stata教程11-raschtest模块进行项目反应试题分析

xxxspy 2018-12-23 17:17:55
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本篇文章中用到的很多代码来自这里: https://www.stata-journal.com/sjpdf.html?articlenum=st0119 , 英文好的朋友可以直接看这个文章就行。

另外, 这篇教程只是展示一下stata在IRT分析时能做什么, 并没有对IRT的相关知识进行介绍。 另外我们也没有对raschtest命令如何使用进行详细的结果, 所以这篇文章只需要花费10秒浏览一下即可。

安装

1
ssc install raschtest
输出(stream):
checking raschtest consistency and verifying not already installed... installing into c:\ado\plus\... installation complete.

数据加载

前几篇文章都是用的这个数据, 不做介绍, 直接上命令:

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use http://www.stata.com/support/faqs/dta/raschfaq, clear
1
list in 1/10
输出(stream):
+-----------------------------------------------------------------------------------------------------------------------------+ | math1 math2 math3 math4 math5 math6 math7 math8 math9 math10 subj_id | |-----------------------------------------------------------------------------------------------------------------------------| 1. | correct correct correct incorrect incorrect incorrect incorrect incorrect incorrect incorrect 1 | 2. | correct correct incorrect incorrect correct correct correct incorrect incorrect incorrect 2 | 3. | correct correct incorrect incorrect correct incorrect incorrect incorrect incorrect incorrect 3 | 4. | correct correct correct incorrect correct incorrect correct incorrect incorrect incorrect 4 | 5. | correct correct correct incorrect incorrect correct correct correct incorrect incorrect 5 | |-----------------------------------------------------------------------------------------------------------------------------| 6. | correct correct correct incorrect correct incorrect incorrect correct correct incorrect 6 | 7. | correct correct correct incorrect incorrect correct incorrect incorrect incorrect incorrect 7 | 8. | correct correct correct incorrect correct correct incorrect correct correct correct 8 | 9. | correct correct correct incorrect correct correct correct incorrect correct incorrect 9 | 10. | correct correct correct correct correct incorrect incorrect correct correct incorrect 10 | +-----------------------------------------------------------------------------------------------------------------------------+

模型拟合

从输出结果中我们可以看到, 我们常用的拟合指标都已经列出, 包括infit/outfit, R1c 和 Andersen LR test等。

1
raschtest math*, method(cml) mean id(subj_id) autogroup
输出(stream):
Estimation method: Conditional maximum likelihood (CML) Number of items: 10 Number of groups: 4 (2 of them are used to compute the statistics of test) Number of individuals: 120 Number of individuals with missing values: 0 (removed) Number of individuals with nul or perfect score: 5 Conditional log-likelihood: -435.3507 Log-likelihood: -576.6596 Difficulty Standardized Items parameters std Err. R1c df p-value Outfit Infit U ----------------------------------------------------------------------------- math1 -1.70772 0.24144 0.308 1 0.5790 0.016 0.970 0.209 math2 -1.46646 0.22711 0.016 1 0.8988 0.031 -0.220 0.366 math3 -0.91556 0.20332 0.262 1 0.6085 -1.065 -0.687 -0.969 math4 -0.25695 0.19002 0.071 1 0.7893 0.258 0.755 0.939 math5 -0.17952 0.18948 0.340 1 0.5597 -0.717 -0.249 -0.180 math6 0.20572 0.18994 1.650 1 0.1989 -0.284 0.517 0.497 math7 0.32291 0.19111 0.821 1 0.3648 -0.832 -0.390 -0.553 math8 0.95453 0.20591 3.251 1 0.0714 -2.023 -2.449 -2.496 math9 1.19694 0.21547 0.439 1 0.5077 -0.641 -0.574 -0.773 math10 1.84612 0.25220 1.696 1 0.1929 0.902 1.675 1.232 ----------------------------------------------------------------------------- R1c test R1c= 8.621 9 0.4730 Andersen LR test Z= 9.178 9 0.4210 ----------------------------------------------------------------------------- The mean of the difficulty parameters is fixed to 0 Ability Expected Group Score parameters std Err. Freq. Score ll -------------------------------------------------------------- 0 0 -3.625 1.608 3 0.42 -------------------------------------------------------------- 1 1 -2.314 0.999 5 1.29 -160.6491 2 -1.574 0.833 4 2.20 3 -0.993 0.759 16 3.12 4 -0.480 0.724 21 4.06 -------------------------------------------------------------- 2 5 0.002 0.713 24 5.00 -270.1128 6 0.484 0.723 22 5.94 7 0.992 0.757 13 6.87 8 1.570 0.832 4 7.80 9 2.310 0.999 6 8.71 -------------------------------------------------------------- 3 10 3.628 1.612 2 9.58 --------------------------------------------------------------

绘图

如果你想要同时生成各种图, 你可以这么写:

1
raschtest math*, method(cml) mean autogroup icc information graph fitgraph id(subj_id)
输出(stream):
Estimation method: Conditional maximum likelihood (CML) Number of items: 10 Number of groups: 4 (2 of them are used to compute the statistics of test) Number of individuals: 120 Number of individuals with missing values: 0 (removed) Number of individuals with nul or perfect score: 5 Conditional log-likelihood: -435.3507 Log-likelihood: -576.6596 Difficulty Standardized Items parameters std Err. R1c df p-value Outfit Infit U ----------------------------------------------------------------------------- math1 -1.70772 0.24144 0.308 1 0.5790 0.016 0.970 0.209 math2 -1.46646 0.22711 0.016 1 0.8988 0.031 -0.220 0.366 math3 -0.91556 0.20332 0.262 1 0.6085 -1.065 -0.687 -0.969 math4 -0.25695 0.19002 0.071 1 0.7893 0.258 0.755 0.939 math5 -0.17952 0.18948 0.340 1 0.5597 -0.717 -0.249 -0.180 math6 0.20572 0.18994 1.650 1 0.1989 -0.284 0.517 0.497 math7 0.32291 0.19111 0.821 1 0.3648 -0.832 -0.390 -0.553 math8 0.95453 0.20591 3.251 1 0.0714 -2.023 -2.449 -2.496 math9 1.19694 0.21547 0.439 1 0.5077 -0.641 -0.574 -0.773 math10 1.84612 0.25220 1.696 1 0.1929 0.902 1.675 1.232 ----------------------------------------------------------------------------- R1c test R1c= 8.621 9 0.4730 Andersen LR test Z= 9.178 9 0.4210 ----------------------------------------------------------------------------- The mean of the difficulty parameters is fixed to 0 Ability Expected Group Score parameters std Err. Freq. Score ll -------------------------------------------------------------- 0 0 -3.625 1.608 3 0.42 -------------------------------------------------------------- 1 1 -2.314 0.999 5 1.29 -160.6491 2 -1.574 0.833 4 2.20 3 -0.993 0.759 16 3.12 4 -0.480 0.724 21 4.06 -------------------------------------------------------------- 2 5 0.002 0.713 24 5.00 -270.1128 6 0.484 0.723 22 5.94 7 0.992 0.757 13 6.87 8 1.570 0.832 4 7.80 9 2.310 0.999 6 8.71 -------------------------------------------------------------- 3 10 3.628 1.612 2 9.58 -------------------------------------------------------------- icc graph icc graph icc graph icc graph icc graph icc graph icc graph icc graph icc graph icc graph

icc graph

icc graph
icc graph
icc graph
icc graph
icc graph

省略其他ICC….

总结

这篇教程只是展示一下stata在IRT分析时能做什么, 并没有对IRT的相关知识进行介绍。目前来看, raschtest命令的功能还比较有限, 如果想要用到stata还需要自己对IRT理论模型有更深的理解才行, 相对于conquest/winsteps等软件, raschtest还不太适合小白用户的使用。

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