Rational Economic Data Revisions

The efficient handling of provisional economic time series data is considered. Procedures are justified in terms of a testable hypothesis regarding the nature of data revisions. The hypothesis leads to explicit and practical variance expressions for measurement errors. Testing and estimation issues are dealt with. An efficient dual filter is developed for recursive signal estimation. Techniques are applied to the Canadian index of production data.     

Health-related determinants of perceived quality of life: a comparison between first-year university students and their working peers

This study compares aspects of the health-related behaviors and working life of first-year university students with those of their working counterparts and assesses the roles of these aspects as predictors of each groups' perceived quality of life (QoL). Subjects were taken from two cross-sectional data sets (a student survey and a population survey) from the Swedish central-western region of Osterg?tland. Male and female respondents aged 20-35 years were extracted and comparison were made considering in turn socio-demographic characteristics, health-related behaviors, exposure to abusive events at work and perceived QoL. University students smoked and used oral snuff in smaller proportions, they were not as frequent drinkers as their working counterparts, but they tended to drink more when they did drink. Threats and violence were less prevalent among students, but sexual harassment, were almost as common. The strongest predictors of perceived QoL for both groups are expected and former perceived QoL, followed by current self-rated health.     

Sample size for estimating a binomial proportion: comparison of different methods

The poor performance of the Wald method for constructing confidence intervals (CIs) for a binomial proportion has been demonstrated in a vast literature. The related problem of sample size determination needs to be updated and comparative studies are essential to understanding the performance of alternative methods. In this paper, the sample size is obtained for the Clopper–Pearson, Bayesian (Uniform and Jeffreys priors), Wilson, Agresti–Coull, Anscombe, and Wald methods. Two two-step procedures are used: one based on the expected length (EL) of the CI and another one on its first-order approximation. In the first step, all possible solutions that satisfy the optimal criterion are obtained. In the second step, a single solution is proposed according to a new criterion (e.g. highest coverage probability (CP)). In practice, it is expected a sample size reduction, therefore, we explore the behavior of the methods admitting 30% and 50% of losses. For all the methods, the ELs are inflated, as expected, but the coverage probabilities remain close to the original target (with few exceptions). It is not easy to suggest a method that is optimal throughout the range (0, 1) for p. Depending on whether the goal is to achieve CP approximately or above the nominal level different recommendations are made.     

Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size

Principal axis factoring (PAF) and maximum likelihood factor analysis (MLFA) are two of the most popular estimation methods in exploratory factor analysis. It is known that PAF is better able to recover weak factors and that the maximum likelihood estimator is asymptotically efficient. However, there is almost no evidence regarding which method should be preferred for different types of factor patterns and sample sizes. Simulations were conducted to investigate factor recovery by PAF and MLFA for distortions of ideal simple structure and sample sizes between 25 and 5000. Results showed that PAF is preferred for population solutions with few indicators per factor and for overextraction. MLFA outperformed PAF in cases of unequal loadings within factors and for underextraction. It was further shown that PAF and MLFA do not always converge with increasing sample size. The simulation findings were confirmed by an empirical study as well as by a classic plasmode, Thurstone's box problem. The present results are of practical value for factor analysts.     

Algorithmic parameterization of mixed treatment comparisons

Mixed Treatment Comparisons (MTCs) enable the simultaneous meta-analysis (data pooling) of networks of clinical trials comparing ??2 alternative treatments. Inconsistency models are critical in MTC to assess the overall consistency between evidence sources. Only in the absence of considerable inconsistency can the results of an MTC (consistency) model be trusted. However, inconsistency model specification is non-trivial when multi-arm trials are present in the evidence structure. In this paper, we define the parameterization problem for inconsistency models in mathematical terms and provide an algorithm for the generation of inconsistency models. We evaluate running-time of the algorithm by generating models for 15 published evidence structures.