Time series data

[Paul Joseph]

Paul Joseph

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20:14

to lav...@googlegroups.com

I greet you well, my question might be trivia. Can I use time series data for SEM especially path analysis. If yes, do I need to text for stationarity? Really need your help

On Wed, Sep 14, 2022, 12:09 PM 'mirijam.l...@arcor.de' via lavaan <lav...@googlegroups.com> wrote:

Hello,

I am trying to calculate a structural equation model. However, I get the following error messages:

"Warning messages:
  1: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats,  :
                         lavaan WARNING:
                         Could not compute standard errors! The information matrix could not be

                         inverted. This may be a symptom that the model is not identified.
                       2: In lav_test_satorra_bentler(lavobject = NULL, lavsamplestats =        lavsamplestats,  : lavaan WARNING: could not invert information matrix needed for robust test statistic"

I don't know if it is important, but all items with AW represent proportional scores.

My model looks like follows:

Strukturmodell <- '
  ##Faktor loading
  negaff =~ PI01_01 + PI04_19 + PI01_07 + PI03_25 + PI04_13 + PI03_31
  versch =~ PI01_04 + PI04_22 + PI01_10 + PI03_28 + PI04_16 + PI03_34
  antago =~ PI01_02 + PI04_20 + PI01_08 + PI03_26 + PI04_14 + PI03_32
  disin =~ PI01_03 + PI04_21 + PI01_09 + PI03_27 + PI04_15 + PI03_33
  anan =~ PI01_06 + PI04_18  + PI01_12 + PI04_24 + PI03_30 + PI03_36
  psycho =~ PI01_05 + PI04_23 + PI01_11 + PI03_29 + PI04_17 + PI03_35
  Reif =~ AW_D21_affiliation + AW_D22_altruisum + AW_D23_anticipation + AW_D24_humor +
          AW_D25_self_assertion + AW_D26_self_observation + AW_D27_sublimation +
          AW_D28_suppression
  HemmVer =~ AW_D7_autistic_fantasy + AW_D10_denial + AW_L5_neurotic + AW_D20_isolation_affects +
        AW_D19_intellectualization +  AW_D18_undoing + AW_D17_displacement +
        AW_D16_reaction_formation + AW_D15_dissociation1 + AW_D15_dissociation2 + AW_D14_repression
  Unreif =~ AW_D8_projection + AW_D9_rationalization +  AW_D12_idealization + AW_D13_devaluation +
        AW_D4_splitting_object_image + AW_D5_splitting_self_image + AW_D6_projective_identification +
        AW_D1_acting_out + AW_D2_help_rejecting_complaining + AW_D3_passive_aggression1 +
        AW_D3_passive_aggression2
 ##covariances
   Reif ~~ HemmVer
   Reif ~~ Unreif
   Reif ~~ negaff
   Reif ~~ versch
   Reif ~~ antago
   Reif ~~ disin
   Reif ~~ anan
   Reif ~~psycho
   HemmVer ~~ Unreif
   HemmVer ~~ negaff
   HemmVer ~~ versch
   HemmVer ~~ antago
   HemmVer ~~ disin
   HemmVer ~~ anan
   HemmVer ~~psycho
   Unreif ~~ Unreif
   Unreif ~~ negaff
   Unreif ~~ versch
   Unreif ~~ antago
   Unreif ~~ disin
   Unreif ~~ anan
   Unreif ~~ psycho
   AW_odf ~~ HemmVer
   AW_odf ~~ Unreif
   AW_odf ~~ negaff
   AW_odf ~~ versch
   AW_odf ~~ antago
   AW_odf ~~ disin
   AW_odf ~~ anan
   AW_odf ~~ psycho
   negaff ~~  versch
   negaff ~~  antago
   negaff ~~  disin
   negaff ~~  anan
   negaff ~~  psycho
   versch ~~ antago
   versch ~~ disin
   versch ~~ anan
   versch ~~ psycho
   antago ~~ disin
   antago ~~ anan
   antago ~~ psycho
   disin ~~ anan
   disin ~~ psycho
   anan ~~ psycho
'
model_fit2 <- sem(data=df_re, model = strukturmodell, meanstructure = TRUE, estimator = "WLSMV", ordered = TRUE)

summary(model_fit2, fit.measures = TRUE, rsquare = TRUE)

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