Chapter I - Turning milestones into measurement ( van Buuren & Eekhout, 2021) - highlights the concepts and tools needed to obtain a quantitative score from a set of developmental milestones.

In practice, we typically want to make the following types of comparisons:

•      Compare development within the same child over time;

•      Compare the development of two children of the same age;

•      Compare the development of two children of different ages;

•      Compare the development of groups of children of different ages.

To do this well, we need an interval scale with a fixed unit of development. We argued that the simple Rasch model is a very suitable candidate to provide us with such a unit. The Rasch model is simple, fast, and we found that it fits child developmental data very well ( Jacobusse et al., 2006)( van Buuren, 2014). The Rasch model has a long history, but -unfortunately- it is almost unknown outside the field of psychometrics. We highlighted the concepts of the model that are of direct relevance to child development. Using data collected by the Dutch Development Instrument, we demonstrated that the model and its estimates behave as intended for children in the open population, for prematurely born-children, and children living in a low- and middle-income country.

As our approach breaks with the traditional paradigm that emphasises different domains of child development, we expected a slow uphill battle for acceptance. We have now gained the interest from various prominent authors in the field, and from organisations who recognise the value of a one-number-summary for child development. In analogy to traditional growth charts, it is entirely possible to track children, or groups of children, on a developmental chart over time. Those and other applications of the technology may eventually win over some more souls.

It is straightforward to apply the D-score methodology, as explained in Chapter I: Turning milestones into measurement ( van Buuren & Eekhout, 2021), for measurements observed by one instrument. In practice, however, there is a complication. We often need to deal with multiple, partially overlapping tools. For example, our data may contain

•      different versions of the same instrument (e.g., Bayley I, II and III);

•      different language versions of the same tool;

•      different tools administered to the same sample;

•      different tools administered to different samples;

•      and so on.

Since there are over 150 different instruments to measure child development ( Fernald et al., 2017), the chances are high that our data also hold data observed by multiple tools.

It is not apparent how to obtain comparable scores from different instruments. Tools may have idiosyncratic instructions to calculate total scores, distinctive domain definitions, unique compositions of norm groups, different floors and ceilings, or combinations of these.

This chapter addresses the problem how to define and calculate the D-score based on data coming from multiple sources, using various instruments administered at varying ages. We explain techniques that systematically exploit the overlap between tools to create comparable scores. For example, many instruments have variations on milestones like Can stack two blocks, Can stand or Says baba. By carefully mapping out the similarities between instruments, we can construct a constrained measurement model informed by subject matter knowledge. As a result, we can map different instruments onto the same scale.

Many of the techniques are well known within psychometrics and educational research. This chapter translates the concepts to the field of child development.

We all like our children to grow and prosper. The first 1000 days refers to the time needed for a child to grow from conception to its second birthday. During this period, the architecture of the developing brain is very open to the influence of relationships and experiences. It is a time of rapid change that lays the groundwork for later health and happiness.

Professionals and parents consider it necessary to monitor children’s development. While we can track the child’s physical growth by growth charts to identify children with signs of potential delay, there are no charts for monitoring child development. To create such charts, we need to have a unit of development, similar to units like centimetres or kilograms.

The D-score is a way to define a unit of child development. With the D-score, we see that progress is much faster during infancy, and that different children develop at different rates. The D-score also allows us to define a “normal” range that we can use to filter out those who are following a more pathological course. There is good evidence that early identification and early intervention improve the outcomes of children ( Britto et al., 2017). Early intervention is crucial for children with developmental disabilities because barriers to healthy development early in life impede progress at each subsequent stage.

Monitoring child development provides caregivers and parents with reliable information about the child and an opportunity to intervene at an early age. Understanding the developmental health of populations of children allows organisations and policymakers to make informed decisions about programmes that support children’s greatest needs ( Bellman et al., 2013).

We believe that there can be one scale for measuring child development and that this scale is useful for many applications. We also believe that there cannot be one instrument for measuring child development that is suitable for all situations. In general, the tool needs tailoring to the setting.

We see that practitioners often view instruments and scales as exchangeable. In daily practice, the practitioner would pick a particular tool to measure a specific faculty, which then effectively produces a “scale score.” Each tool produces its own score, which then feeds into the diagnostic and monitoring process.

We have always found it difficult to explain that scales and instruments are different things. For us, a scale is a continuous concept, like “distance,” “temperature” or “child development,” and the instrument is the way to assign values to the particular object being measured. For measuring distance, we use devices like rods, tapes, sonar, radar, geo-location, or red-shift detection, and we can express the results as the location under the underlying scale (e.g., number of meters). It would undoubtedly be an advance if we could establish a unit of child development, and express the measurement as the number of units. If we succeed, we can compare child development scores, that are measured through different devices. This chapter explores the theory and practice for making that happen.

We aim for three broad audiences:

•      Professionals in the field of child growth and development;

•      Policymakers in international settings;

•      Statisticians, methodologists, and data scientists.

Professionals in child development are constantly faced with the problem that different instruments for measuring child development yield incomparable scores. This chapter introduces and illustrates sound psychometric techniques for extracting comparable scores from existing instruments. We hope that our approach will ease communication between professionals.

Policymakers in international settings are looking for simple, versatile, and cheap instruments to gain insight into the effectiveness of interventions. The ability to measure child development by a single number enhances priority setting and leads to a more accurate understanding of policy effects.

The text may appeal to statisticians and data scientists for the simplicity of the concepts, for the (somewhat unusual) application of statistical models to discard data, for the ease of interpretation of the result, and for the availability of software.

The Global Child Development Group (GCDG) collected longitudinal data from 16 cohorts. The objective of the study was to develop a population-based measure to monitor early child development across ages and countries. The requirements for inclusion were

1.        direct assessment of child development;

2.        availability of individual milestone scores;

3.        spanning ages between 0–5 years;

4.        availability of follow-up measures, at ages 5–10 years.

The effort resulted in a database containing individual data from over 16,000 children from 11 countries. The world map below ( Figure 2.1) colors the countries included in the study. Section 2.2 briefly describes each cohort. Section 2.3 reviews the measurement instruments.

The GCDG data comprises of birth cohorts, impact evaluation studies and instrument evaluation studies. Table 2.1 displays a brief overview of the instruments used in each sub-study.

The cohorts have different designs, age ranges and assessment instruments. Figure 2.2 displays the age range of developmental assessments per cohort, coloured according to the instruments.

A brief description of each cohort follows:

The Bangladesh study ( GCDG-BGD-7MO) was an impact evaluation study including 1862 children around the age of 18 months. The Bayley Scale for Infant and Toddler Development-II ( by2) was administered and long-term follow-up data were available for the Wechsler Preschool and Primary Scale of Intelligence (WPPSI) at 5 years ( Tofail et al., 2008).

The Brazil 1 study ( GCDG-BRA-1) was a birth-cohort with 3 measurement moments: 644 children at 3 months, 1412 children at 6 months and 1362 children at 12 months. The investigators administered the Denver Developmental Screening Test-II ( den) in each round. Long-term follow-up data were available for the Wechsler Adult Intelligence Scale (WAIS) at 18 years ( Victora et al., 2006).

The Brazil 2 study ( GCDG-BRA-2) was a birth-cohort with measurements of 3907 children at 12 months and 3869 children at 24 months. Both occasions collected data on the Battelle Development Inventory ( bat) ( Moura et al., 2010).

The Chile 1 study ( GCDG-CHL-1) was an impact evaluation study of 128 children assessed at 6 months, 1732 children at 12 months and 279 at 18 months. The by1 was administered at each of the three waves. Long-term follow-up data were available for the WPPSI at 5–6 years ( Lozoff et al., 2003).

The Chile 2 study ( GCDG-CHL-2) consists of a birth-cohort of 4869 children. The investigators measured child development by the Battelle Developmental Inventory ( bat) at 7–23 months. A total of 9201 children aged 24–58 responded to the Test de Desarrollo Psicomotor ( tep) at 24–58 months. For the latter group, follow-up data were available for the Peabody Picture Vocabulary Test (PPVT) at 5–6 years ( Conteras & González, 2015).

The China study ( GCDG-CHN) was an impact evaluation study that contained 990 children assessed with the by3 at 18 months ( Lozoff et al., 2016).

The Colombia 1 study ( GCDG-COL-LT45M) was an impact evaluation study that comprised two waves. Wave 1 contained 704 children at 12–24 months and wave 2 631 children at 24–41 months. The by3 was administered at each wave. Long-term follow-up data were available for PPVT at 4–6 years ( Attanasio et al., 2014).

The Colombia 2 study ( GCDG-COL-LT42M) was an instrument validation study where all 1311 children aged 6–42 months were measured the by3. Also, there are data for a subgroup of 658 children on den, the Ages and Stages Questionnaire ( aqi), and the bat screener. Long-term follow-up data were available for the Fifth Wechsler Intelligence Scale for Children (WISC-V) and the PPVT ( Rubio-Codina et al., 2016).

An impact evaluation study in Ecuador( GCDG-ECU) yielded data from 667 children between 0–35 months on the Barrera Moncada ( bar). Long-term follow-up data were available for the PPVT at 5–8 and 9–12 years [ Paxson & Schady, 2010].

The Ethiopia study ( GCDG-ETH) was a birth-cohort with 193 children of 12 months in the first wave, 440 children of 30 months at the second wave, and 456 children of 42 months at the third wave. The investigators used the same instrument ( by3) for all waves. Long-term follow-up data were available for the PPVT at 10–11 years [ Hanlon et al., 2009].

The Jamaica 1 study ( GCDG-JAM-LBW) was an impact evaluation study that collected data on the Griffiths Mental Development Scales ( gri) for 225 children aged 15 months (first wave), and 218 children of aged 24 months (second wave). Long-term follow-up data were available for WPPSI and PPVT at 6 years ( Walker et al., 2004).

The Jamaica 2 study ( GCDG-JAM-STUNTED) was an impact evaluation study with data on the gri for 159 children at 9–24 months, 21–36 months, and at 33–48 months. Long-term follow-up data were available for sbi, Raven’s Coloured Progressive Matrices (Ravens), and PPVT at 7–8 years and the WAIS at 17–18 years ( Grantham-McGregor et al., 1991).

The Madagascar study ( GCDG-MDG) was an impact evaluation study that used the sbi for 205 children aged 34–42 months. Long-term follow-up data were available for sbi and PPVT at 7–11 years ( Fernald et al., 2011).

The Netherlands 1 study ( GCDG-NLD-SMOCC) was an instrument validation study with a total of 9 waves. At each wave the Dutch Developmental instrument ( ddi) (In the Netherlands known as Van Wiechenschema) was administered. The first wave included 1985 children at 1 month, wave 2 1807 children at 2 months, wave 3 1963 children at 3 months, wave 4 1919 children at 6 months, wave 5 1881 children at 9 months, wave 6 1802 children at 12 months, wave 7 1776 children at 15 months, wave 8 1787 children at 18 months, and wave 9 1815 children at 24 months ( Herngreen et al., 1992).

The Netherlands 2 study ( GCDG-NLD-2) was an instrument validation study with a total of five waves. This study resembles GCDG-NLD-SMOCC but for older children. Wave 1 included 1016 children at 24 months, wave 2 995 children at 30 months, wave 3 1592 children at 36 months, wave 4 1592 children at 42 months, and wave 5 1024 children at 48 months ( Doove, 2010).

The South Africa study ( GCDG-ZAF) was a birth cohort with four waves. The first wave included 485 children and second wave 275 children, who were assessed at 6 and 12 months, respectively, with the by1 and the gri. The third wave included 1802 children and the fourth wave 1614 children, assessed at 24 and 48 months, respectively, with the Vineland Social Maturity Scale ( vin) ( Richter et al., 2007).

The Bayley Scales for Infant and Toddler Development (by1, by2, by3) aim to assess infants and toddlers, aged 1–42 months. The current version is the by3, but some GCDG cohorts used earlier versions (i.e. by1 and by2) ( Bayley, 1969, 1993, 2006). The 326 items of the by3 measure three domains: Cognitive items, Motor items (with fine and gross motor items), and Language items (with expressive and receptive items). The by2 contains 277 items and has two additional subscales: Social-Emotional and Adaptive Behavior. by1 contains 229 items.

The Denver Developmental Screening Test (den) is aimed to identify developmental problems in children up to age six. The 125 dichotomous test items are distributed over the age range from birth to six years. The Denver covers four domains: personal-social, fine motor and adaptive, language, and gross motor. The test items are all directly observed by an examiner and are not dependent on parent report ( Frankenburg et al., 1992) ( Frankenburg et al., 1990).

The Griffiths Mental Development Scales (gri) measure the rate of development in infants and young children in six developmental areas: locomotor, personal-social, hearing and language, eye and hand coordination, performance and practical reasoning ( Griffiths, 1967).

The Battelle Developmental Inventory (bat) measures key developmental skills in children from birth to 7 years, 11 months. The instrument contains 450 items distributed over five domains: adaptive, personal-social, communication, motor, and cognitive ( Newborg, 2005).

The Vineland Social Maturity Scale (vin) is a test to assess social competence. The instrument contains eight subscales that measure communication skills, general self-help ability, locomotion skills, occupation skills, self-direction, self-help eating, self-help dressing and socialisation skills ( Doll, 1953).

The Dutch Developmental Instrument (ddi) measures early child development during the ages 0–4 years. The instrument consists of 75 milestones spread over three domains: fine motor, adaptive, personal and social behaviour; communication; and gross motor ( Schlesinger-Was, 1981).

The Barrera Moncada (bar) is a Spanish instrument that measures the growth and psychological development of children ( Barrera Moncada, 1981).

The Test de Desarrollo Psicomotor (tep) is an instrument to evaluate toddlers aged 2 to 5 years on their development. The items come from three sub-tests: 16 items assess coordination; 24 items measure language skills and 12 items tap into motor skills ( Haeussler & Marchant, 1999).

The Ages and Stages Questionnaire (aqi) measures developmental progress in children aged 2 mo – 5.5 yrs. The instrument distinguishes development in five areas: personal-social, gross motor, fine motor, problem solving, and communication. The caregiver completes 30 items per age intervals and ( Squires & Bricker, 2009).

The Stanford Binet Intelligence Scales (sbi) is a cognitive ability and intelligence test to diagnose developmental deficiencies in young children. The items divide into five subtests: fluid reasoning, knowledge, quantitative reasoning, visual-spatial processing, and working memory ( Roid, 2003)( Hagen & Stattler, 1986).

The ultimate goal is to compare child development across populations and cultures. A complication is that measurements are made by different instruments. To do deal with this issue, we harmonize the data included in the GCDG cohorts. In particular, we process the milestone responses such that the following requirements hold:

•      Every milestone in an instrument has a unique name and a descriptive label;

•      Every milestone occupies one column in the dataset;

•      Item scores are (re)coded as: 1 = PASS; 0 = FAIL;

•      Items not administered or not answered are a missing value;

•      Every row in the dataset corresponds to a unique cohort-child-age combination.

Cohorts and milestones need to be connected. There are several ways to connect cohorts:

•      Two cohorts are directly connected if they use the same instrument;

•      Two cohorts are indirectly connected if both connect to a third cohort that connects them.

Likewise, instruments can be connected:

•      Two instruments are directly connected if the same cohort measures both;

•      Two instruments are indirectly connected if both connect to a third instrument that connects them.

An X in Table 3.1 identifies which cohorts use which instruments. The linkage table shows that studies from China, Colombia, and Ethiopia are directly connected (by by3). Brazil 1 indirectly connects to these studies through den. Some cohorts (e.g., Chile 1 and Ecuador) do not link to any other study. Likewise, we might say that aqi, bat, by3, and den are directly connected. Note that no indirect connections exist to this instrument group.

Table 3.1 is a somewhat simplified version of the linkage pattern. As we saw in section 2.2, there are substantial age differences between the cohorts. The linked instrument linkage table shows the counts of the number of registered scores per age group. What appears in Table 3.1 as one test may comprise of two disjoint subsets, and hence some cohorts may not be connected after all.

Connectedness is a necessary - though not sufficient - requirement for parameter identification. If two cohorts are not connected, we cannot distinguish between the following two alternative explanations:

•      Any differences between studies can be attributed to the ability of the children;

•      Any differences between studies can be attributed to the difficulties of the instruments.

The data do not contain the necessary information to discriminate between these two explanations. Since many cohorts in Table 3.1 are unconnected, it seems that we are stuck.

The next section suggests a way out of the dilemma.

Many instruments for measuring child development have appeared since the works of Shirley (1933) and Gesell (1943). It is no surprise that their contents show substantial overlap. All tools assess events like starting to see, hear, smile, fetch, crawl, walk, speak, and think. We will exploit this overlap to bridge different instruments. For example, Table 3.2 displays the labels of milestones from six instruments. All items probe the ability of the child to formulate “sentences” of two words.

The idea is to check whether these milestones measure development in the same way. If this is found to be true, then we may formally restrict the difficulty levels of these milestones to be identical. This restriction provides a formal bridge between the instruments. We repeat the process for all groups of similar-looking items.

A first step in the bridging process is to group items from different instruments by similarity. As the by3 is relatively long and is the most often used instrument, it provides a convenient starting point. Subject matter experts experienced in child development mapped items from other tools to by3 items. These experts evaluated the similarity of wordings and descriptions in reference manuals. Also, they mapped same-skill items across other instruments into groups if these did not map onto by3 items.

Figure 3.1 connects similar items and hence visualises connections between instruments for the fine motor domain. Items are displayed in the wheel, coloured by instrument. In the online application we organised item mappings into five domains: fine motor ( FM), gross motor ( GM), cognitive ( COG), receptive ( REC), and expressive ( EXP). The Prev and Next buttons allow us to visit other domains.

Another way to explore the similarity of milestones from different instruments is to plot the probability of passing by age. Figure 3.2 shows two examples. The first graph presents the age curves of a group of four cognitive items for assessing the ability to put a cube or block in a cup or box. The milestones are administered in different studies and seem to work similarly. The second plot shows a similar graph for items that assess the ability to build a tower of six cubes or blocks. These milestones have similar age patterns as well.

Figure 3.3 presents two examples of weak item mappings. Notable timing differences exist for the “babbles” and “bangs” milestones, which suggests that we should not take these as bridges.

While these plots are suggestive, their interpretation is surprisingly complicated. We may find that age profiles of two milestones A and B administered in samples 1 and 2, respectively, are identical if

•      A and B are equally difficult and samples 1 and 2 have the same maturation level;

•      A is more difficult than B and sample 1 is more advanced than 2.

Similarly, we may find that the age profile for A is earlier than B if

•      A is easier than B and if samples 1 and 2 have the same level of maturation;

•      A and B are equally difficult and if sample 1 is more advanced than sample 2.

Note that the age curves confound difficulty and ability, and hence cannot be used to evaluate the quality of the item map.

What we need to do is separate difficulty and ability. For this, we need a formal statistical model. The next section introduces the concepts required in such a model.

An equate group is a set of two or more milestones that measure the same thing in (perhaps slightly) different ways. Table 3.2 contains an example of an equate group, containing items that measure the ability to form two-word sentences. Also, Figure 3.2 and Figure 3.3 show examples of equate groups.

Equate groups vary in quality. We can use high-quality equate groups to link instruments by restricting the difficulty of all milestones in the equate group to be identical. Equate groups thus provide a method for bridging different tools.

Figure 4.1 displays items from three different instruments with overlapping sets of milestones. The shared items make up equate groups, as presented by the arrows between them. In the example, all three instruments share one milestone (“walk alone”). The “sitting” and “clap hand” items appear in two tools. So in total, there are three equate groups.

Patterns as in Figure 4.1 occur if we have multiple forms of the same instrument. Although in theory, there might be sequence effects, the usual working assumption is that we may ignore them. Equate groups with truly shared items that work in the same way across samples are of high quality. We may collect the responses on identical items into the same column of the data matrix. As a consequence, usual estimation methods will automatically produce one difficulty estimate for that column (i.e. common item).

The procedure described above is known as concurrent calibration. See Kim & Cohen (1998) for more background. The method simultaneously estimates the item parameters for all instruments. Concurrent calibration is an attractive option for various reasons:

•      It yields a common latent scale across all instruments;

•      It is efficient because it calibrates all items in a single run;

•      It produces more stable estimates for common items in small samples.

However, concurrent calibration depends on a strict distinction between items that are indeed the same across instruments and items that differ.

In practice, strict black-white distinctions may not be possible. Items that measure the same skill may have been adapted to suit the format of the instrument (e.g. number of response options, question formulation, and so on). Also, investigators may have altered the item to suit the local language and cultural context. Such changes may or may not affect the measurement properties. The challenge is to find out whether items measure the underlying construct in the same way.

In practice, we may need to perform concurrent calibration to multiple - perhaps slightly dissimilar - milestones. When confronted with similar - but not identical - items, our strategy is first to form provisional equate groups. We then explore, test and rearrange these equate groups, in the hope of finding enough high-quality equate groups that will bridge instruments.

An equate group is a collection of items. Content matter experts may form equate groups by evaluating the contents of items and organising them into groups with similar meaning. The modelling phase takes this set of equate groups (which may be hundreds) as input. Based on the analytic result, we may activate or modify equate groups. It is useful to distinguish between active and passive equate groups. What do we mean by these terms?

•       Active equate group: The analysis treats all items within an active equate group as one super-item. The items obtain the same difficulty estimate and are assumed to yield equivalent measurements. As the items in an active equate group may originate from different instruments, such a group acts as a bridge between instruments.

•       Passive equate group: Any non-active equate groups are called passive. The model does not restrict the difficulty estimates, i.e., the milestones within a passive equate group will have separate difficulty estimates.

Since active equate groups bridge different instruments, they have an essential role in the analysis. In general, we will set the status of an equate group to active only if we believe that the milestones in that group measure the underlying construct in the same way. Note that this does not necessarily imply that all items need to be identical. In Table 3.2, for example, small differences exist in item formulation. We may nevertheless believe that these are irrelevant and ignore these in practice. Reversely, there is no guarantee that the same milestone will measure child development in the same way in different samples. For example, a milestone like “climb stairs” ( Figure 4.2) could be more difficult (and more dangerous) for children who have never seen a staircase.

The data analysis informs decisions to activate equate groups. The following steps implement our strategy for forming and enabling equate groups:

•      Content matter experts compare milestones from different instruments and sort similar milestones into equate groups. It may be convenient to select one instrument as a starting point, and map items from others to that (see section 3.2);

•      Visualise age profiles of mapped items (see section 3.3). Verify the plausibility of potential matches through similar age profiles. Break up mappings for which age profiles appear implausible. This step requires both statistical and subject matter expertise;

•      Fit the model to the data using a subset of equate groups as active. Review the quality of the solution and optimise the quality of the links between tools by editing the equate group structure. The technical details of this model are explained in section 4.4. Refit the model until (1) active equate groups link all cohorts and instruments, (2) active equate groups are distributed over the full-scale range (rather than being centred at one point);

•      Assess the quality of equate groups by the infit and outfit (see section 4.6).

•      Test performance of the equate groups across subgroups or cohorts by methods designed to detect differential item functioning (see section 4.7).

The application of equate groups is needed to connect different instruments to a universal scale. The technique is especially helpful in the situation where abilities differ across cohorts.

If the cohort abilities are relatively uniform (for example as a result of experimental design) and if the risk of misspecification of the equate groups is high, a good alternative is to rely on the equality of ability distribution. In our application, this was not an option due to the substantial age variation between cohorts.

The Rasch model is the preferred measurement model for child development data. Chapter I, Section 4 ( van Buuren & Eekhout, 2021) provides an introduction of the Rasch model geared towards the D-score.

The Rasch model expresses the probability of passing an item as a logistic function of the difference between the person ability β _n and the item difficulty δ _i . Table 4.1 explains the symbols used in equation (4.1). Formula (4.1) defines the model as

One way to interpret the formula is as follows. The logarithm of the odds that a person with ability β _n passes an item of difficulty δ _i is equal to the difference β _i – δ _i ( Wright & Masters, 1982). See the logistic model in Chapter 1, Section 4.6.1 ( van Buuren & Eekhout, 2021) for more detail.

In model (4.1) every milestone i has one parameter δ _i . We extend the Rasch model by restricting the δ _i of all items within the same equate group to the same value. We thereby effectively say that these items are interchangeable measures of child development.

Estimation of the parameter for the equate group is straightforward. Wright & Masters (1982) present a simple method for aligning two test forms with common items. There are three steps:

•      Estimate the separate δ _i ’s per item;

•      Combine these estimates into δ _q by calculating their weighted average;

•      Overwrite each δ _i by δ _q .

Suppose that Q is the collection of items in equate group q, and that w _i is the number of respondents for item i. The parameter estimate δ _q for the equate group is

The end goal for using the equate group method to model development items is to measure development on one common latent scale, the D-score. That way, the measure (i.e. D-score) can be obtained, irrespective of which instrument is used in which population.

Figure 4.3 displays the D-score estimates by age in three cohorts from the GCDG study: Netherlands 1 ( GCDG-NLS-SMOCC), Ethiopia ( GCDG-ETH) and Colombia 2 ( GCDG-COL-LT42M) for two different analyses. As described in section 2.2, the Netherlands 1 study administered the ddi; Ethiopia measured children by the by3; and Colombia collected data on by3, den, aqi and bdi. Accordingly, there is an overlap in items between Ethiopia and Colombia via the by3, but the Netherlands 1 cohort is not linked.

We created the plot on the left-handed side without active equate groups. The large overlap between Ethiopian and Columbian children occurs because the scales for these studies are linked naturally via shared items from by3. Since the ddi instrument is not connected, the Dutch cohort follows a different track. While we can compare D-scores between Ethiopia and Colombia, it is nonsensical to compare Dutch to either Ethiopia or Colombia. The right-handed side plot is based on an analysis that used active equate groups to link the cohorts. Since the analysis connected the scales for all three cohorts, we can now compare D-scores obtained between all three cohorts.

This example demonstrates that active equate groups form the key for converting ability estimates for children from different cohorts using different instruments onto the same scale.

It is essential to activate only those equate groups for which the assumption of equivalent measurement holds. We have already seen the item fit and person fit diagnostics of the Rasch model. This section describes a similar measure for the quality of an active equate group.

4.6.1 Equate fit. Section 6 of Chapter I ( van Buuren & Eekhout, 2021) defines the observed response of person n on item i as x _ni . The accompanying standardized residual z _ni is the difference between x _ni and the expected response P _ni , divided by the expected binomial standard deviation,

with variances W _ni = P _ni (1 – P _ni ).

Equate infit is an extension of item infit that takes an aggregate over all items i in active equate group q, i.e.,

Likewise, we calculate Equate outfit of group q as

where N _i is the total number of responses observed on item i. The interpretation of these diagnostics is the same as for item infit and item outfit.

Note that these definitions implicitly assume that the expected response P _ni is calculated under a model in which all items in equate group q have the same difficulty. This is not true for passive equate groups. Of course, no one can stop us from calculating the above equate fit statistics for passive groups, but such estimates would ignore the between-item variation in difficulties, and hence gives a too optimistic estimate of quality. The bottom line is: The interpretation of the equate fit statistics should be restricted to active equate groups only.

4.6.2 Examples of well fitting equate groups. The evaluation of equate fit involves comparing the observed probabilities of endorsing the items in the equate group to the estimated probability of endorsing the items in the equate group. For an equate group there is an empirical curve for each item in the equate group and one shared estimated curve. The empirical curves should all be close together, and close to the estimated curve for a good equate fit.

Figure 4.4 shows a diagnostic plot for equate groups REC6 (Turns head to sound of bell) and GM42 (Walks alone). The items within REC6 have slightly different formats in the Bayley I ( by1), Dutch Development Instrument ( ddi), and the Denver ( den). The empirical curves in the upper figure show good overlap, but note that hardly any negative responses were recorded for four of the five studies, so the shared estimate depends primarily on the Dutch sample. Items from equate group GM42 appear in six instruments: bar, by1, by2, by3, ddi, and gri. Also, here the empirical data are close together, and even a little steeper than the fitted dashed line, which indicates a good equate fit. The infit and outfit indices, shown in the upper left corners, confirm the good fit (fit < 1).

4.6.3 Examples of equate groups with poor equate fit. Poor fitting equate groups are best treated as passive equate groups, so that items in those groups are not restricted to the same difficulty. Empirical item curves with different locations and slopes indicate a poor fit. Additionally, the equate fit indices will indicate a poor fit (fit > 1).

Figure 4.5 shows examples for groups COG24 (Bangs in play / Bangs 2 blocks) and EXP12 (Babbles). In both cases there is substantial variation in location between the empirical curves. For COG24 we find that the fitted curve is closer to the den item, which suggests that the equate difficulty is mostly based on the den item. Items from equate group EXP12 have a different format in instruments by1, ddi and gri. The empirical curves, with different colours for each instrument, are not close to each other, nor close to the fitted curve. Note that all infit and outfit statistics are fairly high, indicating poor fit. Both equates are candidates for deactivation in a next modelling step.

Items within an active equate group should work in the same way across the different cohorts, i.e., they have no differential item functioning (DIF). The assumption of no DIF is critical for active equate groups. If violated, restricting the difficulty parameters as equal across cohorts may introduce unwanted bias in comparisons between cohorts. This section illustrates the role of DIF in equate groups.

4.7.1 Good equate groups without DIF. Chapter I ( van Buuren & Eekhout, 2021) discusses the role of DIF in the evaluation of the fit of items to the Rasch model. This section illustrates similar issues in the context of equate groups.

Figure 4.6 shows the empirical curves of two equate groups, FM31 (two cubes) and EXP26 (two-word sentence). All curves are close to each other, so there is no differential item functioning here.

4.7.2 Poor equate groups with DIF for study. Figure 4.7 plots the empirical curves for equate groups GM44 (throws ball) and EXP23 (5 or more words). The substantial variation between these curves is a sign of differential item functioning. For example, Throws ball is easier for children in the South-Africa cohort (purple curve; GCDG-ZAF) and more difficult for children in Colombia (blue curve; GCDG-COL-LT42M). In other words, the probability of passing the item given the D-score (i.e. item difficulty) differs between the cohorts. Likewise, there is differential item functioning for Says more than 5 words. This milestone is easier for children in Jamaica (yellow and pink curves; GCDG-JAM-LBW and GCDG-JAM-STUNTED) than for children from Ecuador (green; GCDG-ECU).

5.1.1 Data combination. Section 2.1 provides an overview of the data collected by Global Child Development Group. The group collected item level measurements obtained on 12 instruments for measuring child development across 16 cohorts.

We coded every item as 0 (FAIL), 1 (PASS) or missing. For some instrument we did some additional recoding to restrict to these two response categories. The Battelle Developmental Inventory scores items as 0 (FAIL), 1, or 2, depending on the level of skill demonstrated or time taken to complete the task. We joined categories 1 and 2 for these items. The ASQ items were originally scored as 0 (not yet), 5 (sometimes) and 10 (succeeds). We recoded both 5 and 10 to 1.

We concatenated the datasets from the GCDG cohorts cohort. The resulting data matrix has 71403 rows (child-visit combinations) and 1572 columns (items) collected from 36345 unique children. We removed 233 items that had fewer than 10 observations in a category. The remaining 1339 items were candidates for analysis. The total number of observed scores was equal to about 2.8 million pass/fail responses. While this is a large number of measurements, about 97 percent of the entries in the matrix are missing.

5.1.2 Equate group formation. A group of 13 subject-matter experts from the Global Child Development Group cross-walked the available instruments for similar milestones. This group

•      developed an item coding schema;

•      matched similarly appearing items stemming from different instruments;

•      formed an opinion about the quality of each match;

•      noted peculiarities of the matches;

•      reported the results as a series of detailed Excel spreadsheets.

The group evaluated around 1500 milestones. After several days, this highly-skilled, intensive labour resulted in a series of spreadsheets. Figure 5.1 shows an example. These sheets formed the basis of an initial list of 184 equate groups, each consisting of at least two items.

The analytic challenge is twofold:

•      to find a subset of items that form a scale;

•      to find a subset of equate groups with items similar enough to bridge instruments.

Note that both subsets are related, i.e., changing one affects the other. Thus, we cannot first identify items and then equate groups, or first identify equate groups followed by the items. Rather we need to find the two subsets in an iterative fashion, primarily by hand. This section describes some of the modelling issues the analyst needs to confront.

In general, we look for a final model that

•      preserves the items that best fit the Rasch model;

•      uses active equate groups with items that behave the same across many cohorts and instruments;

•      displays reasonable age-conditional distributions of the D-scores;

•      has difficulty estimates that are similar to previous estimates.

The modelling strategy is a delicate balancing act to achieve all of the above objectives. Particular actions that we could take to improve a given model are:

•      remove bad items;

•      inactivate bad equate groups;

•      break up bad equate groups;

•      move items from one equate group to another;

•      create new equate groups;

•      remove entire instruments;

•      remove persons;

•      remove studies.

In order to steer our actions, we look at the following diagnostics (in order of importance):

•      quality of equate groups (both visually and through infit);

•      plausibility of the distribution of the D-score by age per study;

•      correspondence of difficulty estimates from published (single study) Dutch data and the new model;

•      infit of the items remaining in the model.

Various routes are possible and may result in different final models. The strategy adopted here is to thicken active equate groups by covering as many studies as possible, in the hope of minimizing the number of active equates needed.

Figure 5.2 is a display of the D-score by age for the GCDG-COL-LT42M cohort under four models. D-score by age visualizations for all cohort are can be found via this link. As a rough reference to compare, the grey curves in the back represent the Dutch model as calculated from the SMOCC study. In order to speed up the calculations, the figure shows a random subsample of 25% of all points. Manipulate the plot controls to switch cohorts.

All models contain 1339 items, but differ in the number of active equate groups. The most salient features per model are:

•       1339_θ: No equate groups, so different instruments in different cohorts are fitted independently;

•       1339_11: Connects all cohorts through one or more equated items using 11 equate groups in total;

•       1339_33: There are 33 equate groups that bridge cohort and instruments;

•       1339_184: Maximally connects instruments and cohort by all equate groups.

Comparison of the D-score distribution by age across these models yields various insights:

•      The location of cohorts on the vertical scale depends on the number of active equate groups. For example, for Madagascar (MDG) the points are located around 52 when no equate groups are activated, whereas if all are activated it is about 68.

•      The age trend depends on the number of active equate groups. For example, for Colombia (COL) or Ethiopia (ETH), the model without equate groups has a shallow age trend, whereas it is steep for the 1339_184 model.

•      The vertical spread depends on the number of equate groups. For example, the spread in the Chile-2 (CHL-2) cohort substantially increases with the number of active equates.

•      Model 1339_0 for the Dutch NLD-SMOCC cohort is equivalent to the model fitted to the SMOCC study alone. Introducing equate groups compresses the range of scores, especially at the higher end.

We have now seen that the number of active equate groups has a large effect on the model. The next sections look into the equate groups in more detail.

Figure 5.3 displays the percentage of children that pass milestones at various ages for equate group EXP 26. Subject matter experts clustered similar items stemming from different instruments into equate groups. There are 184 equate groups that contain two or more milestones; the percentage pass by age for the items in these equate groups are shown here.

Most age profiles show a rising pattern, as expected, though some (e.g. FM17 or EXP11) have one item showing a negative relation with age. Equate EXP26 combines two-word sentences items from seven instruments into one plot. The item difficulties expressed as age-equivalents (c.f. Section 3.1.2, Chapter I ( van Buuren & Eekhout, 2021)) for these cohorts vary between 20–25 months. By comparison, equate group EXP18 ( says two words) shows more heterogeneity across cohorts, and is therefore, less likely to be useful for equating. Equate group FM31 ( stack two blocks) is another example of a promising example. By comparison, FM38 ( stack 6–8 blocks) shows additional heterogeneity. As a last example, consider GM42 ( walks alone), which has a similar age profile across cohorts, whereas GM44 ( throws ball) or GM49 ( walk down stairs) are more heterogeneous.

We could follow different strategies in selecting which equate groups to activate. One strategy would be to include as many equate groups as possible (e.g. all 184 equates) so as to build as many bridges as possible between different instruments. A more selective strategy would be to activate a subset of promising equates and leave others inactive. The following section compares four different approaches.

This visualization shows how the passing percentage depends on the child’s D-score as calculated under four models. All models include the same 1339 milestones, but differ in the number of active equates. The grey curve corresponds to the estimate made under the assumption that milestones are equally difficult. Good milestones for bridging instruments will have a tight bundle of curves. For example, as shown in Figure 5.4, equate EXP26 has tight bundles especially in models 1339_11 and 1339_33. By comparison, the curves of the two extreme models vary considerably: the model without any bridges ( 1339_θ) or the model with all bridges ( 1339_184) are thus less than ideal. The shallow grey curve of model 1339_184 indicates a poorer overall fit.

Outfit and infit statistics measure the residual deviation of the items to the grey curve. High values (e.g. above 1.4) are undesirable and indicate lack of fit to the model. For example, the fit statistics for EXP26 in model 1339_184 (1.70 and 1.25) indicate a mediocre fit, whereas EXP26 in models 1339_33 and 1339_11 fits well. Sometimes the individual item curves are steeper than the grey curve. This indicates that these milestones are more discriminative than the combined item. Model 1339_θ lacks a grey curve and has no fit statistics for equate groups, because in that model, the combined item is not activated.

The probability curves provide a quick visual method for spotting promising and problematic equate groups. Examples of promising equate groups include COG36, FM31, GM26 and GM42. A little more weak are FM26 (has more variability), FM52 (looks promising, but has a problem with the item grigcd4θ2 from the GCDG_JAM_STUNTED cohort), and GM35 (does not align cohort GCDG-ZAF). In such cases, one may wish to move an item out of an equate group, combine equate groups, or inactivate troublesome links.

Until now we only looked at models that include all 1339 items. In practice, we may improve upon the model by selecting the subset of milestones that fit the Rasch model. The next section looks in this modelling step in more detail.

Item infit and outfit are convenient statistics for selecting the milestones that fit the model. Figure 5.5 displays the infit and outfit statistics of model 1339_11. The correlation between infit and outfit is high ( r = 0.84). The expected value of the infit and outfit statistics for a perfect fit is 1.0. The centre of infit and outfit in Figure 5.5 is approximately 1.0, so on average one could say the items fit the model. Note however that fit values above and below the values of 1.0 are qualitatively different. Item with fit statistics exceeding 1.0 fit the model less well than expected ( underfit), whereas items with fit statistics lower than 1.0 fit the model better than expected ( overfit). See Chapter 1, Section 6.1 ( van Buuren & Eekhout, 2021) for more details.

Some practitioners remove both underfitting and overfitting items. However, we like to preserve overfitting items and be more strict in removing items that underfit. The idea is that preservation of the best fitting items may increase scale length, and hence reliability and measurement precision. Figure 5.5 draws two cut-off lines at 1.0. Taking items with infit < 1.0 and outfit < 1.0 will select 631 out of 1339 items for further modelling.

A practical problem of item removal is that it also affects equate group composition. By default, a removed item will also be removed from the equate group, so item removal may reduce the size of an equate group below two items. For passive equates this is no problem, since passive equates do no affect the estimates. However, removal of an underfitting item from an active equate group will break the bridge between the instrument it pertains to and the rest of the item set. Potentially this can result in substantial effects on the D-score distribution of the cohort, as demonstrated in Figure 5.2. As a solution, we force any items that are members of active equate groups to remain in the analysis. If that leads to substantially worse equate fit in the next model, we must search for alternative equate groups that bridge the same instruments and that are less sensitive to misfit.

5.7.1 Instrument fit. Some instruments fit better than others. Figure 5.6 shows the box plots of outfit per instrument. Instruments bar, by1, ddi and vin generally fit well, whereas discrepancies between model and data are larger for bat, by2 and sbi. Through additional modelling, we found that it was extremely difficult to get enough high-quality bridge items that could link bat (Battelle Development Inventory) to the other instruments. We also found that models without the Battelle were able to better discriminate children in the upper range of the D-score scale. We therefore opted to remove bat from the model, even though this meant that one cohort ( GCDG-BRA-2) had to be dropped from the analysis.

It is not clear why bat does not fit. Perhaps the scoring system of the Battelle in three categories invokes scoring behaviour that is different from the PASS/FAIL scoring used by most other instruments, even though this appears to be less of a troublesome aspect in aqi, which also uses three response categories.

5.7.2 Splitting, combining and selecting equate groups. Most of the modelling effort went into finding a set of high-quality equate groups that link the instruments. For example, we tried to bridge the South-African study placing vinxxc016 (uses a short sentence) into EXP26 (two-word sentences) and EXP36 (sentences of 3 or more words), but neither option led to a reasonable model. On the surface, milestone reasonable model. On the surface, milestone by3gmd06 (balances on right foot, 2 seconds) appears to fit within GM60 (balances on foot), but the analysis showed large discrepancies with the other items in the groups, so it had to be taken out.

Subject-matter experts identified 38 items that were thought to be cross-culturally incompatible. Table 5.1 provides an overview. Many of such milestones involve a specific language concept (such as a pronoun), refer to stairs (less common in rural settings), help in house or clothing behaviour. These items have different meanings in different contexts, so they were not used to bridge instruments.

Item information is a psychometric measure that quantifies the sensitivity of the item to changes in the person’s ability. An item is most sensitive around the D-score value where the PASS probability equals the FAIL probability, which corresponds to the item difficulty ( δ _i ). One unit change around δ _i has a large effect on the probability of endorsing, while one unit change far away from δ _i has negligible impact. Suppose person A had passing probability 0.7 for some item. The information delivered by that item for person A is the product 0.7 × (1.0 – 0.7) = 0.21. Suppose person B has a D-score that coincides with the difficulty level of the item. In that case, the information for B equals 0.5 × (1 – 0.5) = 0.25, the maximum. Likewise, for a person C with high ability, the information could be 0.98*0.02 = 0.02, so that item carries almost no information for person C.

The information is inversely related to the error of measurement. More information amounts to less measurement error. For each response in the data, we can compute the amount of information it contributed to the model D-score. By summing the information over persons, we obtain a measure of certainty about the difficulty estimate of the item. This sum of information incorporates both the number of administrations and the quality of the match between person abilities and item difficulty.

Figure 5.7 displays the summed information for each item, divided into four grades: A(best) to D (worst). The information grade measures the stability of the difficulty estimate. Most items receive grades higher than C. In total, 30 milestones have grade D. Adding these items to future studies may yield important additional information.

The red circles indicate active equate groups. Most have grade A, so we have a lot of information about the items that form the active equate groups. Table 5.2 displays more detailed information for the active equate groups. The sample sizes are reasonably large. Many information statistics are well is above 100; the criterion for Grade A. The interpretation of this criterion is as follows. Suppose that we obtain a sample of 400 persons who are all perfectly calibrated to the item of interest. In that case, the information for that item will be equal to 100.

Unfortunately, there is no single index of model fit that we can optimise. Modelling is more like a balancing act among multiple competing objectives, such as

•      preserving as many items as possible that fit the model;

•      finding high-quality active equate groups that span many cohorts and instruments;

•      picking active equate groups for which we have enough information;

•      providing reasonable age-conditional distributions of the D-score;

•      representing various developmental domains in a fair way;

•      preserving well-fitting historical models as new data become available;

•      maintaining a reasonable calculation time.

This section showed various modelling techniques and ways to assess the validity of the model. In real life, we fitted a total number of 140 models on the data and made many choices that weigh the above objectives. The final model for the GCDG data consists of 565 items (originating from 14 instruments) that fit the Rasch model and that connect through 18 equate groups. Due to the sparseness of data at the very young ages, the quality of the model is best for ages between 4–36 months.

Model 565_18 formed the basis of the publication by Weber et al. (2019). Additional detail on model 565_18 is available through the dmodel shiny app.

This display shows the scatterplot of the D-score by age separately for each cohort, Figure 6.1 presents the D-score by age for the GCDG-COL-LT52M study. Remember from section 2.1 that each study selected its own set of instruments to collect the data. The scatterplots demonstrate a significant advance made possible by the D-score: We can plot the developmental scores of children from different cohorts, with different ages, using different instruments, on the same vertical axis.

The five blue lines guide the eye. These lines indicate the locations of the -2SD, -1SD, 0SD, +1SD and +2SD quantiles at each age in the combined data. Section 5.4, in Chapter I ( van Buuren & Eekhout, 2021) motivates the idea and provides some technical details. We’ll come back to these lines in section 7.2.

By and large, the data in every study follow the blue lines. Perhaps the most obvious exception is the GCDG-JAM-STUNTED cohort, where older children somewhat exceed the D-score range. It is unknown whether this is real, or due to a sub-optimal calibration of the instrument.

Figure 6.2 plots the same data with D-score transformed into age standardized scores (DAZ) for study GCDG-COL-LT42M. The distributions of the age standardized scores for all studies are displayed here. Replacing the D-score by the DAZ emphasises the differences both within and between studies. The majority of observations lies between the -2 SD and +2 SD lines in all cohorts. Using DAZ makes is easier to spot deviating trends, e.g., for the Jamaican or Ethiopian data.

The EAP algorithm estimates the D-score from a set of PASS/FAIL scores. The standard deviation of the posterior distribution (or sem: standard error of measurement) quantifies the imprecision of the D-score estimate. The sem is inversely related to the number of items. Thus, when we administer more milestones, the sem of the D-score drops.

Figure 6.3 shows that the sem drops off rapidly when the number of items is low and stabilises after about 35 items. Apart from test length, the precision of the D-score also depends on item information (c.f. section 5.8). Administering items that are too easy, or too difficult, does not improve precision. The figure suggests that - in practice - a single D-score cannot be more precise than 0.5 D-score units.

One may wonder whether the sem depends on age. Figure 6.4 suggests that this is not the case. The average DAZ is close to zero everywhere, as expected. The interval DAZ ± sem will cover the true, but unknown, DAZ in about 68% of the cases. While the interval varies somewhat across ages, there is no systematic age trend.

Does precision vary with studies? The answer is yes. Figure 6.5 plots the same information as before but now only for GCDG-COL-LT42M. The standard error of measurement around de age-standardized D-scores (DAZ) for each cohort can be found here. Individual data points are added to give a feel for the design. The Colombia cohort GCDG-COL-LT45M, Figure 6.5, administered the Bayley-III, where each child answered on average 45 items, so the sem is small. In contrast, the Dutch cohort GCDG-NLD-SMOCC collected data on a screener consisting of about ten relatively easy milestones, so the sem is relatively large. As a result, the Colombian D-scores are much more precise than the Dutch. These differences in precision between cohorts is also reflected in Figure 6.6. This figure shows the pooled standard error of measurement within each cohort.

The ordering of studies depends on test length and item information. Table 6.1 shows the median number of items per child (test length) and the probability to pass the item. The Ethiopian cohort GCDG-ETH administered 39 milestones with a median probability of 0.66. In contrast, the South Africa study GCDG-ZAF measures 12 items which were all very easy for the sample at hand (median probability of 1.0). One may thus well explain the extremes by test length and item information.

In general, the design of the study has a significant impact on the precision of the measurement. Our ongoing work addresses the question how one may construct a measurement instrument that will be optimally precise given the goals of the research.

The D-score is a one-number summary of early child development. Traditional instruments distinguish domains (like motor, communication, language and cognitive development) and some provide ways to calculate a total score. The D-score, on the other hand, is based on the notion that child development is a unidimensional latent construct and hence does not provide domain scores. And thus, the question is how the D-score represents domains.

This section explores the following two questions:

•      Can we break down the D-score by domain contribution, and if so, can we evaluate whether the D-score fairly represents all domains?

•      Can we calculate domain-specific D-scores?

6.3.1 Domain coverage of the scale. For many items in the D-score model, we had expert information available as to which domain the item belongs. For each item, we calculated the proportion of times the experts assigned it to one of five domains: Fine Motor, Gross Motor, Expressive, Receptive, Cognitive. We then calculated the distribution of domain by age.

Figure 6.7 shows the domain composition of the D-score across different levels of ability. Note that we miss domain information for a few items. The share of gross-motor is large in early development (e.g., between 15 and 30 months), and gradually tapers off at higher levels. Reversely, the percentage of cognition and language is relatively small before 30 months but rapidly rises as the child matures. These transitions in domain composition look both reasonable and valid.

6.3.2 Domain-specific D-scores. Suppose we select a domain of interest and calculate the D-score only from items that substantially load onto that domain. We then get a domain-specific D-score. Items that relate to multiple domains contribute to multiple domain-specific D-scores.

Figure 6.8 displays the standardized domain-specific D-score (i.e. DAZ) per cohort. The DAZ strips out irrelevant age variation, and thus enhances comparability between cohorts. The error bars around the scores depict the sem interval. We observe some variation in domain-specific DAZ scores within cohorts. Still, these differences are relatively small and well within the margins of error. This analysis suggests that the D-score is an excellent overall summary of the domain-specific D-scores.

The D-score methodology assumes that child development is a unidimensional scale. As a consequence, the correlations between different domain-specific D-scores are extremely high ( r > 0.95). It is more interesting to study the correlation between the DAZ equivalent of the domain-specific scores.

Table 6.2 lists the Pearson correlation matrix of the DAZ and the five domain-specific DAZ scores. All correlations between the DAZ and the domain-specific scores are high, thus confirming the generic character of the D-score and DAZ. We find high inter-domain correlations for the cognitive-receptive, cognitive-fine motor and expressive-receptive pairs. The gross motor domain appears as somewhat distinct from the four other domains. Its position may be genuine, but could also be related to the smaller number of responses on gross motor milestones in the GCDG data.

Figure 6.9 displays individual scores for a 3 year old boy. The filled bars indicate the number of available items per domain. The vertical white line that crosses the horizontal axis at value 5 indicates a threshold for a minimum number of items needed for a D-score. Note that the number of items for Gross Motor in this example is meagre (only three items). The grey vertical line indicates the value of the overall D-score (68.55 D). The nearby dashed lines are located at one sem (0.53 D) distance. The coloured points are the domain-specific D-scores with the sem around in error bars. The plot visualises that the boys’ scores on language domains (i.e. Expressive and Receptive) are low as compared to the motor and cognitive domains. A systematic discrepancy between various domain-specific scores might be an early warning sign for developmental delay.

The UN Sustainable Development Goals form a universal call to action to end poverty, protect the planet and improve the lives and prospects of everyone, everywhere. All UN Member States adopted the 17 Goals in 2015. The SDG 4 target to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. SDG 4.2 reads as:

To measure progress, the UN defined indicator 4.2.1 as follows:

On July 22, 2020, the indicator was changed into

The exclusion of children 0–24 months is at variance with the importance of healthy growth and development during the first 1000 days of life. Indeed, the UN restricted the age range for practical concerns. Loizillon et al. (2017) report:

The careful scientific approach underlying the D-score fills the gap for children aged 0–24 months. Also, the D-score methodology enables extensions to ages beyond 24 months, permits back-calculation of D-scores from existing data, and acts as a linking pin to compare child development from birth onwards.

The cohorts included in the GCDG study represent a wide range of countries and instruments (see Section 2.1). Combining existing data from such a wide range of countries to create the D-score, is undoubtedly challenging, but doable. Although, in all fairness, we note that obtaining accurate comparisons between world-wide populations requires additional representative (existing) data beyond what is available here.

In 2006, the World Health Organisation (WHO) published the WHO Child Growth Standards. These standards specify “how children should grow” and form the basis for widely used anthropometric indicators such as stunting and wasting. We advocate a similar approach for child development. More in particular, the following steps:

1.      Measure child development on an interval scale;

2.      Estimate the age-conditional reference distribution for normal child development;

3.      Define the indicator developmentally on track as the proportion above a chosen cut-off.

Step 1 is solved by the D-score. Step 2 borrows from well-tested statistical methodology for constructing growth standards ( Borghi et al., 2006). Step 3 can be done in different ways, but a applying a simple cut-off fits easily with regular practice in reporting international comparisons.

Figure 7.1 demonstrates steps 2 and 3 in more detail. In the online visualization you can click ‘Next’ to advance these series of six steps:

1.        Plot the D-score by age;

2.        Model the relation between age and D-score by an LMS model. In practice, this amounts to smoothing three curves representing the median, coefficient of variation and the skewness.

3.        Present the centile lines for the model;

4.        Plot the age-standardized scores for development (DAZ);

5.        Draw standard deviation lines to indicate the location at ±1 and ±2 standard deviation from the mean;

6.        Count observations above the -2 SD line as on-track. Count observation below the -2 SD lines as off-track (red dots).

Note: These SD lines build upon on a convenience sample. The GCDG cohorts are not representative samples, and the countries are not representative of the world. While we should not over-interpret these references, they play a central role in a stepwise, principled approach to define “developmentally on track.”

Using the definition from the previous section, we can calculate the percentage of children that are developmentally on track. Table 7.1 summarises this statistic by country. At a cut-off value of -2 SD, we expect that about 97.7% of the children will be on track. The actual country estimates fall into the range 93.9 - 99.9 and are thus near the theoretical value. This close correspondence shows that the definition and estimation procedure work as expected.

Bear in mind that the measurements leading up to these estimates come from different instruments. It is gratifying to see how well we can do with historical data, thanks to the robust underlying measurement model. Of course, comparability only gets better if all countries would use the same instrument. However, using the same tool everywhere is not a requirement.

Weber et al. (2019) thoroughly discuss concurrent, discriminant and predictive validity of the D-score using the GCDG data. In this section, we concentrate on the relation between the D-score and stunting, a popular measure of impaired height growth in children due to nutrition problems. The WHO defines stunted growth as a height-for-age Z-score below the -2 SD line of the WHO Child Growth Standards (HAZ < -2.0).

Figure 7.2 plots the percentage off-track and percentage stunting per country. This plot reveals two exciting features:

•      The variation in stunting is much larger than the variation off-track development. One might speculate that height is more dependent on the environment than off-track development, and hence more variable.

•      Stunted growth and off-track development are unrelated. Ranking countries by stunting or by off-track development yields substantially different orders. This finding provides clear counter-evidence to the argument that stunted growth is as a proxy for delayed development. It may even be the case the child development and physical growth are different maturation processes that develop largely independently.

However, this is not the whole story. Figure 7.3 reveals a consistent difference in DAZ between stunted and non-stunted children of about 0.2 - 0.3 SD. There could be factors at the child level that affect both development and height growth. For example, low-income families may lack the resources for adequate nutrition, which may impact both child development and physical growth.

The exact nature of the relation between stunting and development is still obscure. The D-score provides a means to study the intriguing interplay between both measures in more detail.

There are multiple ways to define on-track development. Here we will use the method outlined in Section 7.2. Ideally, we would like to fit the age-conditional reference distribution on a sample of children with normal, healthy development. As noted before, we calculated the references used in Section 7.2 from a convenience sample. They may not be representative of healthy development.

Assuming we place the cut-off value at -2 SD, we may subdivide the observed D-scores into off-track and on-track. Figure 8.1 colours the regions of the D-score for children considered on-track (green) and off-track (red). The regions indicate the expected locations of D-scores in practice. Although one could find D-score outside the coloured areas, such should be very rare. The occurrence of such cases may indicate an error in the calculation of the D-score, most likely caused by setting an incorrect age variable.

Preventing observations in the red region requires us to form an idea about the factors that determine the off-track probability. The next section looks into this topic.

We already know many of the factors that influence early child development. A higher level of education in the family promotes development. Infectious diseases like malaria slow down growth. Access to adequate nutrition, clean water and a stimulating, prosperous and safe environment is favourable for healthy development. And so on. Unfortunately, we do not have data on most factors, so we need to limit ourselves to a few background characteristics.

Table 8.1 compares the frequency distributions of various factors for children on-track versus off-track. There are only tiny differences between boys and girls. Children with low birth weight (< 2500 gr) are more at risk for off-track development. This estimate does not correct for gestational age. We discussed techniques for such corrections elsewhere.

The influence of maternal education on off-track development follows the expected trend. Interestingly, it seems that a rural environment could prevent off-track development. We note that original measures of maternal education and residence were harmonised across studies. It would, therefore, also be interesting to study the impact per cohort using the actual factor coding.

We predicted DAZ by linear regressions with predictors country, sex, birth weight, maternal education, height for age and residential area. The percentage of explained variance was 11 percent. Figure 8.2 depicts the relative contributions of the individual factors to the prediction. Country differences explain over half the variances, followed by maternal education. Contributions of height-for-age (HAZ), low birth weight and residence are about equal in magnitude.

These analyses only scratch the surface. It is nowadays common to analyse the impact of interventions on height and HAZ by multivariate techniques and machine learning methods. The D-score and DAZ are drop-in replacements that allow similar procedures to study which factors contribute to healthy child development worldwide.

We developed the initial D-score methodology for just one instrument. In practice, however, we need to deal with data collected on multiple, partially overlapping tools. This chapter addressed the problem how to define and calculate the D-score based on data coming from various sources, using multiple instruments administered at varying ages.

We had longitudinal data available from 16 cohorts, collected with 15 tools to measure child development at various ages. Our analytic strategy to define a D-score from these data consists of the following steps:

1.        Make an inventory of instruments and cohorts;

2.        Combine all measurements into one dataset;

3.        Find out which shared instruments connect cohorts;

4.        Place similar items from different instruments into equate groups;

5.        Find the best set of active equate groups;

6.        Estimate item difficulty using a restricted Rasch model that requires the estimates of all items within an active equate group to be identical;

7.        Weed out items that do not fit the model.

We need to perform steps 5, 6 and 7 in an iterative fashion. Depending on the result, we may also need to redefine, combine or break up equate groups (step 4).

These techniques are well-known within psychometrics and educational research. Our approach builds upon a well-grounded and robust theory of psychological measurement. We, therefore, expect that repeating our method on other data will lead to very similar results.

A novel aspect in our methodology is the systematic formation of candidate equate groups by subject-matter experts based on similarity in concept and content. Our subsequent testing and tailoring of each equate group given the data provide empirical evidence of its quality for connecting instruments. While anchoring tests by itself is not novel, we are not aware of any work aimed at identifying the best set of active equate groups on this scale.

The final model retains 565 items and employs 18 equate groups. Given the difficulty estimates from that model, we can estimate the D-score and DAZ for each measurement.

Figure 6.1 reveals that all cohorts show a rapidly rising age trend in the D-score, which matches the earlier finding that child development is faster in younger children.

Figure 6.2 shows large overlaps in the DAZ distributions between cohorts. This finding suggests that the level of child development is similar in different regions of the world. Some studies display more variability in DAZ than others, which is likely to be related to differences in measurement error, as the number of milestones differs widely.

Observe that we used all cohorts for modelling, which may have made them appear more similar than they are. It would be good if we could verify the apparent similarities in level and variability of child development in different regions by other data that were not part of the modelling.

The D-score is a universal scale of early child development. The D-score does not depend on a particular instrument. Instead, we can calculate a D-score as long as appropriate difficulty estimates are available for the tool at hand. This feature makes the D-score methodology flexible and helpful for international comparisons.

Of course, the ideal situation for international comparisons would be that all countries collect child development data in the same way. In practice, this ideal may be difficult to achieve. Also, we cannot change past data. In these less-than-ideal worlds, the D-score presents a convenient, conscientious and timely alternative.

As an example, we outlined a generic strategy on how to advance on SDG 4.2.1. We use the D-score to operationalise the concept developmentally on track. We calculated age-conditional references of the D-score, analogous to the WHO Multicentre Growth Reference Study. We may then define cut-off values. Children above the cut-off then count as developmentally on track.

While we highlighted the principles, much work still needs to be done. First, there are over 150 instruments for child development, and our current key covers only a fraction of these. We are actively expanding the key using additional data, so as time passes the coverage of tools will go up. Second, we calculated the references on a mix of studies, some of which include special populations. Thus, we cannot interpret the current reference values as portraying normal development. We hope that the inclusion of healthy population data will improve the usefulness of the references as a standard for child development.

The D-score metric is a generic measure of child development. It summarises child development by one number. We found that D-score fairly represents development domains over the entire scale. Due to its generic nature, the D-score is less suitable for measuring a specific domain. It may then be better to use a specialised tool that accesses motor, cognitive or communication faculties. For example, think of sub-scales from the Bayley, ASQ, Griffiths, and so on. Note that also in those cases, one still has the option of calculating a D-score.

The opposite scenario may also be of interest. Suppose we want to measure generic development AND identify any areas of slow growth. Extending the measurement by adding more items from domains with a higher failure rate will then increase precision in areas of suspected delay.

Since we based the D-score on a statistical model, we may create instruments customised to the exact needs of the study. Population-based studies may require a short measure consisting of a handful of items per child, and aggregate scores over many children to achieve precision. Intervention studies aim for a precise estimate for the intervention effect. If group sizes are small, we may administer a more extended test to achieve the same precision and vice versa. At the other end of the spectrum, for clinical purposes, we want a precise estimate for one particular person, so here we will administer a relatively long test. The good news is: As long as we pick items from the statistical model, the D-score in those three cases are all values on the same scale.

Our ongoing work targets tailoring instruments to a study design and discusses all of these options. And more.

The raw data needed to replicate these analyses are not public, so we cannot share it with this publication. However, the reader can apply for access to the data through the study contact. The table given below contains the contact information for each cohort included in this publication.

A subset of studies made their study data publicly available under a CC BY 4.0 license ( https://creativecommons.org/licenses/by/4.0/) ¹ . Authorship remains with the study coordinator, but users are free to redistribute, alter and combine the data, on the condition of giving appropriate credit with any redistributions of the material. The URL of the public data is https://d-score.org/childdevdata/.