董信煌;李慶章;
頁: 151-176
日期: 2009/04
摘要: 因果解釋性研究是實證研究中很重要的一種研究方法，在實證研究中學者常使用複迴歸方法來驗證研究模式並找到顯著因子。貝氏迴歸是一種不同於複迴歸的分析工具，它使用事後機率抽取樣本來做統計推論，由於馬可夫鏈蒙地卡羅演算法可以有效率依機率分佈來抽取樣本，貝氏迴歸分析已變得越來越可行。本研究提出一個基於複迴歸分析結果的啟發式方法來建構貝氏迴歸分析的資訊事前機率，來自於兩個不同的實證研究資料將被用來測試此方法，這兩個實證研究皆是在探討資訊系統對績效的影響。偏離資訊法則顯示出此一啟發式方法能顯著的改善使用非資訊事前機率塑模的貝氏迴歸分析，當信任區間被用來尋找顯著因子時，我們發現此一新方法能找到更細膩的因子且可以設計出更好的方法來診斷資訊系統問題。
關鍵字: 研究方法;因果解釋性研究;資訊系統影響;貝氏迴歸;模式選擇;

因果解釋性研究的啟發式貝氏迴歸方法－以資訊系統影響研究為例

Abstract: Causal explanatory study is a very important research method in empirical research whereof research models are frequently validated by multiple linear regressions (MLR) with significant factors sought. An alternative to MLR is Bayesian regressions where statistical inferences are made with samples drawn from posterior distributions. Efficient simulation algorithms of the Markov chain Monte Carlo type have made Bayesian regressions practical. We propose a heuristic method based on the outputs of MLR to construct informative priors for Bayesian regressions. Data collected from two empirical studies of information systems (IS) impact on performance is used to demonstrate the proposed method. Deviance information criterion shows that this heuristic procedure significantly improves a Bayesian modeling with uninformative priors. When credible intervals are used to locate significant factors, it is found that the heuristic Bayesian approach, capable of finding delicate drivers, can help design better diagnostics for IS problems.
Keywords: Research methods;casual explanatory study;IS impact;Bayesian regressions;model selection;

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