scholarly journals Social Integration of Second Generation Students in the Italian School System

2021 ◽  
Author(s):  
Francesco Giovinazzi ◽  
Daniela Cocchi
Keyword(s):  
Social Integration ◽  
School System ◽  
Latent Variable ◽  
Missing Values ◽  
Second Generation ◽  
Variable Model ◽  
Qualitative Variable ◽  
Italian School ◽  
Variable Approach ◽  
The Impact

AbstractCultural divides and prejudice complicate the processes of integration and acculturation of migrant families living in a foreign country. Evaluating the impact of such phenomenon can be crucial for social stability and policy making. In this context, the education system has a leading role in fostering and attaining social integration, in particular when it comes to younger sections of the migrant population. In this work, we propose a method for the construction of a quantitative indicator capturing social integration of second generation students in the Italian school system according to areas defined by nationality of the students and administrative region in which they attend school. The indicator, based on survey data, is estimated by means of a 2-step methodology. In the first step, we choose an individual qualitative variable capturing social integration at the unit level, and we compute a first direct estimate of the indicator as the proportion of highly integrated students in each area. Such variable is isolated following alternatively a proxy variable approach or a latent variable model approach. In the second step, we make use of two alternative small area models to improve the estimates, dealing with missing values, low sample size and high variability in smaller domains. At the end, the 2-step methodology results in 4 alternative versions of a synthetic indicator of social integration, that can be used to rank nationalities and administrative regions.

A latent variable approach to measuring wartime sexual violence

2020 ◽  
Vol 57 (6) ◽  
pp. 728-739
Author(s):  
Jule Krüger ◽  
Ragnhild Nordås
Keyword(s):  
Sexual Violence ◽  
Latent Variable ◽  
Best Practice ◽  
Temporal Trends ◽  
Credible Interval ◽  
Future Research ◽  
Variable Model ◽  
Multiple Indicators ◽  
Variable Approach ◽  
Security Problem

Conflict-related sexual violence is an international security problem and is sometimes used as a weapon of war. It is also a complex and hard-to-observe phenomenon, constituting perhaps one of the most hidden forms of wartime violence. Latent variable models (LVM) offer a promising avenue to account for differences in observed measures. Three annual human rights sources report on the sexual violence practices of armed conflict actors around the world since 1989 and were coded into ordinal indicators of conflict-year prevalence. Because information diverges significantly across these measures, we currently have a poor scientific understanding with regard to trends and patterns of the problem. In this article, we use an LVM approach to leverage information across multiple indicators of wartime sexual violence to estimate its true extent, to express uncertainty in the form of a credible interval, and to account for temporal trends in the underlying data. We argue that a dynamic LVM parametrization constitutes the best fit in this context. It outperforms a static latent variable model, as well as analysis of observed indicators. Based on our findings, we argue that an LVM approach currently constitutes the best practice for this line of inquiry and conclude with suggestions for future research.

The Impact of Traffic Information Acquisition on the Traffic Conditions of the Athens Greater Area

2015 ◽  
pp. 174-191
Author(s):  
Athena Tsirimpa ◽  
Amalia Polydoropoulou
Keyword(s):  
Information Acquisition ◽  
Latent Variable ◽  
Traffic Information ◽  
Activity Scheduling ◽  
Variable Model ◽  
Travel Information ◽  
Traffic Conditions ◽  
Real Time Information ◽  
The Impact ◽  
Time Information

The main objective of this article is to gain fundamental understanding on the effect of real time information acquisition, on the traffic conditions of the Athens greater area. Activity scheduling is a dynamic process, where individuals often need to modify their schedule, as a result of new insights. Research so far hasn't analyzed the effect of traffic information acquisition, in activity scheduling, although several studies have been conducted to capture the factors that influence the rescheduling of activities. An integrated latent variable model has been estimated, that predicts the probability of rescheduling activities as a function of flexibility, mode choice constraints and travel information. The analysis of the results indicates that one of the biggest impacts of traffic information acquisition is reflected in the rescheduling of activities. Therefore, traffic information not only can significantly improve the travel experience of individuals but may directly affect the performance of the transportation system.

scholarly journals role of perceived satisfaction and the built environment on the frequency of cycle-commuting

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Tomás Echiburú ◽  
Ricardo Hurtubia ◽  
Juan Carlos Muñoz
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Latent Variable ◽  
Structural Equation ◽  
Subjective Experience ◽  
Central Business District ◽  
Variable Model ◽  
Perceived Satisfaction ◽  
New Findings ◽  
The Impact

Understanding how several street attributes influence the frequency of cycle commuting is relevant for policymaking in urban planning. However, to better understand the impact of the built environment on people's choices, we must understand the subjective experience of individuals while cycling. This study examines the relationship between perceived satisfaction and the attributes of the built environment along the route. Data was collected from a survey carried out within one district of Santiago’s central business district (N=2,545). It included socio-demographic information, origin-destination and route, travel behavior habits, and psychometric indicators. Two models were estimated. The first, a satisfaction latent variable model by mode, confirms previous findings in the literature, such as the correlation between cycling and a more enjoyable experience, while adding some new findings. For instance, satisfaction increases with distance and the number of trips per week. The second is a hybrid ordered logit model for cycle commuting frequency that includes satisfaction, through a structural equation, that shows this latent variable plays a significant role in travel behavior. The presence of buses along the route decreases cycling satisfaction and frequency, while the trip length and the availability of cycle paths has the opposite effect for male and female cyclists. These results allow us to understand the main factors that deliver satisfaction to cyclists and therefore induce frequent cycle commuting. Overall, our study provides evidence of the need for policymakers to focus their strategies so as to effectively promote cycling among different types of commuters.

scholarly journals Optimizing Detection of True Within-Person Effects for Intensive Measurement Designs: A Comparison of Multilevel SEM and Unit-Weighted Scale Scores

2020 ◽  
Author(s):  
Jonathan Rush ◽  
Philippe Rast ◽  
Scott Michael Hofer
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Variable Model ◽  
Alternative Approach ◽  
Scale Scores ◽  
Weighted Model ◽  
The Impact

Intensive repeated measurement designs are frequently used to investigate within-person variation over relatively brief intervals of time. The majority of research utilizing these designs rely on unit-weighted scale scores, which assume that the constructs are measured without error. An alternative approach makes use of multilevel structural equation models (MSEM), which permit the specification of latent variables at both within-person and between-person levels. These models disaggregate measurement error from systematic variance, which should result in less biased within-person estimates and larger effect sizes. Differences in power, precision, and bias between multilevel unit-weighted and MSEM models were compared through a series of Monte Carlo simulations. Results based on simulated data revealed that precision was consistently poorer in the MSEM models than the unit-weighted models, particularly when reliability was low. However, the degree of bias was considerably greater in the unit-weighted model than the latent variable model. Although the unit-weighted model consistently underestimated the effect of a covariate, it generally had similar power relative to the MSEM model due to the greater precision. Considerations for scale development and the impact of within-person reliability are highlighted.

scholarly journals Latent Variable Modeling and Adaptive Testing for Experimental Cognitive Psychopathology Research

2020 ◽  
pp. 001316442091989
Author(s):  
Michael L. Thomas ◽  
Gregory G. Brown ◽  
Virginie M. Patt ◽  
John R. Duffy
Keyword(s):  
Clinical Trials ◽  
Latent Variable ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Cognitive Testing ◽  
Cognitive Tasks ◽  
Latent Variable Modeling ◽  
Variable Model ◽  
Translational Studies ◽  
The Impact

The adaptation of experimental cognitive tasks into measures that can be used to quantify neurocognitive outcomes in translational studies and clinical trials has become a key component of the strategy to address psychiatric and neurological disorders. Unfortunately, while most experimental cognitive tests have strong theoretical bases, they can have poor psychometric properties, leaving them vulnerable to measurement challenges that undermine their use in applied settings. Item response theory–based computerized adaptive testing has been proposed as a solution but has been limited in experimental and translational research due to its large sample requirements. We present a generalized latent variable model that, when combined with strong parametric assumptions based on mathematical cognitive models, permits the use of adaptive testing without large samples or the need to precalibrate item parameters. The approach is demonstrated using data from a common measure of working memory—the N-back task—collected across a diverse sample of participants. After evaluating dimensionality and model fit, we conducted a simulation study to compare adaptive versus nonadaptive testing. Computerized adaptive testing either made the task 36% more efficient or score estimates 23% more precise, when compared to nonadaptive testing. This proof-of-concept study demonstrates that latent variable modeling and adaptive testing can be used in experimental cognitive testing even with relatively small samples. Adaptive testing has the potential to improve the impact and replicability of findings from translational studies and clinical trials that use experimental cognitive tasks as outcome measures.

scholarly journals The Impact of Social Isolation on Subjective Health: An Instrumental Variable Approach

2018 ◽  
Author(s):  
Emiliano Sironi ◽  
Amelie Nadine Wolff
Keyword(s):  
Social Isolation ◽  
Instrumental Variable ◽  
Subjective Health ◽  
European Social Survey ◽  
Perceived Health Status ◽  
Variable Model ◽  
Significant Deterioration ◽  
Variable Approach ◽  
Using Data ◽  
The Impact

We investigate the relationship between social isolation and subjective health, considering that this relationship is potentially affected by endogeneity due to the presence of self-reported measures. Thus, if an increase in social isolation may impact the perception on health, alternative paths of causality may also be hypothesized. Using data from round 7 of the European Social Survey, we estimate an instrumental variable model in which isolation is explained as being a member of an ethnic minority and having experienced some serious family conflicts in the past. Our results confirm that changes in social isolation influence subjective general health. In particular, greater isolation produces a strong and significant deterioration of the perceived health status. With respect to the literature on social isolation and health, we try to advance it by supporting a path of causality running from social isolation to subjective health.

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