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Is the Development of Diversification in Executive Functioning Universal? Longitudinal Evidence from Ghana

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Title: Is the Development of Diversification in Executive Functioning Universal? Longitudinal Evidence from Ghana
Language: English
Authors: Anahid S. Modrek (ORCID 0000-0002-0474-5777), Sharon Wolf
Source: Social Development. 2024 33(4).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 17
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Descriptors: Executive Function, Preschool Children, Young Children, Children, Factor Analysis, Developmental Stages, Factor Structure, Measures (Individuals), Longitudinal Studies, Foreign Countries
Geographic Terms: Ghana
DOI: 10.1111/sode.12764
ISSN: 0961-205X
1467-9507
Abstract: The component structure of executive functioning (EF) has been shown to change across development. Empirical research examining this in Sub-Saharan Africa is limited. We report the development of EF component structure with a large sample of Ghanaian children (n = 2,979) followed longitudinally from ages 3 through 12 across six waves. Existing literature suggests unitary models of EF (components loading onto a single factor) early in childhood, with development across childhood and into adolescence resulting in a more diversified EF model (components loading onto two- or three-factors). To test these developmental differences, participants completed EF batteries that measured EF components: working memory, short-term memory, inhibition, and cognitive flexibility/shifting. We employed confirmatory factor analysis to test factor models in 3- to 4-year-olds, 5- to 6-year-olds, 7- to 9-year-olds, and 10- to 12-year-olds. Contrary to existing literature, a two-factor EF model best explained EF performance as early as 3-4 years of age. Findings suggest that diversification of EF components may emerge earlier in childhood than expected in some contexts, questioning the universality in the timing of unification and diversification of EF structure.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1481534
Database: ERIC
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  Value: <anid>AN0180281944;bsp01nov.24;2024Oct17.08:12;v2.2.500</anid> <title id="AN0180281944-1">Is the development of diversification in executive functioning universal? Longitudinal evidence from Ghana </title> <p>The component structure of executive functioning (EF) has been shown to change across development. Empirical research examining this in Sub‐Saharan Africa is limited. We report the development of EF component structure with a large sample of Ghanaian children (n = 2,979) followed longitudinally from ages 3 through 12 across six waves. Existing literature suggests unitary models of EF (components loading onto a single factor) early in childhood, with development across childhood and into adolescence resulting in a more diversified EF model (components loading onto two‐ or three‐factors). To test these developmental differences, participants completed EF batteries that measured EF components: working memory, short‐term memory, inhibition, and cognitive flexibility/shifting. We employed confirmatory factor analysis to test factor models in 3‐ to 4‐year‐olds, 5‐ to 6‐year‐olds, 7‐ to 9‐year‐olds, and 10‐ to 12‐year‐olds. Contrary to existing literature, a two‐factor EF model best explained EF performance as early as 3–4 years of age. Findings suggest that diversification of EF components may emerge earlier in childhood than expected in some contexts, questioning the universality in the timing of unification and diversification of EF structure.</p> <p>Keywords: cognitive flexibility; diversification; early maturation; executive functioning; Ghana; Sub‐Saharan Africa; working memory</p> <hd id="AN0180281944-2">INTRODUCTION</hd> <p>Long has executive functioning (EF) been regarded as a general ability to monitor and regulate thoughts and attention. EF is an umbrella term that encompasses different components such as planning, memory (short‐term and working), inhibition, and switching/cognitive flexibility. Of these, memory, inhibition, and switching are considered core processes that support broader learning (Blair & Razza, [<reflink idref="bib6" id="ref1">6</reflink>]; Miyake & Friedman, [<reflink idref="bib61" id="ref2">61</reflink>]; Modrek & Ramirez, [<reflink idref="bib64" id="ref3">64</reflink>]; Modrek et al., [<reflink idref="bib65" id="ref4">65</reflink>]) and important aspects of temperament, such as self‐restraint (Posner & Rothbart, [<reflink idref="bib79" id="ref5">79</reflink>]; Nigg, [<reflink idref="bib69" id="ref6">69</reflink>]; Kochanska et al., [<reflink idref="bib51" id="ref7">51</reflink>]). Less known, however, is how the structure and potential function of EF develops longitudinally.</p> <p>EF comprises different constructs that show patterns of similarity, as well as patterns of dissimilarity, a concept described as "unity and diversity." That is, different EF components share similar functions, while those same components may be separable due to other functions. In this present work, with a sample of Ghanaian youth we ask: How does the unification or diversification of EF components change across development? We investigate EF components that are most frequently studied: working memory (monitoring), inhibition (the ability to block irrelevant information or interferences from attentional focus), and cognitive flexibility/switching (shifting between tasks). Because of shared (unity) and distinct (diversity) functions of EF components, it has been conceptualized as a construct where the variance is either fully shared (unification) or separate constructs that can be discriminant (diversification).</p> <p>Unified models of EF (showing significant overlap between different components) have been found to show differential outcomes compared to diversified models of EF (where different components remain statistically distinguishable). For example, with a sample of over 800 U.S. toddlers, single‐factor (unified) EF models were linked to self‐restraint at age 17 (Friedman et al., [<reflink idref="bib33" id="ref8">33</reflink>]), a finding that held even after controlling for intelligence. Such results suggest the structure of EF is related to real‐world, long‐term outcomes and behaviors (Friedman & Miyake, [<reflink idref="bib34" id="ref9">34</reflink>]).</p> <p>A plethora of studies have investigated the structure of EF testing unification and diversification where a conclusive answer as to when diversification develops remains in question. In theory, when functions between different EF components are distinguishable, they show "diversity," and when EF components significantly overlap, showing shared variance, they show "unity" (Teuber, [<reflink idref="bib87" id="ref10">87</reflink>]). While different EF components correlate with one another, (demonstrating unity), they simultaneously show distinguishability and low interrelatedness (also demonstrating diversity) in EF component structure (Miyake & Friedman, [<reflink idref="bib61" id="ref11">61</reflink>]). Theoretically, when EF components are distinguishable, showing "diversity," they can be tested as loading onto two or more, not one, factor suggesting that distinct EF components developed differently and relate more with some components but not others. Empirically, confirmatory factor analysis (CFA), a form of structural equation modeling, can assess EF component structure, enabling researchers to utilize models testing the relationship between different EF components. This provides a proxy of how some EF components may be more exercised than others, thus having a more distinctive development, or, a more unified development (showing "unity," and loading on a single factor). For example, with a sample of 7‐year‐old children in the United Kingdom, Bull and Scerif ([<reflink idref="bib11" id="ref12">11</reflink>]) found inhibition was found to be positively associated with switching (unity), while these same components with a sample of U.S. adults showed (diversity) more dissimilarity (Miyake et al., [<reflink idref="bib63" id="ref13">63</reflink>]). Such literature confirms that EF components can be interrelated while simultaneously showing distinguishability. Inhibition has been considered focal to identifying EF component structure given most EF components require inhibitory control (Miyake et al., [<reflink idref="bib63" id="ref14">63</reflink>]). Switching is similarly considered a strong contributor to EF component structure given its crucial role when learning, as it supports shifting between new rules and different tasks (Heaton et al., [<reflink idref="bib40" id="ref15">40</reflink>]). The associations between these constructs, however, may vary across development.</p> <hd id="AN0180281944-3">Development of EF structure</hd> <p>Findings on changes in EF component structure across development are not consistent. For instance, using cross‐sectional data, studies have found memory and inhibition to be inseparable (i.e., showing unity) from ages 4 to 9.5 years, but distinguishable (i.e., showing diversity) in 9.5‐ and 14.5‐year‐olds (Shing et al., [<reflink idref="bib84" id="ref16">84</reflink>]). In contrast, other studies have found inhibition, shifting, and memory to be inseparable, (i.e., showing unity) up to age 13 (Xu et al., [<reflink idref="bib100" id="ref17">100</reflink>]). Most existing work testing developmental differences of EF structure is limited to cross‐sectional data. Few studies have investigated the component structure of EF development by following a sample longitudinally. A major goal of the current work is, thus, to investigate developmental changes in the EF component structure across childhood and into early adolescence with a sample, longitudinally.</p> <p>While EF constructs have been found to be intercorrelated in adult samples, they have also shown significant distinguishability, suggesting they are independent constructs and EF structure diversifies by adulthood. A three‐factor component structure of EF was first proposed by Miyake, Emerson, and Friedman ([<reflink idref="bib62" id="ref18">62</reflink>]) with a sample of young U.S. adults. Specifically, three‐factor models best explained the structure of EF—that working memory, inhibition, and shifting should be operationalized as three distinct components of EF (diversified), rather than a singular (unified) composite construct (Miyake et al., [<reflink idref="bib63" id="ref19">63</reflink>]). It was concluded that working memory, inhibition, and shifting were distinguishable (with moderate to low correlations). This does not suggest that EF is fully diversified, and thus not unified, but instead it suggests that a diversified model was achieved even though components of executive function were still related and thus understandably, still showing unity. That is, Miyake and colleagues demonstrated three distinct, but interrelated EF components ([<reflink idref="bib63" id="ref20">63</reflink>]). Since then, data from behavioral and neuroimaging studies have supported diversification of EF constructs in adults (emphasizing distinction between working memory, inhibition, and shifting), with literature suggesting diversification of EF emerges later in development, specifically in the transition from adolescence into adulthood (Miyake et al., [<reflink idref="bib63" id="ref21">63</reflink>]).</p> <p>A multi‐factor model of EF component structure, such as a three‐factor model, is most commonly found in adults and has not been found in young children. Across several studies with 2‐ to 6‐year‐olds in the United States, EF components were consistently found to load on a single factor, with inhibition and working memory showing strong interrelatedness, suggesting unification in EF component structure; the authors did not test shifting (Wiebe et al., [<reflink idref="bib93" id="ref22">93</reflink>]). In line with these data, a longitudinal study with 4‐ and 6‐year‐old children from the United Kingdom found a single‐factor EF component structure (demonstrating unity) across working memory, inhibition, and planning (Hughes et al., [<reflink idref="bib42" id="ref23">42</reflink>]). Brydges and colleagues ([<reflink idref="bib10" id="ref24">10</reflink>]) similarly tested EF structure with children in Australia (7‐ to 9‐years‐old), finding shifting, inhibition, and working memory to be a single‐factor (showing unity) of EF structure. In an extensive review of EF literature comprising 46 samples and over 9,000 participants, Karr and colleagues ([<reflink idref="bib48" id="ref25">48</reflink>]) reviewed trends of single‐factor (unity) versus multi‐factor (diversity) patterns of EF component structure across development, finding a unidimensional, one‐factor model of EF most often utilized with children.</p> <p>While overall findings with young children show a general trend of a unified model of EF component structure, findings with late‐childhood and adolescence are mixed, where both one‐factor (unified) and multi‐factor (diversified) models of EF component structures have been found (Karr et al., [<reflink idref="bib48" id="ref26">48</reflink>]). For example, with a sample of 7‐ to 15‐year‐old participants in east China, a single‐factor EF model (showing unity) best explained EF structure with participants aged 7 to 12, while a three‐factor model (showing diversification) best explained EF structure with participants aged 13 to 15 with EF components inhibition, working memory, and shifting, suggesting diversification of EF component structure developing into adolescence (Xu et al., [<reflink idref="bib100" id="ref27">100</reflink>]). In contrast, with a sample of children, adolescents, and adults from Netherlands (<reflink idref="bib7" id="ref28">7</reflink>, 11, 15, and 21 year olds), no changes in EF component structure were found with age. In this cross‐sectional study, only working memory and shifting were separable in all age groups including the adult group (Huizinga et al., [<reflink idref="bib44" id="ref29">44</reflink>]). With a sample of children and adolescents from east China, EF component structure was found to gradually separate with age. Specifically, memory maintenance and inhibitory control were not separable with participants 4‐ to 9.5‐years‐old (showing unity), but separable in 9.5‐ to 14.5‐year‐olds (showing diversification) by late childhood (Shing et al., [<reflink idref="bib84" id="ref30">84</reflink>]).</p> <p>Taken together, studies that identify a one‐factor EF component structure (demonstrating unity) include young children compared to data providing evidence for two‐ or three‐factor EF component structure (demonstrating diversity) that include middle to late childhood, adolescent, and adult participants. To our knowledge, few studies longitudinally test timing of changes in EF component structure. Most studies have collapsed, for example, late childhood with early‐adolescence and compared them cross‐sectionally to a sample of collapsed participants from early childhood with middle childhood. Indeed, different components of EF have distinct developmental milestones during critical periods within childhood alone, showing heightened development during middle childhood followed by more gradual growth and development into adolescence. One arguable reason for many of the mixed results within Western contexts is researchers working with school‐aged participants, where participants were collapsed across age ranges studied, for example, middle childhood to post‐adolescence (Wu et al., [<reflink idref="bib99" id="ref31">99</reflink>]). Additionally, working memory, inhibition, and shifting have different developmental trajectories. Another potential reason for mixed results within Western contexts is that different studies utilize different measures to assess EF (Obradović, & Willoughby [<reflink idref="bib74" id="ref32">74</reflink>]; Obradović, [<reflink idref="bib72" id="ref33">72</reflink>]). For instance, inhibition being measured with the Flankers task and Go/no‐go task in some work, while being measured with the Tower of London and Matching Familiar Figures Test in other work.</p> <p>While many studies indeed find a one‐factor structure for much younger children, most of these studies have been with cross‐sectional samples from the United States, Europe, Australia or Asia. Given that environmental factors affect the development of EF (Carlson, [<reflink idref="bib15" id="ref34">15</reflink>]; Lewis et al., [<reflink idref="bib53" id="ref35">53</reflink>]) and Sub‐Saharan youth are extremely understudied in EF literature (Nielsen et al., [<reflink idref="bib68" id="ref36">68</reflink>]), we examine questions of the development of EF structure with a longitudinal sample of Ghanaian youth.</p> <hd id="AN0180281944-4">Executive function across contexts</hd> <p>EFs have been considered culturally universal (Obradović et al., [<reflink idref="bib75" id="ref37">75</reflink>]). However, EF development may be impacted by environmental characteristics, such as exposure to poverty or poor parenting, which can impact EF performance and changes in brain structure supporting it (Ellwood‐Lowe et al., [<reflink idref="bib26" id="ref38">26</reflink>]; Raver et al., [<reflink idref="bib81" id="ref39">81</reflink>]). It has been found that, for instance, changing kindergarten classroom environments can change the development of children's EF (Wolf & McCoy, [<reflink idref="bib96" id="ref40">96</reflink>]). There is also evidence suggesting that intervening in environments with children as early as 2 years of age can support the development of EF in at‐risk samples (Blair et al., [<reflink idref="bib7" id="ref41">7</reflink>]). For example, in a longitudinal analysis on the development of executive function abilities in early childhood, Blair and colleagues ([<reflink idref="bib7" id="ref42">7</reflink>]) found changes in environmental factors such as maternal sensitivity were positively associated with positive development in child executive function performance, suggesting environments that are intervened on, such as parenting quality, can have a positive effect on executive function development in early childhood. Less understood, however, is whether such findings extend to the development of EF <emph>structure</emph>, and not just performance.</p> <p>A fruitful direction for the study of EF development is to shift its conceptual focus from being culturally universal to emphasizing environmental factors that explain differences or divergence in its development. One way to achieve this is to understand if and why the structure of EF components develop and diversify differently. EF develops in line with expectations to meet specific tasks and demands in the environment (Doebel, [<reflink idref="bib23" id="ref43">23</reflink>]), pivoting EF as a contextually contingent outcome where developmental differences in EF, such as differences in its component structure, reflect an adaptive process that applies for some individuals, but not others. This perspective would explain both individual and group differences in EF component structure development as experience‐driven. A child's EF component structure may develop because of environmental expectations and respective exertion of self‐regulation (e.g., cognitive control) in responding and adapting to context‐specific goals. More specifically, some children struggle practicing restraint (Kochanska et al., [<reflink idref="bib51" id="ref44">51</reflink>]), while there are contextual factors (e.g., adults conveying expectations of behaviors; Toner et al., [<reflink idref="bib88" id="ref45">88</reflink>]), that support the notion that EF development depends on effortful control of wanting to meet those expectations (Kochanska & Knaack, [<reflink idref="bib52" id="ref46">52</reflink>]; Modrek & Kuhn, [<reflink idref="bib66" id="ref47">66</reflink>]). Such effortful control is an aspect of temperament related to frontal lobe development, supporting executive function activation and performance explaining changes in its structural development (Eisenberg et al., [<reflink idref="bib25" id="ref48">25</reflink>]; Posner & Rothbart, [<reflink idref="bib78" id="ref49">78</reflink>]; Rueda et al., [<reflink idref="bib82" id="ref50">82</reflink>]).</p> <p>The development of EF component structure, and maturation, is potentially contextually contingent and experience‐driven, and not fully age dependent. For example, when adhering to cultural expectations, there might be some behaviors that are considered favorable and expected in one environment, but not another (Simmons et al., [<reflink idref="bib85" id="ref51">85</reflink>]; Nketia et al., [<reflink idref="bib70" id="ref52">70</reflink>]). One example is that of a high‐stress environment, rendering a child to respond more frequently with rapid and reactive responses (i.e., fight response) appropriate from a survival adaptative perspective, which is in contrast to EF employing self‐restraint, like inhibitory control (i.e., freeze or flight response). This would lead to distinct EF components, such as inhibitory control, developing differently between these two contexts. This suggests EF components develop not always with age, but with experience. In low‐support environments, with tasks with a high cognitive load put on children more frequently, EF will necessarily develop to adapt to such environmental demands and tasks.</p> <p>While the majority of EF research has been conducted in the United States, China, Western Europe, and other Western high‐income countries, there is a growing literature on studying EF in low‐ and middle‐income regions, and more specifically in Sub‐Saharan Africa (Obradović & Willoughby, [<reflink idref="bib74" id="ref53">74</reflink>]). Measures have included performance‐based assessments (e.g., Obradović et al., [<reflink idref="bib75" id="ref54">75</reflink>]; Wolf & McCoy, [<reflink idref="bib96" id="ref55">96</reflink>]), tablet‐based assessments (Ford et al., [<reflink idref="bib32" id="ref56">32</reflink>]; Suntheimer et al., [<reflink idref="bib86" id="ref57">86</reflink>]; Willoughby et al., [<reflink idref="bib94" id="ref58">94</reflink>]), and assessor‐ and teacher‐reports (Ahmed et al., [<reflink idref="bib1" id="ref59">1</reflink>]). Much of this work has been conducted in early childhood, with one recent study focused on middle childhood (Suntheimer et al., [<reflink idref="bib86" id="ref60">86</reflink>]), and nearly all operationalize EF as a single construct. To our knowledge, no studies to date have examined the factor structure of EF with Sub‐Saharan African youth. A recent review of EF studies in Africa results showed a broad consensus on EF definitions, little consistency in how dimensions of EF were investigated, and mixed findings on how EF might be associated with other variables like academic achievement (Ezeugwu et al., [<reflink idref="bib28" id="ref61">28</reflink>]).</p> <hd id="AN0180281944-5">Executive function development in Sub‐Saharan Africa</hd> <p>Majority of children in West Africa experience physical punishment or psychological aggression (Wolf & Suntheimer, [<reflink idref="bib97" id="ref62">97</reflink>]). Additionally, children engage in labor at high rates, specifically Ghanaian youth who are involved in rigorous, economic work activities from an early age (UNICEF, [<reflink idref="bib91" id="ref63">91</reflink>]). Living conditions of youth in Ghana and Sub‐Saharan Africa have been investigated, with little known about the effects on EF and the development of its structure. In the present work, we test the development of unity versus diversity in EF components with a sample of Ghanaian children, hypothesizing that its structure might emerge differently. Specifically, we posit diversification of EF might emerge earlier on due to the demands and stress put on children earlier on in life. This hypothesis is supported by the adversity‐induced acceleration hypothesis (Callaghan & Tottenham, [<reflink idref="bib14" id="ref64">14</reflink>]) suggesting adversities experienced early in life facilitate heightened changes across development. Adversity experienced with early caregiving has shown to accelerate timing of development in brain regions that support the development of skills needed to adapt (Gee et al., [<reflink idref="bib35" id="ref65">35</reflink>]; Gunner & Quevedo, [<reflink idref="bib38" id="ref66">38</reflink>]), corroborating with preservative behaviors that heighten EF activity (Kirkham et al., [<reflink idref="bib50" id="ref67">50</reflink>]; Zelazo et al., [<reflink idref="bib103" id="ref68">103</reflink>]).</p> <p>Given documentation that young children in Sub‐Saharan Africa face high rates of delay in meeting basic developmental milestones, and Ghanaian youth in particular showing high rates of violent discipline and engaging in labor and work‐related activities at high rates, such interpersonal hardships might explain developmental patterns of early onset of EF structure diversification as a developmental outcome of skills that provide an adaptive advantage (Lu et al., [<reflink idref="bib55" id="ref69">55</reflink>]; McCoy et al., [<reflink idref="bib58" id="ref70">58</reflink>]; UNICEF, [<reflink idref="bib91" id="ref71">91</reflink>]). Long have such disadvantages and experiences of youth in Ghana and the Sub‐Saharan Africa region more broadly been identified with little known about the effects on EF structure, and how this might facilitate earlier diversification of EF component structure. Such interpersonal hardships might explain developmental patterns of early onset of EF structure diversification.</p> <hd id="AN0180281944-6">Present study</hd> <p>In the present work we examine developmental changes in the structure of EF across childhood in a longitudinal sample of youth in Ghana, examining changes in the unity or diversity of EF component structures across development. Our results provide an important addition to the current evidence‐base about the development of EF under different levels of adverse conditions.</p> <p>We hypothesize developmental differences in the factor structure of EF will emerge early, starting as a more mature, diversified model with this Ghanaian sample. Specifically, we posit a two‐factor model of EF will best explain the structure of EF components in earlier years (3 to 4 years of age) given the societal demands and living conditions of Ghanaian youth. Our results have implications to contribute to the existing narrative on EF literature and development, and for global initiatives to improve the development of children's executive functions (Aurora et al., [<reflink idref="bib3" id="ref72">3</reflink>]; Ball et al., [<reflink idref="bib4" id="ref73">4</reflink>]; Jukes et al., [<reflink idref="bib46" id="ref74">46</reflink>]; Khan et al., [<reflink idref="bib49" id="ref75">49</reflink>]).</p> <hd id="AN0180281944-7">METHODS</hd> <p></p> <hd id="AN0180281944-8">Participants</hd> <p>Participants were part of a longitudinal study that began in fall 2015 and spring 2016 (<emph>N</emph> = 2979; <emph>M<subs>age</subs></emph> = 5.2 years, SD = 1.3) (Wolf et al., [<reflink idref="bib98" id="ref76">98</reflink>]; Wolf, [<reflink idref="bib95" id="ref77">95</reflink>]). Children were originally sampled from 240 representative public and private preprimary schools across six districts in the Greater Accra Region (Wolf et al., [<reflink idref="bib98" id="ref78">98</reflink>]) and were subsequently followed. We use data collected in six waves over 7 years (fall 2015; spring 2016; spring 2017; spring 2018; summer 2021; winter 2022).</p> <p>The analytic sample differed from the original baseline sample in the following ways: First, we restructured the data to long structure, such that each observation coded represented data of a child at a particular wave, for example, one child has data for waves 1, 2, and 3, and so forth, while another child has data for waves 1, 2, (not 3), and so forth. (c.f., Lloyd et al., [<reflink idref="bib54" id="ref79">54</reflink>]; Young & Johnson, [<reflink idref="bib102" id="ref80">102</reflink>]). Next, each EF task composite average was computed only if more than half of its corresponding items/sub‐tasks were available (non‐missing). Composite averages were not standardized, but all take values from 0 to 1 (i.e., indicating 0% to 100% correct). They did not have identical means or variance which is acceptable for the present analyses (given confirmatory model fit is unaffected whether unstandardized or standardized measures are utilized). Out of a total of 3,867 children, 2,979 had non‐missing age and contributed at least one observation (i.e., assessment data from at least one wave). Thus, with the long structure model of the data, there are a total of 16,855 observations in the dataset, but 3,127 are from participants missing age data, concluding with an effective <emph>N</emph> = 13,728 (with age and at least one task/score not missing).</p> <hd id="AN0180281944-9">Procedures</hd> <p>Children's executive function skills were assessed directly in their schools at each wave by trained Ghanaian enumerators who were randomly assigned to students. Enumerators received extensive training with the research team and study protocols and had prior experience with research methodology. Enumerators spoke English, Hausa languages, as well as Ga, Twi, and Ewe. Data collectors worked with administrators to designate a few quiet spaces on the school grounds where assessments could be conducted. To help children feel comfortable, at each data collection wave, the data collectors chatted with children informally for several minutes prior to beginning assessments; assessments were conducted in the language in which the child was most comfortable.</p> <p>The study was approved by University of Pennsylvania's ethics review boards. The rights of the participants were protected, and applicable human research subject guidelines were followed in this research.</p> <hd id="AN0180281944-10">EF instruments</hd> <p>For Wave 1, Wave 2, and Wave 3, three dimensions of executive function were measured using the International Development and Early Learning Assessment (IDELA; Pisani et al., [<reflink idref="bib77" id="ref81">77</reflink>]). <emph>Short‐term Memory</emph> was measured using the forward digit span. For this task, children were asked to repeat the digit span (where the assessors read aloud five‐digit sequences, beginning with two digits and increasing up to six digits) and marked the child responses as correct or incorrect. <emph>Working memory</emph> was assessed using the backward Digit Span. The same procedure was followed with students to repeat the digit span in the reverse order. Of note, is that younger participants in our sample (i.e. 3‐year‐olds) could not complete the backward digit span task, thus we had huge floor effects suggesting the need to use forward digit span as a more developmentally appropriate task for our assessment. Finally, <emph>inhibitory control</emph> was assessed using the head–toes task (adapted from the head‐knees‐toes‐shoulder task; McClelland et al., [<reflink idref="bib57" id="ref82">57</reflink>]). When the assessor touched his or her head, children were asked to touch their toes, and vice versa in a series of five items. In Wave 4, we again assessed <emph>working memory</emph> using the forward Digit Span where the same procedure was followed with students repeating the digit span in the same order. In Wave 4, the stacked, long structure of the data indicated significant gaps between some of the observations where overlap between participant EF measures in prior waves were missing, resulting in insufficient non‐missing overlap, thus in Wave 4 EF scores were effectively imputed based on the correlation between short‐term memory and working memory from previous waves. Wave 5 was omitted due to an intentional subsample of 400. No child EF data were collected during Wave 6.</p> <p>For Wave 7 and Wave 8, we utilized the Hearts and Flowers task and memory tasks. The Hearts and Flower task measured inhibitory control and cognitive flexibility (Davidson et al., [<reflink idref="bib20" id="ref83">20</reflink>]). The task comprised four blocks: heart, flowers, slow mixed, and fast mixed. The hearts block included congruent trials which measured inhibitory control, and the flowers block included incongruent trials, the remaining mixed blocks measured cognitive flexibility and varied by speed, (fast vs. slow). An image of either a heart or a flower was shown to children on one side of the tablet screen. A memory game was assessed to measure both short‐term and working memory. To assess short‐term memory, a sequence of green‐colored squares would light up in an unpredictable pattern where the "forward" block required children to touch squares in the same order they lit up. To assess working memory, in the "backward" block, children were required to touch squares in reverse order (Obradovic, [<reflink idref="bib73" id="ref84">73</reflink>]). Performance was based on correct responses.</p> <hd id="AN0180281944-11">Analytic plan</hd> <p>Due to shared (unity) functions between EF components and distinguishability (diversity) between them, CFA (see Karr et al., [<reflink idref="bib48" id="ref85">48</reflink>]), a form of structural equation modeling, has been deemed the appropriate tool to assess EF component structure enabling researchers to select and infer models testing the relationship between different EF components (MacCallum & Austin, [<reflink idref="bib56" id="ref86">56</reflink>]) allowing for evaluation and validation of EF structure (Brown & Moore, [<reflink idref="bib9" id="ref87">9</reflink>]). For instance, CFA estimates different EF components as a structure by omitting unique variance from each task to address the common measurement error across different EF tasks (see Engle et al., [<reflink idref="bib27" id="ref88">27</reflink>]; Jackson et al., [<reflink idref="bib45" id="ref89">45</reflink>]; Kane et al., [<reflink idref="bib47" id="ref90">47</reflink>]; Miyake et al., [<reflink idref="bib63" id="ref91">63</reflink>]; Miyake, Emerson, & Friedman, [<reflink idref="bib62" id="ref92">62</reflink>]; Shah & Miyake, [<reflink idref="bib83" id="ref93">83</reflink>]).</p> <p>To test our focal hypothesis, we first describe the models of interest to be tested: one‐ versus two‐factor loading of EF (see theoretical models, Table 1). Existing work suggests three‐factor models of EF emerge around adulthood. Two‐factor models, however, have been generally found to emerge by adolescence or late childhood. Thus, our testing of two‐factor models were theory driven in testing <emph>heightened</emph> development of EF in the very young children in our sample, to see if they had developed EF structure of what is more common with older children (e.g., two‐factor models). When employing empirical models to test CFA loading of EF, we account for the fact that a child participant may contribute up to two observations (i.e., at ages 3 and 4) when we observe <emph>p</emph>‐values (i.e., testing path coefficients), we thus specified a cluster‐robust variance in our models. We utilized maximum likelihood estimation for missing data (a common statistical tool used to address implicit imputation of missing data) valid under the assumption that data are missing at random.</p> <p>1 TABLE Theoretical models tested.</p> <p> <ephtml> <table><thead><tr><th>Theoretical models</th><th /></tr></thead><tbody><tr><td>One‐factor (1F), Unification</td><td>EF components are not distinguishable. All EF tasks load on a single latent factor.</td></tr><tr><td>Two‐factor (2F), Diversification</td><td>EF components are distinguishable, with at least one component not collapsing with another.For example, short‐term memory/working memory collapsed + shifting, where shifting is distinguishable from working memory and short‐term memory, though short‐term memory and working memory are not distinguished.</td></tr></tbody></table> </ephtml> </p> <p>We first report confirmatory factor analyses on the whole sample testing these models. We next run CFA of both one‐ and two‐factor models and report fit indices testing developmental differences in structure of EF. Participant performance scores were subsetted by age bracket, across waves. Chi‐square (χ2), χ2/df, root‐mean‐square error of approximation (RMSEA), comparative fit index (CFI), and Akaike Information Criterion (AIC), the most common and widely established fit indices, were used to evaluate fit of each EF component structure model (see Bentler & Bonett, [<reflink idref="bib5" id="ref94">5</reflink>]). Following confirmatory factor model fit assessments (see Byrne et al., [<reflink idref="bib13" id="ref95">13</reflink>]), since the two models are testing separate factor loading and not nested within one another, optimal model fit was deemed contingent upon a non‐significant χ2 value at the .05 level, a larger coefficient of determination (CoD), CFI, a smaller RMSEA, and smaller AIC values. Given empirical evidence demonstrating difference scores have poor psychometric properties, we do not report difference scores between models as valid indices (Draheim et al., [<reflink idref="bib24" id="ref96">24</reflink>]; Griffin et al., [<reflink idref="bib37" id="ref97">37</reflink>]; Hughes et al., [<reflink idref="bib43" id="ref98">43</reflink>]; Miller & Ulrich, [<reflink idref="bib60" id="ref99">60</reflink>]; Paap & Sawi, [<reflink idref="bib76" id="ref100">76</reflink>]; Yangüez et al., [<reflink idref="bib101" id="ref101">101</reflink>]).</p> <hd id="AN0180281944-12">RESULTS</hd> <p>CFA was employed to evaluate factor structure of EF comparisons across age groups (Table 1). In line with prior work, we first tested for the best fitting model on the entire sample of 3‐ to 12‐year‐olds, see Table 2. The results of CFA (Table 2) goodness of fit indices on the entire sample provide insights suggesting an inconclusive optimal factor loading for the entire sample.</p> <p>2 TABLE Goodness of fit indices with whole sample, ages 3 to 12.</p> <p> <ephtml> <table><thead><tr><th>Model</th><th><italic>X</italic><sup>2</sup></th><th><italic>p</italic></th><th>CoD</th><th>RMSEA</th><th>CFI</th><th>AIC</th></tr></thead><tbody><tr><td>1F</td><td>8.69</td><td>p < .01</td><td>.78</td><td>.019</td><td>.99</td><td>−1524.570</td></tr><tr><td>2F</td><td>8.69</td><td>p < .001</td><td>.78</td><td>.030</td><td>.99</td><td>−1522.570</td></tr></tbody></table> </ephtml> </p> <p>1 Abbreviations: AIC, Akaike Information criterion; CFI, comparative fit index; CoD, coefficient of determination; RMSEA, root‐mean‐square error of approximation.</p> <p>Indices presented in Table 2 confirm theory guiding our age differentiation hypotheses and analytic method that factor loading with this sample needs to be tested by age bandwidth. Next, to assess EF factor construct by each age group (3‐ to 4‐year‐olds, 5‐ to 6‐year‐olds, 7‐ to 9‐year‐olds, and 10‐ to 12‐year‐olds), CFA models testing both one‐factor and two‐factor models were run with the four age groups followed by CFA testing fit indices, respectively. Age groups were, for example, 3‐ to 4‐year‐olds (preschoolers), 5‐ to 6‐year‐olds (early elementary), 7‐ to 9‐year‐olds (middle childhood), and 10‐ to12‐year‐olds (early adolescence).</p> <p>Testing our hypothesis that factor structure of EF may change across age groups, alternative models were tested separately for each age group using CFA without nesting the models within one another. We next report CFA indices of goodness of fit indices between one‐ versus two‐factor models by age. (For full models and fit indices, see Tables S1–S8). Below, Table 3 presents the CFA model fit results of each factor loading (one‐ versus two‐factor) by age group. Our first model (with 3‐ to 4‐year‐olds) is a one‐factor CFA which we find, based on the statistical fit indices, is not a strong fit (see Table 3) for this age group. We next test and compare to a two‐factor model (on 3–4‐year‐olds) finding the two‐factor model is a significant and optimal fit. This tells us preliminarily that EF structure has diversified early on, with a two‐factor CFA model being more optimal in the 3–4‐year‐old age group. Indeed, the two‐factor model reported significant and strong fit indices, compared to the one‐factor model which did not (CoD = .916, <emph>p =</emph> .6512 and CoD = .628, <emph>p</emph> < .001, respectively). While this contradicts existing literature, it confirms our hypothesis of expected early diversification of EF with young Ghanaian children. These results suggest that working memory, short‐term memory, and inhibition are statistically dissociable in 3‐ to 4‐year‐olds. This is one of the first reports of such early emergence of EF structure diversification. Correlations within this age group demonstrated significant but generally low to medium associations between EF tasks (range <emph>r</emph> = .095 to <emph>r =</emph> .428, and <emph>p <</emph> .05) confirming interrelatedness, but also substantial non‐shared variance.</p> <p>3 TABLE Goodness of fit indices for one‐factor versus two‐factor CFA models by age group.</p> <p> <ephtml> <table><thead><tr><th>Age</th><th>Model</th><th><italic>X</italic><sup>2</sup></th><th><italic>p</italic></th><th>CoD</th><th>RMSEA</th><th>df</th><th>CFI</th><th>AIC</th></tr></thead><tbody><tr><td>3 to 4</td><td>1F</td><td>13.94</td><td>p < .001</td><td>.628</td><td>.061</td><td>2</td><td>.947</td><td>−67.161</td></tr><tr><td>2F</td><td>0.2</td><td>p = .6512</td><td>.916</td><td>0</td><td>1</td><td>1</td><td>−78.895</td></tr><tr><td>5 to 6</td><td>1F</td><td>13.21</td><td>p < .001</td><td>.512</td><td>.087</td><td>2</td><td>.865</td><td>−2364.646</td></tr><tr><td>2F</td><td>0.17</td><td>p = .676</td><td>.793</td><td>0</td><td>1</td><td>1</td><td>−2375.686</td></tr><tr><td>7 to 9</td><td>1F</td><td>4.45</td><td>p = .1078</td><td>.769</td><td>.072</td><td>2</td><td>.985</td><td>−165.163</td></tr><tr><td>2F</td><td>4.45</td><td>p < .05</td><td>.88</td><td>.121</td><td>1</td><td>.979</td><td>−163.163</td></tr><tr><td>10 to 12</td><td>1F</td><td>8.796</td><td>p < .01</td><td>.754</td><td>.036</td><td>2</td><td>.996</td><td>−1289.453</td></tr><tr><td>2F</td><td>9.09</td><td>p < .001</td><td>.806</td><td>.054</td><td>1</td><td>.995</td><td>−1287.453</td></tr></tbody></table> </ephtml> </p> <p>2 Abbreviations: 1F, one‐factor; 2F, two‐factor; AIC, Akaike Information criterion; CFI, comparative fit index; CoD, coefficient of determination; RMSEA, root‐mean‐square error of approximation.</p> <p>As the sample ages from 5 to 6 years old, we continue to see a significant difference between one versus two factor models, with the two‐factor model remaining substantially better. This continues to confirm our hypothesis and contradict existing literature of single unitary factor loading to be the more common EF structure in early childhood. Indeed, the two‐factor model reported significant and strong fit indices, compared to the one‐factor model which did not (CoD = .793, <emph>p =</emph> .676 and CoD = .512, <emph>p</emph> < .001, respectively). Correlations within this age group demonstrated significant but generally low to medium associations between EF tasks (range <emph>r</emph> = .070 to <emph>r =</emph> .358, and <emph>p <</emph> .05), like the 3–4‐year‐old sample, showing significant relations but ample non‐shared variance between the EF constructs resulting in a significantly better two‐factor, diversified, EF structure model.</p> <p>By 7 to 9 years of age, a two‐factor model seems more optimal, but not significantly better than the one‐factor model. Although the two models reported possible fit, it was in fact the two‐factor EF structure model that was more admissible compared to the unitary, one‐factor model (CoD = .88, <emph>p</emph> < .05 and CoD = .769, <emph>p</emph> = .107, respectively). Correlations within this age group demonstrated significant, medium associations between EF tasks (range <emph>r</emph> = .451 to <emph>r =</emph> .585, and <emph>p <</emph> .05). By 10 to 12 years of age, EF structure continues to show a two‐factor model as optimal, though also not significantly different. Although the two models in this age group similarly demonstrated possible fit, it was still the two‐factor EF structure model that was found to be more admissible compared to the unitary, one‐factor model (CoD = .806, <emph>p</emph> < .001, and CoD = .754, <emph>p</emph> < .01, respectively). Correlations within this age group demonstrated significant, low to medium associations between EF tasks (range <emph>r</emph> = .086 to <emph>r =</emph> .556 and <emph>p <</emph> .05) persisting in showing substantial non‐shared variance.</p> <p>In sum, our initial hypothesis was confirmed by results pointing to a two‐factor CFA model of EF structure as more optimal than a one‐factor model of EF structure in children as young as 3‐ to 4‐years‐old. This is evidence of EF structure diversifying earlier than expected, with a two‐factor CFA model being the best model fit with the 3–4‐year‐old age group and persisting as the optimal EF factor structure in 5‐ to 6‐year‐olds who also demonstrated having a two‐factor EF structure model as the strongest fit. While a two‐factor model persisted as the optimal EF structure for 7‐ to 9‐year‐old children and children 10 to 12 years of age, the better model fit was not statistically significant. Still, CFA fit indices suggest two‐factor models the stronger fit across age groups, with possible need to test three‐ and four‐factor EF structure models on older age groups (7‐ to 9‐years‐old and 10‐ to 12‐years‐old).</p> <hd id="AN0180281944-13">DISCUSSION</hd> <p>The current study examined the structure of EF across different age‐periods from early childhood to early adolescence in West Africa with a large, longitudinal sample of children from Ghana. We administrated a battery of EF measures for working memory, short‐term memory, and shifting/cognitive flexibility and then employed CFA to characterize the optimal factor structure of EF among four time points: 3–4, 5–6, 7–9, and 10–12 years of age. We re‐examined theories of unification versus diversification of EF structure across development in a sample of West African children, a demographic rarely represented in research on EF development (Nielsen et al., [<reflink idref="bib68" id="ref102">68</reflink>]).</p> <p>We found early emergence of diversification in EF <emph>structure</emph> for Ghanaian children. Our findings indicate, in contrast to existing literature, that as early as 3 years of age, a two‐factor model best accounted for EF structure, suggesting EF components separated into dissociable domains as early as age 3. With this sample of Ghanaian children, factor structure of EF did not start from a unified, one‐factor model in preschool and develop into to a diversified, multi‐factor model. Instead, we found our focal hypothesis supported by contextually bound theories of EF, demonstrating diversification (e.g., two‐factor loading) of EF structure as having already emerged early on in development.</p> <p>This trend of diversification with this sample persists through ages 5–6, where a two‐factor model continued to best fit the data. By ages 7 to 9 and 10 to 12, neither a one‐ nor two‐factor model was found to be significantly better than the other, though a two‐factor model remained optimal based on goodness of fit statistics. This might be due to the possibility that in this sample, a three‐factor model might be starting to emerge after age 7, but additional measures would have needed to be collected in order to test this. Our results provide data highlighting potential variability in <emph>when</emph> diversification develops.</p> <p>Our findings provide evidence of early emergence of diversification of EF components, disconfirming the more commonly supported age differentiation hypothesis that argues structure divergence, namely EF components, starts with a more unified, general EF ability earlier on in life and evolves into a more diversified multi‐factor structure with age. For example, with a sample of Chinese youth, Xu et al. ([<reflink idref="bib100" id="ref103">100</reflink>]) found constructs of working memory, inhibition, and shifting best loaded as a single (unified) factor model up to age 12, and only started to show diversification at 13 years of age. Here, with this sample of Ghanaian youth, we found such diversification to emerge as early as age 3. While inconsistency in findings may stem from different tasks utilized, it is still possible that these differences might be attributed to lived experiences of the samples.</p> <p>In U.S. contexts, children from lower income backgrounds experience slower EF development relative to children in higher socioeconomic categories, suggesting later onset of diversification (Farah et al., [<reflink idref="bib29" id="ref104">29</reflink>]; Noble et al., [<reflink idref="bib71" id="ref105">71</reflink>]). We find that in Ghana, EF did not show <emph>delayed</emph> diversification, but rather, <emph>early</emph> emergence. While delayed development of EF may delay diversification (Tucker‐Drob, [<reflink idref="bib90" id="ref106">90</reflink>]), our results question what might explain its early onset. Why might this be the case?</p> <p>We offer a cohesive, cross‐disciplinary interpretation of these results, corroborated by neuropsychological research and developmental science suggesting EF is experience‐dependent and driven by cultural expectations. A study conducted in Japan by Moriguchi and Hiraki ([<reflink idref="bib67" id="ref107">67</reflink>]) argued a functional developmental approach of the prefrontal cortex during preschool years, specifically the development and activation of EF (e.g., shifting strategies) as shown to correlate with the inferior prefrontal cortex. They found 5‐year‐old children exhibited "adult‐like" prefrontal activation, which contrasts with more common evidence showing young children fail to engage the right inferior prefrontal cortex (Crone et al., [<reflink idref="bib18" id="ref108">18</reflink>]). This difference ultimately relies on the different strategies used by the individual, thereby activating different parts of the brain. Compared to adults, children tend to use different strategies, thus explaining why children exhibit different neural activation (Bunge et al., [<reflink idref="bib12" id="ref109">12</reflink>]). However, it is possible in low‐support environments, children adopt similar if not the same strategies as an adult, employing "adult‐like" strategies, and instigating early activation in prefrontal cortex regions. In both child and adult participants, the prefrontal cortex has been found to contribute to early activation, and development, of EF components such as shifting (Moriguchi & Hiraki, [<reflink idref="bib67" id="ref110">67</reflink>]).</p> <p>Our results pivot towards understanding the relation between early, heightened EF development (e.g., potential prefrontal activation) as a result of adaptive or "perseverative behaviors"—an interpretation supported by developmental psychology suggesting functional brain development may help young children to improve adaptability (Kirkham et al., [<reflink idref="bib50" id="ref111">50</reflink>]; Zelazo et al., [<reflink idref="bib103" id="ref112">103</reflink>]). The observed early emergence in diversification of EF structure supports this speculation. The prefrontal cortex may be responsible for adaptive behaviors and what are known as "perseverative tendencies"—that is, early emergence and development of EF might seem "adult like" early on, for sake of adaptation. Nonetheless, more studies engaging youth from diverse backgrounds might better establish generalizability of these findings.</p> <p>The adversity‐induced acceleration hypothesis (Callaghan & Tottenham, [<reflink idref="bib14" id="ref113">14</reflink>]) proposes links between specific types of adversities experienced early in life and respective neurocognitive changes across development. Adversity experienced with early caregiving has shown to accelerate timing of development in brain regions that support the development of skills needed to adapt (Gee et al., [<reflink idref="bib35" id="ref114">35</reflink>]; Gunner & Quevedo, [<reflink idref="bib38" id="ref115">38</reflink>]), corroborating with preservative behaviors that heighten EF activity (Kirkham et al., [<reflink idref="bib50" id="ref116">50</reflink>]; Zelazo et al., [<reflink idref="bib103" id="ref117">103</reflink>]). While there is literature linking lower socioeconomic status environments to neurocognitive delays in development (Brito & Noble, [<reflink idref="bib8" id="ref118">8</reflink>]; Rakesh & Whittle, [<reflink idref="bib80" id="ref119">80</reflink>]), there is also literature suggesting lower income environments might increase the speed of development—that is, heightened pace of neural development in children via the acceleration of biological aging (Almonaitiene et al., [<reflink idref="bib2" id="ref120">2</reflink>]; Danese et al., [<reflink idref="bib19" id="ref121">19</reflink>]; Davis et al., [<reflink idref="bib21" id="ref122">21</reflink>]; Tooley et al., [<reflink idref="bib89" id="ref123">89</reflink>]; McDermott et al., [<reflink idref="bib59" id="ref124">59</reflink>]).</p> <p>There is reason to differentiate between types of adversity children experience, specifically distinguishing between adversity considered interpersonal (e.g., issues with caregiving) and socioeconomic disadvantage (e.g., growing up in poverty) given differential effects on child developmental and cognitive outcomes (Vannucci et al., [<reflink idref="bib92" id="ref125">92</reflink>]). A recent meta‐analysis of over 27,000 youth found <emph>interpersonal</emph> early adversity was linked to <emph>larger</emph> front limbic volume development, while <emph>socioeconomic disadvantage</emph> was linked to <emph>smaller</emph> temporal‐limbic volume development (Vanucci et al., [<reflink idref="bib92" id="ref126">92</reflink>]). In other words, interpersonal adversity early on in life might explain early maturation and heightened activity of frontal systems (i.e., EF developing faster), explaining why EF structure diversification might emerge earlier.</p> <p>Children are born with a universal cognitive architecture, and the development of such architecture depends on the environments in which they develop. Differences in expectations, such as environments with adversity where responsibilities are put on children at a young age may alter this architecture (Hair et al., [<reflink idref="bib39" id="ref127">39</reflink>]), such as the development of the structure of EF, or more specifical constructs like working memory (Fitzpatrick et al., [<reflink idref="bib30" id="ref128">30</reflink>]; Fitzpatrick, [<reflink idref="bib31" id="ref129">31</reflink>]). For example, when examining working memory in the prefrontal cortex, children, like adults, activated the prefrontal cortex more during the high memory load conditions (compared to the low memory load condition), suggesting heightened activation based on the task demands (Casey et al., [<reflink idref="bib16" id="ref130">16</reflink>]). Gomez‐Lavin ([<reflink idref="bib36" id="ref131">36</reflink>]) argued that working memory is a "cultural invention." Developmental changes can be seen as adaptive processes that are culturally contingent, and not necessarily an outcome of genetics (Heyes, [<reflink idref="bib41" id="ref132">41</reflink>]). The extent to which EF has developed, and how its structure has formed, can thus largely be due to culture—how it is nurtured (Diamond, [<reflink idref="bib22" id="ref133">22</reflink>]). This of course raises question of what is known about current EF structure and the development of unification versus diversification. Are the conclusions literature has drawn about the onset of diversification in EF largely due to a generalizable, universal developmental phenomenon of EF, or are these conclusions based off the specific cultures that these studies' samples stemmed from (i.e., U.S., Australia, etc.)?</p> <p>In this sample of Ghanaian children, 77.7% and 82.9% of children experienced at least one form of physical punishment or psychological aggression, respectively, in the past month in the second wave of the study (Wolf & Suntheimer, [<reflink idref="bib97" id="ref134">97</reflink>]). National data in Ghana shows higher rates: 94.0% of children experienced at least one form of violent discipline in the past month (UNICEF, [<reflink idref="bib91" id="ref135">91</reflink>]). Further, Ghanaian children engage in labor at high rates, with 21.8% of 5‐ to‐11‐year‐olds and 50.2% of 15‐ to‐17‐year‐olds involved in economic work activities (UNICEF, [<reflink idref="bib91" id="ref136">91</reflink>]). Experiences of youth in Ghana and the Sub‐Saharan Africa region have long been identified, with little known about the effects on EF. These interpersonal hardships might explain early developmental onset of EF structure diversification.</p> <hd id="AN0180281944-14">LIMITATIONS AND CONCLUSIONS</hd> <p>There are limitations in the present work that should be addressed in forthcoming research efforts. A concern is we are not able to thoroughly test larger multi‐factor models given limited EF tasks available in this dataset. Exploratory analyses did indicate that, for example, three‐factor models may emerge as the optimal models with participants as young as age 5 in this sample, even though this limitation in data did not allow for acceptable fit testing of three‐ or four‐factor loadings that may have shed light on a potential multi‐factor EF model emerging at the end of middle childhood (i.e., our older samples of 10‐ to 12‐year‐olds). Second, participants were given different batteries of the same EF construct based on their age to ensure age‐appropriate instruments were utilized. This may create variability within the longitudinal analyses, even though clustering was employed in models to account for this. Third, the older children's (7–9‐ and 10–12‐year‐olds) findings were inconclusive in that one‐factor versus two‐factor were not statistically significantly different than one another (even though the two‐factor models had stronger goodness of fit), which nonetheless reiterates our first limitation that testing of three‐factor and possibly four‐factor models was likely needed to identify the best fitting model. It is also possible that, for the older age groups in our sample, the fit statistics were not optimal because measures of other domains of EF were not available in that sample which may very well become more important, and distinguished, later on in life. Given such limitations, it would be worthwhile to extend the findings utilizing a broader range of tasks, and extend these efforts with multiple, additional EF components, and across broader global samples. At minimum, these findings point to the study of EF factor structure in Sub‐Saharan Africa and across diverse contexts more generally as an area worthy of further investigation. Finally, our sample is limited to children enrolled in preprimary school in 2015 in six disadvantaged districts in the Greater Accra region of Ghana. This sample is not generalizable to all of Ghana, including children growing up in rural regions where school enrollment rates are lower and poverty and hardship are higher (Cooke et al., [<reflink idref="bib17" id="ref137">17</reflink>]), nor do they generalize to other Sub‐Saharan Africa countries.</p> <p>Our findings encourage researchers to examine university and diversity of EF in longitudinal samples, and to further understand the universality of EF structure and time of diversification onset across diverse samples and contexts. Indeed, adversity has been shown to hinder development; a deeper examination of how adversity might also hasten development, such as early diversification in structure of EF is needed. At minimum, these results suggest a need to understand how the structure and function of EF might not only vary across contexts, but how its developmental, structural pattern might change because of adversity. Further developing an evidence‐based inquiry is needed to contribute to a global research agenda, crucial for understanding social development to inform child cognitive development around the world.</p> <hd id="AN0180281944-15">ACKNOWLEDGMENTS</hd> <p>We acknowledge the original grants that funded the broader project from which the data were drawn, including the UBS Optimus Foundation and the World Bank Strategic Impact Evaluation Fund and Early Learning Partnership, and the British Academy's Early Childhood Development Programme and GCRF Early Childhood Education, the United Kingdom Government's Global Challenges Research Fund and by the (former) Department for International Development. The views and opinions expressed herein are those of the authors only, and do not represent the official views and opinions of any funding agency.</p> <hd id="AN0180281944-16">CONFLICT OF INTEREST STATEMENT</hd> <p>We certify that there is no conflict of interest/competing interest to be declared.</p> <hd id="AN0180281944-17">DATA AVAILABILITY STATEMENT</hd> <p>The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy or ethical restrictions.</p> <hd id="AN0180281944-18">ETHICS STATEMENT</hd> <p>The rights of the participants were protected, and applicable human research subject guidelines were followed in this research. 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An: EJ1481534
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Is the Development of Diversification in Executive Functioning Universal? Longitudinal Evidence from Ghana
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Anahid+S%2E+Modrek%22">Anahid S. Modrek</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0474-5777">0000-0002-0474-5777</externalLink>)<br /><searchLink fieldCode="AR" term="%22Sharon+Wolf%22">Sharon Wolf</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Social+Development%22"><i>Social Development</i></searchLink>. 2024 33(4).
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 17
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Executive+Function%22">Executive Function</searchLink><br /><searchLink fieldCode="DE" term="%22Preschool+Children%22">Preschool Children</searchLink><br /><searchLink fieldCode="DE" term="%22Young+Children%22">Young Children</searchLink><br /><searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Factor+Analysis%22">Factor Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Developmental+Stages%22">Developmental Stages</searchLink><br /><searchLink fieldCode="DE" term="%22Factor+Structure%22">Factor Structure</searchLink><br /><searchLink fieldCode="DE" term="%22Measures+%28Individuals%29%22">Measures (Individuals)</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+Studies%22">Longitudinal Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Ghana%22">Ghana</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/sode.12764
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0961-205X<br />1467-9507
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The component structure of executive functioning (EF) has been shown to change across development. Empirical research examining this in Sub-Saharan Africa is limited. We report the development of EF component structure with a large sample of Ghanaian children (n = 2,979) followed longitudinally from ages 3 through 12 across six waves. Existing literature suggests unitary models of EF (components loading onto a single factor) early in childhood, with development across childhood and into adolescence resulting in a more diversified EF model (components loading onto two- or three-factors). To test these developmental differences, participants completed EF batteries that measured EF components: working memory, short-term memory, inhibition, and cognitive flexibility/shifting. We employed confirmatory factor analysis to test factor models in 3- to 4-year-olds, 5- to 6-year-olds, 7- to 9-year-olds, and 10- to 12-year-olds. Contrary to existing literature, a two-factor EF model best explained EF performance as early as 3-4 years of age. Findings suggest that diversification of EF components may emerge earlier in childhood than expected in some contexts, questioning the universality in the timing of unification and diversification of EF structure.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1481534
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1481534
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  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/sode.12764
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
    Subjects:
      – SubjectFull: Executive Function
        Type: general
      – SubjectFull: Preschool Children
        Type: general
      – SubjectFull: Young Children
        Type: general
      – SubjectFull: Children
        Type: general
      – SubjectFull: Factor Analysis
        Type: general
      – SubjectFull: Developmental Stages
        Type: general
      – SubjectFull: Factor Structure
        Type: general
      – SubjectFull: Measures (Individuals)
        Type: general
      – SubjectFull: Longitudinal Studies
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Ghana
        Type: general
    Titles:
      – TitleFull: Is the Development of Diversification in Executive Functioning Universal? Longitudinal Evidence from Ghana
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    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Anahid S. Modrek
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          Name:
            NameFull: Sharon Wolf
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          Dates:
            – D: 01
              M: 11
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 0961-205X
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              Value: 1467-9507
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            – Type: volume
              Value: 33
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Social Development
              Type: main
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