r lavaan中的CFA：用命令fitmeasures（）的拟合度措施的不同结果与摘要
我正在计算具有以下模型的验证性因素分析：
library(lavaan)
CFA <- "
A =~ BK01_01_z+BK03_01_z+ BK03_03_z+ BK03_04_z+BK03_05_z+ BK03_07_z+ BK03_08_z+ BK05_01_z+BK05_02_z+ BK05_03_z+ BK05_04_z
B=~GK04_01_z + GK04_02_z+ GK04_03_z+GK04_04_z+GK04_05_z
C =~ GS09_01_z+GS09_02_z
Z=~A+B+C
"
fit <- cfa(CFA, data = df_clean, estimator ="WLSMV",
ordered = c("GS09_01_z",
"GS09_02_z"))
如您所见，有两个序数（二进制）变量应该加载到一个因素上。数据是非正常的也可能重要的。
当我现在查看结果时，我会为不同的命令获得不同的结果。
与：
summary(fit, fit.measures=TRUE)
我得到RMSEA = 0.069; CFI = 0.663; TLI = 0.609
with：
fitmeasures(fit, c("cfi","rmsea","srmr","tli"))
这些是结果：
CFI = 0.964; RMSEA = 0.041; SRMR = 0.060; TLI = 0.958
我试图搜索我的问题，但是我找不到为什么？也许有人遇到了类似的问题？
I'm calculating a confirmatory factor analysis with the following model:
library(lavaan)
CFA <- "
A =~ BK01_01_z+BK03_01_z+ BK03_03_z+ BK03_04_z+BK03_05_z+ BK03_07_z+ BK03_08_z+ BK05_01_z+BK05_02_z+ BK05_03_z+ BK05_04_z
B=~GK04_01_z + GK04_02_z+ GK04_03_z+GK04_04_z+GK04_05_z
C =~ GS09_01_z+GS09_02_z
Z=~A+B+C
"
fit <- cfa(CFA, data = df_clean, estimator ="WLSMV",
ordered = c("GS09_01_z",
"GS09_02_z"))
As you can see, there are two ordinal (binary) variables that are supposed to load onto one factor. It may also be important that the data is non-normal.
When I'm looking at the results now, I'm getting different results for different commands.
With:
summary(fit, fit.measures=TRUE)
I'm getting RMSEA = 0.069; CFI = 0.663; TLI = 0.609
with:
fitmeasures(fit, c("cfi","rmsea","srmr","tli"))
these are the results:
cfi = 0.964; rmsea = 0.041; srmr = 0.060; tli = 0.958
I've tried to search for my problem, but I couldn't find out why? Maybe someone has encountered a similar issue?
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您可能会获得标准结果与强大的结果。
例如，当使用替代估计器（例如MLM而不是ML）时，您将获得标准和强大的结果。
我自己尝试过，并且使用 fitmeasures 我得到了标准结果，而不是强大的结果。检查您是否也是如此。
J.
You might be getting Standard results vs Robust results.
For instance, when using alternative estimators such as MLM instead of ML, you get both a Standard and a Robust result.
I have tried it myself and by using fitmeasures I get the standard results, not the robust. Check if that is the case for you as well.
J.