summary(data)
##        X              sr_age      sr_gender   q6_me_inf      
##  Min.   :  0.00   Min.   :18.00   F:206     Min.   :0.00000  
##  1st Qu.: 89.25   1st Qu.:23.00   M:136     1st Qu.:0.00000  
##  Median :176.50   Median :27.00             Median :0.00000  
##  Mean   :176.56   Mean   :27.78             Mean   :0.06433  
##  3rd Qu.:264.75   3rd Qu.:32.00             3rd Qu.:0.00000  
##  Max.   :353.00   Max.   :40.00             Max.   :1.00000  
##  q6_close_person_inf q6_close_person_died q6_media_valence   covid_worry   
##  Min.   :0.00000     Min.   :0.0000       Min.   :-3.0000   Min.   :1.125  
##  1st Qu.:0.00000     1st Qu.:0.0000       1st Qu.:-2.0000   1st Qu.:3.875  
##  Median :0.00000     Median :0.0000       Median : 0.0000   Median :4.750  
##  Mean   :0.05263     Mean   :0.1082       Mean   :-0.6404   Mean   :4.579  
##  3rd Qu.:0.00000     3rd Qu.:0.0000       3rd Qu.: 1.0000   3rd Qu.:5.500  
##  Max.   :1.00000     Max.   :1.0000       Max.   : 3.0000   Max.   :7.000  
##  covid_avoidance_beh covid_spec_anxiety covid_prob_estimates covid_end_est  
##  Min.   :1.000       Min.   :1.000      Min.   : 1.667       Min.   :-16.0  
##  1st Qu.:5.667       1st Qu.:4.667      1st Qu.:31.667       1st Qu.:135.6  
##  Median :6.000       Median :5.500      Median :43.333       Median :211.5  
##  Mean   :5.909       Mean   :5.275      Mean   :44.006       Mean   :260.6  
##  3rd Qu.:6.667       3rd Qu.:6.167      3rd Qu.:56.667       3rd Qu.:333.0  
##  Max.   :7.000       Max.   :7.000      Max.   :88.333       Max.   :988.0  
##     stai_ta         stai_sa        sticsa_ta       sticsa_sa    
##  Min.   :21.00   Min.   :19.00   Min.   :21.00   Min.   :21.00  
##  1st Qu.:37.00   1st Qu.:33.00   1st Qu.:27.00   1st Qu.:24.00  
##  Median :46.00   Median :41.00   Median :34.00   Median :30.00  
##  Mean   :46.27   Mean   :41.41   Mean   :35.60   Mean   :33.04  
##  3rd Qu.:56.00   3rd Qu.:49.00   3rd Qu.:41.75   3rd Qu.:39.00  
##  Max.   :76.00   Max.   :76.00   Max.   :84.00   Max.   :84.00  
##       bdi             cat       
##  Min.   : 0.00   Min.   : 0.00  
##  1st Qu.: 5.00   1st Qu.:20.00  
##  Median :11.00   Median :33.00  
##  Mean   :12.52   Mean   :32.95  
##  3rd Qu.:17.00   3rd Qu.:44.00  
##  Max.   :50.00   Max.   :80.00

Model 1: Factors predicting worry

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## covid_worry ~ stai_sa + stai_ta + bdi + cat + q6_me_inf + q6_close_person_inf +  
##     q6_close_person_died + q6_media_valence + (1 | sr_gender) +  
##     (1 | sr_age)
##    Data: data
## 
## REML criterion at convergence: 1083.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0591 -0.5896  0.0731  0.7092  2.7336 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  sr_age    (Intercept) 0.04658  0.2158  
##  sr_gender (Intercept) 0.01526  0.1235  
##  Residual              1.21642  1.1029  
## Number of obs: 342, groups:  sr_age, 23; sr_gender, 2
## 
## Fixed effects:
##                        Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)            3.299882   0.338197  76.023700   9.757 4.78e-15 ***
## stai_sa                0.026709   0.007616 320.836764   3.507 0.000518 ***
## stai_ta               -0.016770   0.010300 326.554951  -1.628 0.104441    
## bdi                    0.003916   0.011400 329.057680   0.343 0.731464    
## cat                    0.023560   0.005926 325.470701   3.975 8.66e-05 ***
## q6_me_inf              0.461098   0.256643 327.244778   1.797 0.073313 .  
## q6_close_person_inf    0.330748   0.277377 328.258916   1.192 0.233960    
## q6_close_person_died   0.535593   0.196760 329.555920   2.722 0.006832 ** 
## q6_media_valence      -0.007806   0.039852 332.021386  -0.196 0.844824    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##               (Intr) stai_s stai_t bdi    cat    q6_m_n q6_cls_prsn_n
## stai_sa       -0.295                                                 
## stai_ta       -0.663 -0.348                                          
## bdi            0.542 -0.190 -0.461                                   
## cat            0.179 -0.097 -0.404 -0.221                            
## q6_me_inf     -0.023 -0.001 -0.004 -0.106  0.067                     
## q6_cls_prsn_n -0.001  0.023 -0.048  0.055 -0.023 -0.193              
## q6_cls_prsn_d -0.099 -0.048  0.075 -0.075 -0.007  0.087  0.068       
## q6_med_vlnc   -0.005  0.064 -0.017  0.018  0.054  0.123  0.025       
##               q6_cls_prsn_d
## stai_sa                    
## stai_ta                    
## bdi                        
## cat                        
## q6_me_inf                  
## q6_cls_prsn_n              
## q6_cls_prsn_d              
## q6_med_vlnc   -0.021
## Start:  AIC=156.74
## covid_worry ~ 1
## 
##                        Df Sum of Sq    RSS    AIC
## + cat                   1    81.507 456.17 102.52
## + stai_sa               1    78.794 458.88 104.54
## + bdi                   1    55.019 482.66 121.82
## + stai_ta               1    51.156 486.52 124.55
## + sr_gender             1    15.963 521.71 148.43
## + q6_close_person_died  1    10.446 527.23 152.03
## + q6_me_inf             1     8.059 529.62 153.57
## + sr_age                1     6.072 531.60 154.85
## + q6_media_valence      1     5.251 532.43 155.38
## <none>                              537.68 156.74
## + q6_close_person_inf   1     2.830 534.85 156.93
## 
## Step:  AIC=102.51
## covid_worry ~ cat
## 
##                        Df Sum of Sq    RSS     AIC
## + stai_sa               1    16.094 440.08  92.231
## + sr_age                1    13.471 442.70  94.263
## + q6_close_person_died  1     8.654 447.52  97.964
## + sr_gender             1     6.900 449.27  99.302
## + q6_me_inf             1     5.084 451.09 100.681
## <none>                              456.17 102.515
## + bdi                   1     1.846 454.32 103.128
## + q6_close_person_inf   1     1.833 454.34 103.137
## + q6_media_valence      1     0.830 455.34 103.892
## + stai_ta               1     0.053 456.12 104.475
## - cat                   1    81.507 537.68 156.737
## 
## Step:  AIC=92.23
## covid_worry ~ cat + stai_sa
## 
##                        Df Sum of Sq    RSS     AIC
## + sr_age                1   15.0905 424.99  82.298
## + q6_close_person_died  1    7.7177 432.36  88.180
## + stai_sa:cat           1    7.3153 432.76  88.498
## + sr_gender             1    4.7395 435.34  90.528
## + stai_ta               1    4.0285 436.05  91.086
## + q6_me_inf             1    3.9669 436.11  91.134
## <none>                              440.08  92.231
## + q6_close_person_inf   1    1.9284 438.15  92.729
## + q6_media_valence      1    0.3825 439.69  93.933
## + bdi                   1    0.1317 439.94  94.129
## - stai_sa               1   16.0937 456.17 102.515
## - cat                   1   18.8065 458.88 104.542
## 
## Step:  AIC=82.3
## covid_worry ~ cat + stai_sa + sr_age
## 
##                        Df Sum of Sq    RSS    AIC
## + stai_sa:cat           1    8.9438 416.04 77.023
## + q6_close_person_died  1    7.9202 417.07 77.864
## + sr_gender             1    5.2730 419.71 80.028
## + stai_ta               1    3.6634 421.32 81.337
## + q6_me_inf             1    2.9689 422.02 81.900
## <none>                              424.99 82.298
## + q6_close_person_inf   1    1.9543 423.03 82.721
## + stai_sa:sr_age        1    0.8889 424.10 83.582
## + cat:sr_age            1    0.0938 424.89 84.222
## + q6_media_valence      1    0.0522 424.93 84.256
## + bdi                   1    0.0300 424.96 84.273
## - sr_age                1   15.0905 440.08 92.231
## - stai_sa               1   17.7129 442.70 94.263
## - cat                   1   21.0418 446.03 96.825
## 
## Step:  AIC=77.02
## covid_worry ~ cat + stai_sa + sr_age + cat:stai_sa
## 
##                        Df Sum of Sq    RSS    AIC
## + q6_close_person_died  1    7.2147 408.83 73.041
## + sr_gender             1    4.3122 411.73 75.460
## + q6_me_inf             1    3.6104 412.43 76.043
## + stai_ta               1    3.4036 412.64 76.214
## <none>                              416.04 77.023
## + q6_close_person_inf   1    2.0544 413.99 77.330
## + stai_sa:sr_age        1    0.4601 415.58 78.645
## + bdi                   1    0.2613 415.78 78.809
## + q6_media_valence      1    0.1527 415.89 78.898
## + cat:sr_age            1    0.0043 416.04 79.020
## - cat:stai_sa           1    8.9438 424.99 82.298
## - sr_age                1   16.7190 432.76 88.498
## 
## Step:  AIC=73.04
## covid_worry ~ cat + stai_sa + sr_age + q6_close_person_died + 
##     cat:stai_sa
## 
##                                Df Sum of Sq    RSS    AIC
## + q6_me_inf                     1    4.6754 404.15 71.107
## + sr_gender                     1    4.3068 404.52 71.419
## + cat:q6_close_person_died      1    4.1006 404.73 71.593
## + stai_sa:q6_close_person_died  1    3.6395 405.19 71.983
## + stai_ta                       1    2.9349 405.89 72.577
## + q6_close_person_inf           1    2.7593 406.07 72.725
## <none>                                      408.83 73.041
## + q6_close_person_died:sr_age   1    2.0424 406.78 73.328
## + stai_sa:sr_age                1    0.5364 408.29 74.592
## + q6_media_valence              1    0.2164 408.61 74.860
## + bdi                           1    0.1510 408.68 74.914
## + cat:sr_age                    1    0.0001 408.83 75.041
## - q6_close_person_died          1    7.2147 416.04 77.023
## - cat:stai_sa                   1    8.2383 417.07 77.864
## - sr_age                        1   16.8535 425.68 84.857
## 
## Step:  AIC=71.11
## covid_worry ~ cat + stai_sa + sr_age + q6_close_person_died + 
##     q6_me_inf + cat:stai_sa
## 
##                                Df Sum of Sq    RSS    AIC
## + sr_gender                     1    4.0254 400.13 69.684
## + cat:q6_close_person_died      1    3.8955 400.26 69.795
## + q6_me_inf:sr_age              1    3.6549 400.50 70.000
## + stai_sa:q6_close_person_died  1    3.4223 400.73 70.199
## + stai_ta                       1    3.4165 400.74 70.204
## <none>                                      404.15 71.107
## + q6_close_person_died:sr_age   1    2.1582 401.99 71.276
## + q6_close_person_inf           1    1.5802 402.57 71.767
## + stai_sa:sr_age                1    0.8135 403.34 72.418
## + cat:q6_me_inf                 1    0.4290 403.72 72.744
## - q6_me_inf                     1    4.6754 408.83 73.041
## + stai_sa:q6_me_inf             1    0.0690 404.08 73.049
## + cat:sr_age                    1    0.0579 404.09 73.058
## + q6_media_valence              1    0.0419 404.11 73.072
## + bdi                           1    0.0232 404.13 73.087
## - q6_close_person_died          1    8.2797 412.43 76.043
## - cat:stai_sa                   1    8.8998 413.05 76.556
## - sr_age                        1   15.6358 419.79 82.089
## 
## Step:  AIC=69.68
## covid_worry ~ cat + stai_sa + sr_age + q6_close_person_died + 
##     q6_me_inf + sr_gender + cat:stai_sa
## 
##                                  Df Sum of Sq    RSS    AIC
## + q6_close_person_died:sr_gender  1    8.1617 391.96 64.635
## + stai_sa:sr_gender               1    4.7083 395.42 67.635
## + stai_sa:q6_close_person_died    1    4.6622 395.46 67.675
## + cat:q6_close_person_died        1    4.5812 395.55 67.745
## + q6_me_inf:sr_gender             1    4.3316 395.79 67.961
## + q6_me_inf:sr_age                1    3.6338 396.49 68.563
## + stai_ta                         1    2.8448 397.28 69.243
## + q6_close_person_died:sr_age     1    2.4965 397.63 69.543
## <none>                                        400.13 69.684
## + q6_close_person_inf             1    1.6114 398.51 70.304
## + cat:sr_gender                   1    1.3246 398.80 70.550
## + stai_sa:sr_age                  1    0.9853 399.14 70.840
## - sr_gender                       1    4.0254 404.15 71.107
## + cat:q6_me_inf                   1    0.3526 399.77 71.382
## - q6_me_inf                       1    4.3939 404.52 71.419
## + stai_sa:q6_me_inf               1    0.1260 400.00 71.576
## + bdi                             1    0.1047 400.02 71.594
## + sr_gender:sr_age                1    0.0853 400.04 71.611
## + cat:sr_age                      1    0.0811 400.05 71.614
## + q6_media_valence                1    0.0341 400.09 71.655
## - cat:stai_sa                     1    7.9515 408.08 74.413
## - q6_close_person_died            1    8.2389 408.37 74.654
## - sr_age                          1   16.0572 416.18 81.140
## 
## Step:  AIC=64.64
## covid_worry ~ cat + stai_sa + sr_age + q6_close_person_died + 
##     q6_me_inf + sr_gender + cat:stai_sa + q6_close_person_died:sr_gender
## 
##                                  Df Sum of Sq    RSS    AIC
## + stai_sa:sr_gender               1    4.5496 387.42 62.643
## + q6_me_inf:sr_gender             1    3.4347 388.53 63.625
## + q6_me_inf:sr_age                1    3.2574 388.71 63.781
## <none>                                        391.96 64.635
## + stai_ta                         1    2.2456 389.72 64.670
## + q6_close_person_inf             1    1.6173 390.35 65.221
## + cat:q6_close_person_died        1    1.5878 390.38 65.247
## + cat:sr_gender                   1    1.3510 390.61 65.455
## + q6_close_person_died:sr_age     1    1.1526 390.81 65.628
## + stai_sa:sr_age                  1    0.5675 391.40 66.140
## - q6_me_inf                       1    4.1039 396.07 66.198
## + stai_sa:q6_close_person_died    1    0.4477 391.52 66.245
## + bdi                             1    0.2089 391.76 66.453
## + cat:q6_me_inf                   1    0.2012 391.76 66.460
## + sr_gender:sr_age                1    0.1084 391.86 66.541
## + stai_sa:q6_me_inf               1    0.0604 391.90 66.583
## + q6_media_valence                1    0.0093 391.96 66.627
## + cat:sr_age                      1    0.0073 391.96 66.629
## - cat:stai_sa                     1    7.8329 399.80 69.402
## - q6_close_person_died:sr_gender  1    8.1617 400.13 69.684
## - sr_age                          1   15.0341 407.00 75.508
## 
## Step:  AIC=62.64
## covid_worry ~ cat + stai_sa + sr_age + q6_close_person_died + 
##     q6_me_inf + sr_gender + cat:stai_sa + q6_close_person_died:sr_gender + 
##     stai_sa:sr_gender
## 
##                                  Df Sum of Sq    RSS    AIC
## + q6_me_inf:sr_age                1    2.9919 384.42 61.991
## + q6_me_inf:sr_gender             1    2.8063 384.61 62.156
## <none>                                        387.42 62.643
## + stai_ta                         1    1.9418 385.47 62.924
## + q6_close_person_inf             1    1.8742 385.54 62.984
## + cat:q6_close_person_died        1    1.6838 385.73 63.153
## + q6_close_person_died:sr_age     1    1.4228 385.99 63.384
## + stai_sa:q6_close_person_died    1    0.5900 386.83 64.121
## + stai_sa:sr_age                  1    0.4825 386.93 64.216
## + bdi                             1    0.3248 387.09 64.356
## + sr_gender:sr_age                1    0.3019 387.11 64.376
## - q6_me_inf                       1    4.4362 391.85 64.537
## + cat:sr_gender                   1    0.0541 387.36 64.595
## + cat:q6_me_inf                   1    0.0209 387.39 64.624
## + q6_media_valence                1    0.0139 387.40 64.630
## - stai_sa:sr_gender               1    4.5496 391.96 64.635
## + cat:sr_age                      1    0.0024 387.41 64.640
## + stai_sa:q6_me_inf               1    0.0000 387.42 64.643
## - q6_close_person_died:sr_gender  1    8.0030 395.42 67.635
## - cat:stai_sa                     1   10.0627 397.48 69.412
## - sr_age                          1   15.7389 403.15 74.262
## 
## Step:  AIC=61.99
## covid_worry ~ cat + stai_sa + sr_age + q6_close_person_died + 
##     q6_me_inf + sr_gender + cat:stai_sa + q6_close_person_died:sr_gender + 
##     stai_sa:sr_gender + sr_age:q6_me_inf
## 
##                                  Df Sum of Sq    RSS    AIC
## + q6_me_inf:sr_gender             1    2.2926 382.13 61.945
## <none>                                        384.42 61.991
## + stai_ta                         1    1.7732 382.65 62.410
## - sr_age:q6_me_inf                1    2.9919 387.42 62.643
## + cat:q6_close_person_died        1    1.4714 382.95 62.680
## + q6_close_person_inf             1    1.4307 382.99 62.716
## + q6_close_person_died:sr_age     1    0.9739 383.45 63.124
## + stai_sa:sr_age                  1    0.6789 383.74 63.387
## + stai_sa:q6_close_person_died    1    0.5378 383.89 63.512
## + stai_sa:q6_me_inf               1    0.5182 383.90 63.530
## + cat:q6_me_inf                   1    0.4527 383.97 63.588
## + bdi                             1    0.3848 384.04 63.649
## + sr_gender:sr_age                1    0.3369 384.09 63.691
## - stai_sa:sr_gender               1    4.2841 388.71 63.781
## + cat:sr_gender                   1    0.1243 384.30 63.881
## + q6_media_valence                1    0.0261 384.40 63.968
## + cat:sr_age                      1    0.0092 384.41 63.983
## - q6_close_person_died:sr_gender  1    7.6498 392.07 66.730
## - cat:stai_sa                     1   10.7780 395.20 69.448
## 
## Step:  AIC=61.95
## covid_worry ~ cat + stai_sa + sr_age + q6_close_person_died + 
##     q6_me_inf + sr_gender + cat:stai_sa + q6_close_person_died:sr_gender + 
##     stai_sa:sr_gender + sr_age:q6_me_inf + q6_me_inf:sr_gender
## 
##                                  Df Sum of Sq    RSS    AIC
## <none>                                        382.13 61.945
## - q6_me_inf:sr_gender             1    2.2926 384.42 61.991
## - sr_age:q6_me_inf                1    2.4782 384.61 62.156
## + stai_ta                         1    1.4824 380.65 62.616
## + cat:q6_close_person_died        1    1.3507 380.78 62.734
## + q6_close_person_inf             1    1.2127 380.92 62.858
## + q6_close_person_died:sr_age     1    0.9183 381.21 63.123
## + cat:q6_me_inf                   1    0.7774 381.35 63.249
## - stai_sa:sr_gender               1    3.7498 385.88 63.285
## + stai_sa:sr_age                  1    0.7179 381.41 63.302
## + stai_sa:q6_close_person_died    1    0.5889 381.54 63.418
## + bdi                             1    0.5292 381.60 63.471
## + stai_sa:q6_me_inf               1    0.2632 381.87 63.710
## + sr_gender:sr_age                1    0.2157 381.91 63.752
## + q6_media_valence                1    0.0485 382.08 63.902
## + cat:sr_gender                   1    0.0461 382.08 63.904
## + cat:sr_age                      1    0.0262 382.10 63.922
## - q6_close_person_died:sr_gender  1    6.9653 389.10 66.123
## - cat:stai_sa                     1   12.0924 394.22 70.600
## 
## Call:
## lm(formula = covid_worry ~ cat + stai_sa + sr_age + q6_close_person_died + 
##     q6_me_inf + sr_gender + cat:stai_sa + q6_close_person_died:sr_gender + 
##     stai_sa:sr_gender + sr_age:q6_me_inf + q6_me_inf:sr_gender, 
##     data = data)
## 
## Coefficients:
##                     (Intercept)                              cat  
##                       0.3553818                        0.0546831  
##                         stai_sa                           sr_age  
##                       0.0629031                        0.0411359  
##            q6_close_person_died                        q6_me_inf  
##                       0.0868381                        2.2240730  
##                      sr_genderM                      cat:stai_sa  
##                       0.4789147                       -0.0008757  
## q6_close_person_died:sr_genderM               stai_sa:sr_genderM  
##                       0.9592581                       -0.0185480  
##                sr_age:q6_me_inf             q6_me_inf:sr_genderM  
##                      -0.0520796                       -0.7384578
## Warning: Some predictor variables are on very different scales: consider
## rescaling
## boundary (singular) fit: see ?isSingular
## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: covid_worry ~ cat + stai_sa + q6_close_person_died + q6_me_inf +  
##     cat:stai_sa + (1 | stai_sa:sr_gender) + (1 | sr_age) + (1 |  
##     sr_gender) + (1 | sr_age:q6_me_inf) + (1 | q6_me_inf:sr_gender) +  
##     (1 | q6_close_person_died:sr_gender)
##    Data: data
## 
## REML criterion at convergence: 1070.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9963 -0.6113  0.0689  0.6941  3.0070 
## 
## Random effects:
##  Groups                         Name        Variance  Std.Dev. 
##  stai_sa:sr_gender              (Intercept) 7.512e-02 2.741e-01
##  sr_age:q6_me_inf               (Intercept) 5.071e-10 2.252e-05
##  sr_age                         (Intercept) 6.680e-02 2.585e-01
##  q6_close_person_died:sr_gender (Intercept) 9.534e-02 3.088e-01
##  q6_me_inf:sr_gender            (Intercept) 3.291e-02 1.814e-01
##  sr_gender                      (Intercept) 0.000e+00 0.000e+00
##  Residual                                   1.093e+00 1.045e+00
## Number of obs: 342, groups:  
## stai_sa:sr_gender, 93; sr_age:q6_me_inf, 38; sr_age, 23; q6_close_person_died:sr_gender, 4; q6_me_inf:sr_gender, 4; sr_gender, 2
## 
## Fixed effects:
##                        Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)           2.022e+00  5.196e-01  1.182e+01   3.891 0.002203 ** 
## cat                   4.572e-02  1.237e-02  2.905e+02   3.696 0.000262 ***
## stai_sa               4.589e-02  1.218e-02  2.006e+02   3.768 0.000216 ***
## q6_close_person_died  6.332e-01  3.648e-01  1.703e+00   1.736 0.245964    
## q6_me_inf             4.503e-01  3.061e-01  1.345e+00   1.471 0.332205    
## cat:stai_sa          -6.465e-04  2.753e-04  2.604e+02  -2.348 0.019614 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cat    stai_s q6_c__ q6_m_n
## cat         -0.686                            
## stai_sa     -0.817  0.664                     
## q6_cls_prs_ -0.253 -0.020 -0.025              
## q6_me_inf   -0.124  0.038 -0.006  0.035       
## cat:stai_sa  0.747 -0.930 -0.841  0.022 -0.037
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
## convergence code: 0
## boundary (singular) fit: see ?isSingular
## Warning: Some predictor variables are on very different scales: consider
## rescaling
## boundary (singular) fit: see ?isSingular
## Warning: Some predictor variables are on very different scales: consider
## rescaling

## Warning: Some predictor variables are on very different scales: consider
## rescaling
## boundary (singular) fit: see ?isSingular
## Warning: Some predictor variables are on very different scales: consider
## rescaling

## Warning: Some predictor variables are on very different scales: consider
## rescaling
## boundary (singular) fit: see ?isSingular
## Warning: Some predictor variables are on very different scales: consider
## rescaling

## Warning: Some predictor variables are on very different scales: consider
## rescaling
## boundary (singular) fit: see ?isSingular
## Warning: Some predictor variables are on very different scales: consider
## rescaling

## Warning: Some predictor variables are on very different scales: consider
## rescaling
## boundary (singular) fit: see ?isSingular
## Warning: Some predictor variables are on very different scales: consider
## rescaling

## Warning: Some predictor variables are on very different scales: consider
## rescaling
## boundary (singular) fit: see ?isSingular
## Warning: Some predictor variables are on very different scales: consider
## rescaling
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## covid_worry ~ cat + stai_sa + q6_close_person_died + q6_me_inf + 
##     (1 | stai_sa:sr_gender) + (1 | sr_age) + (1 | sr_gender) + 
##     (1 | sr_age:q6_me_inf) + (1 | q6_me_inf:sr_gender) + (1 | 
##     q6_close_person_died:sr_gender) + cat:stai_sa
##                                      npar  logLik    AIC     LRT Df Pr(>Chisq)
## <none>                                 13 -535.28 1096.5                      
## (1 | stai_sa:sr_gender)                12 -536.25 1096.5 1.94915  1    0.16268
## (1 | sr_age)                           12 -535.86 1095.7 1.16408  1    0.28062
## (1 | sr_gender)                        12 -535.28 1094.5 0.00000  1    0.99992
## (1 | sr_age:q6_me_inf)                 12 -535.28 1094.5 0.00000  1    1.00000
## (1 | q6_me_inf:sr_gender)              12 -535.36 1094.7 0.16997  1    0.68014
## (1 | q6_close_person_died:sr_gender)   12 -536.76 1097.5 2.96410  1    0.08513
##                                       
## <none>                                
## (1 | stai_sa:sr_gender)               
## (1 | sr_age)                          
## (1 | sr_gender)                       
## (1 | sr_age:q6_me_inf)                
## (1 | q6_me_inf:sr_gender)             
## (1 | q6_close_person_died:sr_gender) .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Model 2: Factors predicting covid-specific anxiety

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: covid_spec_anxiety ~ stai_sa + stai_ta + bdi + cat + q6_me_inf +  
##     q6_close_person_inf + q6_close_person_died + q6_media_valence +  
##     (1 | sr_gender) + (1 | sr_age)
##    Data: data
## 
## REML criterion at convergence: 1107.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8111 -0.5467  0.1567  0.6945  2.1127 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  sr_age    (Intercept) 0.04511  0.2124  
##  sr_gender (Intercept) 0.07299  0.2702  
##  Residual              1.30803  1.1437  
## Number of obs: 342, groups:  sr_age, 23; sr_gender, 2
## 
## Fixed effects:
##                        Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)           4.545e+00  3.886e-01  1.334e+01  11.694 2.17e-08 ***
## stai_sa               1.428e-02  7.915e-03  3.274e+02   1.804  0.07211 .  
## stai_ta              -1.252e-02  1.068e-02  3.287e+02  -1.172  0.24194    
## bdi                   2.109e-04  1.182e-02  3.302e+02   0.018  0.98578    
## cat                   1.829e-02  6.150e-03  3.284e+02   2.975  0.00315 ** 
## q6_me_inf             2.061e-01  2.660e-01  3.291e+02   0.775  0.43911    
## q6_close_person_inf  -5.197e-01  2.874e-01  3.298e+02  -1.808  0.07151 .  
## q6_close_person_died  2.992e-01  2.039e-01  3.306e+02   1.468  0.14310    
## q6_media_valence     -1.048e-01  4.128e-02  3.320e+02  -2.539  0.01156 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##               (Intr) stai_s stai_t bdi    cat    q6_m_n q6_cls_prsn_n
## stai_sa       -0.268                                                 
## stai_ta       -0.597 -0.348                                          
## bdi            0.490 -0.191 -0.460                                   
## cat            0.161 -0.094 -0.405 -0.222                            
## q6_me_inf     -0.021  0.000 -0.005 -0.107  0.067                     
## q6_cls_prsn_n -0.001  0.022 -0.049  0.055 -0.023 -0.193              
## q6_cls_prsn_d -0.089 -0.048  0.075 -0.075 -0.007  0.087  0.068       
## q6_med_vlnc   -0.004  0.063 -0.017  0.018  0.054  0.123  0.025       
##               q6_cls_prsn_d
## stai_sa                    
## stai_ta                    
## bdi                        
## cat                        
## q6_me_inf                  
## q6_cls_prsn_n              
## q6_cls_prsn_d              
## q6_med_vlnc   -0.021
## Start:  AIC=150.2
## covid_spec_anxiety ~ 1
## 
##                        Df Sum of Sq    RSS    AIC
## + cat                   1    41.059 486.44 124.49
## + stai_sa               1    34.378 493.12 129.15
## + bdi                   1    23.556 503.94 136.58
## + sr_gender             1    23.149 504.35 136.85
## + stai_ta               1    21.587 505.91 137.91
## + q6_media_valence      1    16.431 511.07 141.38
## + q6_close_person_died  1     4.242 523.26 149.44
## + q6_close_person_inf   1     3.109 524.39 150.18
## <none>                              527.50 150.20
## + sr_age                1     2.816 524.68 150.37
## + q6_me_inf             1     1.534 525.96 151.20
## 
## Step:  AIC=124.49
## covid_spec_anxiety ~ cat
## 
##                        Df Sum of Sq    RSS    AIC
## + sr_gender             1    14.932 471.51 115.82
## + q6_media_valence      1     9.629 476.81 119.65
## + sr_age                1     6.418 480.02 121.94
## + stai_sa               1     5.185 481.25 122.82
## + q6_close_person_inf   1     3.993 482.45 123.67
## + q6_close_person_died  1     3.435 483.00 124.06
## <none>                              486.44 124.49
## + q6_me_inf             1     0.677 485.76 126.01
## + stai_ta               1     0.269 486.17 126.30
## + bdi                   1     0.140 486.30 126.39
## - cat                   1    41.059 527.50 150.20
## 
## Step:  AIC=115.82
## covid_spec_anxiety ~ cat + sr_gender
## 
##                        Df Sum of Sq    RSS    AIC
## + q6_media_valence      1     9.273 462.23 111.03
## + sr_age                1     7.156 464.35 112.59
## + q6_close_person_inf   1     3.970 467.54 114.93
## + stai_sa               1     3.393 468.11 115.35
## + q6_close_person_died  1     3.310 468.20 115.41
## <none>                              471.51 115.82
## + q6_me_inf             1     0.484 471.02 117.47
## + bdi                   1     0.263 471.24 117.63
## + stai_ta               1     0.216 471.29 117.67
## + cat:sr_gender         1     0.039 471.47 117.80
## - sr_gender             1    14.932 486.44 124.49
## - cat                   1    32.842 504.35 136.85
## 
## Step:  AIC=111.03
## covid_spec_anxiety ~ cat + sr_gender + q6_media_valence
## 
##                              Df Sum of Sq    RSS    AIC
## + sr_age                      1    5.7160 456.52 108.78
## + q6_close_person_inf         1    4.6028 457.63 109.61
## + q6_close_person_died        1    3.6275 458.61 110.34
## <none>                                    462.23 111.03
## + stai_sa                     1    2.6463 459.59 111.07
## + q6_media_valence:sr_gender  1    2.4471 459.79 111.22
## + cat:q6_media_valence        1    2.1573 460.08 111.43
## + stai_ta                     1    0.3145 461.92 112.80
## + bdi                         1    0.1350 462.10 112.93
## + q6_me_inf                   1    0.0809 462.15 112.97
## + cat:sr_gender               1    0.0328 462.20 113.01
## - q6_media_valence            1    9.2726 471.51 115.82
## - sr_gender                   1   14.5759 476.81 119.65
## - cat                         1   27.1330 489.37 128.54
## 
## Step:  AIC=108.78
## covid_spec_anxiety ~ cat + sr_gender + q6_media_valence + sr_age
## 
##                              Df Sum of Sq    RSS    AIC
## + q6_close_person_inf         1    4.5333 451.98 107.36
## + q6_close_person_died        1    3.7053 452.81 107.99
## + q6_media_valence:sr_gender  1    3.1758 453.34 108.39
## + stai_sa                     1    3.1068 453.41 108.44
## <none>                                    456.52 108.78
## + cat:q6_media_valence        1    2.5901 453.93 108.83
## + sr_gender:sr_age            1    1.4840 455.03 109.66
## + q6_media_valence:sr_age     1    0.3171 456.20 110.54
## + bdi                         1    0.2943 456.22 110.56
## + stai_ta                     1    0.1942 456.32 110.63
## + cat:sr_age                  1    0.1515 456.37 110.66
## + cat:sr_gender               1    0.0478 456.47 110.74
## + q6_me_inf                   1    0.0232 456.49 110.76
## - sr_age                      1    5.7160 462.23 111.03
## - q6_media_valence            1    7.8323 464.35 112.59
## - sr_gender                   1   15.2537 471.77 118.02
## - cat                         1   30.1048 486.62 128.62
## 
## Step:  AIC=107.36
## covid_spec_anxiety ~ cat + sr_gender + q6_media_valence + sr_age + 
##     q6_close_person_inf
## 
##                                        Df Sum of Sq    RSS    AIC
## + q6_media_valence:sr_gender            1    3.2056 448.78 106.93
## + q6_close_person_died                  1    3.0830 448.90 107.02
## + stai_sa                               1    3.0140 448.97 107.07
## <none>                                              451.98 107.36
## + cat:q6_media_valence                  1    2.0717 449.91 107.79
## + sr_gender:sr_age                      1    1.7041 450.28 108.07
## + q6_close_person_inf:sr_age            1    1.4489 450.54 108.26
## - q6_close_person_inf                   1    4.5333 456.52 108.78
## + q6_close_person_inf:sr_gender         1    0.4482 451.54 109.02
## + q6_me_inf                             1    0.3467 451.64 109.10
## + bdi                                   1    0.2552 451.73 109.17
## + stai_ta                               1    0.1351 451.85 109.26
## + cat:sr_age                            1    0.1335 451.85 109.26
## + q6_media_valence:sr_age               1    0.1324 451.85 109.26
## + cat:q6_close_person_inf               1    0.1169 451.87 109.27
## + q6_close_person_inf:q6_media_valence  1    0.0053 451.98 109.36
## + cat:sr_gender                         1    0.0002 451.98 109.36
## - sr_age                                1    5.6464 457.63 109.61
## - q6_media_valence                      1    8.4167 460.40 111.67
## - sr_gender                             1   15.2128 467.20 116.68
## - cat                                   1   30.7231 482.71 127.85
## 
## Step:  AIC=106.93
## covid_spec_anxiety ~ cat + sr_gender + q6_media_valence + sr_age + 
##     q6_close_person_inf + sr_gender:q6_media_valence
## 
##                                        Df Sum of Sq    RSS    AIC
## + stai_sa                               1    3.2161 445.56 106.47
## + q6_close_person_died                  1    3.1985 445.58 106.48
## + cat:q6_media_valence                  1    2.9612 445.82 106.66
## <none>                                              448.78 106.93
## - sr_gender:q6_media_valence            1    3.2056 451.98 107.36
## + sr_gender:sr_age                      1    1.2223 447.56 108.00
## + q6_close_person_inf:sr_age            1    0.9606 447.82 108.19
## - q6_close_person_inf                   1    4.5631 453.34 108.39
## + bdi                                   1    0.3908 448.39 108.63
## + q6_me_inf                             1    0.3888 448.39 108.63
## + q6_close_person_inf:sr_gender         1    0.3326 448.45 108.67
## + cat:q6_close_person_inf               1    0.1128 448.67 108.84
## + stai_ta                               1    0.1031 448.68 108.85
## + cat:sr_gender                         1    0.0831 448.70 108.86
## + q6_media_valence:sr_age               1    0.0813 448.70 108.87
## + cat:sr_age                            1    0.0604 448.72 108.88
## + q6_close_person_inf:q6_media_valence  1    0.0078 448.77 108.92
## - sr_age                                1    6.3746 455.15 109.75
## - cat                                   1   31.1349 479.91 127.87
## 
## Step:  AIC=106.47
## covid_spec_anxiety ~ cat + sr_gender + q6_media_valence + sr_age + 
##     q6_close_person_inf + stai_sa + sr_gender:q6_media_valence
## 
##                                        Df Sum of Sq    RSS    AIC
## + stai_sa:q6_media_valence              1    5.6724 439.89 104.09
## + cat:q6_media_valence                  1    3.0114 442.55 106.15
## + q6_close_person_died                  1    2.9462 442.62 106.20
## <none>                                              445.56 106.47
## + stai_sa:cat                           1    2.2558 443.31 106.73
## - stai_sa                               1    3.2161 448.78 106.93
## + stai_ta                               1    1.9437 443.62 106.97
## - sr_gender:q6_media_valence            1    3.4077 448.97 107.07
## + stai_sa:sr_gender                     1    1.5085 444.05 107.31
## + q6_close_person_inf:sr_age            1    1.3101 444.25 107.46
## + sr_gender:sr_age                      1    1.3010 444.26 107.47
## - q6_close_person_inf                   1    4.4679 450.03 107.88
## + stai_sa:sr_age                        1    0.4157 445.15 108.15
## + q6_close_person_inf:sr_gender         1    0.3070 445.26 108.23
## + q6_me_inf                             1    0.2660 445.30 108.26
## + q6_media_valence:sr_age               1    0.1475 445.42 108.36
## + cat:sr_age                            1    0.1331 445.43 108.37
## + cat:q6_close_person_inf               1    0.1038 445.46 108.39
## + cat:sr_gender                         1    0.0531 445.51 108.43
## + stai_sa:q6_close_person_inf           1    0.0382 445.52 108.44
## + bdi                                   1    0.0216 445.54 108.45
## + q6_close_person_inf:q6_media_valence  1    0.0017 445.56 108.47
## - sr_age                                1    6.8913 452.45 109.72
## - cat                                   1   10.7821 456.35 112.65
## 
## Step:  AIC=104.09
## covid_spec_anxiety ~ cat + sr_gender + q6_media_valence + sr_age + 
##     q6_close_person_inf + stai_sa + sr_gender:q6_media_valence + 
##     q6_media_valence:stai_sa
## 
##                                        Df Sum of Sq    RSS    AIC
## <none>                                              439.89 104.09
## + stai_ta                               1    2.4986 437.39 104.14
## + q6_close_person_died                  1    2.0638 437.83 104.48
## + stai_sa:sr_gender                     1    1.6119 438.28 104.83
## - q6_close_person_inf                   1    3.8144 443.71 105.04
## + stai_sa:sr_age                        1    1.3302 438.56 105.05
## + stai_sa:cat                           1    1.0481 438.84 105.27
## + q6_close_person_inf:sr_age            1    1.0339 438.86 105.28
## + sr_gender:sr_age                      1    1.0298 438.86 105.28
## + q6_media_valence:sr_age               1    0.4653 439.43 105.72
## + cat:q6_close_person_inf               1    0.3140 439.58 105.84
## + q6_close_person_inf:sr_gender         1    0.2603 439.63 105.88
## - sr_gender:q6_media_valence            1    4.9833 444.87 105.94
## + q6_me_inf                             1    0.1322 439.76 105.98
## + cat:q6_media_valence                  1    0.0984 439.79 106.01
## + stai_sa:q6_close_person_inf           1    0.0603 439.83 106.04
## + bdi                                   1    0.0555 439.84 106.04
## + cat:sr_gender                         1    0.0552 439.84 106.04
## + q6_close_person_inf:q6_media_valence  1    0.0297 439.86 106.06
## + cat:sr_age                            1    0.0018 439.89 106.08
## - q6_media_valence:stai_sa              1    5.6724 445.56 106.47
## - sr_age                                1    7.9462 447.84 108.21
## - cat                                   1   10.4921 450.38 110.15
## 
## Call:
## lm(formula = covid_spec_anxiety ~ cat + sr_gender + q6_media_valence + 
##     sr_age + q6_close_person_inf + stai_sa + sr_gender:q6_media_valence + 
##     q6_media_valence:stai_sa, data = data)
## 
## Coefficients:
##                 (Intercept)                          cat  
##                    3.496896                     0.013691  
##                  sr_genderM             q6_media_valence  
##                   -0.300963                    -0.443623  
##                      sr_age          q6_close_person_inf  
##                    0.026035                    -0.474920  
##                     stai_sa  sr_genderM:q6_media_valence  
##                    0.016915                     0.165248  
##    q6_media_valence:stai_sa  
##                    0.006757
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: covid_spec_anxiety ~ cat + q6_media_valence + q6_close_person_inf +  
##     stai_sa + q6_media_valence:stai_sa + (1 | sr_gender) + (1 |  
##     sr_age) + (1 | sr_gender:q6_media_valence)
##    Data: data
## 
## REML criterion at convergence: 1099.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.7912 -0.6127  0.1420  0.6789  2.1647 
## 
## Random effects:
##  Groups                     Name        Variance Std.Dev.
##  sr_age                     (Intercept) 0.03615  0.1901  
##  sr_gender:q6_media_valence (Intercept) 0.04842  0.2200  
##  sr_gender                  (Intercept) 0.06035  0.2457  
##  Residual                               1.28022  1.1315  
## Number of obs: 342, groups:  
## sr_age, 23; sr_gender:q6_media_valence, 10; sr_gender, 2
## 
## Fixed effects:
##                            Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                4.146742   0.318249   6.200031  13.030 9.76e-06 ***
## cat                        0.012762   0.004817 328.983677   2.649  0.00846 ** 
## q6_media_valence          -0.392076   0.152826 124.327597  -2.565  0.01149 *  
## q6_close_person_inf       -0.507262   0.278433 330.848280  -1.822  0.06938 .  
## stai_sa                    0.015284   0.007296 327.370007   2.095  0.03694 *  
## q6_media_valence:stai_sa   0.006206   0.003245 327.136131   1.913  0.05668 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cat    q6_md_ q6_c__ stai_s
## cat          0.051                            
## q6_med_vlnc  0.344  0.036                     
## q6_cls_prs_ -0.069 -0.032 -0.038              
## stai_sa     -0.642 -0.576 -0.342  0.045       
## q6_md_vln:_ -0.350 -0.027 -0.915  0.063  0.405
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## covid_spec_anxiety ~ cat + q6_media_valence + q6_close_person_inf + 
##     stai_sa + (1 | sr_gender) + (1 | sr_age) + (1 | sr_gender:q6_media_valence) + 
##     q6_media_valence:stai_sa
##                                  npar  logLik    AIC    LRT Df Pr(>Chisq)
## <none>                             10 -549.91 1119.8                     
## (1 | sr_gender)                     9 -550.41 1118.8 1.0027  1     0.3166
## (1 | sr_age)                        9 -550.84 1119.7 1.8524  1     0.1735
## (1 | sr_gender:q6_media_valence)    9 -551.20 1120.4 2.5885  1     0.1076

Model 3: Factors predicting avoidance behaviours

## boundary (singular) fit: see ?isSingular
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: covid_avoidance_beh ~ stai_sa + stai_ta + bdi + cat + q6_me_inf +  
##     q6_close_person_inf + q6_close_person_died + q6_media_valence +  
##     (1 | sr_gender) + (1 | sr_age)
##    Data: data
## 
## REML criterion at convergence: 974.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.7913 -0.4466  0.1595  0.6877  1.4582 
## 
## Random effects:
##  Groups    Name        Variance  Std.Dev.
##  sr_age    (Intercept) 0.000e+00 0.000000
##  sr_gender (Intercept) 1.146e-08 0.000107
##  Residual              9.060e-01 0.951835
## Number of obs: 342, groups:  sr_age, 23; sr_gender, 2
## 
## Fixed effects:
##                        Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)            5.541219   0.275975 332.639911  20.079   <2e-16 ***
## stai_sa                0.003356   0.006477 332.916465   0.518   0.6046    
## stai_ta               -0.001441   0.008799 332.999707  -0.164   0.8700    
## bdi                    0.002444   0.009715 332.999510   0.252   0.8015    
## cat                    0.005638   0.005053 332.990378   1.116   0.2653    
## q6_me_inf              0.211416   0.219148 332.999447   0.965   0.3354    
## q6_close_person_inf   -0.221348   0.236650 332.998118  -0.935   0.3503    
## q6_close_person_died   0.372657   0.167680 332.998124   2.222   0.0269 *  
## q6_media_valence      -0.058038   0.033791 332.998073  -1.718   0.0868 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##               (Intr) stai_s stai_t bdi    cat    q6_m_n q6_cls_prsn_n
## stai_sa       -0.310                                                 
## stai_ta       -0.697 -0.342                                          
## bdi            0.563 -0.185 -0.466                                   
## cat            0.204 -0.114 -0.406 -0.213                            
## q6_me_inf     -0.020 -0.009  0.001 -0.105  0.060                     
## q6_cls_prsn_n  0.006  0.023 -0.053  0.054 -0.018 -0.196              
## q6_cls_prsn_d -0.095 -0.048  0.071 -0.072 -0.011  0.084  0.059       
## q6_med_vlnc   -0.001  0.062 -0.021  0.014  0.063  0.121  0.024       
##               q6_cls_prsn_d
## stai_sa                    
## stai_ta                    
## bdi                        
## cat                        
## q6_me_inf                  
## q6_cls_prsn_n              
## q6_cls_prsn_d              
## q6_med_vlnc   -0.018       
## convergence code: 0
## boundary (singular) fit: see ?isSingular
## Start:  AIC=-22.44
## covid_avoidance_beh ~ 1
## 
##                        Df Sum of Sq    RSS     AIC
## + cat                   1    7.0513 311.36 -28.099
## + bdi                   1    5.8799 312.53 -26.815
## + stai_sa               1    5.6981 312.71 -26.616
## + stai_ta               1    5.0863 313.33 -25.948
## + q6_close_person_died  1    4.8781 313.53 -25.721
## + q6_media_valence      1    4.4953 313.92 -25.303
## + sr_gender             1    2.1721 316.24 -22.782
## <none>                              318.41 -22.441
## + q6_me_inf             1    1.0553 317.36 -21.576
## + sr_age                1    0.7277 317.68 -21.223
## + q6_close_person_inf   1    0.5402 317.87 -21.021
## 
## Step:  AIC=-28.1
## covid_avoidance_beh ~ cat
## 
##                        Df Sum of Sq    RSS     AIC
## + q6_close_person_died  1    4.5111 306.85 -31.091
## + q6_media_valence      1    2.9986 308.36 -29.409
## <none>                              311.36 -28.099
## + sr_age                1    1.4615 309.90 -27.709
## + sr_gender             1    1.1555 310.21 -27.371
## + stai_sa               1    0.7882 310.57 -26.966
## + q6_me_inf             1    0.7326 310.63 -26.905
## + q6_close_person_inf   1    0.6929 310.67 -26.861
## + bdi                   1    0.5598 310.80 -26.715
## + stai_ta               1    0.0950 311.27 -26.204
## - cat                   1    7.0513 318.41 -22.441
## 
## Step:  AIC=-31.09
## covid_avoidance_beh ~ cat + q6_close_person_died
## 
##                            Df Sum of Sq    RSS     AIC
## + q6_media_valence          1    3.2075 303.64 -32.684
## <none>                                  306.85 -31.091
## + sr_age                    1    1.5210 305.33 -30.790
## + q6_me_inf                 1    1.1226 305.73 -30.344
## + sr_gender                 1    1.1156 305.73 -30.336
## + stai_sa                   1    0.6409 306.21 -29.806
## + q6_close_person_inf       1    0.4324 306.42 -29.573
## + bdi                       1    0.4152 306.44 -29.554
## + stai_ta                   1    0.1284 306.72 -29.234
## + cat:q6_close_person_died  1    0.0073 306.84 -29.099
## - q6_close_person_died      1    4.5111 311.36 -28.099
## - cat                       1    6.6844 313.53 -25.721
## 
## Step:  AIC=-32.68
## covid_avoidance_beh ~ cat + q6_close_person_died + q6_media_valence
## 
##                                         Df Sum of Sq    RSS     AIC
## <none>                                               303.64 -32.684
## + sr_age                                 1    1.1363 302.51 -31.967
## + sr_gender                              1    1.0586 302.58 -31.879
## + q6_me_inf                              1    0.6829 302.96 -31.455
## + q6_close_person_inf                    1    0.5520 303.09 -31.307
## + stai_sa                                1    0.4473 303.19 -31.189
## - q6_media_valence                       1    3.2075 306.85 -31.091
## + bdi                                    1    0.3109 303.33 -31.035
## + stai_ta                                1    0.0923 303.55 -30.788
## + cat:q6_close_person_died               1    0.0696 303.57 -30.763
## + q6_close_person_died:q6_media_valence  1    0.0038 303.64 -30.689
## + cat:q6_media_valence                   1    0.0006 303.64 -30.685
## - q6_close_person_died                   1    4.7201 308.36 -29.409
## - cat                                    1    5.1832 308.82 -28.896
## 
## Call:
## lm(formula = covid_avoidance_beh ~ cat + q6_close_person_died + 
##     q6_media_valence, data = data)
## 
## Coefficients:
##          (Intercept)                   cat  q6_close_person_died  
##             5.583868              0.007413              0.378555  
##     q6_media_valence  
##            -0.062836
## 
## Call:
## lm(formula = covid_avoidance_beh ~ cat + q6_close_person_died + 
##     q6_media_valence, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5729 -0.4608  0.1715  0.6439  1.4122 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           5.583868   0.113849  49.046   <2e-16 ***
## cat                   0.007413   0.003086   2.402   0.0168 *  
## q6_close_person_died  0.378555   0.165150   2.292   0.0225 *  
## q6_media_valence     -0.062836   0.033254  -1.890   0.0597 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9478 on 338 degrees of freedom
## Multiple R-squared:  0.04639,    Adjusted R-squared:  0.03792 
## F-statistic:  5.48 on 3 and 338 DF,  p-value: 0.001093

Model 4: Factors predicting covid probability estimates

## boundary (singular) fit: see ?isSingular
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: covid_prob_estimates ~ stai_sa + bdi + stai_ta + cat + q6_me_inf +  
##     q6_close_person_inf + q6_close_person_died + q6_media_valence +  
##     (1 | sr_gender) + (1 | sr_age)
##    Data: data
## 
## REML criterion at convergence: 2868.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1880 -0.6488 -0.0319  0.6849  3.0821 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  sr_age    (Intercept)   0.0     0.00   
##  sr_gender (Intercept)   0.0     0.00   
##  Residual              267.4    16.35   
## Number of obs: 342, groups:  sr_age, 23; sr_gender, 2
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)           41.4858     4.7411 333.0000   8.750  < 2e-16 ***
## stai_sa                0.1985     0.1113 333.0000   1.784  0.07529 .  
## bdi                    0.1942     0.1669 333.0000   1.164  0.24538    
## stai_ta               -0.4297     0.1512 333.0000  -2.843  0.00475 ** 
## cat                    0.3123     0.0868 333.0000   3.597  0.00037 ***
## q6_me_inf              2.1666     3.7648 333.0000   0.575  0.56535    
## q6_close_person_inf    3.3875     4.0655 333.0000   0.833  0.40531    
## q6_close_person_died   4.2018     2.8807 333.0000   1.459  0.14561    
## q6_media_valence      -1.0718     0.5805 333.0000  -1.846  0.06572 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##               (Intr) stai_s bdi    stai_t cat    q6_m_n q6_cls_prsn_n
## stai_sa       -0.310                                                 
## bdi            0.563 -0.185                                          
## stai_ta       -0.697 -0.342 -0.466                                   
## cat            0.204 -0.114 -0.213 -0.406                            
## q6_me_inf     -0.020 -0.009 -0.105  0.001  0.060                     
## q6_cls_prsn_n  0.006  0.023  0.054 -0.053 -0.018 -0.196              
## q6_cls_prsn_d -0.095 -0.048 -0.072  0.071 -0.011  0.084  0.059       
## q6_med_vlnc   -0.001  0.062  0.014 -0.021  0.063  0.121  0.024       
##               q6_cls_prsn_d
## stai_sa                    
## bdi                        
## stai_ta                    
## cat                        
## q6_me_inf                  
## q6_cls_prsn_n              
## q6_cls_prsn_d              
## q6_med_vlnc   -0.018       
## convergence code: 0
## boundary (singular) fit: see ?isSingular
## Start:  AIC=1945.02
## covid_prob_estimates ~ 1
## 
##                        Df Sum of Sq    RSS    AIC
## + cat                   1    6817.9  93505 1923.0
## + stai_sa               1    4498.1  95825 1931.3
## + bdi                   1    4125.4  96198 1932.7
## + stai_ta               1    2143.0  98180 1939.6
## + q6_media_valence      1    2116.9  98206 1939.7
## + sr_gender             1     933.0  99390 1943.8
## + q6_close_person_died  1     777.0  99546 1944.4
## <none>                              100323 1945.0
## + q6_me_inf             1     395.3  99928 1945.7
## + q6_close_person_inf   1     244.4 100079 1946.2
## + sr_age                1     212.9 100110 1946.3
## 
## Step:  AIC=1922.95
## covid_prob_estimates ~ cat
## 
##                        Df Sum of Sq    RSS    AIC
## + q6_media_valence      1    1134.8  92370 1920.8
## + stai_ta               1     796.2  92709 1922.0
## + sr_age                1     653.1  92852 1922.5
## + q6_close_person_died  1     636.0  92869 1922.6
## <none>                               93505 1923.0
## + stai_sa               1     333.5  93172 1923.7
## + sr_gender             1     322.4  93183 1923.8
## + q6_me_inf             1     211.1  93294 1924.2
## + q6_close_person_inf   1     159.5  93346 1924.4
## + bdi                   1      52.5  93453 1924.8
## - cat                   1    6817.9 100323 1945.0
## 
## Step:  AIC=1920.77
## covid_prob_estimates ~ cat + q6_media_valence
## 
##                        Df Sum of Sq   RSS    AIC
## + stai_ta               1     857.2 91513 1919.6
## + q6_close_person_died  1     684.3 91686 1920.2
## <none>                              92370 1920.8
## + sr_age                1     502.3 91868 1920.9
## + sr_gender             1     304.5 92066 1921.6
## + stai_sa               1     250.7 92120 1921.8
## + cat:q6_media_valence  1     190.1 92180 1922.1
## + q6_close_person_inf   1     120.6 92250 1922.3
## + q6_me_inf             1     100.6 92270 1922.4
## + bdi                   1      32.0 92338 1922.7
## - q6_media_valence      1    1134.8 93505 1923.0
## - cat                   1    5835.8 98206 1939.7
## 
## Step:  AIC=1919.58
## covid_prob_estimates ~ cat + q6_media_valence + stai_ta
## 
##                            Df Sum of Sq   RSS    AIC
## + stai_sa                   1    1206.6 90307 1917.0
## + bdi                       1     750.1 90763 1918.8
## + stai_ta:cat               1     738.1 90775 1918.8
## + q6_close_person_died      1     650.3 90863 1919.2
## <none>                                  91513 1919.6
## + stai_ta:q6_media_valence  1     491.3 91022 1919.7
## + sr_age                    1     440.0 91073 1919.9
## + sr_gender                 1     290.1 91223 1920.5
## + cat:q6_media_valence      1     247.0 91266 1920.7
## - stai_ta                   1     857.2 92370 1920.8
## + q6_me_inf                 1     158.3 91355 1921.0
## + q6_close_person_inf       1     144.2 91369 1921.0
## - q6_media_valence          1    1195.8 92709 1922.0
## - cat                       1    5069.7 96583 1936.0
## 
## Step:  AIC=1917.05
## covid_prob_estimates ~ cat + q6_media_valence + stai_ta + stai_sa
## 
##                            Df Sum of Sq   RSS    AIC
## + stai_sa:cat               1    1296.8 89010 1914.1
## + stai_sa:stai_ta           1    1246.3 89060 1914.3
## + stai_sa:q6_media_valence  1     789.4 89517 1916.0
## + stai_ta:cat               1     739.2 89568 1916.2
## + q6_close_person_died      1     545.8 89761 1917.0
## <none>                                  90307 1917.0
## + sr_age                    1     501.6 89805 1917.1
## + stai_ta:q6_media_valence  1     480.4 89826 1917.2
## + bdi                       1     446.1 89861 1917.3
## + cat:q6_media_valence      1     284.1 90023 1918.0
## + q6_close_person_inf       1     173.9 90133 1918.4
## + sr_gender                 1     150.9 90156 1918.5
## + q6_me_inf                 1     143.3 90163 1918.5
## - q6_media_valence          1    1035.8 91343 1919.0
## - stai_sa                   1    1206.6 91513 1919.6
## - stai_ta                   1    1813.1 92120 1921.8
## - cat                       1    4181.0 94488 1930.5
## 
## Step:  AIC=1914.1
## covid_prob_estimates ~ cat + q6_media_valence + stai_ta + stai_sa + 
##     cat:stai_sa
## 
##                            Df Sum of Sq   RSS    AIC
## + bdi                       1    930.15 88080 1912.5
## + sr_age                    1    617.35 88393 1913.7
## <none>                                  89010 1914.1
## + q6_close_person_died      1    479.07 88531 1914.2
## + stai_sa:q6_media_valence  1    430.54 88579 1914.4
## + stai_ta:q6_media_valence  1    257.70 88752 1915.1
## + q6_me_inf                 1    194.32 88816 1915.3
## + q6_close_person_inf       1    181.19 88829 1915.4
## + cat:q6_media_valence      1    121.16 88889 1915.6
## + stai_sa:stai_ta           1    104.23 88906 1915.7
## + sr_gender                 1     91.39 88919 1915.8
## + stai_ta:cat               1      1.50 89008 1916.1
## - q6_media_valence          1   1177.94 90188 1916.6
## - cat:stai_sa               1   1296.78 90307 1917.0
## - stai_ta                   1   1745.71 90756 1918.7
## 
## Step:  AIC=1912.51
## covid_prob_estimates ~ cat + q6_media_valence + stai_ta + stai_sa + 
##     bdi + cat:stai_sa
## 
##                            Df Sum of Sq   RSS    AIC
## + stai_sa:bdi               1   1102.42 86977 1910.2
## + sr_age                    1    719.03 87361 1911.7
## + bdi:cat                   1    573.38 87506 1912.3
## <none>                                  88080 1912.5
## + q6_close_person_died      1    381.59 87698 1913.0
## + stai_sa:q6_media_valence  1    348.22 87732 1913.2
## + bdi:q6_media_valence      1    221.24 87859 1913.7
## + q6_close_person_inf       1    217.95 87862 1913.7
## + bdi:stai_ta               1    216.57 87863 1913.7
## + stai_sa:stai_ta           1    171.70 87908 1913.8
## + stai_ta:q6_media_valence  1    148.82 87931 1913.9
## + q6_me_inf                 1    133.73 87946 1914.0
## + sr_gender                 1    120.75 87959 1914.0
## - bdi                       1    930.15 89010 1914.1
## + cat:q6_media_valence      1     54.27 88026 1914.3
## + stai_ta:cat               1      0.09 88080 1914.5
## - q6_media_valence          1   1158.15 89238 1915.0
## - cat:stai_sa               1   1780.86 89861 1917.3
## - stai_ta                   1   2623.82 90704 1920.5
## 
## Step:  AIC=1910.2
## covid_prob_estimates ~ cat + q6_media_valence + stai_ta + stai_sa + 
##     bdi + cat:stai_sa + stai_sa:bdi
## 
##                            Df Sum of Sq   RSS    AIC
## - cat:stai_sa               1      0.20 86978 1908.2
## + sr_age                    1    774.29 86203 1909.1
## <none>                                  86977 1910.2
## + q6_close_person_died      1    369.85 86608 1910.7
## + stai_sa:q6_media_valence  1    325.22 86652 1910.9
## + bdi:q6_media_valence      1    238.24 86739 1911.3
## + q6_close_person_inf       1    238.00 86739 1911.3
## + bdi:cat                   1    167.25 86810 1911.5
## + q6_me_inf                 1    132.34 86845 1911.7
## + stai_ta:q6_media_valence  1    131.92 86845 1911.7
## + sr_gender                 1    111.55 86866 1911.8
## + bdi:stai_ta               1     65.75 86912 1911.9
## + stai_sa:stai_ta           1     58.55 86919 1912.0
## + cat:q6_media_valence      1     47.27 86930 1912.0
## + stai_ta:cat               1      5.22 86972 1912.2
## - q6_media_valence          1   1046.46 88024 1912.3
## - stai_sa:bdi               1   1102.42 88080 1912.5
## - stai_ta                   1   2951.48 89929 1919.6
## 
## Step:  AIC=1908.2
## covid_prob_estimates ~ cat + q6_media_valence + stai_ta + stai_sa + 
##     bdi + stai_sa:bdi
## 
##                            Df Sum of Sq   RSS    AIC
## + sr_age                    1     773.1 86205 1907.2
## <none>                                  86978 1908.2
## + q6_close_person_died      1     370.0 86608 1908.7
## + stai_sa:q6_media_valence  1     321.8 86656 1908.9
## + q6_close_person_inf       1     238.1 86740 1909.3
## + bdi:q6_media_valence      1     235.9 86742 1909.3
## + bdi:cat                   1     142.0 86836 1909.6
## + q6_me_inf                 1     132.0 86846 1909.7
## + stai_ta:q6_media_valence  1     131.5 86846 1909.7
## + sr_gender                 1     111.7 86866 1909.8
## + bdi:stai_ta               1      65.6 86912 1909.9
## + cat:q6_media_valence      1      47.4 86930 1910.0
## + stai_sa:stai_ta           1      44.7 86933 1910.0
## + stai_ta:cat               1       2.6 86975 1910.2
## + stai_sa:cat               1       0.2 86977 1910.2
## - q6_media_valence          1    1050.1 88028 1910.3
## - stai_sa:bdi               1    2883.1 89861 1917.3
## - stai_ta                   1    2953.9 89932 1917.6
## - cat                       1    3235.9 90214 1918.7
## 
## Step:  AIC=1907.15
## covid_prob_estimates ~ cat + q6_media_valence + stai_ta + stai_sa + 
##     bdi + sr_age + stai_sa:bdi
## 
##                            Df Sum of Sq   RSS    AIC
## + stai_sa:sr_age            1     811.6 85393 1905.9
## + bdi:sr_age                1     522.2 85682 1907.1
## <none>                                  86205 1907.2
## + cat:sr_age                1     430.5 85774 1907.4
## + stai_sa:q6_media_valence  1     378.3 85826 1907.6
## + q6_media_valence:sr_age   1     372.6 85832 1907.7
## + q6_close_person_died      1     370.3 85834 1907.7
## + bdi:q6_media_valence      1     278.7 85926 1908.0
## + stai_ta:sr_age            1     257.6 85947 1908.1
## + q6_close_person_inf       1     247.0 85957 1908.2
## - sr_age                    1     773.1 86978 1908.2
## + stai_ta:q6_media_valence  1     157.8 86047 1908.5
## - q6_media_valence          1     866.3 87071 1908.6
## + sr_gender                 1     131.3 86073 1908.6
## + bdi:cat                   1      97.5 86107 1908.8
## + q6_me_inf                 1      95.8 86109 1908.8
## + bdi:stai_ta               1      76.5 86128 1908.8
## + cat:q6_media_valence      1      64.7 86140 1908.9
## + stai_sa:stai_ta           1      41.0 86164 1909.0
## + stai_ta:cat               1       5.5 86199 1909.1
## + stai_sa:cat               1       1.4 86203 1909.1
## - stai_ta                   1    2986.9 89191 1916.8
## - stai_sa:bdi               1    3109.8 89314 1917.3
## - cat                       1    3339.4 89544 1918.1
## 
## Step:  AIC=1905.91
## covid_prob_estimates ~ cat + q6_media_valence + stai_ta + stai_sa + 
##     bdi + sr_age + stai_sa:bdi + stai_sa:sr_age
## 
##                            Df Sum of Sq   RSS    AIC
## + stai_sa:q6_media_valence  1     685.4 84708 1905.2
## + q6_media_valence:sr_age   1     573.9 84819 1905.6
## + bdi:q6_media_valence      1     562.0 84831 1905.7
## <none>                                  85393 1905.9
## + q6_close_person_died      1     390.7 85002 1906.3
## + stai_ta:q6_media_valence  1     363.7 85029 1906.5
## - q6_media_valence          1     788.3 86181 1907.0
## - stai_sa:sr_age            1     811.6 86205 1907.2
## + q6_close_person_inf       1     171.7 85221 1907.2
## + sr_gender                 1     168.4 85224 1907.2
## + q6_me_inf                 1     144.9 85248 1907.3
## + cat:q6_media_valence      1     144.7 85248 1907.3
## + bdi:cat                   1      98.4 85295 1907.5
## + stai_ta:sr_age            1      67.8 85325 1907.6
## + bdi:stai_ta               1      53.0 85340 1907.7
## + stai_sa:stai_ta           1      26.7 85366 1907.8
## + bdi:sr_age                1      12.6 85380 1907.9
## + cat:sr_age                1       4.9 85388 1907.9
## + stai_ta:cat               1       1.6 85391 1907.9
## + stai_sa:cat               1       0.3 85393 1907.9
## - stai_sa:bdi               1    2818.6 88212 1915.0
## - stai_ta                   1    2902.2 88295 1915.3
## - cat                       1    3262.6 88656 1916.7
## 
## Step:  AIC=1905.15
## covid_prob_estimates ~ cat + q6_media_valence + stai_ta + stai_sa + 
##     bdi + sr_age + stai_sa:bdi + stai_sa:sr_age + q6_media_valence:stai_sa
## 
##                            Df Sum of Sq   RSS    AIC
## + q6_media_valence:sr_age   1     851.5 83856 1903.7
## <none>                                  84708 1905.2
## - q6_media_valence:stai_sa  1     685.4 85393 1905.9
## + q6_close_person_died      1     277.9 84430 1906.0
## + q6_close_person_inf       1     204.8 84503 1906.3
## + sr_gender                 1     147.3 84560 1906.6
## + q6_me_inf                 1     123.0 84584 1906.7
## + bdi:cat                   1     109.9 84598 1906.7
## + stai_sa:stai_ta           1      84.5 84623 1906.8
## + bdi:q6_media_valence      1      70.4 84637 1906.9
## + stai_ta:sr_age            1      42.3 84665 1907.0
## + bdi:sr_age                1      41.5 84666 1907.0
## + cat:q6_media_valence      1      27.4 84680 1907.0
## + bdi:stai_ta               1      26.4 84681 1907.0
## + stai_sa:cat               1       9.5 84698 1907.1
## + stai_ta:cat               1       4.0 84704 1907.1
## + cat:sr_age                1       2.7 84705 1907.1
## + stai_ta:q6_media_valence  1       1.0 84706 1907.2
## - stai_sa:sr_age            1    1118.7 85826 1907.6
## - stai_sa:bdi               1    2165.9 86873 1911.8
## - stai_ta                   1    2975.0 87683 1915.0
## - cat                       1    3351.4 88059 1916.4
## 
## Step:  AIC=1903.7
## covid_prob_estimates ~ cat + q6_media_valence + stai_ta + stai_sa + 
##     bdi + sr_age + stai_sa:bdi + stai_sa:sr_age + q6_media_valence:stai_sa + 
##     q6_media_valence:sr_age
## 
##                                   Df Sum of Sq   RSS    AIC
## <none>                                         83856 1903.7
## + stai_sa:q6_media_valence:sr_age  1     302.6 83553 1904.5
## + q6_close_person_inf              1     289.8 83566 1904.5
## + bdi:q6_media_valence             1     154.8 83701 1905.1
## + q6_me_inf                        1     145.2 83711 1905.1
## + bdi:cat                          1     138.5 83718 1905.1
## - q6_media_valence:sr_age          1     851.5 84708 1905.2
## + q6_close_person_died             1     132.6 83723 1905.2
## + stai_sa:stai_ta                  1     109.9 83746 1905.2
## + sr_gender                        1      95.5 83760 1905.3
## + bdi:sr_age                       1      59.9 83796 1905.5
## + bdi:stai_ta                      1      38.6 83817 1905.5
## + stai_ta:sr_age                   1      28.9 83827 1905.6
## - q6_media_valence:stai_sa         1     963.1 84819 1905.6
## + cat:sr_age                       1      18.3 83838 1905.6
## + cat:q6_media_valence             1      17.2 83839 1905.6
## + stai_ta:q6_media_valence         1      13.3 83843 1905.7
## + stai_sa:cat                      1       3.3 83853 1905.7
## + stai_ta:cat                      1       2.7 83853 1905.7
## - stai_sa:sr_age                   1    1473.5 85329 1907.7
## - stai_sa:bdi                      1    1680.3 85536 1908.5
## - stai_ta                          1    2965.5 86822 1913.6
## - cat                              1    3407.2 87263 1915.3
## 
## Call:
## lm(formula = covid_prob_estimates ~ cat + q6_media_valence + 
##     stai_ta + stai_sa + bdi + sr_age + stai_sa:bdi + stai_sa:sr_age + 
##     q6_media_valence:stai_sa + q6_media_valence:sr_age, data = data)
## 
## Coefficients:
##              (Intercept)                       cat          q6_media_valence  
##                 53.69574                   0.30990                  -9.85129  
##                  stai_ta                   stai_sa                       bdi  
##                 -0.50627                  -0.27298                   1.20757  
##                   sr_age               stai_sa:bdi            stai_sa:sr_age  
##                 -0.81281                  -0.01889                   0.02917  
## q6_media_valence:stai_sa   q6_media_valence:sr_age  
##                  0.09179                   0.17900
## boundary (singular) fit: see ?isSingular
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: covid_prob_estimates ~ cat + q6_media_valence + stai_ta + stai_sa +  
##     bdi + stai_sa:bdi + q6_media_valence:stai_sa + (1 | sr_age) +  
##     (1 | stai_sa:sr_age) + (1 | q6_media_valence:sr_age)
##    Data: data
## 
## REML criterion at convergence: 2884.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.20258 -0.66695  0.01711  0.70283  2.88701 
## 
## Random effects:
##  Groups                  Name        Variance Std.Dev.
##  stai_sa:sr_age          (Intercept)  18.52    4.303  
##  q6_media_valence:sr_age (Intercept)   0.00    0.000  
##  sr_age                  (Intercept)   0.00    0.000  
##  Residual                            241.03   15.525  
## Number of obs: 342, groups:  
## stai_sa:sr_age, 291; q6_media_valence:sr_age, 95; sr_age, 23
## 
## Fixed effects:
##                            Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)               30.837083   5.723054 292.678759   5.388 1.47e-07 ***
## cat                        0.305264   0.085221 324.715528   3.582 0.000393 ***
## q6_media_valence          -3.251682   2.041488 327.793442  -1.593 0.112169    
## stai_ta                   -0.518620   0.149433 328.809345  -3.471 0.000589 ***
## stai_sa                    0.554724   0.148265 313.411687   3.741 0.000218 ***
## bdi                        1.350561   0.401937 333.527070   3.360 0.000869 ***
## stai_sa:bdi               -0.022302   0.007261 324.579827  -3.072 0.002310 ** 
## q6_media_valence:stai_sa   0.049564   0.045786 328.508786   1.083 0.279819    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cat    q6_md_ stai_t stai_s bdi    st_s:b
## cat          0.180                                          
## q6_med_vlnc  0.145 -0.017                                   
## stai_ta     -0.480 -0.400  0.029                            
## stai_sa     -0.585 -0.101 -0.171 -0.338                     
## bdi         -0.290 -0.120  0.170 -0.302  0.490              
## stai_sa:bdi  0.521  0.040 -0.177  0.126 -0.600 -0.914       
## q6_md_vln:_ -0.149  0.033 -0.961 -0.033  0.188 -0.178  0.189
## convergence code: 0
## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## covid_prob_estimates ~ cat + q6_media_valence + stai_ta + stai_sa + 
##     bdi + (1 | sr_age) + (1 | stai_sa:sr_age) + (1 | q6_media_valence:sr_age) + 
##     stai_sa:bdi + q6_media_valence:stai_sa
##                               npar  logLik    AIC    LRT Df Pr(>Chisq)
## <none>                          12 -1442.3 2908.5                     
## (1 | sr_age)                    11 -1442.3 2906.5 0.0000  1     1.0000
## (1 | stai_sa:sr_age)            11 -1442.3 2906.7 0.1807  1     0.6708
## (1 | q6_media_valence:sr_age)   11 -1442.3 2906.5 0.0000  1     1.0000

Model 5: Factors predicting covid end estimates

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## covid_end_est ~ stai_sa + stai_ta + bdi + cat + q6_me_inf + q6_close_person_inf +  
##     q6_close_person_died + q6_media_valence + (1 | sr_gender) +  
##     (1 | sr_age)
##    Data: data
## 
## REML criterion at convergence: 4458.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6730 -0.6467 -0.2132  0.3940  4.1084 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  sr_age    (Intercept)  2307.56  48.037 
##  sr_gender (Intercept)    47.66   6.903 
##  Residual              30172.47 173.702 
## Number of obs: 342, groups:  sr_age, 23; sr_gender, 2
## 
## Fixed effects:
##                       Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)          284.80866   52.35948 191.65166   5.439 1.62e-07 ***
## stai_sa                1.09728    1.19757 297.59739   0.916  0.36027    
## stai_ta               -2.21732    1.62850 323.30604  -1.362  0.17428    
## bdi                    2.56728    1.80340 325.95276   1.424  0.15553    
## cat                    0.04162    0.93490 318.54983   0.045  0.96452    
## q6_me_inf            -41.71554   40.60039 324.14298  -1.027  0.30497    
## q6_close_person_inf  -35.46152   43.91733 324.51678  -0.807  0.41999    
## q6_close_person_died  -7.35946   31.17228 325.93730  -0.236  0.81351    
## q6_media_valence     -17.13726    6.32842 330.15561  -2.708  0.00712 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##               (Intr) stai_s stai_t bdi    cat    q6_m_n q6_cls_prsn_n
## stai_sa       -0.296                                                 
## stai_ta       -0.679 -0.348                                          
## bdi            0.554 -0.186 -0.463                                   
## cat            0.182 -0.102 -0.401 -0.221                            
## q6_me_inf     -0.023 -0.002 -0.005 -0.106  0.067                     
## q6_cls_prsn_n -0.005  0.024 -0.047  0.056 -0.025 -0.192              
## q6_cls_prsn_d -0.105 -0.050  0.077 -0.076 -0.006  0.089  0.072       
## q6_med_vlnc   -0.008  0.067 -0.015  0.017  0.052  0.124  0.025       
##               q6_cls_prsn_d
## stai_sa                    
## stai_ta                    
## bdi                        
## cat                        
## q6_me_inf                  
## q6_cls_prsn_n              
## q6_cls_prsn_d              
## q6_med_vlnc   -0.023
## Start:  AIC=3555.9
## covid_end_est ~ 1
## 
##                        Df Sum of Sq      RSS    AIC
## + sr_age                1    862680 10280347 3530.3
## + q6_media_valence      1    276404 10866623 3549.3
## <none>                              11143027 3555.9
## + bdi                   1     47145 11095882 3556.4
## + stai_sa               1     39400 11103627 3556.7
## + sr_gender             1     29906 11113121 3557.0
## + q6_close_person_inf   1     28512 11114514 3557.0
## + cat                   1     13910 11129117 3557.5
## + q6_me_inf             1     10115 11132912 3557.6
## + stai_ta               1      4845 11138182 3557.7
## + q6_close_person_died  1       368 11142659 3557.9
## 
## Step:  AIC=3530.34
## covid_end_est ~ sr_age
## 
##                        Df Sum of Sq      RSS    AIC
## + q6_media_valence      1    209653 10070694 3525.3
## + bdi                   1    122265 10158082 3528.2
## + stai_sa               1     99453 10180894 3529.0
## <none>                              10280347 3530.3
## + cat                   1     58017 10222331 3530.4
## + stai_ta               1     36916 10243431 3531.1
## + q6_close_person_inf   1     26460 10253888 3531.5
## + q6_me_inf             1     23739 10256609 3531.5
## + sr_gender             1     14744 10265603 3531.8
## + q6_close_person_died  1        23 10280325 3532.3
## - sr_age                1    862680 11143027 3555.9
## 
## Step:  AIC=3525.29
## covid_end_est ~ sr_age + q6_media_valence
## 
##                           Df Sum of Sq      RSS    AIC
## + bdi                      1     79496  9991198 3524.6
## + stai_sa                  1     59019 10011675 3525.3
## <none>                                 10070694 3525.3
## + q6_me_inf                1     48727 10021967 3525.6
## + q6_media_valence:sr_age  1     36767 10033927 3526.0
## + q6_close_person_inf      1     35389 10035305 3526.1
## + cat                      1     27997 10042697 3526.3
## + sr_gender                1     19944 10050750 3526.6
## + stai_ta                  1     15551 10055143 3526.8
## + q6_close_person_died     1        24 10070670 3527.3
## - q6_media_valence         1    209653 10280347 3530.3
## - sr_age                   1    795929 10866623 3549.3
## 
## Step:  AIC=3524.58
## covid_end_est ~ sr_age + q6_media_valence + bdi
## 
##                           Df Sum of Sq      RSS    AIC
## + bdi:sr_age               1     92376  9898822 3523.4
## + q6_me_inf                1     66872  9924326 3524.3
## <none>                                  9991198 3524.6
## + bdi:q6_media_valence     1     44213  9946985 3525.1
## - bdi                      1     79496 10070694 3525.3
## + q6_close_person_inf      1     36111  9955087 3525.3
## + q6_media_valence:sr_age  1     29496  9961702 3525.6
## + stai_ta                  1     28366  9962832 3525.6
## + sr_gender                1     26422  9964776 3525.7
## + stai_sa                  1      5228  9985970 3526.4
## + cat                      1      1921  9989277 3526.5
## + q6_close_person_died     1       144  9991054 3526.6
## - q6_media_valence         1    166884 10158082 3528.2
## - sr_age                   1    854867 10846065 3550.7
## 
## Step:  AIC=3523.41
## covid_end_est ~ sr_age + q6_media_valence + bdi + sr_age:bdi
## 
##                           Df Sum of Sq      RSS    AIC
## + bdi:q6_media_valence     1     92267  9806555 3522.2
## + q6_me_inf                1     58544  9840278 3523.4
## <none>                                  9898822 3523.4
## + q6_close_person_inf      1     51580  9847241 3523.6
## + stai_ta                  1     30380  9868441 3524.4
## - sr_age:bdi               1     92376  9991198 3524.6
## + sr_gender                1     21170  9877652 3524.7
## + q6_media_valence:sr_age  1     18986  9879836 3524.7
## + stai_sa                  1      2927  9895894 3525.3
## + cat                      1      1081  9897741 3525.4
## + q6_close_person_died     1         9  9898813 3525.4
## - q6_media_valence         1    151270 10050091 3526.6
## 
## Step:  AIC=3522.2
## covid_end_est ~ sr_age + q6_media_valence + bdi + sr_age:bdi + 
##     q6_media_valence:bdi
## 
##                           Df Sum of Sq     RSS    AIC
## + q6_me_inf                1     67565 9738990 3521.8
## <none>                                 9806555 3522.2
## + q6_close_person_inf      1     50304 9756251 3522.4
## + sr_gender                1     23110 9783445 3523.4
## - q6_media_valence:bdi     1     92267 9898822 3523.4
## + stai_ta                  1     22292 9784263 3523.4
## + stai_sa                  1      7779 9798776 3523.9
## + q6_media_valence:sr_age  1      6524 9800031 3524.0
## + q6_close_person_died     1      1383 9805172 3524.2
## + cat                      1        45 9806510 3524.2
## - sr_age:bdi               1    140430 9946985 3525.1
## 
## Step:  AIC=3521.84
## covid_end_est ~ sr_age + q6_media_valence + bdi + q6_me_inf + 
##     sr_age:bdi + q6_media_valence:bdi
## 
##                              Df Sum of Sq     RSS    AIC
## + q6_me_inf:sr_age            1     73330 9665660 3521.3
## <none>                                    9738990 3521.8
## - q6_me_inf                   1     67565 9806555 3522.2
## + q6_close_person_inf         1     30101 9708889 3522.8
## + stai_ta                     1     22543 9716447 3523.0
## + sr_gender                   1     21043 9717948 3523.1
## + q6_me_inf:q6_media_valence  1     13385 9725605 3523.4
## - q6_media_valence:bdi        1    101287 9840278 3523.4
## + stai_sa                     1      7871 9731119 3523.6
## + q6_media_valence:sr_age     1      7555 9731435 3523.6
## + bdi:q6_me_inf               1      6974 9732017 3523.6
## + q6_close_person_died        1      4248 9734742 3523.7
## + cat                         1       405 9738586 3523.8
## - sr_age:bdi                  1    132845 9871835 3524.5
## 
## Step:  AIC=3521.25
## covid_end_est ~ sr_age + q6_media_valence + bdi + q6_me_inf + 
##     sr_age:bdi + q6_media_valence:bdi + sr_age:q6_me_inf
## 
##                              Df Sum of Sq     RSS    AIC
## <none>                                    9665660 3521.3
## + q6_close_person_inf         1     44253 9621407 3521.7
## - sr_age:q6_me_inf            1     73330 9738990 3521.8
## - q6_media_valence:bdi        1     83640 9749300 3522.2
## + stai_ta                     1     24584 9641076 3522.4
## + sr_gender                   1     20879 9644780 3522.5
## + q6_media_valence:sr_age     1     12900 9652760 3522.8
## + q6_me_inf:q6_media_valence  1     10348 9655312 3522.9
## + stai_sa                     1      5273 9660387 3523.1
## + q6_close_person_died        1      3392 9662268 3523.1
## + cat                         1      1825 9663835 3523.2
## + bdi:q6_me_inf               1         1 9665659 3523.3
## - sr_age:bdi                  1    155265 9820925 3524.7
## 
## Call:
## lm(formula = covid_end_est ~ sr_age + q6_media_valence + bdi + 
##     q6_me_inf + sr_age:bdi + q6_media_valence:bdi + sr_age:q6_me_inf, 
##     data = data)
## 
## Coefficients:
##          (Intercept)                sr_age      q6_media_valence  
##               95.125                 4.778               -29.750  
##                  bdi             q6_me_inf            sr_age:bdi  
##               -7.793               203.483                 0.372  
## q6_media_valence:bdi      sr_age:q6_me_inf  
##                1.121                -8.999
## boundary (singular) fit: see ?isSingular
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## covid_end_est ~ q6_media_valence + bdi + q6_me_inf + q6_media_valence:bdi +  
##     (1 | sr_age) + (1 | sr_age:bdi) + (1 | sr_age:q6_me_inf)
##    Data: data
## 
## REML criterion at convergence: 4471
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.5672 -0.4473 -0.1333  0.3273  2.8759 
## 
## Random effects:
##  Groups           Name        Variance Std.Dev.
##  sr_age:bdi       (Intercept) 17646    132.84  
##  sr_age:q6_me_inf (Intercept)  2692     51.89  
##  sr_age           (Intercept)     0      0.00  
##  Residual                     13144    114.65  
## Number of obs: 342, groups:  sr_age:bdi, 288; sr_age:q6_me_inf, 38; sr_age, 23
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)          243.1160    20.8048  67.6435  11.686   <2e-16 ***
## q6_media_valence     -23.9584    10.1642 262.9609  -2.357   0.0191 *  
## bdi                    1.6221     1.1448 285.7020   1.417   0.1576    
## q6_me_inf            -45.6164    42.0590 132.5097  -1.085   0.2801    
## q6_media_valence:bdi   0.6668     0.6341 323.2652   1.052   0.2938    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) q6_md_ bdi    q6_m_n
## q6_med_vlnc  0.212                     
## bdi         -0.656 -0.234              
## q6_me_inf   -0.157  0.105 -0.105       
## q6_md_vlnc: -0.186 -0.815  0.359 -0.043
## convergence code: 0
## boundary (singular) fit: see ?isSingular
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## covid_end_est ~ q6_media_valence + bdi + q6_me_inf + (1 | sr_age) + 
##     (1 | sr_age:bdi) + (1 | sr_age:q6_me_inf) + q6_media_valence:bdi
##                        npar  logLik    AIC     LRT Df Pr(>Chisq)    
## <none>                    9 -2235.5 4489.0                          
## (1 | sr_age)              8 -2235.5 4487.0  0.0000  1  1.0000000    
## (1 | sr_age:bdi)          8 -2241.7 4499.5 12.4369  1  0.0004209 ***
## (1 | sr_age:q6_me_inf)    8 -2235.8 4487.5  0.5315  1  0.4659704    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1