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AbstractWhen researchers design an experiment, they usually hold potentially relevant features of the experiment constant. We call these details the "topic" of the experiment. For example, researchers studying the impact of party cues on attitudes must inform respondents of the parties' positions on a particular policy. In doing so, researchers implement just one of many possible designs . Clifford, Leeper, and Rainey (2023. "Generalizing Survey Experiments Using Topic Sampling: An Application to Party Cues." Forthcoming in Political Behavior. https://doi.org/10.1007/s11109-023-09870-1) argue that researchers should implement many of the possible designs in parallel—what they call "topic sampling"—to generalize to a larger population of topics. We describe two estimators for topic-sampling designs: First, we describe a nonparametric estimator of the typical effect that is unbiased under the assumptions of the design; and second, we describe a hierarchical model that researchers can use to describe the heterogeneity. We suggest describing the heterogeneity across topics in three ways: (1) the standard deviation in treatment effects across topics, (2) the treatment effects for particular topics, and (3) how the treatment effects for particular topics vary with topic-level predictors. We evaluate the performance of the hierarchical model using the Strengthening Democracy Challenge megastudy and show that the hierarchical model works well.

Abstract
We present a sampling algorithm for an ordered population with elements that have a measure of size. The algorithm enables one to select a sample with specified probabilities and with efficiency for the estimated mean between that of one per stratum and that of two per stratum. The algorithm contains a design parameter for the efficiency of the estimated mean relative to the efficiency of the estimated variance of the estimated mean. For a variable highly correlated with the order, it is possible for both the efficiency of the estimated mean and the efficiency of the estimated variance for an intermediate design to be greater than that for the two-per-stratum design. For most studied populations, the variance of the estimated mean declines and the variance of the estimated variance increases as one moves from the two-per-stratum design toward the one-per-stratum design. We illustrate the trade-off between the variance of the estimated mean and the variance of the estimated variance using an autoregressive process. An estimator of the variance of the estimated mean and a replication form for variance estimation are given.

A sample in which one person per household - to obtain valid, independent responses - is interviewed can be selfweighting 'if the households are selected with probability proportional to the respective sizes.' To illustrate, suppose that city blocks are groupd in S strata. 'Out of the total Bi blocks in the i-th stratum, bi blocks are selected with replacement, the probability of selection of the ij-th block being (pi)*ij.' The design can be made self-weighting 'if the sample households in the ij-th block are selected proportional to the respective household sizes, i.e. (formula not translated) where (formula not translated) total current pop of the ij-th block obtained after houselisting.' In this case, the estimated aggregate (x) (formula not translated) of the characteristic under study is: (formula not translated) where XXX is the characteristic for the selected individual in the ijk-th household.' Self-weighting occurs when (formula not translated) where f is the predetermined constant (formula not translated) over-all sampling fraction. Formula for determining the f-values under alternative sampling instructions are discussed. C. M. Coughenour.

The purpose of this paper is to provide a typology of sampling designs for qualitative researchers. We introduce the following sampling strategies: (a) parallel sampling designs, which represent a body of sampling strategies that facilitate credible comparisons of two or more different subgroups that are extracted from the same levels of study; (b) nested sampling designs, which are sampling strategies that facilitate credible comparisons of two or more members of the same subgroup, wherein one or more members of the subgroup represent a sub-sample of the full sample; and (c) multilevel sampling designs, which represent sampling strategies that facilitate credible comparisons of two or more subgroups that are extracted from different levels of study.

Before conducting a survey, researchers frequently ask themselves how large the resulting sample of respondents needs to be to answer their research questions. In this guideline, we discuss how sample size calculation is affected by the sampling design. We give practical advice on how to conduct sample size calculation for complex samples.

The worsening of Ecuador's socioeconomic conditions and the rapid inflow of Venezuelan migrants demand a rapid government response. Representative information on the migration and host communities is vital for evidence-based policy design. This study presents an innovative methodology based on the use of big data for sampling design of a representative survey of migrants and host communities' populations. This approach tackles the difficulties posed by the lack of information on the total number of Venezuelan migrants—regular and irregular—and their geographical location in the country. The total estimated population represents about 3 percent of the total Ecuadoran population. Venezuelans settled across urban areas, mainly in Quito, Guayaquil, and Manta (Portoviejo). The strategy implemented may be useful in designing similar exercises in countries with limited information (that is, lack of a recent census or migratory registry) and scarce resources for rapidly gathering socioeconomic data on migrants and host communities for policy design.

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. Adapted from the source document.