Title: A latent factor model with a nonnegative component with application to cheating detection in educational teaching
Speaker: Dr Yunxiao Chen, Department of Statistics, LSE
Abstract: In this talk, we introduce a new family of latent factor models, which add non-negative constraints on some latent factors and the corresponding loading parameters. This model is motivated by an application to cheating detection in educational testing, where a subset of examinees may have cheated in the exam on a subset of leaked test items. The goal is to detect both cheating examinees and leaked items based on item response data. The proposed model captures normal item response behavior using an unconstrained latent factor component and captures the cheating behavior using a non-negative latent factor component. Thanks to the latent variable modeling formulation, marginal false discovery rate (mFDR) and marginal false non-discovery rate (mFNR) can be defined for the detection of cheating examinees and compromised items, respectively. They can be estimated from data under an empirical Bayes framework. The proposed model is applied to a real data example and successfully recovers the cheating examinees and leaked items that have been flagged by the testing program. Finally, extensions are discussed, including (1) the incorporation of response time, and (2) the general theory for the decomposition of a non-sparse low rank matrix and a sparse low rank matrix.
Title: Modelling within-household associations in household panel studies
Speaker: Professor Fiona Steele, Department of Statistics, LSE
Abstract: Household panel data provide valuable information about the extent of similarity in coresidents’ attitudes and behaviours. However, existing analysis approaches do not allow for the complex association structures that arise due to changes in household composition over time. We propose a flexible marginal modelling approach where the changing correlation structure between individuals is modelled directly. A key component of our correlation model specification is a ‘superhousehold’, a form of social network in which pairs of observations from different individuals are connected (directly or indirectly) by coresidence. These superhouseholds partition observations into clusters with a nonstandard and highly variable correlation structure. Our approach is applied in an analysis of individuals’ attitudes towards gender roles using British Household Panel Survey data. We find strong evidence of between-individual correlation before, during and after coresidence, with large differences among spouses, parent-child, other family, and unrelated pairs.
Joint work with Paul Clarke (ISER, University of Essex) and Jouni Kuha (Departments of Statistics & Methodology, LSE).
Title: The emergence of inequality in network cooperation games: A meta re-analysis of experimental data
Speaker: Dr Milena Tsvetkova
Abstract: We re-analyze 18 network cooperation experiments, comprising 96 experimental conditions altogether, to investigate how the structure and rules of our interactions affect inequality in social groups. We find that network clustering, network rigidity, and punishment institutions lead to more unequal distributions of earnings. These results complement previous explanations for why we observe higher inequality in sedentary agrarian communities compared to nomadic groups.