Exploring teachers’ career self-management. Considering the roles of organizational career management,occupational self-efficacy,and learning goal orientation.

The current study examines how organizational career management – i.e. activities undertaken by schools in order to plan and manage teachers’ careers – relates to teachers’ career self-management – i.e. teachers steering their careers by means of searching for opportunities, networking, or seeking supervisory support. Moreover, it examines the mediating roles of occupational self-efficacy and learning goal orientation in this relationship. Mediation analysis in SPSS, using the PROCESS macro of survey data from 220 Dutch secondary school teachers, showed that positive relationships between organizational career management and career self-management were mediated by occupational self-efficacy and learning goal orientation.     

Sparse matrices,and the estimation of variance components by likelihood methods

It is generally considered that analysis of variance by maximum likelihood or its variants is computationally impractical, despite existing techniques for reducing computational effect per iteration and for reducing the number of iterations to convergence. This paper shows thata major reduction in the overall computational effort can be achieved through the use of sparse-matrix algorithms that take advantage of the factorial designs that characterize most applications of large analysis-of-variance problems. In this paper, an algebraic structure for factorial designsis developed. Through this structure, it is shown that the required computations can be arranged so that sparse-matrix methods result in greatly reduced storage and time requirements.     

Search With Dirichlet Priors: Estimation and Implications for Consumer Demand

This article is an empirical application of the search model with an unknown distribution, as introduced by Rothschild in 1974. For searchers who hold Dirichlet priors, we develop a novel characterization of optimal search behavior. Our solution delivers easily computable formulas for the ex-ante purchase probabilities as outcomes of search, as required by discrete-choice-based estimation. Using our method, we investigate the consequences of consumer learning on the properties of search-generated demand. Holding search costs constant, the search model from a known distribution predicts larger price elasticities, mainly for the lower-priced products. We estimate a search model with Dirichlet priors, on a dataset of prices and market shares of S&P 500 mutual funds. We find that the assumption of no uncertainty in consumer priors leads to substantial biases in search cost estimates.     

Bayesian Analysis of Multiple Hypothesis Testing with Applications to Microarray Experiments

Recently, the field of multiple hypothesis testing has experienced a great expansion, basically because of the new methods developed in the field of genomics. These new methods allow scientists to simultaneously process thousands of hypothesis tests. The frequentist approach to this problem is made by using different testing error measures that allow to control the Type I error rate at a certain desired level. Alternatively, in this article, a Bayesian hierarchical model based on mixture distributions and an empirical Bayes approach are proposed in order to produce a list of rejected hypotheses that will be declared significant and interesting for a more detailed posterior analysis. In particular, we develop a straightforward implementation of a Gibbs sampling scheme where all the conditional posterior distributions are explicit. The results are compared with the frequentist False Discovery Rate (FDR) methodology. Simulation examples show that our model improves the FDR procedure in the sense that it diminishes the percentage of false negatives keeping an acceptable percentage of false positives.     

Environmental risk assessment model for low dose extrapolation ∗

Use of experimental data from animal studies to estimate human risk due to long-term exposure to very low doses of chemicals in the environment poses a number of biological and statistical problems. One of the statistical problems is to extrapolate the animal dose-response relation from the high dose levels where data are available to low dose, which humans might encounter. Here, a quantal dose-response model is developed based on a multi-hit theory of toxic response. The development of the model utilizes a weighted Lagrange-Poisson distribution for the number of hits. When spontaneous background toxic response is included, the model involves three unknown parameters. The maximum likelihood estimators for these parameters are given as the solution of a nonlinear iterative algorithm. The use of this model for low-dose extrapolation is indicated. The results are applied to nine sets of toxic response data.     

Approximate tests of hypotheses in regression models with grouped data

Techniques for testing hypotheses about parameters in the regression models under the situation of grouped data are provided. A test statistic similar to conventional F statistic is considered. A simulation study performed for a few cases shows that the proposed statistic has an approximate F distribution and is useful in applications.     

Efficiency analysis of ten estimation procedures for quantitative linear models with autocorrelated errors

Many estimation procedures for quantitative linear models with autocorrelated errors have been proposed in the literature. A number of these procedures have been compared in various ways for different sample sizes and autocorrelation parameters values and for structured or random explanatory vaiables. In this paper, we revisit three situations that were considered to some extent in previous studies, by comparing ten estimation procedures: Ordinary Least Squares (OLS), Generalized Least Squares (GLS), estimated Generalized Least Squares (six procedures), Maximum Likelihood (ML), and First Differences (FD). The six estimated GLS procedures and the ML procedure differ in the way the error autocovariance matrix is estimated. The three situations can be defined as follows: Case 1, the explanatory variable x in the simple linear regression is fixed; Case 2,x is purely random; and Case 3x is first-order autoregressive. Following a theoretical presentation, the ten estimation procedures are compared in a Monte Carlo study conducted in the time domain, where the errors are first-order autoregressive in Cases 1-3. The measure of comparison for the estimation procedures is their efficiency relative to OLS. It is evaluated as a function of the time series length and the magnitude and sign of the error autocorrelation parameter. Overall, knowledge of the model of the time series process generating the errors enhances efficiency in estimated GLS. Differences in the efficiency of estimation procedures between Case 1 and Cases 2 and 3 as well as differences in efficiency among procedures in a given situation are observed and discussed.     

A quantitative field study of the decision behaviour of four shopfloor schedulers

In this article the decision behaviour of four production schedulers in a truck manufacturing company is investigated by means of a quantitative model. The model consists of there parts: performance variables, action variables and disturbance variables. The outcomes show that there is a large difference between schedulers that apparently have the same type of decision problem. Another interesting finding is that some scheduling actions work positively in the short term, but negatively over a longer term. Other results, along with methodological issues of quantitative research, are discussed.     

试析中国大陆高校通识教育的模式

由于对通识教育理念认识的差异，大陆高校的通识教育模式也各不相同。对目前大陆高校通识教育的模式进行了梳理，并分析了大陆高校实施通识教育的经验与启示。     

Non-linear state space modelling of fisheries biomass dynamics by using Metropolis-Hastings within-Gibbs sampling

State space modelling and Bayesian analysis are both active areas of applied research in fisheries stock assessment. Combining these two methodologies facilitates the fitting of state space models that may be non-linear and have non-normal errors, and hence it is particularly useful for modelling fisheries dynamics. Here, this approach is demonstrated by fitting a non-linear surplus production model to data on South Atlantic albacore tuna ( Thunnus alalunga ). The state space approach allows for random variability in both the data (the measurement of relative biomass) and in annual biomass dynamics of the tuna stock. Sampling from the joint posterior distribution of the unobservables was achieved by using Metropolis-Hastings within-Gibbs sampling.