Statistics in the Social Sciences:The Best of Two Worlds

Hoe kun je statistiek gebruiken als brug tussen wetenschappelijke theorieën en onderzoeksdata? Deze vraag fascineert Marijtje van Duijn mateloos. Twee wereldenAls statisticus binnen de vakgroep sociologie vormt Marijtje van Duijn de schakel tussen onderzoeksvragen en data. Van Duijn: “In onderzoek moeten de onderzoeksvraag en de statistische modellen uiteraard goed op elkaar aansluiten, maar het is ook belangrijk om te kijken in hoeverre de onderzoeksvraag met de data beantwoord wordt of kan worden”. Deze link tussen theorie en empirie staat dan ook centraal haar oratie.Sociale netwerkanalyseVan Duijns specialisatie is de zogeheten sociale netwerkanalyse, een methode die het mogelijk maakt om relaties tussen mensen te analyseren. “Bij sociologie wordt veel onderzoek naar sociale netwerken gedaan. De structuur van deze netwerken vereist een speciale aanpak, want een normale regressie analyse is niet mogelijk. Als groep zijn we de afgelopen twintig jaar heel hard bezig geweest met het ontwikkelen van methoden die recht doen aan de complexiteit van deze netwerken”.PuzzelenStatistische modellen voor sociale netwerkanalyse staan bekend als niet eenvoudig uit te voeren en te interpreteren, maar dat is juist wat van Duijn aantrekt. “Het zijn inderdaad soms ingewikkelde analyses. En ook de vraagstellingen zijn complex. Het is niet rechttoe rechtaan, maar het is een uitdagende puzzel. Ik vind het leuk om samen met de onderzoekers te kijken hoe we hun vragen zo goed mogelijk kunnen beantwoorden”

The achievement gap in Indonesia? Organizational and ideological differences between private Islamic schools

This study examines the effects of different types of private Islamic schools on student achievement and achievement gaps. We formulate hypotheses, drawing on an education production function approach that outlines differences in investment and resource allocation decisions across these tracks and streams. We tested our hypotheses using Indonesian data collected in 2013 on 156,952 students nested in 3,150 schools in 366 municipalities. Using multilevel regression analyses, we found that student achievement and achievement gaps vary over private Islamic school tracks and streams. Even though student achievement and achievement gaps are strongly determined by student and family characteristics, our findings suggest that differences between school tracks and streams also play an important role. Moreover, our study revealed a large variability in student achievement and achievement gaps between municipalities

Balance in Family Triads:How Intergenerational Relationships Affect the Adult Sibling Relationship

In order to understand the interdependency between intergenerational and adult sibling relationships, a family systems perspective is applied to identify a smaller?empirically analyzable?relational unit of analysis, that is, the sibling?parent?sibling triad. Using balance theory, triadic configurations are derived that represent enhancement, compensation, and loyalty conflicts. The hypotheses are tested for three relational dimensions: support exchange, contact, and conflict. Multilevel analyses of 549 sibling?parent?sibling triads from the Netherlands Kinship Panel data show strong evidence for enhancement, whereas some indication was obtained for sibling relationships being affected by loyalty conflicts. The results underscore and substantiate interdependency between intergenerational and adult sibling relationships

No Longer Discrete:Modeling the Dynamics of Social Networks and Continuous Behavior

The dynamics of individual behavior are related to the dynamics of the social structures in which individuals are embedded. This implies that in order to study social mechanisms such as social selection or peer influence, we need to model the evolution of social networks and the attributes of network actors as interdependent processes. The stochastic actor-oriented model is a statistical approach to study network-attribute coevolution based on longitudinal data. In its standard specification, the coevolving actor attributes are assumed to be measured on an ordinal categorical scale. Continuous variables first need to be discretized to fit into such a modeling framework. This article presents an extension of the stochastic actor-oriented model that does away with this restriction by using a stochastic differential equation to model the evolution of a continuous attribute. We propose a measure for explained variance and give an interpretation of parameter sizes. The proposed method is illustrated by a study of the relationship between friendship, alcohol consumption, and self-esteem among adolescents

Psychoeducation for hypochondriasis:A comparison of a cognitive-behavioural approach and a problem-solving approach

In this study, two 6-week psychoeducational courses for hypochondriasis are compared, one based on the cognitive-behavioural approach, and the other on the problem-solving approach. Effects of both courses on hypochondriacal complaints, depression, trait anxiety, and number of problems encountered in daily life, are measured pre-treatment, posttreatment, and at 1- and 6-month follow-up. Participants (N = 48, of whom 4 dropped out), suffering from DSM-IV hypochondriasis, were randomized into one of the two course conditions. Results showed beneficial effects of both courses. Few differential treatment effects were found: in both conditions all effect measures decreased significantly over time (p <0.01). However, between- and inter-individual variability in decrease-patterns was of considerable size, leading to large deviations from the mean pattern. Acceptability and feasibility of both courses were rated highly by their respective participants. It is concluded that both courses can be considered equally beneficial and effective over time, with the effects evident immediately after treatment and maintained over the follow-up period. (c) 2006 Elsevier Ltd. All rights reserved

CSNE: Conditional Signed Network Embedding

Signed networks are mathematical structures that encode positive and negative relations between entities such as friend/foe or trust/distrust. Recently, several papers studied the construction of useful low-dimensional representations (embeddings) of these networks for the prediction of missing relations or signs. Existing embedding methods for sign prediction generally enforce different notions of status or balance theories in their optimization function. These theories, however, are often inaccurate or incomplete, which negatively impacts method performance. In this context, we introduce conditional signed network embedding (CSNE). Our probabilistic approach models structural information about the signs in the network separately from fine-grained detail. Structural information is represented in the form of a prior, while the embedding itself is used for capturing fine-grained information. These components are then integrated in a rigorous manner. CSNE's accuracy depends on the existence of sufficiently powerful structural priors for modelling signed networks, currently unavailable in the literature. Thus, as a second main contribution, which we find to be highly valuable in its own right, we also introduce a novel approach to construct priors based on the Maximum Entropy (MaxEnt) principle. These priors can model the \emph{polarity} of nodes (degree to which their links are positive) as well as signed \emph{triangle counts} (a measure of the degree structural balance holds to in a network). Experiments on a variety of real-world networks confirm that CSNE outperforms the state-of-the-art on the task of sign prediction. Moreover, the MaxEnt priors on their own, while less accurate than full CSNE, achieve accuracies competitive with the state-of-the-art at very limited computational cost, thus providing an excellent runtime-accuracy trade-off in resource-constrained situations