為了處理在願付價格的研究中，極端受訪者對於估計結果所造成的誤差。本文沿用Tsai(2005)所建議採用的三要素混合模型，將受訪者區分為價格再高都願意支付、願意支付合理價格以及價格再低都不願意支付等三種類型。在評估願付價格時，以加速失敗模型(accelerated failure time model，簡稱AFT model)針對願意支付合理價格的受訪者進行估計，並且在考慮不同起價點可能會造成不同程度的起價點偏誤(starting point bias)或是定錨效果(anchoring effect)的情形下，提出一個起價點偏誤調整模型來做探討。我們並以CVDFACTS中的高血壓之願付價格資料進行實證分析。分析結果發現，教育程度越高的男性對於能降低高血壓病患罹患心臟血管相關疾病之新藥願意付較高的金額。此外我們也發現在此筆資料中，不同起價點確實會造成不同程度的偏誤，經由偏誤調整後會得到較高的願付金額。 / A study of willingness-to-pay often suffers from the bias introduced by extreme respondents who are willing to or not willing to pay any price. To overcome the problem, a three-component model proposed by Tsai (2005) is adopted. Under such a circumstance, respondents are classified into three categories, i.e. respondents who are willing to pay any price, unwilling to pay any price, or willing to pay a reasonable price. The willingness-to-pay for those subjects who are willing to pay a reasonable price is again modeled by an accelerated failure time model (AFT model). In this study, we, however, propose an unified model that allows us to look into the issue related to starting point bias and anchoring effect, simultaneously. Willingness-to-pay for cardiovascular disease treatment from a longitudinal follow-up survey- CVDFACTS, is investigated using the new model. Through the use of the model, we are able to detect the effects of starting point biases, and make a proper adjustment accordingly. Our analysis indicates that male respondents with higher education level have an inclination to pay higher price for the new treatment. Besides, we also discover that starting point bias does exist in this dataset.

本文主要著重在處理條件評估法下，「不知道」受訪者的回應。當「不知道」受訪者的產生機制並未符合完全隨機時，考量他們的真實意向就顯得極為重要。 文中使用中央研究院生醫所在其研究計畫「竹東及朴子地區心臟血管疾病長期追蹤研究」（CardioVascular Disease risk FACtor Two-township Study，簡稱CVDFACTS）第五循環中的研究調查資料。 　　由於以往的文獻對於「不知道」受訪者的處理，皆有不足之處。如Wang (1997)所提出的方法，就只能針對某種特定的「不知道」受訪者來做處理；而Caudill and Groothuis (2005)所提的方法，由於將「不知道」受訪者的差補與願付價格的估計分開，亦使其估計結果不具備一些好的性質。在本文中，我們提出一個能同時處理「不知道」受訪者且估計願付價格的方法。除了使得統計上較有效率外，也保有EM演算法的一個特性：願付價格模型中的估計參數為最大概似估計值。此外，在加入三要素混合模型(Tsai (2005))後，我們也可避免用到極端受訪者的訊息去差補那些「不知道」受訪者的意向。 　　在分析願付價格的過程中，我們發現此筆資料的「不知道」受訪者，其產生的機制為隨機，而非為完全隨機，這意謂著不考量「不知道」受訪者的分析結果，必定會產生偏差。而在比較有考量「不知道」受訪者與沒有的情況後，其結果確實應證了我們的想法：只要「不知道」受訪者不是完全隨機產生的，那麼不考量他們必定會產生某種程度的偏差。 / This paper investigates how to deal with “Don’t Know” (DK) responses in contingent valuation surveys, which must be taken into consideration when they are not completely at random. The data we use is collected from the fifth cycle of the Cardiovascular Disease Risk Factor Two-township Study (CVDFACTS), which is a series of long-term surveys conducted by the Institute of Biomedical Sciences, Academia Sinica. Previous methods used in dealing with DK responses have not been satisfactory because they only focus on some types of DK respondents (Wang (1997)), or separate the imputation of DK responses from the WTP estimation (Caudill and Groothuis (2005)). However, in this paper, we introduce an integrated method to cope with the incomplete data caused by DK responses. Besides being more efficient, the single-step method guarantees maximum likelihood estimates of the WTP model to be obtained due to the good property that the EM algorithm possesses. Furthermore, by adding the concept of the three-component mixture model (Tsai (2005)), some extreme information are drawn out when imputing the DK inclinations. In this hypertension data, the mechanism of the DK responses is “Don’t know at random”, which means the analysis of DK-dropped results in a bias. By using our method, the difference between DK-dropped and DK-included is actually revealed, which proves our suspicion that a DK-dropped analysis is accompanied by a biased result when DK is not completely at random.