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.

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.

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.

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.

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.

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.