Read data

Date import is not included in the HTML-file.

Recoding

names(data)
##  [1] "X"                  "TIME"               "TIME.1"            
##  [4] "AFF_WORRY"          "WORRY_RECESSION"    "WORRY_EMPLOYMENT"  
##  [7] "POL_HOME"           "POL_NONE"           "POL_MASK"          
## [10] "POL_FREEDOM"        "POL_SCHOOL"         "TRUST_SCIENCE"     
## [13] "TRUST_GOVERN"       "AGE"                "GENDER"            
## [16] "EDUCATION"          "CHRONIC"            "INFECTED"          
## [19] "INFEC_P_NOT_TESTED" "INFEC_P_CONFIRMED"  "INFEC_P_RECOVERED" 
## [22] "INFEC_P_DEAD"       "INFEC_P_NONE"       "INFEC_P_DONTKNOW"  
## [25] "INFECTED_PEERS"
#remove row names

data <- data[,-1]


#remove measurement occasions 1 to 3

data <- data[data$TIME > 3,]


#measurement occasion onset at 0

data$TIME <- data$TIME - 4


#AFF_Worry = Health Worry -> low value = more worry
#WORRY_RECESSION = Economic Worry -> high value = more worry 
#WORRY_EMPLOYMENT = Economic Worry -> high value = more worry 
#POL_NONE = social Distancing Acceptance -> high value = non-acceptance
#POL_HOME = social Distancing Acceptance -> high value = acceptance


#Zero values are missing values according to the documentation on the following variables
data$POL_NONE[data$POL_NONE == 0] <- NA
data$WORRY_EMPLOYMENT[data$WORRY_EMPLOYMENT == 0] <- NA


#recoding for easier interpretation of acceptance and worry variables


data$POL_NONE <- 8 - data$POL_NONE
data$AFF_WORRY <- 8 - data$AFF_WORRY

Descriptive Statistics and Plot of Mean Values and Confidence Intervals

library(psych)




descriptives <- describeBy(data[,c("AFF_WORRY","WORRY_RECESSION","WORRY_EMPLOYMENT","POL_HOME","POL_NONE")], group = data$TIME, mat = T, na.rm =T)

print(descriptives)
##                    item group1 vars    n     mean       sd median  trimmed
## AFF_WORRY1            1      0    1 1113 5.336029 1.501895    6.0 5.518519
## AFF_WORRY2            2      1    1 1028 5.326848 1.567122    6.0 5.529126
## AFF_WORRY3            3      2    1 1022 4.965753 1.662457    5.0 5.135697
## AFF_WORRY4            4      3    1 1032 4.886628 1.612793    5.0 5.029056
## AFF_WORRY5            5      4    1 1006 4.787276 1.648903    5.0 4.914392
## AFF_WORRY6            6      5    1 1018 4.793713 1.662437    5.0 4.922794
## AFF_WORRY7            7      6    1 1007 4.571003 1.615246    5.0 4.660471
## AFF_WORRY8            8      7    1 1013 4.562685 1.664934    5.0 4.646116
## AFF_WORRY9            9      8    1  972 4.514403 1.705023    5.0 4.592545
## AFF_WORRY10          10      9    1  925 4.517838 1.684201    5.0 4.592443
## AFF_WORRY11          11     10    1  955 4.637696 1.650438    5.0 4.732026
## AFF_WORRY12          12     11    1  993 4.435045 1.684267    5.0 4.513208
## AFF_WORRY13          13     12    1 1010 4.526733 1.748220    5.0 4.622525
## WORRY_RECESSION1     14      0    2 1113 5.424978 1.475211    6.0 5.613917
## WORRY_RECESSION2     15      1    2 1028 5.404669 1.475426    6.0 5.586165
## WORRY_RECESSION3     16      2    2 1022 5.348337 1.516476    6.0 5.536675
## WORRY_RECESSION4     17      3    2 1032 5.266473 1.513509    5.5 5.444310
## WORRY_RECESSION5     18      4    2 1006 5.182903 1.435286    5.0 5.322581
## WORRY_RECESSION6     19      5    2 1018 5.244597 1.519488    5.0 5.411765
## WORRY_RECESSION7     20      6    2 1007 5.131082 1.574112    5.0 5.289963
## WORRY_RECESSION8     21      7    2 1013 5.136229 1.546768    5.0 5.290999
## WORRY_RECESSION9     22      8    2  972 5.170782 1.499622    5.0 5.322622
## WORRY_RECESSION10    23      9    2  925 5.166486 1.484538    5.0 5.323887
## WORRY_RECESSION11    24     10    2  955 5.139267 1.510438    5.0 5.286275
## WORRY_RECESSION12    25     11    2  993 5.053374 1.587556    5.0 5.208805
## WORRY_RECESSION13    26     12    2 1010 5.022772 1.605391    5.0 5.169554
## WORRY_EMPLOYMENT1    27      0    3 1026 2.820663 2.069759    2.0 2.542579
## WORRY_EMPLOYMENT2    28      1    3  988 2.926113 2.061763    2.0 2.672980
## WORRY_EMPLOYMENT3    29      2    3  990 2.880808 2.044978    2.0 2.622475
## WORRY_EMPLOYMENT4    30      3    3 1001 2.749251 2.047453    2.0 2.474407
## WORRY_EMPLOYMENT5    31      4    3  968 2.667355 1.902724    2.0 2.394330
## WORRY_EMPLOYMENT6    32      5    3  986 2.771805 1.987692    2.0 2.502532
## WORRY_EMPLOYMENT7    33      6    3  963 2.716511 1.987907    2.0 2.433204
## WORRY_EMPLOYMENT8    34      7    3  983 2.741607 2.032919    2.0 2.463787
## WORRY_EMPLOYMENT9    35      8    3  922 2.785249 2.000402    2.0 2.516260
## WORRY_EMPLOYMENT10   36      9    3  881 2.866061 2.004596    2.0 2.617021
## WORRY_EMPLOYMENT11   37     10    3  918 2.875817 1.980781    2.0 2.638587
## WORRY_EMPLOYMENT12   38     11    3  946 2.823467 2.066492    2.0 2.538259
## WORRY_EMPLOYMENT13   39     12    3  948 2.601266 2.029164    1.0 2.276316
## POL_HOME1            40      0    4 1113 5.087152 1.917954    6.0 5.332211
## POL_HOME2            41      1    4 1028 4.801556 1.952084    5.0 4.991505
## POL_HOME3            42      2    4 1022 4.362035 2.020475    4.0 4.452323
## POL_HOME4            43      3    4 1032 4.187016 2.037216    4.0 4.233656
## POL_HOME5            44      4    4 1006 3.965209 2.017284    4.0 3.956576
## POL_HOME6            45      5    4 1018 3.711198 2.093933    4.0 3.639706
## POL_HOME7            46      6    4 1007 3.393247 2.009984    3.0 3.245353
## POL_HOME8            47      7    4 1013 3.317868 1.995956    3.0 3.151665
## POL_HOME9            48      8    4  972 3.361111 1.981441    3.0 3.206941
## POL_HOME10           49      9    4  925 3.060541 1.894572    3.0 2.863698
## POL_HOME11           50     10    4  955 3.170681 1.989801    3.0 2.984314
## POL_HOME12           51     11    4  993 3.036254 1.958927    3.0 2.826415
## POL_HOME13           52     12    4 1010 2.724752 1.888750    2.0 2.454208
## POL_NONE1            53      0    5 1113 5.666667 1.716753    6.0 5.967452
## POL_NONE2            54      1    5 1028 5.500000 1.780288    6.0 5.788835
## POL_NONE3            55      2    5 1019 5.210991 1.899136    6.0 5.467564
## POL_NONE4            56      3    5 1029 5.222546 1.912491    6.0 5.492121
## POL_NONE5            57      4    5 1001 5.226773 1.874973    6.0 5.495630
## POL_NONE6            58      5    5 1018 4.837917 2.003011    5.0 5.039216
## POL_NONE7            59      6    5 1007 4.719960 1.998267    5.0 4.895911
## POL_NONE8            60      7    5 1013 4.820336 2.046713    5.0 5.024661
## POL_NONE9            61      8    5  972 4.718107 1.987282    5.0 4.897172
## POL_NONE10           62      9    5  925 4.880000 1.938357    5.0 5.091768
## POL_NONE11           63     10    5  955 4.832461 1.955263    5.0 5.019608
## POL_NONE12           64     11    5  993 4.797583 2.021636    5.0 4.996226
## POL_NONE13           65     12    5 1010 5.063366 1.960953    6.0 5.318069
##                       mad min max range        skew    kurtosis         se
## AFF_WORRY1         1.4826   1   7     6 -0.87222553  0.27401188 0.04501860
## AFF_WORRY2         1.4826   1   7     6 -0.88438864  0.13123010 0.04887718
## AFF_WORRY3         1.4826   1   7     6 -0.67147310 -0.33511658 0.05200258
## AFF_WORRY4         1.4826   1   7     6 -0.60596559 -0.35982154 0.05020405
## AFF_WORRY5         1.4826   1   7     6 -0.52339019 -0.50513722 0.05198718
## AFF_WORRY6         1.4826   1   7     6 -0.51904111 -0.52153032 0.05210403
## AFF_WORRY7         1.4826   1   7     6 -0.46057949 -0.40851962 0.05090072
## AFF_WORRY8         1.4826   1   7     6 -0.44060543 -0.61152526 0.05231091
## AFF_WORRY9         1.4826   1   7     6 -0.35461050 -0.74381950 0.05468865
## AFF_WORRY10        1.4826   1   7     6 -0.35599443 -0.68517010 0.05537618
## AFF_WORRY11        1.4826   1   7     6 -0.45746773 -0.55481769 0.05340691
## AFF_WORRY12        1.4826   1   7     6 -0.39551707 -0.59721805 0.05344859
## AFF_WORRY13        1.4826   1   7     6 -0.41260017 -0.71300374 0.05500920
## WORRY_RECESSION1   1.4826   1   7     6 -0.88596955  0.34491731 0.04421875
## WORRY_RECESSION2   1.4826   1   7     6 -0.84204469  0.27868034 0.04601727
## WORRY_RECESSION3   1.4826   1   7     6 -0.86159736  0.29888142 0.04743621
## WORRY_RECESSION4   2.2239   1   7     6 -0.85203253  0.34037547 0.04711346
## WORRY_RECESSION5   1.4826   1   7     6 -0.71143848  0.31850390 0.04525217
## WORRY_RECESSION6   1.4826   1   7     6 -0.76801844  0.17726087 0.04762374
## WORRY_RECESSION7   1.4826   1   7     6 -0.67177325 -0.17592721 0.04960449
## WORRY_RECESSION8   1.4826   1   7     6 -0.70959246  0.03109443 0.04859822
## WORRY_RECESSION9   1.4826   1   7     6 -0.72776342  0.20329000 0.04810041
## WORRY_RECESSION10  1.4826   1   7     6 -0.82388576  0.37326470 0.04881132
## WORRY_RECESSION11  1.4826   1   7     6 -0.70525634  0.13484209 0.04887663
## WORRY_RECESSION12  1.4826   1   7     6 -0.68546914 -0.04837782 0.05037957
## WORRY_RECESSION13  1.4826   1   7     6 -0.64578141 -0.16570343 0.05051498
## WORRY_EMPLOYMENT1  1.4826   1   7     6  0.73938570 -0.82352960 0.06461691
## WORRY_EMPLOYMENT2  1.4826   1   7     6  0.66166103 -0.90574308 0.06559343
## WORRY_EMPLOYMENT3  1.4826   1   7     6  0.69032314 -0.87383723 0.06499368
## WORRY_EMPLOYMENT4  1.4826   1   7     6  0.75937280 -0.84108463 0.06471379
## WORRY_EMPLOYMENT5  1.4826   1   7     6  0.79502013 -0.61202515 0.06115588
## WORRY_EMPLOYMENT6  1.4826   1   7     6  0.75431755 -0.72780610 0.06330101
## WORRY_EMPLOYMENT7  1.4826   1   7     6  0.82009279 -0.64668582 0.06405941
## WORRY_EMPLOYMENT8  1.4826   1   7     6  0.77868071 -0.78522234 0.06484003
## WORRY_EMPLOYMENT9  1.4826   1   7     6  0.73349352 -0.78022293 0.06587972
## WORRY_EMPLOYMENT10 1.4826   1   7     6  0.66665059 -0.87053714 0.06753654
## WORRY_EMPLOYMENT11 1.4826   1   7     6  0.62965172 -0.89475636 0.06537551
## WORRY_EMPLOYMENT12 1.4826   1   7     6  0.77566580 -0.76100376 0.06718746
## WORRY_EMPLOYMENT13 0.0000   1   7     6  0.95704814 -0.46739491 0.06590418
## POL_HOME1          1.4826   1   7     6 -0.76806175 -0.54255839 0.05748978
## POL_HOME2          2.9652   1   7     6 -0.55422463 -0.84400424 0.06088382
## POL_HOME3          2.9652   1   7     6 -0.26898229 -1.11142194 0.06320159
## POL_HOME4          2.9652   1   7     6 -0.15339108 -1.20153650 0.06341576
## POL_HOME5          2.9652   1   7     6 -0.04554016 -1.18045807 0.06360160
## POL_HOME6          2.9652   1   7     6  0.08601338 -1.31591806 0.06562795
## POL_HOME7          2.9652   1   7     6  0.31012490 -1.13460979 0.06333996
## POL_HOME8          2.9652   1   7     6  0.39216603 -1.04425598 0.06271136
## POL_HOME9          2.9652   1   7     6  0.33528834 -1.05925369 0.06355474
## POL_HOME10         2.9652   1   7     6  0.53501358 -0.81229361 0.06229316
## POL_HOME11         2.9652   1   7     6  0.48967552 -1.01500773 0.06438844
## POL_HOME12         2.9652   1   7     6  0.56023866 -0.90052955 0.06216466
## POL_HOME13         1.4826   1   7     6  0.83278369 -0.47976545 0.05943109
## POL_NONE1          1.4826   1   7     6 -1.18663308  0.36462351 0.05145888
## POL_NONE2          1.4826   1   7     6 -1.05973987  0.06310681 0.05552566
## POL_NONE3          1.4826   1   7     6 -0.79352291 -0.55991654 0.05949341
## POL_NONE4          1.4826   1   7     6 -0.87366736 -0.42432255 0.05961998
## POL_NONE5          1.4826   1   7     6 -0.86818218 -0.36196873 0.05926222
## POL_NONE6          2.9652   1   7     6 -0.54427840 -0.95094857 0.06277827
## POL_NONE7          2.9652   1   7     6 -0.45763135 -1.02838306 0.06297073
## POL_NONE8          2.9652   1   7     6 -0.55371994 -0.99068917 0.06430610
## POL_NONE9          2.9652   1   7     6 -0.50615435 -0.93698550 0.06374211
## POL_NONE10         2.9652   1   7     6 -0.61983379 -0.75183530 0.06373280
## POL_NONE11         2.9652   1   7     6 -0.53488241 -0.91631872 0.06327083
## POL_NONE12         2.9652   1   7     6 -0.56806846 -0.90875229 0.06415466
## POL_NONE13         1.4826   1   7     6 -0.76442813 -0.59682671 0.06170303
descriptives$vars <-  factor(descriptives$vars)


levels(descriptives$vars) <- c("AFF_WORRY (recoded)","WORRY_RECESSION","WORRY_EMPLOYMENT","POL_HOME","POL_NONE (recoded)")

descriptives$TIME <- as.numeric(descriptives$group1)


library(ggplot2)
## 
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
## 
##     %+%, alpha
theme_set(theme_minimal())



descriptives$lower <- descriptives$mean - descriptives$se * qnorm(.025)
descriptives$upper <- descriptives$mean + descriptives$se * qnorm(.025)

pd <- position_dodge(0.2)

# Visualization
 ggplot(descriptives, aes(x = TIME, y = mean, group=vars)) + 
  geom_line(aes(color = vars),position=pd) + 
  scale_color_manual(values = c("blue","darkred", "red", "green", "darkgreen"))  +
    geom_point(position=pd, size=1) +
  geom_errorbar(aes(ymin=lower, ymax=upper), width=.1,position=pd) +  
   scale_x_continuous(name = element_blank(),
    breaks=c(0:9),
    labels=c("24.03", "31.03.", "07.04.", "14.04.", "21.04.", "28.04.","05.05.","12.05.","19.05.","26.05.")
    ) + 
   scale_y_continuous(name = element_blank(), limits = c(1,7), breaks = 1:7)

Multigroup Analysis

H1 - H3

Model with non-standardized variables

Time is treated as group variable to test H1, H2, H3. The tested model is completely unrestricted (within each measurement occasion). Every restriction of the model (across measurement occasions) results in significant misfit. Thus, a saturated baseline model is chosen to test H1 to H3.

library(lavaan)
## This is lavaan 0.6-6
## lavaan is BETA software! Please report any bugs.
## 
## Attaching package: 'lavaan'
## The following object is masked from 'package:psych':
## 
##     cor2cov
m <- "


WORRY_RECESSION ~~ AFF_WORRY
WORRY_RECESSION ~~ POL_NONE
POL_NONE ~~ AFF_WORRY
WORRY_RECESSION ~~ POL_HOME
POL_HOME ~~ AFF_WORRY
WORRY_EMPLOYMENT ~~ AFF_WORRY
WORRY_EMPLOYMENT ~~ WORRY_RECESSION
WORRY_EMPLOYMENT ~~ POL_NONE
WORRY_EMPLOYMENT ~~ POL_HOME
POL_HOME ~~ POL_NONE



"



fit <- sem(m, data = data, std.lv = F, fixed.x = F, group = "TIME")

summary(fit, standardized = T)
## lavaan 0.6-6 ended normally after 169 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of free parameters                        260
##                                                       
##   Number of observations per group:               Used       Total
##     0                                             1026        1113
##     1                                              988        1028
##     2                                              987        1022
##     3                                              998        1032
##     4                                              963        1006
##     5                                              986        1018
##     6                                              963        1007
##     7                                              983        1013
##     8                                              922         972
##     9                                              881         925
##     10                                             918         955
##     11                                             946         993
##     12                                             948        1010
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
##   Test statistic for each group:
##     0                                            0.000
##     1                                            0.000
##     2                                            0.000
##     3                                            0.000
##     4                                            0.000
##     5                                            0.000
##     6                                            0.000
##     7                                            0.000
##     8                                            0.000
##     9                                            0.000
##     10                                           0.000
##     11                                           0.000
##     12                                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## 
## Group 1 [0]:
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   WORRY_RECESSION ~~                                                       
##     AFF_WORRY            0.572    0.072    7.959    0.000    0.572    0.257
##     POL_NONE             0.222    0.080    2.754    0.006    0.222    0.086
##   POL_NONE ~~                                                              
##     AFF_WORRY            1.052    0.088   11.979    0.000    1.052    0.403
##   WORRY_RECESSION ~~                                                       
##     POL_HOME             0.318    0.089    3.565    0.000    0.318    0.112
##   POL_HOME ~~                                                              
##     AFF_WORRY            0.632    0.092    6.854    0.000    0.632    0.219
##   WORRY_EMPLOYMENT ~~                                                      
##     AFF_WORRY            0.133    0.097    1.368    0.171    0.133    0.043
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.509    0.097    5.252    0.000    0.509    0.166
##   POL_NONE ~~                                                              
##     WORRY_EMPLOYME      -0.642    0.114   -5.647    0.000   -0.642   -0.179
##   POL_HOME ~~                                                              
##     WORRY_EMPLOYME      -0.226    0.124   -1.823    0.068   -0.226   -0.057
##   POL_NONE ~~                                                              
##     POL_HOME             1.314    0.111   11.783    0.000    1.314    0.396
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    5.390    0.046  116.529    0.000    5.390    3.638
##     POL_NONE          5.622    0.054  103.944    0.000    5.622    3.245
##     POL_HOME          5.082    0.060   84.928    0.000    5.082    2.651
##     WORRY_EMPLOYME    2.821    0.065   43.673    0.000    2.821    1.363
##     AFF_WORRY         5.302    0.047  112.788    0.000    5.302    3.521
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    2.195    0.097   22.650    0.000    2.195    1.000
##     POL_NONE          3.001    0.133   22.650    0.000    3.001    1.000
##     POL_HOME          3.674    0.162   22.650    0.000    3.674    1.000
##     WORRY_EMPLOYME    4.280    0.189   22.650    0.000    4.280    1.000
##     AFF_WORRY         2.267    0.100   22.650    0.000    2.267    1.000
## 
## 
## Group 2 [1]:
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   WORRY_RECESSION ~~                                                       
##     AFF_WORRY            0.475    0.076    6.275    0.000    0.475    0.204
##     POL_NONE             0.037    0.084    0.434    0.664    0.037    0.014
##   POL_NONE ~~                                                              
##     AFF_WORRY            1.223    0.098   12.481    0.000    1.223    0.433
##   WORRY_RECESSION ~~                                                       
##     POL_HOME             0.185    0.092    2.025    0.043    0.185    0.065
##   POL_HOME ~~                                                              
##     AFF_WORRY            0.931    0.102    9.127    0.000    0.931    0.303
##   WORRY_EMPLOYMENT ~~                                                      
##     AFF_WORRY            0.121    0.104    1.173    0.241    0.121    0.037
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.615    0.099    6.226    0.000    0.615    0.202
##   POL_NONE ~~                                                              
##     WORRY_EMPLOYME      -0.506    0.119   -4.269    0.000   -0.506   -0.137
##   POL_HOME ~~                                                              
##     WORRY_EMPLOYME       0.066    0.128    0.514    0.607    0.066    0.016
##   POL_NONE ~~                                                              
##     POL_HOME             1.314    0.118   11.090    0.000    1.314    0.377
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    5.403    0.047  115.024    0.000    5.403    3.659
##     POL_NONE          5.465    0.057   95.866    0.000    5.465    3.050
##     POL_HOME          4.819    0.062   77.886    0.000    4.819    2.478
##     WORRY_EMPLOYME    2.926    0.066   44.632    0.000    2.926    1.420
##     AFF_WORRY         5.302    0.050  105.606    0.000    5.302    3.360
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    2.180    0.098   22.226    0.000    2.180    1.000
##     POL_NONE          3.210    0.144   22.226    0.000    3.210    1.000
##     POL_HOME          3.782    0.170   22.226    0.000    3.782    1.000
##     WORRY_EMPLOYME    4.247    0.191   22.226    0.000    4.247    1.000
##     AFF_WORRY         2.490    0.112   22.226    0.000    2.490    1.000
## 
## 
## Group 3 [2]:
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   WORRY_RECESSION ~~                                                       
##     AFF_WORRY            0.448    0.082    5.491    0.000    0.448    0.178
##     POL_NONE            -0.058    0.092   -0.627    0.530   -0.058   -0.020
##   POL_NONE ~~                                                              
##     AFF_WORRY            1.289    0.109   11.860    0.000    1.289    0.408
##   WORRY_RECESSION ~~                                                       
##     POL_HOME            -0.002    0.098   -0.016    0.987   -0.002   -0.001
##   POL_HOME ~~                                                              
##     AFF_WORRY            0.992    0.112    8.875    0.000    0.992    0.295
##   WORRY_EMPLOYMENT ~~                                                      
##     AFF_WORRY           -0.049    0.108   -0.458    0.647   -0.049   -0.015
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.680    0.101    6.754    0.000    0.680    0.220
##   POL_NONE ~~                                                              
##     WORRY_EMPLOYME      -0.654    0.125   -5.235    0.000   -0.654   -0.169
##   POL_HOME ~~                                                              
##     WORRY_EMPLOYME       0.019    0.131    0.148    0.882    0.019    0.005
##   POL_NONE ~~                                                              
##     POL_HOME             1.507    0.131   11.468    0.000    1.507    0.392
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    5.348    0.048  110.832    0.000    5.348    3.528
##     POL_NONE          5.198    0.060   85.973    0.000    5.198    2.737
##     POL_HOME          4.349    0.064   67.525    0.000    4.349    2.149
##     WORRY_EMPLOYME    2.874    0.065   44.315    0.000    2.874    1.411
##     AFF_WORRY         4.961    0.053   93.612    0.000    4.961    2.980
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    2.298    0.103   22.215    0.000    2.298    1.000
##     POL_NONE          3.607    0.162   22.215    0.000    3.607    1.000
##     POL_HOME          4.093    0.184   22.215    0.000    4.093    1.000
##     WORRY_EMPLOYME    4.152    0.187   22.215    0.000    4.152    1.000
##     AFF_WORRY         2.773    0.125   22.215    0.000    2.773    1.000
## 
## 
## Group 4 [3]:
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   WORRY_RECESSION ~~                                                       
##     AFF_WORRY            0.436    0.079    5.556    0.000    0.436    0.179
##     POL_NONE            -0.173    0.092   -1.889    0.059   -0.173   -0.060
##   POL_NONE ~~                                                              
##     AFF_WORRY            1.206    0.105   11.484    0.000    1.206    0.390
##   WORRY_RECESSION ~~                                                       
##     POL_HOME            -0.221    0.098   -2.264    0.024   -0.221   -0.072
##   POL_HOME ~~                                                              
##     AFF_WORRY            0.697    0.106    6.559    0.000    0.697    0.212
##   WORRY_EMPLOYMENT ~~                                                      
##     AFF_WORRY            0.214    0.105    2.044    0.041    0.214    0.065
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.585    0.100    5.878    0.000    0.585    0.189
##   POL_NONE ~~                                                              
##     WORRY_EMPLOYME      -0.830    0.127   -6.561    0.000   -0.830   -0.212
##   POL_HOME ~~                                                              
##     WORRY_EMPLOYME       0.483    0.132    3.650    0.000    0.483    0.116
##   POL_NONE ~~                                                              
##     POL_HOME             1.237    0.129    9.577    0.000    1.237    0.318
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    5.265    0.048  109.981    0.000    5.265    3.481
##     POL_NONE          5.215    0.061   86.146    0.000    5.215    2.727
##     POL_HOME          4.202    0.064   65.300    0.000    4.202    2.067
##     WORRY_EMPLOYME    2.745    0.065   42.434    0.000    2.745    1.343
##     AFF_WORRY         4.873    0.051   95.283    0.000    4.873    3.016
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    2.287    0.102   22.338    0.000    2.287    1.000
##     POL_NONE          3.658    0.164   22.338    0.000    3.658    1.000
##     POL_HOME          4.133    0.185   22.338    0.000    4.133    1.000
##     WORRY_EMPLOYME    4.178    0.187   22.338    0.000    4.178    1.000
##     AFF_WORRY         2.610    0.117   22.338    0.000    2.610    1.000
## 
## 
## Group 5 [4]:
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   WORRY_RECESSION ~~                                                       
##     AFF_WORRY            0.340    0.078    4.367    0.000    0.340    0.142
##     POL_NONE             0.063    0.087    0.724    0.469    0.063    0.023
##   POL_NONE ~~                                                              
##     AFF_WORRY            1.413    0.110   12.824    0.000    1.413    0.454
##   WORRY_RECESSION ~~                                                       
##     POL_HOME            -0.034    0.094   -0.360    0.719   -0.034   -0.012
##   POL_HOME ~~                                                              
##     AFF_WORRY            0.699    0.110    6.365    0.000    0.699    0.210
##   WORRY_EMPLOYMENT ~~                                                      
##     AFF_WORRY           -0.065    0.102   -0.641    0.522   -0.065   -0.021
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.328    0.089    3.681    0.000    0.328    0.119
##   POL_NONE ~~                                                              
##     WORRY_EMPLOYME      -0.545    0.117   -4.678    0.000   -0.545   -0.152
##   POL_HOME ~~                                                              
##     WORRY_EMPLOYME      -0.015    0.123   -0.123    0.902   -0.015   -0.004
##   POL_NONE ~~                                                              
##     POL_HOME             1.394    0.130   10.733    0.000    1.394    0.369
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    5.178    0.047  111.250    0.000    5.178    3.585
##     POL_NONE          5.211    0.061   86.081    0.000    5.211    2.774
##     POL_HOME          3.979    0.065   61.349    0.000    3.979    1.977
##     WORRY_EMPLOYME    2.671    0.061   43.533    0.000    2.671    1.403
##     AFF_WORRY         4.774    0.053   89.357    0.000    4.774    2.879
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    2.086    0.095   21.943    0.000    2.086    1.000
##     POL_NONE          3.529    0.161   21.943    0.000    3.529    1.000
##     POL_HOME          4.051    0.185   21.943    0.000    4.051    1.000
##     WORRY_EMPLOYME    3.625    0.165   21.943    0.000    3.625    1.000
##     AFF_WORRY         2.748    0.125   21.943    0.000    2.748    1.000
## 
## 
## Group 6 [5]:
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   WORRY_RECESSION ~~                                                       
##     AFF_WORRY            0.350    0.081    4.307    0.000    0.350    0.138
##     POL_NONE            -0.132    0.097   -1.360    0.174   -0.132   -0.043
##   POL_NONE ~~                                                              
##     AFF_WORRY            1.431    0.116   12.366    0.000    1.431    0.428
##   WORRY_RECESSION ~~                                                       
##     POL_HOME            -0.207    0.101   -2.041    0.041   -0.207   -0.065
##   POL_HOME ~~                                                              
##     AFF_WORRY            0.677    0.113    5.975    0.000    0.677    0.194
##   WORRY_EMPLOYMENT ~~                                                      
##     AFF_WORRY            0.009    0.105    0.089    0.929    0.009    0.003
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.454    0.097    4.672    0.000    0.454    0.150
##   POL_NONE ~~                                                              
##     WORRY_EMPLOYME      -0.775    0.129   -6.002    0.000   -0.775   -0.195
##   POL_HOME ~~                                                              
##     WORRY_EMPLOYME       0.177    0.133    1.334    0.182    0.177    0.043
##   POL_NONE ~~                                                              
##     POL_HOME             1.508    0.142   10.618    0.000    1.508    0.359
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    5.242    0.048  108.418    0.000    5.242    3.453
##     POL_NONE          4.820    0.064   75.537    0.000    4.820    2.406
##     POL_HOME          3.700    0.067   55.470    0.000    3.700    1.767
##     WORRY_EMPLOYME    2.772    0.063   43.810    0.000    2.772    1.395
##     AFF_WORRY         4.801    0.053   90.454    0.000    4.801    2.881
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    2.305    0.104   22.204    0.000    2.305    1.000
##     POL_NONE          4.015    0.181   22.204    0.000    4.015    1.000
##     POL_HOME          4.387    0.198   22.204    0.000    4.387    1.000
##     WORRY_EMPLOYME    3.947    0.178   22.204    0.000    3.947    1.000
##     AFF_WORRY         2.778    0.125   22.204    0.000    2.778    1.000
## 
## 
## Group 7 [6]:
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   WORRY_RECESSION ~~                                                       
##     AFF_WORRY            0.384    0.083    4.604    0.000    0.384    0.150
##     POL_NONE            -0.159    0.102   -1.553    0.120   -0.159   -0.050
##   POL_NONE ~~                                                              
##     AFF_WORRY            1.398    0.113   12.326    0.000    1.398    0.433
##   WORRY_RECESSION ~~                                                       
##     POL_HOME            -0.056    0.102   -0.545    0.586   -0.056   -0.018
##   POL_HOME ~~                                                              
##     AFF_WORRY            0.811    0.107    7.551    0.000    0.811    0.251
##   WORRY_EMPLOYMENT ~~                                                      
##     AFF_WORRY           -0.078    0.103   -0.754    0.451   -0.078   -0.024
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.611    0.103    5.906    0.000    0.611    0.194
##   POL_NONE ~~                                                              
##     WORRY_EMPLOYME      -0.569    0.129   -4.398    0.000   -0.569   -0.143
##   POL_HOME ~~                                                              
##     WORRY_EMPLOYME       0.311    0.129    2.419    0.016    0.311    0.078
##   POL_NONE ~~                                                              
##     POL_HOME             1.144    0.134    8.512    0.000    1.144    0.285
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    5.115    0.051  100.048    0.000    5.115    3.224
##     POL_NONE          4.687    0.065   72.666    0.000    4.687    2.342
##     POL_HOME          3.395    0.065   52.575    0.000    3.395    1.694
##     WORRY_EMPLOYME    2.717    0.064   42.428    0.000    2.717    1.367
##     AFF_WORRY         4.542    0.052   87.357    0.000    4.542    2.815
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    2.517    0.115   21.943    0.000    2.517    1.000
##     POL_NONE          4.007    0.183   21.943    0.000    4.007    1.000
##     POL_HOME          4.015    0.183   21.943    0.000    4.015    1.000
##     WORRY_EMPLOYME    3.948    0.180   21.943    0.000    3.948    1.000
##     AFF_WORRY         2.603    0.119   21.943    0.000    2.603    1.000
## 
## 
## Group 8 [7]:
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   WORRY_RECESSION ~~                                                       
##     AFF_WORRY            0.434    0.084    5.169    0.000    0.434    0.167
##     POL_NONE            -0.183    0.101   -1.811    0.070   -0.183   -0.058
##   POL_NONE ~~                                                              
##     AFF_WORRY            1.372    0.117   11.702    0.000    1.372    0.402
##   WORRY_RECESSION ~~                                                       
##     POL_HOME            -0.052    0.099   -0.526    0.599   -0.052   -0.017
##   POL_HOME ~~                                                              
##     AFF_WORRY            0.844    0.109    7.719    0.000    0.844    0.254
##   WORRY_EMPLOYMENT ~~                                                      
##     AFF_WORRY            0.128    0.108    1.177    0.239    0.128    0.038
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.642    0.103    6.247    0.000    0.642    0.203
##   POL_NONE ~~                                                              
##     WORRY_EMPLOYME      -0.856    0.135   -6.339    0.000   -0.856   -0.206
##   POL_HOME ~~                                                              
##     WORRY_EMPLOYME       0.400    0.130    3.085    0.002    0.400    0.099
##   POL_NONE ~~                                                              
##     POL_HOME             1.183    0.135    8.765    0.000    1.183    0.291
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    5.140    0.050  103.781    0.000    5.140    3.310
##     POL_NONE          4.800    0.065   73.705    0.000    4.800    2.351
##     POL_HOME          3.318    0.063   52.292    0.000    3.318    1.668
##     WORRY_EMPLOYME    2.742    0.065   42.304    0.000    2.742    1.349
##     AFF_WORRY         4.556    0.053   85.526    0.000    4.556    2.728
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    2.412    0.109   22.170    0.000    2.412    1.000
##     POL_NONE          4.168    0.188   22.170    0.000    4.168    1.000
##     POL_HOME          3.959    0.179   22.170    0.000    3.959    1.000
##     WORRY_EMPLOYME    4.129    0.186   22.170    0.000    4.129    1.000
##     AFF_WORRY         2.790    0.126   22.170    0.000    2.790    1.000
## 
## 
## Group 9 [8]:
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   WORRY_RECESSION ~~                                                       
##     AFF_WORRY            0.378    0.087    4.363    0.000    0.378    0.145
##     POL_NONE            -0.040    0.100   -0.398    0.691   -0.040   -0.013
##   POL_NONE ~~                                                              
##     AFF_WORRY            1.537    0.124   12.439    0.000    1.537    0.449
##   WORRY_RECESSION ~~                                                       
##     POL_HOME            -0.086    0.099   -0.868    0.385   -0.086   -0.029
##   POL_HOME ~~                                                              
##     AFF_WORRY            1.075    0.118    9.134    0.000    1.075    0.315
##   WORRY_EMPLOYMENT ~~                                                      
##     AFF_WORRY            0.182    0.113    1.604    0.109    0.182    0.053
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.619    0.102    6.074    0.000    0.619    0.204
##   POL_NONE ~~                                                              
##     WORRY_EMPLOYME      -0.856    0.134   -6.379    0.000   -0.856   -0.215
##   POL_HOME ~~                                                              
##     WORRY_EMPLOYME       0.368    0.131    2.805    0.005    0.368    0.093
##   POL_NONE ~~                                                              
##     POL_HOME             1.118    0.135    8.257    0.000    1.118    0.283
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    5.156    0.050  103.214    0.000    5.156    3.399
##     POL_NONE          4.684    0.066   71.360    0.000    4.684    2.350
##     POL_HOME          3.371    0.065   51.563    0.000    3.371    1.698
##     WORRY_EMPLOYME    2.785    0.066   42.301    0.000    2.785    1.393
##     AFF_WORRY         4.502    0.057   79.614    0.000    4.502    2.622
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    2.301    0.107   21.471    0.000    2.301    1.000
##     POL_NONE          3.973    0.185   21.471    0.000    3.973    1.000
##     POL_HOME          3.940    0.184   21.471    0.000    3.940    1.000
##     WORRY_EMPLOYME    3.997    0.186   21.471    0.000    3.997    1.000
##     AFF_WORRY         2.948    0.137   21.471    0.000    2.948    1.000
## 
## 
## Group 10 [9]:
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   WORRY_RECESSION ~~                                                       
##     AFF_WORRY            0.457    0.086    5.292    0.000    0.457    0.181
##     POL_NONE            -0.052    0.098   -0.532    0.595   -0.052   -0.018
##   POL_NONE ~~                                                              
##     AFF_WORRY            1.547    0.122   12.684    0.000    1.547    0.473
##   WORRY_RECESSION ~~                                                       
##     POL_HOME            -0.006    0.096   -0.063    0.950   -0.006   -0.002
##   POL_HOME ~~                                                              
##     AFF_WORRY            0.674    0.110    6.119    0.000    0.674    0.211
##   WORRY_EMPLOYMENT ~~                                                      
##     AFF_WORRY            0.151    0.114    1.328    0.184    0.151    0.045
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.471    0.102    4.602    0.000    0.471    0.157
##   POL_NONE ~~                                                              
##     WORRY_EMPLOYME      -0.526    0.133   -3.971    0.000   -0.526   -0.135
##   POL_HOME ~~                                                              
##     WORRY_EMPLOYME       0.427    0.129    3.300    0.001    0.427    0.112
##   POL_NONE ~~                                                              
##     POL_HOME             1.150    0.131    8.799    0.000    1.150    0.310
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    5.163    0.051  102.219    0.000    5.163    3.444
##     POL_NONE          4.841    0.066   73.823    0.000    4.841    2.487
##     POL_HOME          3.058    0.064   47.683    0.000    3.058    1.606
##     WORRY_EMPLOYME    2.866    0.067   42.461    0.000    2.866    1.431
##     AFF_WORRY         4.490    0.057   79.276    0.000    4.490    2.671
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    2.248    0.107   20.988    0.000    2.248    1.000
##     POL_NONE          3.789    0.181   20.988    0.000    3.789    1.000
##     POL_HOME          3.623    0.173   20.988    0.000    3.623    1.000
##     WORRY_EMPLOYME    4.014    0.191   20.988    0.000    4.014    1.000
##     AFF_WORRY         2.827    0.135   20.988    0.000    2.827    1.000
## 
## 
## Group 11 [10]:
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   WORRY_RECESSION ~~                                                       
##     AFF_WORRY            0.229    0.083    2.751    0.006    0.229    0.091
##     POL_NONE            -0.002    0.098   -0.019    0.985   -0.002   -0.001
##   POL_NONE ~~                                                              
##     AFF_WORRY            1.326    0.116   11.454    0.000    1.326    0.408
##   WORRY_RECESSION ~~                                                       
##     POL_HOME            -0.010    0.100   -0.103    0.918   -0.010   -0.003
##   POL_HOME ~~                                                              
##     AFF_WORRY            0.786    0.112    6.991    0.000    0.786    0.237
##   WORRY_EMPLOYMENT ~~                                                      
##     AFF_WORRY            0.140    0.108    1.288    0.198    0.140    0.043
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.580    0.101    5.761    0.000    0.580    0.194
##   POL_NONE ~~                                                              
##     WORRY_EMPLOYME      -0.455    0.129   -3.530    0.000   -0.455   -0.117
##   POL_HOME ~~                                                              
##     WORRY_EMPLOYME       0.466    0.131    3.543    0.000    0.466    0.118
##   POL_NONE ~~                                                              
##     POL_HOME             0.668    0.131    5.103    0.000    0.668    0.171
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    5.146    0.050  103.026    0.000    5.146    3.400
##     POL_NONE          4.804    0.065   74.344    0.000    4.804    2.454
##     POL_HOME          3.197    0.066   48.492    0.000    3.197    1.600
##     WORRY_EMPLOYME    2.876    0.065   44.013    0.000    2.876    1.453
##     AFF_WORRY         4.634    0.055   84.657    0.000    4.634    2.794
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    2.290    0.107   21.424    0.000    2.290    1.000
##     POL_NONE          3.833    0.179   21.424    0.000    3.833    1.000
##     POL_HOME          3.991    0.186   21.424    0.000    3.991    1.000
##     WORRY_EMPLOYME    3.919    0.183   21.424    0.000    3.919    1.000
##     AFF_WORRY         2.751    0.128   21.424    0.000    2.751    1.000
## 
## 
## Group 12 [11]:
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   WORRY_RECESSION ~~                                                       
##     AFF_WORRY            0.321    0.088    3.660    0.000    0.321    0.120
##     POL_NONE            -0.223    0.105   -2.129    0.033   -0.223   -0.069
##   POL_NONE ~~                                                              
##     AFF_WORRY            1.460    0.121   12.086    0.000    1.460    0.427
##   WORRY_RECESSION ~~                                                       
##     POL_HOME            -0.284    0.102   -2.791    0.005   -0.284   -0.091
##   POL_HOME ~~                                                              
##     AFF_WORRY            0.594    0.109    5.424    0.000    0.594    0.179
##   WORRY_EMPLOYMENT ~~                                                      
##     AFF_WORRY            0.249    0.114    2.194    0.028    0.249    0.072
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.718    0.109    6.571    0.000    0.718    0.219
##   POL_NONE ~~                                                              
##     WORRY_EMPLOYME      -0.595    0.137   -4.335    0.000   -0.595   -0.142
##   POL_HOME ~~                                                              
##     WORRY_EMPLOYME       0.358    0.132    2.706    0.007    0.358    0.088
##   POL_NONE ~~                                                              
##     POL_HOME             0.510    0.130    3.914    0.000    0.510    0.128
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    5.053    0.052   97.829    0.000    5.053    3.181
##     POL_NONE          4.742    0.066   72.046    0.000    4.742    2.342
##     POL_HOME          3.060    0.064   47.908    0.000    3.060    1.558
##     WORRY_EMPLOYME    2.823    0.067   42.046    0.000    2.823    1.367
##     AFF_WORRY         4.442    0.055   80.968    0.000    4.442    2.632
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    2.524    0.116   21.749    0.000    2.524    1.000
##     POL_NONE          4.098    0.188   21.749    0.000    4.098    1.000
##     POL_HOME          3.860    0.177   21.749    0.000    3.860    1.000
##     WORRY_EMPLOYME    4.266    0.196   21.749    0.000    4.266    1.000
##     AFF_WORRY         2.847    0.131   21.749    0.000    2.847    1.000
## 
## 
## Group 13 [12]:
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   WORRY_RECESSION ~~                                                       
##     AFF_WORRY            0.308    0.092    3.336    0.001    0.308    0.109
##     POL_NONE            -0.310    0.104   -2.986    0.003   -0.310   -0.097
##   POL_NONE ~~                                                              
##     AFF_WORRY            1.482    0.122   12.171    0.000    1.482    0.430
##   WORRY_RECESSION ~~                                                       
##     POL_HOME            -0.112    0.099   -1.124    0.261   -0.112   -0.037
##   POL_HOME ~~                                                              
##     AFF_WORRY            0.746    0.110    6.768    0.000    0.746    0.225
##   WORRY_EMPLOYMENT ~~                                                      
##     AFF_WORRY            0.132    0.115    1.143    0.253    0.132    0.037
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.641    0.109    5.907    0.000    0.641    0.195
##   POL_NONE ~~                                                              
##     WORRY_EMPLOYME      -0.611    0.131   -4.656    0.000   -0.611   -0.153
##   POL_HOME ~~                                                              
##     WORRY_EMPLOYME       0.383    0.125    3.056    0.002    0.383    0.100
##   POL_NONE ~~                                                              
##     POL_HOME             0.528    0.122    4.317    0.000    0.528    0.142
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    5.004    0.053   95.301    0.000    5.004    3.095
##     POL_NONE          5.018    0.064   78.452    0.000    5.018    2.548
##     POL_HOME          2.746    0.061   44.669    0.000    2.746    1.451
##     WORRY_EMPLOYME    2.601    0.066   39.491    0.000    2.601    1.283
##     AFF_WORRY         4.497    0.057   79.203    0.000    4.497    2.572
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     WORRY_RECESSIO    2.614    0.120   21.772    0.000    2.614    1.000
##     POL_NONE          3.878    0.178   21.772    0.000    3.878    1.000
##     POL_HOME          3.582    0.165   21.772    0.000    3.582    1.000
##     WORRY_EMPLOYME    4.113    0.189   21.772    0.000    4.113    1.000
##     AFF_WORRY         3.056    0.140   21.772    0.000    3.056    1.000
 fit_free <- sem(m,
            data = data,
            group = "TIME")

 fit_intercept <- sem(m,
            data = data,
            group = "TIME", group.equal = c("intercepts"))



 anova(fit_free, fit_intercept)
## Chi-Squared Difference Test
## 
##               Df    AIC    BIC  Chisq Chisq diff Df diff Pr(>Chisq)    
## fit_free       0 246491 248424    0.0                                  
## fit_intercept 60 248013 249500 1641.5     1641.5      60  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 fit_load <- sem(m,
            data = data,
            group = "TIME", group.equal = c("loadings"))


anova(fit_free,fit_load)
## Warning in lavTestLRT(object = new("lavaan", version = "0.6.6", call =
## lavaan::lavaan(model = m, : lavaan WARNING: some models have the same degrees of
## freedom
## Chi-Squared Difference Test
## 
##          Df    AIC    BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit_free  0 246491 248424     0                              
## fit_load  0 246491 248424     0          0       0
fit_resv <- sem(m,
            data = data,
            group = "TIME", group.equal = c("loadings","residuals"))

anova(fit_free,fit_resv)
## Chi-Squared Difference Test
## 
##          Df    AIC    BIC  Chisq Chisq diff Df diff Pr(>Chisq)    
## fit_free  0 246491 248424   0.00                                  
## fit_resv 60 246501 247988 129.98     129.98      60  4.458e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit_res <- sem(m,
            data = data,
            group = "TIME", group.equal = c("loadings","residual.covariances"))



anova(fit_free,  fit_res)
## Chi-Squared Difference Test
## 
##           Df    AIC    BIC  Chisq Chisq diff Df diff Pr(>Chisq)    
## fit_free   0 246491 248424   0.00                                  
## fit_res  120 246576 247617 324.92     324.92     120  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Output

Export of mean value estimates and confidence intervals of the target model (for Table 1)

est <- parameterestimates(fit_free, ci = TRUE, level = 0.95, standardized = T)




est <- est[est$op == "~1",]



print(est)
##                  lhs op rhs block group   est    se       z pvalue ci.lower
## 16   WORRY_RECESSION ~1         1     1 5.390 0.046 116.529      0    5.299
## 17          POL_NONE ~1         1     1 5.622 0.054 103.944      0    5.516
## 18          POL_HOME ~1         1     1 5.082 0.060  84.928      0    4.965
## 19  WORRY_EMPLOYMENT ~1         1     1 2.821 0.065  43.673      0    2.694
## 20         AFF_WORRY ~1         1     1 5.302 0.047 112.788      0    5.210
## 36   WORRY_RECESSION ~1         2     2 5.403 0.047 115.024      0    5.311
## 37          POL_NONE ~1         2     2 5.465 0.057  95.866      0    5.353
## 38          POL_HOME ~1         2     2 4.819 0.062  77.886      0    4.698
## 39  WORRY_EMPLOYMENT ~1         2     2 2.926 0.066  44.632      0    2.798
## 40         AFF_WORRY ~1         2     2 5.302 0.050 105.606      0    5.203
## 56   WORRY_RECESSION ~1         3     3 5.348 0.048 110.832      0    5.253
## 57          POL_NONE ~1         3     3 5.198 0.060  85.973      0    5.079
## 58          POL_HOME ~1         3     3 4.349 0.064  67.525      0    4.222
## 59  WORRY_EMPLOYMENT ~1         3     3 2.874 0.065  44.315      0    2.747
## 60         AFF_WORRY ~1         3     3 4.961 0.053  93.612      0    4.858
## 76   WORRY_RECESSION ~1         4     4 5.265 0.048 109.981      0    5.171
## 77          POL_NONE ~1         4     4 5.215 0.061  86.146      0    5.097
## 78          POL_HOME ~1         4     4 4.202 0.064  65.300      0    4.076
## 79  WORRY_EMPLOYMENT ~1         4     4 2.745 0.065  42.434      0    2.619
## 80         AFF_WORRY ~1         4     4 4.873 0.051  95.283      0    4.773
## 96   WORRY_RECESSION ~1         5     5 5.178 0.047 111.250      0    5.086
## 97          POL_NONE ~1         5     5 5.211 0.061  86.081      0    5.092
## 98          POL_HOME ~1         5     5 3.979 0.065  61.349      0    3.852
## 99  WORRY_EMPLOYMENT ~1         5     5 2.671 0.061  43.533      0    2.551
## 100        AFF_WORRY ~1         5     5 4.774 0.053  89.357      0    4.669
## 116  WORRY_RECESSION ~1         6     6 5.242 0.048 108.418      0    5.148
## 117         POL_NONE ~1         6     6 4.820 0.064  75.537      0    4.695
## 118         POL_HOME ~1         6     6 3.700 0.067  55.470      0    3.569
## 119 WORRY_EMPLOYMENT ~1         6     6 2.772 0.063  43.810      0    2.648
## 120        AFF_WORRY ~1         6     6 4.801 0.053  90.454      0    4.697
## 136  WORRY_RECESSION ~1         7     7 5.115 0.051 100.048      0    5.015
## 137         POL_NONE ~1         7     7 4.687 0.065  72.666      0    4.561
## 138         POL_HOME ~1         7     7 3.395 0.065  52.575      0    3.268
## 139 WORRY_EMPLOYMENT ~1         7     7 2.717 0.064  42.428      0    2.591
## 140        AFF_WORRY ~1         7     7 4.542 0.052  87.357      0    4.440
## 156  WORRY_RECESSION ~1         8     8 5.140 0.050 103.781      0    5.043
## 157         POL_NONE ~1         8     8 4.800 0.065  73.705      0    4.672
## 158         POL_HOME ~1         8     8 3.318 0.063  52.292      0    3.194
## 159 WORRY_EMPLOYMENT ~1         8     8 2.742 0.065  42.304      0    2.615
## 160        AFF_WORRY ~1         8     8 4.556 0.053  85.526      0    4.452
## 176  WORRY_RECESSION ~1         9     9 5.156 0.050 103.214      0    5.058
## 177         POL_NONE ~1         9     9 4.684 0.066  71.360      0    4.556
## 178         POL_HOME ~1         9     9 3.371 0.065  51.563      0    3.243
## 179 WORRY_EMPLOYMENT ~1         9     9 2.785 0.066  42.301      0    2.656
## 180        AFF_WORRY ~1         9     9 4.502 0.057  79.614      0    4.391
## 196  WORRY_RECESSION ~1        10    10 5.163 0.051 102.219      0    5.064
## 197         POL_NONE ~1        10    10 4.841 0.066  73.823      0    4.713
## 198         POL_HOME ~1        10    10 3.058 0.064  47.683      0    2.932
## 199 WORRY_EMPLOYMENT ~1        10    10 2.866 0.067  42.461      0    2.734
## 200        AFF_WORRY ~1        10    10 4.490 0.057  79.276      0    4.379
## 216  WORRY_RECESSION ~1        11    11 5.146 0.050 103.026      0    5.048
## 217         POL_NONE ~1        11    11 4.804 0.065  74.344      0    4.677
## 218         POL_HOME ~1        11    11 3.197 0.066  48.492      0    3.068
## 219 WORRY_EMPLOYMENT ~1        11    11 2.876 0.065  44.013      0    2.748
## 220        AFF_WORRY ~1        11    11 4.634 0.055  84.657      0    4.527
## 236  WORRY_RECESSION ~1        12    12 5.053 0.052  97.829      0    4.952
## 237         POL_NONE ~1        12    12 4.742 0.066  72.046      0    4.613
## 238         POL_HOME ~1        12    12 3.060 0.064  47.908      0    2.935
## 239 WORRY_EMPLOYMENT ~1        12    12 2.823 0.067  42.046      0    2.692
## 240        AFF_WORRY ~1        12    12 4.442 0.055  80.968      0    4.334
## 256  WORRY_RECESSION ~1        13    13 5.004 0.053  95.301      0    4.901
## 257         POL_NONE ~1        13    13 5.018 0.064  78.452      0    4.893
## 258         POL_HOME ~1        13    13 2.746 0.061  44.669      0    2.625
## 259 WORRY_EMPLOYMENT ~1        13    13 2.601 0.066  39.491      0    2.472
## 260        AFF_WORRY ~1        13    13 4.497 0.057  79.203      0    4.386
##     ci.upper std.lv std.all std.nox
## 16     5.481  5.390   3.638   3.638
## 17     5.728  5.622   3.245   3.245
## 18     5.199  5.082   2.651   2.651
## 19     2.947  2.821   1.363   1.363
## 20     5.394  5.302   3.521   3.521
## 36     5.495  5.403   3.659   3.659
## 37     5.576  5.465   3.050   3.050
## 38     4.940  4.819   2.478   2.478
## 39     3.055  2.926   1.420   1.420
## 40     5.400  5.302   3.360   3.360
## 56     5.442  5.348   3.528   3.528
## 57     5.316  5.198   2.737   2.737
## 58     4.475  4.349   2.149   2.149
## 59     3.001  2.874   1.411   1.411
## 60     5.065  4.961   2.980   2.980
## 76     5.358  5.265   3.481   3.481
## 77     5.334  5.215   2.727   2.727
## 78     4.329  4.202   2.067   2.067
## 79     2.872  2.745   1.343   1.343
## 80     4.973  4.873   3.016   3.016
## 96     5.269  5.178   3.585   3.585
## 97     5.329  5.211   2.774   2.774
## 98     4.106  3.979   1.977   1.977
## 99     2.791  2.671   1.403   1.403
## 100    4.878  4.774   2.879   2.879
## 116    5.337  5.242   3.453   3.453
## 117    4.946  4.820   2.406   2.406
## 118    3.831  3.700   1.767   1.767
## 119    2.896  2.772   1.395   1.395
## 120    4.905  4.801   2.881   2.881
## 136    5.215  5.115   3.224   3.224
## 137    4.814  4.687   2.342   2.342
## 138    3.521  3.395   1.694   1.694
## 139    2.842  2.717   1.367   1.367
## 140    4.644  4.542   2.815   2.815
## 156    5.237  5.140   3.310   3.310
## 157    4.927  4.800   2.351   2.351
## 158    3.443  3.318   1.668   1.668
## 159    2.869  2.742   1.349   1.349
## 160    4.661  4.556   2.728   2.728
## 176    5.254  5.156   3.399   3.399
## 177    4.813  4.684   2.350   2.350
## 178    3.499  3.371   1.698   1.698
## 179    2.914  2.785   1.393   1.393
## 180    4.613  4.502   2.622   2.622
## 196    5.262  5.163   3.444   3.444
## 197    4.970  4.841   2.487   2.487
## 198    3.184  3.058   1.606   1.606
## 199    2.998  2.866   1.431   1.431
## 200    4.601  4.490   2.671   2.671
## 216    5.244  5.146   3.400   3.400
## 217    4.931  4.804   2.454   2.454
## 218    3.326  3.197   1.600   1.600
## 219    3.004  2.876   1.453   1.453
## 220    4.741  4.634   2.794   2.794
## 236    5.154  5.053   3.181   3.181
## 237    4.871  4.742   2.342   2.342
## 238    3.185  3.060   1.558   1.558
## 239    2.955  2.823   1.367   1.367
## 240    4.549  4.442   2.632   2.632
## 256    5.107  5.004   3.095   3.095
## 257    5.143  5.018   2.548   2.548
## 258    2.866  2.746   1.451   1.451
## 259    2.730  2.601   1.283   1.283
## 260    4.608  4.497   2.572   2.572
est$vars <- est$lhs
 
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
est <- est %>% mutate_if(is.numeric, round, digits=3) 

est$CI <- paste(est$ci.lower, est$ci.upper, sep = " - ")


export <- data.frame(vars = est$vars, time = est$group, est = est$est, ci = est$CI)

export <- export[with(export, order(vars, time)),] 


write.csv2(export, file = "results_H1_to_H3.csv")

Export of standard deviation estimates of the target model (for Table 1)

est <- parameterestimates(fit_free, ci = TRUE, level = 0.95, standardized = T)




est <- est[est$lhs == est$rhs,]

est$sd <- sqrt(est$est)


print(est)
##                  lhs op              rhs block group   est    se      z pvalue
## 11   WORRY_RECESSION ~~  WORRY_RECESSION     1     1 2.195 0.097 22.650      0
## 12          POL_NONE ~~         POL_NONE     1     1 3.001 0.133 22.650      0
## 13          POL_HOME ~~         POL_HOME     1     1 3.674 0.162 22.650      0
## 14  WORRY_EMPLOYMENT ~~ WORRY_EMPLOYMENT     1     1 4.280 0.189 22.650      0
## 15         AFF_WORRY ~~        AFF_WORRY     1     1 2.267 0.100 22.650      0
## 31   WORRY_RECESSION ~~  WORRY_RECESSION     2     2 2.180 0.098 22.226      0
## 32          POL_NONE ~~         POL_NONE     2     2 3.210 0.144 22.226      0
## 33          POL_HOME ~~         POL_HOME     2     2 3.782 0.170 22.226      0
## 34  WORRY_EMPLOYMENT ~~ WORRY_EMPLOYMENT     2     2 4.247 0.191 22.226      0
## 35         AFF_WORRY ~~        AFF_WORRY     2     2 2.490 0.112 22.226      0
## 51   WORRY_RECESSION ~~  WORRY_RECESSION     3     3 2.298 0.103 22.215      0
## 52          POL_NONE ~~         POL_NONE     3     3 3.607 0.162 22.215      0
## 53          POL_HOME ~~         POL_HOME     3     3 4.093 0.184 22.215      0
## 54  WORRY_EMPLOYMENT ~~ WORRY_EMPLOYMENT     3     3 4.152 0.187 22.215      0
## 55         AFF_WORRY ~~        AFF_WORRY     3     3 2.773 0.125 22.215      0
## 71   WORRY_RECESSION ~~  WORRY_RECESSION     4     4 2.287 0.102 22.338      0
## 72          POL_NONE ~~         POL_NONE     4     4 3.658 0.164 22.338      0
## 73          POL_HOME ~~         POL_HOME     4     4 4.133 0.185 22.338      0
## 74  WORRY_EMPLOYMENT ~~ WORRY_EMPLOYMENT     4     4 4.178 0.187 22.338      0
## 75         AFF_WORRY ~~        AFF_WORRY     4     4 2.610 0.117 22.338      0
## 91   WORRY_RECESSION ~~  WORRY_RECESSION     5     5 2.086 0.095 21.943      0
## 92          POL_NONE ~~         POL_NONE     5     5 3.529 0.161 21.943      0
## 93          POL_HOME ~~         POL_HOME     5     5 4.051 0.185 21.943      0
## 94  WORRY_EMPLOYMENT ~~ WORRY_EMPLOYMENT     5     5 3.625 0.165 21.943      0
## 95         AFF_WORRY ~~        AFF_WORRY     5     5 2.748 0.125 21.943      0
## 111  WORRY_RECESSION ~~  WORRY_RECESSION     6     6 2.305 0.104 22.204      0
## 112         POL_NONE ~~         POL_NONE     6     6 4.015 0.181 22.204      0
## 113         POL_HOME ~~         POL_HOME     6     6 4.387 0.198 22.204      0
## 114 WORRY_EMPLOYMENT ~~ WORRY_EMPLOYMENT     6     6 3.947 0.178 22.204      0
## 115        AFF_WORRY ~~        AFF_WORRY     6     6 2.778 0.125 22.204      0
## 131  WORRY_RECESSION ~~  WORRY_RECESSION     7     7 2.517 0.115 21.943      0
## 132         POL_NONE ~~         POL_NONE     7     7 4.007 0.183 21.943      0
## 133         POL_HOME ~~         POL_HOME     7     7 4.015 0.183 21.943      0
## 134 WORRY_EMPLOYMENT ~~ WORRY_EMPLOYMENT     7     7 3.948 0.180 21.943      0
## 135        AFF_WORRY ~~        AFF_WORRY     7     7 2.603 0.119 21.943      0
## 151  WORRY_RECESSION ~~  WORRY_RECESSION     8     8 2.412 0.109 22.170      0
## 152         POL_NONE ~~         POL_NONE     8     8 4.168 0.188 22.170      0
## 153         POL_HOME ~~         POL_HOME     8     8 3.959 0.179 22.170      0
## 154 WORRY_EMPLOYMENT ~~ WORRY_EMPLOYMENT     8     8 4.129 0.186 22.170      0
## 155        AFF_WORRY ~~        AFF_WORRY     8     8 2.790 0.126 22.170      0
## 171  WORRY_RECESSION ~~  WORRY_RECESSION     9     9 2.301 0.107 21.471      0
## 172         POL_NONE ~~         POL_NONE     9     9 3.973 0.185 21.471      0
## 173         POL_HOME ~~         POL_HOME     9     9 3.940 0.184 21.471      0
## 174 WORRY_EMPLOYMENT ~~ WORRY_EMPLOYMENT     9     9 3.997 0.186 21.471      0
## 175        AFF_WORRY ~~        AFF_WORRY     9     9 2.948 0.137 21.471      0
## 191  WORRY_RECESSION ~~  WORRY_RECESSION    10    10 2.248 0.107 20.988      0
## 192         POL_NONE ~~         POL_NONE    10    10 3.789 0.181 20.988      0
## 193         POL_HOME ~~         POL_HOME    10    10 3.623 0.173 20.988      0
## 194 WORRY_EMPLOYMENT ~~ WORRY_EMPLOYMENT    10    10 4.014 0.191 20.988      0
## 195        AFF_WORRY ~~        AFF_WORRY    10    10 2.827 0.135 20.988      0
## 211  WORRY_RECESSION ~~  WORRY_RECESSION    11    11 2.290 0.107 21.424      0
## 212         POL_NONE ~~         POL_NONE    11    11 3.833 0.179 21.424      0
## 213         POL_HOME ~~         POL_HOME    11    11 3.991 0.186 21.424      0
## 214 WORRY_EMPLOYMENT ~~ WORRY_EMPLOYMENT    11    11 3.919 0.183 21.424      0
## 215        AFF_WORRY ~~        AFF_WORRY    11    11 2.751 0.128 21.424      0
## 231  WORRY_RECESSION ~~  WORRY_RECESSION    12    12 2.524 0.116 21.749      0
## 232         POL_NONE ~~         POL_NONE    12    12 4.098 0.188 21.749      0
## 233         POL_HOME ~~         POL_HOME    12    12 3.860 0.177 21.749      0
## 234 WORRY_EMPLOYMENT ~~ WORRY_EMPLOYMENT    12    12 4.266 0.196 21.749      0
## 235        AFF_WORRY ~~        AFF_WORRY    12    12 2.847 0.131 21.749      0
## 251  WORRY_RECESSION ~~  WORRY_RECESSION    13    13 2.614 0.120 21.772      0
## 252         POL_NONE ~~         POL_NONE    13    13 3.878 0.178 21.772      0
## 253         POL_HOME ~~         POL_HOME    13    13 3.582 0.165 21.772      0
## 254 WORRY_EMPLOYMENT ~~ WORRY_EMPLOYMENT    13    13 4.113 0.189 21.772      0
## 255        AFF_WORRY ~~        AFF_WORRY    13    13 3.056 0.140 21.772      0
##     ci.lower ci.upper std.lv std.all std.nox    sd
## 11     2.005    2.385  2.195       1       1 1.482
## 12     2.742    3.261  3.001       1       1 1.732
## 13     3.356    3.992  3.674       1       1 1.917
## 14     3.909    4.650  4.280       1       1 2.069
## 15     2.071    2.464  2.267       1       1 1.506
## 31     1.988    2.372  2.180       1       1 1.476
## 32     2.927    3.493  3.210       1       1 1.792
## 33     3.448    4.115  3.782       1       1 1.945
## 34     3.872    4.621  4.247       1       1 2.061
## 35     2.270    2.710  2.490       1       1 1.578
## 51     2.095    2.500  2.298       1       1 1.516
## 52     3.289    3.926  3.607       1       1 1.899
## 53     3.732    4.454  4.093       1       1 2.023
## 54     3.786    4.519  4.152       1       1 2.038
## 55     2.528    3.017  2.773       1       1 1.665
## 71     2.086    2.487  2.287       1       1 1.512
## 72     3.337    3.979  3.658       1       1 1.913
## 73     3.771    4.496  4.133       1       1 2.033
## 74     3.811    4.544  4.178       1       1 2.044
## 75     2.381    2.839  2.610       1       1 1.616
## 91     1.900    2.272  2.086       1       1 1.444
## 92     3.214    3.844  3.529       1       1 1.879
## 93     3.690    4.413  4.051       1       1 2.013
## 94     3.301    3.949  3.625       1       1 1.904
## 95     2.503    2.994  2.748       1       1 1.658
## 111    2.102    2.509  2.305       1       1 1.518
## 112    3.661    4.370  4.015       1       1 2.004
## 113    3.999    4.774  4.387       1       1 2.094
## 114    3.599    4.295  3.947       1       1 1.987
## 115    2.533    3.023  2.778       1       1 1.667
## 131    2.292    2.742  2.517       1       1 1.587
## 132    3.649    4.365  4.007       1       1 2.002
## 133    3.656    4.373  4.015       1       1 2.004
## 134    3.595    4.300  3.948       1       1 1.987
## 135    2.371    2.836  2.603       1       1 1.613
## 151    2.198    2.625  2.412       1       1 1.553
## 152    3.800    4.537  4.168       1       1 2.042
## 153    3.609    4.309  3.959       1       1 1.990
## 154    3.764    4.494  4.129       1       1 2.032
## 155    2.543    3.037  2.790       1       1 1.670
## 171    2.091    2.511  2.301       1       1 1.517
## 172    3.610    4.336  3.973       1       1 1.993
## 173    3.581    4.300  3.940       1       1 1.985
## 174    3.632    4.362  3.997       1       1 1.999
## 175    2.679    3.218  2.948       1       1 1.717
## 191    2.038    2.458  2.248       1       1 1.499
## 192    3.435    4.142  3.789       1       1 1.946
## 193    3.285    3.962  3.623       1       1 1.903
## 194    3.639    4.389  4.014       1       1 2.003
## 195    2.563    3.090  2.827       1       1 1.681
## 211    2.081    2.500  2.290       1       1 1.513
## 212    3.482    4.184  3.833       1       1 1.958
## 213    3.625    4.356  3.991       1       1 1.998
## 214    3.561    4.278  3.919       1       1 1.980
## 215    2.499    3.002  2.751       1       1 1.658
## 231    2.296    2.751  2.524       1       1 1.589
## 232    3.729    4.468  4.098       1       1 2.024
## 233    3.512    4.208  3.860       1       1 1.965
## 234    3.881    4.650  4.266       1       1 2.065
## 235    2.590    3.104  2.847       1       1 1.687
## 251    2.379    2.849  2.614       1       1 1.617
## 252    3.529    4.228  3.878       1       1 1.969
## 253    3.260    3.904  3.582       1       1 1.893
## 254    3.743    4.483  4.113       1       1 2.028
## 255    2.781    3.331  3.056       1       1 1.748
est$vars <- est$lhs
 
library(dplyr)

est <- est %>% mutate_if(is.numeric, round, digits=3) 

est$CI <- paste(est$ci.lower, est$ci.upper, sep = " - ")


export <- data.frame(vars = est$vars, time = est$group, sd = est$sd, est = est$est, ci = est$CI)

export <- export[with(export, order(vars, time)),] 


write.csv2(export, file = "results_H1_to_H3_sd.csv")

ANOVAs and Tukey-Post-Hoc-Tests

Additional analyses that were conducted to account for multiple-testing.

# ANOVAs

summary(aov(data$AFF_WORRY ~ as.factor(data$TIME)))
##                         Df Sum Sq Mean Sq F value Pr(>F)    
## as.factor(data$TIME)    12   1123   93.58   34.53 <2e-16 ***
## Residuals            13081  35447    2.71                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(aov(data$POL_HOME ~ as.factor(data$TIME)))
##                         Df Sum Sq Mean Sq F value Pr(>F)    
## as.factor(data$TIME)    12   6541   545.0   138.7 <2e-16 ***
## Residuals            13081  51408     3.9                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(aov(data$POL_NONE ~ as.factor(data$TIME)))
##                         Df Sum Sq Mean Sq F value Pr(>F)    
## as.factor(data$TIME)    12   1171   97.58   26.19 <2e-16 ***
## Residuals            13070  48704    3.73                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 11 observations deleted due to missingness
summary(aov(data$WORRY_EMPLOYMENT ~ as.factor(data$TIME)))
##                         Df Sum Sq Mean Sq F value Pr(>F)  
## as.factor(data$TIME)    12     98   8.169   2.006 0.0199 *
## Residuals            12507  50921   4.071                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 574 observations deleted due to missingness
summary(aov(data$WORRY_RECESSION ~ as.factor(data$TIME)))
##                         Df Sum Sq Mean Sq F value   Pr(>F)    
## as.factor(data$TIME)    12    195  16.231   7.032 6.65e-13 ***
## Residuals            13081  30195   2.308                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Tukey-Post-Hoc Tests

a1 <- aov(data$AFF_WORRY ~ as.factor(data$TIME))
posthoc <- TukeyHSD(x=a1, conf.level=0.95)
print(posthoc)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = data$AFF_WORRY ~ as.factor(data$TIME))
## 
## $`as.factor(data$TIME)`
##               diff        lwr          upr     p adj
## 1-0   -0.009180502 -0.2451218  0.226760834 1.0000000
## 2-0   -0.370275326 -0.6065764 -0.133974223 0.0000161
## 3-0   -0.449400844 -0.6851044 -0.213697330 0.0000000
## 4-0   -0.548752409 -0.7860311 -0.311473713 0.0000000
## 5-0   -0.542315588 -0.7788586 -0.305772591 0.0000000
## 6-0   -0.765025772 -1.0022426 -0.527808966 0.0000000
## 7-0   -0.773343657 -1.0101914 -0.536495960 0.0000000
## 8-0   -0.821625459 -1.0610740 -0.582176940 0.0000000
## 9-0   -0.818190913 -1.0608650 -0.575516783 0.0000000
## 10-0  -0.698332416 -0.9389159 -0.457748915 0.0000000
## 11-0  -0.900983434 -1.1390765 -0.662890329 0.0000000
## 12-0  -0.809296078 -1.0463279 -0.572264303 0.0000000
## 2-1   -0.361094824 -0.6020270 -0.120162669 0.0000524
## 3-1   -0.440220342 -0.6805664 -0.199874261 0.0000001
## 4-1   -0.539571907 -0.7814629 -0.297680875 0.0000000
## 5-1   -0.533135086 -0.7743045 -0.291965682 0.0000000
## 6-1   -0.755845270 -0.9976756 -0.514014947 0.0000000
## 7-1   -0.764163155 -1.0056314 -0.522694889 0.0000000
## 8-1   -0.812444957 -1.0564648 -0.568425111 0.0000000
## 9-1   -0.809010411 -1.0561962 -0.561824599 0.0000000
## 10-1  -0.689151914 -0.9342856 -0.444018251 0.0000000
## 11-1  -0.891802932 -1.1344929 -0.649112968 0.0000000
## 12-1  -0.800115576 -1.0417644 -0.558466751 0.0000000
## 3-2   -0.079125518 -0.3198248  0.161573747 0.9971969
## 4-2   -0.178477083 -0.4207190  0.063764881 0.4146893
## 5-2   -0.172040262 -0.4135616  0.069481122 0.4726616
## 6-2   -0.394750446 -0.6369318 -0.152569104 0.0000052
## 7-2   -0.403068331 -0.6448881 -0.161248520 0.0000026
## 8-2   -0.451350132 -0.6957179 -0.206982412 0.0000001
## 9-2   -0.447915587 -0.6954448 -0.200386350 0.0000002
## 10-2  -0.328057090 -0.5735370 -0.082577131 0.0006934
## 11-2  -0.530708107 -0.7737478 -0.287668366 0.0000000
## 12-2  -0.439020751 -0.6810209 -0.197020644 0.0000001
## 4-3   -0.099351565 -0.3410106  0.142307501 0.9796121
## 5-3   -0.092914744 -0.3338515  0.148021998 0.9880991
## 6-3   -0.315624928 -0.5572232 -0.074026630 0.0010793
## 7-3   -0.323942813 -0.5651787 -0.082706920 0.0006295
## 8-3   -0.372224615 -0.6160145 -0.128434710 0.0000325
## 9-3   -0.368790069 -0.6157489 -0.121831251 0.0000574
## 10-3  -0.248931572 -0.4938363 -0.004026804 0.0421205
## 11-3  -0.451582590 -0.6940414 -0.209123829 0.0000001
## 12-3  -0.359895234 -0.6013119 -0.118478609 0.0000599
## 5-4    0.006436821 -0.2360411  0.248914751 1.0000000
## 6-4   -0.216273363 -0.4594087  0.026861929 0.1411938
## 7-4   -0.224591248 -0.4673664  0.018183933 0.1033536
## 8-4   -0.272873050 -0.5181862 -0.027559881 0.0142751
## 9-4   -0.269438504 -0.5179012 -0.020975849 0.0198381
## 10-4  -0.149580007 -0.3960011  0.096841133 0.7257066
## 11-4  -0.352231025 -0.5962214 -0.108240690 0.0001303
## 12-4  -0.260543669 -0.5034984 -0.017588900 0.0227791
## 6-5   -0.222710184 -0.4651276  0.019707183 0.1094893
## 7-5   -0.231028069 -0.4730843  0.011028120 0.0786248
## 8-5   -0.279309871 -0.5239115 -0.034708235 0.0099555
## 9-5   -0.275875325 -0.5236355 -0.028115158 0.0140827
## 10-5  -0.156016828 -0.4017296  0.089695988 0.6614415
## 11-5  -0.358667846 -0.6019428 -0.115392912 0.0000788
## 12-5  -0.266980490 -0.5092168 -0.024744181 0.0161190
## 7-6   -0.008317885 -0.2510326  0.234396808 1.0000000
## 8-6   -0.056599687 -0.3018530  0.188653619 0.9999226
## 9-6   -0.053165141 -0.3015687  0.195238410 0.9999657
## 10-6   0.066693356 -0.1796682  0.313054902 0.9995877
## 11-6  -0.135957662 -0.3798878  0.107972486 0.8258894
## 12-6  -0.044270306 -0.2871646  0.198624019 0.9999942
## 8-7   -0.048281802 -0.2931781  0.196614508 0.9999860
## 9-7   -0.044847256 -0.2928983  0.203203833 0.9999947
## 10-7   0.075011241 -0.1709949  0.321017400 0.9986416
## 11-7  -0.127639777 -0.3712110  0.115931436 0.8799392
## 12-7  -0.035952421 -0.2784863  0.206581436 0.9999994
## 9-8    0.003434546 -0.2471011  0.253970181 1.0000000
## 10-8   0.123293043 -0.1252181  0.371804193 0.9161144
## 11-8  -0.079357975 -0.3254590  0.166743016 0.9976660
## 12-8   0.012329381 -0.2327450  0.257403724 1.0000000
## 10-9   0.119858497 -0.1317621  0.371479109 0.9367959
## 11-9  -0.082792521 -0.3320330  0.166448001 0.9969068
## 12-9   0.008894835 -0.2393320  0.257121695 1.0000000
## 11-10 -0.202651018 -0.4498564  0.044554413 0.2438185
## 12-10 -0.110963662 -0.3571471  0.135219727 0.9578774
## 12-11  0.091687356 -0.1520629  0.335437569 0.9904144
a1 <- aov(data$POL_HOME ~ as.factor(data$TIME))
posthoc <- TukeyHSD(x=a1, conf.level=0.95)
print(posthoc)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = data$POL_HOME ~ as.factor(data$TIME))
## 
## $`as.factor(data$TIME)`
##              diff        lwr          upr     p adj
## 1-0   -0.28559542 -0.5697322 -0.001458641 0.0474149
## 2-0   -0.72511662 -1.0096867 -0.440546580 0.0000000
## 3-0   -0.90013634 -1.1839867 -0.616285959 0.0000000
## 4-0   -1.12194309 -1.4076904 -0.836195774 0.0000000
## 5-0   -1.37595341 -1.6608148 -1.091092073 0.0000000
## 6-0   -1.69390457 -1.9795774 -1.408231785 0.0000000
## 7-0   -1.76928412 -2.0545124 -1.484055840 0.0000000
## 8-0   -1.72604073 -2.0144011 -1.437680360 0.0000000
## 9-0   -2.02661130 -2.3188562 -1.734366428 0.0000000
## 10-0  -1.91647121 -2.2061984 -1.626744020 0.0000000
## 11-0  -2.05089807 -2.3376262 -1.764169978 0.0000000
## 12-0  -2.36239937 -2.6478493 -2.076949405 0.0000000
## 2-1   -0.43952120 -0.7296683 -0.149374127 0.0000399
## 3-1   -0.61454092 -0.9039822 -0.325099640 0.0000000
## 4-1   -0.83634767 -1.1276495 -0.545045860 0.0000000
## 5-1   -1.09035799 -1.3807908 -0.799925214 0.0000000
## 6-1   -1.40830915 -1.6995379 -1.117080449 0.0000000
## 7-1   -1.48368870 -1.7744814 -1.192896012 0.0000000
## 8-1   -1.44044531 -1.7343108 -1.146579832 0.0000000
## 9-1   -1.74101588 -2.0386940 -1.443337729 0.0000000
## 10-1  -1.63087579 -1.9260826 -1.335668980 0.0000000
## 11-1  -1.76530264 -2.0575666 -1.473038703 0.0000000
## 12-1  -2.07680394 -2.3678141 -1.785793815 0.0000000
## 3-2   -0.17501972 -0.4648863  0.114846884 0.7327979
## 4-2   -0.39682648 -0.6885509 -0.105102049 0.0004836
## 5-2   -0.65083680 -0.9416935 -0.359980140 0.0000000
## 6-2   -0.96878796 -1.2604394 -0.677136532 0.0000000
## 7-2   -1.04416751 -1.3353835 -0.752951462 0.0000000
## 8-2   -1.00092411 -1.2952085 -0.706639703 0.0000000
## 9-2   -1.30149468 -1.5995864 -1.003402958 0.0000000
## 10-2  -1.19135460 -1.4869784 -0.895730752 0.0000000
## 11-2  -1.32578145 -1.6184666 -1.033096281 0.0000000
## 12-2  -1.63728275 -1.9287159 -1.345849582 0.0000000
## 4-3   -0.22180676 -0.5128292  0.069215706 0.3575202
## 5-3   -0.47581708 -0.7659697 -0.185664484 0.0000043
## 6-3   -0.79376823 -1.0847175 -0.502818953 0.0000000
## 7-3   -0.86914778 -1.1596606 -0.578634935 0.0000000
## 8-3   -0.82590439 -1.1194930 -0.532315826 0.0000000
## 9-3   -1.12647496 -1.4238798 -0.829070174 0.0000000
## 10-3  -1.01633488 -1.3112660 -0.721403715 0.0000000
## 11-3  -1.15076173 -1.4427472 -0.858776216 0.0000000
## 12-3  -1.46226303 -1.7529935 -1.171532529 0.0000000
## 5-4   -0.25401032 -0.5460189  0.037998276 0.1661514
## 6-4   -0.57196148 -0.8647617 -0.279161242 0.0000000
## 7-4   -0.64734103 -0.9397076 -0.354974462 0.0000000
## 8-4   -0.60409764 -0.8995206 -0.308674652 0.0000000
## 9-4   -0.90466821 -1.2038840 -0.605452394 0.0000000
## 10-4  -0.79452812 -1.0912854 -0.497770840 0.0000000
## 11-4  -0.92895497 -1.2227849 -0.635125033 0.0000000
## 12-4  -1.24045627 -1.5330391 -0.947873435 0.0000000
## 6-5   -0.31795116 -0.6098868 -0.026015497 0.0187858
## 7-5   -0.39333071 -0.6848314 -0.101830003 0.0005710
## 8-5   -0.35008732 -0.6446534 -0.055521209 0.0055032
## 9-5   -0.65065789 -0.9490277 -0.352288059 0.0000000
## 10-5  -0.54051780 -0.8364221 -0.244613533 0.0000001
## 11-5  -0.67494465 -0.9679131 -0.381976250 0.0000000
## 12-5  -0.98644595 -1.2781636 -0.694728334 0.0000000
## 7-6   -0.07537955 -0.3676733  0.216914172 0.9997501
## 8-6   -0.03213616 -0.3274871  0.263214736 1.0000000
## 9-6   -0.33270673 -0.6318514 -0.033562092 0.0143012
## 10-6  -0.22256664 -0.5192522  0.074118873 0.3840576
## 11-6  -0.35699349 -0.6507509 -0.063236036 0.0038623
## 12-6  -0.66849479 -0.9610048 -0.375984747 0.0000000
## 8-7    0.04324339 -0.2516776  0.338164366 0.9999995
## 9-7   -0.25732718 -0.5560474  0.041392998 0.1778096
## 10-7  -0.14718709 -0.4434446  0.149070440 0.9153146
## 11-7  -0.28161394 -0.5749391  0.011711259 0.0744802
## 12-7  -0.59311524 -0.8851912 -0.301039298 0.0000000
## 9-8   -0.30057057 -0.6022828  0.001141668 0.0519814
## 10-8  -0.19043048 -0.4897047  0.108843732 0.6582325
## 11-8  -0.32485733 -0.6212291 -0.028485601 0.0173082
## 12-8  -0.63635864 -0.9314940 -0.341223262 0.0000000
## 10-9   0.11014009 -0.1928788  0.413158929 0.9929247
## 11-9  -0.02428676 -0.3244393  0.275865809 1.0000000
## 12-9  -0.33578807 -0.6347199 -0.036856214 0.0125090
## 11-10 -0.13442685 -0.4321286  0.163274925 0.9573005
## 12-10 -0.44592815 -0.7423991 -0.149457189 0.0000478
## 12-11 -0.31150130 -0.6050421 -0.017960535 0.0258620
a1 <- aov(data$POL_NONE ~ as.factor(data$TIME))
posthoc <- TukeyHSD(x=a1, conf.level=0.95)
print(posthoc)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = data$POL_NONE ~ as.factor(data$TIME))
## 
## $`as.factor(data$TIME)`
##               diff         lwr          upr     p adj
## 1-0   -0.166666667 -0.44334624  0.110012904 0.7358897
## 2-0   -0.455675499 -0.73298952 -0.178361480 0.0000041
## 3-0   -0.444120505 -0.72073018 -0.167510833 0.0000081
## 4-0   -0.439893440 -0.71850605 -0.161280830 0.0000131
## 5-0   -0.828749181 -1.10613430 -0.551364066 0.0000000
## 6-0   -0.946706389 -1.22488165 -0.668531123 0.0000000
## 7-0   -0.846331030 -1.12407346 -0.568588604 0.0000000
## 8-0   -0.948559671 -1.22935198 -0.667767358 0.0000000
## 9-0   -0.786666667 -1.07124153 -0.502091801 0.0000000
## 10-0  -0.834205934 -1.11632920 -0.552082671 0.0000000
## 11-0  -0.869083585 -1.14828645 -0.589880717 0.0000000
## 12-0  -0.603300330 -0.88125862 -0.325342043 0.0000000
## 2-1   -0.289008832 -0.57174943 -0.006268235 0.0396389
## 3-1   -0.277453839 -0.55950364  0.004595964 0.0590133
## 4-1   -0.273226773 -0.55724115  0.010787605 0.0730932
## 5-1   -0.662082515 -0.94489284 -0.379272186 0.0000000
## 6-1   -0.780039722 -1.06362509 -0.496454358 0.0000000
## 7-1   -0.679664363 -0.96282516 -0.396503570 0.0000000
## 8-1   -0.781893004 -1.06804594 -0.495740068 0.0000000
## 9-1   -0.620000000 -0.90986555 -0.330134454 0.0000000
## 10-1  -0.667539267 -0.95499833 -0.380080199 0.0000000
## 11-1  -0.702416918 -0.98701035 -0.417823486 0.0000000
## 12-1  -0.436633663 -0.72000619 -0.153261136 0.0000255
## 3-2    0.011554994 -0.27111720  0.294227191 1.0000000
## 4-2    0.015782059 -0.26885042  0.300414536 1.0000000
## 5-2   -0.373073683 -0.65650474 -0.089642628 0.0009342
## 6-2   -0.491030890 -0.77523529 -0.206826493 0.0000008
## 7-2   -0.390655531 -0.67443628 -0.106874779 0.0003765
## 8-2   -0.492884172 -0.77965060 -0.206117746 0.0000010
## 9-2   -0.330991168 -0.62146236 -0.040519973 0.0102559
## 10-2  -0.378530435 -0.66660021 -0.090460659 0.0009656
## 11-2  -0.413408086 -0.69861836 -0.128197809 0.0001185
## 12-2  -0.147624831 -0.43161686  0.136367193 0.8859369
## 4-3    0.004227065 -0.27971922  0.288173351 1.0000000
## 5-3   -0.384628676 -0.66737062 -0.101886729 0.0004831
## 6-3   -0.502585883 -0.78610305 -0.219068715 0.0000003
## 7-3   -0.402210525 -0.68530302 -0.119118029 0.0001883
## 8-3   -0.504439165 -0.79052452 -0.218353813 0.0000004
## 9-3   -0.342546161 -0.63234499 -0.052747333 0.0059861
## 10-3  -0.390085428 -0.67747722 -0.102693637 0.0005058
## 11-3  -0.424963080 -0.70948856 -0.140437601 0.0000571
## 12-3  -0.159179825 -0.44248411  0.124124456 0.8177092
## 5-4   -0.388855742 -0.67355749 -0.104153996 0.0004443
## 6-4   -0.506812949 -0.79228459 -0.221341303 0.0000003
## 7-4   -0.406437590 -0.69148748 -0.121387705 0.0001736
## 8-4   -0.508666231 -0.79668863 -0.220643827 0.0000004
## 9-4   -0.346773227 -0.63848445 -0.055062006 0.0054839
## 10-4  -0.394312494 -0.68363259 -0.104992399 0.0004647
## 11-4  -0.429190145 -0.71566322 -0.142717068 0.0000529
## 12-4  -0.163406890 -0.44866711  0.121853327 0.7969844
## 6-5   -0.117957207 -0.40223098  0.166316562 0.9780108
## 7-5   -0.017581849 -0.30143208  0.266268380 1.0000000
## 8-5   -0.119810489 -0.40664567  0.167024690 0.9767961
## 9-5    0.042082515 -0.24845656  0.332621586 0.9999996
## 10-5  -0.005456752 -0.29359497  0.282681466 1.0000000
## 11-5  -0.040334404 -0.32561381  0.244945002 0.9999997
## 12-5   0.225448851 -0.05861260  0.509510300 0.2922908
## 7-6    0.100375359 -0.18424707  0.384997791 0.9946145
## 8-6   -0.001853282 -0.28945265  0.285746086 1.0000000
## 9-6    0.160039722 -0.13125382  0.451333265 0.8398330
## 10-6   0.112500455 -0.17639851  0.401399415 0.9870623
## 11-6   0.077622804 -0.20842495  0.363670554 0.9995775
## 12-6   0.343406059  0.05857298  0.628239138 0.0043914
## 8-7   -0.102228641 -0.38940937  0.184952092 0.9941369
## 9-7    0.059664363 -0.23121586  0.350544587 0.9999784
## 10-7   0.012125096 -0.27635711  0.300607307 1.0000000
## 11-7  -0.022752555 -0.30837940  0.262874285 1.0000000
## 12-7   0.243030700 -0.04137967  0.527441072 0.1878024
## 9-8    0.161893004 -0.13190075  0.455686763 0.8370227
## 10-8   0.114353737 -0.17706598  0.405773458 0.9861898
## 11-8   0.079476086 -0.20911733  0.368069502 0.9995090
## 12-8   0.345259341  0.05786984  0.632648846 0.0046472
## 10-9  -0.047539267 -0.34260534  0.247526802 0.9999985
## 11-9  -0.082416918 -0.37469195  0.209858109 0.9993759
## 12-9   0.183366337 -0.10772001  0.474452680 0.6732846
## 11-10 -0.034877651 -0.32476620  0.255010901 0.9999999
## 12-10  0.230905604 -0.05778444  0.519595645 0.2803422
## 12-11  0.265783255 -0.02005349  0.551620003 0.0989809
a1 <- aov(data$WORRY_EMPLOYMENT ~ as.factor(data$TIME))
posthoc <- TukeyHSD(x=a1, conf.level=0.95)
print(posthoc)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = data$WORRY_EMPLOYMENT ~ as.factor(data$TIME))
## 
## $`as.factor(data$TIME)`
##               diff         lwr         upr     p adj
## 1-0    0.105450592 -0.19255426  0.40345544 0.9944455
## 2-0    0.060145313 -0.23770615  0.35799678 0.9999818
## 3-0   -0.071412019 -0.36842943  0.22560539 0.9998802
## 4-0   -0.153307396 -0.45287647  0.14626168 0.8968695
## 5-0   -0.048857494 -0.34701628  0.24930129 0.9999982
## 6-0   -0.104151865 -0.40412083  0.19581710 0.9953300
## 7-0   -0.079055444 -0.37744614  0.21933526 0.9996696
## 8-0   -0.035413310 -0.33880319  0.26797657 1.0000000
## 9-0    0.045398526 -0.26168708  0.35248414 0.9999995
## 10-0   0.055154225 -0.24858359  0.35889204 0.9999944
## 11-0   0.002804462 -0.29855162  0.30416054 1.0000000
## 12-0  -0.219396945 -0.52058759  0.08179370 0.4341800
## 2-1   -0.045305280 -0.34595640  0.25534584 0.9999993
## 3-1   -0.176862611 -0.47668747  0.12296225 0.7625854
## 4-1   -0.258757988 -0.56111081  0.04359484 0.1858816
## 5-1   -0.154308086 -0.45526367  0.14664749 0.8955934
## 6-1   -0.209602457 -0.51235150  0.09314658 0.5213978
## 7-1   -0.184506036 -0.48569138  0.11667931 0.7132017
## 8-1   -0.140863903 -0.44700279  0.16527499 0.9508473
## 9-1   -0.060052066 -0.36985390  0.24974976 0.9999884
## 10-1  -0.050296367 -0.35678007  0.25618734 0.9999982
## 11-1  -0.102646130 -0.40676961  0.20147735 0.9964079
## 12-1  -0.324847538 -0.62880709 -0.02088799 0.0237527
## 3-2   -0.131557332 -0.43122973  0.16811507 0.9655736
## 4-2   -0.213452709 -0.51565435  0.08874894 0.4871733
## 5-2   -0.109002807 -0.40980650  0.19180089 0.9931141
## 6-2   -0.164297177 -0.46689523  0.13830088 0.8506675
## 7-2   -0.139200756 -0.44023433  0.16183282 0.9490225
## 8-2   -0.095558623 -0.40154821  0.21043096 0.9982850
## 9-2   -0.014746787 -0.32440107  0.29490750 1.0000000
## 10-2  -0.004991087 -0.31132565  0.30134348 1.0000000
## 11-2  -0.057340850 -0.36131403  0.24663233 0.9999914
## 12-2  -0.279542258 -0.58335143  0.02426691 0.1081018
## 4-3   -0.081895377 -0.38327501  0.21948425 0.9995715
## 5-3    0.022554525 -0.27742333  0.32253238 1.0000000
## 6-3   -0.032739846 -0.33451697  0.26903728 1.0000000
## 7-3   -0.007643425 -0.30785179  0.29256494 1.0000000
## 8-3    0.035998708 -0.26917906  0.34117648 1.0000000
## 9-3    0.116810545 -0.19204156  0.42566265 0.9899453
## 10-3   0.126566244 -0.17895743  0.43208991 0.9783015
## 11-3   0.074216481 -0.22893949  0.37737245 0.9998550
## 12-3  -0.147984926 -0.45097644  0.15500659 0.9246511
## 5-4    0.104449902 -0.19805464  0.40695444 0.9955631
## 6-4    0.049155532 -0.25513334  0.35344440 0.9999985
## 7-4    0.074251953 -0.22848118  0.37698508 0.9998521
## 8-4    0.117894086 -0.18976767  0.42555584 0.9887337
## 9-4    0.198705922 -0.11260086  0.51001270 0.6534849
## 10-4   0.208461622 -0.09954325  0.51646649 0.5596181
## 11-4   0.156111859 -0.14954453  0.46176825 0.8982000
## 12-4  -0.066089549 -0.37158283  0.23940373 0.9999614
## 6-5   -0.055294370 -0.35819493  0.24760619 0.9999941
## 7-5   -0.030197949 -0.33153560  0.27113970 1.0000000
## 8-5    0.013444184 -0.29284455  0.31973292 1.0000000
## 9-5    0.094256020 -0.21569388  0.40420592 0.9986766
## 10-5   0.104011720 -0.20262166  0.41064510 0.9962365
## 11-5   0.051661957 -0.25261236  0.35593627 0.9999974
## 12-5  -0.170539451 -0.47464991  0.13357101 0.8196914
## 7-6    0.025096421 -0.27803243  0.32822527 1.0000000
## 8-6    0.068738554 -0.23931259  0.37678970 0.9999461
## 9-6    0.149550391 -0.16214122  0.46124200 0.9334635
## 10-6   0.159306090 -0.14908773  0.46769991 0.8904286
## 11-6   0.106956327 -0.19909200  0.41300465 0.9950425
## 12-6  -0.115245081 -0.42113051  0.19064035 0.9902772
## 8-7    0.043642133 -0.26287237  0.35015663 0.9999996
## 9-7    0.124453969 -0.18571903  0.43462697 0.9833331
## 10-7   0.134209669 -0.17264922  0.44106856 0.9665583
## 11-7   0.081859906 -0.22264167  0.38636148 0.9996161
## 12-7  -0.140341502 -0.44467935  0.16399635 0.9500472
## 9-8    0.080811836 -0.23417340  0.39579708 0.9997632
## 10-8   0.090567536 -0.22115476  0.40228983 0.9991574
## 11-8   0.038217773 -0.27118426  0.34761981 0.9999999
## 12-8  -0.183983635 -0.49322454  0.12525727 0.7519010
## 10-9   0.009755699 -0.30556468  0.32507608 1.0000000
## 11-9  -0.042594064 -0.35562085  0.27043273 0.9999998
## 12-9  -0.264795471 -0.57766299  0.04807205 0.2000672
## 11-10 -0.052349763 -0.36209298  0.25739345 0.9999975
## 12-10 -0.274551171 -0.58413343  0.03503109 0.1444125
## 12-11 -0.222201408 -0.52944724  0.08504443 0.4465574
a1 <- aov(data$WORRY_RECESSION ~ as.factor(data$TIME))
posthoc <- TukeyHSD(x=a1, conf.level=0.95)
print(posthoc)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = data$WORRY_RECESSION ~ as.factor(data$TIME))
## 
## $`as.factor(data$TIME)`
##               diff        lwr          upr     p adj
## 1-0   -0.020308277 -0.2380684  0.197451811 1.0000000
## 2-0   -0.076640943 -0.2947331  0.141451189 0.9947857
## 3-0   -0.158504670 -0.3760453  0.059035922 0.4337459
## 4-0   -0.242074954 -0.4610693 -0.023080561 0.0155103
## 5-0   -0.180380289 -0.3986957  0.037935097 0.2326428
## 6-0   -0.293895115 -0.5128324 -0.074957843 0.0006340
## 7-0   -0.288748515 -0.5073451 -0.070151910 0.0008725
## 8-0   -0.254195645 -0.4751927 -0.033198632 0.0089745
## 9-0   -0.258491052 -0.4824651 -0.034516988 0.0085471
## 10-0  -0.285710522 -0.5077551 -0.063665988 0.0014342
## 11-0  -0.371603923 -0.5913500 -0.151857878 0.0000017
## 12-0  -0.402205261 -0.6209718 -0.183438762 0.0000001
## 2-1   -0.056332666 -0.2786990  0.166033657 0.9997925
## 3-1   -0.138196392 -0.3600218  0.083629018 0.6894691
## 4-1   -0.221766676 -0.4450180  0.001484634 0.0535263
## 5-1   -0.160072011 -0.3826573  0.062513278 0.4562413
## 6-1   -0.273586838 -0.4967821 -0.050391559 0.0033527
## 7-1   -0.268440238 -0.4913014 -0.045579117 0.0044572
## 8-1   -0.233887368 -0.4591034 -0.008671287 0.0332437
## 9-1   -0.238182774 -0.4663209 -0.010044692 0.0312933
## 10-1  -0.265402245 -0.4916463 -0.039158176 0.0067170
## 11-1  -0.351295645 -0.5752843 -0.127306968 0.0000157
## 12-1  -0.381896983 -0.6049247 -0.158869217 0.0000011
## 3-2   -0.081863727 -0.3040151  0.140287652 0.9920028
## 4-2   -0.165434010 -0.3890092  0.058141189 0.4073481
## 5-2   -0.103739345 -0.3266495  0.119170800 0.9465704
## 6-2   -0.217254172 -0.4407734  0.006265077 0.0663193
## 7-2   -0.212107572 -0.4352931  0.011078005 0.0817393
## 8-2   -0.177554702 -0.4030919  0.047982447 0.3049934
## 9-2   -0.181850108 -0.4103052  0.046604935 0.2877356
## 10-2  -0.209069579 -0.4356333  0.017494101 0.1055424
## 11-2  -0.294962980 -0.5192745 -0.070651478 0.0009523
## 12-2  -0.325564318 -0.5489163 -0.102212338 0.0001037
## 4-3   -0.083570284 -0.3066075  0.139466935 0.9907360
## 5-3   -0.021875619 -0.2442462  0.200494937 1.0000000
## 6-3   -0.135390445 -0.3583716  0.087590688 0.7253214
## 7-3   -0.130243846 -0.3528905  0.092402809 0.7727472
## 8-3   -0.095690975 -0.3206948  0.129312884 0.9731828
## 9-3   -0.099986382 -0.3279150  0.127942199 0.9657769
## 10-3  -0.127205853 -0.3532387  0.098826960 0.8160382
## 11-3  -0.213099253 -0.4368745  0.010676038 0.0802543
## 12-3  -0.243700591 -0.4665141 -0.020887131 0.0177967
## 5-4    0.061694665 -0.1620983  0.285487647 0.9995038
## 6-4   -0.051820161 -0.2762199  0.172579528 0.9999220
## 7-4   -0.046673562 -0.2707409  0.177393766 0.9999745
## 8-4   -0.012120691 -0.2385304  0.214289051 1.0000000
## 9-4   -0.016416098 -0.2457326  0.212900436 1.0000000
## 10-4  -0.043635569 -0.2710679  0.183796766 0.9999897
## 11-4  -0.129528969 -0.3547178  0.095659875 0.7923638
## 12-4  -0.160130307 -0.3843634  0.064102769 0.4683067
## 6-5   -0.113514826 -0.3372519  0.110222260 0.9025570
## 7-5   -0.108368227 -0.3317720  0.115035513 0.9281597
## 8-5   -0.073815357 -0.2995684  0.151937683 0.9973358
## 9-5   -0.078110763 -0.3067789  0.150557416 0.9959838
## 10-5  -0.105330234 -0.3321088  0.121448360 0.9473414
## 11-5  -0.191223634 -0.4157522  0.033304936 0.1920128
## 12-5  -0.221824972 -0.4453950  0.001745008 0.0541598
## 7-6    0.005146600 -0.2188649  0.229158101 1.0000000
## 8-6    0.039699470 -0.1866550  0.266053963 0.9999962
## 9-6    0.035404063 -0.1938579  0.264666049 0.9999991
## 10-6   0.008184593 -0.2191927  0.235561926 1.0000000
## 11-6  -0.077708808 -0.3028421  0.147424487 0.9955774
## 12-6  -0.108310146 -0.3324874  0.115867145 0.9301378
## 8-7    0.034552870 -0.1914721  0.260577876 0.9999992
## 9-7    0.030257464 -0.1986792  0.259194147 0.9999998
## 10-7   0.003037993 -0.2240113  0.230087325 1.0000000
## 11-7  -0.082855407 -0.3076574  0.141946611 0.9919900
## 12-7  -0.113456745 -0.3373013  0.110387854 0.9031930
## 9-8   -0.004295407 -0.2355252  0.226934368 1.0000000
## 10-8  -0.031514877 -0.2608762  0.197846416 0.9999998
## 11-8  -0.117408278 -0.3445451  0.109728578 0.8899738
## 12-8  -0.148009616 -0.3741989  0.078179704 0.6147396
## 10-9  -0.027219471 -0.2594506  0.205011673 1.0000000
## 11-9  -0.113112871 -0.3431473  0.116921589 0.9210690
## 12-9  -0.143714209 -0.3728131  0.085384700 0.6794589
## 11-10 -0.085893400 -0.3140496  0.142262789 0.9903440
## 12-10 -0.116494738 -0.3437076  0.110718166 0.8956213
## 12-11 -0.030601338 -0.2555686  0.194365888 0.9999998

H4 - H7

Model with standardized variables

Analyses of within measurement occasion standardized relationships. To facilitate the interpretation of results, a within-group-z-standardization is conducted before multigroup analyses take place. Once more, the analyzed within-group model is saturated. Likelihood ratio tests show that all restrictions - apart from restricting variances of mean values and variances of standardized variables to be equal - significantly impaired model fit.

library(lavaan)

#Within-group-centering and standardization


library(tidyverse)
## -- Attaching packages -------------------------------------------------- tidyverse 1.3.0 --
## v tibble  3.0.1     v purrr   0.3.4
## v tidyr   1.1.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.5.0
## -- Conflicts ----------------------------------------------------- tidyverse_conflicts() --
## x ggplot2::%+%()   masks psych::%+%()
## x ggplot2::alpha() masks psych::alpha()
## x dplyr::filter()  masks stats::filter()
## x dplyr::lag()     masks stats::lag()
data <- group_by(data, TIME) %>% mutate(POL_HOME.sd = as.numeric(scale(POL_HOME)))
data <- group_by(data, TIME) %>% mutate(POL_NONE.sd = as.numeric(scale(POL_NONE)))

data <- group_by(data, TIME) %>% mutate(WORRY_RECESSION.sd = as.numeric(scale(WORRY_RECESSION)))
data <- group_by(data, TIME) %>% mutate(AFF_WORRY.sd = as.numeric(scale(AFF_WORRY)))
data <- group_by(data, TIME) %>% mutate(WORRY_EMPLOYMENT.sd = as.numeric(scale(WORRY_EMPLOYMENT)))

data$AFF_WORRY <- data$AFF_WORRY.sd
data$WORRY_RECESSION <- data$WORRY_RECESSION.sd
data$WORRY_EMPLOYMENT <- data$WORRY_EMPLOYMENT.sd

data$POL_HOME <- data$POL_HOME.sd
data$POL_NONE <- data$POL_NONE.sd



data$int1 <- data$AFF_WORRY * data$WORRY_RECESSION
data$int2 <- data$AFF_WORRY * data$WORRY_EMPLOYMENT



# regression models



m <- "




# Regressions
POL_NONE   ~ AFF_WORRY + WORRY_RECESSION + WORRY_EMPLOYMENT +  int1 + int2
POL_HOME  ~ AFF_WORRY + WORRY_RECESSION + WORRY_EMPLOYMENT +  int1 + int2

WORRY_RECESSION ~~ AFF_WORRY
WORRY_RECESSION ~~ int1
int1 ~~ AFF_WORRY
WORRY_RECESSION ~~ int2
int2 ~~ AFF_WORRY

WORRY_EMPLOYMENT ~~ AFF_WORRY
WORRY_EMPLOYMENT ~~ WORRY_RECESSION
WORRY_EMPLOYMENT ~~ int1
WORRY_EMPLOYMENT ~~ int2


int1 ~~ int2
POL_HOME ~~ POL_NONE

"





 fit_free <- sem(m, 
            data = data, 
            group = "TIME")
 
 fit_load <- sem(m, 
            data = data, 
            group = "TIME", group.equal = c("loadings","intercepts"))
 
 
 
 
 anova(fit_free, fit_load)
## Chi-Squared Difference Test
## 
##          Df    AIC    BIC  Chisq Chisq diff Df diff Pr(>Chisq)
## fit_free  0 247046 250429  0.000                              
## fit_load 84 246911 249670 32.979     32.979      84          1
fit_resv <- sem(m, 
            data = data, 
            group = "TIME", group.equal = c("loadings","residuals","intercepts"))

anova(fit_load,fit_resv)
## Chi-Squared Difference Test
## 
##           Df    AIC    BIC  Chisq Chisq diff Df diff Pr(>Chisq)
## fit_load  84 246911 249670 32.979                              
## fit_resv 168 246797 248931 86.558     53.579      84     0.9961
fit_res <- sem(m, 
            data = data, 
            group = "TIME", group.equal = c("loadings","residual.covariances","residuals","intercepts"))


anova(fit_res,fit_resv)
## Chi-Squared Difference Test
## 
##           Df    AIC    BIC   Chisq Chisq diff Df diff Pr(>Chisq)    
## fit_resv 168 246797 248931  86.558                                  
## fit_res  300 246878 248030 431.392     344.83     132  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit_reg <- sem(m, 
            data = data, 
            group = "TIME", group.equal = c("loadings","regressions","residuals","intercepts"))


anova(fit_resv,  fit_reg)
## Chi-Squared Difference Test
## 
##           Df    AIC    BIC   Chisq Chisq diff Df diff Pr(>Chisq)    
## fit_resv 168 246797 248931  86.558                                  
## fit_reg  288 246810 248052 339.907     253.35     120  1.478e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# target model is a model with equal intercepts and variances across measurement occasions (this is surprising given that variables are z-standardized within measurement occasions)

summary(fit_resv, standardized = T, rsquare = T)
## lavaan 0.6-6 ended normally after 58 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of free parameters                        455
##   Number of equality constraints                   168
##                                                       
##   Number of observations per group:               Used       Total
##     0                                             1026        1113
##     1                                              988        1028
##     2                                              987        1022
##     3                                              998        1032
##     4                                              963        1006
##     5                                              986        1018
##     6                                              963        1007
##     7                                              983        1013
##     8                                              922         972
##     9                                              881         925
##     10                                             918         955
##     11                                             946         993
##     12                                             948        1010
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                86.558
##   Degrees of freedom                               168
##   P-value (Chi-square)                           1.000
##   Test statistic for each group:
##     0                                           16.731
##     1                                           11.780
##     2                                            7.243
##     3                                            8.806
##     4                                            6.846
##     5                                            3.360
##     6                                            8.437
##     7                                            0.577
##     8                                            3.295
##     9                                            3.362
##     10                                           5.861
##     11                                           5.574
##     12                                           4.686
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## 
## Group 1 [0]:
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   POL_NONE ~                                                            
##     AFF_WORRY         0.425    0.029   14.860    0.000    0.425    0.427
##     WORRY_RECESSIO    0.035    0.029    1.180    0.238    0.035    0.035
##     WORRY_EMPLOYME   -0.211    0.028   -7.485    0.000   -0.211   -0.211
##     int1              0.075    0.025    2.996    0.003    0.075    0.086
##     int2              0.061    0.027    2.259    0.024    0.061    0.064
##   POL_HOME ~                                                            
##     AFF_WORRY         0.213    0.031    6.843    0.000    0.213    0.215
##     WORRY_RECESSIO    0.087    0.032    2.716    0.007    0.087    0.088
##     WORRY_EMPLOYME   -0.085    0.031   -2.755    0.006   -0.085   -0.085
##     int1              0.056    0.027    2.032    0.042    0.056    0.064
##     int2              0.041    0.029    1.379    0.168    0.041    0.043
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   AFF_WORRY ~~                                                             
##     WORRY_RECESSIO       0.257    0.029    8.912    0.000    0.257    0.255
##   WORRY_RECESSION ~~                                                       
##     int1                -0.255    0.033   -7.612    0.000   -0.255   -0.222
##   AFF_WORRY ~~                                                             
##     int1                -0.163    0.035   -4.687    0.000   -0.163   -0.142
##   WORRY_RECESSION ~~                                                       
##     int2                -0.007    0.033   -0.228    0.820   -0.007   -0.007
##   AFF_WORRY ~~                                                             
##     int2                -0.098    0.032   -3.016    0.003   -0.098   -0.093
##     WORRY_EMPLOYME       0.042    0.031    1.345    0.179    0.042    0.042
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.167    0.030    5.531    0.000    0.167    0.166
##   WORRY_EMPLOYMENT ~~                                                      
##     int1                -0.009    0.036   -0.257    0.797   -0.009   -0.008
##     int2                 0.122    0.032    3.790    0.000    0.122    0.116
##   int1 ~~                                                                  
##     int2                 0.238    0.036    6.703    0.000    0.238    0.199
##  .POL_NONE ~~                                                              
##    .POL_HOME             0.276    0.023   12.032    0.000    0.276    0.326
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.29.)   -0.016    0.008   -1.978    0.048   -0.016   -0.016
##    .POL_HOM (.30.)   -0.000    0.009   -0.051    0.959   -0.000   -0.000
##     AFF_WOR (.31.)   -0.006    0.009   -0.648    0.517   -0.006   -0.006
##     WORRY_R (.32.)   -0.003    0.009   -0.361    0.718   -0.003   -0.003
##     WORRY_E (.33.)   -0.001    0.009   -0.088    0.930   -0.001   -0.001
##     int1    (.34.)    0.160    0.010   15.777    0.000    0.160    0.140
##     int2    (.35.)    0.029    0.009    3.095    0.002    0.029    0.028
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.22.)    0.778    0.010   79.139    0.000    0.778    0.780
##    .POL_HOM (.23.)    0.924    0.012   79.139    0.000    0.924    0.935
##     AFF_WOR (.24.)    1.006    0.013   79.095    0.000    1.006    1.000
##     WORRY_R (.25.)    1.008    0.013   79.105    0.000    1.008    1.000
##     WORRY_E (.26.)    0.999    0.013   79.090    0.000    0.999    1.000
##     int1    (.27.)    1.305    0.016   79.105    0.000    1.305    1.000
##     int2    (.28.)    1.103    0.014   79.097    0.000    1.103    1.000
## 
## R-Square:
##                    Estimate
##     POL_NONE          0.220
##     POL_HOME          0.065
## 
## 
## Group 2 [1]:
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   POL_NONE ~                                                            
##     AFF_WORRY         0.437    0.029   15.172    0.000    0.437    0.436
##     WORRY_RECESSIO   -0.029    0.030   -0.986    0.324   -0.029   -0.029
##     WORRY_EMPLOYME   -0.149    0.029   -5.170    0.000   -0.149   -0.148
##     int1              0.086    0.026    3.349    0.001    0.086    0.098
##     int2              0.051    0.028    1.834    0.067    0.051    0.053
##   POL_HOME ~                                                            
##     AFF_WORRY         0.290    0.031    9.230    0.000    0.290    0.287
##     WORRY_RECESSIO    0.015    0.032    0.469    0.639    0.015    0.015
##     WORRY_EMPLOYME    0.008    0.031    0.252    0.801    0.008    0.008
##     int1              0.079    0.028    2.816    0.005    0.079    0.089
##     int2             -0.026    0.030   -0.871    0.384   -0.026   -0.027
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   AFF_WORRY ~~                                                             
##     WORRY_RECESSIO       0.202    0.030    6.666    0.000    0.202    0.201
##   WORRY_RECESSION ~~                                                       
##     int1                -0.190    0.035   -5.405    0.000   -0.190   -0.166
##   AFF_WORRY ~~                                                             
##     int1                 0.101    0.036    2.798    0.005    0.101    0.088
##   WORRY_RECESSION ~~                                                       
##     int2                -0.048    0.033   -1.435    0.151   -0.048   -0.045
##   AFF_WORRY ~~                                                             
##     int2                -0.006    0.033   -0.194    0.846   -0.006   -0.006
##     WORRY_EMPLOYME       0.037    0.032    1.160    0.246    0.037    0.037
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.204    0.030    6.741    0.000    0.204    0.203
##   WORRY_EMPLOYMENT ~~                                                      
##     int1                -0.051    0.036   -1.399    0.162   -0.051   -0.044
##     int2                 0.077    0.033    2.335    0.020    0.077    0.074
##   int1 ~~                                                                  
##     int2                 0.288    0.035    8.168    0.000    0.288    0.240
##  .POL_NONE ~~                                                              
##    .POL_HOME             0.246    0.024   10.229    0.000    0.246    0.291
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.29.)   -0.016    0.008   -1.978    0.048   -0.016   -0.016
##    .POL_HOM (.30.)   -0.000    0.009   -0.051    0.959   -0.000   -0.000
##     AFF_WOR (.31.)   -0.006    0.009   -0.648    0.517   -0.006   -0.006
##     WORRY_R (.32.)   -0.003    0.009   -0.361    0.718   -0.003   -0.003
##     WORRY_E (.33.)   -0.001    0.009   -0.088    0.930   -0.001   -0.001
##     int1    (.34.)    0.160    0.010   15.777    0.000    0.160    0.140
##     int2    (.35.)    0.029    0.009    3.095    0.002    0.029    0.028
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.22.)    0.778    0.010   79.139    0.000    0.778    0.772
##    .POL_HOM (.23.)    0.924    0.012   79.139    0.000    0.924    0.904
##     AFF_WOR (.24.)    1.006    0.013   79.095    0.000    1.006    1.000
##     WORRY_R (.25.)    1.008    0.013   79.105    0.000    1.008    1.000
##     WORRY_E (.26.)    0.999    0.013   79.090    0.000    0.999    1.000
##     int1    (.27.)    1.305    0.016   79.105    0.000    1.305    1.000
##     int2    (.28.)    1.103    0.014   79.097    0.000    1.103    1.000
## 
## R-Square:
##                    Estimate
##     POL_NONE          0.228
##     POL_HOME          0.096
## 
## 
## Group 3 [2]:
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   POL_NONE ~                                                            
##     AFF_WORRY         0.414    0.029   14.501    0.000    0.414    0.420
##     WORRY_RECESSIO   -0.056    0.030   -1.906    0.057   -0.056   -0.057
##     WORRY_EMPLOYME   -0.151    0.029   -5.223    0.000   -0.151   -0.153
##     int1              0.015    0.026    0.594    0.553    0.015    0.018
##     int2              0.026    0.028    0.930    0.352    0.026    0.027
##   POL_HOME ~                                                            
##     AFF_WORRY         0.305    0.031    9.797    0.000    0.305    0.303
##     WORRY_RECESSIO   -0.056    0.032   -1.748    0.080   -0.056   -0.056
##     WORRY_EMPLOYME    0.022    0.031    0.710    0.478    0.022    0.022
##     int1              0.016    0.028    0.572    0.568    0.016    0.018
##     int2              0.006    0.030    0.198    0.843    0.006    0.006
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   AFF_WORRY ~~                                                             
##     WORRY_RECESSIO       0.180    0.031    5.859    0.000    0.180    0.179
##   WORRY_RECESSION ~~                                                       
##     int1                -0.179    0.035   -5.054    0.000   -0.179   -0.156
##   AFF_WORRY ~~                                                             
##     int1                -0.014    0.036   -0.386    0.699   -0.014   -0.012
##   WORRY_RECESSION ~~                                                       
##     int2                -0.045    0.033   -1.360    0.174   -0.045   -0.043
##   AFF_WORRY ~~                                                             
##     int2                 0.042    0.033    1.250    0.211    0.042    0.040
##     WORRY_EMPLOYME      -0.014    0.032   -0.450    0.653   -0.014   -0.014
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.222    0.030    7.434    0.000    0.222    0.222
##   WORRY_EMPLOYMENT ~~                                                      
##     int1                -0.047    0.036   -1.311    0.190   -0.047   -0.042
##     int2                 0.019    0.033    0.571    0.568    0.019    0.018
##   int1 ~~                                                                  
##     int2                 0.322    0.035    9.298    0.000    0.322    0.269
##  .POL_NONE ~~                                                              
##    .POL_HOME             0.265    0.024   11.219    0.000    0.265    0.313
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.29.)   -0.016    0.008   -1.978    0.048   -0.016   -0.016
##    .POL_HOM (.30.)   -0.000    0.009   -0.051    0.959   -0.000   -0.000
##     AFF_WOR (.31.)   -0.006    0.009   -0.648    0.517   -0.006   -0.006
##     WORRY_R (.32.)   -0.003    0.009   -0.361    0.718   -0.003   -0.003
##     WORRY_E (.33.)   -0.001    0.009   -0.088    0.930   -0.001   -0.001
##     int1    (.34.)    0.160    0.010   15.777    0.000    0.160    0.140
##     int2    (.35.)    0.029    0.009    3.095    0.002    0.029    0.028
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.22.)    0.778    0.010   79.139    0.000    0.778    0.797
##    .POL_HOM (.23.)    0.924    0.012   79.139    0.000    0.924    0.910
##     AFF_WOR (.24.)    1.006    0.013   79.095    0.000    1.006    1.000
##     WORRY_R (.25.)    1.008    0.013   79.105    0.000    1.008    1.000
##     WORRY_E (.26.)    0.999    0.013   79.090    0.000    0.999    1.000
##     int1    (.27.)    1.305    0.016   79.105    0.000    1.305    1.000
##     int2    (.28.)    1.103    0.014   79.097    0.000    1.103    1.000
## 
## R-Square:
##                    Estimate
##     POL_NONE          0.203
##     POL_HOME          0.090
## 
## 
## Group 4 [3]:
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   POL_NONE ~                                                            
##     AFF_WORRY         0.425    0.028   14.981    0.000    0.425    0.425
##     WORRY_RECESSIO   -0.071    0.029   -2.444    0.015   -0.071   -0.071
##     WORRY_EMPLOYME   -0.224    0.029   -7.791    0.000   -0.224   -0.223
##     int1              0.095    0.025    3.769    0.000    0.095    0.108
##     int2              0.003    0.027    0.101    0.920    0.003    0.003
##   POL_HOME ~                                                            
##     AFF_WORRY         0.234    0.031    7.561    0.000    0.234    0.233
##     WORRY_RECESSIO   -0.116    0.032   -3.629    0.000   -0.116   -0.115
##     WORRY_EMPLOYME    0.121    0.031    3.868    0.000    0.121    0.120
##     int1              0.082    0.027    2.994    0.003    0.082    0.093
##     int2              0.046    0.030    1.554    0.120    0.046    0.048
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   AFF_WORRY ~~                                                             
##     WORRY_RECESSIO       0.180    0.031    5.905    0.000    0.180    0.179
##   WORRY_RECESSION ~~                                                       
##     int1                -0.215    0.035   -6.217    0.000   -0.215   -0.188
##   AFF_WORRY ~~                                                             
##     int1                -0.079    0.036   -2.201    0.028   -0.079   -0.069
##   WORRY_RECESSION ~~                                                       
##     int2                -0.036    0.033   -1.077    0.282   -0.036   -0.034
##   AFF_WORRY ~~                                                             
##     int2                -0.056    0.033   -1.672    0.095   -0.056   -0.053
##     WORRY_EMPLOYME       0.065    0.032    2.058    0.040    0.065    0.065
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.191    0.030    6.304    0.000    0.191    0.190
##   WORRY_EMPLOYMENT ~~                                                      
##     int1                -0.032    0.036   -0.896    0.370   -0.032   -0.028
##     int2                 0.117    0.033    3.597    0.000    0.117    0.112
##   int1 ~~                                                                  
##     int2                 0.236    0.036    6.551    0.000    0.236    0.197
##  .POL_NONE ~~                                                              
##    .POL_HOME             0.241    0.024   10.006    0.000    0.241    0.284
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.29.)   -0.016    0.008   -1.978    0.048   -0.016   -0.016
##    .POL_HOM (.30.)   -0.000    0.009   -0.051    0.959   -0.000   -0.000
##     AFF_WOR (.31.)   -0.006    0.009   -0.648    0.517   -0.006   -0.006
##     WORRY_R (.32.)   -0.003    0.009   -0.361    0.718   -0.003   -0.003
##     WORRY_E (.33.)   -0.001    0.009   -0.088    0.930   -0.001   -0.001
##     int1    (.34.)    0.160    0.010   15.777    0.000    0.160    0.140
##     int2    (.35.)    0.029    0.009    3.095    0.002    0.029    0.028
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.22.)    0.778    0.010   79.139    0.000    0.778    0.773
##    .POL_HOM (.23.)    0.924    0.012   79.139    0.000    0.924    0.915
##     AFF_WOR (.24.)    1.006    0.013   79.095    0.000    1.006    1.000
##     WORRY_R (.25.)    1.008    0.013   79.105    0.000    1.008    1.000
##     WORRY_E (.26.)    0.999    0.013   79.090    0.000    0.999    1.000
##     int1    (.27.)    1.305    0.016   79.105    0.000    1.305    1.000
##     int2    (.28.)    1.103    0.014   79.097    0.000    1.103    1.000
## 
## R-Square:
##                    Estimate
##     POL_NONE          0.227
##     POL_HOME          0.085
## 
## 
## Group 5 [4]:
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   POL_NONE ~                                                            
##     AFF_WORRY         0.447    0.029   15.500    0.000    0.447    0.446
##     WORRY_RECESSIO   -0.022    0.029   -0.754    0.451   -0.022   -0.022
##     WORRY_EMPLOYME   -0.140    0.029   -4.894    0.000   -0.140   -0.140
##     int1              0.057    0.025    2.256    0.024    0.057    0.064
##     int2             -0.004    0.027   -0.147    0.883   -0.004   -0.004
##   POL_HOME ~                                                            
##     AFF_WORRY         0.209    0.031    6.659    0.000    0.209    0.213
##     WORRY_RECESSIO   -0.040    0.031   -1.285    0.199   -0.040   -0.041
##     WORRY_EMPLOYME    0.006    0.031    0.196    0.844    0.006    0.006
##     int1              0.050    0.027    1.820    0.069    0.050    0.058
##     int2             -0.017    0.030   -0.552    0.581   -0.017   -0.018
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   AFF_WORRY ~~                                                             
##     WORRY_RECESSIO       0.142    0.032    4.496    0.000    0.142    0.141
##   WORRY_RECESSION ~~                                                       
##     int1                -0.035    0.037   -0.935    0.350   -0.035   -0.030
##   AFF_WORRY ~~                                                             
##     int1                 0.114    0.036    3.128    0.002    0.114    0.099
##   WORRY_RECESSION ~~                                                       
##     int2                -0.002    0.034   -0.062    0.951   -0.002   -0.002
##   AFF_WORRY ~~                                                             
##     int2                 0.022    0.034    0.651    0.515    0.022    0.021
##     WORRY_EMPLOYME      -0.021    0.032   -0.642    0.521   -0.021   -0.021
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.119    0.032    3.765    0.000    0.119    0.119
##   WORRY_EMPLOYMENT ~~                                                      
##     int1                -0.002    0.037   -0.060    0.953   -0.002   -0.002
##     int2                 0.047    0.034    1.393    0.164    0.047    0.045
##   int1 ~~                                                                  
##     int2                 0.190    0.037    5.092    0.000    0.190    0.159
##  .POL_NONE ~~                                                              
##    .POL_HOME             0.265    0.024   11.072    0.000    0.265    0.313
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.29.)   -0.016    0.008   -1.978    0.048   -0.016   -0.016
##    .POL_HOM (.30.)   -0.000    0.009   -0.051    0.959   -0.000   -0.000
##     AFF_WOR (.31.)   -0.006    0.009   -0.648    0.517   -0.006   -0.006
##     WORRY_R (.32.)   -0.003    0.009   -0.361    0.718   -0.003   -0.003
##     WORRY_E (.33.)   -0.001    0.009   -0.088    0.930   -0.001   -0.001
##     int1    (.34.)    0.160    0.010   15.777    0.000    0.160    0.140
##     int2    (.35.)    0.029    0.009    3.095    0.002    0.029    0.028
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.22.)    0.778    0.010   79.139    0.000    0.778    0.771
##    .POL_HOM (.23.)    0.924    0.012   79.139    0.000    0.924    0.950
##     AFF_WOR (.24.)    1.006    0.013   79.095    0.000    1.006    1.000
##     WORRY_R (.25.)    1.008    0.013   79.105    0.000    1.008    1.000
##     WORRY_E (.26.)    0.999    0.013   79.090    0.000    0.999    1.000
##     int1    (.27.)    1.305    0.016   79.105    0.000    1.305    1.000
##     int2    (.28.)    1.103    0.014   79.097    0.000    1.103    1.000
## 
## R-Square:
##                    Estimate
##     POL_NONE          0.229
##     POL_HOME          0.050
## 
## 
## Group 6 [5]:
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   POL_NONE ~                                                            
##     AFF_WORRY         0.440    0.028   15.531    0.000    0.440    0.439
##     WORRY_RECESSIO   -0.078    0.029   -2.714    0.007   -0.078   -0.078
##     WORRY_EMPLOYME   -0.187    0.028   -6.553    0.000   -0.187   -0.186
##     int1             -0.022    0.025   -0.877    0.381   -0.022   -0.025
##     int2              0.046    0.027    1.676    0.094    0.046    0.048
##   POL_HOME ~                                                            
##     AFF_WORRY         0.207    0.031    6.709    0.000    0.207    0.210
##     WORRY_RECESSIO   -0.102    0.031   -3.239    0.001   -0.102   -0.104
##     WORRY_EMPLOYME    0.058    0.031    1.869    0.062    0.058    0.059
##     int1              0.008    0.027    0.292    0.771    0.008    0.009
##     int2             -0.013    0.030   -0.453    0.651   -0.013   -0.014
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   AFF_WORRY ~~                                                             
##     WORRY_RECESSIO       0.140    0.031    4.489    0.000    0.140    0.139
##   WORRY_RECESSION ~~                                                       
##     int1                -0.156    0.036   -4.378    0.000   -0.156   -0.136
##   AFF_WORRY ~~                                                             
##     int1                 0.010    0.036    0.284    0.777    0.010    0.009
##   WORRY_RECESSION ~~                                                       
##     int2                -0.035    0.034   -1.036    0.300   -0.035   -0.033
##   AFF_WORRY ~~                                                             
##     int2                -0.014    0.034   -0.410    0.682   -0.014   -0.013
##     WORRY_EMPLOYME       0.003    0.032    0.097    0.923    0.003    0.003
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.152    0.031    4.891    0.000    0.152    0.151
##   WORRY_EMPLOYMENT ~~                                                      
##     int1                -0.035    0.036   -0.973    0.331   -0.035   -0.031
##     int2                 0.029    0.033    0.876    0.381    0.029    0.028
##   int1 ~~                                                                  
##     int2                 0.233    0.036    6.408    0.000    0.233    0.194
##  .POL_NONE ~~                                                              
##    .POL_HOME             0.273    0.023   11.634    0.000    0.273    0.322
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.29.)   -0.016    0.008   -1.978    0.048   -0.016   -0.016
##    .POL_HOM (.30.)   -0.000    0.009   -0.051    0.959   -0.000   -0.000
##     AFF_WOR (.31.)   -0.006    0.009   -0.648    0.517   -0.006   -0.006
##     WORRY_R (.32.)   -0.003    0.009   -0.361    0.718   -0.003   -0.003
##     WORRY_E (.33.)   -0.001    0.009   -0.088    0.930   -0.001   -0.001
##     int1    (.34.)    0.160    0.010   15.777    0.000    0.160    0.140
##     int2    (.35.)    0.029    0.009    3.095    0.002    0.029    0.028
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.22.)    0.778    0.010   79.139    0.000    0.778    0.772
##    .POL_HOM (.23.)    0.924    0.012   79.139    0.000    0.924    0.949
##     AFF_WOR (.24.)    1.006    0.013   79.095    0.000    1.006    1.000
##     WORRY_R (.25.)    1.008    0.013   79.105    0.000    1.008    1.000
##     WORRY_E (.26.)    0.999    0.013   79.090    0.000    0.999    1.000
##     int1    (.27.)    1.305    0.016   79.105    0.000    1.305    1.000
##     int2    (.28.)    1.103    0.014   79.097    0.000    1.103    1.000
## 
## R-Square:
##                    Estimate
##     POL_NONE          0.228
##     POL_HOME          0.051
## 
## 
## Group 7 [6]:
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   POL_NONE ~                                                            
##     AFF_WORRY         0.446    0.029   15.450    0.000    0.446    0.448
##     WORRY_RECESSIO   -0.092    0.030   -3.100    0.002   -0.092   -0.093
##     WORRY_EMPLOYME   -0.114    0.029   -3.932    0.000   -0.114   -0.114
##     int1              0.016    0.026    0.625    0.532    0.016    0.019
##     int2             -0.024    0.028   -0.853    0.394   -0.024   -0.025
##   POL_HOME ~                                                            
##     AFF_WORRY         0.262    0.031    8.350    0.000    0.262    0.263
##     WORRY_RECESSIO   -0.068    0.032   -2.083    0.037   -0.068   -0.068
##     WORRY_EMPLOYME    0.100    0.032    3.146    0.002    0.100    0.099
##     int1              0.051    0.028    1.809    0.070    0.051    0.058
##     int2             -0.039    0.031   -1.278    0.201   -0.039   -0.041
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   AFF_WORRY ~~                                                             
##     WORRY_RECESSIO       0.150    0.031    4.782    0.000    0.150    0.149
##   WORRY_RECESSION ~~                                                       
##     int1                -0.207    0.035   -5.864    0.000   -0.207   -0.181
##   AFF_WORRY ~~                                                             
##     int1                 0.064    0.037    1.742    0.082    0.064    0.056
##   WORRY_RECESSION ~~                                                       
##     int2                -0.049    0.034   -1.463    0.143   -0.049   -0.047
##   AFF_WORRY ~~                                                             
##     int2                 0.062    0.034    1.823    0.068    0.062    0.058
##     WORRY_EMPLOYME      -0.025    0.032   -0.770    0.441   -0.025   -0.025
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.194    0.031    6.301    0.000    0.194    0.193
##   WORRY_EMPLOYMENT ~~                                                      
##     int1                -0.055    0.037   -1.490    0.136   -0.055   -0.048
##     int2                -0.016    0.034   -0.484    0.628   -0.016   -0.016
##   int1 ~~                                                                  
##     int2                 0.305    0.035    8.603    0.000    0.305    0.254
##  .POL_NONE ~~                                                              
##    .POL_HOME             0.179    0.026    6.941    0.000    0.179    0.211
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.29.)   -0.016    0.008   -1.978    0.048   -0.016   -0.016
##    .POL_HOM (.30.)   -0.000    0.009   -0.051    0.959   -0.000   -0.000
##     AFF_WOR (.31.)   -0.006    0.009   -0.648    0.517   -0.006   -0.006
##     WORRY_R (.32.)   -0.003    0.009   -0.361    0.718   -0.003   -0.003
##     WORRY_E (.33.)   -0.001    0.009   -0.088    0.930   -0.001   -0.001
##     int1    (.34.)    0.160    0.010   15.777    0.000    0.160    0.140
##     int2    (.35.)    0.029    0.009    3.095    0.002    0.029    0.028
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.22.)    0.778    0.010   79.139    0.000    0.778    0.782
##    .POL_HOM (.23.)    0.924    0.012   79.139    0.000    0.924    0.921
##     AFF_WOR (.24.)    1.006    0.013   79.095    0.000    1.006    1.000
##     WORRY_R (.25.)    1.008    0.013   79.105    0.000    1.008    1.000
##     WORRY_E (.26.)    0.999    0.013   79.090    0.000    0.999    1.000
##     int1    (.27.)    1.305    0.016   79.105    0.000    1.305    1.000
##     int2    (.28.)    1.103    0.014   79.097    0.000    1.103    1.000
## 
## R-Square:
##                    Estimate
##     POL_NONE          0.218
##     POL_HOME          0.079
## 
## 
## Group 8 [7]:
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   POL_NONE ~                                                            
##     AFF_WORRY         0.423    0.029   14.832    0.000    0.423    0.425
##     WORRY_RECESSIO   -0.088    0.029   -2.985    0.003   -0.088   -0.088
##     WORRY_EMPLOYME   -0.202    0.029   -7.036    0.000   -0.202   -0.203
##     int1              0.008    0.026    0.302    0.763    0.008    0.009
##     int2             -0.050    0.028   -1.798    0.072   -0.050   -0.053
##   POL_HOME ~                                                            
##     AFF_WORRY         0.264    0.031    8.505    0.000    0.264    0.264
##     WORRY_RECESSIO   -0.086    0.032   -2.685    0.007   -0.086   -0.086
##     WORRY_EMPLOYME    0.107    0.031    3.413    0.001    0.107    0.107
##     int1             -0.011    0.028   -0.384    0.701   -0.011   -0.012
##     int2             -0.045    0.030   -1.488    0.137   -0.045   -0.047
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   AFF_WORRY ~~                                                             
##     WORRY_RECESSIO       0.168    0.031    5.445    0.000    0.168    0.167
##   WORRY_RECESSION ~~                                                       
##     int1                -0.165    0.036   -4.653    0.000   -0.165   -0.144
##   AFF_WORRY ~~                                                             
##     int1                 0.046    0.036    1.272    0.203    0.046    0.040
##   WORRY_RECESSION ~~                                                       
##     int2                -0.034    0.034   -1.020    0.308   -0.034   -0.032
##   AFF_WORRY ~~                                                             
##     int2                 0.025    0.034    0.733    0.463    0.025    0.023
##     WORRY_EMPLOYME       0.038    0.032    1.181    0.238    0.038    0.038
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.204    0.030    6.736    0.000    0.204    0.203
##   WORRY_EMPLOYMENT ~~                                                      
##     int1                -0.035    0.036   -0.956    0.339   -0.035   -0.030
##     int2                 0.027    0.033    0.805    0.421    0.027    0.026
##   int1 ~~                                                                  
##     int2                 0.339    0.034    9.856    0.000    0.339    0.282
##  .POL_NONE ~~                                                              
##    .POL_HOME             0.202    0.025    8.040    0.000    0.202    0.238
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.29.)   -0.016    0.008   -1.978    0.048   -0.016   -0.016
##    .POL_HOM (.30.)   -0.000    0.009   -0.051    0.959   -0.000   -0.000
##     AFF_WOR (.31.)   -0.006    0.009   -0.648    0.517   -0.006   -0.006
##     WORRY_R (.32.)   -0.003    0.009   -0.361    0.718   -0.003   -0.003
##     WORRY_E (.33.)   -0.001    0.009   -0.088    0.930   -0.001   -0.001
##     int1    (.34.)    0.160    0.010   15.777    0.000    0.160    0.140
##     int2    (.35.)    0.029    0.009    3.095    0.002    0.029    0.028
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.22.)    0.778    0.010   79.139    0.000    0.778    0.780
##    .POL_HOM (.23.)    0.924    0.012   79.139    0.000    0.924    0.919
##     AFF_WOR (.24.)    1.006    0.013   79.095    0.000    1.006    1.000
##     WORRY_R (.25.)    1.008    0.013   79.105    0.000    1.008    1.000
##     WORRY_E (.26.)    0.999    0.013   79.090    0.000    0.999    1.000
##     int1    (.27.)    1.305    0.016   79.105    0.000    1.305    1.000
##     int2    (.28.)    1.103    0.014   79.097    0.000    1.103    1.000
## 
## R-Square:
##                    Estimate
##     POL_NONE          0.220
##     POL_HOME          0.081
## 
## 
## Group 9 [8]:
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   POL_NONE ~                                                            
##     AFF_WORRY         0.462    0.029   15.712    0.000    0.462    0.453
##     WORRY_RECESSIO   -0.040    0.030   -1.325    0.185   -0.040   -0.039
##     WORRY_EMPLOYME   -0.226    0.030   -7.564    0.000   -0.226   -0.220
##     int1             -0.013    0.026   -0.480    0.631   -0.013   -0.014
##     int2             -0.087    0.029   -3.010    0.003   -0.087   -0.089
##   POL_HOME ~                                                            
##     AFF_WORRY         0.319    0.032    9.949    0.000    0.319    0.314
##     WORRY_RECESSIO   -0.091    0.033   -2.768    0.006   -0.091   -0.089
##     WORRY_EMPLOYME    0.097    0.033    2.985    0.003    0.097    0.095
##     int1              0.034    0.029    1.176    0.240    0.034    0.038
##     int2             -0.010    0.031   -0.316    0.752   -0.010   -0.010
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   AFF_WORRY ~~                                                             
##     WORRY_RECESSIO       0.144    0.032    4.477    0.000    0.144    0.143
##   WORRY_RECESSION ~~                                                       
##     int1                -0.119    0.037   -3.210    0.001   -0.119   -0.104
##   AFF_WORRY ~~                                                             
##     int1                 0.075    0.037    1.990    0.047    0.075    0.065
##   WORRY_RECESSION ~~                                                       
##     int2                -0.046    0.035   -1.337    0.181   -0.046   -0.044
##   AFF_WORRY ~~                                                             
##     int2                -0.039    0.035   -1.121    0.262   -0.039   -0.037
##     WORRY_EMPLOYME       0.052    0.033    1.593    0.111    0.052    0.052
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.203    0.031    6.508    0.000    0.203    0.203
##   WORRY_EMPLOYMENT ~~                                                      
##     int1                -0.048    0.037   -1.271    0.204   -0.048   -0.042
##     int2                 0.079    0.034    2.305    0.021    0.079    0.075
##   int1 ~~                                                                  
##     int2                 0.312    0.036    8.643    0.000    0.312    0.260
##  .POL_NONE ~~                                                              
##    .POL_HOME             0.173    0.026    6.555    0.000    0.173    0.204
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.29.)   -0.016    0.008   -1.978    0.048   -0.016   -0.015
##    .POL_HOM (.30.)   -0.000    0.009   -0.051    0.959   -0.000   -0.000
##     AFF_WOR (.31.)   -0.006    0.009   -0.648    0.517   -0.006   -0.006
##     WORRY_R (.32.)   -0.003    0.009   -0.361    0.718   -0.003   -0.003
##     WORRY_E (.33.)   -0.001    0.009   -0.088    0.930   -0.001   -0.001
##     int1    (.34.)    0.160    0.010   15.777    0.000    0.160    0.140
##     int2    (.35.)    0.029    0.009    3.095    0.002    0.029    0.028
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .POL_NON (.22.)    0.778    0.010   79.139    0.000    0.778    0.743
##    .POL_HOM (.23.)    0.924    0.012   79.139    0.000    0.924    0.890
##     AFF_WOR (.24.)    1.006    0.013   79.095    0.000    1.006    1.000
##     WORRY_R (.25.)    1.008    0.013   79.105    0.000    1.008    1.000
##     WORRY_E (.26.)    0.999    0.013   79.090    0.000    0.999    1.000
##     int1    (.27.)    1.305    0.016   79.105    0.000    1.305    1.000
##     int2    (.28.)    1.103    0.014   79.097    0.000    1.103    1.000
## 
## R-Square:
##                    Estimate
##     POL_NONE          0.257
##     POL_HOME          0.110
## 
## 
## Group 10 [9]:
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   POL_NONE ~                                                            
##     AFF_WORRY         0.498    0.030   16.456    0.000    0.498    0.490
##     WORRY_RECESSIO   -0.086    0.031   -2.740    0.006   -0.086   -0.084
##     WORRY_EMPLOYME   -0.144    0.030   -4.775    0.000   -0.144   -0.142
##     int1             -0.004    0.027   -0.151    0.880   -0.004   -0.005
##     int2              0.000    0.029    0.008    0.994    0.000    0.000
##   POL_HOME ~                                                            
##     AFF_WORRY         0.215    0.033    6.512    0.000    0.215    0.217
##     WORRY_RECESSIO   -0.057    0.034   -1.665    0.096   -0.057   -0.057
##     WORRY_EMPLOYME    0.117    0.033    3.554    0.000    0.117    0.118
##     int1              0.017    0.030    0.573    0.567    0.017    0.020
##     int2             -0.055    0.032   -1.711    0.087   -0.055   -0.058
## 
## Covariances:
##                       Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   AFF_WORRY ~~                                                             
##     WORRY_RECESSIO       0.182    0.032    5.609    0.000    0.182    0.181
##   WORRY_RECESSION ~~                                                       
##     int1                -0.246    0.036   -6.777    0.000   -0.246   -0.214
##   AFF_WORRY ~~                                                             
##     int1                 0.025    0.039    0.638    0.524    0.025    0.021
##   WORRY_RECESSION ~~                                                       
##     int2                -0.040    0.035   -1.124    0.261   -0.040   -0.038
##   AFF_WORRY ~~                                                             
##     int2                -0.042    0.035   -1.180    0.238   -0.042   -0.040
##     WORRY_EMPLOYME       0.044    0.034    1.321    0.186    0.044    0.044
##   WORRY_RECESSION ~~                                                       
##     WORRY_EMPLOYME       0.156    0.033    4.778    0.000    0.156    0.