Differential item functioning of material deprivation assessment in households with or without children.

BACKGROUND: Composite multidimensional indices are broadly used to measure child poverty and social exclusion. Many of such indices are based on EU-SILC data or similar large scale complex sampling surveys, with the household as unit of analysis. Indicators related to households with or without children may quantify the intended attribute differently depending on the household structure and characteristics of individuals, potentially compromising the assessment. METHODS: We conducted statistical modelling and hypotheses tests using a two-parameter logistic item response model (IRM) and the likelihood-ratio test for DIF verification. Methods were applied to 2020 EU-SILC Portuguese data comprising 11,367 households representing a population of 4,099,052. Statistical analysis have allowed for the survey sampling design. CONCLUSION: Our findings demonstrate differential item functioning in the assessment material deprivation in households with or without children.

Utility-driven assessment of anonymized data via clustering.

In this study, clustering is conceived as an auxiliary tool to identify groups of special interest. This approach was applied to a real dataset concerning an entire Portuguese cohort of higher education Law students. Several anonymized clustering scenarios were compared against the original cluster solution. The clustering techniques were explored as data utility models in the context of data anonymization, using k-anonymity and (ε, Î´ï»¿)-differential as privacy models. The purpose was to assess anonymized data utility by standard metrics, by the characteristics of the groups obtained, and the relative risk (a relevant metric in social sciences research). For a matter of self-containment, we present an overview of anonymization and clustering methods. We used a partitional clustering algorithm and analyzed several clustering validity indices to understand to what extent the data structure is preserved, or not, after data anonymization. The results suggest that for low dimensionality/cardinality datasets the anonymization procedure easily jeopardizes the clustering endeavor. In addition, there is evidence that relevant field-of-study estimates obtained from anonymized data are biased.

Persistence and Academic Expectations in Higher-Education Students / Persistencia y Expectativas Académicas en Educación Superior

Background: The article focuses on the relationship between students’ expectations and persistence in the context of higher education. It explores the role that high expectations play in increasing the probability of adult students’ persistence, controlling for individual sociodemographic attributes, skills preparation, values, and commitments. Method: A multilevel logistic model was applied to data on 2,697 first-year students who were enrolled in 54 programmes at a Portuguese public university during 2015-2016. Results: The findings suggest that high academic expectations are relevant to older students, since such expectations increase their likelihood of persistence. Being admitted to their first-choice programmes and differences in their study habits also contribute to increasing the probability of persistence. In the presence of such motivational and behavioural attributes, we did not find statistically significant differences according to students’ socioeconomic background or gender. Our results also suggest that the relationship between prior academic achievement and persistence varies randomly across programmes. Conclusions: This institutional research study gives evidence towards the relevance of taking into account the level of programmes/courses in order to support interventions that effectively meet the students´ expectations and, thus, could increase the probability of persistence for all students entering HE. (AU)

Antecedentes: el artículo se centra en la relación entre expectativas y persistencia de los estudiantes en educación superior. Explora el papel que juegan las altas expectativas en el aumento de la persistencia, controlando los atributos sociodemográficos individuales, la preparación de habilidades, etc. Método: se aplicó un modelo logístico multinivel a los datos de 2.697 estudiantes de primer año que se matricularon en 54 programas en una universidad pública portuguesa durante 2015-2016. Resultados: las altas expectativas académicas son relevantes para estudiantes mayores, ya que aumentan su probabilidad de persistencia. Ser admitido en sus programas de primera elección y las diferencias en sus hábitos de estudio también contribuyen a aumentar la probabilidad de persistencia. En presencia de tales atributos motivacionales y de comportamiento, no encontramos diferencias estadísticamente significativas de acuerdo con los antecedentes socioeconómicos o el género de los estudiantes. Nuestros resultados sugieren que la relación entre el GPA de la escuela secundaria y la persistencia varía aleatoriamente entre programas. Conclusiones: la relevancia de tomar en cuenta el nivel de programas / cursos para apoyar intervenciones que satisfagan de manera efectiva las expectativas de los estudiantes y, por lo tanto, puedan incrementar la persistencia de los estudiantes que ingresan a la ES. (AU)