应该不是~~~~~~~~~~~

看上去是好东西，不过就是不明白，谁要是有好的课件，发一个给到我
cathay52@163.com

结构方程分析的是面上的东西，还是非常笼统的，而IRT是以项目为基础进行研究的，更加细致。

不是，sem属于线性模型（虽然最近有非线性的sem模型了），基于相关矩阵或协方差矩阵，irt模型是非线性的模型，精确程度自然是irt更加精确，要说从统计学的角度上来说，线性模型只是非线性模型的一个特例。

从应用的角度讲,结构方程是做因素分析和相关的,而IRT是做项目分析的,分析项目的难度区分度,难度等参数,两者风马牛不相及,但也有研究者将两者对接的,提高各阶段的测量精度
我觉得这个说法有些道理，我是在看一个美国教授的讲稿时有这个想法的，因为这个美国教授先讲IRT
理论论，然后再讲SEM，所以我就想IRT是不是SEM的特例？因为这个教授在讲义中对IRT推广得SEM。
现在关键问题是找不到这个讲义了，要不我就上传一份。
再说SEM是从整体分析，IRT是一个项目，有可能是子项目，所以容易产生我的这个想法。
希望知道的朋友多说说。
非常感谢！
祝好！

谢谢分享。学习中。

以下文字引自Reckase的Multidimensional Item Response Theory (pp. 70-71)，有心的同学不妨找他提到的以下文献学习一下：

Bock RD, Aitkin M (1981) Marginal maximum likelihood estimation of item parameters: Applications
of an EM algorithm. Psychometrika 46:443–459
Bock RD, Gibbons R, Muraki E (1988) Full information item factor analysis. Applied Psychological
Measurement 12:261–280
McDonald RP (1967) Nonlinear factor analysis. Psychometric Monograph 15
Samejima F (1974) Normal ogive model on the continuous response level in the multidimensional
space. Psychometrika 39:111–121

总之，Reckase第一段话的大意是：the statistical procedures are virtually identical。

3.3.3 Comparison of the Factor Analytic and MIRT Approaches
Factor analysis and MIRT have virtually identical statistical formulations when
they are applied to matrices of item responses. This can be noted from a comparison
of the models presented by Bock and Aitken (1981), Samejima (1974),
and McDonald (1967). In fact, the software for the full information factor analysis
methodology presented by Bock et al. (1988) can be used for either factor analysis
or MIRT.
If the statistical procedures are virtually identical, what is the difference in the
two types of methodology? First, factor analysis is thought of as a data reduction
technique. The goal is usually to find the smallest number of factors that
reproduces the observed correlation matrix. MIRT is a technique for modeling
the interaction between persons and test items. Reckase and Hirsch (1991) have
shown that using too few dimensions might degrade the modeling of the item/person interactions, but using too many dimensions does not cause serious problems. Thus,
MIRT may be a better analysis tool when it is not thought of as a data reduction technique.
It is a method for modeling the meaningful contributors to the interactions of
people with test items.
Second, factor analysis typically ignores the characteristics of the input variables
while MIRT focuses on them. Analyzing the correlation matrix implies that differences
in means and variances of variables is of little or no consequence. On the
one hand, newer versions of factor analysis, such as structural equation modeling,
do consider means, variances, and covariances, but not for the purpose of getting a
better understanding of the input variables. MIRT, on the other hand, focuses on the
differences in means and variances of the item scores because they are directly related
to important characteristics of test items such as difficulty and discrimination.
These item characteristics are actively used in MIRT applications.
Finally, MIRT analyses actively work to find solutions that use the same latent
space across tests and samples. The goal is to keep a common coordinate system
for all analyses so that the items will have parameter estimates on common metrics.
Having item parameters on a common metric support their storage in an item bank
for use in test forms construction and computerized adaptive testing. Factor analysis
procedures have developed some procedures for putting the solutions into common
coordinate systems, but those methods are not widely used. Instead, factor analysis
methods now emphasize confirmatory methods and structural equation modeling.
The methods developed for factor analysis like Procrustes rotations are now being
used to place MIRT solutions onto common scales. Such methods are an extension
of unidimensional IRT methods for test equating and linking of calibrations. Developing
methods for linking calibrations from different tests and examinee samples is
a major part of the research on MIRT. These methods will be described in detail in
Chap. 9 of this book.

对于一道题是否合适作为试题，结构方程不也是会给出的？是不是从实用角度，是不是已经大概的覆盖了IRT的一个功能，毕竟IRT所谓的难度、区分度，也都是为了这个，只是它能测量所谓的每个人的能力。

嗯，两者还是不同的，非线性的贝叶斯结构方程模型等，不过这个一直不火，没有判断指标。

SEM淘汰试题是因为影响结构效度，IRT是因为难度和区分度，举例来说前者淘汰数学考试中的语文题，后者淘汰二年级数学考试中一年级和三年级的题，两种方法都是编测量工具用的，这就是它俩应用角度的共同点，异同就是我前面说的那些，并且一般两者很少一起使用，因为后者一般要求单因素模型，前者就是做多因素的，前者一般用在考试中，后者一般用在心理人格问卷中

谢谢，可能是我学习的较少，但我所见的文章比较多都是用IRT在做考试这块，也有极少会结合SEM，

你说的对，考试基本都是IRT做的，一般管理学和心理学问卷用SEM