Evaluating Validity Properties of 25 Race-Related Scales

Racial attitudes, beliefs, and motivations lie at the center of many of the most influential theories of prejudice and discrimination. The extent to which such theories can meaningfully explain behavior hinges on accurate measurement of these latent constructs. We evaluated the validity properties of 25 race-related scales in a sample of 1,031,207 respondents using modern approaches such as dynamic fit indices, Item Response Theory, and nomological nets. Despite showing adequate internal reliability, many scales demonstrated poor model fit and had latent score distributions showing clear floor or ceiling effects, results that illustrate deficiencies in measures’ ability to capture their intended construct. Nomological nets further suggested that the theoretical space of “racial prejudice” is crowded with scales that may not actually capture meaningfully distinct latent constructs. We provide concrete recommendations for scale selection and renovation and outline implications for overlooking measurement issues in the study of prejudice and discrimination.

The reliability and validity of a screening scale for online gaming disorder among Chinese adolescents and young adults

Background In recent years, there have been frequent reports of gaming disorder in China, with more focus on young people. We developed and psychometrically tested a Gaming Disorder screening scale (i.e., Gaming Disorder Screening Scale - GDSS) for Chinese adolescents and young adults, based on the existing scales and diagnostic criteria, but also considering the development status of China. Methods For testing content and criterion validity, 1747 participants competed the GDSS and the Internet Addiction Test (IAT). After 15 days, 400 participants were retested with the scales for to assess test-retest reliability. Besides, 200 game players were interviewed for a diagnosis of gaming disorder. Results The Cronbach’s alpha coefficient on the GDSS was 0.93. The test-retest coefficient of 0.79. Principal components analysis identified three factors accounting for 62.4% of the variance; behavior, functioning, cognition and emotion. Confirmatory factor analysis showed a good model fit to the data (χ2 /df = 5.581; RMSEA =0.074; TLI = 0.916, CFI = 0.928). The overall model fit was significantly good in the measurement invariance tested across genders and different age groups. Based on the clinical interview, the screening cut-off point was determined to be ≥47 (sensitivity 41.4%, specificity 82.3%). Conclusions The GDSS demonstrated good reliability and validity aspects for screening online gaming disorder among Chinese adolescents and young adults.

Using Bayesian state-space models to understand the population dynamics of the dominant malaria vector, Anopheles funestus in rural Tanzania

Background: It is often assumed that the population dynamics of the malaria vector Anopheles funestus, its role in malaria transmission and the way it responds to interventions are similar to the more elaborately characterized An. gambiae. However, An. funestus has several unique ecological features that could generate distinct transmission dynamics and responsiveness to interventions. The objectives of this work were to develop a model which will; 1) reconstruct the population dynamics, survival, and fecundity of wild An. funestus populations in southern Tanzania, 2) quantify impacts of density dependence on the dynamics, and 3) assess seasonal fluctuations in An. funestus demography. Through quantifying the population dynamics of An. funestus, this model will enable analysis of how their stability and response to interventions may different from that of An. gambiae s.l.Methods: A Bayesian State Space Model (SSM) based on mosquito life history was fit to time series data on the abundance of female An. funestus s.s. collected over 2 years in southern Tanzania. Prior values of fitness and demography were incorporated from empirical data on larval development, adult survival and fecundity from laboratory-reared first generation progeny of wild caught An. funestus. The model was structured to allow larval and adult fitness traits to vary seasonally in response to environmental covariates (i.e. temperature and rainfall), and for density dependency in larvae. We measured the effects of density dependence and seasonality through counterfactual examination of model fit with or without these covariates.Results: The model accurately reconstructed the seasonal population dynamics of An. funestus and generated biologically-plausible values of their survival larval, development and fecundity in the wild. This model suggests that An-funestus survival and fecundity annual pattern was highly variable across the year, but did not show consistent seasonal trends either rainfall or temperature. While the model fit was somewhat improved by inclusion of density dependence, this was a relatively minor effect and suggests that this process is not as important for An. funestus as it is for An. gambiae populations.Conclusion: The model's ability to accurately reconstruct the dynamics and demography of An. funestus could potentially be useful in simulating the response of these populations to vector control techniques deployed separately or in combination. The observed and simulated dynamics also suggests that An. funestus could be playing a role in year-round malaria transmission, with any apparent seasonality attributed to other vector species.

A Flexible Mixed Model for Clustered Count Data

Clustered count data are commonly modeled using Poisson regression with random effects to account for the correlation induced by clustering. The Poisson mixed model allows for overdispersion via the nature of the within-cluster correlation, however, departures from equi-dispersion may also exist due to the underlying count process mechanism. We study the cross-sectional COM-Poisson regression model—a generalized regression model for count data in light of data dispersion—together with random effects for analysis of clustered count data. We demonstrate model flexibility of the COM-Poisson random intercept model, including choice of the random effect distribution, via simulated and real data examples. We find that COM-Poisson mixed models provide comparable model fit to well-known mixed models for associated special cases of clustered discrete data, and result in improved model fit for data with intermediate levels of over- or underdispersion in the count mechanism. Accordingly, the proposed models are useful for capturing dispersion not consistent with commonly used statistical models, and also serve as a practical diagnostic tool.

The Estimation of Critical Angle in Climbing as a Measure of Maximal Metabolic Steady State

Purpose: Sport climbing is a technical, self-paced sport, and the workload is highly variable and mainly localized to the forearm flexors. It has not proved effective to control intensity using measures typical of other sports, such as gas exchange thresholds, heart rate, or blood lactate. Therefore, the purposes of the study were to (1) determine the possibility of applying the mathematical model of critical power to the estimation of a critical angle (CA) as a measure of maximal metabolic steady state in climbing and (2) to compare this intensity with the muscle oxygenation breakpoint (MOB) determined during an exhaustive climbing task.Materials and Methods: Twenty-seven sport climbers undertook three to five exhaustive ascents on a motorized treadwall at differing angles to estimate CA, and one exhaustive climbing test with a progressive increase in angle to determine MOB, assessed using near-infrared spectroscopy (NIRS).Results: Model fit for estimated CA was very high (R2 = 0.99; SEE = 1.1°). The mean peak angle during incremental test was −17 ± 5°, and CA from exhaustive trials was found at −2.5 ± 3.8°. Nine climbers performing the ascent 2° under CA were able to sustain the task for 20 min with perceived exertion at 12.1 ± 1.9 (RPE). However, climbing 2° above CA led to task failure after 15.9 ± 3.0 min with RPE = 16.4 ± 1.9. When MOB was plotted against estimated CA, good agreement was stated (ICC = 0.80, SEM = 1.5°).Conclusion: Climbers, coaches, and researchers may use a predefined route with three to five different wall angles to estimate CA as an analog of critical power to determine a maximal metabolic steady state in climbing. Moreover, a climbing test with progressive increases in wall angle using MOB also appears to provide a valid estimate of CA.

Sizing an open-channel woodchip bioreactor to treat nitrate from agricultural tile-drainage and achieve water quality targets

Woodchip bioreactors are capable of removing nitrate from agricultural runoff and subsurface tile drain water, alleviating human health hazards and harmful discharge to the environment. Water pumped from agricultural tile drain sumps to nearby ditches or channels could be cost effectively diverted through a woodchip bioreactor to remove nitrate prior to discharge into local waterways. Sizing the bioreactor to achieve targeted outlet concentrations within a minimum footprint is important to minimizing cost. Determining the necessary bioreactor size should involve a hydrological component as well as reaction type and rates. We measured inflow and outflow nitrate concentrations in a pumped open-channel woodchip bioreactor over a 13-month period and used a tanks-in-series approach to model hydrology and estimate parameter values for reaction kinetics. Both zero-order and first-order reaction kinetics incorporating the Arrhenius equation for temperature dependence were modeled. The zero-order model fit the data better. The rate coefficients (k = 17.5 g N m−3 day−1 and theta = 1.12 against Tref = 20 °C) can be used for estimating the size of a woodchip bioreactor to treat nitrate in agricultural runoff from farm blocks on California's central coast. We present an Excel model for our tanks-in-series hydrology to aid in estimating bioreactor size.

Biomedical students’ course preference and links with quality of life and psychological distress

Introduction: This study investigates psychological distress and quality of life (QoL) amongst first year premedical and health science students. The primary aim of this study was to investigate potential differences in QoL and psychological distress between students who sought entry into a medicine programme when compared to those opting for a non-medicine career. Methods: We examined participant responses to measures of QoL, psychological distress, and course preference (medicine or other). A structural equation model was conducted to consider the interrelationships among future course preference, gender, QoL, depression, anxiety and stress. Results: Three hundred and sixty-five students completed the online survey. An a priori conceptual model was developed and then evaluated using a structural equation model. The values obtained for RMSEA (0.027), CFI (0.999), and SRMR (0.016) indicated an excellent model fit. The overall model fit statistic, chi-square (χ2 = 7.626, df=6, p= .267), confirmed a good model fit. Students aiming to enrol in medicine generated higher psychological health and environmental QoL scores compared to their non-medicine oriented peers. In addition, physical QoL and psychological health QoL scores significantly predicted psychological distress measures. Conclusion: The study raises a potential debate regarding placing students with mixed career intentions into the same course and the potential implications this may have on teaching in interprofessional and large student groups in relation to wellbeing, pedagogy, equity, and expenditure. The findings clearly indicated that medical students are not as adversely impacted upon in terms of QoL and psychological distress compared with their non-medical peers.

Process Corresponding Implications Associated with a Conclusive Model-Fit Current-Voltage Characteristic Curves

NFinFET transistors with various fin widths (110 nm, 115 nm, and 120 nm) are put into measurements, and the data are collected. By using the modified model, the measure data is fitted. Several parameters in the formula of modified model are determined to make both the measured data and the fitting data almost as close as possible. Those parameters are listed and analyzed, including kN (proportional to channel width and gate oxide capacitor, and inversely proportional to the channel length) λ (the inverse of Early Voltage), and sometimes Vth (Threshold Voltage). By kN, the appropriate process control can be high lighted, the corresponding channel concentration can be calculated and thus many implicit physical quantities may be exploited.

Measuring Achievement, Affiliation, and Power Motives in Mobility Situations: Development of the Multi-Motive Grid Mobility

The current study introduces the Multi-Motive Grid Mobility (MMG-M) in an age-stratified sample (N = 206) that aims to disentangle six motive components – hope of success, hope of affiliation, hope of power, fear of failure, fear of rejection, and fear of power – in mobility-related and mobility-unrelated scenarios. Similar to the classical Multi-Motive Grid (MMG), we selected 14 picture scenarios representing seven mobility and seven non-mobility situations. The scenarios were combined with 12 statements from the MMG. Both the MMG-M and MMG were assessed to allow comparability between psychometric criteria. The results of confirmatory factor analyses yielded a good model fit for a six-factor solution with an additional mobility factor for the MMG-M. Internal consistency of the items was similar to the MMG. Lastly, we investigated associations between the motive components and mobility-related variables. We found that risk awareness was positively related to all fear components in both mobility and non-mobility scenarios. Most importantly, physical constraint was positively associated with fear of rejection and fear of power in mobility situations underlining the importance to create support systems to reduce these concerns in people’s everyday lives.