CAJ | 학술논문

分别采用四维度和十五维度Rasch模型分析包含项目内多维度结构的科学测验数据，估计两种维度结构下维度分数的信度。结果表明，对比相应的单维模型而言，四维度与十五维度Rasch模型均能够极大提高各内容维度上分数估计的信度。四维度与十五维度Rasch模型拟合结果的比较表明，对于总长度固定的测验，维度数目的增加能够补偿子维度长度减少引起的信度损失。但是这一作用必须以维度间较高的相关性为前提。
분별채용사유도화십오유도Rasch모형분석포함항목내다유도결구적과학측험수거，고계량충유도결구하유도분수적신도。결과표명，대비상응적단유모형이언，사유도여십오유도Rasch모형균능구겁대제고각내용유도상분수고계적신도。사유도여십오유도Rasch모형의합결과적비교표명，대우총장도고정적측험，유도수목적증가능구보상자유도장도감소인기적신도손실。단시저일작용필수이유도간교고적상관성위전제。
Due to broad content coveiage and limited testing time, the large scale assessment is challenged by the bandwidth-fidelity dilemma. This study is to explore how the multi-dimensional Rasch model would reliability in the within-item multi-dimensionality data. The results demonstrate that both 4-dimensional imp and rove 15- dimensional Rasch models fit data, which supports the construct validity of the test. The uni-dimensional model underestimates the correlation between domains due to measurement error. The multi-dimensional Rasch analysis yields a higher level of measurement precision and a more appropriate estimate for the correlation between domains as compared to uni-dimensional approach. The comparison between 4-dimension and 15-dimension analysis shows that the increase on the number of dimensions can compensate the effect of scale length reduction to a certain extent, as long as the correlations between the specific domain and the others are relatively high enough. In conclusion, the multi-dimensional Rasch analysis yields more reliable domain score than uni-dimensional Rasch model in the within-item multidimenionality context. For the test with fixed length, reliable scores can be reported on the more specified content domains, as long as there are high correlations between domains.