The Effect of Probing "Don't Know" Responses on Measurement Quality and Nonresponse in Surveys

In survey interviews, “Don’t know” (DK) responses are commonly treated as missing data. One way to reduce the rate of such responses is to probe initial DK answers with a follow-up question designed to encourage respondents to give substantive, non- DK responses. However, such probing can also reduce data quality by introducing additional or differential measurement error. We propose a latent variable model for analyzing the effects of probing on responses to survey questions. The model makes it possible to separate measurement effects of probing from true differences between respondents who do and do not require probing. We analyze new data from an exper- iment which compared responses to two multi-item batteries of questions with and without probing. In this study, probing reduced the rate of DK responses by around a half. However, it also had substantial measurement effects, in that probed answers were often weaker measures of constructs of interest than were unprobed answers. These effects were larger for questions on attitudes than for pseudo-knowledge ques- tions on perceptions of external facts. The results provide evidence against the use of probing of “Don’t know” responses, at least for the kinds of items and respondents considered in this study

Collective treatment of High Energy Thresholds in SUSY - GUTs

Supersymmetric GUTs are the most natural extension of the Standard model unifying electroweak and strong forces. Despite their indubitable virtues, among these the gauge coupling unification and the quantization of the electric charge, one of their shortcomings is the large number of parameters used to describe the high energy thresholds (HET), which are hard to handle. We present a new method according to which the effects of the HET, in any GUT model, can be described by fewer parameters that are randomly produced from the original set of the parameters of the model. In this way, regions favoured by the experimental data are easier to locate, avoiding a detailed and time consuming exploration of the parameter space, which is multidimensional even in the most economic unifying schemes. To check the efficiency of this method, we directly apply it to a SUSY SO(10) GUT model in which the doublet-triplet splitting is realized through the Dimopoulos-Wilczek mechanism. We show that the demand of gauge coupling unification, in conjunction with precision data, locates regions of the parameter space in which values of the strong coupling \astrong are within the experimental limits, along with a suppressed nucleon decay, mediated by a higgsino driven dimension five operators, yielding lifetimes that are comfortably above the current experimental bounds. These regions open up for values of the SUSY breaking parameters m_0, M_1/2 < 1 TeV being therefore accessible to LHC.Comment: 21 pages, 8 figures, UA-NPPS/BSM-10/02 (added

A modified weighted pairwise likelihood estimator for a class of random effects models

Composite likelihood estimation has been proposed in the literature for handling intractable likelihoods. In particular, pairwise likelihood estimation has been recently proposed to estimate models with latent variables and random effects that involve high dimensional integrals. Pairwise estimators are asymptotically consistent and normally distributed but not the most efficient among consistent estimators. Vasdekis et al. (Biostatistics 15:677-689, 2014) proposed a weighted estimator that is found to be more efficient than the unweighted pairwise estimator produced by separate maximizations of pairwise likelihoods. In this paper, we propose a modification to that weighted estimator that leads to simpler computations and study its performance through simulations and a real application