Evaluating the Equal-Interval Hypothesis with Test Score Scales

In psychometrics, it is difficult to verify that measurement instruments can be used to produce numeric values with the desirable property that differences between units are equal-interval because the attributes being measured are latent. The theory of additive conjoint measurement (e.g., Krantz, Luce, Suppes, \u26 Tversky, 1971, ACM) guarantees that interval scales are possible---for latent and manifest variables alike---if certain axioms hold. However, ACM was initially developed under the assumption that data could be gathered to test the axioms that was free from measurement error. It wasn\u27t until Karabatsos (2001) that the methodology allowed for measurement error. In this dissertation, an improved version of Karabatsos\u27s methodology is applied to simulated and empirical data to test whether such data are consistent with the axioms. It is first shown that the methodology behaves reasonably using data simulated to meet the cancellation axioms of ACM. It is then shown that the methodology is capable of distinguishing data simulated to meet the axioms from data that is not. In particular, it is demonstrated that the methodology is sensitive to item-side violations of the axioms. Empirical examples are then used to illustrate the fact that test score data may or may not conform to the ACM axioms. Empirical demonstration shows that an existing test scale thought to satisfy the ACM axioms using the Karabatsos (2001) approach does not do so using the modified approach here. Since not all data may meet the ACM axioms (and hence not warrant interval interpretations), this dissertation also examines whether scale distortions can lead to erroneous conclusions. At the score-level, an approach was developed using ``difference matrices\u27\u27 to highlight the fact that when the Rasch (1960) model is applied to certain non-Rasch data, the estimates will be more compressed at lower abilities. This same phenomena was noted in two simulations meant to capture how educational assessment data is used---with respect to schools and educational interventions---although the effects of the distortions were small

The social genome of friends and schoolmates in the National Longitudinal Study of Adolescent to Adult Health

Our study reported significant findings of a “social genome” that can be quantified and studied to understand human health and behavior. In a national sample of more than 5,000 American adolescents, we found evidence of social forces that act to make friends and schoolmates more genetically similar to one another compared with random pairs of unrelated individuals. This subtle genetic similarity was observed across the entire genome and at sets of genomic locations linked with specific traits—educational attainment and body mass index—a phenomenon we term “social–genetic correlation.” We also find evidence of a “social–genetic effect” such that the genetics of a person’s friends and schoolmates influenced their own education, even after accounting for the person’s own genetics

Wide Educational Disparities in Young Adult Cardiovascular Health

Widening educational differences in overall health and recent stagnation in cardiovascular disease mortality rates highlight the critical need to describe and understand educational disparities in cardiovascular health (CVH) among U.S. young adults. We use two data sets representative of the U.S. population to examine educational disparities in CVH among young adults (24–34) coming of age in the 21st century: the National Health and Nutrition Examination Survey (2005–2010; N= 689) and the National Longitudinal Study of Adolescent to Adult Health (2007–2008; N=11,200). We employ descriptive statistics and regression analysis. The results show that fewer than one in four young adults had good CVH (at least 5 out of 7 ideal cardiovascular indicators). Young adults who had not attained a college degree demonstrate particularly disadvantaged CVH compared with their college-educated peers. Such educational disparities persist after accounting for a range of confounders, including individuals’ genetic propensity to develop coronary artery disease. The results indicate that the CVH of today’s young adults is troubling and especially compromised for individuals with lower levels of educational attainment. These results generate substantial concern about the future CVH of the US population, particularly for young adults with a low level of education

Erratum: Testing the key assumption of heritability estimates based on genome-wide genetic relatedness

Comparing genetic and phenotypic similarity among unrelated individuals seems a promising way to quantify the genetic component of traits while avoiding the problematic assumptions plaguing twin- and other kin-based estimates of heritability. One approach uses a Genetic Relatedness Estimation through Maximum Likelihood (GREML) model for individuals who are related at less than .025 to predict their phenotypic similarity by their genetic similarity. Here we test the key underlying assumption of this approach: that genetic relatedness is orthogonal to environmental similarity. Using data from the Health and Retirement Study (and two other surveys), we show two unrelated individuals may be more likely to have been reared in a similar environment (urban versus non-urban setting) if they are genetically similar. This effect is not eliminated by controls for population structure. However, when we include this environmental confound in GREML models, heritabilities do not change substantially and thus potential bias in estimates of most biological phenotypes is probably minimal

Is the Gene-Environment Interaction Paradigm Relevant to Genome-Wide Studies? The Case of Education and Body Mass Index

This study uses data from the Framingham Heart Study to examine the relevance of the gene-environment interaction paradigm for genome-wide association studies (GWAS). We use completed college education as our environmental measure and estimate the interactive effect of genotype and education on body mass index (BMI) using 260,402 single-nucleotide polymorphisms (SNPs). Our results highlight the sensitivity of parameter estimates obtained from GWAS models and the difficulty of framing genome-wide results using the existing gene-environment interaction typology. We argue that SNP-environment interactions across the human genome are not likely to provide consistent evidence regarding genetic influences on health that differ by environment. Nevertheless, genome-wide data contain rich information about individual respondents, and we demonstrate the utility of this type of data. We highlight the fact that GWAS is just one use of genome-wide data, and we encourage demographers to develop methods that incorporate this vast amount of information from respondents into their analyses

Gene–environment interactions related to body mass: School policies and social context as environmental moderators

This paper highlights the role of institutional resources and policies, whose origins lie in political processes, in shaping the genetic etiology of body mass among a national sample of adolescents. Using data from Waves I and II of the National Longitudinal Study of Adolescent Health, we decompose the variance of body mass into environmental and genetic components. We then examine the extent to which the genetic influences on body mass are different across the 134 schools in the study. Taking advantage of school differences in both health-related policies and social norms regarding body size, we examine how institutional resources and policies alter the relative impact of genetic influences on body mass. For the entire sample, we estimate a heritability of .82, with the remaining .18 due to unique environmental factors. However, we also show variation about this estimate and provide evidence suggesting that social norms and institutional policies often mask genetic vulnerabilities to increased weight. Empirically, we demonstrate that more-restrictive school policies and policies designed to curb weight gain are also associated with decreases the proportion of variance in body mass that is due to additive genetic influences

Rapid online assessment of reading ability

Published18 March 2021An accurate model of the factors that contribute to individual differences in reading ability depends on data collection in large, diverse and representative samples of research participants. However, that is rarely feasible due to the constraints imposed by standardized measures of reading ability which require test administration by trained clinicians or researchers. Here we explore whether a simple, two-alternative forced choice, time limited lexical decision task (LDT), self-delivered through the webbrowser, can serve as an accurate and reliable measure of reading ability. We found that performance on the LDT is highly correlated with scores on standardized measures of reading ability such as the Woodcock-Johnson Letter Word Identification test (r = 0.91, disattenuated r = 0.94). Importantly, the LDT reading ability measure is highly reliable (r = 0.97). After optimizing the list of words and pseudowords based on item response theory, we found that a short experiment with 76 trials (2–3 min) provides a reliable (r = 0.95) measure of reading ability. Thus, the self-administered, Rapid Online Assessment of Reading ability (ROAR) developed here overcomes the constraints of resourceintensive, in-person reading assessment, and provides an efficient and automated tool for effective online research into the mechanisms of reading (dis)ability.We would like to thank the Pavlovia and PsychoPy team for their support on the browser-based experiments. This work was funded by NIH NICHD R01HD09586101, research grants from Microsoft and Jacobs Foundation Research Fellowship to J.D.Y