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.

The assessment of test data for the presence of differential item functioning (DIF) is a key component of instrument development and validation. Among the many methods that have been used successfully in such analyses is the mixture modeling approach. Using this approach to identify the presence of DIF has been touted as potentially superior for gaining insights into the etiology of DIF, as compared to using intact groups. Recently, researchers have expanded on this work to incorporate multilevel mixture modeling, for cases in which examinees are nested within schools. The current study further expands on this multilevel mixture modeling for DIF detection by using a multidimensional multilevel mixture model that incorporates multiple measured dimensions, as well as the presence of multiple subgroups in the population. This model was applied to a national sample of third-grade students who completed math and language tests. Results of the analysis demonstrate that the multidimensional model provides more complete information regarding the nature of DIF than do separate unidimensional models.

Open-ended questions are a common component in surveys and questionnaires that are used in the social sciences. Such items can provide researchers with insights into respondents' attitudes and opinions that cannot be easily gleaned from closed-response items based on a traditional Likert-type response format. However, the use of openended items can also be associated with a set of analytic problems, particularly in terms of identifying coherent themes that are supported by the data. Topic models present the researcher with a statistically based tool for identifying underlying topics within text, based on how words group together, much in the way that factor analysis uses correlations among observed variables to identify potential latent variables. The current study demonstrates the use of topic models with open-ended response items in order to illustrate how the resulting topics can both provide insights into respondents' attitudes and create variables that can be used in data analyses with closed-ended item responses. A full illustration of topic modeling in this context is given and discussion of the utility of these models is provided. What is the significance of this article for the general public?This study focused on the use of topic modeling to gain insights into open-ended survey responses. The article reports the results of these analyses, highlighting the utility of topic modeling for identifying themes underlying free-form responses provided by respondents to survey items. These topics can yield insights into respondents' thoughts and also provide researchers with additional variables (in the form of topics) that can be used in other statistical analyses.

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.

This study provides baseline data to assist researchers in conducting future studies exploring the developmental trajectories of young gifted learners on measures of cognitive ability and achievement. The study includes common neuropsychological tests associated with preliteracy and the early-reading process as well as markers for inattention and executive functioning skills. Using a sample of kindergarteners identified as gifted, the results indicated that despite intelligence quotient scores in the very superior range and high means on traditional achievement measures, great variability was observed within the sample on several benchmarking measures of cognitive, academic, neuropsychological, and executive functioning. Additionally, only an average mean score on a visual-motor processing neuropsychological measure was obtained. Four neuropsychological measures provided important loadings in canonical correlations with achievement: Oromotor Sequences, Repetition of Nonsense Words, Beery-Buktenica Developmental Test of Visual-Motor Integration scores, and Speeded Naming. In addition to providing baseline data on these measures, the results also offer support for defining giftedness as a developmental process.