多变量分析基于生育进程的总和生育率及其组成部分的指标。
Multivariate analysis of parity progression-based measures of the total fertility rate and its components.
机构信息
Population and Health Studies, East-West Center, 1601 East-West Road, Honolulu, HI 96848, USA.
出版信息
Demography. 2010 Feb;47(1):97-124. doi: 10.1353/dem.0.0087.
This article describes a methodology for applying a discrete-time survival model-the complementary log-log model-to estimate effects of socioeconomic variables on (1) the total fertility rate and its components and (2) trends in the total fertility rate and its components. For the methodology to be applicable, the total fertility rate (TFR) must be calculated from parity progression ratios (PPRs). The components of the TFR are PPRs, the total marital fertility rate (TMFR), and the TFR itself as measures of the quantum of fertility, and mean and median ages at first marriage and mean and median closed birth intervals by birth order as measures of the tempo or timing of fertility. The focus is on effects of predictor variables on these measures rather than on coefficients, which are often difficult to interpret in the complex models that are considered. The methodology is applicable to both period and cohort data. It is illustrated by application to data from the 1993, 1998, and 2003 Demographic and Health Surveys (DHS) in the Philippines.
本文描述了一种将离散时间生存模型——互补对数-log 模型应用于估计社会经济变量对（1）总和生育率及其组成部分和（2）总和生育率及其组成部分趋势的影响的方法。为了使该方法适用，总和生育率（TFR）必须根据生育进程比（PPR）来计算。TFR 的组成部分是 PPR、总婚姻生育率（TMFR）和 TFR 本身，它们是生育数量的衡量标准，而初婚年龄的均值和中位数以及按出生顺序的闭区间生育间隔的均值和中位数是生育节奏或时间的衡量标准。重点是预测变量对这些衡量标准的影响，而不是对系数的影响，因为在考虑的复杂模型中，系数往往难以解释。该方法适用于时期和队列数据。本文通过应用于菲律宾 1993 年、1998 年和 2003 年人口与健康调查（DHS）的数据来说明该方法。
Zotero 插件