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Standard stratified sampling (SSS) is a popular non-random sampling scheme. Maximum likelihood estimator (MLE) is inconsistent if some sampled strata depend on the response variable Y (‘endogenous samples’) or if some Y-dependent strata are not sampled at all (‘truncated sample’ – a missing data problem). Various versions of MLE have appeared in the literature, and this paper reviews practical likelihood-based estimators for endogenous or truncated samples in SSS. Also a new estimator ‘Estimated-EX MLE’ is introduced using an extra random sample on X (not on Y) to estimate the distribution EX of X. As information on Y may be hard to get, this estimator's data demand is weaker than an extra random sample on Y in some other estimators. The estimator can greatly improve the efficiency of ‘Fixed-X MLE’ which conditions on X, even if the extra sample size is small. In fact, Estimated-EX MLE does not estimate the full FX as it needs only a sample average using the extra sample. Estimated-EX MLE can be almost as efficient as the ‘Known-FX MLE’. A small-scale simulation study is provided to illustrate these points.

We show how to use a smoothed empirical likelihood approach to conduct efficient semiparametric inference in models characterized as conditional moment equalities when data are collected by variable probability sampling. Results from a simulation experiment suggest that the smoothed empirical likelihood based estimator can estimate the model parameters very well in small to moderately sized stratified samples.

We extend Vuong’s (1989) model-selection statistic to allow for complex survey samples. As a further extension, we use an M-estimation setting so that the tests apply to general estimation problems – such as linear and nonlinear least squares, Poisson regression and fractional response models, to name just a few – and not only to maximum likelihood settings. With stratified sampling, we show how the difference in objective functions should be weighted in order to obtain a suitable test statistic. Interestingly, the weights are needed in computing the model-selection statistic even in cases where stratification is appropriately exogenous, in which case the usual unweighted estimators for the parameters are consistent. With cluster samples and panel data, we show how to combine the weighted objective function with a cluster-robust variance estimator in order to expand the scope of the model-selection tests. A small simulation study shows that the weighted test is promising.

Educational achievement as measured by students’ performance in international tests is receiving scant attention in the educational research. The launch of these tests helped the educational community to better understand the factors underlying students’ achievement and guided the policy makers in their policies. However, Middle East and North African (MENA) countries were underrepresented in these discussions mainly because of data scarcity. Fortunately, Trends in International Mathematics and Science Study (TIMSS) 2007 data provides data for 15 MENA countries for students at the eighth grade and for many MENA countries; this is the first time that such data is available. This chapter aims to present estimates of students’ family background over mathematics and science scores. The results show that students with abundant home resources perform better than those with less home resources. Moreover, expatriate students in Gulf countries perform better than natives.

“The Elephant in the Corner: A Cautionary Tale About Measurement Error in Treatment Effects Models” by Daniel L. Millimet discusses the current use of the unobserved-outcome framework to estimate population-averaged treatment effects, and it exposes the sensitivity of these estimators to assumption of no measurement error. The Monte Carlo simulation evidence in this chapter indicates that “nonclassical measurement error in the covariates, mean-reverting measurement error in the outcome, and simultaneous measurement errors in the outcome, treatment assignment, and covariates have a dramatic, adverse effect on the performance of the various estimators even with relatively small and infrequent errors” (Millimet article, p. 1–39). To some extent, all the estimators analyzed by Millimet are based on weak functional form assumptions and use semiparametric or nonparametric methods. Millimet's results indicate the need for measurement error models be they parametric or nonparametric models, see Schennach (2007), Hu and Schennach (2008), and Matzkin (2007) for some recent research in nonparametric approaches. Chapter 7 develops a Bayesian estimator that can handle some of the measurement errors discussed in this chapter.

The purpose of this paper is to examine the roles social networks play in households’ contribution to the provision of small hydraulic facilities (SHFs) in rural China.

This paper adopts a sample-selection ordered probit model (Greene and Hensher, 2010) to estimate the impacts of overall social-network intensity, of the number of strong ties (relatives), and of the number of weak ties (friends), using data on 1,064 representative households collected from three provinces (Inner Mongolia, Ningxia, and Shandong).

The numbers of strong ties and weak ties both have significant impacts on households’ willingness to contribute to SHFs provision, but only the latter has a significant impact on their level of contribution. More specifically, a one standard deviation increase in the number of weak ties (i.e. friends) is associated with a 6.6 percent increase in households’ propensity of contributing more than 550 yuan and a 8.2 percent decrease in their propensity of contributing less than 100 yuan.

This paper is the first to examine the impacts of social networks on households’ contribution to SHFs provision in rural China. Its finding is of great policy relevance-fostering and maintaining social networks (e.g. through rural cooperatives) can significantly increase households’ contribution to public-good provision.

This study aims to examine whether the cyber supply chain risk management (CSCRM) practices adopted by manufacturing firms contribute to achieving cyber supply chain (CSC) visibility. Studies have highlighted the necessity of having visibility across interconnected supply chains. Thus, this study examines the extent of CSCRM practices enabling CSC visibility to act as a mediator in achieving CSC performance.

A survey method was used to obtain data from the electrical and electronics manufacturing firms registered with the Federations of Malaysian Manufacturers directory. Data from 130 respondents were analysed using IBM SPSS and PLS-SEM.

This study empirically proves a dedicated governance team's integral role in setting the security tone within its CSC. The result also confirms the significant role that CSC visibility plays in achieving CSC performance. As theorised in the literature, there is also a strong direct relationship between CSC visibility and CSC performance, assuring manufacturing firms that investments and policies devised to improve CSC visibility are fruitful.

The significance of supply chain visibility in an integrated supply chain is recognised and studied using analytical models, behavioural techniques and case studies. Substantial empirical evidence on the CSCRM practices which contributes towards achieving supply chain visibility is still elusive. This study's major contribution lies in identifying CSCRM practices that can contribute towards achieving CSC visibility, and the mediating role CSC visibility plays in achieving CSC performance.

The commitment to statistical process control programmes is becoming commonplace in US industry. However, some companies are experiencing failure of these programmes, particularly in multi‐strata (population) production processes. Even if such a process is in a state of statistical control, there is a high likelihood that one or more strata could drift away from the target owing to an assignable cause. The success of a QC programme depends on the ability of a quality control practitioner to detect this shift with a greater statistical power (sensitivity) and take corrective actions. Addresses the problem faced by the multi‐strata production process of a local manufacturing company in detecting a single stratum shift from the target with a high level of sensitivity. Proposes the selection of an appropriate sampling method (stratified or random) to have a strong bearing on the relative sensitivity of detecting the above shift in a single stratum. Develops power curves for the above mentioned process under stratified and random sampling scenarios, when a shift occurs in a single stratum. Examines the relationship of sample size to the threshold level of the stratum shift and the preferred sampling method.