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Testing of hypothesis, also known as sample-testing, is a common feature with almost every social and management research. We draw conclusion on population (characteristics) based on available sample information, following certain statistical principles. This paper will introduce the fundamental concepts with suitable examples, mostly in Indian context. This section is expected to help scholar readers, to learn, how hypothesis tests for differences means (or proportions) take different forms, depending on whether the samples are large or small; and also to appreciate hypothesis-testing techniques, on how it could be used in similar decision-making situations, elsewhere.

Over the last few decades, precarious work rose as an important feature of socioeconomic insecurity in contemporary Europe. The following study asks: How do labor market institutions and labor market conditions shape work precarity in Europe? This research captures the elusive concept of precarious work by measuring the degree to which a job (1) is insecure and uncertain, (2) offers poor prospects of career mobility, and (3) puts workers in an economically insecure position with low pay. Building on two theoretical paradigms, the Varieties of Capitalism and the Power Resource Theory, this study derives and tests hypotheses about how macro-level factors shape the variation in the distribution of precarious work in 32 European countries. Combining individual-level data from the 2010 European Working Conditions Survey with country-level data from multiple sources, my findings suggest that work precarity decreases in countries with high percentages of employees in all enterprises receiving continual training, high percentages of all enterprises providing on-the-job training for employees, and high levels of spending on active labor market policies.

Importance sampling is a popular Monte Carlo method used in a variety of areas in econometrics. When the variance of the importance sampling estimator is infinite, the central limit theorem does not apply and estimates tend to be erratic even when the simulation size is large. The authors consider asymptotic trimming in such a setting. Specifically, the authors propose a bias-corrected tail-trimmed estimator such that it is consistent and has finite variance. The authors show that the proposed estimator is asymptotically normal, and has good finite-sample properties in a Monte Carlo study.

Socio-economic shocks have been the sources of burden to smallholder farmers, and the recent COVID-19 pandemics, can be termed a shock that could lead to distortions in the operations of the commodity value chain. The study, therefore, examined the effect of the COVID-19 pandemic on income and access to economic input such as labour, seeds, fertilisers, herbicides, before and during the COVID-19 pandemic. The study was carried out in Kano State, Nigeria. Multi-stage sampling technique was employed in the selection of 260 respondents comprising producers (115), marketers (60), processors (46) and transporters (29). Primary data were collected from stakeholders in tomato value chain through the use of a structured questionnaire. Data collected were analysed using descriptive statistics. Findings revealed that tomato value chain in Kano State, Nigeria, is male-dominated. The average age of stakeholders ranged between 43 years (transporters) and 51 years (processors). Results further revealed a significant relationship in income of marketers' pre- and during COVID-19 pandemic. However, non-significant relationship was observed in the income of producers, processors and transporters prior and during COVID-19 pandemic. There was also no significant difference in the economic value of seeds, manure and fertiliser before and during COVID-19 pandemic. The most important constraints to the producers were the high cost of farm chemical procured while the marketers were confronted with high cost of transportation. The processors were faced with high processing cost and transporters experienced request for undue levies by market authorities. The study recommends that mechanism be devised to improve availability of inputs to producers and dissemination of adequate market information to stakeholders in the tomato value chain regularly to improve contribution of the commodity to food security and economic development.

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.

The purpose of this chapter is to provoke thinking about the directions in which the travel survey toolkit should move in the near future based on the author's personal experience and as an outcome of the Travel Survey Methods conference. The chapter begins with a brief historical review that attempts to show some of the major elements of change that have occurred in travel survey methods over the past 40–50 years. A more detailed review is provided about developments over the past 10–15 years. The chapter then explores a number of emerging challenges, including telephone contact of potential respondents, computer-assisted surveys, Internet surveys, mixed-mode surveys, the impacts of language and literacy and the potentials of mobile technologies. Based on this, the chapter then considers future directions that should be pursued. The chapter suggests that it has been changes in survey methodology that have, in the past, sometimes enabled and at other times led to changes in modelling paradigms, and that this may be an appropriate time for travel survey methodology again to enable changes in modelling paradigms. A speculative specification of a new household travel survey that makes use of a number of these developments is then offered. The chapter ends with some concluding remarks that issue a challenge to the travel survey community to think ‘outside the box’ and foster change and improvement in the accuracy and representativeness of travel surveys.

The National Health Interview Survey on Disability, fielded between 1994 and 1997, was one of the most comprehensive and most complex surveys on disability ever conducted. This chapter describes the background of the NHIS-D, its design and contents, its technical characteristics, and initial efforts to disseminate its findings. It provides a basic background on the NHIS-D for readers of this volume, and provides analysts working with the NHIS-D insight into some of the design and development issues that have not been previously described in public documents.