5,010 research outputs found
Bayesian binary quantile regression for the analysis of Bachelor-Master transition
The multi-cycle organization of modern university systems stimulates the
interest in studying the progression to higher level degree courses during the
academic career. In particular, after the achievement of the first level
qualification (Bachelor degree), students have to decide whether to continue
their university studies, by enrolling in a second level (Master) programme, or
to conclude their training experience. In this work we propose a binary
quantile regression approach to analyze the Bachelor-Master transition
phenomenon with the adoption of the Bayesian inferential perspective. In
addition to the traditional predictors of academic outcomes, such as the
personal characteristics and the field of study, different aspects of the
student's performance are considered. Moreover, a new contextual variable,
indicating the type of university regulations, is taken into account in the
model specification. The utility of the Bayesian binary quantile regression to
characterize the non-continuation decision after the first cycle studies is
illustrated with an application to administrative data of Bachelor graduates at
the School of Economics of Sapienza University of Rome and compared with a more
conventional logistic regression approach.Comment: 24 pages, 7 figures and 3 table
Bayesian psychometric scaling
In educational and psychological studies, psychometric methods are involved in the measurement of constructs, and in constructing and validating measurement instruments. Assessment results are typically used to measure student proficiency levels and test characteristics. Recently, Bayesian item response models received considerable attention to analyze test data and to measure latent variables. Bayesian psychometric modeling allows to include prior information about the assessment in addition to information available in the observed response data. An introduction is given to Bayesian psychometric modeling, and it is shown that this approach is very flexible, provides direct estimates of student proficiencies, and depends less on asymptotic results. Various Bayesian item response models are discussed to provide insight in Bayesian psychometric scaling and the Bayesian way of making psychometric inferences. This is done according to a general multilevel modeling approach, where observations are nested in students and items, and students are nested in schools. Different examples are given to illustrate the influence of prior information, the effects of clustered response data following a PISA study, and Bayesian methods for scale construction
Bias and Equivalence in Cross-Cultural Research
Bias and equivalence are key concepts in the methodology of cross-cultural studies. Bias is a generic term for any challenge of the comparability of cross-cultural data; bias leads to invalid conclusions. The demonstration of equivalence (lack of bias) is a prerequisite for any cross-cultural comparison. we first describe considerations that are relevant when choosing instruments in a cross-cultural study, notably the question of whether an existing or new instrument is to be preferred.We then describe the definition, manifestation, and sources of three types of bias (construct, method, and item bias), and three levels of equivalence (construct, measurement unit, and full score equivalence). We provide strategies to minimize bias and achieve equivalence that apply either to the design, implementation, or statistical analysis phase of a study. The need to integrate these strategies in cross-cultural studies is emphasized so as to increase the validity of conclusions regarding cross-cultural similarities and differences and rule out alternative explanations of cross-cultural differences
ltm: An R Package for Latent Variable Modeling and Item Response Analysis
The R package ltm has been developed for the analysis of multivariate dichotomous and polytomous data using latent variable models, under the Item Response Theory approach. For dichotomous data the Rasch, the Two-Parameter Logistic, and Birnbaum's Three-Parameter models have been implemented, whereas for polytomous data Semejima's Graded Response model is available. Parameter estimates are obtained under marginal maximum likelihood using the Gauss-Hermite quadrature rule. The capabilities and features of the package are illustrated using two real data examples.
Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data
The analysis of binary three-way data (i.e., persons who indicate which attributes apply to each of a set of objects) may be of interest in several substantive domains as sensory profiling, marketing research or personality assessment. Latent class probabilistic latent feature models (LCPLFMs) may be used to explain binary object-attribute associations on the basis of a small number of binary latent variables (called latent features). As LCPLFMs aim to model object-attribute associations using a small number of latent features they may be more suited to analyze data with many objects/attributes than standard multilevel latent class models which do not include such a dimension reduction. In this paper we describe new functions of the plfm package for analyzing binary three-way data with LCPLFMs. The new functions provide a flexible modeling approach as they allow to (1) specify different assumptions for modeling statistical dependencies between object-attribute pairs, (2) use different assumptions for modeling parameter heterogeneity across persons, (3) conduct a confirmatory analysis by constraining specific parameters to pre-specified values, (4) inspect results with print, summary and plot methods. As an illustration, the models are applied to analyze data on the perception of midsize cars, and to study the situational determinants of anger-related behavior
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