Sociological Methods & Research最新文献

Meta-analysis is a statistical method that combines quantitative findings from previous studies. It has been increasingly used to obtain more credible results in a wide range of scientific fields. Combining the results of relevant studies allows researchers to leverage study similarities while modeling potential sources of between-study heterogeneity. This paper provides a review of the core methodologies of meta-analysis that we consider most relevant to sociological research. After developing the foundation of the fixed-effects and random-effects models of meta-analysis, this paper illustrates the utility of the method with regression coefficients reported from two sets of social science studies. We explain the various steps of the process including constructing the meta-sample from primary studies; estimating the fixed- and random-effects models; analyzing the source of heterogeneity across studies; assessing publication bias. We conclude with a discussion of steps that could be taken to strengthen the development of meta-analysis in sociological research, which will eventually increase the credibility of sociological inquiry via a knowledge-cumulative process.

Previous research reveals that the visual design of open-ended questions should match the response task so that respondents can infer the expected response format. Based on a web survey including specific probes in a list-style open-ended question format, we experimentally tested the effects of varying numbers of answer boxes on several indicators of response quality. Our results showed that using multiple small answer boxes instead of one large box had a positive impact on the number and variety of themes mentioned, as well as on the conciseness of responses to specific probes. We found no effect on the relevance of themes and the risk of item non-response. Based on our findings, we recommend using multiple small answer boxes instead of one large box to convey the expected response format and improve response quality in specific probes. This study makes a valuable contribution to the field of web probing, extends the concept of response quality in list-style open-ended questions, and provides a deeper understanding of how visual design features affect cognitive response processes in web surveys.

In this paper, we explore the use of Facebook targeted advertisements for the collection of survey data. We illustrate the potential of survey sampling and recruitment on Facebook through the example of building a large employee-employer linked dataset as part of The Shift Project. We describe the workflow process of targeting, creating, and purchasing survey recruitment advertisements on Facebook. We address concerns about sample selectivity, and apply post-stratification weighting techniques to adjust for differences between our sample and that of "gold-standard" data sources. We then compare univariate and multi-variate relationships in the Shift data against the Current Population Survey and the National Longitudinal Survey of Youth-1997. Finally, we provide an example of the utility of the firm-level nature of the data by showing how firm-level gender composition is related to wages. We conclude by discussing some important remaining limitations of the Facebook approach, as well as highlighting some unique strengths of the Facebook targeting advertisement approach, including the ability for rapid data collection in response to research opportunities, rich and flexible sample targeting capabilities, and low cost, and we suggest broader applications of this technique.

Quantile regression (QR) provides an alternative to linear regression (LR) that allows for the estimation of relationships across the distribution of an outcome. However, as highlighted in recent research on the motherhood penalty across the wage distribution, different procedures for conditional and unconditional quantile regression (CQR, UQR) often result in divergent findings that are not always well understood. In light of such discrepancies, this paper reviews how to implement and interpret a range of LR, CQR, and UQR models with fixed effects. It also discusses the use of Quantile Treatment Effect (QTE) models as an alternative to overcome some of the limitations of CQR and UQR models. We then review how to interpret results in the presence of fixed effects based on a replication of Budig and Hodges’s work on the motherhood penalty using NLSY79 data.