A Community Data Sharing Resource: The LDbase Data Repository.

The purpose of this invited paper is to show the learning disabilities field what LDbase is, why it's important for the field, what it offers the field, and examples of how you can leverage LDbase in your own work.

Educational experiences of U.S. children during the 2020-2021 school year in the context of the COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic is a historic event impacting children around the globe. Prior research on the educational experiences of children during the COVID-19 pandemic focused almost exclusively on spring 2020. This article extends this literature past the initial shock of spring 2020, capturing the first full school year (2020-2021) during the COVID-19 pandemic. This registered report study utilized a national sample of 1,666 United States twins in kindergarten through 12th grade from 43 states to provide the current descriptive report of children's educational experiences during this time, as reported by their parents. Specifically, we reported on school format, parents' role in education, parent-teacher interactions, schoolwork struggles, technology access, and school services. About half of children attended in-person schooling, with many children switching from online to in-person throughout the school year. Parents saw the pandemic as a risk to their children's education. During the 2020-2021 school year of the pandemic, parents felt they had a larger role in their children's education and were less satisfied in their interactions with teachers than what they experienced during the prepandemic part of the 2019-2020 school year. Children experienced more schoolwork struggles than they had in previous years, and this was similar across age groups. For most constructs, results were equivalent across age group, but parents of younger children tended to provide more schoolwork help. Overall, this article highlights the disruptions in their educational environments that children continued to experience through the first full school year (2020-2021) of the COVID-19 pandemic. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Where you live matters: visualizing environmental effects on reading attainment.

BACKGROUND: The way in which socioeconomic status (SES) moderates the etiology of reading attainment has been explored many times, with past work often finding that genetic influences are suppressed under conditions of socioeconomic deprivation and more fully realized under conditions of socioeconomic advantage: a gene-SES interaction. Additionally, past work has pointed toward the presence of gene-location interactions, with the relative influence of genes and environment varying across geographic regions of the same country/state. METHOD: This study investigates the extent to which SES and geographical location interact to moderate the genetic and environmental components of reading attainment. Utilizing data from 2,135 twin pairs in Florida (mean age 13.82 years, range 10.71-17.77), the study operationalized reading attainment as reading comprehension scores from a statewide test and SES as household income. We applied a spatial twin analysis procedure to investigate how twin genetic and environmental estimates vary by geographic location. We then expanded this analysis to explore how the moderating role of SES on said genetic and environmental influences also varied by geographic location. RESULTS: A gene-SES interaction was found, with heritability of reading being suppressed in lower- (23%) versus higher-SES homes (78%). The magnitude of the moderating parameters were not consistent by location, however, and ranged from -0.10 to 0.10 for the moderating effect on genetic influences, and from -0.30 to 0.05 for the moderating effect on environmental influences. For smaller areas and those with less socioeconomic variability, the magnitude of the genetic moderating parameter was high, giving rise to more fully realized genetic influences on reading there. CONCLUSIONS: SES significantly influences reading variability. However, a child's home location matters in both the overall etiology and how strongly SES moderates said etiologies. These results point toward the presence of multiple significant environmental factors that simultaneously, and inseparably, influence the underlying etiology of reading attainment.

Genetics can inform causation, but the concepts and language we use matters.

Madole & Harden describe how genetics can be used in a causal framework. We agree with many of their opinions but argue that comparing within-family designs to experiments is unnecessary and that the proposed influence of genetics on behavior can be better described as inus conditions.

Literacy skills seem to fuel literacy enjoyment, rather than vice versa.

Children who like to read and write tend to be better at it. This association is typically interpreted as enjoyment impacting engagement in literacy activities, which boosts literacy skills. We fitted direction-of-causation models to partial data of 3690 Finnish twins aged 12. Literacy skills were rated by the twins' teachers and literacy enjoyment by the twins themselves. A bivariate twin model showed substantial genetic influences on literacy skills (70%) and literacy enjoyment (35%). In both skills and enjoyment, shared-environmental influences explained about 20% in each. The best-fitting direction-of-causation model showed that skills impacted enjoyment, while the influence in the other direction was zero. The genetic influences on skills influenced enjoyment, likely via the skills→enjoyment path. This indicates an active gene-environment correlation: children with an aptitude for good literacy skills are more likely to enjoy reading and seek out literacy activities. To a lesser extent, it was also the shared-environmental influences on children's skills that propagated to influence children's literacy enjoyment. Environmental influences that foster children's literacy skills (e.g., families and schools), also foster children's love for reading and writing. These findings underline the importance of nurturing children's literacy skills. HIGHLIGHTS: It's known that how much children enjoy reading and writing and how good they are at it correlates ∼0.30, but causality remains unknown. We tested the direction of causation in 3690 twins aged 12. Literacy skills impacted literacy enjoyment, but not the other way around. Genetics influence children's literacy skills and how much they like and choose to read and write, indicating genetic niche picking.

Measuring CHAOS? Evaluating the short-form Confusion, Hubbub And Order Scale.

The Confusion, Hubbub and Order Scale (CHAOS) - short form - is a survey tool intended to capture information about home environments. It is widely used in studies of child and adolescent development and psychopathology, particularly twin studies. The original long form of the scale comprised 15 items and was validated in a sample of infants in the 1980s. The short form of the scale was developed in the late 1990s and contains six items, including four from the original scale, and two new items. This short form has not been validated and is the focus of this study. We use five samples drawn from twin studies in Australia, the UK, and the USA, and examine measurement invariance of the CHAOS short-form. We first compare alternate confirmatory factor models for each group; we next test between-group configural, metric and scalar invariance; finally, we examine predictive validity of the scale under different conditions. We find evidence that a two-factor configuration of the six items is more appropriate than the commonly used one-factor model. Second, we find measurement non-invariance across groups at the metric invariance step, with items performing differently depending on the sample. We also find inconsistent results in tests of predictive validity using family-level socioeconomic status and academic achievement as criterion variables. The results caution the continued use of the short-form CHAOS in its current form and recommend future revisions and development of the scale for use in developmental research.

Data Envelopment Analysis (DEA) in the Educational Sciences.

Many of the analytical models commonly used in educational research often aim to maximize explained variance and identify variable importance within models. These models are useful for understanding general ideas and trends, but give limited insight into the individuals within said models. Data envelopment analysis (DEA), is a method rooted in organizational management that makes such insights possible. Unlike models alluded to above, DEA does not explain variance. Instead, it explains how efficiently an individual utilizes their inputs to produce outputs, and identifies which input is not being utilized optimally. This paper provides a history and usages of DEA from fields outside of education, and describes the math and processes behind it. This paper then extends DEA's usage into the educational field using a study on child reading ability. Using students from the Project KIDS dataset (n=1987), DEA is demonstrated using a simple view of reading framework, identifying individual efficiency levels in using reading-based skills to achieve reading comprehension, determining which skills are being underutilized, and classifying new subsets of readers. New subsets of readers were identified using this method, with implications for more targeted interventions.

Understanding Heritability in the Context of Reading Ability and Instruction.

In this paper, we discuss what heritability is and how it is measured, and explain why estimates of heritability are not always the same in different scientific papers. After providing this foundational knowledge, we bust some common myths about heritability. We end with discussing how teachers can use their knowledge about heritability in their own practice.

A How-To Guide for Open-Science Practices in Special Education Research.

Open-science reforms, which aim to increase credibility and access of research, have the potential to benefit the research base in special education, as well as practice and policy informed by that research base. Awareness of open science is increasing among special education researchers. However, relatively few researchers in the field have experience using multiple open-science practices, and few practical guidelines or resources have been tailored to special education researchers to support their exploration and adoption of open science. In this paper, we described and provided guidelines and resources for applying five core open-science practices-preregistration, registered reports, data sharing, materials sharing, and open-access publishing-in special education research.

From the trajectory of heritability to the heritability of trajectories.

Although compelling and insightful, the proposal by Uchiyama et al. largely neglects within-person change over time, arguably the central topic of interest within their framework. Longitudinal behavioural genetics modelling suggests that the heritability of trajectories is low, in contrast to high and increasing cross-sectional heritability across development. Better understanding of the mechanisms of trajectories remains a crucial outstanding challenge.

Developmental behavioral genetics research on school achievement is missing vulnerable children, to our detriment.

Gene-environment processes tell us how genetic predispositions and environments work together to influence children in schools. One type of gene-environment process that has been extensively studied using behavioral genetics methods is a gene-by-environment interaction. A gene-by-environment interaction shows us when the effect of your context on a phenotype differs depending on your genetic predispositions, or vice versa, when the effect of your genetic predispositions on a phenotype differs depending on your context. Developmental behavioral geneticists interested in children's school achievement have examined many different contexts within the gene-by-environment interaction model, including contexts measured from within children's home and school environments. However, this work has been overwhelmingly focused on WEIRD samples children, leaving us with non-inclusive scientific evidence. This can lead to detrimental outcomes when we overgeneralize this non-inclusive scientific evidence to racialized groups. We conclude with a call to include racialized children in more research samples.

Exploring Individual Differences in Response to Reading Intervention: Data from Project KIDS (Kids and Individual Differences in Schools).

This manuscript provides information on datasets pertaining to Project KIDS. Datasets include behavioral and achievement data for over 4,000 students between five and twelve years old participating in nine randomized control trials of reading instruction and intervention between 2005-2011, and information on home environments of a subset of 442 students collected via parent survey in 2013. All data is currently stored on an online data repository and freely available. Data might be of interest to researchers interested in individual differences in reading development and response to instruction and intervention, as well as to instructors of data analytic methods such as hierarchical linear modeling and psychometrics.

Examining mindset and grit in concurrent and future reading comprehension: A twin study.

Noncognitive factors have gained attention in recent years as potential intervention targets for academic achievement improvement in students. Two notable facets, intelligence mindset and grit, have been of particular interest. Both have been shown to consistently improve educational outcomes, although little work has focused on reading ability. As such, we examined the relation between both grit and mindset on current, future, and change in reading comprehension ability in a twin sample. We used data from 422 twin pairs (171 monozygotic pairs, 251 dizygotic pairs) drawn from the Florida Twin Project on Reading, Behavior and Environment (Taylor et al., 2019). The racial composition of the sample included 1.00% American Indian or Alaska Native, 2.25% Asian, 13.25% Black or African American, 22.63% Hispanic, 1.00% Native Hawaiian or Other Pacific Islander, 56.13% White, and 3.75% more than 1 race. The household income of the sample at time 1 was 16.15% below $25,000, 18.06% $25,000-49,999, 36.34% $50,000-99,999, and 29.45% $100,000 or more and closely align with the overall composition reported for the state of Florida (United States Census Bureau, 2021). Twins were on average 13 years old when the questionnaire and first reading ability measure were collected, and on average 15 years old when the second reading ability measure was collected. Weak and moderate positive correlations were found between both mindset and grit and with each reading ability score and neither were significantly related to change in reading ability. Twin modeling suggested little to no common genetic or environmental influences between mindset and grit to reading ability. In total, our results do not lend support to the notion of mindset or grit being a mechanism of change for reading ability. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Assessing Measurement Invariance Across Multiple Groups: When Is Fit Good Enough?

Complex research questions often need large samples to obtain accurate estimates of parameters and adequate power. Combining extant data sets into a large, pooled data set is one way this can be accomplished without expending resources. Measurement invariance (MI) modeling is an established approach to ensure participant scores are on the same scale. There are two major problems when combining independent data sets through MI. First, sample sizes will often be large leading to small differences becoming noninvariant. Second, not all data sets may include the same combination of measures. In this article, we present a method that can deal with both these problems and is user friendly. It is a combination of generating random normal deviates for variables missing completely in combination with assessing model fit using the root mean square error of approximation good enough principle, based on the hypothesis that the difference between groups is not zero but small. We demonstrate the method by examining MI across eight independent data sets and compare the MI decisions of the traditional and good enough approach. Our results show the approach has potential in combining educational data.

Intelligence Can Be Used to Make a More Equitable Society but Only When Properly Defined and Applied.

In the US, undeniable evidence shows that socioeconomic inequities explain a high proportion of individual differences in school achievement. Although not all countries show this same effect due to socioeconomic status, it is consistently found that social inequities lead to achievement gaps. These achievement gaps then manifest into trajectories that set some individuals on a path of lower incomes, poorer health and higher mortality, lower wellbeing, and other poor adult outcomes. Like James Flynn so handily reminded the scientific literature that achievement gaps are explainable by environmental factors, the inequities we see around the world are based on environments some children are exposed to. In his work, Flynn stated his belief that the suppression of scientific work on intelligence would continue to lead to social inequities. We wish to take this idea and move it forward. We believe that the scientific construct of intelligence plays a key role in helping create a more equitable society through science. We also believe that the poor perception of intelligence, rooted in historical realities, means that it will continue to be misunderstood, feared, and misused, limiting how effective it could be in helping to close gaps in achievement and in creating a more equitable society.

Next Directions in Measurement of the Home Mathematics Environment: An International and Interdisciplinary Perspective.

This article synthesizes findings from an international virtual conference, funded by the National Science Foundation (NSF), focused on the home mathematics environment (HME). In light of inconsistencies and gaps in research investigating relations between the HME and children's outcomes, the purpose of the conference was to discuss actionable steps and considerations for future work. The conference was composed of international researchers with a wide range of expertise and backgrounds. Presentations and discussions during the conference centered broadly on the need to better operationalize and measure the HME as a construct - focusing on issues related to child, family, and community factors, country and cultural factors, and the cognitive and affective characteristics of caregivers and children. Results of the conference and a subsequent writing workshop include a synthesis of core questions and key considerations for the field of research on the HME. Findings highlight the need for the field at large to use multi-method measurement approaches to capture nuances in the HME, and to do so with increased international and interdisciplinary collaboration, open science practices, and communication among scholars.

Language Variation in the Writing of African American Students: Factors Predicting Reading Achievement.

Purpose This study aims to examine the predictive relation between measures obtained from African American students' written narrative language samples and reading achievement, as measured by standardized academic assessments. Method Written language samples were elicited from 207 African American students in Grades 1-8. The samples were examined for morphosyntactic variations from standardized written Generalized American English (GAE). These variations were categorized as either (a) specific to African American English (AAE) or (b) neutral across AAE and standardized written GAE (i.e., considered ungrammatical both in AAE and in standardized written GAE). Structural equation modeling was employed to then examine the predictive relation between the density of AAE-specific forms in students' writing and their performance on standardized assessments of literacy and reading vocabulary. This relation was examined while accounting for the density of dialect-neutral morphosyntactic forms, reported family income, age, and written sample length. Results The written samples were highly variable in terms of morphosyntax. Younger students and those from lower income homes tended to use AAE-specific forms at higher rates. However, the density of AAE-specific forms did not significantly predict standardized literacy scores or reading vocabulary after accounting for dialect-neutral variations, income, and sample length. Conclusions These results support the ongoing need to better understand the language, literacy, and overall academic development of students from all backgrounds. It may be essential to focus on dialect-neutral language forms (i.e., morphosyntactic forms that are consistent across both AAE and standardized written GAE) in written samples to maximize assessment validity across students who speak varying dialects of English. Supplemental Material https://doi.org/10.23641/asha.16879558.

The home math environment and math achievement: A meta-analysis.

Mathematical thinking is in high demand in the global market, but approximately 6 percent of school-age children across the globe experience math difficulties (Shalev et al., 2000). The home math environment (HME), which includes all math-related activities, attitudes, beliefs, expectations, and utterances in the home, may be associated with children's math development. To examine the relation between the HME and children's math abilities, a preregistered meta-analysis was conducted to estimate the average weighted correlation coefficient (r) between the HME and children's math achievement and how potential moderators (i.e., assessment, study, and sample features) might contribute to study heterogeneity. A multilevel correlated effects model using 631 effect sizes from 64 quantitative studies comprising 68 independent samples found a positive, statistically significant average weighted correlation of r = .13 (SE = .02, p < .001). Our combined sensitivity analyses showed that the present findings were robust and that the sample of studies has evidential value. A number of assessment, study, and sample characteristics contributed to study heterogeneity, showing that no single feature of HME research was driving the large between-study differences found for the association between the HME and children's math achievement. These findings indicate that children's environments and interactions related to their learning are supported in the specific context of math learning. Our results also show that the HME represents a setting in which children learn about math through social interactions with their caregivers (Vygotsky, 1978) and what they learn depends on the influence of many levels of environmental input (Bronfenbrenner, 1979) and the specificity of input children receive (Bornstein, 2002). (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The Differential Relations Between ADHD and Reading Comprehension: A Quantile Regression and Quantile Genetic Approach.

This paper extends the understanding of the relation between ADHD and reading comprehension, through examining how this relation differs depending on the quantile an individual falls in for each. Samples from three twin projects around the United States were used (Florida Twin Project, Colorado component of International Longitudinal Twin Study of Early Reading Development, & Western Reserve Reading and Math Projects). Phenotypic analysis using quantile regression showed relations between ADHD related behaviors and reading comprehension to be stronger in the lower quantiles of reading comprehension in two of three samples. A new method was developed extending this analysis into the bivariate genetic space. Results of this quantile genetic analysis revealed that overlapping common environmental influences accounted for a larger proportion of variance in the lower quantiles of these variables in two of three samples. Finally, in all three samples the phenotypic relation was strongest when shared environmental influences accounted for a larger proportion of the overall variance.