社会形势预警的几种建模方法比较

Based on social survey data conducted by local research group in some counties executed in the nearly past five years in China, the author proposed and solved two kernel problems in the field of social situation forecasting: i) How can the attitudes’ data on individual level be integrated with social situation data on macrolevel; ii) How can the powers of forecasting models’ constructed by different statistic methods be compared? Five integrative statistics were applied to the research: 1) algorithm average (MEAN); 2) standard deviation (SD); 3) coefficient variability (CV); 4) mixed secondary moment (M2); 5) Tendency (TD). To solve the former problem, the five statistics were taken to synthesize the individual and mocrolevel data of social situations on the levels of counties’ regions, and form novel integrative datasets, from the basis of which, the latter problem was accomplished by the author: modeling methods such as Multiple Regression Analysis (MRA), Discriminant Analysis (DA) and Support Vector Machine (SVM) were used to construct several forecasting models. Meanwhile, on the dimensions of stepwise vs. enter, short-term vs. long-term forecasting and different integrative (statistic) models, meta-analysis and power analysis were taken to compare the predicting power of each model within and among modeling methods. Finally, it can be concluded from the research of the dissertation: 1) Exactly significant difference exists among different integrative (statistic) models, in which, tendency (TD) integrative models have the highest power, but coefficient variability (CV) ones have the lowest; 2) There is no significant difference of the power between stepwise and enter models as well as short-term and long-term forecasting models; 3) There is significant difference among models constructed by different methods, of which, support vector machine (SVM) has the highest statistic power. This research founded basis in all facets for exploring the optimal forecasting models of social situation’s more deeply, further more, it is the first time methods of meta-analysis and power analysis were immersed into the assessments of such forecasting models.

基于近5年来本课题组对全国部分县的社会调查数据，作者提出并解决了社会形势预测领域的两大核心问题：i）个体水平的态度数据如何与宏观水平的社会形势数据整合？ ii） 不同统计方法所建构的预测模型的各类效力如何比较？对于前一问题，作者采用了：平均数（MEAN）、标准差（SD）、变异系数（CV）、混合二阶矩（M2）和势（TD）等五个整合统计量在各个县级行政区的水平上将个体态度与宏观社会形势数据整合，形成新的整合数据集；并在此数据集基础上，作者解决了后一问题，即，逐一采用多元回归分析（MRA）、判别分析（DA）和支持向量机（SVM）等建模方法构筑多个预测模型。然后按逐步-迫入法、近期-远期预测、不同整合（统计）模型等维度，采用元分析和统计效力分析的方法，在各个建模方法内和各个建模方法间，分层次比较各模型的预测效力。 最终，本文研究得出如下结论：1）不同整合（统计）模型的统计效力间确实存在显著性差异，其中，势（TD）整合模型的统计效力最高，而变异系数（CV）整合模型的统计效力最低；2）逐步模型与迫入模型间，近期预测与远期预测间，各模型的统计效力差异均不显著；3）不同建模方法所构建的模型的统计效力有显著性差异，支持向量机（SVM）模型的统计效力最高。 该研究为进一步地探索最优社会形势预测模型进行了全面的铺垫，并且将元分析与统计效力的方法首次融入到形势预测模型的评估中。