Person-fit偵測作假之效用- 非參數試題反應理論的模擬與應用

Abstract

在心理測驗中，作假的偵測是一個很重要的議題，因為其效果乃影響著變項間的關係、模型測試的正確性、以及測驗的公平性。目前，社會期許量表已被廣泛的應用於作假偵測，但增加題數，則亦增加作答者的負荷。因此，本研究欲探究應用person-fit統計數作為解決方法的可能性。雖然過去已有研究使用參數型的試題反應理論下的person-fit技術進行作假偵測，然而，參數型的試題反應理論的諸多假設，如：大樣本、常態分配、以及多題數等，在實際資料分析中並不容易滿足，因而導致不正確的結果及應用。據此，本研究乃聚焦於探究非參數試題反應理論下的person-fit技術之應用效用，取其使用情境較彈性，且更接近實際的情境之優點。\n 本研究使用模擬資料及實際資料進行研究假設的檢驗。在研究一中，依據不同的樣本數、樣本能力分配、作假動機以及題目的異常率，以R產生模擬作答並求出person-fit數值，進而比較參數型與非參數型各person-fit指標的偵測率（detection rate），作為效用判斷之依據。研究二則將此技術應用於實際資料中，以社會期許量表與一份興趣量表進行本研究所採用之三種統計數（lz, U3p與Guttman errors）的偵測檢證，以瞭解其在實際情境中的實用性。\n 研究結果指出，較佳的person-fit統計數需視不同的情境而定。Guttman errors最適合用於當樣本數小於100人，受試者能力值為常態分配及低闊峰，而作答異常率僅為部分的情況。當作答異常率達到100％，受試者能力分配為負偏態及低闊峰，且作假程度嚴重時，以U3p的偵測效果較佳。而lz則最適用於各種中等程度的作假情境。從實際資料的分析結果，指出不論是大樣本或小樣本，能力分配為常態性的假設皆不容易被滿足，且應用person-fit統計數於作假偵測是可行的，特別是使用非參數型的U3p指標。

Faking detection is a crucial issue because of the effect on the hypothesized relation among variables, model testing, and test fairness. Aside from the Social Desirable Scale, which has often been used in detecting faking, we explored the possibility of an alternative method, which is the person-fit statistics of nonparametric item response theory (NIRT). In the scope of parametric item response theory (PIRT), the person-fit technique has been used in faking detection. Although the PIRT assumptions such as large sample size, normal distribution, and number of items are difficult to achieve, numerous researchers still adopt conventional methods, leading to inaccurate results and implications. Using NIRT person-fit may be more flexible and closer to the practical condition based on NIRT features, and are therefore the focus of this study. \n\nWe used both simulated and real data to test the hypothesis. In Study 1, the data were simulated and varied in sample size, distribution, faking motivation, and aberrant rate, to investigate the accuracy of person-fit estimating between PIRT and NIRT. In Study 2, the technique using person-fit as a faking detection tool was applied to empirical data to evaluate its use in a practical context. \n\nThe results indicate that superior person-fit statistics are conditional. The Guttman error detection rate was higher when the sample size was less than 100, when partial item-faking existed in the scale, and in normal and platykurtic distributions. When the aberrant rate is 100% with severe faking, U3p outperformed other indicators in the negatively skewed and platykurtic distribution. Comparatively, lz could be adopted in all median-faking conditions. Our empirical study found that the normal distribution of ability is not easy to satisfy across a small and large sample size. Adopting person-fit statistics for faking detection is feasible, particularly for U3p.