抗作假人格迫選測驗中瑟斯頓IRT模型的影響因素
發(fā)布時間：2018-12-26 14:06
【摘要】：使用蒙特卡洛(Monte Carlo)模擬研究的方法,探討應用瑟斯頓IRT模型對抗作假迫選測驗計分時需滿足的編制條件,考察了測驗所測特質個數(2或5個)、每維度包含陳述數量(10或20個)、單維配對題目比例(0或20%)和正負向陳述間配對題目比例(0、20%或40%)對模型估計的影響.結果如下:1)特質個數對模型估計有顯著影響,測驗所測特質個數越多,模型估計越準確;2)陳述數量影響模型估計,測驗包含陳述數量越多,模型估計越精確,且當特質個數較少時,陳述數量的影響更大;3)測驗中單維配對題目的比例基本不影響瑟斯頓IRT模型的估計精度;4)測驗中加入一定比例(約20%)的正負向陳述間配對題目可提高模型的估計精度,且特質個數較少時,該因素的影響更大.最后,在研究結果的基礎上給出了開發(fā)抗作假人格迫選測驗的建議.
[Abstract]:By using Monte Carlo (Monte Carlo) simulation method, this paper discusses the compiling conditions to be satisfied when using Thurston IRT model to counter the false selection test, and investigates the number of traits (2 or 5) measured in the test. Each dimension includes the effects of the number of statements (10 or 20), the proportion of one-dimensional pairs (0 or 20%) and the proportion of positive and negative statements (0% or 40%) on the model estimation. The results are as follows: 1) the number of traits has a significant impact on the model estimation, the more the number of traits measured, the more accurate the model estimation; 2) the number of statements affects the estimation of the model. The more statements the test contains, the more accurate the model estimation is, and the more the number of statements is when the number of traits is small; 3) the proportion of one-dimensional paired questions in the test has little effect on the estimation accuracy of Thurston IRT model. 4) adding a certain proportion (about 20%) of positive and negative statements to the test can improve the estimation accuracy of the model, and the influence of this factor is greater when the number of traits is small. Finally, based on the results of the study, some suggestions on the development of anti-false personality forced selection test are given.
【作者單位】： 北京師范大學心理學部;未來教育高精尖創(chuàng)新中心;
【基金】：中央高�；究蒲袠I(yè)務費專項資金資助
【分類號】：B841.7
本文編號：2392225
[Abstract]:By using Monte Carlo (Monte Carlo) simulation method, this paper discusses the compiling conditions to be satisfied when using Thurston IRT model to counter the false selection test, and investigates the number of traits (2 or 5) measured in the test. Each dimension includes the effects of the number of statements (10 or 20), the proportion of one-dimensional pairs (0 or 20%) and the proportion of positive and negative statements (0% or 40%) on the model estimation. The results are as follows: 1) the number of traits has a significant impact on the model estimation, the more the number of traits measured, the more accurate the model estimation; 2) the number of statements affects the estimation of the model. The more statements the test contains, the more accurate the model estimation is, and the more the number of statements is when the number of traits is small; 3) the proportion of one-dimensional paired questions in the test has little effect on the estimation accuracy of Thurston IRT model. 4) adding a certain proportion (about 20%) of positive and negative statements to the test can improve the estimation accuracy of the model, and the influence of this factor is greater when the number of traits is small. Finally, based on the results of the study, some suggestions on the development of anti-false personality forced selection test are given.
【作者單位】： 北京師范大學心理學部;未來教育高精尖創(chuàng)新中心;
【基金】：中央高�；究蒲袠I(yè)務費專項資金資助
【分類號】：B841.7
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