Eye-Tracking Data: New Insights on Response Order Effects and Other Cognitive Shortcuts in Survey Responding

Survey researchers since Cannell have worried that respondents may take various shortcuts to reduce the effort needed to complete a survey. The evidence for such shortcuts is often indirect. For instance, preferences for earlier versus later response options have been interpreted as evidence that respondents do not read beyond the first few options. This is really only a hypothesis, however, that is not supported by direct evidence regarding the allocation of respondent attention. In the current study, we used a new method to more directly observe what respondents do and do not look at by recording their eye movements while they answered questions in a Web survey. The eye-tracking data indicate that respondents do in fact spend more time looking at the first few options in a list of response options than those at the end of the list; this helps explain their tendency to select the options presented first regardless of their content. In addition, the eye-tracking data reveal that respondents are reluctant to invest effort in reading definitions of survey concepts that are only a mouse click away or paying attention to initially hidden response options. It is clear from the eye-tracking data that some respondents are more prone to these and other cognitive shortcuts than others, providing relatively direct evidence for what had been suspected based on more conventional measures.     

The frisch-waugh theorem and generalized least squares

We discuss the standard linear regression model with nonspherical disturbances, where some regressors are annihilated by considering only the residuals from an auxiliary regression, and where, analogous to the Frisch-Waugh procedure, the original GLS procedure is applied to the transformed data. We call this procedure pseudo-GLS and give conditions for pseudo-GL5 to be equal to genuine GLS. We also show via examples that these conditions are often violated in empirical applications, and that the Frisch-Waugh Theorem still “works” with nonspherical disturbances if efficient estimation is applied to both the original and the transformed data.     

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BAYESIAN HIDDEN MARKOV MODELS FOR LONGITUDINAL COUNTS

This paper describes a Bayesian approach to modelling carcinogenity in animal studies where the data consist of counts of the number of tumours present over time. It compares two autoregressive hidden Markov models. One of them models the transitions between three latent states: an inactive transient state, a multiplying state for increasing counts and a reducing state for decreasing counts. The second model introduces a fourth tied state to describe non‐zero observations that are neither increasing nor decreasing. Both these models can model the length of stay upon entry of a state. A discrete constant hazards waiting time distribution is used to model the time to onset of tumour growth. Our models describe between‐animal‐variability by a single hierarchy of random effects and the within‐animal variation by first‐order serial dependence. They can be extended to higher‐order serial dependence and multi‐level hierarchies. Analysis of data from animal experiments comparing the influence of two genes leads to conclusions that differ from those of Dunson (2000). The observed data likelihood defines an information criterion to assess the predictive properties of the three‐ and four‐state models. The deviance information criterion is appropriately defined for discrete parameters.     

A Rank-Sum-Type Test for Paired Data with Multiple Endpoints

Clinical trials and other types of studies often examine the effects of a particular treatment or experimental condition on a number of different response variables. Although the usual approach for analysing such data is to examine each variable separately, this can increase the chance of false positive findings. Bonferroni's inequality or Hotelling's T² statistic can be employed to control the overall type I error rate, but these tests generally lack power for alternatives in which the treatment improves the outcome on most or all of the endpoints. For the comparison of independent groups, O'Brien (1984) developed a rank-sum type test that has greater power than the Bonferroni and T² procedures when one treatment is uniformly better (i.e. for all endpoints) than the other treatment(s). In this paper we adapt the rank-sum test to studies involving paired data and demonstrate that it, too, has power advantages for such alternatives. Simulation results are described, and an example from a study measuring the effects of sleep loss on glucose metabolism is presented to illustrate the methodology.     

世纪之交西方史学的转折点

在20世纪的最后30年里,西方及西方以外的历史思考和历史写作方面都出现了根本性的变化。现代历史学家确信:历史是连续的、片面的过程;坚持自己科学性权利的学术研究会把来源于客观学术研究的历史叙述置于世界历史的框架之中。到了20世纪,把历史看作“人文科学”的一种特殊类型的历史研究观逐渐被把历史看作一种“社会科学”的历史观所代替。然而,对社会科学和现代化普遍持有的信心在20世纪七、八十年代受到后现代主义哲学家和文学批评家的最激烈的声讨,也受到来自历史学家,尤其是文化历史学家的较为平静的挑战,他们的论点都导致了认识论的相对论。无论如何,只有很少数的历史工作者同意后现代主义者关于“不存在客观标准”的观点,但后现代主义和文化马克思主义的某些中心思想却对日益向文化史转变的社会历史作品产生了很大影响。统一的思想或历史倾向让路给了向小单位、地方和边缘的集中关注,与之同时则是政治史研究地位的下降。在通向21世纪的世纪交替时期,我们已经看到了广泛的研究方法的多元化。     

Bayesian palaeoclimate reconstruction

Summary.  We consider the problem of reconstructing prehistoric climates by using fossil data that have been extracted from lake sediment cores. Such reconstructions promise to provide one of the few ways to validate modern models of climate change. A hierarchical Bayesian modelling approach is presented and its use, inversely, is demonstrated in a relatively small but statistically challenging exercise: the reconstruction of prehistoric climate at Glendalough in Ireland from fossil pollen. This computationally intensive method extends current approaches by explicitly modelling uncertainty and reconstructing entire climate histories. The statistical issues that are raised relate to the use of compositional data (pollen) with covariates (climate) which are available at many modern sites but are missing for the fossil data. The compositional data arise as mixtures and the missing covariates have a temporal structure. Novel aspects of the analysis include a spatial process model for compositional data, local modelling of lattice data, the use, as a prior, of a random walk with long-tailed increments, a two-stage implementation of the Markov chain Monte Carlo approach and a fast approximate procedure for cross-validation in inverse problems. We present some details, contrasting its reconstructions with those which have been generated by a method in use in the palaeoclimatology literature. We suggest that the method provides a basis for resolving important challenging issues in palaeoclimate research. We draw attention to several challenging statistical issues that need to be overcome.     

Power of the Neyman Smooth Test for Evaluating Multivariate Forecast Densities

We compare and investigate Neyman's smooth test, its components, and the Kolmogorov-Smirnov (KS) goodness-of-fit test for testing the uniformity of multivariate forecast densities. Simulations indicate that the KS test lacks power when the forecast distributions are misspecified, especially for correlated sequences of random variables. Neyman's smooth test and its components work well in samples of size typically available, although there sometimes are size distortions. The components provide directed diagnosis regarding the kind of departure from the null. For illustration, the tests are applied to forecast densities obtained from a bivariate threshold model fitted to high-frequency financial data.     

Using a Box–Cox transformation in the analysis of longitudinal data with incomplete responses

We analyse longitudinal data on CD4 cell counts from patients who participated in clinical trials that compared two therapeutic treatments: zidovudine and didanosine. The investigators were interested in modelling the CD4 cell count as a function of treatment, age at base-line and disease stage at base-line. Serious concerns can be raised about the normality assumption of CD4 cell counts that is implicit in many methods and therefore an analysis may have to start with a transformation. Instead of assuming that we know the transformation (e.g. logarithmic) that makes the outcome normal and linearly related to the covariates, we estimate the transformation, by using maximum likelihood, within the Box–Cox family. There has been considerable work on the Box–Cox transformation for univariate regression models. Here, we discuss the Box–Cox transformation for longitudinal regression models when the outcome can be missing over time, and we also implement a maximization method for the likelihood, assumming that the missing data are missing at random.     

REGION, RELIGIOUS COMMITMENT, AND LIFE SATISFACTION AMONG BLACK AMERICANS

Using data from the National Survey of Black Americans (NSBA), this research examines three sets of hypotheses regarding the effects of religious commitment on life satisfaction. Further, given evidence of historical geographical differences in black culture and social structure, this study explores the regional specificity of religious influences on life satisfaction. Religious participation contributes to subjective well-being only among non-southern blacks, while private religiosity is unrelated to well-being in any analysis. There are also denominational effects: non-southern members of traditional black denominations (i.e., Baptists and Methodists) and southern Catholics report particularly high levels of life satisfaction. In addition, the effects of religion on life satisfaction are contingent upon age for non-southerners only. This study concludes with a discussion of directions for future research on religion and mental health.