######################################################
## data file: PlannedBehavior.csv
######################################################
df <- read.csv("http://commres.net/wiki/_media/r/plannedbehavior.csv")
head(df)
str(df)
######################################################
# attitude
# norms
# control
# intention
# behavior
######################################################
# install.packages("lavaan")
library(lavaan)

# Specify model
specmod <- "
intention ~ attitude + norms + control 
"
# Estimate model
fitmod <- sem(specmod, data=df)

# Summarize model
summary(fitmod, fit.measures=TRUE, rsquare=TRUE)

<coce>
######################################################

## data file: PlannedBehavior.csv
######################################################
df ← read.csv(“http://commres.net/wiki/_media/r/plannedbehavior.csv”)
head(df)

attitude norms control intention behavior

1 2.31 2.31 2.03 2.50 2.62

2 4.66 4.01 3.63 3.99 3.64
3 3.85 3.56 4.20 4.35 3.83
4 4.24 2.25 2.84 1.51 2.25
5 2.91 3.31 2.40 1.45 2.00
6 2.99 2.51 2.95 2.59 2.20

str(df)

'data.frame': 199 obs. of 5 variables:
$ attitude : num 2.31 4.66 3.85 4.24 2.91 2.99 3.96 3.01 4.77 3.67 …
$ norms : num 2.31 4.01 3.56 2.25 3.31 2.51 4.65 2.98 3.09 3.63 …
$ control : num 2.03 3.63 4.2 2.84 2.4 2.95 3.77 1.9 3.83 5 …
$ intention: num 2.5 3.99 4.35 1.51 1.45 2.59 4.08 2.58 4.87 3.09 …
$ behavior : num 2.62 3.64 3.83 2.25 2 2.2 4.41 4.15 4.35 3.95 …

######################################################
# attitude
# norms
# control
# intention
# behavior
######################################################
# install.packages(“lavaan”)
library(lavaan)

This is lavaan 0.6-9
lavaan is FREE software! Please report any bugs.
Warning message:
패키지 ‘lavaan’는 R 버전 4.1.2에서 작성되었습니다

# Specify model
specmod ← “

+ intention ~ attitude + norms + control
+ ”

# Estimate model
fitmod ← sem(specmod, data=df)

# Summarize model
summary(fitmod, fit.measures=TRUE, rsquare=TRUE)

lavaan 0.6-9 ended normally after 11 iterations

Estimator                                         ML
Optimization method                           NLMINB
Number of model parameters                         4
                                                    
Number of observations                           199
                                                    

Model Test User Model:

                                                    
Test statistic                                 0.000
Degrees of freedom                                 0

Model Test Baseline Model:

Test statistic                                91.633
Degrees of freedom                                 3
P-value                                        0.000

User Model versus Baseline Model:

Comparative Fit Index (CFI)                    1.000
Tucker-Lewis Index (TLI)                       1.000

Loglikelihood and Information Criteria:

Loglikelihood user model (H0)               -219.244
Loglikelihood unrestricted model (H1)       -219.244
                                                    
Akaike (AIC)                                 446.489
Bayesian (BIC)                               459.662
Sample-size adjusted Bayesian (BIC)          446.990

Root Mean Square Error of Approximation:

RMSEA                                          0.000
90 Percent confidence interval - lower         0.000
90 Percent confidence interval - upper         0.000
P-value RMSEA <= 0.05                             NA

Standardized Root Mean Square Residual:

SRMR                                           0.000

Parameter Estimates:

Standard errors                             Standard
Information                                 Expected
Information saturated (h1) model          Structured

Regressions:

                 Estimate  Std.Err  z-value  P(>|z|)
intention ~                                         
  attitude          0.352    0.058    6.068    0.000
  norms             0.153    0.059    2.577    0.010
  control           0.275    0.058    4.740    0.000

Variances:

                 Estimate  Std.Err  z-value  P(>|z|)
 .intention         0.530    0.053    9.975    0.000

R-Square:

                 Estimate
  intention         0.369

</code>