Solving the many-variables problem in MICE with principal component regression

Multiple Imputation (MI) is one of the most popular approaches to addressing missing values in questionnaires and surveys. MI with multivariate imputation by chained equations (MICE) allows flexible imputation of many types of data. In MICE, for each variable under imputation, the imputer needs to specify which variables should act as predictors in the imputation model. The selection of these predictors is a difficult, but fundamental, step in the MI procedure, especially when there are many variables in a data set. In this project, we explore the use of principal component regression (PCR) as a univariate imputation method in the MICE algorithm to automatically address the many-variables problem that arises when imputing large social science data. We compare different implementations of PCR-based MICE with a correlation-thresholding strategy through two Monte Carlo simulation studies and a case study. We find the use of PCR on a variable-by-variable basis to perform best and that it can perform closely to expertly designed imputation procedures

High-dimensional imputation for the social sciences:A comparison of state-of-the-art methods

Including a large number of predictors in the imputation model underlying a multiple imputation (MI) procedure is one of the most challenging tasks imputers face. A variety of high-dimensional MI techniques can help, but there has been limited research on their relative performance. In this study, we investigated a wide range of extant high-dimensional MI techniques that can handle a large number of predictors in the imputation models and general missing data patterns. We assessed the relative performance of seven high-dimensional MI methods with a Monte Carlo simulation study and a resampling study based on real survey data. The performance of the methods was defined by the degree to which they facilitate unbiased and confidence-valid estimates of the parameters of complete data analysis models. We found that using lasso penalty or forward selection to select the predictors used in the MI model and using principal component analysis to reduce the dimensionality of auxiliary data produce the best results

Ongelijkheid in Nederland

Introductie themanummer Ongelijkheid in Nederlan

Multiculturalism and moderate secularism

What is sometimes talked about as the ‘post-secular’ or a ‘crisis of secularism’ is, in Western Europe, quite crucially to do with the reality of multiculturalism. By which I mean not just the fact of new ethno-religious diversity but the presence of a multiculturalist approach to this diversity, namely: the idea that equality must be extended from uniformity of treatment to include respect for difference; recognition of public/private interdependence rather than dichotomized as in classical liberalism; the public recognition and institutional accommodation of minorities; the reversal of marginalisation and a remaking of national citizenship so that all can have a sense of belonging to it. I think that equality requires that this ethno-cultural multiculturalism should be extended to include state-religion connexions in Western Europe, which I characterise as ‘moderate secularism’, based on the idea that political authority should not be subordinated to religious authority yet religion can be a public good which the state should assist in realising or utilising. I discuss here three multiculturalist approaches that contend this multiculturalising of moderate secularism is not the way forward. One excludes religious groups and secularism from the scope of multiculturalism (Kymlicka); another largely limits itself to opposing the ‘othering’ of groups such as Jews and Muslims (Jansen); and the third argues that moderate secularism is the problem not the solution (Bhargava)

Can we rate public support for democracy in a comparable way? Cross-national equivalence of democratic attitudes in the World Value Survey

In this study we examine the cross-cultural equivalence of two scales that measure attitudes toward democracy across 36 countries in the World Value Survey (WVS) 2000. We examine the equivalence of these scales in order to explore if we can meaningfully compare democratic attitudes across countries. Multiple group confirmatory factor analyses (MGCFA) is applied to answer this question. The analyses indicate that the scales may be compared but only to a certain extent and not across all the countries. We close this article by discussing the implications of the findings

A Blessing and a Curse? Political Institutions in the Growth and Decay of Generalized Trust: A Cross-National Panel Analysis, 1980–2009

Despite decades of research on social capital, studies that explore the relationship between political institutions and generalized trust–a key element of social capital–across time are sparse. To address this issue, we use various cross-national public-opinion data sets including the World Values Survey and employ pooled time-series OLS regression and fixed- and random-effects estimation techniques on an unbalanced panel of 74 countries and 248 observations spread over a 29-year time period. With these data and methods, we investigate the impact of five political-institutional factors–legal property rights, market regulations, labor market regulations, universality of socioeconomic provisions, and power-sharing capacity–on generalized trust. We find that generalized trust increases monotonically with the quality of property rights institutions, that labor market regulations increase generalized trust, and that power-sharing capacity of the state decreases generalized trust. While generalized trust increases as the government regulation of credit, business, and economic markets decreases and as the universality of socioeconomic provisions increases, both effects appear to be more sensitive to the countries included and the modeling techniques employed than the other political-institutional factors. In short, we find that political institutions simultaneously promote and undermine generalized trust