships between baseline measures of mother tongue and LOI receptive vocabulary and scores on subsequent vocabulary assessments, we estimated a series of ordinary least squares (OLS) regression models to examine the associations between baseline agestandardized receptive vocabulary scores in all languages (English, Swahili, and Luo) and English receptive vocabulary at follow-up. We repeated this analysis for follow-up measures of child expressive vocabulary as well as Swahili and Luo receptive vocabulary; we present these results as supplemental analyses. Our final aim was to examine the association between caregiver literacy and child vocabulary at two time points. We used OLS regression to examine the association between baseline caregiver literacy in Luo and English and child English and Luo receptive and expressive vocabulary scores at follow-up. To test whether the relationship between baseline mother tongue and LOI receptive vocabulary scores and follow-up LOI receptive vocabulary varied with caregiver literacy, we estimated OLS regression models that included both caregiver literacy and baseline child receptive vocabulary (in English, Luo, and Swahili). All regressions used age-adjusted _z-_ scores for child vocabulary, and standard errors were adjusted for household clustering. All statistical analyses were conducted using Stata 14.2.

# **Descriptive Statistics**

**Results**

11
MULTILINGUAL CHILD ASSESSMENT The average age of children in the study was 54.42 months (range 24–83 months) ( **Table 1** ). About one-quarter (27%) of caregivers were illiterate. Maternal and household characteristics were similar to those observed in the representative 2014 Kenya Demographic and Health Survey sample for the study area.

# **Psychometric Properties of the Instruments**

The internal consistency of the vocabulary measures ranged from  = 0.57 to 0.90: Cronbach’s alphas were lowest for the expressive and English receptive tests and highest for the MDAT language test ( **Supplemental Table 1** ). The internal consistency of the receptive vocabulary assessments was higher for Luo (  = 0.78) and Swahili (  = 0.76) than for English (  = 0.57). The IRR of the receptive vocabulary tests was  = 1 for Luo,  = 0.89 for Swahili, and  = 0.95 for English. The internal consistency of the expressive vocabulary test was  = 0.67, while the IRR was  = 0.95. The internal consistency of the MDAT fine motor and language tests was  = 0.94 and  = 0.90, respectively. IRR of the total score for each measure was  = 0.93 for fine motor and  = 0.86 for language.

Correlations among the baseline child development assessments ranged from _r_ = 0.32 to 0.56, and all correlations were statistically significant at the _p_ < 0.001 level ( **Supplemental Table 2** ). The three age-normalized receptive vocabulary scores were all moderately correlated with each other, the expressive vocabulary score, and the MDAT scores, while the expressive vocabulary score was also moderately correlated with both MDAT scores. The MDAT tests had the strongest correlation ( _r_ = 0.56) with each other; among the vocabulary assessments, they had the strongest correlations with Luo vocabulary ( _r_ = 0.48–0.49).

12
MULTILINGUAL CHILD ASSESSMENT The associations between baseline child, caregiver, and household characteristics with child age-adjusted child development scores are presented in **Supplemental Table 3** . In bivariate regression analyses adjusted for household clustering, child height-for-age _z-_ score was significantly associated with all child development assessments (  = 0.25–0.33, SD = 0.03–0.04, _p_ < 0.001 for all). Caregiver characteristics (education, literacy, and cognition) were most strongly associated with child expressive vocabulary and MDAT scores, while caregiver depressive symptoms were not associated with any child assessments. Finally, household characteristics were not consistently associated with child assessments.

# **The Role of Language in Child Development Assessment**

At baseline, 2-year-old children knew, on average, 3.45 of 27 (SD = 3.40) Luo receptive vocabulary words, 3.40 of 34 (SD = 3.41) English receptive vocabulary words, 5.08 of 31 (SD = 4.20) Swahili receptive vocabulary words, and 1.71 of 20 expressive vocabulary words in any language (SD = 2.02) ( **Table 2** ). Children’s vocabulary progressed with age ( **Figure 2** ), such that 6-year-olds knew, on average, 14.48 (SD = 5.39) Luo, 9.17 (SD = 3.70) English, and 11.94 (SD = 5.43) Swahili receptive words, and 9.78 expressive words in any language (SD = 5.83).

Overall, 189 children answered the expressive vocabulary test entirely in Luo, while 13 children answered entirely in English, and 6 children answered entirely in Swahili ( **Table 2** ). The other 297 children (58%) answered in more than one language; the number of children answering in only one language decreased with age. Across all ages, children answered more expressive vocabulary words in Luo, followed by English and then Swahili. The fraction of expressive responses given in Luo decreased from about 89% among 2-year-olds to about 67% among 6- year-olds, while the fraction of responses given in English increased from about 5% among 2- year-olds to about 29% among 6-year-olds ( **Table 2** ). The percentage of responses given in 13
MULTILINGUAL CHILD ASSESSMENT Swahili was small (7% among 2-year-olds) and decreased slightly with age. The youngest children showed a clear preference for expressing themselves in their mother tongue, as was evident in the patterns of response in our expressive vocabulary test ( **Figure 3** ).

# **Children’s vocabulary at baseline and follow-up**

In bivariate analyses, the baseline measure for each language was most strongly associated with the corresponding follow-up measure ( **Tables 3, S4, & S5** ; Models 1–3). When baseline measures of the other languages were included in the analyses (Models 4–6), however, our follow-up receptive English measure performed differently from other languages in at least two ways: first, English benefited most from the inclusion of baseline measures of the other two languages; second, English was much more strongly associated with the next-best baseline assessment than were other follow-up languages ( **Figure 4** ). The baseline Luo assessment was also notable for being the next-strongest correlate of both follow-up English and follow-up Swahili.

Child baseline Luo receptive vocabulary was significantly associated with follow-up English receptive vocabulary (  = 0.18, SD = 0.05, _p_ < 0.001), even after accounting for baseline English (  = 0.26, SD = 0.05, _p_ < 0.001) and Swahili (  = 0.10, SD = 0.05, _p_ = 0.032; Model 6) ( **Table 3** ). Adding Luo to the English test (moving from Model 1 to Model 4 or 6) increased the _R_ -squared substantially ( **Figure 5** ). There was a greater gain in _R_ -squared by testing in both English and Luo (Model 4: _R_ -squared = 0.1737) than English alone, or in English and Swahili (Model 5: _R_ -squared = 0.1560). Testing in all three languages yielded an _R_ -squared of 0.1821 (Model 6). In contrast, baseline receptive vocabulary for all languages was not associated with children’s Swahili or Luo vocabulary at follow-up ( **Supplemental Tables 4 and 5** ). Though the other languages were sometimes statistically significant in the full models (Model 6), they did 14
MULTILINGUAL CHILD ASSESSMENT little to increase the overall explanatory power above the bivariate model of only baseline receptive vocabulary scores in the same language (Model 1).

Children’s expressive vocabulary at follow-up was significantly associated with baseline receptive vocabulary measures in all three languages; Luo receptive vocabulary had the strongest association (  = 0.26, SE = 0.06, _p_ < 0.001; Model 8) ( **Supplemental Table 6** ). When baseline expressive vocabulary was included in the full model, however, the receptive vocabulary measures were no longer statistically significant (Model 9).

# **Associations Between Caregiver Literacy and Child Vocabulary**

In our final analyses, we examined caregiver baseline and child follow-up measures in English (the primary LOI at older grade levels) and Luo (the mother tongue for 95% of our sample). After adjusting for caregiver education and household wealth, caregiver literacy in Luo was significantly associated with children’s receptive vocabulary in English (  = 0.11, SD = 0.05, _p_ = 0.045), while caregiver literacy in English was not ( **Table 4** ). Caregiver literacy in either language was not significantly associated with children’s receptive vocabulary in Luo or their expressive vocabulary. Moreover, controlling for caregiver literacy (in English and Luo) did not alter the pattern of associations between children’s baseline receptive vocabulary (in English, Luo, and Swahili) and follow-up English receptive vocabulary. Similar patterns were observed among the children of literate and illiterate caregivers, though baseline Swahili receptive vocabulary was more strongly associated with endline English receptive vocabulary among the children of literate caregivers ( **Supplemental Table 7** ).

**Discussion**

15
MULTILINGUAL CHILD ASSESSMENT In this study, we assessed the language development of 2-to-6-year-old multilingual children at two time points in a rural, ethnically homogenous region of Kenya. Notably, we found that English receptive vocabulary was less strongly associated with other measures of children’s language development than expected, especially among the youngest children. Instead, baseline Luo receptive vocabulary seemed best able to capture general language skill. Specifically, children’s baseline Luo receptive vocabulary was significantly associated with English receptive vocabulary at follow-up, even after taking baseline English receptive vocabulary into account. Luo was also the form of receptive vocabulary most strongly associated with subsequent expressive vocabulary.

Children’s follow-up English receptive scores were significantly associated with their baseline receptive vocabulary scores in all three languages. However, English receptive vocabulary at baseline was not a significant correlate of children’s later receptive vocabulary in Swahili or Luo. These findings suggest that, when measuring children’s vocabulary at a very young age, an assessment of mother tongue receptive vocabulary provides a strong indication of overall language ability, while LOI receptive vocabulary does not provide a full assessment of vocabulary development. Our findings are consistent with existing work in the study area, in which children demonstrated greater reading fluency in English than in Luo, but significantly lower reading comprehension scores in English than in Luo (Piper, Schroeder, & Trudell, 2016). While children’s familiarity with English through their classroom exposure is high, their actual understanding of English is often quite low (Trudell & Piper, 2014). This situation is likely to be common to many African contexts since many children learn to read in a language other than their mother tongue (Lewis et al., 2016).

16
MULTILINGUAL CHILD ASSESSMENT In the process of vocabulary development, children typically first acquire receptive knowledge of a word (i.e., they recognize and understand the word when it is spoken or read), only later developing the ability to produce the word (expressive vocabulary) either by speaking or writing (Burger & Chong, 2011). By age six, children’s receptive vocabulary is usually larger than their expressive vocabulary, although they may also learn to say words before they fully understand them (Burger & Chong, 2011). In our examination of the relationship between children’s receptive and expressive vocabulary, we found that the strongest measures of language development at follow-up were baseline expressive vocabulary (in any language) followed by receptive vocabulary in Luo. However, expressive vocabulary is often not measured in research studies in LMICs—for example, because very young children can be too shy to respond, or may respond correctly in any one of several languages, which makes it more complicated to code responses. Our finding that 59% of children used multiple languages in their expressive responses confirmed our assumption that code-switching was common.

Caregivers’ literacy in mother tongue at baseline provided an indicator of children’s school readiness (as measured by English vocabulary), while caregivers’ English literacy skills at baseline did not. Additionally, caregiver literacy at baseline in either language was not associated with children’s vocabulary in Luo at follow-up. There may be several reasons for these findings. First, young children are often cared for by multiple caregivers, including other children. As a result, their Luo vocabulary may depend less on their primary caregiver’s literacy because they hear conversation among other family and caregivers in Luo. Conversely, most families and neighbors do not converse in English, so children would have less regular exposure to the language. Greater caregiver engagement in stimulating activities with their children was associated with higher English, but not Luo receptive vocabulary, suggesting that caregivers may 17
MULTILINGUAL CHILD ASSESSMENT deliberately teach their children English words. Finally, while our measurement of caregiver literacy was designed to be at a second-grade reading level, it proved more difficult for caregivers in English than in Luo; only three caregivers could read all five words in the English sentence.

A central limitation to this study is that it took place among a rural and ethnically homogenous group of children, so the findings may not generalize to an urban or ethnically mixed setting (Hungi, Njangi, Wekulo, & Ngware, 2017). In mixed ethnicity households or very diverse communities, the associations between mother tongue vocabulary and subsequent LOI vocabulary may not be as strong. However, even within this homogenous group, we had to navigate a multilingual environment to implement language assessments, which presented several inherent challenges. <mark>First, items (e.g., “playground”) that perform well in high-income</mark> contexts may be unknown to children in other settings. Additionally, concepts that are <mark>represented by a single more difficult word in the original test language may translate to a phrase built from much simpler words: for example, “nest” translates in Luo to</mark> _<mark>od</mark>_ <mark>(“covering” or “housing”)</mark> _<mark>winyo</mark>_ <mark>(“bird”) (Capen, 1998), making it an easier word in Luo than the same word in English; thus, the ordering of item difficulty may no longer be appropriate. Finally, even linguistically accurate translations may not retain what some have called “psychological similarity” (van de Vijver & Poortinga, 2005).</mark> This is when an item taken from one setting may not have the same psychological meaning in a different context, such as “What do we do before crossing the road?” Therefore, a core strength of our study is the rigorous adaptation, translation, and validation process that we performed for our assessments and our testing of children across a broad age range in multiple languages. This process allowed us to document more fully how children’s vocabulary in different languages evolves with age and how receptive vocabulary 18
MULTILINGUAL CHILD ASSESSMENT measures in mother tongue and the LOI were associated with vocabulary development 5–6 weeks later.

In a multilingual context, as is common in LMICs, there is a question of how best to support young children’s language and cognitive development. Should pre-primary educational materials be in the local mother tongue—i.e., children’s first language—or in English, the language in which children will eventually be instructed and tested in primary school? Our findings raise the possibility that to best support the language development of children before school age, early childhood interventions—especially those targeting parents—might do well to include instruction and materials in mother tongue, as a child’s first language lays the foundation for learning in other languages and for general readiness for school (see also Altan & Hoff, 2018; Hoff & Ribot, 2017).

A recent review of language of instruction policies in Eastern and Southern Africa found that 14 of 21 countries introduce English as the LOI before fifth grade (Trudell, 2016a). However, it may be particularly challenging in Africa to implement UNESCO’s guidelines of at least six years of mother tongue education because of the continent’s high degree of linguistic heterogeneity. As a concrete example, Kenya’s formal educational policy mandates that early primary instruction be conducted in the mother tongue in rural areas and in Swahili in urban areas––with a transition to English at Grade 4 in either case–– however, this policy is only loosely followed in practice, illustrating the practical challenges inherent in such complex environments (Trudell, 2016b; Manyonyi, Mbori, & Okwako, 2016).

Vocabulary assessment of young children in only one language, particularly if not in their mother tongue, risks inadequately capturing children’s development. Foundational work in the study of bilingual education has pointed out the interdependence of language skills across 19
MULTILINGUAL CHILD ASSESSMENT languages for bilingual children, but has focused exclusively on high-income country examples (e.g., Cummins, 1979). Monolinguals and bilinguals may learn school-centric words in the LOI equally quickly, but bilingual children may differentially know home-centric words in their first language rather than the LOI, thereby complicating the interpretation of assessments conducted in a single language (Bialystok, Luk, Peets, & Yang, 2010). As a specific example of the interplay of languages in Africa, Shin et al. (2015) found that in Malawi, Chichewa literacy in Grade 2 was a predictor of subsequent English skills in Grade 3.

A recent study in Kenya found no additional benefit from mother tongue instruction in primary school on children’s language development, but only assessed children’s linguistic development in English and Swahili (Piper, Zuilkowski, Kwayumba, & Oyanga, 2018). However, a separate study found that the PRIMR program (which provides teacher training and instructional supports to improve language and math skills in early primary grades) improved oral reading fluency and reading comprehension in mother tongue (Piper, Zuilkowski, & Ong’ele, 2016). Our findings suggest that receptive vocabulary in a child’s mother tongue may be a particularly important measure of linguistic development, even when the outcome of interest is the language of instruction. Children’s vocabulary in their mother tongue may better reflect the level of stimulation and conversation they receive at home, while children’s vocabulary in the LOI indicates their exposure to that language. Multilingual testing of parents and children is essential in order to understand the developmental status of multilingual children as well as factors that affect their development in LMICs.

Data availability: The data that support the findings of this study are available upon reasonable request to <u>oozier@worldbank.org.</u> 20
MULTILINGUAL CHILD ASSESSMENT

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29
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# Child Age in Months

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# Language Measured at Endline

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MULTILINGUAL CHILD ASSESSMENT Table 1. Descriptive characteristics of children, caregivers, and households at baseline

||Obs.|Mean|SD|Min.|Max.|
|---|---|---|---|---|---|
|_Child characteristics:_||||||
|Child age in months|505|54.42|17.51|24|83|
|Child is male|505|0.53|0.50|0|1|
|Height-for-age_z-_score|489|–0.23|1.40|–4.58|4.39|
|Child is stunted (HAZ <–2SD)|489|0.08|0.27|0|1|
|_Primary caregiver characteristics:_||||||
|Caregiver is child's mother|353|0.85|0.36|0|1|
|Caregiver is child's father|353|0.02|0.14|0|1|
|Caregiver is child's grandmother|353|0.11|0.31|0|1|
|Caregiver mother tongue is Luo|353|0.95|0.23|0|1|
|Caregiver education: no formal schooling|353|0.02|0.15|0|1|
|Caregiver education: incomplete primary<br>school|353|0.48|0.50|0|1|
|Caregiver education: completed primary,<br>not secondary|353|0.41|0.49|0|1|
|Caregiver education: completed secondary||||||
|school|353|0.08|0.28|0|1|
|Caregiver illiterate|353|0.27|0.45|0|1|
|Caregiver working memory (out of 20)|353|2.43|1.74|0|9|
|Caregiver depressive symptoms (out of 26)|346|10.33|5.36|0|26|
|_Household characteristics:_||||||
|Household size|353|3.03|0.85|2|6|
|Any children's books in the home|353|0.13|0.34|0|1|
|Number of children's books in the home|353|0.22|0.71|0|5|
|Someone has read to the child in past 3||||||
|days|353|0.48|0.50|0|1|
|Family care indicators score (out of 17)|353|7.95|3.58|0|17|
|Rooms per person|353|0.89|0.43|0.20|3.00|
|Household has cement floor|353|0.24|0.43|0|1|
|Household has iron roof|353|0.96|0.20|0|1|
|Household has electricity|353|0.30|0.46|0|1|
|Household has latrine|353|0.69|0.46|0|1|
|Household wealth index|353|0.04|2.22|–3.51|9.80|

Notes. Summary statistics on 505 children and 353 caregivers for whom baseline data is available. Baseline height data is missing for 16 children. Seven caregivers declined to answer the questions on depressive symptoms.

35
MULTILINGUAL CHILD ASSESSMENT Table 2. Descriptive statistics of child measures at baseline

||2 y|ears|3 ye|ars|4 ye|ars|5 y|ears|6 y|ears|Overall|
|---|---|---|---|---|---|---|---|---|---|---|---|
||N =|106|N =|76|N =|110|N =|113|N =|105|N = 505|
||mean|sd/n|mean|sd/n|mean|sd/n|mean|sd/n|mean|sd/n|%|
|Receptive vocabulary||||||||||||
|English (out of 34)|3.40|3.41|4.26|3.44|6.51|3.76|8.58|3.92|9.17|3.70||
|Swahili (out of 31)|3.45|3.40|5.19|3.96|8.44|4.81|11.04|5.00|14.48|5.39||
|Luo(out of 27)|5.08|4.20|5.99|3.87|8.77|3.87|11.53|4.90|11.94|5.43||
|Expressive vocabulary (out||||||||||||
|of 30)|1.71|2.02|2.60|2.37|4.82|3.86|8.73|5.33|9.78|5.83||
|% of responses in English|4.44%|15.29%|6.42%|16.30%|15.57%|24.08%|28.34%|26.04%|29.02%|27.10%|19.21%|
|% of responses in Swahili|7.02%|25.54%|4.90%|18.24%|5.03%|16.13%|4.83%|12.97%|3.80%|7.28%|4.96%|
|% of responses in Luo|88.54%|28.78%|88.68%|24.97%|79.39%|30.25%|66.83%|29.00%|67.18%|29.13%|75.83%|
|% answered only in English|0.94%|1|0.00%|0|2.73%|3|3.54%|4|4.76%|5|2.55%|
|% answered only in Swahili|3.77%|4|1.32%|1|0.91%|1|0.00%|0|0.00%|0|1.18%|
|% answered only in Luo|44.34%|47|59.21%|45|47.27%|52|21.24%|24|20.00%|21|37.06%|
|Multiple language<br>response|50.94%|54|39.47%|30|49.09%|54|75.22%|85|75.24%|79|59.22%|
||N =|106|N =|76|N =|110|N =|113|N =|105|N = 505|
|Adapted MDAT fine motor||||||||||||
|(out of 43)|5.52|3.62|12.99|5.34|18.34|4.85|22.65|4.85|25.98|5.05||
|Adapted MDAT language<br>(out of 36)|10.22|1.93|13.81|3.28|16.93|3.27|20.78|2.92|22.22|2.64||

Notes. English, Swahili, and Luo vocabulary are raw total receptive vocabulary scores, measured using three assessments based on the British Picture Vocabulary Scale (BPVS). Expressive vocabulary raw scores were measured using a tool developed from the PPVT. Percent (%) English, Swahili, and Luo expressive are the mean (and sd) percent of responses given in each language for the expressive vocabulary test. Only in English, Swahili, and Luo are the percent (and n) of children who answered the expressive vocabulary exclusively in each language. Multiple language response are the percent (and n) of children who answered in more than one language. The adapted MDAT are the raw scores from the Kenya adaptation of the Malawi Developmental Assessment Tool (MDAT).

36
MULTILINGUAL CHILD ASSESSMENT Table 3. The association between baseline receptive vocabulary and follow-up English receptive vocabulary

|Outcome: Follow-up English receptive<br>|||||||
|---|---|---|---|---|---|---|
|vocabulary|e1|e2|e3|e4|e5|e6|
||b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|
|Receptive vocabulary in English (_z-_score)|0.3554****|||0.2811****|0.3035****|0.2593****|
||0.0478|||0.0477|0.0487|0.0485|
||<0.001|||<0.001|<0.001|<0.001|
|Receptive vocabulary in Swahili (_z-_score)||0.2669****|||0.1618****|0.1034**|
|||0.0472|||0.0469|0.048|
|||<0.001|||0.0006|0.0318|
|Receptive vocabulary in Luo (_z-_score)|||0.3157****|0.2139****||0.1812****|
||||0.0465|0.0451||0.0463|
||||<0.001|<0.001||0.0001|
|Constant|–0.0119|–0.0025|–0.003|–0.0104|–0.0107|–0.0099|
||0.0469|0.0483|0.0474|0.0454|0.0458|0.045|
||0.7998|0.9585|0.95|0.8187|0.8158|0.8267|
|_R_-squared|0.1332|0.0696|0.1009|0.1737|0.1560|0.1821|
|N. of cases|442|442|442|442|442|442|

Notes. English, Swahili, and Luo receptive vocabulary are measured using three assessments based on the British Picture Vocabulary Scale (BPVS). Vocabulary scores are age-adjusted _z-_ scores for children ages 2 to 6 years. Baseline and follow-up were conducted approximately 5 weeks apart. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01, **** _p_ < 0.001.

37
MULTILINGUAL CHILD ASSESSMENT Table 4. The association of baseline caregiver literacy in English and Luo and child follow-up vocabulary scores

||E<br>|nglish recept<br>|ive vocabul<br>|ary<br>|Lu<br>|o receptiv<br>|e vocabul<br>|ary<br>|E<br>|xpressive<br>|vocabula<br>|ry<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||cg1|cg2|cg3|cg4|cg5|cg6|cg7|cg8|cg9|cg10|cg11|cg12|
||b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|
|Caregiver|||||||||||||
|literacy|||||||||||||
|English|0.070**||–0.057|–0.098|0.0003||–0.040|–0.077|0.049*||0.027|0.021|
||0.028||0.073|0.073|0.029||0.088|0.092|0.029||0.081|0.084|
||0.013||0.431|0.181|0.993||0.651|0.405|0.088||0.737|0.800|
|Luo||0.067***|0.109*|0.110**||0.005|0.034|0.028||0.039*|0.019|0.023|
|||0.022|0.056|0.055||0.023|0.068|0.068||0.022|0.062|0.063|
|||0.002|0.056|0.045||0.815|0.619|0.680||0.082|0.760|0.711|
|Constant|–0.190**|–0.234***|–0.224**|–0.237***|–0.003|–0.021|–0.014|–0.039|–0.142|–0.143|–0.148|–0.143|
||0.089|0.088|0.090|0.087|0.0936|0.092|0.094|0.093|0.090|0.089|0.090|0.091|
||0.034|0.008|0.013|0.007|0.9735|0.822|0.883|0.677|0.117|0.109|0.103|0.118|
|_R_-squared|0.015|0.023|0.024|0.045|0|0|0.001|0.016|0.008|0.008|0.008|0.015|
|N. of cases|442|442|442|442|442|442|442|442|442|442|442|442|

Notes. Receptive English and Luo vocabulary scores are age-adjusted _z-_ scores for children ages 2 to 6 years, measured using separate assessments based on the British Picture Vocabulary Scale (BPVS). Expressive vocabulary _z-_ scores were measured using a tool developed from the PPVT. Caregiver literacy is number of words (out of 5) a caregiver could read from a simple sentence at a second-grade reading level, adapted from the Early Grade Reading Assessment (EGRA). Baseline and follow-up were conducted approximately 5 weeks apart. The first two models for each vocabulary assessment (cg1 & cg2, cg5 & cg6, cg9, & cg10) are bivariate regressions. The third model for each vocabulary assessment (cg3, cg7, and cg11) include caregiver literacy in both languages. The fourth model for each vocabulary assessment (cg4, cg8, and cg12) add controls for caregiver education and household wealth. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01, **** _p_ < 0.001.

38
Supplemental Table 1. Internal consistency and interrater reliability of child development measures

||Obs.|No.<br>items<br>in<br>scale|Cronbach's<br>alpha|Interr|ater reliabilit|y N = 48|
|---|---|---|---|---|---|---|
|||||Percent<br>agreement|Cohen's<br>kappa|Krippendorff's<br>alpha|
|Receptive vocabulary|||||||
|English|505|34|0.569|0.958333|0.954023|0.998914|
|Swahili|505|31|0.757|0.895833|0.888424|0.996739|
|Luo|505|27|0.779|1|1|1|
|Expressive vocabulary|505|20|0.660|0.958333|0.949712|0.99934|
|Adapted MDAT|||||||
|Fine motor|495|32|0.935|0.9375|0.934005|0.999635|
|Language|495|28|0.901|0.875|0.864407|0.996617|

Notes. English, Swahili, and Luo receptive vocabulary were measured using separate assessments based on the British Picture Vocabulary Scale (BPVS). Expressive vocabulary was measured using a tool developed from the PPVT. The adapted MDAT is the Kenya adaptation of the Malawi Developmental Assessment Tool (MDAT).
Supplemental Table 2. Correlation matrix of baseline age-standardized child development measures

|Rece|ptive vocabulary|Expressive|Adapt|ed MDAT|
|---|---|---|---|---|
|English<br>S|wahili<br>Luo|vocabulary|Fine motor|Language|
|Receptive|||||
|vocabulary|||||
|English<br>1|||||
|Swahili<br>0.358958|1||||
|Luo<br>0.364414|0.400669<br>1||||
|Expressive|||||
|vocabulary<br>0.43381|0.434502<br>0.490148|1|||
|adapted MDAT|||||
|Fine motor<br>0.323954|0.430528<br>0.490547|0.447659|1||
|adapted MDAT|||||
|language<br>0.421838|0.319749<br>0.476982|0.465957|0.555477|1|
|Notes. English, Swahili, and Luo rece<br>the British Picture Vocabulary Scale (<br>from the PPVT. The adapted MDAT a<br>Assessment Tool (MDAT). All_z-_scores|ptive vocabulary are_z-_scores, m<br>BPVS). Expressive vocabulary_z-_<br>re the_z-_scores from the Kenya<br>were age adjusted. All correla|easured using s<br>scores were mea<br>adaptation of th<br>tions are signific|eparate assess<br>sured using a<br>e Malawi Deve<br>ant at_p_< 0.00|ments based on<br>tool developed<br>lopmental<br>1.|
Supplemental Table 3. Association between contextual factors and child development scores at baseline using bivariate OLS regression

|||<br>|Receptive<br>|vocabu<br>|lary<br>||Expres<br>|sive<br>|A<br>|dapte<br>|d MDAT<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Engli|sh|Swah|ili|Luo||vocabu|lary|Fine m|otor|Langu|age|
||b|se|b|se|b|se|b|se|b|se|b|se|
|_Child characteristics:_|||||||||||||
|Height-for-age_z-_score|0.25***|0.03|0.19***|0.03|0.26***|0.04|0.3***|0.04|0.34***|0.03|0.33***|0.03|
|_Primary caregiver_<br>_characteristics:_|||||||||||||
|Caregiver completed<br>primary school|0.19*|0.09|0.22**|0.1|0.15|0.09|0.29***|0.1|0.23**|0.1|0.15|0.1|
|Caregiver is literate|0.13|0.1|0.11|0.1|0.03|0.09|0.23**|0.1|0.18*|0.1|0.19*|0.1|
|Caregiver working<br>memory (out of 20)|0.08***|0.03|0.04|0.02|0.03|0.02|0.06**|0.03|0.05*|0.03|0.07**|0.03|
|Caregiver depressive<br>symptoms (out of 26)|.|0.01|.|0.01|–0.01|0.008|0.01|0.01|–0.01|0.01|0.01|0.01|
|_Household_|||||||||||||
|_characteristics:_|||||||||||||
|Household size|–0.11|0.05|–0.08|0.05|–0.15***|0.05|–0.06|0.05|–0.07|0.06|–0.03|0.05|
|Household wealth index|0.03|0.02|0.04**|0.02|–0.01|0.02|0.04**|0.02|0.05**|0.02|0.02|0.02|
|Family care indicators<br>|0.03*|0.01|0.02|0.01|0.01|0.01|0.03*|0.02|0.05***|0.01|0.04**|0.01|
|score(out of 17)<br>Notes. Receptive English a<br>Picture Vocabulary Scale (<br>adapted MDAT are the_z-_s<br>were age adjusted. Baseli|nd Luo voc<br>BPVS). Expr<br>cores from<br>ne and follo|abular<br>essive<br>the Ke<br>w-up|y scores ar<br>vocabular<br>nya adapt<br>were cond|e_z-_sco<br>y_z-_sco<br>ation o<br>ucted a|res, measur<br>res were me<br>f the Malaw<br>pproximatel|ed using<br>asured<br>i Develo<br>y 5 wee|separate<br>using a too<br>pmental A<br>ks apart. *|assess<br>l deve<br>ssessm<br>_p_< 0.1|ments bas<br>loped from<br>ent Tool (<br>, **_p_< 0.0|ed on t<br>the P<br>MDAT<br>5, ***|he British<br>PVT. The<br>). All_z-_sco<br> _p_< 0.01.|<br>res|
Supplemental Table 4. The association between baseline receptive vocabulary and follow-up Swahili receptive vocabulary

||s1|s2|s3|s4|s5|s6|
|---|---|---|---|---|---|---|
||b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|
|Receptive vocabulary in English (_z-_score)|0.2214****||||0.0675|0.0431|
||0.0452||||0.0413|0.0448|
||<0.001||||0.1036|0.3372|
|Receptive vocabulary in Swahili (_z-_score)||0.5031****||0.4578****|0.4798****|0.4476****|
|||0.0419||0.0461|0.0452|0.0473|
|||<0.001||<0.001|<0.001|<0.001|
|Receptive vocabulary in Luo (_z-_score)|||0.2912****|0.1114**||0.0998*|
||||0.0481|0.0473||0.0509|
||||<0.001|0.0191||0.0508|
|Constant|–0.0064|–0.0011|–0.0011|–0.0013|–0.0029|–0.0024|
||0.0475|0.0406|0.0464|0.0405|0.0406|0.0405|
||0.8923|0.9793|0.9805|0.9747|0.9436|0.9524|
|_R_-squared|0.0508|0.2429|0.0843|0.2532|0.2471|0.2548|
|N. of cases|442|442|442|442|442|442|

Notes. Receptive English, Swahili, and Luo vocabulary scores are age-adjusted _z-_ scores for children ages 2 to 6 years, measured using separate assessments based on the British Picture Vocabulary Scale (BPVS). Baseline and follow-up were conducted approximately 5 weeks apart. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01, **** _p_ < 0.001.
Supplemental Table 5. The association between baseline receptive vocabulary and follow-up Luo receptive vocabulary

||l1|l2|l3|l4|l5|l6|
|---|---|---|---|---|---|---|
||b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|
|Receptive vocabulary in English (_z-_score)|0.2780****|||0.1125**||0.0857*|
||0.052|||0.0445||0.0461|
||<0.001|||0.0121||0.0641|
|Receptive vocabulary in Swahili (_z-_score)||0.3340****|||0.1472****|0.1269***|
|||0.0481|||0.0437|0.0455|
|||<0.001|||0.0008|0.0056|
|Receptive vocabulary in Luo (_z-_score)|||0.5168****|0.4761****|0.4590****|0.4359****|
||||0.0443|0.0474|0.0474|0.0492|
||||<0.001|<0.001|<0.001|<0.001|
|Constant|–0.01|–0.0028|–0.0037|–0.0067|–0.0038|–0.0061|
||0.049|0.0477|0.0423|0.0424|0.0419|0.0421|
||0.8383|0.9524|0.9295|0.8742|0.9279|0.8857|
|_R_-squared|0.0797|0.1066|0.2645|0.2759|0.2819|0.2882|
|N. of cases|442|442|442|442|442|442|

Notes. Receptive English, Swahili, and Luo vocabulary scores are age-adjusted _z-_ scores for children ages 2 to 6 years, measured using separate assessments based on the British Picture Vocabulary Scale (BPVS). Baseline and follow-up were conducted approximately 5 weeks apart. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01, **** _p_ < 0.001.
Supplemental Table 6. The association between receptive vocabulary at baseline and expressive vocabulary at follow-up

||x1|x2|x3|x4|x5|x6|x7|x8|x9|
|---|---|---|---|---|---|---|---|---|---|
||b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|
|Receptive vocabulary in|0.3236*|||||0.2361*|0.2127*|0.1728*||
|English (_z-_score)|**|||||**|**|**|0.0347|
||0.0541|||||0.0517|0.0468|0.0473|0.0375|
||<0.01|||||<0.01|<0.01|0.0003|0.3547|
|Receptive vocabulary in||0.3545*|||0.2300*|0.2727*||0.1891*||
|Swahili (_z-_score)||**|||**|**||**|0.0108|
|||0.0528|||0.0505|0.0518||0.052|0.0371|
|||<0.01|||<0.001|<0.001||0.0003|0.7721|
|Receptive vocabulary in Luo (_z-_|||0.3961*||0.3058*||0.3191*|0.2592*||
|score)|||**||**||**|**|0.0217|
||||0.0571||0.0579||0.0542|0.0552|0.0386|
||||<0.01||<0.001||<0.001|<0.001|0.5748|
|Expressive vocabulary (_z-_||||0.7385*|||||0.7084*|
|score)||||**|||||**|
|||||0.0409|||||0.0564|
|||||<0.001|||||<0.001|
|Constant|–0.0176|–0.0092|–0.0098|–0.0019|–0.0098|–0.0156|–0.0154|–0.0144|–0.0032|
||0.0472|0.0457|0.0461|0.0314|0.0439|0.0441|0.0447|0.0431|0.0316|
||0.7094|0.8405|0.8327|0.9529|0.823|0.7242|0.731|0.7387|0.9205|
|_R_-squared|0.1115|0.1239|0.1603|0.5538|0.2042|0.1767|0.2024|0.2306|0.5558|
|N. of cases|442|442|442|442|442|442|442|442|442|

Notes. Receptive English, Swahili, and Luo vocabulary scores are age-adjusted _z-_ scores for children ages 2 to 6 years, measured using separate assessments based on the British Picture Vocabulary Scale (BPVS). Expressive vocabulary _z-_ scores were measured using a tool developed from the PPVT. Baseline and follow-up were conducted approximately 5 weeks apart. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01, **** _p_ < 0.001.
Supplemental Table 7. Relationships between baseline receptive vocabulary and follow-up English receptive vocabulary among children of literate and illiterate caregivers

||e1|e2|e3|e4|e5|e6|
|---|---|---|---|---|---|---|
||b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|
||||Luo li|teracy|English|literacy|
|Sample (above/below median literacy):|Both|Both|Below|above|Below|above|
|Receptive vocabulary in Luo (_z-_score)|0.1812***|0.1788***|0.1412*|0.1925***|0.1511**|0.1932***|
||0.0463|0.046|0.083|0.0547|0.0732|0.0592|
||0.0001|0.0001|0.0915|0.0005|0.0411|0.0013|
|Receptive vocabulary in English (_z-_score)|0.2593***|0.2515***|0.3113***|0.2288***|0.3391***|0.2128***|
||0.0485|0.0488|0.0914|0.0562|0.0802|0.0594|
||<0.001|<0.001|0.0009|0.0001|<0.001|0.0004|
|Receptive vocabulary in Swahili (_z-_score)|0.1034**|0.0998**|0.0564|0.1316**|0.052|0.1336**|
||0.048|0.0481|0.0764|0.0613|0.0698|0.066|
||0.0318|0.0386|0.4623|0.0331|0.4576|0.0446|
|Caregiver literacy in Luo||0.0639|||||
|||0.0429|||||
|||0.1376|||||
|Caregiver literacy in English||–0.018|||||
|||0.0546|||||
|||0.7416|||||
|Constant|–0.0099|–0.1825**|–0.1697**|0.0801|–0.0986|0.0549|
||0.045|0.0776|0.0729|0.0568|0.0681|0.0594|
||0.8267|0.0193|0.0218|0.1605|0.1501|0.3569|
|_R-_squared|0.1821|0.1951|0.1833|0.1796|0.2149|0.1656|
|N. of cases|442|442|158|284|187|255|

Notes. Receptive English, Swahili, and Luo vocabulary scores are age-adjusted _z-_ scores measured using separate assessments based on the British Picture Vocabulary Scale (BPVS). Baseline and follow-up were conducted approximately 5 weeks apart. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01.
MULTILINGUAL CHILD ASSESSMENT

# **Appendix A: Receptive Vocabulary Test Translation and Adaptation**

To create the receptive vocabulary assessments, we first asked local staff, fluent in all languages, to translate and back-translate the first 106 items of the British Picture Vocabulary Scale – III (BPVS III; Dunn, Dunn, & Styles, 2009) into Luo and Swahili. The team also evaluated the translated words (for both languages) across various criteria, including (a) does an easily translated, corresponding word exist? (b) is this word commonly known or used? (c) what is the estimated difficulty of this word for young children? (d) is this word common and the same for both rural and urban populations? (e) is the picture representing the stimulus word culturally appropriate and familiar? (f) are the distractor pictures culturally appropriate and familiar? (g) if the stimulus word or picture is not appropriate, which other picture in the plate could be substituted?

This process produced the following results: for 9 of the stimulus words, the translation to Luo and Swahili resulted in the same word (for example, “money” is referred to as “pesa” in both languages; “airplane” is “ndege” in both languages); 26 words had no commonly known directly translated equivalent in Luo among adult speakers interviewed; 12 words had no equivalent in Swahili, and 11 had no translation in either Luo or Swahili; 13 Luo and 10 Swahili translations were not single words, but were phrases, or words with a qualifier (e.g., “gigantic” was translated as “very big” in Swahili); 5 Swahili and four Luo words were appropriated from English, and were identical or nearly identical to the English word; and for 12 stimulus items, the words were suitable for piloting, but the pictures were inappropriate or unfamiliar. Eight plates had words that were more likely to be known in English and were not translated. Luo was the most difficult language to work with, as it had the most limitations in translating the stimulus words, so we created this list first. To increase our capacity to select the best words possible, we (PK, OO) engaged a focus group of 6 Luo-speaking mothers and teachers to review 32 words for which we either had no suitable translation, or that were candidate items not part of the original BPVS III.

We piloted 58 items with about 30 Luo-speaking children 2-6 years of age, and examined pass rates for each word by age group (younger or older than five years). Words were then grouped by estimated difficulty level (hard, average or easy), and we sought to have roughly the same number of items at each of the three difficulty levels. As we lacked a sufficient number of Luo words across the three categories, we created some new stimulus words based on results from the focus groups (lantana, bull, roar), providing our own plates of pictures for each. The final Luo test includes 27 items, with approximately 9 words in each difficulty level. Of these 27 items, three were new words and pictures, three involved translations including two words (no one-word translation was known), one used a distractor picture to replace the original stimulus picture (“boulder” replaced “mountain”), one replaced a plate with more familiar looking pictures, and one slightly changed the stimulus word (from “applauding” to “clapping,” as our translators were aware of no distinct word for “applauding.”).

We repeated a similar process for creating tests in Swahili (32 items) and English (34 items), each with roughly one-third of items in the three difficulty categories. The Swahili test included two items altered by changing the stimulus word to a distractor picture, and one item with the stimulus word slightly altered (from “sawing” to “cutting”). Nine items were changed for the English test. Five stimulus words were changed to better reflect the English words used for the stimulus picture (e.g., “zipper” was changed to “zip;” “sedan” was changed to “saloon car”); three distractor pictures replaced original stimulus items; and a new item was introduced,
MULTILINGUAL CHILD ASSESSMENT using an existing plate (“thumb”). With our final set of words we tabulated the rate of correct responses for each item, then sorted the items in descending order by the rate of correct responses.

Based on data collected from the full study population, we used item response theory to assess the content validity of the receptive vocabulary measures. This analysis allowed us to understand the relative difficulty and discrimination of the items, and the equivalency of the receptive vocabulary assessments across the three languages. Of five hundred subjects, 71 reached the last (most difficult) Swahili receptive words; 36 reached the most difficult English receptive words; 126 reached the most difficult Luo receptive words. ; 38 reached the most difficult expressive items The intersection of these four groups was unsurprisingly small: four respondents. The maximum item difficulty was higher for the English test, and the English test had more difficult items (3 items > 4) than the Luo test (no items greater than 3), although the estimated discrimination for all items was lower (0.78 and 1.32 respectively) ( **Appendix A, Table 1)** . The Swahili test had more items of higher difficulty than the Luo test (5 items >4), two of which had very high estimated difficulty (floating, 30.60 and blue, 707.01). The Swahili test also had greater overall discrimination than the English test, at 1.22. A two-parameter model would not converge for the English receptive vocabulary, so only the estimated item difficulty is presented. Likelihood ratio tests for the Swahili and Luo receptive vocabulary tests indicated that for both languages, the two-parameter model that allowed discrimination to vary by item was a better fit (p<0.001).
MULTILINGUAL CHILD ASSESSMENT Appendix A Table 1. Receptive Vocabulary Item Difficulty And Discrimination Using IRT (N=505)

||English||Sw|ahili|||Luo||
|---|---|---|---|---|---|---|---|---|
|BPVS plate #||Diff|Item (BPVS plate #)|Diff|Discrim|Item (BPVS plate #)|<br>Diff|Discrim|
|#30||-1.045|kuku (chicken)|-1.202|1.484|guok (dog)|-1.774|2.754|
|#8||-1.454|mpira (#1)|-0.918|2.445|atudo (#2)|-1.143|1.384|
|#9||-1.097|kijiko (#5)|-1.425|2.891|dhok (#7)|-0.289|2.219|
|#6||-0.817|pesa (#12)|-0.918|1.876|rwakruok (#28)|0.121|1.828|
|#17||1.015|kuruka (#4)|0.484|1.365|ma duong (#42)|-0.283|1.134|
|talon||1.177|kunywa (#7)|0.415|2.451|pamo (#99)|0.206|1.323|
|#51||1.629|ndege (#15)|-0.807|2.316|opuk (#16)<br>maonge gimoro|-0.187|1.728|
|#53||0.642|mshipi (#17)|-0.222|1.090|(#21)|0.047|1.804|
|waterfall||2.894|kukata (#41)|-0.410|1.075|chiel (#22)|0.670|0.638|
|#34||1.110|rarua (#61)|0.806|1.166|bwok (#59)|-0.068|1.092|
|#74||1.380|mkulima (#22)|1.714|0.538|ng'ur (#104)|1.266|0.786|
|camel||0.388|chemchemi (#38)|0.936|0.899|turubin (#54)<br>chikruok katolo|3.122|0.387|
|#71||2.604|kuogelea (#11)|1.774|0.602|(#31)|0.641|1.809|
|#23||0.702|kupima (#58)|1.219|1.098|frimbi (#27)|0.600|1.049|
|#47||1.453|salamu (#51)|1.044|1.314|jatedo (#65)|0.884|1.284|
|#48||1.212|ya kugwaruza (#63)|1.109|0.827|pogo (#43)|0.952|1.224|
|#75||3.436|kuelea (#49)|30.599|0.028|mor (#24)|0.641|1.171|
|#94||1.174|moto (#14)|-0.112|0.451|ritho (#92)|0.478|2.699|
|#78||2.335|ngome (#24)|2.479|0.496|osigo (#98)|0.554|2.779|
|#118||0.940|unganisha (#86)|3.182|0.820|od kich (#84)|0.806|1.200|
|#19||3.145|<br>mbavu (#76)|8.685|0.177|lwanda (#32)|-1.250|-0.751|
|#55||3.856|ramani (#56)|-5.889|-0.151|lengo (#50)|2.347|0.779|
|#79||2.353|daraja (#23)|0.405|0.336|ruath (bull)|2.651|0.441|
|#69||2.935|samawati (#10)|707.045|0.002|yie (#83)|1.421|1.767|
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|#97|4.408|manyoya (#46)|4.680|0.527|orido (#49)|1.828|1.660|
|---|---|---|---|---|---|---|---|
|#109|3.055|kiota (#29)|1.154|-0.056|nyabende (lantana)|0.484|0.946|
|#57|4.408|ndoano (#33)|4.991|0.438|okumbo (#40)|1.356|2.034|
|#68|1.393|fisi (#82)|3.600|0.328||||
|#62|3.909|mstatili (#36)|2.049|0.908||||
|#116|3.909|tawi (#39)|3.277|0.624||||
|#35|1.937|kisiwa (#73)|3.171|0.657||||
|#44|4.319|||||||
|#70|3.579|||||||
|#111|3.300|||||||
|Overall Discrim|0.790|||1.215|||1.321|
|Model 1<2:|n.a.|||p<0.001|||p<0.001|

Note. Estimated using IRT one parameter and two parameter logistic models. The first parameter is item difficulty, and the second is item discrimination. One parameter models estimate an overall discrimination that is held constant for each item. Two parameter models allow the item discrimination to vary across items. We used a likelihood ratio test to compare the two models to determine the model with best fit. A two-parameter model would not converge for the English receptive vocabulary, so only item difficulties were estimated.
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# **Appendix B: Expressive Vocabulary Test Creation**

To create the expressive vocabulary measure, we began with images from pieces of the BPVS III plates, local storybook illustrations used in a related project, as well as other simple drawings, we presented up to 200 individual pictures to 61 children 2 to 6 years of age, asking them to name the object or concept the picture showed. We recorded responses in the language (English, Swahili or Luo) children used.

We then reviewed pass rates (in any language) by age (younger or older than 5 years) for each picture, and discarded words with no clear response (e.g., multiple responses for any picture). We again grouped words as hard, average or easy, based on response by age. This resulted in 20 items (scores range from 0-20), and children received credit for responding in any of the three languages. Four items overlapped with those occurring in the receptive tests (two words on the Swahili list, and two on the English list). The rest were a mix of distractors in the BPVS, and stimulus words. A local artist then created all 20 original drawings used in our assessment, displayed below. A child could respond in any of the three languages to score a pass for expressing the word verbally. During instrument development and pretesting, we worked with our field team to identify words that were similar across languages, and understand how they would be differentiated. English and Swahili are not a tonal languages, while Luo is. Thus, for all similar words, the field team agreed on the differences in their pronunciation that would identify a child’s response in a given language.

The full expressive vocabulary assessment is provided below, with the intention of making the tool freely available for use for research purposes only. The tool is not meant for, and should not be used for diagnostic purposes. We did not establish any norms. It is also not intended for use as an instructional aid. The images were printed on flashcards, single sided, and with one image per card. Children were asked in their preferred language, “What is this?” for each item. Children were not instructed as to which language to respond in, but responses in any language were accepted. We did not provide further instruction because code-switching during conversation is very common in this area, and very young children may not be aware which language they are actually speaking for a given word. Thus, a child could respond to each item in the 20-item test in English, Luo, or Swahili to score a pass for expressing the word verbally. Administration ended with three consecutive fails after the first 10 items.

<u>Appendix B Table 1: Expressive Vocabulary Assessment</u>

|Item|Stimulus Image|Stimulus Word||Accepted Respon|ses|
|---|---|---|---|---|---|
||Practice Items||English|Swahili|Luo|
|1||Shoe|Shoe|Kiatu<br>Viatu|Wuoche|
|2||Cow|Cow|Ng’ombe|Ruath<br>Dwasi<br>Dhiang|
|3||Frog|Frog|Chura|Ogwal|
<!-- Start of picture text --> ese<br><!-- End of picture text -->
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|14|Circle|Circle|Mviringo|Sako|
|---|---|---|---|---|
|15|Zipper|Zipper|Zippi<br>Zip|Ring’|
|16|Hippo|Hippo|Kiboko|Rao|
|17|Hyena|Hyena|Fisi|Ondiek<br>Otoyo|
|18|Piano|Piano|Kinanda|Kinanda|
|19|Warthog|Warthog|Ngiri|Njiri|
|20|Wrench|Wrench<br>Spanner|Spana|Spana|
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<!-- Start of picture text --> i we<br>i ~ Vala<br>VS<br><!-- End of picture text -->
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# i
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<!-- Start of picture text --> is, fe<br>es ~~<br><h<br>h<br>Ne |<br>|<br>1<br>ISS<br>am<br>(ap<br><!-- End of picture text -->
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# **Appendix C: MDAT Expansion**

The adaptation of the MDAT as used in the current study involved multiple steps, drawing from experiences of the study authors (PK, LCHF) using the subscales in other studies and contexts (e.g., author citation redacted). First, we added some items piloted and recommended by the first author of the MDAT (M. Gladstone, personal communication) that had shown good variability in pass rates by age, but were excluded from the final MDAT because they did not meet criteria for inclusion (i.e., they were not passed by 100% of children at the highest age band). Next, we added items that assessed behaviors and knowledge considered important for preparing children for primary school (learning letters, numbers, cardinality, writing letters). W <mark>e sought to increase the difficulty level of the fine motor/perception subscale by adding Object-based Pattern Reasoning Assessment (OPRA) items from the Zambia Early Child Development Scale (Zuilkowski et al., 2016). The concept of the OPRA is similar to twodimensional pattern reasoning tests, such as the Ravens Progressive Matrices, but differs in that rather than using pictures, the test uses familiar materials (beans, beads, bottle tops) to make patterns. We added 6 items from the OPRA: two easy items (completing a pattern with one object missing, all of the other objects the same), two medium difficulty items (completing a pattern with two alternating objects), and two hard items (completing a pattern with three alternating objects).</mark> Adaptation of the language subscale included piloting objects used for testing receptive and expressive vocabulary, in order to produce a set of objects that were familiar in the local context. The number of objects presented to the child was increased from 10 to 14, and these included objects easily known by young children (torch, soap) and those that were more challenging (sieve, wick). <mark>Finally, as our sample included children 24-35 months of age, we added items from the original MDAT appropriate for testing children at the lower age range.</mark> After some adjustments to administration and scoring, the language subscale showed good variability in scores across the age span. We then piloted the fine motor/perception and language subscales with 112 children 36-71 months of age in a nearby, non-study area in Kenya. We excluded items passed by all children during piloting and dropped a few items with the same difficulty level of other items to reduce the overall length of the subscales. The final adapted test had 21 fine motor/perception items and 26 language items, with start and stop rules to reduce the number of items administered (see **Table 1** ).

Appendix C Table 1: Comparison of original MDAT Language items with Western Kenya <u>MDAT</u>

|Item|Fine Motor/Perception|Item|Western Kenya MDAT|
|---|---|---|---|
|1|Follows mother’s or guardian’s<br>face/object to the midline||Not applicable|
|2|Follows object or fixes and follows<br>on face or bright object (red<br>pompom) with eyes through 180<br>degrees.||Not applicable|
|3|Puts hands together/awareness of<br>hands/puts in front of eyes/mouth||Not applicable|
|4|Reaches out for a large thing eg.<br>Rattle or red yarn||Not applicable|
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|5|When holding objects, tends to put<br>them in mouth||Not applicable|
|---|---|---|---|
|6|Grasps hold of a large thing e.g.<br>Handle of the rattle or plastic spoon||Not applicable|
|7|Can pick up a larger object from the<br>ground||Not applicable|
|8|Can see a small object such as a<br>piece of maize or a bean||Not applicable|
|9|<br>Transfers objects from one hand to<br>another hand||Not applicable|
|10|Picks up small things with all four<br>fingers in a RAKING fashion||Not applicable|
|11|Strikes on object with another in<br>imitation with the examiner||Not applicable|
|12|Finds object under the cloth||Not applicable|
|13|Neat pincer grasp, picks up maize or<br>bean with thumb and one finger|START<br><36<br>MONTHS|Neat pincer grasp, picks up maize or<br>bean with thumb and one finger|
|14|Puts blocks in and out of cup in<br>imitation|14|Puts blocks in and out of cup in<br>imitation|
|15|Pushes a little car along|15|DELETED|
|16|Puts blocks into bottle in imitation|16|Puts blocks into bottle in imitation|
|17|Dumps blocks out of bottle<br>purposefully|17|Dumps blocks out of bottle<br>purposefully|
||Screws jar lid on and off|18|Screws jar lid on and off|
|18|Scribbles on paper (straight<br>scribble)|25||
|19|Scribbles on paper (circular<br>scribble)|START<br>36-59<br>MONTHS||
|20|Tower of 2 blocks|21/24|Puts pegs into board in up to 2<br>minutes/30 SECONDS|
|21|Puts pegs into board in up to 2<br>minutes|NEW|Makes train with up to 5 blocks|
|22|Tower of 4 blocks|START<br>60-71<br>MONTHS||
|23|Tower of 6 blocks|20, 22, 24|Builds tower with up to 6 blocks|
|24|Puts pegs into board in up to 30 secs|<br>30|<br>Can make a bridge with 3<br>blocks**FOR ALL: SKIP TO<br>HEAVIEST BOX IF FAIL**|
|25|Unscrews and screws back on the<br>cap of the Chiponde bottle|NEW|Can make a bridge with 6 blocks|
|26|<br>Can put 6 hair beads on to a shoe<br>lace (thread themon)|NEW|Can make stairs with 6 blocks|
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|27|Copies a vertical line ( as drawn by<br>the examiner) with charcoal/chalk<br>within 30 degrees|29|Picks heaviest box|
|---|---|---|---|
|28|Picks longest stick 3 of 3|18/19|Scribbles in any way|
|29|Picks heaviest box 3 of 3 – is the<br>child able to tell you which box is<br>the heaviest?||**IF >=36 MONTHS AND FAILS:<br>SKIP TO FOLDS **|
|30|Can make a bridge with bricks:||**END IF FAILS SMALL BRIDGE,<br>HEAVIEST BOX AND<br>SCRIBBLES**|
|31|Makes a doll or complicated car out<br>of clay|27|Copies a vertical line within about 30<br>degrees|
|32|Copies a circle (needs to be<br>complete) with chalk or in the sand<br>with a stick|34|Copies a circle|
|33|Copies a cross with chalk|33|Copies a cross|
|34|Can draw a square||**END IF FAILS LINE, CIRCLE,<br>CROSS**|
|35||NEW|Can color within lines|
|36||34|Copies a square|
|37||NEW|Can copy letters**E C A M J H**|
|38||NEW|Can fold paper into quarters|
|39|||**END IF FAILS 3 OF ABOVE<br>(COLORING, SQUARE, WRITING<br>4 LETTERS AND FOLDING)**|
|40||NEW|Can copy a pattern of 4 bottle tops<br>**END IF FAILS 4 BOTTLE<br>TOPS**|
|41||NEW|Can copy a pattern of 6 bottle tops<br>**END IF FAILS 6 BOTTLE<br>TOPS**|
|42||NEW|Beans and beads|
|43||43||

<u>Table 2: Comparison of original MDAT Language items with Western Kenya MDAT</u>

|Item|Language/Hearing|Item|Western Kenya MDAT|
|---|---|---|---|
||Startles or jumps/responds to<br>sounds|||
|**1**|Happy vocalising or making<br>sounds–not crying|||
|2|Laughs/chuckles|||
|3|Turns to voice – if you are out of<br>sight, does she/he look in the<br>directionofyourvoice orsound|||
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|4|Uses single syllables or sounds, for<br>example Ma, Pa, Da, Ba|||
|---|---|---|---|
|5|Responds to his or her name, turns<br>and looks at you|||
|6|Uses 2/4 syllable babble such as<br>dada, mama, mimi, tata, but not<br>specifically at anything or any<br>person|||
|7|Understands when being cautioned<br>about danger, for example when<br>saying “no” to child, they stop even<br>briefly|||
|8|Indicates by gesture to say “No”|START <36<br>MONTHS STOP<br>RULE: IF 3 OF<br>LAST FIVE<br>ADMINISTERED<br>ARE FAILED,<br>STOP|Child shakes head or uses<br>other gesture when means<br>“no” (ask parent/caregiver if<br>not observed)|
|9|Follows simple commands (1<br>stage)|10|Child jabbers: makes sentence-<br>like utterances, even if cannot<br>use all real words (ask<br>parent/caregiver if not<br>observed). If child already<br>speaks in sentences at least<br>some of the time, score YES.|
|10|Unclear talk/jabber in sentences|11, 13|Child uses**6 or more** words<br>(ask parent/caregiver if not<br>observed)|
|11|Says 2 words, but words other than<br>mama/dada|12|Child uses 2 or more words<br>together to form some type of<br>phrase with subject/object verb<br>(“Mama go”) (ask<br>parent/caregiver if not<br>observed)|
|12|Says 2 words together|16|Child speaks in sentences at<br>least some of the time (ask<br>parent/caregiver if not<br>observed)|
|13|Says 6 words (words other than<br>mama/dada)|9|Child follows one stage<br>command. Can do up to 2<br>times (differential scoring<br>based on which trial passed:<br>2=passed first time, 1=passed<br>second time, 0=failed all<br>times)|
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|14|Follows 2 stage commands||START 36-59 MONTHS<br>STOP RULE: IF 3 OF LAST<br>FIVE ADMINISTERED ARE<br>FAILED, STOP|
|---|---|---|---|
|15|Identifies objects in the basket – at<br>least 5|19|Can tell you first name or<br>nickname – how child is<br>known (can be observed<br>incidentally or if not observed,<br>ask child to tell you name)<br>This will be asked at<br>beginning of FM and if fails,<br>asked again here.|
|16|Speaks clearly in sentences|17|Can point to 5 or more body<br>parts (YES/NO) Record all<br>pointed to. Head, toes, tongue,<br>hard. Move nose eyes ears or<br>mouth (1) to language test –<br>eyes hair and hand to language<br>test with cup pencil and comb.|
|17|Points to body parts >1|14|Follows 2 stage command.<br>Can do up to 2 times<br>(differential scoring based on<br>which trial passed: 2=passed<br>first time, 1=passed second<br>time, 0=failed all times)|
|18|Names 5 objects in the basket|18|Can**name** 5 or more objects<br>you point to if under 36<br>months. Otherwise, must name<br>10 or more objects you point<br>to to pass. Record all named|
|19|Knows his or her first name|15|Can**identify** (point to) 5 or<br>more objects you name if<br>under 36 months. Otherwise,<br>must identify 10 or more<br>objects you point to to pass.<br>Record all identified|
|20|Knows actions of objects||START 60-71 MONTHS<br>STOP RULE: IF 3 OF LAST<br>FIVE ADMINISTERED ARE<br>FAILED, STOP|
|21|Identifies objects – 10|20|<br>Can**point** to 3 or more objects<br>linked to action (“Which one<br>is for sweeping?”) Record all<br>identified|
|22|Names (can say it) 10 objects|25|Can**tell** you what 3 or more<br>objects are used for (“For|
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||||washing”) Record all verbal<br>descriptions|
|---|---|---|---|
|23|Is able to categorise things|23|Can list4+ foods Record all<br>foods named OR|
|24|Is able to follow a three stage<br>command|23|Can list4+ animals Record all<br>animals named|
|25|Is able to tell you the use of objects|24|Can perform a 3-stage<br>command Can do 1 time.<br>Child must do correctly to<br>PASS|
|26|Can do remember back 2 syllables|27|Can answer 2 or more<br>questions about what to do in<br>certain situations (hungry,<br>tired, cold)|
|27|Knows 2 of 3 questions relating to<br>the understanding certain concepts|28|Correctly answers questions<br>about BOTH adjectives<br>(faster, bigger)|
|28|Understands the adjectives such as<br>“faster”|30|Knows 3 OR MORE<br>prepositions and follows tasks<br>related to this (under, on,<br>between, etc.)|
|29|Can do remember back 4 syllables|31|Understands and passes 2 OR<br>MORE concepts of opposites|
|30|Can understands prepositions|32/33|Can count to 5 correctly|
|31|Understands the concept of<br>opposites|NEW|Can count to 10 correctly|
|32|Knows quantities – up to 3|NEW|Can create sets of objects (1, 3<br>or 5) PASS IF CHILD CAN<br>DO 2 OF THESE|
|33|Knows quantities–up to 5|NEW|Can name 2 colors|
|34||NEW|Can recognize and name 3 or<br>more written letters in first<br>name|
|35||NEW|Knows age|
|36||NEW|Knows where they currently<br>live|