Journal paper
篇名(英)Heterogeneity of Multi-level Primary Sampling Units on Population Inference in Scientific Research
作者姓名(中)蔡良庭; 楊志堅
作者姓名(英)Liang-Ting Tsai; Chih-Chien Yang
Abstract本研究以Bootstrap 重複取樣程序,評估分層隨機取樣(stratified random sampling)及規模等比率取樣(probability proportional to size),對於調查研究的母體推論影響。調查研究的資料 常包含多層次的結構,而分析必須搭配適當的取樣權重,才能正確推 論母體特性(蔡良庭、楊志堅,2008)。權重的計算又隨不同取樣設 計而計算方式不同,但此權重計算的差異對於母體特性的推論,卻最 常被忽略,也需更進一步驗證。本研究以數值模擬方法並搭配臺灣社 會變遷調查的實徵資料分析,探討不同權重的計算對於母體推論的影 響。結果顯示,若忽略了取樣單位間的樣本數差異,採用分層隨機取 樣設計時,取樣數愈多,母群體特性的推論愈不精確。規模等比率的 取樣設計及權重計算能提供更精準的母體推論。
摘要(英)In this study, the Str. RS (stratified random sampling) and PPS (probability proportional to size) sampling procedures were used to evaluate the heterogeneity of multi-level primary sampling units on population inference in social science research. When a social science survey has complexly designed sampling frames, the Str. RS and PPS are often applied. However, the effects of different sampling weights within these two methods on the inference of population characteristics are often neglected. A numerical simulation study and a real data analysis on Taiwan Social Change Survey data with a further extension confirmatory factor analysis model based on Yang and Tsai (2008) were proposed in this study. Independent variables manipulated in this study include the sampling designs, data type (continuous or categorical), sampling size, and heterogeneity of PSU. The results suggest the PPS sampling design can provide a more precise parameter estimate of CFA models in a complexly designed sampling frame survey, whether the data type is continuous or categorical. We summarize the findings and recommend the PPS multistage procedure based on the bootstrap method that can be used in practical social science survey applications.
Keyword取樣設計; 取樣權重; 確認性因素分析; 分層隨機取樣; 規模等比率取樣
關鍵字(英)sampling design; sampling weight; confirmatory factor analysis; stratified random sampling; probability proportional to size
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