統(tǒng)計模型的“不確定性”問題與傾向值方法
發(fā)布時間：2018-09-10 19:51
【摘要】：量化社會學研究往往基于特定的統(tǒng)計模型展開。近十幾年來日益流行的傾向值方法也不例外,其在實施過程中需要同時擬合估計傾向值得分的"傾向值模型"與估計因果關系的"結果模型"。然而,無論是其模型形式還是系數(shù)估計,統(tǒng)計模型本身都具有不可忽視的"不確定性"問題。本研究在傾向值分析方法的框架下,系統(tǒng)梳理和闡釋了模型形式不確定性與模型系數(shù)不確定性的內涵及其處理方法。通過分析"蒙特卡洛模擬"數(shù)據(jù)與經(jīng)驗調查數(shù)據(jù),本文展示了在使用傾向值方法進行因果估計的過程中,研究者如何通過"貝葉斯平均法"進行多個備選傾向值模型的選擇,以及如何通過聯(lián)合估計解決傾向值模型與估計模型中的系數(shù)不確定性問題。本文的研究也表明,在考慮傾向值估計過程的不確定性之后,結果模型中對于因果關系的估計呈現(xiàn)更小的置信區(qū)間和更高的統(tǒng)計效率。
[Abstract]:Quantitative sociological studies are often based on specific statistical models. The tendency value method, which has become more and more popular in recent years, is no exception. In the process of implementation, it is necessary to fit the "tendency value model" and the "result model" of estimating causality. However, no matter its model form or coefficient estimation, the statistical model itself has the "uncertainty" problem which can not be ignored. Under the framework of the tendency value analysis method, the connotation and treatment methods of the model formal uncertainty and the model coefficient uncertainty are systematically summarized and explained in this study. Based on the analysis of Monte Carlo Simulation data and empirical survey data, this paper shows how to select multiple alternative tendency value models by Bayesian average method in the process of causality estimation using tendency value method. And how to solve the problem of coefficient uncertainty in the model and estimation model by joint estimation. It is also shown that the estimation of causality in the result model presents a smaller confidence interval and a higher statistical efficiency after considering the uncertainty of the estimation process of the tendency value.
【作者單位】： 復旦大學社會學系;
【基金】：國家社科基金青年項目(15CSH030) 上海市教育委員會科研創(chuàng)新項目(15ZS001) 復旦大學“卓學人才計劃”項目的支持~~
【分類號】：C8
本文編號：2235443
[Abstract]:Quantitative sociological studies are often based on specific statistical models. The tendency value method, which has become more and more popular in recent years, is no exception. In the process of implementation, it is necessary to fit the "tendency value model" and the "result model" of estimating causality. However, no matter its model form or coefficient estimation, the statistical model itself has the "uncertainty" problem which can not be ignored. Under the framework of the tendency value analysis method, the connotation and treatment methods of the model formal uncertainty and the model coefficient uncertainty are systematically summarized and explained in this study. Based on the analysis of Monte Carlo Simulation data and empirical survey data, this paper shows how to select multiple alternative tendency value models by Bayesian average method in the process of causality estimation using tendency value method. And how to solve the problem of coefficient uncertainty in the model and estimation model by joint estimation. It is also shown that the estimation of causality in the result model presents a smaller confidence interval and a higher statistical efficiency after considering the uncertainty of the estimation process of the tendency value.
【作者單位】： 復旦大學社會學系;
【基金】：國家社科基金青年項目(15CSH030) 上海市教育委員會科研創(chuàng)新項目(15ZS001) 復旦大學“卓學人才計劃”項目的支持~~
【分類號】：C8
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