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