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How rich are the rich compared with the poor? What’s the chance for a given person to get better living standards than his parents, or to get a higher place in the social hierarchy? Which countries are more/less equalitarian? Those questions are important both for social sciences researchers and for citizens. The first part of this course aims to introduce international and national indicators in term of human & economic development such as IDH, Gini, GDP, Gross National Happiness and the different ways to measure inequality. In the second part, students will learn how people’s life chance, gender inequalities, social reproduction, and education inequalities are impacting China and western societies.
本书是作者与巴黎和里尔社会学专业的大学生共同完成的对大学生，特别是文科大学生，进行的关于教育制度的调查。作者提出，以文化不平等社会学为基础的教学方法是真正具有合理性的方法，可有助于减少在教育和文化面前的不平等。因此，作者大肆批判能力神授论，反对以培养和选择出身优越的精英为方向的传统教育，也反对面向按一定规格批量生产专家的技术统治论的教育。在作者的心中，真正民主的教育，是无条件面向尽可能多的人的。
This book introduces R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. It teaches how to wrangle- transform your datasets into a form convenient for analysis; how to program- use powerful R tools for solving data problems with greater clarity and ease; how to explore- examine your data, generate hypotheses, and quickly test them; how to model- provide a low-dimensional summary that captures true signals in your dataset, and how to communicate- use R Markdown for integrating prose, code, and results.
This third edition introduces logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Beginning with an introduction to the logistic regression model, the book discusses the multiple logistic regression model, interpretation of the fitted logistic regression model, model-building strategies and methods for logistic regression, assessing the fit of the model, application of logistic regression with different sampling models, logistic regression for matched case-control studies, logistic regression models for multinomial and ordinal outcomes, logistic regression models for the analysis of correlated data, and some special topics.
This book includes detailed discussions of goodness of fit, indices of predictive efficiency, and standardized logistic regression coefficients, and examples using SAS and SPSS are included. It explicates the estimation, interpretation, and diagnostics of such logistic regression models. The logistic counterparts to the OLS statistics- the R2, the standard error of estimate, the t ratio, and the slope- are systematically presented. Traditional regression diagnostics- the studentized residual, leverage, dbeta- are included in an innovative logistic protocol of diagnostics. The last chapter dissects the problem of a polytomous dependent variable, with multiple ordered or unordered categories.
This book covers the basics of R, how to get data and manipulate variables, and introductory to intermediate data analysis all in R. The book will guide the readers through the basics of RStudio, differences between primary data and secondary data, how to examine, clean, and subset data and variables, how to create good empirical hypotheses, univariate and descriptive statistics, measures of central tendency and dispersion, how to graphically display data and quantitative relationships, the foundations and origins of hypothesis testing and the notion of statistical significance, testing bivariate relationships, etc.
