Multiple Indicators
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2021 ◽  
Author(s):  
Musheer A. Aljaberi ◽  
Naser A. Alareqe ◽  
Mousa A. Qasem ◽  
Abdulsamad Alsalahi ◽  
Sarah Noman ◽  
...  

Abstract Background: Due to the limited research examining the psychological impact of coronavirus disease 2019 (COVID-19), our study aims to investigate the effect of the COVID-19 pandemic on psychological outcomes and assess the differences between participants with and without post-traumatic stress disorder (PTSD) on the psychological outcomes as latent factors and items. Methods: An online survey was conducted on 999 participants. The Impact of Event Scale-Revised (IES-R) assessed the psychological impact, while outcomes were measured by Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder (GAD-7), and Insomnia Severity Index (ISI). A series of Confirmatory Factor Analysis (CFA), structural equation model (SEM), Multiple Indicators and Multiple Causes (MIMIC), and Differential Item Functioning (DIF) were conducted. Results: The IES-R has significant effects on the psychological outcomes. Participants with PTSD have a higher impact on latent factors (depression, anxiety, and insomnia) than those without PTSD. Among participants, 36.5% experienced moderate to severe symptoms of depression, and 32.6% had mild depressive symptoms. For anxiety, 23.7% of respondents experienced moderate to severe anxiety symptoms, and 33.1% had mild symptoms of anxiety. For the ISI, 51.5% of participants experienced symptoms of insomnia. Conclusion: the IES-R for COVID-19 has a significant impact on depression, anxiety, and insomnia at the level of latent constructs and observed variables.


Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2608
Author(s):  
Anna M. Wagner ◽  
Katrina E. Bennett ◽  
Glen E. Liston ◽  
Christopher A. Hiemstra ◽  
Dan Cooley

Snow plays a major role in the hydrological cycle. Variations in snow duration and timing can have a negative impact on water resources. Excluding predicted changes in snowmelt rates and amounts could result in deleterious infrastructure, military mission, and asset impacts at military bases across the US. A change in snowpack can also lead to water shortages, which in turn can affect the availability of irrigation water. We performed trend analyses of air temperature, snow water equivalent (SWE) at 22 SNOTEL stations, and streamflow extremes for selected rivers in the snow-dependent and heavily irrigated Yakima River Basin (YRB) located in the Pacific Northwest US. There was a clear trend of increasing air temperature in this study area over a 30 year period (water years 1991–2020). All stations indicated an increase in average air temperatures for December (0.97 °C/decade) and January (1.12 °C/decade). There was also an upward trend at most stations in February (0.28 °C/decade). In December–February, the average air temperatures were 0.82 °C/decade. From these trends, we estimate that, by 2060, the average air temperatures for December–February at most (82%) stations will be above freezing. Furthermore, analysis of SWE from selected SNOTEL stations indicated a decreasing trend in historical SWE, and a shift to an earlier peak SWE was also assumed to be occurring due of the shorter snow duration. Decreasing trends in snow duration, rain-on-snow, and snowmelt runoff also resulted from snow modeling simulations of the YRB and the nearby area. We also observed a shift in the timing of snowmelt-driven peak streamflow, as well as a statistically significant increase in winter maximum streamflow and decrease in summer maximum and minimum streamflow trends by 2099. From the streamflow trends and complementary GEV analysis, we show that the YRB basin is a system in transition with earlier peak flows, lower snow-driven maximum streamflow, and higher rainfall-driven summer streamflow. This study highlights the importance of looking at changes in snow across multiple indicators to develop future infrastructure and planning tools to better adapt and mitigate changes in extreme events.


2021 ◽  
Vol 13 (18) ◽  
pp. 10377
Author(s):  
Jetnipit Kunchai ◽  
Dissakoon Chonsalasin ◽  
Buratin Khampirat

To help address educational inequalities in student backgrounds and career plans, the measurement of career aspirations can provide crucial information about inequality related to career opportunities. Therefore, this study analyzed the factor structure and psychometric properties of the Career Aspiration Scale-Revised (CAS-R) and the effects of sociodemographic variables on the CAS-R. The study participants were 590 undergraduates at three community colleges in rural Thailand. Confirmatory factor analysis (CFA) was conducted to investigate the validity of the CAS-R three-factor model. The Multiple Indicators Multiple Causes (MIMIC) model with and without differential item functioning (DIF) were applied to investigate the effects of sociodemographic factors as covariates on the CAS-R and specific item response. The CFA results supported the 18-item CAS-R’s three-factor structure because the items well represented latent factors and because the subscales met research standards for reliability and validity. The MIMIC model showed that only the year of study (first-year students) had a positive direct effect on leadership, achievement, and educational aspirations. The MIMIC model with DIF indicated that gender, year of study, major, and paternal education caused inequality in 10 items. Overall, the results show that the 18-item CAS-R has strong psychometric properties and can accurately assess the career aspirations of Thai students. The MIMIC model’s application allowed researchers to show that sociodemographic background affected the leadership, achievement, and education subscales. Obtaining information on the CAS-R scale’s measurements would be useful for researchers, practitioners, and career counselors interested in helping students develop career aspirations and choices.


2021 ◽  
Vol 22 (3) ◽  
pp. 225-246
Author(s):  
Friedrich Schneider

Zusammenfassung In diesem Überblicksartikel schätzt Friedrich Schneider mit Hilfe des MIMIC-Ansatzes („Multiple Indicators Multiple Causes“) Größe und Entwicklung der Schattenwirtschaft für 157 Länder in den Jahren 1991–2017 und stellt zugleich die einschlägige Literatur vor. Die OECD-Länder haben mit Werten unter 20 Prozent des offiziellen BIP die kleinste Schattenwirtschaft; die Länder in Lateinamerika und Subsahara-Afrika mit durchschnittlich fast 38 beziehungsweise 39 Prozent die größte. Der Durchschnitt über alle Länder liegt bei 30,9 Prozent. Die Schattenwirtschaft schrumpft dabei über die Zeit. Der durchschnittliche Rückgang beträgt 6,8 Prozentpunkte. Besonders groß ist die Schattenwirtschaft in Ländern wie Bolivien (Georgien) mit 62,9 (61,7) Prozent des BIP; besonders klein ist sie in Ländern wie der Schweiz (den Vereinigten Staaten) mit durchschnittlich 6,4 (7,6) Prozent. Zur Überprüfung der Robustheit werden die MIMIC-Ergebnisse mit denen des Diskrepanz-Ansatzes für 23 Länder verglichen. Für die meisten Länder zeigen sich stabile, ähnliche Ergebnisse. Abschließend wird die Interaktion der Schattenwirtschaft mit der offiziellen Wirtschaft untersucht. Erste Ergebnisse für Pakistan 1976–2015 zeigen kurzfristig einen negativen und langfristig einen positiven Effekt der Schattenwirtschaft auf das BIP.


2021 ◽  
Author(s):  
Elham Shaarbaf Eidgahi ◽  
Malihe Nasiri ◽  
Nourossadat Kariman ◽  
Nastaran Safavi Ardebili ◽  
Masoud Salehi ◽  
...  

Abstract Background Gestational Diabetes Mellitus (GDM) is an underlying cause of maternal and newborn morbidity and mortality all around the world. Timely diagnosis of GDM plays an important role in reducing its adverse consequences and burden. This study aimed to determine diagnostic accuracy of multiple indicators in complete blood count (CBC) test for early prediction of GDM. Methods In this prospective cohort study, the data from 600 pregnant women was analyzed. In the study sample, the two-step approach was utilized for the diagnosis of GDM at 24–28 weeks of gestation. We also used the repeated measures of hemoglobin (Hb), hematocrit (Hct), fasting blood sugar (FBS) and red blood cell count (RBC) in the first and early second trimesters of pregnancy as the longitudinal multiple indicators for early diagnosis of GDM. The classification of pregnant women to GDM and non-GDM groups was performed using a statistical technique based on the random-effects modeling framework. Results Among the sample, 49 women (8.2%) were diagnosed with GDM. In the first and early second trimester of pregnancy, the mean HcT, Hb and FBS of women with GDM was significantly higher than non-GDMs (P < 0.001). The concurrent use of multiple longitudinal data from HcT, Hb, RBC and FBS in the first and early second trimester of pregnancy resulted in a sensitivity, specificity and area under the curve (AUC) of 87%, 70% and 83%, respectively, for early prediction of GDM. Conclusions In general, our findings showed that the concurrent use of repeated measures data on Hct, Hb, FBS and RBC in the first and early second trimester of pregnancy might be utilized as an acceptable tool to predict GDM in the earlier stages.


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