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”

A multilevel p2 model with covariates for the analysis of binary bully-victim network data in multiple classrooms

Many studies have been aimed at defining the exact nature of bullying, identifying bullies and their victims in school classes, investigating the personal and developmental characteristics of bullies and victims, and evaluating intervention programs to prevent bullying (see, e.g. Espelage & Swearer, 2003). Children have different roles in bullying (Schwartz, 2000), and some pairs of children lead to more bullying than others (Coie et al., 1999). Relatively little is known about the dyadic properties of bullies and victims (Rodkin & Berger, in press). Recently, a dual perspective theory of bullying was proposed, focusing on the dyadic nature of the bully-victim relationship (R. Veenstra et al., 2007). This theory is tested on pre-adolescent data from TRAILS (Tracking Adolescents’ Individual Lives Survey). TRAILS is designed to chart and explain the development of mental health and social development from preadolescence into adulthood (De Winter et al., 2005; Oldehinkel, Hartman, De Winter, Veenstra, & Ormel, 2004). Students were asked to report about several of their ties with classmates. This round robin design yields in principle two observations for each relationship between two children A and B, one from the perspective of child A (the nominator or ‘sender’), reporting whether or not s/he bullies child B (the target or ‘receiver’), and vice versa. These two reports may not always coincide and are less likely to be in agreement for a bullying tie than for a friendship tie. The set of dyadic data collected in a closed group forms a social network. Many methods and models have been proposed for social network analysis (see Wasserman & Faust, 1994). For a review on the intricacies of dyadic designs and dyadic data analysis, see Kenny, Kashy, and Cook (2006). We use a multilevel p2 model (Zijlstra, Van Duijn, & Snijders, 2006) to analyze bully network data from 54 classes collected in the TRAILS study. This model takes into account the dependent nature of the data and employs the characteristics of sender and receiver individually and as a dyad. Moreover, class characteristics can be used to explain differences per classroom; for instance, between prevalence rates of bullying in school classes. We follow the dual perspective theory as laid out by Veenstra et al. (2007) but slightly modify the covariates used in the analysis. In the next section we start with the definition and interpretation of the simple p2 model, followed by the multilevel p2 model, and its relation to other models for social network data. In Section 3, we present the data and theory to be tested. After a section introducing the interpretation of p2 model results, we present the results obtained for the dual perspective theory. The final section summarizes and discusses the findings

Teaching scientific integrity through statistics

In the past years, Dutch academia was confronted with several cases of fraud. The Stapel investigation revealed that the prevailing research culture allowed questionable research practices (QRP). As a consequence, there is an ongoing debate on how to prevent academic misconduct. Teaching scientific integrity is an evident solution, although its implementation may be less obvious. In our workshops and classes we have used the principles of statistical reasoning and methodology, especially validity, to help students understand the importance of scientific integrity and the dangers and consequences of QRP. We feel that this approach is more effective than merely discussing principles of scientific integrity, such as verifiability and independence. The explanation may be that students are sufficiently aware of the ethical norms, but fail to see how they apply to or might challenge their own behavior. We will present an outline of our lectures

Extensions of Rasch's multiplicative Poisson model

Consideration will be given to a model developed by Rasch that assumes scores observed on some types of attainment tests can be regarded as realizations of a Poisson process. The parameter of the Poisson distribution is assumed to be a product of two other parameters, one pertaining to the ability of the subject and a second pertaining to the difficulty of the test. Rasch's model is expanded by assuming a prior distribution, with fixed but unknown parameters, for the subject parameters. The test parameters are considered fixed. Secondly, it will be shown how additional between- and within-subjects factors can be incorporated. Methods for testing the fit and estimating the parameters of the model will be discussed, and illustrated by empirical examples

Teaching hypothesis testing: a necessary challenge

The last decades a debate has arisen about the use of hypothesis testing. This has led some teachers to think that confidence intervals and effect sizes need to be taught instead of formal hypothesis testing with p-values. Although we see shortcomings of the use of p-values in statistical inferences and the difficulties in really understanding hypothesis tests, we take a different view. We think that it is essential to understand what the fundamental principles are behind hypothesis testing in order to obtain correct statistical inference by interpreting confidence intervals (and p-values). In our course “Applied Statistics ” for graduate students we designed course material in which we explain the three main approaches of hypothesis testing, Fisher, Neyman-Pearson and Bayesian, using a popular chance game as illustration. In this paper, we will shortly present the highlights of the course material, the results of the evaluation of our teaching, and suggestions for extensions