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Social movement scholars are increasingly interested in Internet activism but have struggled to find robust methods for identifying cases, particularly representative samples of online protest content, given that no population list exists. This article reviews early approaches to this problem, focusing on three recent case sampling designs that attempt to address this problem. The first approach purposively samples from an organizationally based sampling frame. The second approach randomly samples from a SMO-based sampling frame. The third approach mimics user routines to identify populations of "reachable" websites on a given topic, which are then randomly sampled. For each approach, I examine the sampling frame and sampling method to understand how cases were selected, outline the assumptions built into the overall sampling design, and discuss an exemplary research project employing each design. Comparisons of findings from these exemplar studies indicate that sampling designs are extremely consequential. I close by recommending best practices.

This paper provides a framework for developing sampling designs in mixed methods research. First, we present sampling schemes that have been associated with quantitative and qualitative research. Second, we discuss sample size considerations and provide sample size recommendations for each of the major research designs for quantitative and qualitative approaches. Third, we provide a sampling design typology and we demonstrate how sampling designs can be classified according to time orientation of the components and relationship of the qualitative and quantitative sample. Fourth, we present four major crises to mixed methods research and indicate how each crisis may be used to guide sampling design considerations. Finally, we emphasize how sampling design impacts the extent to which researchers can generalize their findings.

The China Family Panel Studies (CFPS) is an on-going, nearly nationwide, comprehensive, longitudinal social survey that is intended to serve research needs on a large variety of social phenomena in contemporary China. In this article, we describe the sampling design of the CFPS sample for its 2010 baseline survey, and methods for constructing weights to adjust for sampling design and survey non-responses. Specifically, the CFPS used a multi-stage probability strategy to reduce operation costs and implicit stratification to increase efficiency. Respondents were oversampled in five provinces or administrative equivalents for regional comparisons. We provide operation details for both sampling and weights construction.

In this paper, we propose a method that estimates the variance of an imputed estimator in a multistage sampling design. The method is based on the rescaling bootstrap for multistage sampling introduced by Preston (Surv Methodol 35(2):227-234, 2009). In his original version, this resampling method requires that the dataset includes only complete cases and no missing values. Thus, we propose two modifications for applying this method to nonresponse and imputation. These modifications are compared to other modifications in a Monte Carlo simulation study. The results of our simulation study show that our two proposed approaches are superior to the other modifications of the rescaling bootstrap and, in many situations, produce valid estimators for the variance of the imputed estimator in multistage sampling designs.

AbstractThe goal of this chapter is to recommend quality criteria to guide evaluators' selections of sampling designs when mixing approaches. First, we contextualize our discussion of quality criteria and sampling designs by discussing the concept of interpretive consistency and how it impacts sampling decisions. Embedded in this discussion are challenges impacting interpretive consistency. Strategies and an integrative sampling framework comprising published frameworks are presented to facilitate evaluators' decisions about selecting sampling designs in accordance to specific criteria. We conclude by presenting an illustrative application of mixed sampling design criteria to a published mixed evaluation study as a heuristic example of a way of embedding sampling criteria in a mixed evaluation. ©Wiley Periodicals, Inc., and the American Evaluation Association.

In the paper, we consider the following problem: Let {πk} be a sequence satisfying 0πkΣ1 (k=1,…, N) and π=n.Tben, is there an unordered sampling design such that, for each k=1,…N, the inclusion probability of unit k is equal to π? It is shown that it can be solved by the straightforward application of the Minkowski‐Farkas theorem.

Research comparing public and private organizations and otherwise analyzing "publicness" involves complex challenges. These include the challenge of designing and attaining adequate samples to represent the two complex categories of "public" and "private," as well as dimensions of publicness, and subcategories and control variables needed for valid comparisons. This review of sampling alternatives begins with discussion of the strengths and weaknesses of the presumably optimal design, a national probability sample of organizations. Due to the expensive nature of such a design-the discussion concentrates on the one such study ever conducted-the discussion then considers the strengths and weaknesses of the purposive samples and samples of opportunity that most research have used. In spite of limitations in representativeness and in accounting for all variables needed to eliminate alternative interpretations, studies using such samples can be aggregated to support conclusions about differences between public and private organizations that are by now well-founded. Researchers should continue to seek opportunities for the optimal large representative samples. Lacking such opportunities, researchers can contribute usefully to analysis of publicness by carefully designing their studies to make them consistent with previous studies and to support aggregation with previous studies. Adapted from the source document.

Following the establishment of the PRC in 1949, the Chinese government built up statistical survey systems that cover the whole of mainland China. The government relies mainly on two systems of complete enumeration surveys used to carry out the central planning of the economy. One is the census type surveys such as the population census and the manufacturing census. The other is the regular reporting system made up of the individual survey units. The paper provides a brief history of household expenditure surveys in China based on articles and tables gleaned from official documents. (DÜI-Sen)