Galesic, Mirta, Henrik Olsson, and Jörg Rieskamp. 2012. Social Sampling Explains Apparent Biases in Judgments of Social Environments. Psychological Science 23 (12): 1515-1523.
Abstract: How people assess their social environments plays a central role in how they evaluate their life circumstances. Using a large probabilistic national sample, we investigated how accurately people estimate characteristics of the general population. For most characteristics, people seemed to underestimate the quality of others’ lives and showed apparent self-enhancement, but for some characteristics, they seemed to overestimate the quality of others’ lives and showed apparent self-depreciation. In addition, people who were worse off appeared to enhance their social position more than those who were better off. We demonstrated that these effects can be explained by a simple social-sampling model. According to the model, people infer how others are doing by sampling from their own immediate social environments. Interplay of these sampling processes and the specific structure of social environments leads to the apparent biases. The model predicts the empirical results better than alternative accounts and highlights the importance of considering environmental structure when studying human cognition.
- Please discuss the central theme of the article. In particular, how do the authors frame the use of sampling to study how people perceive their social environments?
- Discuss the idea that the sampling processes that people use to assess their own and others’ social environments tends to lead to discernible biases. How would people living in the same approximate area with similar characteristics to those around them bias their perspective of their social environment?
- How does this article shed light on people’s inference processes and the environments they live in?
Mouw, Ted, and Ashton M. Verdery. 2012. Network Sampling with Memory: A Proposal for More Efficient Sampling from Social Networks. Sociological Methodology 42 (1): 206-256.
Abstract: Techniques for sampling from networks have grown into an important area of research across several fields. For sociologists, the possibility of sampling from a network is appealing for two reasons: (1) A network sample can yield substantively interesting data about network structures and social interactions, and (2) it is useful in situations in which study populations are difficult or impossible to survey with traditional sampling approaches because of the lack of a sampling frame. Despite its appeal, methodological concerns about the precision and accuracy of network-based sampling methods remain. In particular, recent research has shown that sampling from a network using a random walk–based approach such as respondent-driven sampling (RDS) can result in a high design effect (DE): the ratio of the sampling variance to the sampling variance of simple random sampling (SRS). A high DE means that more cases must be collected to achieve the same level of precision as SRS. In this article, we propose an alternative strategy, network sampling with memory (NSM), which collects network data from respondents to reduce DEs and, correspondingly, the number of interviews needed to achieve a given level of statistical power. NSM combines a “list” mode, in which all individuals on the revealed network list are sampled with the same cumulative probability, with a “search” mode, which gives priority to bridge nodes connecting the current sample to unexplored parts of the network. We test the relative efficiency of NSM compared with RDS and SRS on 162 school and university networks from the National Longitudinal Study of Adolescent Health and Facebook that range in size from 110 to 16,278 nodes. The results show that the average DE for NSM on these 162 networks is 1.16, which is very close to the efficiency of a simple random sample (DE = 1) and 98.5 percent lower than the average DE we observed for RDS.
- Please discuss the benefits of sampling from a network, particularly in terms of generating useful data about structures and social interactions.
- How can network sampling help to remedy some of the shortcomings in traditional sampling approaches
- Discuss the importance of referral-based sampling for hidden populations, specifically in terms of the challenges that are presented in understanding the behaviors of groups not typically included in samples.
Tracy, Paul E. and Danielle Marie Carkin. 2014. Adjusting for Design Effects in Disproportionate Stratified Sampling Designs Through Weighting. Crime & Delinquency 60 (2): 306-325.
Abstract: This article validates the necessity of adjusting for the design effects in disproportionate stratified sampling designs through the use of sample weights. Using data from the 1958 Birth Cohort study, we demonstrate that complex sampling designs introduce sampling error and even sampling bias into sample data. Such sample data are a poor representation of population parameters. These design effects can be addressed through the application of sample weights.
- Discuss the authors’ idea of using sample weights as a way to address sampling error and sampling bias. How will this help to produce more accurate findings?
- Discuss the importance of a representative sample. How is this idea considered in developing a sampling design?
- Please discuss the concept of stratified random sampling. How does it differ from simple random or systematic random sampling techniques?