抗作假人格迫選測(cè)驗(yàn)中瑟斯頓IRT模型的影響因素
發(fā)布時(shí)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(yīng)用瑟斯頓IRT模型對(duì)抗作假迫選測(cè)驗(yàn)計(jì)分時(shí)需滿足的編制條件,考察了測(cè)驗(yàn)所測(cè)特質(zhì)個(gè)數(shù)(2或5個(gè))、每維度包含陳述數(shù)量(10或20個(gè))、單維配對(duì)題目比例(0或20%)和正負(fù)向陳述間配對(duì)題目比例(0、20%或40%)對(duì)模型估計(jì)的影響.結(jié)果如下:1)特質(zhì)個(gè)數(shù)對(duì)模型估計(jì)有顯著影響,測(cè)驗(yàn)所測(cè)特質(zhì)個(gè)數(shù)越多,模型估計(jì)越準(zhǔn)確;2)陳述數(shù)量影響模型估計(jì),測(cè)驗(yàn)包含陳述數(shù)量越多,模型估計(jì)越精確,且當(dāng)特質(zhì)個(gè)數(shù)較少時(shí),陳述數(shù)量的影響更大;3)測(cè)驗(yàn)中單維配對(duì)題目的比例基本不影響瑟斯頓IRT模型的估計(jì)精度;4)測(cè)驗(yàn)中加入一定比例(約20%)的正負(fù)向陳述間配對(duì)題目可提高模型的估計(jì)精度,且特質(zhì)個(gè)數(shù)較少時(shí),該因素的影響更大.最后,在研究結(jié)果的基礎(chǔ)上給出了開(kāi)發(fā)抗作假人格迫選測(cè)驗(yàn)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(xué)心理學(xué)部;未來(lái)教育高精尖創(chuàng)新中心;
【基金】:中央高;究蒲袠I(yè)務(wù)費(fèi)專(zhuān)項(xiàng)資金資助
【分類(lèi)號(hào)】:B841.7
本文編號(hào):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(xué)心理學(xué)部;未來(lái)教育高精尖創(chuàng)新中心;
【基金】:中央高;究蒲袠I(yè)務(wù)費(fèi)專(zhuān)項(xiàng)資金資助
【分類(lèi)號(hào)】:B841.7
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