Measurement Invariance in Comparing Attitudes Toward Immigrants Among Youth Across Europe in 1999 and 2009

2017 ◽  
Vol 47 (4) ◽  
pp. 687-728 ◽  
Author(s):  
Ingrid Munck ◽  
Carolyn Barber ◽  
Judith Torney-Purta
Keyword(s):  
Measurement Invariance ◽  
Civic Education ◽  
Educational Achievement ◽  
International Association ◽  
Secondary Analysis ◽  
Large Data ◽  
Data Sets ◽  
Data Set ◽  
Positive Attitudes ◽  
Education Study

This study applies the alignment method, a technique for assessing measurement equivalence across many groups, to the analysis of adolescents’ support for immigrants’ rights in a pooled data set from the 1999 International Association for the Evaluation of Educational Achievement (IEA) Civic Education Study and the 2009 IEA International Civics and Citizenship Education Study. We examined measurement invariance across 92 groups (country by cohort by gender), finding that a five-item scale was statistically well-grounded for unbiased group comparisons despite the presence of significant noninvariance in some groups. Using the resulting group mean scores, we compared European youth’s attitudes finding that female students had more positive attitudes than did male students across countries and cohorts. An analysis of countries participating in both studies revealed that students in most countries demonstrated more positive attitudes in 2009 than in 1999. The alignment methodology makes it feasible to comprehensively assess measurement invariance in large data sets and to compute aligned factor scores for the full sample that can update existing databases for more efficient further secondary analysis and with metainformation concerning measurement invariance.

Profiles of adolescents’ civic attitudes in sixteen countries: Examining cross-cohort changes from 1999 to 2009

2020 ◽  
Vol 15 (2) ◽  
pp. 79-96
Author(s):  
Carolyn Barber ◽  
Jessica Ross
Keyword(s):  
Civic Education ◽  
Western Europe ◽  
Educational Achievement ◽  
International Association ◽  
Political Process ◽  
Civic Attitudes ◽  
Positive Attitudes ◽  
Students Attitudes ◽  
Citizenship Norms ◽  
Using Data

The purpose of this study is to examine profiles of students’ attitudes toward citizenship norms and inclusiveness in the political process, focusing on changes in the frequency of particular profiles and in the strength of predictors across a decade. Using data from 16 countries participating in the 1999 and 2009 International Association for the Evaluation of Educational Achievement civic education studies, we identified five attitudinal profiles. Profiles defined by negative attitudes toward diverse social groups decreased over time, whereas profiles defined by very positive attitudes toward diverse groups increased, particularly in western Europe. Although some post-Communist countries demonstrated trends toward more positive attitudinal profiles, others trended toward profiles defined by weaker citizenship norms. Across countries and cohorts, more positive profiles were associated with stronger school climates and expectations of civic participation.

scholarly journals Students' Concepts of Democracy

2003 ◽  
Vol 2 (3) ◽  
pp. 396-409 ◽  
Author(s):  
Vera Husfeldt ◽  
Roumiana Nikolova
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Civic Education ◽  
Educational Achievement ◽  
International Association ◽  
School Students ◽  
Upper Secondary ◽  
Confirmatory Factor ◽  
Education Study ◽  
Concepts Of Democracy

In addition to assessing the civic knowledge and skills of adolescents, examining students' concepts of democracy was an important aspect of the International Association for the Evaluation of Educational Achievement (IEA) Civic Education Study. Based on theories and previous research with adults and youth in this area, a set of survey items was developed to cover several models of democracy. In the 1999 IEA Civic Education Study of 14 year olds, the confirmatory factor analysis showed one factor with items relating to the generic or rule of law model. A second factor, participatory democracy, did not meet IEA scaling standards. In contrast, confirmatory factor analysis of upper secondary school students' data revealed a three-factor solution for the democracy items, suggesting that they have more differentiated concepts of democracy than 14 year olds.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

scholarly journals Generation of geometric interpolations of building types with deep variational autoencoders

2020 ◽  
Vol 6 ◽  
Author(s):  
Jaime de Miguel Rodríguez ◽  
Maria Eugenia Villafañe ◽  
Luka Piškorec ◽  
Fernando Sancho Caparrini
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Large Data ◽  
Learning Model ◽  
Large Data Sets ◽  
Data Sets ◽  
Connectivity Map ◽  
Data Set ◽  
3D Objects ◽  
Machine Learning Model

Abstract This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.

A batch-wise non-linear fitting and analysis tool for treating large X-ray diffraction data sets

2006 ◽  
Vol 39 (2) ◽  
pp. 262-266 ◽  
Author(s):  
R. J. Davies
Keyword(s):  
Diffraction Data ◽  
Operation Mode ◽  
Large Data ◽  
Scattering Data ◽  
Data Sets ◽  
Analysis Tool ◽  
Data Set ◽  
X Ray ◽  
Linear Fitting ◽  
Non Linear

Synchrotron sources offer high-brilliance X-ray beams which are ideal for spatially and time-resolved studies. Large amounts of wide- and small-angle X-ray scattering data can now be generated rapidly, for example, during routine scanning experiments. Consequently, the analysis of the large data sets produced has become a complex and pressing issue. Even relatively simple analyses become difficult when a single data set can contain many thousands of individual diffraction patterns. This article reports on a new software application for the automated analysis of scattering intensity profiles. It is capable of batch-processing thousands of individual data files without user intervention. Diffraction data can be fitted using a combination of background functions and non-linear peak functions. To compliment the batch-wise operation mode, the software includes several specialist algorithms to ensure that the results obtained are reliable. These include peak-tracking, artefact removal, function elimination and spread-estimate fitting. Furthermore, as well as non-linear fitting, the software can calculate integrated intensities and selected orientation parameters.

Effects of genotype and lactation number on health and reproductive problems in dairy cows

1997 ◽  
Vol 1997 ◽  
pp. 143-143
Author(s):  
B.L. Nielsen ◽  
R.F. Veerkamp ◽  
J.E. Pryce ◽  
G. Simm ◽  
J.D. Oldham
Keyword(s):  
Dairy Cows ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Variation Analysis ◽  
Genetic Line ◽  
Data Set ◽  
Health Events ◽  
Use Of Data ◽  
Low Incidence

High producing dairy cows have been found to be more susceptible to disease (Jones et al., 1994; Göhn et al., 1995) raising concerns about the welfare of the modern dairy cow. Genotype and number of lactations may affect various health problems differently, and their relative importance may vary. The categorical nature and low incidence of health events necessitates large data-sets, but the use of data collected across herds may introduce unwanted variation. Analysis of a comprehensive data-set from a single herd was carried out to investigate the effects of genetic line and lactation number on the incidence of various health and reproductive problems.

The Old and New Face of Civic Education: Expert, Teacher, and Student Views

2003 ◽  
Vol 2 (3) ◽  
pp. 446-454 ◽  
Author(s):  
Heinrich Mintrop
Keyword(s):  
Social Movements ◽  
Critical Thinking ◽  
Civic Education ◽  
Community Organizing ◽  
Educational Achievement ◽  
International Association ◽  
Political Activity ◽  
Expert Teacher ◽  
The Social ◽  
Teachers And Students

Using the representative database of the Second International Association for the Evaluation of Educational Achievement (IEA) Civic Education Study, this article takes a look at civic education through the lens of expert scholars, teachers, and students. The data reveals that, as some of the experts reported, political interest is not pervasive among students and classrooms are not places where a culture of debate, controversy, and critical thinking flourishes for students. But things have changed if civic education was primarily an imparting of facts about national history and the workings of the political system. As for teachers, now the discourse of rights and the social movements associated with it top the list of curricular concerns. Large majorities of teachers share with national scholars a conceptualization of civic education as critical thinking and value education, repudiating knowledge transformation as ideal, and they recognize the wide gulf that exists between these ideals and reality. As for many students, political disinterest notwithstanding, forms of participation born out of social movements and community organizing are the preferred channels of political activity. And yet, it seems the experts have a point: the field is not where it should be.

scholarly journals Extreme Learning Machine with sigmoid activation function on large data

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3523-3526
Keyword(s):  
Efficient Algorithm ◽  
Large Data ◽  
Activation Function ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Learning Machine ◽  
Sigmoid Activation Function ◽  
State Of Art ◽  
Better Than

This paper describes an efficient algorithm for classification in large data set. While many algorithms exist for classification, they are not suitable for larger contents and different data sets. For working with large data sets various ELM algorithms are available in literature. However the existing algorithms using fixed activation function and it may lead deficiency in working with large data. In this paper, we proposed novel ELM comply with sigmoid activation function. The experimental evaluations demonstrate the our ELM-S algorithm is performing better than ELM,SVM and other state of art algorithms on large data sets.

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