Multilevel analysis code

[Daniel Groß]

Hi,

I would like to do a multilevel analysis (two level) with lavaan. On level 2 I calculate a mediation. The only level 1 variable is UZUF. I would like to know whether my code is correct.

UZUF = Students' satisfaction with the lessons (level 1)

PAallgL = positive affect teacher (level 2)

MHL_12 = activity teacher (level 2)

cd = Students class

model1 <- '
level: 1
UZUF ~~ UZUF

level: 2
PAallgL ~ a*MHL_12
UZUF ~ b*PAallgL + c*MHL_12

indirect := a*b
direct := c
total := c + (a*b)
'

fit <- sem(model1, data = data1, cluster = "cd")

summary(fit, standardized=T, fit.measures=T, rsq = T)

Thank you for your help.

Daniel

[Jeremy Miles]

What happens when you run it?

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[Daniel Groß]

> model1 <- '
+     level: 1
+ UZUF ~~ UZUF
+ level: 2
+ PAallgL ~ a*MHL_12
+           UZUF ~ b*PAallgL + c*MHL_12
+
+ indirect := a*b
+ direct := c
+ total := c + (a*b)
+ '

> fit <- sem(model1, data = data1, cluster = "cd")
>
> summary(fit, standardized=T, fit.measures=T, rsq = T)

lavaan 0.6-2 ended normally after 26 iterations

  Optimization method                           NLMINB

  Number of free parameters                          8

  Number of observations                           945

  Number of clusters [cd]                           49

  Estimator                                         ML

  Model Fit Test Statistic                       0.000

  Degrees of freedom                                 0

Model test baseline model:

  Minimum Function Test Statistic               24.255

  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)               -879.156

  Loglikelihood unrestricted model (H1)       -879.156

  Number of free parameters                          8

  Akaike (AIC)                                1774.313

  Bayesian (BIC)                              1813.122

  Sample-size adjusted Bayesian (BIC)         1787.715

Root Mean Square Error of Approximation:

  RMSEA                                          0.000

  90 Percent Confidence Interval          0.000  0.000

  P-value RMSEA <= 0.05                             NA

Standardized Root Mean Square Residual (corr metric):

  SRMR (within covariance matrix)                0.000

  SRMR (between covariance matrix)               0.000

Parameter Estimates:

  Information                                 Observed

  Observed information based on                Hessian

  Standard Errors                             Standard

Level 1 [within]:

Intercepts:

                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all

   .UZUF              0.000                               0.000    0.000

Variances:

                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all

   .UZUF              0.314    0.015   21.174    0.000    0.314    1.000

Level 2 [cd]:

Regressions:

                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all

  PAallgL ~                                                             

    MHL_12     (a)    0.659    0.157    4.196    0.000    0.659    0.514

  UZUF ~                                                                

    PAallgL    (b)    0.270    0.113    2.394    0.017    0.270    0.393

    MHL_12     (c)    0.082    0.145    0.566    0.571    0.082    0.093

Intercepts:

                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all

   .PAallgL           1.524    0.468    3.257    0.001    1.524    3.313

   .UZUF              1.492    0.408    3.653    0.000    1.492    4.713

Variances:

                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all

   .PAallgL           0.156    0.031    4.950    0.000    0.156    0.736

   .UZUF              0.080    0.020    4.064    0.000    0.080    0.799

R-Square:

                   Estimate

    PAallgL           0.264

    UZUF              0.201

Defined Parameters:

                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all

    indirect          0.178    0.086    2.079    0.038    0.178    0.202

    direct            0.082    0.145    0.566    0.571    0.082    0.093

    total             0.260    0.132    1.977    0.048    0.260    0.295

[Terrence Jorgensen]

I would like to know whether my code is correct.

Looks fine to me.

Terrence D. Jorgensen

Assistant Professor, Methods and Statistics

Research Institute for Child Development and Education, the University of Amsterdam

http://www.uva.nl/profile/t.d.jorgensen

[Daniel Groß]

Thank you for confirming my results and analysis.

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