Not One Pandemic: A Multilevel Mixture Model Investigation of the Relationship Between Poverty and the Course of the COVID-19 Pandemic Death Rate in the United States.

The COVID-19 pandemic, which began in China in late 2019, and subsequently spread across the world during the first several months of 2020, has had a dramatic impact on all facets of life. At the same time, it has not manifested in the same way in every nation. Some countries experienced a large initial spike in cases and deaths, followed by a rapid decline, whereas others had relatively low rates of both outcomes throughout the first half of 2020. The United States experienced a unique pattern of the virus, with a large initial spike, followed by a moderate decline in cases, followed by second and then third spikes. In addition, research has shown that in the United States the severity of the pandemic has been associated with poverty and access to health care services. This study was designed to examine whether the course of the pandemic has been uniform across America, and if not how it differed, particularly with respect to poverty. Results of a random intercept multilevel mixture model revealed that the pandemic followed four distinct paths in the country. The least ethnically diverse (85.1% white population) and most rural (82.8% rural residents) counties had the lowest death rates (0.06/1000) and the weakest link between deaths due to COVID-19 and poverty (b = 0.03). In contrast, counties with the highest proportion of urban residents (100%), greatest ethnic diversity (48.2% nonwhite), and highest population density (751.4 people per square mile) had the highest COVID-19 death rates (0.33/1000), and strongest relationship between the COVID-19 death rate and poverty (b = 46.21). Given these findings, American policy makers need to consider developing responses to future pandemics that account for local characteristics. These responses must take special account of pandemic responses among people of color, who suffered the highest death rates in the nation.

Poverty and Covid-19: Rates of Incidence and Deaths in the United States During the First 10 Weeks of the Pandemic.

The Covid-19 pandemic in the winter and spring of 2020 represents a major challenge to the world health care system that has not been seen perhaps since the influenza pandemic in 1918. The virus has spread across the world, claiming lives on all continents with the exception of Antarctica. Since its arrival in the United States, attention has been paid to how Covid-19 cases and deaths have been distributed across varying socioeconomic and ethnic groups. The goal of this study was to examine this issue during the early weeks of the pandemic, with the hope of shedding some light on how the number of cases and the number of deaths were, or were not related to poverty. Results of this study revealed that during the early weeks of the pandemic more disadvantaged counties in the United States had a larger number of confirmed Covid-19 cases, but that over time this trend changed so that by the beginning of April, 2020 more affluent counties had more confirmed cases of the virus. The number of deaths due to Covid-19 were associated with poorer and more urban counties. Discussion of these results focuses on the possibility that testing for the virus was less available in more disadvantaged counties later in the pandemic than was the case earlier, as the result of an overall lack of adequate testing resources across the nation.

Modeling of self-report behavior data using the generalized covariates in a uniform and shifted binomial mixture model: An empirical example and Monte Carlo simulation.

Social scientists routinely collect data using questionnaires and surveys. Items on these instruments frequently involve scales with multiple ordered options that respondents use to report intensity of feelings or behaviors. Given their popularity, a variety of statistical models have been developed for analyzing data collected using these items. A model that has been recently described for working with ordinal items is the covariates in a uniform and shifted binomial mixture (CUB). The CUB model characterizes responses to ordinal items as a function of two parameters: (a) response feeling (or intensity), and (b) response uncertainty. This model has been extended to include a third parameter measuring likelihood of respondents selecting a socially desirable or safe response, known as the shelter option. This model has been primarily used to investigate items measuring political opinions or product preferences. However, the CUB with a shelter parameter and covariates generalized covariates in a uniform and shifted ninomial mixture model (GeCUB) seems particularly well suited for characterizing self-reported behaviors, particularly those that are not considered positive (i.e., substance abuse). The purpose of this study is to apply this extension of the CUB to the modeling of self-reported substance use behavior by teenagers. Results from the GeCUB model estimation revealed that subjects used the "no use" response as a shelter option at relatively high rates for marijuana use but not for cigarettes or alcohol. In addition, females reported less use and less certainty in their responses than did males. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Quantifying Item Invariance for the Selection of the Least Biased Assessment.

An important aspect of educational and psychological measurement and evaluation of individuals is the selection of scales with appropriate evidence of reliability and validity for inferences and uses of the scores for the population of interest. One aspect of validity is the degree to which a scale fairly assesses the construct(s) of interest for members of different subgroups within the population. Typically, this issue is addressed statistically through assessment of differential item functioning (DIF) of individual items, or differential bundle functioning (DBF) of sets of items. When selecting an assessment to use for a given application (e.g., measuring intelligence), or which form of an assessment to use in a given instance, researchers need to consider the extent to which the scales work with all members of the population. Little research has examined methods for comparing the amount or magnitude of DIF/DBF present in two assessments when deciding which assessment to use. The current simulation study examines 6 different statistics for this purpose. Results show that a method based on the random effects item response theory model may be optimal for instrument comparisons, particularly when the assessments being compared are not of the same length.

Comparison of Methods for Factor Invariance Testing of a 1-Factor Model With Small Samples and Skewed Latent Traits.

A primary underlying assumption for researchers using a psychological scale is that scores are comparable across individuals from different subgroups within the population. In the absence of invariance, the validity of these scores for inferences about individuals may be questionable. Factor invariance testing refers to the methodological approach to assessing whether specific factor model parameters are indeed equivalent across groups. Though much research has investigated the performance of several techniques for assessing invariance, very little work has examined how methods perform under small sample size, and non-normally distributed latent trait conditions. Therefore, the purpose of this simulation study was to compare invariance assessment Type I error and power rates between (a) the normal based maximum likelihood estimator, (b) a skewed-t distribution maximum likelihood estimator, (c) Bayesian estimation, and (d) the generalized structured component analysis model. The study focused on a 1-factor model. Results of the study demonstrated that the maximum likelihood estimator was robust to violations of normality of the latent trait, and that the Bayesian and generalized component models may be useful in particular situations. Implications of these findings for research and practice are discussed.

Methods for the Comparison of Differential Item Functioning across Assessments.

An important aspect of the educational and psychological evaluation of individuals is the selection of scales with appropriate evidence of reliability and validity for inferences and uses of the scores for the population of interest. One key aspect of validity is the degree to which a scale fairly assesses the construct(s) of interest for members of different subgroups within the population. Typically, this issue is addressed statistically through assessment of differential item functioning (DIF) of individual items, or differential test functioning (DTF) of sets of items within the same measure. When selecting an assessment to use for a given application (e.g., measuring intelligence), or which form of an assessment to use for a test administration, researchers need to consider the extent to which the scales work with all members of the population. Little research has examined methods for comparing the amount or magnitude of DIF/DTF present in two or more assessments when deciding which assessment to use. The current study made use of 7 different statistics for this purpose, in the context of intelligence testing. Results demonstrate that by using a variety of effect sizes, the researcher can gain insights into not only which scales may contain the least amount of DTF, but also how they differ with regard to the way in which the DTF manifests itself.

Differential item functioning analysis using a multilevel Rasch mixture model: investigating the impact of disability status and receipt of testing accommodations.

The assessment of differential item functioning (DIF) remains an area of active research in psychometrics and educational measurement. In recent years, methodological innovations involving mixture Rasch models have provided researchers with an additional set of tools for more deeply understanding the root causes of DIF, while at the same time increased interest in the role of disabilities and accommodations has also made itself felt in the measurement community. The current study furthered work in both areas by using the newly described multilevel mixture Rasch model to investigate the presence of DIF associated with disability and accommodation status at both examinee and school levels for a 3rd grade language assessment. Results of the study found that indeed DIF was present at both levels of analysis, and that it was associated with the presence of disabilities and the receipt of accommodations. Implications of these results for both practitioners and researchers are discussed.