【发布时间】：2019-04-05 01:11:42
【问题描述】：
我正在尝试在探索性因素分析后计算 omega 估计值，以估计我找到的组件的可靠性。使用 psych 包中的 omega() 函数，我得到以下输出：
Alpha: 0.8
G.6: 0.86
Omega Hierarchical: 0.37
Omega H asymptotic: 0.43
Omega Total 0.86
Schmid Leiman Factor loadings greater than
0.2
g F1* F2* F3* h2 u2 p2
EMS1 0.30 0.71 0.59 0.41 0.15
EMS3 -0.21 0.64 0.53 0.47 0.05
EMS4 0.62 0.41 0.59 0.04
EMS7 0.34 0.62 0.50 0.50 0.23
EMS8 0.36 0.42 0.32 0.68 0.40
EMS9 0.57 0.33 0.67 0.00
EMS10 0.39 0.20 0.80 0.11
EMS11 0.72 0.51 0.49 0.02
EMS12 0.68 0.46 0.54 0.02
EMS15 0.54 -0.24 0.41 0.59 0.02
EMS16 0.22 0.77 0.63 0.37 0.08
EMS19 0.65 0.52 0.48 0.01
EMS20 0.27 0.53 0.36 0.64 0.21
EMS21 0.62 0.40 0.60 0.04
EMS23 0.63 0.42 0.58 0.07
EMS24 0.68 0.45 0.55 1.02
EMS25 0.73 0.56 0.44 0.95
EMS27 0.45 0.20 0.25 0.75 0.83
EMS28 0.78 0.59 0.41 1.02
EMS34 0.26 0.31 0.48 0.34 0.66 0.20
With eigenvalues of:
g F1* F2* F3*
2.5 3.4 2.9 0.0
general/max 0.73 max/min = Inf
mean percent general = 0.27 with sd = 0.36 and cv of 1.33
Explained Common Variance of the general factor = 0.28
The degrees of freedom are 133 and the fit is 0.8
The number of observations was 601 with Chi Square = 471.81 with prob < 1.9e-39
The root mean square of the residuals is 0.04
The df corrected root mean square of the residuals is 0.05
RMSEA index = 0.066 and the 10 % confidence intervals are 0.059 0.072
BIC = -379.21
Compare this with the adequacy of just a general factor and no group factors
The degrees of freedom for just the general factor are 170 and the fit is 5.4
The number of observations was 601 with Chi Square = 3195.63 with prob < 0
The root mean square of the residuals is 0.22
The df corrected root mean square of the residuals is 0.24
RMSEA index = 0.173 and the 10 % confidence intervals are 0.167 0.177
BIC = 2107.87
Measures of factor score adequacy
g F1* F2* F3*
Correlation of scores with factors 0.9 0.94 0.93 0
Multiple R square of scores with factors 0.8 0.89 0.86 0
Minimum correlation of factor score estimates 0.6 0.78 0.73 -1
Total, General and Subset omega for each subset
g F1* F2* F3*
Omega total for total scores and subscales 0.86 0.82 0.85 NA
Omega general for total scores and subscales 0.37 0.08 0.34 NA
Omega group for total scores and subscales 0.58 0.75 0.51 NA
Warning messages:
1: In fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, :
A loading greater than abs(1) was detected. Examine the loadings carefully.
2: In fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, :
An ultra-Heywood case was detected. Examine the results carefully
3: In cov2cor(t(w) %*% r %*% w) :
diag(.) had 0 or NA entries; non-finite result is doubtful
这就是我调用函数的方式：
omega(df[,items],nfactors=3)
在搜索指导后，我找不到为什么没有为第三个因素计算欧米茄。我不确定这是否与警告消息之一有关：
Warning messages:
1: In fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, :
A loading greater than abs(1) was detected. Examine the loadings carefully.
2: In fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, :
An ultra-Heywood case was detected. Examine the results carefully
3: In cov2cor(t(w) %*% r %*% w) :
diag(.) had 0 or NA entries; non-finite result is doubtful
【问题讨论】：
标签： r reliability psych factor-analysis