scholarly journals Impact of the stringency of lockdown measures on covid-19: A theoretical model of a pandemic

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258205
Author(s):  
Claudio Violato ◽  
Emilio Mauro Violato ◽  
Efrem Mauro Violato
Keyword(s):  
Theoretical Model ◽  
Path Analysis ◽  
Multiple Regression ◽  
Latent Variable ◽  
Mortality Rates ◽  
Infection Rates ◽  
Public Data ◽  
Latent Variable Path Analysis ◽  
Latent Variable Path ◽  
Infection Spread

Background How effective have lockdowns been at reducing the covid-19 infection and mortality rates? Lockdowns influence contact among persons within or between populations including restricting travel, closing schools, prohibiting public gatherings, requiring workplace closures, all designed to slow the contagion of the virus. The purpose of the present study was to assess the impact of lockdown measures on the spread of covid-19 and test a theoretical model of the covid-19 pandemic employing structural equation modelling. Methods Lockdown variables, population demographics, mortality rates, infection rates, and health were obtained for eight countries: Austria, Belgium, France, Germany, Italy, Netherlands, Spain, and the United Kingdom. The dataset, owid-covid-data.csv, was downloaded on 06/01/2020 from: https://github.com/owid/covid-19-data/tree/master/public/data. Infection spread and mortality data were depicted as logistic growth and analyzed with stepwise multiple regression. The overall structure of the covid-19 data was explored through factor analyses leading to a theoretical model that was tested using latent variable path analysis. Results Multiple regression indicated that the time from lockdown had a small but significant effect (β = 0.112, p< 0.01) on reducing the number of cases per million. The stringency index produced the most important effect for mortality and infection rates (β = 0.588,β = 0.702, β = 0.518, β = 0.681; p< 0.01). Exploratory and confirmatory analyses resulted in meaningful and cohesive latent variables: 1) Mortality, 2) Infection Spread, 3) Pop Health Risk, and 4) Health Vulnerability (Comparative Fit Index = 0.91; Standardized Root Mean Square Residual = 0.08). Discussion The stringency index had a large impact on the growth of covid-19 infection and mortality rates as did percentage of population aged over 65, median age, per capita GDP, diabetes prevalence, cardiovascular death rates, and ICU hospital beds per 100K. The overall Latent Variable Path Analysis is theoretically meaningful and coherent with acceptable fit indices as a model of the covid-19 pandemic.

School belonging in adolescents: Psychometric properties of the School Belongingness Scale in Iranian students

2020 ◽  
pp. 1-10 ◽  
Author(s):  
Gökmen Arslan ◽  
Faramarz Asanjarani ◽  
Saeede Bakhtiari ◽  
Fatemeh Hajkhodadadi
Keyword(s):  
Psychometric Properties ◽  
Latent Variable ◽  
Measurement Model ◽  
Social Emotional ◽  
School Belongingness ◽  
Confirmatory Factor ◽  
Factor Measurement ◽  
Latent Variable Path Analysis ◽  
Iranian Students ◽  
Latent Variable Path

Abstract The purpose of the present study is to investigate the initial psychometric properties and cultural adaptation of the School Belongingness Scale (SBS) in a sample of Iranian adolescents. Participants included 324 students, ranging in age between 12 and 18 years (M = 14.68, SD = 1.39). Confirmatory factor analysis indicated that responses to the Farsi version of the SBS were characterised by a two-factor measurement model, and latent variable path analysis results revealed this measurement model was predictive of adolescents’ responses to measures of social, emotional, and behavioural problems (e.g., emotional problems, conduct problems), academic achievement, and prosocial behaviour. These results provide initial evidence suggesting that the scale is psychometrically adequate to measure Iranian students’ sense of belonging at school.

Latent Variable Path Modeling with Partial Least Squares

Technometrics ◽  
1992 ◽  
Vol 34 (1) ◽  
pp. 110 ◽  
Author(s):  
Charles K. Bayne ◽  
Jan-Bernd Lohmöller ◽  
Jan-Bernd Lohmoller
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variable ◽  
Path Modeling ◽  
Latent Variable Path

The Relative Importance of Socioeconomic and Political Variables for Public Policy

1977 ◽  
Vol 71 (2) ◽  
pp. 559-566 ◽  
Author(s):  
Michael S. Lewis-Beck
Keyword(s):  
Public Policy ◽  
Path Analysis ◽  
Multiple Regression ◽  
Partial Correlation ◽  
Policy Outcomes ◽  
Statistical Techniques ◽  
Relative Importance ◽  
Simple Correlation ◽  
Independent Variables ◽  
Political Variables

Since Dawson and Robinson, a dominant issue in the quantitative study of public policy has been the relative importance of socioeconomic and political variables for determining policy outcomes. It is argued here that past efforts to resolve this issue have been unsatisfactory, largely because they relied on inadequate statistical techniques, i.e., simple correlation, partial correlation, or multiple regression. Coefficients from these techniques are irrelevant for all but the most peculiar models of public policy. In general, if the researcher wishes to assess the relative importance of independent variables, it will be necessary to resort to path analysis of a formally constructed causal model. The comparison of “effects coefficients,” derived from path analysis, is offered as the preferred means of evaluating independent variables, superior to comparisons of coefficients from simple correlation, partial correlation, or multiple regression. When the effects coefficients are actually calculated for a popular model of welfare policy, socioeconomic variables appear much more important than political variables, contrary to interpretations coming from the more traditional statistical techniques.

How to Deal With Death: An Empirical Path Analysis of a Simplified Model of Death Anxiety

2018 ◽  
Vol 82 (2) ◽  
pp. 261-277
Author(s):  
Vera Surall ◽  
Inga Steppacher
Keyword(s):  
Life Satisfaction ◽  
Theoretical Model ◽  
Path Analysis ◽  
Mediation Analysis ◽  
Empirical Data ◽  
Death Anxiety ◽  
Demographic Variables ◽  
Model Fit ◽  
Simplified Model ◽  
Death Acceptance

How anxious are you about dying? According to Tomer and Eliason, this depends on various personal circumstances, which they identified in their model on death anxiety. This study aims to verify various aspects of Tomer and Eliason’s theoretical model. We therefore collected data from 652 German participants about demographic variables, religiosity, life satisfaction, death acceptance, and death anxiety. We then conducted a path analysis in order to verify whether the empirical data supported the theoretical model. Our results demonstrate a very good model fit, indicating that the analyzed model is valid and can be maintained. Further mediation analysis demonstrates the specific relations of variables within the model and their influence on death anxiety.

scholarly journals Student Ratings of Teaching Effectiveness: Student Engagement and Course Characteristics

2010 ◽  
Vol 39 (1) ◽  
Author(s):  
Tanya Beran ◽  
Claudio Violato
Keyword(s):  
Student Engagement ◽  
Latent Variable ◽  
Teaching Effectiveness ◽  
Student Ratings ◽  
Path Model ◽  
Course Characteristics ◽  
Student Ratings Of Instruction ◽  
Fit Index ◽  
Student Ratings Of Teaching ◽  
Latent Variable Path

Characteristics of university courses and student engagement were examined in relation to student ratings of instruction. The Universal Student Ratings of Instruction instrument was administered to students at the end of every course at a major Canadian university over a three-year period. Using a two-step analytic procedure, a latent variable path model was created. The model showed a moderate fit to the data (Comparative Fit Index = .88), converged in _0 iterations, with a standardized residual mean error of .03, χ2 (_49) = _988.59, p < .05. The model indicated that course characteristics such as status and description are not directly related to student ratings. Rather, they are mediated by student engagement, which is measured by student attendance and expected grade. It was concluded that, although the model is statistically adequate, many other factors determine how students rate their instructors.  

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