设置 CFA

R 中的因子分析

Jennifer Brussow

Psychometrician

为何进行验证性分析？

验证性分析的优势：

明确指定变量/因子关系
事先检验既有理论
开发新量表时应优先发表此类结果

EFA 结果的路径图

用封装函数设置 CFA

EFA_syn <- structure.sem(EFA_model)

EFA_syn

      Path        Parameter Value
 [1,] "MR5->A1"   "F4A1"    NA   
 [2,] "MR5->A2"   "F4A2"    NA   
 [3,] "MR5->A3"   "F4A3"    NA   
 [4,] "MR5->A4"   "F4A4"    NA   
 [5,] "MR5->A5"   "F4A5"    NA   
 [6,] "MR3->C1"   "F3C1"    NA   
 [7,] "MR3->C2"   "F3C2"    NA   
 [8,] "MR3->C3"   "F3C3"    NA   
 [9,] "MR3->C4"   "F3C4"    NA   
[10,] "MR3->C5"   "F3C5"    NA   
[11,] "MR1->E1"   "F2E1"    NA   
...

封装函数生成的语法

EFA_syn

      Path        Parameter Value
 [1,] "MR5->A1"   "F4A1"    NA

因子4（F4）= EFA 中的因子 MR5
被试的因子水平预测题目反应
封装函数会自动命名参数
NA 值 = 随机选取的起始值

从理论创建 CFA 语法

# Set up syntax specifying which items load onto each factor
theory_syn_eq <- "
AGE: A1, A2, A3, A4, A5     #Agreeableness
CON: C1, C2, C3, C4, C5     #Conscientiousness
EXT: E1, E2, E3, E4, E5     #Extraversion
NEU: N1, N2, N3, N4, N5     #Neuroticism
OPE: O1, O2, O3, O4, O5     #Openness
"

使用简短、易记的因子名
因子名后接冒号
题目用逗号分隔列表

theory_syn <- cfa(text = theory_syn_eq, reference.indicators = FALSE)

让我们写点语法！

R 中的因子分析