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ISSN : 1229-067X

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Vol.32 No.4
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구조방정식모형을 이용한 차별문항기능의 탐지: MACS와 MIMIC의 비교

Detection of differential item functioning using structural equation modeling: A comparison of MACS and MIMIC

한국심리학회지: 일반 / Korean Journal of Psychology: General, (P)1229-067X; (E)2734-1127
2013, v.32 no.4, pp.1023-1052
윤수철 (서울대학교 언어교육원)
이순묵 (성균관대학교)
윤수철, & 이순묵. (2013). 구조방정식모형을 이용한 차별문항기능의 탐지: MACS와 MIMIC의 비교. 한국심리학회지: 일반, 32(4), 1023-1052.
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초록

구조방정식모형을 이용하여 차별문항기능을 탐지하는 방법으로 평균 및 공분산구조(MACS) 모형과 다지표-다원인(MIMIC) 모형의 두 가지를 들 수 있다. 두 모형은 모두 구조방정식모형의 특수한 경우에 해당하지만, 모형 간에 통계적 가정 및 자료의 입력 방식이 다르기 때문에 연구 상황에 따라 그 수행이 상이할 수 있다. 특히 MIMIC 모형은 MACS 모형에 비해 추가적인 가정을 필요로 하기 때문에, 가정이 위배될 경우 균일적 차별문항기능 탐지에 더 불리할 수 있다. 또한 MACS 모형과 달리 MIMIC 모형은 집단변수를 포함한 단일 입력자료를 사용하기 때문에, 집단 간에 표본크기가 상이할 경우 MIMIC 모형이 MACS 모형보다 균일적 차별문항기능 탐지에 더 유리할 것으로 예상할 수 있다. 이러한 가능성에 대해 여러 문헌에서 지적되었음에도 불구하고, 다양한 상황에서 두 모형의 차이를 체계적으로 비교한 연구는 발견되지 않았다. 따라서 다양한 연구 상황을 반영한 몬테 카를로 모의실험(Monte Carlo simulation)을 통해 균일적 차별문항기능 탐지에 대한 두 모형의 수행을 비교하였다. 구체적으로는 집단효과, 측정변수 신뢰도의 차이, 전체 표본크기, 표본크기의 비율, 차별문항기능의 크기, 차별문항기능 탐지 전략 등을 체계적으로 조작하였으며, 이에 따른 두 모형의 수행을 비교하였다. 비교 결과, 추가적인 가정이 위배되는 상황에서도 MIMIC 모형의 균일적 차별문항기능 탐지율이 MACS 모형에 비해 크게 저하되지 않았으며, 표본 크기가 집단 간에 다른 경우 MIMIC 모형이 MACS 모형보다 우수한 탐지율을 보였다. 단, 두 모형의 효과적인 사용을 위해서는 적절한 탐지 전략이 필요하므로, 이에 대해 논의한 후 현실적으로 바람직한 방안을 제안하였다.

keywords
Differential Item Functioning, Structural Equation Modeling, MACS, MIMIC, 차별문항기능, Differential Item Functioning, 구조방정식모형, MACS, MIMIC

Abstract

Two models, MACS and MIMIC, can be used to detect Differential Item Functioning(DIF) in a Structural Equation Modeling framework. Although these two models can be considered as special cases of general Structural Equation Models, they may perform differently in various research contexts due to differences in statistical assumptions and the way in which each model uses data. In particular, since MIMIC model requires some additional assumptions, its performance may decline when those assumptions are not satisfied. Furthermore, the performance of MIMIC model will be superior to that of MACS model when sample sizes vary among groups because the former uses a single dataset including group variable(s), unlike the latter. Although many articles have commented on these predictions, no systematic research comparing the performance of the two models under these circumstances had yet to be conducted. Thus, we investigated the differences in performance of these two models under various conditions, specifically the size of impact, differences in measurement variable reliability, sample size ratio, total sample size, the size of differential item functioning, and the strategy for detecting DIF through a Monte Carlo simulation study. We found that the performance of MIMIC model in detecting uniform DIF did not decline significantly, although one of its additional assumptions was violated. Moreover, MIMIC model was superior to MACS model when sample sizes differed between two groups. Finally, we emphasize the importance of employing appropriate strategies for effective use of the two models to detect uniform DIF.

keywords
Differential Item Functioning, Structural Equation Modeling, MACS, MIMIC, 차별문항기능, Differential Item Functioning, 구조방정식모형, MACS, MIMIC

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