麦当劳欧米茄:R中的警告 [英] McDonalds omega: warnings in R

问题描述

我正在以几种不同的比例来计算欧米茄；并在R中使用不同的omega功能获得针对不同规模的不同警告消息.我的问题是关于如何解释这些警告以及是否安全地报告检索到的omega统计信息.

I'm computing omega for several different scales; and get different warning messages for different scales with different omega functions in R. My questions are regarding how to interpret these warnings and if it is safe to report the retrieved omega statistics.

当我使用从alpha到omega:内部一致性估计普遍问题的实用解决方案"一文中的以下功能时

When I'm using the following function from the article "From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation"

 ci.reliability(subscale1, interval.type="bca", B=1000)

我收到以下警告:

 1: In lav_object_post_check(lavobject) :
   lavaan WARNING: some estimated variances are negative
 2: In lav_object_post_check(lavobject) :
   lavaan WARNING: observed variable error term matrix (theta) is not positive definite; use inspect(fit,"theta") to investigate.

其中可能有很多！

它们是什么意思? 我仍然收到欧米茄的统计数据；他们可以被解释吗?

What do they mean? I still receive omega statistics; can they be interpreted or not?

当我使用该功能时:

  psych::omega(subscale1)

我收到此警告:

 Warning message:
 In GPFoblq(L, Tmat = Tmat, normalize = normalize, eps = eps, maxit =      maxit,  :
   convergence not obtained in GPFoblq. 1000 iterations used.

再次， 这是什么意思;我可以使用获得的欧米茄统计信息吗?

Again, What does it mean; and can I use the omega-statistics that I get?

请注意，这些警告出现在不同的子量表上.因此可以使用其中一个函数来计算一个分量表，而不能使用另一个函数，反之亦然.

Note that these warnings appear on different subscales; so one subscale can be computed using one of the function but not the other and vice versa.

如果有帮助，则Subscale1包含4个项目；样本中的N> 300.另外，我可以对拉瓦兰中的这4个项目进行CFA分析(Chi2 = 11.8，p <.001； CFI = 0.98； RMSEA = 0.123).

If it helps: Subscale1 encompasses 4 items; the sample includes N>300. Also, I can run a CFA analysis on these 4 items in lavaan (Chi2=11.8, p<.001; CFI=0.98; RMSEA=0.123).

推荐答案

您所指的那篇特定文章似乎是Dunn，Baguley和Brunsden撰写的《英国心理学杂志》(2014)，105、399–412©2013. .他们讨论的欧米伽系数实际上就是我和里克·辛巴格(Rick Zinbarg)所说的omega_total. (麦当劳开发了两个欧米茄系数，这导致了这种混乱.)

That particular article to which you are referring seems to be the British Journal of Psychology (2014), 105, 399–412© 2013 by Dunn, Baguley and Brunsden. The omega coefficient they discuss is actually what Rick Zinbarg and I refer to as omega_total. (McDonald developed two omega coefficients which has led to this confusion.)

在我的心理服务包中使用omega时遇到问题.心理中的omega函数旨在查找omega_hiearchical以及omega_total.因此，它尝试(默认情况下)提取三个较低级别的因子，然后依次对这些因子的结果相关性进行因子分解.但是，在您的子量表中只有4个变量，因此找不到有意义的3因子解决方案.您可以指定要查找两个因素:

You are having problems using omega in my psych package. The omega function in psych is meant to find omega_hiearchical as well as omega_total. Thus, it tries (by default) to extract three lower level factors and then, in turn, factor the resulting correlations of those factors. However, with only 4 variables in your sub scale, it can not find a meaningful 3 factor solution. You can specify that you want to find two factors:

omega(subscale1,2) 

，它将起作用.但是，omega_h对于4个项目并不是特别有意义.

and it will work. However, omega_h is not particularly meaningful for 4 items.

与样本量的建议相反，这实际上是由于项目数量所致.

Contrary to the suggestion of sample size, it is actually due to the number of items.

我认为您可能会发现使用心理帮助寻找omega_h的教程对您有帮助:

I think you might find the tutorial for finding omega_h using psych helpful:

[ http://personality-project.org/r/psych/HowTo/R_for_omega.pdf]

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