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This chapter studies a snowball sampling method for social networks with endogenous peer selection. Snowball sampling is a sampling design which preserves the dependence structure of the network. It sequentially collects the information of vertices linked to the vertices collected in the previous iteration. The snowball samples suffer from a sample selection problem because of the endogenous peer selection. The author proposes a new estimation method that uses the relationship between samples in different iterations to correct selection. The author uses the snowball samples collected from Facebook to estimate the proportion of users who support the Umbrella Movement in Hong Kong.

In the field of behavioral disabilities, systematic direct observation (SDO) has been an integral tool for describing and explaining relationships between student and teacher behavior in authentic classroom settings. However, this method of measurement can be resource-intensive and presents a series of complex decisions for investigators. The purpose of this chapter is to review a series of critical decisions investigators must make when developing SDO protocols to address their research questions. After describing each decision point and its relevance to the measurement system, we identify trends and special considerations in the field of behavioral disabilities with respect to each decision. We organize content according to deciding what to measure, deciding how to measure it, and critical steps to prevent system breakdowns. Finally, we identify avenues for research to further the impact of SDO in the field of behavioral disabilities.

A major stumbling block in multivariate discrete data analysis is the problem of evaluating the outcome probabilities that enter the likelihood function. Calculation of these probabilities involves high-dimensional integration, making simulation methods indispensable in both Bayesian and frequentist estimation and model choice. We review several existing probability estimators and then show that a broader perspective on the simulation problem can be afforded by interpreting the outcome probabilities through Bayes’ theorem, leading to the recognition that estimation can alternatively be handled by methods for marginal likelihood computation based on the output of Markov chain Monte Carlo (MCMC) algorithms. These techniques offer stand-alone approaches to simulated likelihood estimation but can also be integrated with traditional estimators. Building on both branches in the literature, we develop new methods for estimating response probabilities and propose an adaptive sampler for producing high-quality draws from multivariate truncated normal distributions. A simulation study illustrates the practical benefits and costs associated with each approach. The methods are employed to estimate the likelihood function of a correlated random effects panel data model of women's labor force participation.

This study compares two methods of data collection for students' social behaviors. One method employed time sampling procedures, while the other method used handheld computerized devices and the Multi-Option Observation System for Experimental Studies (MOOSES) system. Both coding systems were used to assess social behaviors of students with emotional disabilities during writing instruction. The middle-school-aged students, all classified as having EBD, were enrolled in classes to improve their written expression. Students were assessed for on-task, off-task, and multitask behaviors. Results revealed some surprising differences. When students were relatively consistent with attendance and on-task behaviors, the methods yielded comparable results; however, when students were more disruptive and demonstrated more inconsistent behaviors, different patterns emerged. Implications and recommendations for future research and practice are discussed.

Our paper discusses simulation-based Bayesian inference using information from previous draws to build the proposals. The aim is to produce samplers that are easy to implement, that explore the target distribution effectively, and that are computationally efficient and mix well.

Purpose — This study proposes an optimal survey design method for multi-day and multi-period panels that maximizes the statistical power of the parameter of interest under the conditions that non-linear changes in response to a policy intervention over time can be expected.

Design/methodology/approach — The proposed method addresses balances among sample size, survey duration for each wave and frequency of observation. Higher-order polynomial changes in the parameter are also addressed, allowing us to calculate optimal sampling designs for non-linear changes in response to a given policy intervention.

Findings — One of the most important findings is that variation structure in the behaviour of interest strongly influences how surveys are designed to maximize statistical power, while the type of policy to be evaluated does not influence it so much. Empirical results done by using German Mobility Panel data indicate that not only are more data collection waves needed, but longer multi-day periods of behavioural observations per wave are needed as well, with the increase in the non-linearity of the changes in response to a policy intervention.

Originality/value — This study extends previous studies on sampling designs for travel diary survey by dealing with statistical relations between sample size, survey duration for each wave, and frequency of observation, and provides the numerical and empirical results to show how the proposed method works.