StOCNET:Software for the statistical analysis of social networks

StOCNET3 is an open software system in a Windows environment for the advanced statistical analysis of social networks. It provides a platform to make a number of recently developed and therefore not (yet) standard statistical methods available to a wider audience. A flexible user interface utilizing an easily accessible data structure is developed such that new methods can readily be included in the future. As such, it will allow researchers to develop new statistical tools by combining their own programs with routines of the StOCNET system, providing a faster availability of newly developed methods. In this paper we show the current state of the developments. The emphasis is on the implementation and operation of the programs that are included in StOCNET: BLOCKS (for stochastic blockmodeling), p2 (for analyzing binary network data with actor and/or dyadic covariates), SIENA (for analyzing repeated measurements of social networks), and ZO (for calculating probability distributions of statistics). Moreover, we present an overview of future contributions, which will be available in the near future, and of planned activities with respect to the functionality of the StOCNET software. StOCNET is a freeware PC program, and can be obtained from the StOCNET website at http://stat.gamma.rug.nl/stocnet/

Scale construction and evaluation in practice:A review of factor analysis versus item response theory applications

In scale construction and evaluation, factor analysis (FA) and item response theory (IRT) are two methods frequently used to determine whether a set of items reliably measures a latent variable. In a review of 41 published studies we examined which methodology – FA or IRT – was used, and what researchers’ motivations were for applying either method. Characteristics of the studies were compared to gain more insight into the practice of scale analysis. Findings indicate that FA is applied far more often than IRT. Many times it is unclear whether the data justify the chosen method because model assumptions are neglected. We recommended that researchers (a) use substantive knowledge about the items to their advantage by more frequently employing confirmatory techniques, as well as adding item content and interpretability of factors to the criteria in model evaluation; and (b) investigate model assumptions and report corresponding findings. To this end, we recommend more collaboration between substantive researchers and statisticians/psychometricians