# A Meta-Analysis of the Correlation Between Socioeconomic Status and Direct Measures of Young Children's Word Comprehension

Camila Scaff, Joseph Coffey, Alejandrina Cristia

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Camila Scaff, Joseph Coffey, Alejandrina Cristia. A Meta-Analysis of the Correlation Between Socioeconomic Status and Direct Measures of Young Children's Word Comprehension. Infancy, 2025, 30 (4), pp.e70035. 10.1111/infa.70035. hal-05533502

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A meta-analysis of the correlation between socioeconomic status and direct measures of
young children's word comprehension

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Camila SCAFF<sup>2,1</sup>, Joseph R. COFFEY<sup>2</sup>, & Alejandrina CRISTIA<sup>2</sup>

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<sup>1</sup> Human Ecology Group, Institute of Evolutionary Medicine, University of Zurich

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<sup>2</sup> Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'études cognitives, ENS, EHESS, CNRS, PSL University

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Funding for this study came from the Agence Nationale pour la Recherche
[ANR-16-DATA-0004 ACLEW, ANR-14-CE30-0003 MechELex]. AC also acknowledges J.
S. McDonnell Foundation Understanding Human Cognition Scholar Award and the
European Research Council (ERC) under the European Union's Horizon 2020 research and
innovation programme (ExELang, Grant agreement No. 101001095). We wish to thank
Anouck Lassis for help in screening and entering data; and Newton Ingati, at Upwork.com,
for his careful data entry as our secondary coder. The data and code underlying this study
are openly available in OSF at
https://osf.io/wa4pg/?view_only=57fcf238a13542ce9cf341806025a972. The authors have
no conflict of interest to disclose. No ethics approval is required for the present
meta-analysis, and no materials have been reproduced.

Correspondence concerning this article should be addressed to Camila SCAFF, LSCP
29 rue d'Ulm, Paris, France. E-mail: camillescaff@gmail.com
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Abstract

What can we learn from the empirical body of literature on the association between socioeconomic status (SES) and children's word comprehension? To answer this question, a systematic review was carried out on studies in which samples of children varying in SES were directly assessed on their word comprehension abilities. The final analysis included 83 effect sizes from 40 independent samples, representing data from 8,211 children between the ages of 15 and 46 months. Overall, the body of literature appeared healthy, with no significant evidence of reporting or publication biases, although studies were underpowered to detect the weighted average effect size, which was z-transformed *r* = 0.23 (95% CI [0.17,0.3]) over the whole included sample, and z-transformed *r* = 0.15 (95% CI [0.02,0.28]) at the youngest age. Exploratory analyses suggested that the SES-word comprehension correlation strengthened with child age; and that touch-based measures led to smaller SES-word comprehension associations than both looking- and pointing-based measures. We discuss these findings to inform future empirical, theoretical, and applied research.

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*Keywords:* socioeconomic status, income, education, word comprehension, meta-analysis

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Word count: 9,960 words
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A meta-analysis of the correlation between socioeconomic status and direct measures of
young children's word comprehension

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Introduction

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For decades, researchers have studied the extent to which there is structured and meaningful variation in the development of word comprehension, including some that may be attributable to parental socioeconomic status (SES, Fenson et al., 1994; Hart & Risley, 1992, 1995; Walker, Greenwood, Hart, & Carta, 1994). This paper presents a meta-analytic review of the empirical literature bearing on the association between familial SES and preschool word comprehension.

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The American Psychological Association defines SES as “the social standing or class of an individual or group,” generally reflecting household economic and social resources (Saegert et al., 2007). A vast body of evidence demonstrates meaningful group differences in language and cognitive abilities as a function of SES during infancy and childhood, a period characterized by rapid language, motor and cognitive growth (see Weisleder & Marchman, 2018 for a review). Importantly, the association between language and SES appears stronger than that for other neurocognitive systems (Weisleder & Marchman, 2018). Differences as a function of SES in language development have been observed as early as 18 months, and these early disparities can establish developmental trajectories that persist into later childhood (Ferrer et al., 2015) and which have been linked to academic achievement (Hemphill & Tivnan, 2008).

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**Significance of the study.** Understanding when and how these disparities emerge is essential for designing effective interventions. Several hypotheses have been proposed to explain how SES differences shape children's language development. Two dominant models are the *family stress model* and the *family investment model* (e.g., Linver, Brooks-Gunn, & Kohen, 2002; Weisleder & Marchman, 2018). The family stress model suggests that poverty impacts children's development primarily through its effects on parental mental
health (e.g., parental stress and/or depression). The family investment model emphasizes the role of material resources, such as financial and educational opportunities, that support cognitive development. These pathways are thought to influence the quality of caregiver-child communicative interactions, with consequences for children's language outcomes. Notably, approximately 30% of early childhood interventions target language and literacy skills, highlighting the persistent interest in and importance of understanding child language outcomes (Draper et al., 2024; Pace, Alper, Burchinal, Golinkoff, & Hirsh-Pasek, 2019)). It is therefore important to rest such efforts on previous empirical literature, a gap we seek to fill in this analysis.

Systematic reviews and meta-analyses have become key tools in infancy research (e.g., Bergmann et al., 2018), helping to synthesize findings, guide interventions, and identify issues such as systematic gaps or biases. In the domains of cognitive development broadly and linguistic development specifically, at least four meta-analyses looking at the association between childhood language skills and SES have been conducted in recent years, varying in scope and intention. Below, we provide a brief overview of these meta-analyses, with detailed summaries available in supplementary materials (SM1). The meta-analyses by Letourneau, Duffett-Leger, Levac, Watson, and Young-Morris (2011), E. L. Fisher (2017), Holzinger et al. (2020), and Wilding, Brown, and Stumm (2023) reviewed between 7 and 12 independent samples, with sample sizes ranging from 517 to 127,164 participants aged 1.5 to 17 years. They consistently reported small to moderate associations between SES and language outcomes (r ~ .11 to .28), with Wilding et al. (2023) reporting the strongest correlation (r = .28, 95% CI [.23, .32]) in a sample of 130,340 UK monolingual children aged 2–5 years. In addition, none of the four meta-analyses presented above report on power, with only two assessing potential publication bias.

Looking more broadly, Korous, Causadias, Bradley, Luthar, and Levy (2022) examined 14 meta-analyses published between 1982 and 2019 on the relationship between SES and cognitive abilities E. L. Fisher (2017). They highlighted significant limitations in
those 14 meta-analyses: namely, they noted that combining SES components into an overall composite can obscure specific associations and pathways through which SES exerts its effects and emphasized the little attention paid to key moderators that could influence the strength of the observed associations. Particularly, variables like age, sex, race/ethnicity, and geographic location of samples further limit the generalizability and applicability of the findings, a necessary foundation for investigating pathways through which SES influences language development (Linver et al., 2002; as in Weisleder & Marchman, 2018). Additional reviews converge on this critique. A series of recent systematic and meta-analytic reviews of interventions related to language disparities (Finestack et al., 2022; e.g., Walker & Carta, 2020) highlight persistent gaps in the representativeness of research samples; particularly from lower-SES and ethnically diverse families, adaptations to local contexts, and non-English learning backgrounds (Greenwood, Schnitz, Carta, Wallisch, & Irvin, 2020; Heidlage et al., 2020; Larson et al., 2020; Walker & Carta, 2020). Some further emphasize the limited number of language interventions with low-income families focused on infants and toddlers (Greenwood et al., 2020; Walker & Carta, 2020).

Our analysis targets studies of children under four years to better home in on this critical development period, using direct measures of word comprehension, which is a measure of language ability particularly appropriate for this age group. In an attempt to address the limitations highlighted by Korous et al. (2022), we examine potential moderators and sample diversity to assess generalizability. To provide a clearer and more actionable understanding of the SES-language relationship during early childhood, we follow Bergmann et al. (2018)'s recommendation to pay particular attention to potential issues affecting the body of literature, such as publication bias, which could raise concerns about the validity of reported effects.

Potential moderators of the strength of association between SES and infant word comprehension.
**Age.** In the 1-4 years age range studied here, shifts in the association between SES and word comprehension link may reveal differential group-level developmental trajectories. Three possibilities arise. First, we may observe narrowing SES differences over time, for instance, through developmental canalization or as a function of the accumulation of other experiences. However, little evidence supports this(Garc??a & Weiss, 2017). Second, the SES-comprehension link may remain stable, aligning with theories that early-life factors, such as prenatal stress shaping language outcomes (Lautarescu, Craig, & Glover, 2020). This is consistent with a UK meta-analysis showing stable SES-language associations from ages two to five (Wilding et al., 2023). Third, SES disparities may widen with age, as seen in schooling studies (Raudenbush, 2008; but see Raudenbush & Eschmann, 2015).
Cumulative effects could create a positive feedback loop: higher-SES children experience richer linguistic environments, accelerating language processing and word learning (Hurtado, Marchman, & Fernald, 2008). However, increased disparities might also reflect improved measurement reliability with age (Bergmann et al., 2018).

**Task Type.** The methods used to measure word comprehension may influence observed SES effects. Bergmann et al. (2018)'s meta-meta-analysis comparing different experimental methods for children under 5 years old found significant differences in effect sizes among the different techniques, highlighting the importance of task choice in language development studies. Additionally, the type of language assessment used was found to significantly moderate the SES effect in Wilding et al. (2023)'s study of UK children summarized above. To investigate the potential impact of different measurement approaches on the association between socioeconomic status (SES) and word comprehension, we closely examine task types that directly elicit child behavior, such as looking, pointing, or touching tasks. Specifically, we compare three main tasks used in previous studies: looking-while-listening (LWL), pointing vocabulary tests (PVT), and touch-based vocabulary tests (TVT). Although we do not know of studies testing this, the three tasks could vary in the extent to which they depend on executive functions, which
have been found to correlate with familial SES (Lawson, Hook, & Farah, 2018).

Additionally, it has been speculated that different measures of the same task (such as accuracy and response times) might reflect on distinct cognitive processes (Scaff, Fibla, & Cristia, 2022; Smolak, Hendrickson, Zesiger, Poulin-Dubois, & Friend, 2021). It should be noted from the outset, however, that the tasks are not used indiscriminately with children of all ages, so there is some level of confounding between age and task type.

The first task is the “looking while listening” (LWL) procedure, which measures visual attention without requiring an explicit choice. In SES studies using this method (e.g., Fernald, Marchman, & Weisleder, 2013), children see two pictures on a screen while a voice names one. Accuracy is the proportion of time over a fixed window (e.g., 2000 msec) in which the child looks at the correct (or named) picture out of the total time spent looking at either picture. Reaction time is the latency from target word onset in which the child shifts from the not-named to the named picture.

The second group, pointing vocabulary tests (PVT), includes tasks like the Peabody Picture Vocabulary Test. Here, children see four images and must “point to X”, where X is a word spoken by the experimenter and which best corresponds to one of the images. If the child points to the right image, the trial is considered correct; if they point to any of the other three, this results in an error being computed. The test continues until there are no more trials or the child incurs four consecutive errors. Scores thus partially reflect trials completed. The scores are often used to infer vocabulary and language skills, and sometimes IQ (e.g. in our meta-analysis, Fergusson, Beautrais, & Silva, 1982; Robertson & Finer, 1985). PVT likely imposes more task demands than other tests due to increasing difficulty, competition effects, and repetitive structure, which may highlight differences in attention and concentration. Additionally, higher-SES households, where joint book reading is more common, may better prepare children for such tasks. Thus, we expect the strongest SES effects in PVT.
The third task, touch-based vocabulary tests (TVT), requires children to select the correct image on a touchscreen. The most common version (Friend & Keplinger, 2008) uses a large screen with live prompts, while newer adaptations (Rosemberg & Alam, 2021; Scaff et al., 2022) introduce self-paced gameplay and additional linguistic elements (Levine et al., 2020). Unlike PVT, TVT reduces competition by presenting only two images and reinforcing touch responses.

**SES type.** SES is often operationalized as a composite of parental education, income, and occupation, but these components may have distinct effects on language outcomes. For example, parental education correlates strongly with linguistic input and cognitive stimulation, while household income may reflect access to material resources (e.g., books, access to childcare) but could vary depending on the broader socioeconomic context (e.g., national social safety nets). In previous work, SES is estimated using single measures such as annual income or maternal education, as well as composites (Hollingshead, 1975; Nakao & Treas, 1994). While these different SES indicators are often correlated ($r$>.8, Cirino et al., 2002; Rowland, Alcock, & Meints, 2022), Korous et al. (2022) argue that the distinct components of SES (e.g., income, education, occupation) may differentially relate to child outcomes, such that combining them into a composite may obscure the understanding of specific mechanisms driving SES-related effects. In language acquisition research, maternal education is the most commonly used measure, including the studies we consider here. This is at least in part because some researchers have argued that maternal education is the most relevant SES index in the context of language acquisition (Ensminger, Fothergill, Bornstein, & Bradley, 2003; Hoff, 2003).

**Research questions.** In sum, a large body of empirical, conceptual, and theoretical research led us to study the association between familial SES and children's word comprehension measured using direct methods, focusing on children under four years to address an extant gap in research. We defined the following research questions:
1) How much evidence is there bearing on a possible association between SES and infants' word comprehension?

2) How robust is the correlation between SES and word comprehension measured with direct methods in infants and young children?

3) Does this association change as a function of child age, task type, or SES measure?

4) How diverse are samples included in previous work?

5) Is there evidence of publication bias, power limitations, or other potential threats to the validity of these findings?

## Methods

Data and scripts are available on OSF (Scaff, Coffey, & Cristia, 2024). This meta-analytic study was conducted in accordance with the ethical standards of the American Psychological Association. We disclose, in compliance with Wiley's AI policy, the use of LLMs (ChatGPT, Grammarly) solely for grammar and spelling corrections.

**Literature search.** A complete list of searches used to find relevant literature is provided in SM2. In summary, we identified studies through searches of the PubMed and Google Scholar engines using as keywords ("socioeconomic-status" OR "socioeconomic status") AND ("language" OR "vocabulary"); through calls for unpublished data sent through mailing lists; and through backward searches. Together, these search strategies resulted in a database of 577 unique records. The PRISMA flow diagram is in Figure 1.

**Inclusion criteria.** Given our interest in shedding light on the strength of association between SES and word comprehension in infancy, and also considering feasibility, we only included papers with participants who were younger than 4 years old (mean age for the whole sample was less than 48 months). As we explained in the Introduction, we focused on studies where word comprehension was measured directly. In practice, this meant it used one of the following task types: PVT (any edition of the Peabody Picture Vocabulary Test or PPVT, PPVT-R, PPVT-III, or any translation like
the TVIP in Spanish), TVT (proportion of correct responses), or LWL (accuracy). The relationship with SES could have been via the contrast of two groups (e.g., higher versus lower SES, having completed high-school versus graduated college) or via a correlation-based analysis, where SES was treated as a continuous variable. In previous work, SES is estimated using single measures such as annual income or maternal education, as well as composites (Hollingshead, 1975; Nakao & Treas, 1994). Some research suggests that these different measures are highly correlated (Cirino et al., 2002; Rowland et al., 2022). In language acquisition research, maternal education is the most commonly used measure, including the literature we meta-analyze here. This is at least in part because some researchers have argued that maternal education is the most relevant SES index in the context of language acquisition (Ensminger et al., 2003; Hoff, 2003). Finally, we included studies written in English, French, or Spanish, languages that at least one of the authors could read scientific writing.

The search was not restricted by year of publication, nor to specific locations, nor to populations with typical development, so as not to bias included samples (since atypical development may be more prevalent in lower SES).

**Data extraction.** Data were entered by one of the authors and independently by another coder hired via a freelancing platform, who relied only on the studies' manuscripts and a provided codebook. All discrepancies were resolved by verification by the lead author. Whenever information was missing, an attempt was made to contact the authors of the original work. In some cases, this led to the inclusion of studies for which an effect size could not be calculated on the basis of the published work. A given paper could appear several times (i.e., in several rows in the database) provided they had tested different groups of participants or the same children several times or with different measures.

For each included row thus defined, we extracted peer-reviewed status, year of data collection (if not reported, we used year of publication), location (country), native language, mean age of the included children, proportion of ethnically diverse participants in the
study, proportion of female participants, sample lingual status (e.g. monolingual, bilingual, mixed), type of SES measure, type of word comprehension measure, and priority<fnref n="1" />.

We included studies that measured the SES at the level of individual families (based on maternal or paternal education or income, or composite measures such as Hollingshead Index, or the researchers' own composite) as well as at other levels (such as authors explicitly equating neighborhoods with different SES groups, Rosemberg & Alam, 2021). Included studies used either SES as a continuous distribution or contrast studies, comparing children from two or more distinct SES groups. We acknowledge that dichotomies such as high and low SES are arbitrary decisions taken at a given time for a given sample, without consideration of the broader population from which infants are sampled. If more than two categories were available, we decided to calculate the effect size using the most extreme groups to capture the effect of the full range of SES represented in the sample. This happened only once: for Dollaghan et al. (1999), we compared children of college graduates versus mothers who had completed less than high school.

For *word comprehension measure*, we coded LWL, PVT, or TVT, as well as the specific implementation when it was provided. We also indicated whether the SES-comprehension association was based on accuracy (including scores, for PVT) or response times.

For each study that met the criteria, we attempted to retrieve the necessary information to calculate the effect sizes. If a Pearson's correlation coefficient ($r$) was directly provided, it was enough for further analysis. However, this was not always the case. For contrast studies, we calculated the Cohen's $d$ from provided means and standard deviations, and we converted $d$ to $r$ using the following formula: $r = \frac{d}{\sqrt{(d^2+a^2)}}$ (ch. 7 Borenstein, Hedges, Higgins, & Rothstein, 2021). The correction factor $a$ is used when the

<footnote n="1">¹ The column called “priority” coded whether the authors conveyed that SES disparities were central to the study. We checked for this through the article’s title, argumentation, recruitment process, and/or main analysis.</footnote>
groups do not have the same number of participants.

**Analytical procedures.** The *r* correlation coefficient was retrieved or computed for each row in the data by the procedures mentioned above, and transformed to Fisher's *z* (Hedges & Olkin, 2014). The next step was to compute the inverse-variance weights, giving more influence to more precise studies (namely, studies with larger sample sizes). The main model used for this meta-analysis was a random effects model, as recommended by Hedges and Vevea (1998). A random-effects model is better suited to take into account the variance of the “true” effect afforded by each sample and a general interpretation of the overall effect size that takes into account variability between studies.

Analyses were carried out using the R package *robumeta* (Z. Fisher & Tipton, 2015), which includes a robust estimation of parameters in the presence of correlated data. As mentioned above, we allowed studies to contribute multiple data points in two ways. First, if a study included multiple participant groups (e.g., cross-sectional samples at different ages), each group was treated as an independent sample. Second, some studies reported the SES-word comprehension association across multiple analyses from the same participants, for instance, if the task type yields both accuracy and response times. Because the same participants contributed multiple data points, we applied a hierarchical robust model. This statistical approach accounts for variability at different levels of analysis, making it particularly useful when the same participants provide multiple effect sizes (when participants were tested at different ages or when a single task provided multiple measures). The hierarchical model nests these data points within studies, allowing for more accurate estimation of overall effects by taking into account the structure and dependencies of the data.

We used meta-regression to address two research questions related to age: (1) At what age do significant SES-related differences in word comprehension emerge? (2) Are SES-related differences larger in older children than in younger ones? Our meta-regression analysis is expected to reveal two main outcomes, observable through the intercept and age
estimate. For ease of interpretation, the intercept was set at the youngest tested age (15 months). A positive intercept suggests SES effects are already present at this age; a non-significant intercept suggests instead that SES effects on word comprehension are either undetectable or absent at young ages. The estimate for age reflects whether SES effects become smaller or larger with child age.

To assess publication bias and associated analyses, we used the R package *metafor*. These include the funnel plot, Egger's regression test for funnel plot asymmetry, and the trim-and-fill

To assess publication bias and associated analyses, we used the R package *metafor* (Viechtbauer, 2010). These include the funnel plot, Egger's regression test for funnel plot asymmetry (Egger, Smith, Schneider, & Minder, 1997), and the trim-and-fill method.<fnref n="2" />

## Results

1) How much evidence is there bearing on a possible association between SES and infants' word comprehension? The PRISMA flowchart is represented in Figure 1, showing that a total of 45 articles met the eligibility and inclusion criteria (Benzies et al., 2014; Bernier, Carlson, Deschênes, & Matte-Gagné, 2012; DeAnda, Arias-Trejo, Poulin-Dubois, Zesiger, & Friend, 2016; DeAnda, Hendrickson, Zesiger, Poulin-Dubois, & Friend, 2018; Dionne, Boivin, Séguin, Pérusse, & Tremblay, 2008; Dollaghan et al., 1999; Feiring & Lewis, 1981; Fernald & Marchman, 2012; Fernald et al., 2013; Friend, DeAnda, Arias-Trejo, Poulin-Dubois, & Zesiger, 2017; Friend, Schmitt, & Simpson, 2012; Friend, Smolak, Liu, Poulin-Dubois, & Zesiger, 2018; Friend, Smolak, Patrucco-Nanchen, Poulin-Dubois, & Zesiger, 2019; Hayden, Klein, Durbin, & Olino, 2006; Hendrickson, Mitsven, Poulin-Dubois, Zesiger, & Friend, 2015; Hendrickson,

<footnote n="2">² These tools were not yet available in the *robumeta* package at times of analysis, information about the packages and environment are available in Supplementary Materials.</footnote>
Poulin-Dubois, Zesiger, & Friend, 2017; Hurtado, Marchman, & Fernald, 2007, 2008; Koenig, Arunachalam, & Saudino, 2020; Legacy, Zesiger, Friend, & Poulin-Dubois, 2018; Levine et al., 2019; Lo, Rosslund, Chai, Mayor, & Kartushina, 2021; Ludman, Spitz, & Lansdown, 1993; Marchman, Adams, Loi, Fernald, & Feldman, 2016; McDowell, Lonigan, & Goldstein, 2007; Morales, Farkas, Aristotelous, & MacBeth, 2021; Pan, Rowe, Spier, & Tamis-Lemonda, 2004; Patrucco-Nanchen, Friend, Poulin-Dubois, & Zesiger, 2019; Qi, Kaiser, Milan, & Hancock, 2006; Reese & Read, 2000; Robertson & Finer, 1985; Rose, Feldman, & Jankowski, 2015; Rosemberg & Alam, 2021; Rowe, 2008; Scaff et al., 2022; Shavlik, Davis-Kean, Schwab, & Booth, 2021; Smolak et al., 2021; Sohr-Preston et al., 2013; Song, Spier, & Tamis-Lemonda, 2014; Suttora et al., 2017; Verdine et al., 2014; Verkerk et al., 2012; Vohr, Garcia-Coll, & Oh, 1989; Weisleder & Fernald, 2013; Zajac, Raby, & Dozier, 2019). Most data was collected since the 2000s, with only 4 non-independent effect sizes dating from before 2000.

Collectively, the studies reported on 83 non-independent effect sizes, from 40 independent effect sizes (different infant groups). Table 1 gives key characteristics of the included studies and their reported effect sizes. The total number of unique participants across all studies was 8211. Sample sizes varied considerably, with a minimum of 21 and a maximum of 3870, the median sample size being 80 and the mean 205. In what follows, we describe samples based on non-independent effect sizes rather than at the paper or independent infant group levels, because this better represents the data as analyzed, for instance to investigate the modulation of SES-word comprehension effects as a function of child age, since the same children may have been tested at several ages, thus providing multiple effect sizes. It should be noted that one research group reported on the same group of infants across several different papers, such that one group of infants actually contributed 9 data points (at different ages, and tested with different procedures). The mean age was 26.85 months, median = 23.52 (SD 9.29; range 15.06 to 46.30 months). The most represented task measure was TVT, with 39 non-independent effect sizes, followed by
PVT (25 non-independent effect sizes), with the remaining 19 having been collected via LWL. Some studies report both accuracy and response times for the same group of children; most report only one. Response times were available for most LWL and TVT studies. The education-based measures of SES are dramatically over-represented in our sample, with 68 non-independent effect sizes versus 11 using a composite measure, 3 using other measures, and only 1 based on income.

## 2) What is the overall strength correlation between SES and word comprehension measured with direct methods in infants and young children?

The weighted mean effect size over the whole dataset was significantly above zero, with estimated z-transformed $r = .233$, SE = .032, $t(21.7) = 7.175$, $p < .001$, 95% CI = [.166, .30]). Omega², a measure of variation within measures collected from the same children, was zero, suggesting there was no heterogeneity across such repeated measures. tau², a measure of between-study variance, was also surprisingly low, at 0.02, which may curtail our ability to detect moderator effects. Of note, when we fit a simple random effects meta-regression that disregarded repeated measures (using metafor), we also found a low level of total heterogeneity ($tau^2 = 0.03$) but high level of unexplained variance ($I^2 = 73.76$%), which is generally interpreted as suggesting that moderator analyses should be explored.

## 3) Does the SES-word comprehension association change as a function of moderators?
As noted above, an analysis considering repeated measures (using *robumeta*) reveals low between-study variance, whereas one in which information about repeated measures is not integrated (using *metafor*) suggests high unexplained variance that needs to be accounted for by moderators via meta-regressions. In view of such complex results, we reasoned that to best inform our readership, we should declare each moderator separately in multiple meta-regressions that take into account repeated measures, while noting that repeated testing with no correction may result in an inflated risk of false positives. Our conclusions thus remain extremely cautious.
Our highest-powered analysis involved age (see Figure 3). For ease of interpretation, we set the intercept at the youngest tested age (15 months). This intercept was significant and positive (z-transformed $r = .15$, $t(9.20) = 2.61$, $p = 0.028$, 95% CI = [.02, .281]). Furthermore, age had a positive estimate that met the significance threshold ($b = .073$, $t(21.14) = 2.10$, $p = 0.047$). Thus, current evidence suggests that SES effects are present in the youngest ages included here (z-transformed $r = .15$ at 15 months) and that this effect strengthens with infant age: by the oldest ages included (46 months), it is estimated to be z-transformed $r = .341$.

Next, we explore potential differences as a function of the task used (see SM5 for figures). Using the robust variance estimation meta-regression, we tested task type as a categorical predictor. Results varied depending on the reference level, but a consistent pattern emerged. In all models, the intercept, representing the estimated SES effect for the reference task, was significant, indicating that a positive association between SES and word comprehension was present across all task types, though the strength of this association varied. When LWL was the reference category, the SES effect for LWL tasks was significant ($b = .308$, $p = 0.011$). PVT did not differ from LWL ($b = .003$, $p = 0.977$), while TVT showed a trend toward a weaker association ($b = -.173$, $p = 0.097$). With TVT as the reference, the SES effect was again significant ($b = .135$, $p = 0.02$), but both LWL ($b = .173$, $p = 0.097$) and PVT ($b = .176$, $p = 0.004$) showed stronger effect estimates than TVT, and LWL was marginally significant. When PVT was the reference, the SES effect remained significant ($b = .31$, $p < .001$), with no difference from LWL ($b = -.003$, $p = 0.977$) and a significantly weaker effect for TVT ($b = -.176$, $p = 0.004$). These results show that all tasks detect a positive SES-comprehension association, but this effect is consistently weaker in touch-based (TVT) tasks compared to LWL and PVT. Importantly, none of the task differences were moderated by age. Age by task interaction terms were non-significant in all models ($p > .3$), indicating that the observed differences between tasks are not driven by the age of the children tested. We also
tested whether the outcome measure type (accuracy vs. response time) influenced the SES association. This analysis revealed no significant effect (b = .101, p = .147), suggesting that task type, not response modality, is the key methodological moderator. Together, these findings indicate that the association between SES and word comprehension is detectable across all task types.

Finally, despite our initial motivation to explore differences in word comprehension as a function of the type of SES measure, the extreme imbalance of the different SES measures limits our conclusions. For curious readers, a robust variance estimation meta-regression showed no significant difference between education-based effects versus all of the others (b = 0.12, t(10.60) = 1.52, p > .05).]

**3) How diverse are samples included in previous work?** The majority of the data were gathered in the United States (54 non-independent effect sizes, including 1 in which part of the sample was from Mexico). The remaining data came from children tested in the following countries: Canada (8 non-independent effect sizes), Switzerland (7 non-independent effect sizes), Argentina, France, Italy, New Zealand (2 non-independent effect sizes each), Chile, Mexico, The Netherlands, Norway (1 effect size each). With three exceptions (Argentina, Chile, and New Zealand), all of these countries are in North America or Europe. These countries also do not represent extremes in income inequality, with Gini coefficients<fnref n="3" /> ranging from 31 (France) to 45 (Mexico), whereas the observed distribution of Gini in the world in 2020 spanned from 24 (Sweden) to 54 (Colombia; see SM3).

Similarly, for the majority of non-independent effect sizes, English was one of the languages being learnt (51, that is 61%). The frequency of other languages can be largely predicted from the country prevalence just reported on (Spanish 16, French 9, Italian 2, Norwegian 1, Dutch 1). In our sample a total of 60 non-independent studies report

<footnote n="3">³ The World Bank's Gini index “measures the extent to which the distribution of income or consumption among individuals or households within an economy deviates from a perfectly equal distribution”; 0 is perfect equality, 100 perfect inequality.</footnote>
exclusively on monolingual children and 15 report a mixed sample of monolinguals and bilinguals. Only 8 non-independent studies report on samples composed exclusively of bilingual children, all using TVT. Word comprehension in bilingual and mixed samples was measured in considerably different ways across studies. In some cases, assessments were conducted in the child's dominant language of exposure (e.g., Dionne et al., 2008), while in others, the majority language of the country was used regardless of dominance (e.g., Scaff et al., 2022). A few studies administered tests in both the child's first and second languages (e.g., Legacy et al., 2018). This substantial heterogeneity in language of assessment limited our ability to perform fine-grained analyses of bilingualism as a moderator.

Ethnicity was reported for 39 (that is, 47%) non-independent effect sizes. On average, 44% of the participants were considered ethnically diverse, with a range from 0 to 100%. As for the SES range included, this was reported for 74 (that is, 89%) non-independent effect sizes. However, the way in which this range was conveyed was so variable across studies that a consistent quantitative analysis was not possible. For instance, focusing on the 35 non-independent effect sizes that analyzed SES categorically based on education, the following were the reported levels of education for the less-educated group: "no formal education", "no qualification", "completed elementary school", "more than primary", "less than grade 9", "fewer than 10 years of education", "less than high school", "lower secondary school", "high school or less", "some high school up to associate".

4) Is there evidence of publication bias, power limitations, or other potential threats to validity when considering meta-analyzed studies as a whole? We investigated potential validity threats in several ways. To begin with, we looked at funnel plot asymmetries, a widely used test to detect a potential consequence of publication biases. Funnel plots show the standard error of the effect size as a function of effect size. In the presence of publication bias, these plots will display an under-representation of small positive effects (or negative effects) with high variance, which results in asymmetric plots. The test for funnel plot asymmetry met the criterion for
significance (z = -2.06, p = 0.04). However, the asymmetry was not in the direction
consistent with conventional publication bias (missing studies in the bottom left). As seen
in Figure 2, the trim-and-fill method suggested that missing studies were those with high
effect sizes (and high variance). When the funnel is symmetrized, the estimate remains
small to moderate (r = .276).

Next, we considered power. Following Bergmann et al. (2018), we did two kinds of
analyses: checking the power that studies have to find their own effect size; and checking
their power to find the meta-analytic effect size. Assuming one-tailed analyses (e.g., that
higher SES is associated with higher word comprehension scores), the average power across
all studies was around 50% to find their own effect size, and 57% to find the meta-analytic
effect size.

Finally, we considered potential differences as a function of publication status and
study aim. For publication status, the vast majority of included effect sizes came from
studies that were published in peer-reviewed journals (78 non-independent effect sizes).<fnref n="4" />
Since only 5 were not, we did not analyze differences between these two types. As for study
aim, about half of the effect sizes (47) appeared in articles that did not purport to bear on
SES effects. Following Lawson et al. (2018), we inquired whether studies reporting on SES
effects as part of their investigation had larger effects than those that incidentally reported
on SES effects. A robust variance estimation meta-regression declaring this moderator
revealed no significant differences between the two groups of studies (b = .097, t(21.51) =
1.47, p > .05; see SM4 for figures). Overall, although studies tended to be underpowered,
our analyses do not support an interpretation of bias or other potential validity threat in
the included literature.

<footnote n="4">⁴ Recent recommendations for infant research suggest that the inclusion of gray literature, although time-consuming for authors of meta-analyses, does not negatively impact conclusions (Tsuji, Cristia, Frank, & Bergmann, 2020).</footnote>
Discussion

This paper provides a meta-analysis of studies investigating the correlation between socioeconomic status (SES) and directly-measured word comprehension in children under four years of age. Here we synthesized data from 45 articles reporting 83 effect sizes, representing over 8,000 children under the age of four. Across the full dataset, we found a small-to-moderate but robust correlation between socioeconomic status (SES) and children's directly measured word comprehension (z-transformed r = .233). This association was evident as early as 15 months (z = .15) and strengthened with age. Task type also moderated the association: LWL and PVT showed comparable SES effects, while TVT revealed weaker associations. While studies were predominantly conducted in North America and often involved English-learning children, there was no evidence of publication or reporting bias.

The size of the correlation is small to moderate between word comprehension and SES across our whole dataset (z-converted r = .233), which is on the high side compared to associations reported in the previous meta-analyses of SES summarized in the Introduction (see SM1). Interestingly, a previous meta-analysis on executive functions, including older children, found an overall association of only r=0.16 (Lawson et al., 2018). Together, these points of reference reinforce the impression that the effect discovered in the present meta-analysis is considerable in size.

We complement a comprehensive view of the relevant previous literature with an exploration of potential moderators, namely age, task, and SES measure. Regarding age, we asked: (1) When do significant SES-related differences in word comprehension become apparent? (2) Are older children affected to a greater extent? We answered these by examining the intercept and the age estimate in our regression model, respectively. The intercept results of our analysis suggest that SES-related disparities in early word comprehension are present as early as 15 months (estimate z-converted r = .15), consistent
with individual studies showing SES disparities before two years of age (Fernald et al., 2013; Hurtado et al., 2008; Rowe, 2008; Walker et al., 1994).

One important question for future research is whether direct measurements can also
reveal SES effects in even younger infants. Although the LWL task can be used with
infants as young as 6 months (Tincoff & Jusczyk, 1999); bergelson20126], establishing this
effect at younger ages may not be trivial. To obtain a significant result given the estimated
correlation of r = .15 at 15 months, a sample of 377 infants would be required to have a
90% chance of observing this effect at alpha = .05.

Turning to our age estimate results, one must exercise caution when interpreting our
moderator analyses. While one parameter indicated significant variance in the data,
another did not, suggesting that there may be limited power to detect moderator effects
when there is low variability in the data. The mean age of the sample emerged as a
significant moderator, with a stronger level of SES-comprehension association for older as
opposed to younger children. A positive effect of age is consistent with theories that
hypothesize that effects of SES accumulate during development, such as those highlighting
the role of stress on cognitive function (Hackman, Farah, & Meaney, 2010) and/or
linguistic stimulation on language outcomes (Dailey & Bergelson, 2022; Weisleder &
Fernald, 2013). However, an increase in the correlation between SES and comprehension as
children age could also emerge even if theories based on cumulative effects are false, given
the increases in measurement reliability as a function of child age (Bergmann et al., 2018).
It is important to note that our study does not rule out other factors implicated in the
association between SES and child outcomes discussed in the Introduction (e.g. Garc??a &
Weiss, 2017; Lautarescu et al., 2020; Raudenbush, 2008), which could be revisited in future
research.

Following Bergmann et al. (2018), we also examined methodological factors that
might influence the association between SES and word comprehension. Task type emerged
as a significant predictor. Specifically, LWL and PVT reveal SES-comprehension effects that do not significantly differ in size. In contrast, the touch-based TVT data revealed a lower strength of association between SES and word comprehension than the other measures. No differences were observed between accuracy- and response-time-based outcome measures.

However, these task-related findings must be interpreted with caution. The observed differences may reflect variations in how sensitive each task is to SES-related variability, rather than true differences in the underlying association. This may inadvertently amplify SES differences if the format aligns more closely with common practices in higher-SES households, such as the case for PVT task and picture book experiences (Hoff, 2014; Zhu et al., 2025) or relies heavily on executive functions (Lawson et al., 2018). TVT tasks, which are gamified and tablet-based, may undermine observable SES effects due to their accessibility across SES backgrounds. There is evidence that TVT outcomes, particularly tactile response times, may be less specific to lexical processing and more influenced by motor and executive functions (Scaff et al., 2022; Smolak et al., 2021). This could make them less sensitive to SES-related variation, especially when compared to visual-based measures. Indeed, while some studies using TVT have found SES-related differences in accuracy (Friend et al., 2012; Rosemberg & Alam, 2021), others, especially in bilingual or samples outside the US have not (e.g., DeAnda et al., 2016, 2018; Legacy et al., 2018; Patrucco-Nanchen et al., 2019).

In sum, differences across task types likely reflect not only variation in measurement sensitivity but also contextual factors such as familiarity, linguistic background, and test modality. We emphasize the need for future research to directly compare the sensitivity of these tasks within the same populations and across diverse SES and linguistic groups.

We had hoped to explore the SES measure as a moderator. The meta-analysis by Lawson et al. (2018) found that the type of SES measure moderated the association
between SES and executive functions in childhood. The results of our review show that the overwhelming majority of studies used education-based measures as proxies for SES. While this biased distribution doesn't allow us to reliably test its moderator effect, this result might reflect the assumption of the literature of some of the causal explanations of the association between SES and word comprehension. This may reflect the belief that maternal education is the most relevant predictor of children's early language (e.g., Hoff, Burridge, Ribot, & Giguere, 2018). However, education (in particular maternal education) does not capture every one of the complicated causal pathways linking SES to word comprehension as briefly sketched in the Introduction. For example, income is likely to be a more relevant proxy if SES affects children's input through financial scarcity concerns (Ellwood-Lowe, Foushee, & Srinivasan, 2022), aligning with the family stress model, and education will be more meaningful if the causal pathway is through the diversity of adults' vocabulary (Hoff, 2003), aligning with the family investment model. Moreover, to our knowledge, there have not been detailed studies assessing the predictive value of alternative measures of SES concerning child language in geographically diverse samples. Furthermore, different proxies may be more relevant in diverse countries, and the strength of the SES-word comprehension correlation may not be equivalent across all proxies. Also, SES proxies could interact differently as a function of context. Places with low income inequality may see stronger relationships with education than income, whereas income variation may explain more variance in countries with marked income inequality.
Reporting on multiple of these measures in the future, as recommended by Korous et al. (2022), may allow us to disentangle evidence and support for the different causal pathways.

Another aim was to take stock of the diversity of participant samples represented in this literature. In other words, to what extent can we say that this reflects the association between SES and directly-measured word comprehension in the world? The majority of our data points were collected in North America (USA, Canada, and Mexico), and while some samples were ethnically diverse, over half came from children who were learning at least
some English. In exploratory analyses (see SM6), we found that a dichotomous analysis
separating the USA versus all other samples (while controlling for age) yielded a
significantly larger effect for the former than for the latter. This result is consistent with at
least two mutually consistent explanations. The association between SES and word
comprehension may truly be larger in the USA than in the other included countries
(Switzerland, France, Italy, the Netherlands, Norway, New Zealand, Argentina, Chile,
Mexico). In addition, and/or alternatively, the word comprehension paradigms may have
been better adapted to measure SES-related variation in North America than in other
samples. At present, we cannot tease these apart, but we recommend further work
considering both options.

Importantly, our findings align with broader concerns raised in recent reviews of early
language development and intervention: the field often lacks representative samples. Early
intervention reviews [Greenwood et al. (2020); Heidlage et al. (2020); Walker and Carta
(2020)) or meta-analyses (Anderson, Graham, Prime, Jenkins, & Madigan, 2021; Korous et
al., 2022) reveal that most studies have failed to report key demographic characteristics
such as ethnicity, race, and caregiver education and are largely based on samples from
middle- to high-SES, monolingual, and majority-White families. These findings underscore
a broader gap in the literature: the lack of socioeconomic and ethnolinguistic diversity in
the language intervention literature that limits our ability to understand causal pathways
in which SES interacts with language development across contexts. Our own results
reinforce these concerns. For example, we could not include bilingualism as a separate
moderator due to multicollinearity with task type and the fact that it reflects a very
specific subset of countries, mostly the US. All of our included studies bearing on
non-monolinguals use TVT, and nearly all of them were based on North American infants.
Therefore, our results should not be interpreted as the relationship between SES and word
comprehension in the world's population as a whole, but rather as a snapshot of the
current state of the art of the available literature, which is based on a multiply biased
sample, including towards North American and European (Nielsen, Haun, Kärtner, & Legare, 2017), and monolingual participants (Kidd & Garcia, 2022). This is both an important limitation of the current work and an invitation to reconsider the practices that lead to such a biased sample. To de-bias this literature, more developmental research could be done in other places beyond the northern American scope. Ideally, such research would involve local actors to make sure that local language and habits are appropriately taken into account, particularly in decontextualized tests like those used to measure word comprehension, where performance might differ depending on familiarity with testing materials (Zhu et al., 2025). It would also be interesting to consider the roadblocks standing in the way of researchers who are already doing important work to document this kind of phenomenon elsewhere, but may find it difficult to get their work accepted in the current publication circuits (Singh, Cristia, Karasik, Rajendra, & Oakes, 2023).

Turning to our last aim, we set out to assess the validity of this body of research by studying publication biases, power limitations, and other potential threats to validity. Although studies tended to be under-powered, there was no sign of reporting or publication biases based on an analysis of funnel plot asymmetry, nor on whether the study authors presented it as intending to check for SES differences. While this reflects positively on the body of literature represented in the present meta-analysis, these results should be interpreted cautiously. Future research should consider conducting further analyses to additionally assess over-reporting of significant results, such as p-curving (Simonsohn, Nelson, & Simmons, 2014). We also encourage data collection efforts that are less subject to reporting biases, such as cohort studies (e.g., Heude et al., 2016), along the lines of Wilding et al. (2023)'s inclusion criteria. Our analyses thus revealed that there is a sizable body of literature providing empirical estimates of the association between young children's word comprehension (measured directly) and familial SES. Although the participant samples included in this work do not represent children everywhere, there are no obvious red flags that would lead to concerns about the estimates themselves.
Another limitation of our study is related to the challenge of terminology in the literature on children's language development. The terms *word comprehension, vocabulary size, comprehension vocabulary, word recognition, lexical knowledge, and vocabulary skills*, are often used interchangeably. It is often the case that in more interdisciplinary or general scientific outlets these terms are often reduced to “vocabulary” or even “language outcome” (a challenge we addressed by using inclusive search terms). We opted for the term “word comprehension” in this paper because we believe it captures processes common to the direct measures presented here. However, as argued in the Introduction and supported by moderator analyses, the three tasks are not equivalent, which may indicate that other factors are at play.

## Conclusion and future directions

We systematically reviewed the literature for studies in which samples of children varying in SES were assessed on word comprehension. The overall effect size was small to medium, with no evidence for publication bias. Analyses revealed that child age was a significant moderator of this relationship, suggesting that SES differences widen with age. As for further research, we recommend increasing the diversity of the participant samples included in the literature and potentially investigating SES associations even earlier (although large samples would be needed to detect it). What are the causal pathways between SES and word comprehension that ultimately affect lexical outcomes and other downstream processes and skills? Answering this question requires isolating causal relationships, for instance, via interventions with appropriate control groups. That said, intervention studies are costly and difficult to implement. Meta-analytic and systematic reviews like the present one can help by giving preliminary answers that highlight areas in which evidence is clear versus insufficient, and thus help researchers optimally allocate resources when conducting new correlational and intervention studies.
<caption>Table 1</caption>

Included studies. Papers are cited by first author and year regardless of how many authors there are. In Country 'Argent.' stands for Argentina, 'NL' for Netherlands, 'NZ' for New Zealand, 'Switz.' for Switzerland. Age stands for mean age in months; 'N' for number of included participants; 'Lingual' for lingual status, namely monolingual (Monol.), bilingual (Bil.) or mixed samples (Mixed); '% Diverse' for percentage of participants considered ethnically diverse;; 'Test' for word comprehension test (see main text for explanation of acronym), '-acc' stands for accuracy measure and '-RT' for reaction time measure; 'SES' for the type of SES measure, namely education (E), income (I), a composite (C), or other (O); 'Aim' for whether the study investigated SES effects as a main objective of the paper (Y) or not (N); 'r' indicates the correlation between child word comprehension and familial SES.

<table><thead><tr><th>Paper</th><th>Country</th><th>Age</th><th>Lingual</th><th>% Diverse</th><th>N</th><th>Test</th><th>SES</th><th>Aim</th><th>r</th></tr></thead><tbody><tr><td>Benzies 2014</td><td>Canada</td><td>46</td><td>Monol.</td><td>60</td><td>118</td><td>PVT</td><td>E</td><td>N</td><td>.093</td></tr><tr><td>Bernier 2012</td><td>Canada</td><td>37</td><td>Monol.</td><td>—</td><td>62</td><td>PVT</td><td>C</td><td>Y</td><td>.435</td></tr><tr><td>Deanda 2016</td><td>USA</td><td>16</td><td>Mixed</td><td>46</td><td>72</td><td>TVT-acc</td><td>E</td><td>Y</td><td>.262</td></tr><tr><td>Deanda 2016</td><td>USA</td><td>16</td><td>Mixed</td><td>98</td><td>87</td><td>TVT-acc</td><td>E</td><td>Y</td><td>-.036</td></tr><tr><td>DeAnda 2018</td><td>USA</td><td>17</td><td>Mixed</td><td>—</td><td>79</td><td>TVT-acc</td><td>E</td><td>Y</td><td>.284</td></tr><tr><td>DeAnda 2018</td><td>USA</td><td>23</td><td>Mixed</td><td>—</td><td>79</td><td>TVT-acc</td><td>E</td><td>Y</td><td>.393</td></tr><tr><td>DeAnda 2018</td><td>USA</td><td>17</td><td>Monol.</td><td>—</td><td>64</td><td>TVT-acc</td><td>E</td><td>Y</td><td>.075</td></tr><tr><td>DeAnda 2018</td><td>USA</td><td>24</td><td>Monol.</td><td>—</td><td>64</td><td>TVT-acc</td><td>E</td><td>Y</td><td>.056</td></tr><tr><td>DeAnda 2018</td><td>USA</td><td>18</td><td>Bil.</td><td>—</td><td>44</td><td>TVT-acc</td><td>E</td><td>Y</td><td>-.050</td></tr><tr><td>DeAnda 2018</td><td>USA</td><td>24</td><td>Bil.</td><td>—</td><td>44</td><td>TVT-acc</td><td>E</td><td>Y</td><td>.178</td></tr><tr><td>DeAnda 2018</td><td>USA</td><td>18</td><td>Bil.</td><td>—</td><td>44</td><td>TVT-acc</td><td>E</td><td>Y</td><td>-.038</td></tr><tr><td>DeAnda 2018</td><td>USA</td><td>24</td><td>Bil.</td><td>—</td><td>44</td><td>TVT-acc</td><td>E</td><td>Y</td><td>.074</td></tr><tr><td>Dionne 2008</td><td>Canada</td><td>41</td><td>Mixed</td><td>—</td><td>809</td><td>PVT</td><td>E</td><td>N</td><td>.400</td></tr><tr><td>Dollaghan 1999</td><td>USA</td><td>37</td><td>Monol.</td><td>50</td><td>73</td><td>PVT</td><td>E</td><td>Y</td><td>.521</td></tr></tbody></table>
<table><caption>Table 1 continued</caption><thead><tr><th>Paper</th><th>Country</th><th>Age</th><th>Lingual</th><th>% Diverse</th><th>N</th><th>Test</th><th>SES</th><th>Aim</th><th>r</th></tr></thead><tbody><tr><td>Feiring 1981</td><td>USA</td><td>24</td><td>Monol.</td><td>—</td><td>193</td><td>PVT</td><td>C</td><td>Y</td><td>.159</td></tr><tr><td>Fernald 2012</td><td>USA</td><td>18</td><td>Monol.</td><td>—</td><td>82</td><td>LWL-acc</td><td>E</td><td>N</td><td>.254</td></tr><tr><td>Fernald 2012</td><td>USA</td><td>18</td><td>Monol.</td><td>—</td><td>82</td><td>LWL-RT</td><td>E</td><td>N</td><td>.257</td></tr><tr><td>Fernald 2013</td><td>USA</td><td>18</td><td>Monol.</td><td>34</td><td>48</td><td>LWL-acc</td><td>C</td><td>Y</td><td>.520</td></tr><tr><td>Fernald 2013</td><td>USA</td><td>24</td><td>Monol.</td><td>34</td><td>48</td><td>LWL-acc</td><td>C</td><td>Y</td><td>.300</td></tr><tr><td>Fernald 2013</td><td>USA</td><td>18</td><td>Monol.</td><td>34</td><td>48</td><td>LWL-RT</td><td>C</td><td>Y</td><td>.500</td></tr><tr><td>Fernald 2013</td><td>USA</td><td>24</td><td>Monol.</td><td>34</td><td>48</td><td>LWL-RT</td><td>C</td><td>Y</td><td>.450</td></tr><tr><td>Friend 2012</td><td>USA</td><td>18</td><td>Mixed</td><td>28</td><td>16</td><td>TVT-acc</td><td>E</td><td>N</td><td>.106</td></tr><tr><td>Friend 2012</td><td>USA</td><td>18</td><td>Mixed</td><td>28</td><td>50</td><td>TVT-acc</td><td>E</td><td>N</td><td>.223</td></tr><tr><td>Friend 2017</td><td>USA</td><td>24</td><td>Monol.</td><td>—</td><td>56</td><td>TVT-acc</td><td>E</td><td>Y</td><td>.010</td></tr><tr><td>Friend 2017</td><td>Mexico</td><td>23</td><td>Monol.</td><td>—</td><td>26</td><td>TVT-acc</td><td>E</td><td>Y</td><td>.120</td></tr><tr><td>Friend 2018</td><td>USA</td><td>23</td><td>Monol.</td><td>38</td><td>48</td><td>TVT-acc</td><td>E</td><td>N</td><td>.370</td></tr><tr><td>Friend 2019</td><td>Switz.</td><td>16</td><td>Monol.</td><td>7</td><td>58</td><td>TVT-acc</td><td>E</td><td>N</td><td>.239</td></tr><tr><td>Friend 2019</td><td>Switz.</td><td>22</td><td>Monol.</td><td>7</td><td>58</td><td>TVT-acc</td><td>E</td><td>N</td><td>-.040</td></tr><tr><td>Friend 2019</td><td>Switz.</td><td>16</td><td>Monol.</td><td>7</td><td>58</td><td>TVT-RT</td><td>E</td><td>N</td><td>.264</td></tr><tr><td>Friend 2019</td><td>Switz.</td><td>22</td><td>Monol.</td><td>7</td><td>58</td><td>TVT-RT</td><td>E</td><td>N</td><td>.133</td></tr><tr><td>Friend 2019</td><td>Switz.</td><td>36</td><td>Monol.</td><td>7</td><td>60</td><td>PVT</td><td>E</td><td>N</td><td>-.039</td></tr><tr><td>Hayden 2006</td><td>—</td><td>43</td><td>Monol.</td><td>15</td><td>98</td><td>PVT</td><td>C</td><td>N</td><td>.030</td></tr><tr><td>Hendrickson 2015</td><td>USA</td><td>16</td><td>Mixed</td><td>—</td><td>44</td><td>TVT-RT</td><td>E</td><td>N</td><td>.056</td></tr><tr><td>Hendrickson 2015</td><td>USA</td><td>16</td><td>Mixed</td><td>—</td><td>44</td><td>TVT-RT</td><td>E</td><td>N</td><td>.165</td></tr><tr><td>Hendrickson 2017</td><td>USA</td><td>23</td><td>Mixed</td><td>—</td><td>39</td><td>TVT-acc</td><td>E</td><td>N</td><td>.322</td></tr><tr><td>Hendrickson 2017</td><td>USA</td><td>23</td><td>Mixed</td><td>—</td><td>39</td><td>TVT-RT</td><td>E</td><td>N</td><td>.115</td></tr><tr><td>Hendrickson 2017</td><td>USA</td><td>23</td><td>Mixed</td><td>—</td><td>39</td><td>TVT-RT</td><td>E</td><td>N</td><td>.034</td></tr><tr><td>Hurtado 2007</td><td>USA</td><td>24</td><td>Monol.</td><td>100</td><td>49</td><td>LWL-acc</td><td>E</td><td>Y</td><td>.320</td></tr></tbody></table>
<table><caption>Table 1 continued</caption><thead><tr><th>Paper</th><th>Country</th><th>Age</th><th>Lingual</th><th>% Diverse</th><th>N</th><th>Test</th><th>SES</th><th>Aim</th><th>r</th></tr></thead><tbody><tr><td>Hurtado 2007</td><td>USA</td><td>24</td><td>Monol.</td><td>100</td><td>43</td><td>LWL-RT</td><td>E</td><td>Y</td><td>.320</td></tr><tr><td>Hurtado 2008</td><td>USA</td><td>18</td><td>Monol.</td><td>100</td><td>26</td><td>LWL-RT</td><td>E</td><td>N</td><td>-.319</td></tr><tr><td>Hurtado 2008</td><td>USA</td><td>24</td><td>Monol.</td><td>100</td><td>27</td><td>LWL-RT</td><td>E</td><td>N</td><td>-.023</td></tr><tr><td>Koenig 2020</td><td>USA</td><td>36</td><td>Monol.</td><td>–</td><td>126</td><td>LWL-RT</td><td>C</td><td>N</td><td>.098</td></tr><tr><td>Koenig 2020</td><td>USA</td><td>36</td><td>Monol.</td><td>11</td><td>126</td><td>PVT</td><td>C</td><td>N</td><td>.122</td></tr><tr><td>Legacy 2018</td><td>Canada</td><td>24</td><td>Bil.</td><td>–</td><td>38</td><td>TVT-acc</td><td>E</td><td>N</td><td>.074</td></tr><tr><td>Legacy 2018</td><td>Canada</td><td>24</td><td>Bil.</td><td>–</td><td>38</td><td>TVT-acc</td><td>E</td><td>N</td><td>-.254</td></tr><tr><td>Legacy 2018</td><td>Canada</td><td>24</td><td>Bil.</td><td>–</td><td>38</td><td>TVT-RT</td><td>E</td><td>N</td><td>.046</td></tr><tr><td>Legacy 2018</td><td>Canada</td><td>24</td><td>Bil.</td><td>–</td><td>38</td><td>TVT-RT</td><td>E</td><td>N</td><td>.098</td></tr><tr><td>Levine 2019</td><td>USA</td><td>30</td><td>Monol.</td><td>–</td><td>98</td><td>TVT-acc</td><td>E</td><td>Y</td><td>.287</td></tr><tr><td>Ludman 1993</td><td>England</td><td>36</td><td>Monol.</td><td>–</td><td>27</td><td>PVT</td><td>O</td><td>N</td><td>.376</td></tr><tr><td>Marchman 2016</td><td>USA</td><td>18</td><td>Monol.</td><td>–</td><td>30</td><td>LWL-acc</td><td>E</td><td>N</td><td>.698</td></tr><tr><td>Marchman 2016</td><td>USA</td><td>18</td><td>Monol.</td><td>–</td><td>30</td><td>LWL-RT</td><td>E</td><td>N</td><td>.635</td></tr><tr><td>McDowell 2007</td><td>USA</td><td>43</td><td>Monol.</td><td>30</td><td>237</td><td>PVT</td><td>I</td><td>Y</td><td>.565</td></tr><tr><td>Morales 2021</td><td>Chile</td><td>42</td><td>Monol.</td><td>10</td><td>3870</td><td>PVT</td><td>E</td><td>Y</td><td>.283</td></tr><tr><td>Pan 2004</td><td>USA</td><td>36</td><td>Monol.</td><td>8</td><td>49</td><td>PVT</td><td>E</td><td>Y</td><td>.290</td></tr><tr><td>Patrucco 2019</td><td>Switz.</td><td>16</td><td>Monol.</td><td>–</td><td>50</td><td>TVT-acc</td><td>E</td><td>N</td><td>-.093</td></tr><tr><td>Patrucco 2019</td><td>Switz.</td><td>22</td><td>Monol.</td><td>–</td><td>50</td><td>TVT-acc</td><td>E</td><td>N</td><td>-.107</td></tr><tr><td>Qi 2006</td><td>USA</td><td>43</td><td>Monol.</td><td>100</td><td>173</td><td>PVT</td><td>E</td><td>Y</td><td>.267</td></tr><tr><td>Reese 2000</td><td>NZ</td><td>32</td><td>Monol.</td><td>8</td><td>61</td><td>PVT</td><td>E</td><td>Y</td><td>.030</td></tr><tr><td>Reese 2000</td><td>NZ</td><td>40</td><td>Monol.</td><td>8</td><td>61</td><td>PVT</td><td>E</td><td>Y</td><td>.130</td></tr><tr><td>Robertson 1985</td><td>Canada</td><td>44</td><td>Monol.</td><td>22</td><td>167</td><td>PVT</td><td>E</td><td>N</td><td>.330</td></tr><tr><td>Rose 2015</td><td>USA</td><td>36</td><td>Mixed</td><td>87</td><td>111</td><td>PVT</td><td>C</td><td>N</td><td>.370</td></tr><tr><td>Rosemberg 2021</td><td>Argent.</td><td>40</td><td>Monol.</td><td>–</td><td>113</td><td>TVT-acc</td><td>O</td><td>Y</td><td>.449</td></tr></tbody></table>
<table><caption>Table 1 continued</caption><thead><tr><th>Paper</th><th>Country</th><th>Age</th><th>Lingual</th><th>% Diverse</th><th>N</th><th>Test</th><th>SES</th><th>Aim</th><th>r</th></tr></thead><tbody><tr><td>Rosemberg 2021</td><td>Argent.</td><td>40</td><td>Monol.</td><td>–</td><td>113</td><td>TVT-RT</td><td>O</td><td>Y</td><td>.030</td></tr><tr><td>Lo 2021</td><td>Norway</td><td>19</td><td>Monol.</td><td>–</td><td>21</td><td>TVT-acc</td><td>E</td><td>N</td><td>.026</td></tr><tr><td>Rowe 2008</td><td>USA</td><td>42</td><td>Monol.</td><td>28</td><td>45</td><td>PVT</td><td>E</td><td>Y</td><td>.510</td></tr><tr><td>Scaff 2022</td><td>France</td><td>31</td><td>Mixed</td><td>–</td><td>91</td><td>TVT-acc</td><td>E</td><td>Y</td><td>.330</td></tr><tr><td>Scaff 2022</td><td>France</td><td>31</td><td>Mixed</td><td>–</td><td>91</td><td>TVT-RT</td><td>E</td><td>Y</td><td>.000</td></tr><tr><td>Shavlik 2021</td><td>USA</td><td>35</td><td>Monol.</td><td>18</td><td>187</td><td>PVT</td><td>E</td><td>Y</td><td>.430</td></tr><tr><td>Smolak 2021</td><td>USA</td><td>23</td><td>Monol.</td><td>37</td><td>41</td><td>TVT-acc</td><td>E</td><td>N</td><td>.340</td></tr><tr><td>Smolak 2021</td><td>USA</td><td>23</td><td>Monol.</td><td>37</td><td>41</td><td>TVT-RT</td><td>E</td><td>N</td><td>.060</td></tr><tr><td>Smolak 2021</td><td>USA</td><td>23</td><td>Monol.</td><td>37</td><td>41</td><td>TVT-RT</td><td>E</td><td>N</td><td>.050</td></tr><tr><td>Sohr-Preston 2013</td><td>USA</td><td>37</td><td>Monol.</td><td>0</td><td>139</td><td>PVT</td><td>E</td><td>Y</td><td>.280</td></tr><tr><td>Song 2014</td><td>USA</td><td>37</td><td>Monol.</td><td>96</td><td>68</td><td>PVT</td><td>E</td><td>N</td><td>.172</td></tr><tr><td>Suttora 2017</td><td>Italy</td><td>15</td><td>Monol.</td><td>0</td><td>30</td><td>LWL-acc</td><td>E</td><td>N</td><td>-.039</td></tr><tr><td>Suttora 2017</td><td>Italy</td><td>15</td><td>Monol.</td><td>0</td><td>30</td><td>LWL-RT</td><td>E</td><td>N</td><td>-.109</td></tr><tr><td>Verdine 2014</td><td>USA</td><td>44</td><td>Monol.</td><td>–</td><td>97</td><td>PVT</td><td>E</td><td>Y</td><td>.553</td></tr><tr><td>Verkerk 2012</td><td>NL</td><td>44</td><td>Monol.</td><td>–</td><td>149</td><td>PVT</td><td>E</td><td>N</td><td>.278</td></tr><tr><td>Vohr 1989</td><td>USA</td><td>36</td><td>Monol.</td><td>–</td><td>56</td><td>PVT</td><td>C</td><td>N</td><td>.340</td></tr><tr><td>Weisleder 2013</td><td>USA</td><td>19</td><td>Monol.</td><td>100</td><td>29</td><td>LWL-acc</td><td>E</td><td>N</td><td>.540</td></tr><tr><td>Weisleder 2013</td><td>USA</td><td>24</td><td>Monol.</td><td>100</td><td>29</td><td>LWL-acc</td><td>E</td><td>N</td><td>.359</td></tr><tr><td>Weisleder 2013</td><td>USA</td><td>19</td><td>Monol.</td><td>100</td><td>29</td><td>LWL-RT</td><td>E</td><td>N</td><td>.259</td></tr><tr><td>Weisleder 2013</td><td>USA</td><td>24</td><td>Monol.</td><td>100</td><td>29</td><td>LWL-RT</td><td>E</td><td>N</td><td>.232</td></tr><tr><td>Zajac 2019</td><td>USA</td><td>36</td><td>Monol.</td><td>76</td><td>320</td><td>PVT</td><td>E</td><td>N</td><td>.290</td></tr></tbody></table>
<figure><img src="image_2.png" /><figcaption>*Figure 1.* PRISMA flowchart for study inclusion/exclusion: “Age” (children older than 4 years on average), “Other measures” (measures not LWL, PVT, TVT), “Language” (articles in an unfamiliar language). “Studies included in qualitative synthesis” met all criteria except for quantitative SES-language comprehension estimates; when possible authors were contacted for this data. “Studies included in quantitative synthesis” encompassed studies with available SES-language comprehension effect sizes for analysis.</figcaption></figure>
<figure><img src="image_3.png" /></figure>

*Figure 2.* Funnel plot. Each dot represents a single study. The x-axis shows the effect size for the study. The y-axis shows the standard error of the effect estimate; therefore, larger studies with higher precision are nearer the top, and lower-powered studies towards the bottom. Studies imputed by the trim-and-fill method, to symmetrize the plot, are in white.
<figure><img src="image_4.png" /></figure>

*Figure 3*. Effect sizes as a function of age. Shaded areas represent 95% confidence intervals from linear regressions (weighted by sample size using the `geom.smooth` function of the `ggplot` R package).

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