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題名：共同方法偏誤之檢驗方法
作者：陳建帆
作者(外文)：Chen, Chien-Fan
校院名稱：國立陽明交通大學
系所名稱：經營管理研究所
指導教授：丁承
學位類別：博士
出版日期：2023
主題關鍵詞：偏誤；卡方差異檢定；拔靴法；調整的Bootstrap p值檢驗；共同方法變異（CMV）；共同方法偏誤（CMB）；驗證性因素分析（CFA）；方法效應大小；蒙地卡羅模擬；ULMC法；bias；chi-square difference test；bootstrapping；adjusted bootstrap p value；common method variance (CMV)；common method bias (CMB)；confirmatory factor analysis (CFA)；method effect size；Monte Carlo simulation；ULMC technique
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鑑於期刊審稿人對於共同方法變異（common method variance, CMV）的影響所引起的問題日益重視，調查研究者可能無法避免提供該研究中假設關係並未被CMV扭曲的證據。 然而，CMV本身不必然會對所估計之參數造成統計上有意義的偏差或效應。換言之，假使CMV因為資料來自單一方法而無法避免，其效應有可能太過輕微而不足以影響研究結論。
然而, 文獻上對於CMV影響是否與如何造成真正的偏誤卻無清楚明確的闡述。本文假設當方法效應很小時，儘管有CMV的存在，顯著之共同方法偏誤（common method bias, CMB）卻不必然發生。其中CMV效應大小是以 / ( ) (Cohen, 1988, pp. 410–412)來衡量進而適當辨識出偏差的程度。本研究包括兩大部分。第一部分，我們檢視了方法效應大小對於驗證性因素分析（CFA）中之因素負荷量和相關性估計值的影響，我們使用不同類型的模型以及樣本大小和不同分布特徵的數據進行操作。第二部分，我們比較了幾種方法來檢驗CMB，以獲得更多的統計上有意義的見解。蒙地卡羅模擬結果顯示，無論數據是否服從常態分佈，當平均方法效應小於「中等」水平時，估計值都不存在CMB。為了更實際應用的目的，我們檢視檢驗CMB的幾種方法，結果顯示卡方差異檢定（由Williams等人進行的Method-R和Method-S模型）表現不佳。相比其他方法，本研究提出了一個整合調整p-value的拔靴法（Bollen和Stine，1992）與改良型ULMC（Ding et al.,2023）的方法，該法在檢驗CMB的表現良好。最後本文使用模擬資料和真實資料示範所提檢驗CMB的方法。在實證研究中所面臨CMV的威脅，我們建議可使用本文所提方法來檢驗CMB。
In light of the growing concern among journal reviewers regarding the impact of common method variance (CMV), survey researchers may imperatively provide evidences that the hypothesized relationship in the researches were not distorted. However, CMV itself is not necessarily the meaningful biases or effects in parameter estimates. Stated differently, should CMV should unavoidably exist in data due to the same method, the amount of method effects may be too trivial to change the conclusions.
Whether and how CMV leads to biases is an important issue but seems not well addressed in the literature. We hypothesize that, when the sizes of the method effects are small, despite the presence of CMV the significant CMB（common method bias, CMB） in parameter estimates are unnecessarily found. The amount of CMV can be measured by Effect size( = / (1 ) (Cohen, 1988, pp. 410–412) to properly identify the biasing level. We first discuss the influence of the method effect size on the biases in estimates of trait loadings and trait correlations based on confirmatory factor analysis (CFA) in study 1. And then the conclusion had been confirmed by using different types of model with various sample size and distributional characteristic datasets. Second, several methods had been compared for their ability to address CMB, that is presented in study 2 to gain more statistical insights for addressing meaningful biases. The Monte-Carlo simulation results have indicated that, regardless of whether the data are normal or nonnormal, when the average effect size is smaller than “medium” level, the CMB would not exist. For more piratical application, several methods have been used in detecting common method bias (CMB). It appears that the chi-square difference (restricted vs. unrestricted) test, commonly used in the literature, performs poorly. This study proposes an approach to integrate the modified bootstrap method (Bollen and Stine, 1992) with the refined unmeasured latent method construct (ULMC) technique (Ding et al.,2023) and shows that the approach performs well. The approach has been illustrated by using a simulated dataset and a real dataset. We suggest that the proposed approach be used to detect CMB for empirical studies facing the threat of CMV.
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