Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Nandana Sengupta, Madeleine Udell, Nathan Srebro, James Evans Abstract: Sociological Methodology, Ahead of Print. Social science approaches to missing values predict avoided, unrequested, or lost information from dense data sets, typically surveys. The authors propose a matrix factorization approach to missing data imputation that (1) identifies underlying factors to model similarities across respondents and responses and (2) regularizes across factors to reduce their overinfluence for optimal data reconstruction. This approach may enable social scientists to draw new conclusions from sparse data sets with a large number of features, for example, historical or archival sources, online surveys with high attrition rates, or data sets created from Web scraping, which confound traditional imputation techniques. The authors introduce matrix factorization techniques and detail their probabilistic interpretation, and they demonstrate these techniques’ consistency with Rubin’s multiple imputation framework. The authors show via simulations using artificial data and data from real-world subsets of the General Social Survey and National Longitudinal Study of Youth cases for which matrix factorization techniques may be preferred. These findings recommend the use of matrix factorization for data reconstruction in several settings, particularly when data are Boolean and categorical and when large proportions of the data are missing. Citation: Sociological Methodology PubDate: 2022-10-22T07:32:03Z DOI: 10.1177/00811750221125799
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Warren TenHouten, Lorne Schussel, Maria F. Gritsch, Charles D. Kaplan Abstract: Sociological Methodology, Ahead of Print. Because all aspects of social life have a mental component, sociology’s focus is not society alone but mind and society. Insofar as mind is an emergent level of brainwork, the description and measurement of mindwork amidst social interaction can be accomplished by neurometric measurement methodology. The authors’ topic, hyperscanning, involves the simultaneous recording of either hemodynamic or neuroelectric measurement of brain activity in two (or more) interacting individuals. The authors consider two hyperscanning methods, functional magnetic resonance imaging and electroencephalography (EEG). Although functional magnetic resonance imaging provides excellent spatial resolution of brain-region activation, the temporal resolution of EEG is unmatched. EEG’s low spatial resolution has been overcome by low-resolution electromagnetic tomography. Hyperscanning studies show that interpersonal coordination of action includes mutual entrainment or synchronization of neural dynamics, flow of information between brains, and causal effects of one brain upon another with respect to social-signaling processes involving fairness, reciprocity, trust, competition, cooperation, and leadership. Citation: Sociological Methodology PubDate: 2022-10-15T12:23:43Z DOI: 10.1177/00811750221128790
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Petrus te Braak, Theun Pieter van Tienoven, Joeri Minnen, Ignace Glorieux Abstract: Sociological Methodology, Ahead of Print. Previous research has shown that a prolonged recall period is associated with lower data quality in time-diary research. In these studies, the recall period is roughly estimated on the basis of the period between the assigned diary day and the agreed collection day. Because this is so rudimentary, little is known about the duration of the mean recall period and its consequences for data quality. Recent advances in online methodology now allow a better investigation of the recall period using time stamps. Using a refined indicator, the authors examine the duration of the recall period, to what extent this duration is related to socioeconomic characteristics, and how a prolonged recall period affects data quality. The authors demonstrate that using online time-diary data collected from 8,535 teachers in Belgium, the mean recall period is less than 24 hr for most respondents, although respondents with many time constraints have extended recall periods. Additionally, a prolonged recall period indeed has negative consequences for data quality. Quality deterioration already arises several hours after an activity has been completed, much sooner than previous research has indicated. Citation: Sociological Methodology PubDate: 2022-10-05T12:17:19Z DOI: 10.1177/00811750221126499
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Kazuo Yamaguchi Abstract: Sociological Methodology, Ahead of Print. This article introduces a new causal analytic method for survival analysis that retains the framework of Rubin’s causal model as an alternative to the marginal structural model (MSM). The major limitation of the MSM is a systematic bias in the effects of past treatments when the method is applied to the hazard rate analysis of nonrepeatable events in the presence of unobserved heterogeneity. This systematic bias is demonstrated in the article. The method introduced here assumes a semiparametric conditional-incidence-rate model and provides consistent estimates of the effects of present and past treatments on the conditional cumulative-incidence rate in the analysis of nonrepeatable events in the presence of unobserved heterogeneity. Unlike the MSM, which requires a sequential and cumulative use of the inverse-probability-of-treatment weighting many times for data with many time points, the new method uses the inverse-probability-of-treatment weighing only twice sequentially for estimation of the present and past treatment effects at each time of entry into treatment, and not cumulatively across different treatment entry times. Analysis of the conditional-incidence rate can also provide a more efficient parameter estimate for the treatment effect than the hazard rate model in cases where a majority of sample persons experience the event and thereby cease to be members of the risk set of the hazard rate during the period of observation. An application to an analysis of sexual initiation demonstrates that leaving home promotes sexual initiation, especially premarital sexual initiation, because it greatly increases the rate of premarital sexual initiation during the year after leaving home. Citation: Sociological Methodology PubDate: 2022-07-30T06:30:54Z DOI: 10.1177/00811750221114857
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Lisa Lucile Owens First page: 121 Abstract: Sociological Methodology, Ahead of Print. The author explores interactions with one research subject who feigns credentials and invents stories in order to participate in social science research interviews online. The possibility of intentional deception among interviewees in virtually mediated fieldwork is a critical consideration in the context of the recent extensive pivot to online-based fieldwork during the need for social distancing associated with the coronavirus disease 2019 pandemic. Following this rapid shift in what is generally accepted as the “gold standard” for social science research interviews, widespread use of online-based interviewing methods will likely endure as equivalent to in-person methods. A methodological case study with implications for virtually mediated fieldwork, this article highlights some of the advantages and disadvantages of virtually mediated interviews and provides practical suggestions. Citation: Sociological Methodology PubDate: 2022-06-22T07:13:40Z DOI: 10.1177/00811750221106777
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Andrew Carr First page: 141 Abstract: Sociological Methodology, Ahead of Print. To understand how income inequality affects individuals and communities, researchers must have accurate measures of income inequality at lower geographic levels, such as counties, school districts, and census tracts. Studies of income inequality, however, are constrained by the tabular format in which censuses publish income data. In this article, the author proposes a new method, Lorenz interpolation, for estimating income inequality from binned income data. Using public microsample data from the American Community Survey (ACS), the author shows that Lorenz interpolation produces more accurate and reliable income inequality estimates than do alternative estimation methods. Then, using restricted ACS income data obtained through a Federal Statistical Research Data Center, the author evaluates the accuracy of Lorenz interpolation at the census tract and school district levels. Lorenz interpolation produces reliable school district–level estimates, but the method produces less reliable estimates for some income inequality measures at the tract level. These findings indicate that researchers should refrain from estimating tract-level income inequality measures from tabular data. They also show that aggregating tract income distributions to higher geographic levels can produce valid estimates of income inequality. Citation: Sociological Methodology PubDate: 2022-04-07T01:25:12Z DOI: 10.1177/00811750221085586
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Beatriz Gallo Cordoba, George Leckie, William J. Browne First page: 162 Abstract: Sociological Methodology, Ahead of Print. Ethnic achievement gaps are often explained in terms of student and school factors. The decomposition of these gaps into their within- and between-school components has therefore been applied as a strategy to quantify the overall influence of each set of factors. Three competing approaches have previously been proposed, but each is limited to the study of student-school decompositions of the gap between two ethnic groups (e.g., White and Black). The authors show that these approaches can be reformulated as mediation models facilitating new extensions to allow additional levels in the school system (e.g., classrooms, school districts, geographic areas) and multiple ethnic groups (e.g., White, Black, Hispanic, Asian). The authors illustrate these extensions using administrative data for high school students in Colombia and highlight the increased substantive insights and nuanced policy implications they afford. Citation: Sociological Methodology PubDate: 2022-06-09T05:11:14Z DOI: 10.1177/00811750221099503
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Dennis M. Feehan, Vo Hai Son, Abu Abdul-Quader First page: 193 Abstract: Sociological Methodology, Ahead of Print. Researchers increasingly use aggregate relational data to learn about the size and distribution of survey respondents’ weak-tie personal networks. Aggregate relational data are collected by asking questions about respondents’ connectedness to many different groups (e.g., “How many teachers do you know'”). This approach can be powerful, but to use aggregate relational data, researchers must locate external information about the size of each group from a census or administrative records (e.g., the number of teachers in the population). This need for external information makes aggregate relational data difficult or impossible to collect in many settings. Here, the authors show that relatively simple modifications can overcome this need for external data, significantly increasing the flexibility of the method and weakening key assumptions required by the associated estimators. The key idea is to estimate the size of these groups from the sample of survey respondents, rather than relying on external sources of information. These methods are appropriate for using a sample survey to study the size and distribution of weak-tie network connections. They can also be used as part of the network scale-up method to estimate the size of hidden populations. The authors illustrate this approach with two empirical studies: a large simulation study and original household survey data collected in Hanoi, Vietnam. Citation: Sociological Methodology PubDate: 2022-07-05T12:47:05Z DOI: 10.1177/00811750221109568
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Molly M. King First page: 220 Abstract: Sociological Methodology, Ahead of Print. Researchers often need to work with categorical income data. The typical nonparametric (including midpoint) and parametric estimation methods used to estimate summary statistics both have advantages, but they carry assumptions that cause them to deviate in important ways from real-world income distributions. The method introduced here, random empirical distribution imputation (REDI), imputes discrete observations using binned income data, while also calculating summary statistics. REDI achieves this through random cold-deck imputation from a real-world reference data set (demonstrated here using the Current Population Survey Annual Social and Economic Supplement). This method can be used to reconcile bins between data sets or across years and handle top incomes. REDI has other advantages for computing values of an income distribution that is nonparametric, bin consistent, area and variance preserving, continuous, and computationally fast. The author provides proof of concept using two years of the American Community Survey. The method is available as the redi command for Stata. Citation: Sociological Methodology PubDate: 2022-07-14T09:53:10Z DOI: 10.1177/00811750221108086
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Scott M. Lynch, Emma Zang First page: 254 Abstract: Sociological Methodology, Ahead of Print. Multistate life table methods are an important tool for producing easily understood measures of population health. Most contemporary uses of these methods involve sample data, thus requiring techniques for capturing uncertainty in estimates. In recent decades, several methods have been developed to do so. Among these methods, the Bayesian approach proposed by Lynch and Brown has several unique advantages. However, the approach is limited to estimating years to be spent in only two living states, such as “healthy” and “unhealthy.” In this article, the authors extend this method to allow for large state spaces with “quasi-absorbing” states. The authors illustrate the new method and show its advantages using data from the Health and Retirement Study to investigate U.S. regional differences in years of remaining life to be spent with diabetes, chronic conditions, and disabilities. The method works well and yields rich output for reporting and subsequent analyses. The expanded method also should facilitate the use of multistate life tables to address a wider array of social science research questions. Citation: Sociological Methodology PubDate: 2022-07-23T07:22:32Z DOI: 10.1177/00811750221112398
Hybrid journal (It can contain Open Access articles)
