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the simulation of Thurstone Item Response Theory, include fixed forced test and adaptive forced test.

Project description

the simulation of Thurstone Item Response Theory, include fixed forced test and adaptive forced test. 模拟瑟斯顿项目反应理论,包括固定测验和自适应测验。

瑟斯顿IRT模型简介和应用

瑟斯顿IRT模型主要应用于迫选式非认知测验(人格测验,动机测验,兴趣测验等)。

固定测验模拟

模拟100个被试,30个维度,每个维度10个陈述,每道题3个陈述,所以下面这个陈述总共有100题

from tirt import SimFixedTirt

fixed_tirt = SimFixedTirt(subject_nums=100, trait_size=30, items_size_per_dim=10)
theta_list = fixed_tirt.sim()
score_list = fixed_tirt.scores

for i, theta in enumerate(theta_list):
    print score_list[i]
    print theta

自适应测验模拟

模拟1个被试,题库600道题,30个维度,首先随机抽10题,第二阶段抽合适的题40道题,总共50道题

from tirt import SimAdaptiveTirt

sat = SimAdaptiveTirt(subject_nums=1, item_size=600, trait_size=30, max_sec_item_size=40)
sat.sim()

for key, value in sat.thetas.items():
    print sat.scores[key]
    print value

一致性

迫选测验通常都没有测谎量表(迫选测验本身抗作假),而衡量被试是否认真作答有更好的一致性分数

from tirt import irt_consistency_score, sim_scores, BayesProbitModel, gen_item_dict, SimFixedTirt
from tirt.utils import random_params

# 生成试题字典
item_dict = gen_item_dict(30, 10, block_size=3)
# 生成试题参数
a, b = random_params(item_dict, 30, block_size=3)
# 生成随机得分
scores = sim_scores(30, 10, 10)

for score in scores:
    model = BayesProbitModel(a, b, score=score)
    # 打印一致性
    print irt_consistency_score(model)

model = SimFixedTirt(trait_size=30, items_size_per_dim=10, subject_nums=100, model='bayes_probit')
model.sim()
print model.get_consistency_scores()

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