Linear mixed effects
model
Enjoyment
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ group * game.experience.thirds * time + (1 | subject)
## Data: filter(long_data.thirds, construct == "enjoyment")
##
## REML criterion at convergence: 1281.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.12394 -0.47115 0.05627 0.67388 2.35026
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 1.393 1.180
## Residual 14.966 3.869
## Number of obs: 242, groups: subject, 121
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 36.4286 1.5287 216.4313
## groupI -0.5119 1.9236 216.4313
## groupS -0.9911 1.8329 216.4313
## groupT -1.9841 2.0383 216.4313
## game.experience.thirdsMedium 0.5714 2.0383 216.4313
## game.experience.thirdsHigh -3.2857 1.8723 216.4313
## timepost -7.4286 2.0678 109.0000
## groupI:game.experience.thirdsMedium -5.5881 2.6746 216.4313
## groupS:game.experience.thirdsMedium -3.0089 2.6873 216.4313
## groupT:game.experience.thirdsMedium 2.1841 2.7583 216.4313
## groupI:game.experience.thirdsHigh 0.7440 2.6293 216.4313
## groupS:game.experience.thirdsHigh 4.0149 2.6934 216.4313
## groupT:game.experience.thirdsHigh 5.5079 2.5858 216.4313
## groupI:timepost 2.9286 2.6020 109.0000
## groupS:timepost 2.7411 2.4793 109.0000
## groupT:timepost 6.3175 2.7571 109.0000
## game.experience.thirdsMedium:timepost 2.6508 2.7571 109.0000
## game.experience.thirdsHigh:timepost 8.0714 2.5326 109.0000
## groupI:game.experience.thirdsMedium:timepost 1.5492 3.6179 109.0000
## groupS:game.experience.thirdsMedium:timepost 2.2867 3.6351 109.0000
## groupT:game.experience.thirdsMedium:timepost -7.4397 3.7310 109.0000
## groupI:game.experience.thirdsHigh:timepost -1.0714 3.5566 109.0000
## groupS:game.experience.thirdsHigh:timepost -6.7173 3.6433 109.0000
## groupT:game.experience.thirdsHigh:timepost -9.8770 3.4977 109.0000
## t value Pr(>|t|)
## (Intercept) 23.830 < 2e-16 ***
## groupI -0.266 0.790399
## groupS -0.541 0.589252
## groupT -0.973 0.331424
## game.experience.thirdsMedium 0.280 0.779477
## game.experience.thirdsHigh -1.755 0.080686 .
## timepost -3.592 0.000493 ***
## groupI:game.experience.thirdsMedium -2.089 0.037849 *
## groupS:game.experience.thirdsMedium -1.120 0.264096
## groupT:game.experience.thirdsMedium 0.792 0.429316
## groupI:game.experience.thirdsHigh 0.283 0.777464
## groupS:game.experience.thirdsHigh 1.491 0.137509
## groupT:game.experience.thirdsHigh 2.130 0.034293 *
## groupI:timepost 1.126 0.262842
## groupS:timepost 1.106 0.271333
## groupT:timepost 2.291 0.023867 *
## game.experience.thirdsMedium:timepost 0.961 0.338461
## game.experience.thirdsHigh:timepost 3.187 0.001876 **
## groupI:game.experience.thirdsMedium:timepost 0.428 0.669346
## groupS:game.experience.thirdsMedium:timepost 0.629 0.530624
## groupT:game.experience.thirdsMedium:timepost -1.994 0.048649 *
## groupI:game.experience.thirdsHigh:timepost -0.301 0.763800
## groupS:game.experience.thirdsHigh:timepost -1.844 0.067935 .
## groupT:game.experience.thirdsHigh:timepost -2.824 0.005643 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 24 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: impasse_data_enjoyment
##
## REML criterion at convergence: 319.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.03901 -0.46619 0.02843 0.63772 1.47574
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 7.681 2.771
## Residual 10.761 3.280
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 35.917 1.240 46.017 28.972
## game.experience.thirdsMedium -5.017 1.839 46.017 -2.728
## game.experience.thirdsHigh -2.542 1.960 46.017 -1.297
## timepost -4.500 1.339 27.000 -3.360
## game.experience.thirdsMedium:timepost 4.200 1.986 27.000 2.114
## game.experience.thirdsHigh:timepost 7.000 2.117 27.000 3.306
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## game.experience.thirdsMedium 0.00899 **
## game.experience.thirdsHigh 0.20121
## timepost 0.00234 **
## game.experience.thirdsMedium:timepost 0.04386 *
## game.experience.thirdsHigh:timepost 0.00268 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.674
## gm.xprnc.tH -0.632 0.426
## timepost -0.540 0.364 0.342
## gm.xprnc.M: 0.364 -0.540 -0.230 -0.674
## gm.xprnc.H: 0.342 -0.230 -0.540 -0.632 0.426
## contrast game.experience.thirds estimate SE df t.ratio p.value
## 1 pre - post Low 4.5 1.339223 27 3.3601571 0.002335746
## 2 pre - post Medium 0.3 1.467045 27 0.2044926 0.839502229
## 3 pre - post High -2.5 1.640207 27 -1.5241982 0.139087864
## cohens_d
## 1 3.3601571
## 2 0.2044926
## 3 -1.5241982
## contrast time estimate SE df t.ratio p.value
## 1 Low - Medium post 0.8166667 1.838773 46.01735 0.4441368 0.89722303
## 2 Low - High post -4.4583333 1.960138 46.01735 -2.2744993 0.06962378
## 3 Medium - High post -5.2750000 2.037036 46.01735 -2.5895473 0.03367334
## cohens_d
## 1 0.4441368
## 2 -2.2744993
## 3 -2.5895473
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium pre 5.016667 1.838773 46.01735 2.728269 0.02394844 2.728269
## 2 Low - High pre 2.541667 1.960138 46.01735 1.296677 0.40426069 1.296677
## 3 Medium - High pre -2.475000 2.037036 46.01735 -1.215001 0.45049941 -1.215001
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low -4.5 1.34 27 -7.248 -1.75
## Medium -0.3 1.47 27 -3.310 2.71
## High 2.5 1.64 27 -0.865 5.87
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -4.2 1.99 27 -2.114 0.1057
## Low - High -7.0 2.12 27 -3.306 0.0073
## Medium - High -2.8 2.20 27 -1.272 0.4225
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: tutorial_data_enjoyment
##
## REML criterion at convergence: 314.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4670 -0.4698 -0.0564 0.7047 1.8885
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 2.397e-18 1.548e-09
## Residual 1.259e+01 3.548e+00
## Number of obs: 62, groups: subject, 31
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 34.444 1.183 56.000 29.127
## game.experience.thirdsMedium 2.756 1.630 56.000 1.690
## game.experience.thirdsHigh 2.222 1.564 56.000 1.420
## timepost -1.111 1.672 56.000 -0.664
## game.experience.thirdsMedium:timepost -4.789 2.305 56.000 -2.077
## game.experience.thirdsHigh:timepost -1.806 2.212 56.000 -0.816
## Pr(>|t|)
## (Intercept) <2e-16 ***
## game.experience.thirdsMedium 0.0965 .
## game.experience.thirdsHigh 0.1610
## timepost 0.5092
## game.experience.thirdsMedium:timepost 0.0424 *
## game.experience.thirdsHigh:timepost 0.4179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.725
## gm.xprnc.tH -0.756 0.548
## timepost -0.707 0.513 0.535
## gm.xprnc.M: 0.513 -0.707 -0.388 -0.725
## gm.xprnc.H: 0.535 -0.388 -0.707 -0.756 0.548
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## contrast game.experience.thirds estimate SE df t.ratio p.value
## 1 pre - post Low 1.111111 1.672417 28 0.6643743 0.5118854415
## 2 pre - post Medium 5.900000 1.586594 28 3.7186568 0.0008886793
## 3 pre - post High 2.916667 1.448356 28 2.0137776 0.0537330888
## cohens_d
## 1 0.6643743
## 2 3.7186568
## 3 2.0137776
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium post 2.0333333 1.630071 56 1.2473896 0.4307261 1.2473896
## 2 Low - High post -0.4166667 1.564403 56 -0.2663423 0.9616767 -0.2663423
## 3 Medium - High post -2.4500000 1.519048 56 -1.6128518 0.2487019 -1.6128518
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium pre -2.7555556 1.630071 56 -1.690451 0.2177845 -1.690451
## 2 Low - High pre -2.2222222 1.564403 56 -1.420492 0.3373915 -1.420492
## 3 Medium - High pre 0.5333333 1.519048 56 0.351097 0.9343901 0.351097
## boundary (singular) fit: see help('isSingular')
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low -1.11 1.67 28 -4.54 2.3147
## Medium -5.90 1.59 28 -9.15 -2.6500
## High -2.92 1.45 28 -5.88 0.0502
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium 4.79 2.31 28 2.077 0.1129
## Low - High 1.81 2.21 28 0.816 0.6963
## Medium - High -2.98 2.15 28 -1.389 0.3603
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: original_data_enjoyment
##
## REML criterion at convergence: 317.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1793 -0.4440 0.0000 0.7105 2.4866
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 16.17 4.022
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 36.4286 1.5200 54.0000 23.966
## game.experience.thirdsMedium 0.5714 2.0267 54.0000 0.282
## game.experience.thirdsHigh -3.2857 1.8616 54.0000 -1.765
## timepost -7.4286 2.1496 54.0000 -3.456
## game.experience.thirdsMedium:timepost 2.6508 2.8662 54.0000 0.925
## game.experience.thirdsHigh:timepost 8.0714 2.6327 54.0000 3.066
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## game.experience.thirdsMedium 0.77906
## game.experience.thirdsHigh 0.08322 .
## timepost 0.00108 **
## game.experience.thirdsMedium:timepost 0.35915
## game.experience.thirdsHigh:timepost 0.00339 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.750
## gm.xprnc.tH -0.816 0.612
## timepost -0.707 0.530 0.577
## gm.xprnc.M: 0.530 -0.707 -0.433 -0.750
## gm.xprnc.H: 0.577 -0.433 -0.707 -0.816 0.612
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## contrast game.experience.thirds estimate SE df t.ratio
## 1 pre - post Low 7.4285714 2.149617 27 3.4557651
## 2 pre - post Medium 4.7777778 1.895784 27 2.5202121
## 3 pre - post High -0.6428571 1.520009 27 -0.4229299
## p.value cohens_d
## 1 0.001829909 3.4557651
## 2 0.017946398 2.5202121
## 3 0.675696003 -0.4229299
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium post -3.222222 2.026678 54 -1.5899031 0.2586934 -1.5899031
## 2 Low - High post -4.785714 1.861623 54 -2.5707216 0.0340474 -2.5707216
## 3 Medium - High post -1.563492 1.718200 54 -0.9099592 0.6364142 -0.9099592
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium pre -0.5714286 2.026678 54 -0.2819533 0.95715857 -0.2819533
## 2 Low - High pre 3.2857143 1.861623 54 1.7649730 0.19107483 1.7649730
## 3 Medium - High pre 3.8571429 1.718200 54 2.2448740 0.07273952 2.2448740
## boundary (singular) fit: see help('isSingular')
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low -7.429 2.15 27 -11.84 -3.018
## Medium -4.778 1.90 27 -8.67 -0.888
## High 0.643 1.52 27 -2.48 3.762
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -2.65 2.87 27 -0.925 0.6296
## Low - High -8.07 2.63 27 -3.066 0.0131
## Medium - High -5.42 2.43 27 -2.231 0.0840
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: safe_data_enjoyment
##
## REML criterion at convergence: 323.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3330 -0.7145 0.1312 0.7582 1.9247
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 18.37 4.286
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 35.4375 1.0716 54.0000 33.071
## game.experience.thirdsMedium -2.4375 1.8560 54.0000 -1.313
## game.experience.thirdsHigh 0.7292 2.0519 54.0000 0.355
## timepost -4.6875 1.5154 54.0000 -3.093
## game.experience.thirdsMedium:timepost 4.9375 2.6248 54.0000 1.881
## game.experience.thirdsHigh:timepost 1.3542 2.9018 54.0000 0.467
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## game.experience.thirdsMedium 0.19464
## game.experience.thirdsHigh 0.72370
## timepost 0.00313 **
## game.experience.thirdsMedium:timepost 0.06536 .
## game.experience.thirdsHigh:timepost 0.64262
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.577
## gm.xprnc.tH -0.522 0.302
## timepost -0.707 0.408 0.369
## gm.xprnc.M: 0.408 -0.707 -0.213 -0.577
## gm.xprnc.H: 0.369 -0.213 -0.707 -0.522 0.302
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## contrast game.experience.thirds estimate SE df t.ratio
## 1 pre - post Low 4.687500 1.515433 27 3.0931752
## 2 pre - post Medium -0.250000 2.143146 27 -0.1166509
## 3 pre - post High 3.333333 2.474692 27 1.3469690
## p.value cohens_d
## 1 0.004566763 3.0931752
## 2 0.908000032 -0.1166509
## 3 0.189187733 1.3469690
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium post -2.5000000 1.856019 54 -1.3469690 0.3758391 -1.3469690
## 2 Low - High post -2.0833333 2.051906 54 -1.0153161 0.5705055 -1.0153161
## 3 Medium - High post 0.4166667 2.314862 54 0.1799963 0.9823033 0.1799963
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium pre 2.4375000 1.856019 54 1.3132948 0.3940570 1.3132948
## 2 Low - High pre -0.7291667 2.051906 54 -0.3553606 0.9328473 -0.3553606
## 3 Medium - High pre -3.1666667 2.314862 54 -1.3679720 0.3647028 -1.3679720
## boundary (singular) fit: see help('isSingular')
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low -4.69 1.52 27 -7.80 -1.58
## Medium 0.25 2.14 27 -4.15 4.65
## High -3.33 2.47 27 -8.41 1.74
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -4.94 2.62 27 -1.881 0.1635
## Low - High -1.35 2.90 27 -0.467 0.8874
## Medium - High 3.58 3.27 27 1.095 0.5256
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
Tables for
enjoyment
Enjoyment Impasse
Contrasts
Enjoyment
Tutorial Contrasts
Enjoyment
Original Contrasts
Curiosity
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ group * game.experience.thirds * time + (1 | subject)
## Data: filter(long_data.thirds, construct == "curiosity")
##
## REML criterion at convergence: 1243.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9875 -0.6479 0.0319 0.7012 2.5876
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 13.68 3.699
## Number of obs: 242, groups: subject, 121
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 39.1429 1.3981 218.0000
## groupI 0.4405 1.7592 218.0000
## groupS 0.9821 1.6763 218.0000
## groupT 0.7460 1.8641 218.0000
## game.experience.thirdsMedium 3.0794 1.8641 218.0000
## game.experience.thirdsHigh -0.2857 1.7123 218.0000
## timepost -3.7143 1.9772 218.0000
## groupI:game.experience.thirdsMedium -4.3627 2.4461 218.0000
## groupS:game.experience.thirdsMedium -3.8294 2.4577 218.0000
## groupT:game.experience.thirdsMedium -3.0683 2.5226 218.0000
## groupI:game.experience.thirdsHigh -2.5476 2.4047 218.0000
## groupS:game.experience.thirdsHigh -0.3393 2.4633 218.0000
## groupT:game.experience.thirdsHigh 0.1468 2.3649 218.0000
## groupI:timepost 2.1310 2.4879 218.0000
## groupS:timepost 1.2768 2.3706 218.0000
## groupT:timepost 3.6032 2.6363 218.0000
## game.experience.thirdsMedium:timepost 1.4921 2.6363 218.0000
## game.experience.thirdsHigh:timepost 6.5000 2.4216 218.0000
## groupI:game.experience.thirdsMedium:timepost -0.4087 3.4593 218.0000
## groupS:game.experience.thirdsMedium:timepost 1.0704 3.4758 218.0000
## groupT:game.experience.thirdsMedium:timepost -1.3810 3.5675 218.0000
## groupI:game.experience.thirdsHigh:timepost -1.7917 3.4008 218.0000
## groupS:game.experience.thirdsHigh:timepost -2.3958 3.4836 218.0000
## groupT:game.experience.thirdsHigh:timepost -7.2222 3.3444 218.0000
## t value Pr(>|t|)
## (Intercept) 27.997 < 2e-16 ***
## groupI 0.250 0.80253
## groupS 0.586 0.55854
## groupT 0.400 0.68940
## game.experience.thirdsMedium 1.652 0.09999 .
## game.experience.thirdsHigh -0.167 0.86764
## timepost -1.879 0.06164 .
## groupI:game.experience.thirdsMedium -1.784 0.07589 .
## groupS:game.experience.thirdsMedium -1.558 0.12067
## groupT:game.experience.thirdsMedium -1.216 0.22519
## groupI:game.experience.thirdsHigh -1.059 0.29058
## groupS:game.experience.thirdsHigh -0.138 0.89057
## groupT:game.experience.thirdsHigh 0.062 0.95055
## groupI:timepost 0.857 0.39265
## groupS:timepost 0.539 0.59072
## groupT:timepost 1.367 0.17311
## game.experience.thirdsMedium:timepost 0.566 0.57200
## game.experience.thirdsHigh:timepost 2.684 0.00783 **
## groupI:game.experience.thirdsMedium:timepost -0.118 0.90606
## groupS:game.experience.thirdsMedium:timepost 0.308 0.75840
## groupT:game.experience.thirdsMedium:timepost -0.387 0.69907
## groupI:game.experience.thirdsHigh:timepost -0.527 0.59884
## groupS:game.experience.thirdsHigh:timepost -0.688 0.49234
## groupT:game.experience.thirdsHigh:timepost -2.159 0.03190 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 24 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: impasse_data_curiosity
##
## REML criterion at convergence: 318.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6231 -0.5097 0.0491 0.5936 1.6457
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 16.57 4.071
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 39.583 1.175 54.000 33.681
## game.experience.thirdsMedium -1.283 1.743 54.000 -0.736
## game.experience.thirdsHigh -2.833 1.858 54.000 -1.525
## timepost -1.583 1.662 54.000 -0.953
## game.experience.thirdsMedium:timepost 1.083 2.465 54.000 0.439
## game.experience.thirdsHigh:timepost 4.708 2.628 54.000 1.792
## Pr(>|t|)
## (Intercept) <2e-16 ***
## game.experience.thirdsMedium 0.4648
## game.experience.thirdsHigh 0.1332
## timepost 0.3450
## game.experience.thirdsMedium:timepost 0.6621
## game.experience.thirdsHigh:timepost 0.0788 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.674
## gm.xprnc.tH -0.632 0.426
## timepost -0.707 0.477 0.447
## gm.xprnc.M: 0.477 -0.707 -0.302 -0.674
## gm.xprnc.H: 0.447 -0.302 -0.707 -0.632 0.426
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## contrast game.experience.thirds estimate SE df t.ratio p.value
## 1 pre - post Low 1.583333 1.662023 27 0.9526544 0.3492165
## 2 pre - post Medium 0.500000 1.820655 27 0.2746265 0.7856937
## 3 pre - post High -3.125000 2.035554 27 -1.5352086 0.1363691
## cohens_d
## 1 0.9526544
## 2 0.2746265
## 3 -1.5352086
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium post 0.200 1.743144 54 0.1147352 0.9927697 0.1147352
## 2 Low - High post -1.875 1.858198 54 -1.0090421 0.5744154 -1.0090421
## 3 Medium - High post -2.075 1.931096 54 -1.0745193 0.5338408 -1.0745193
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium pre 1.283333 1.743144 54 0.7362175 0.7431608 0.7362175
## 2 Low - High pre 2.833333 1.858198 54 1.5247747 0.2874999 1.5247747
## 3 Medium - High pre 1.550000 1.931096 54 0.8026530 0.7030030 0.8026530
## boundary (singular) fit: see help('isSingular')
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low -1.58 1.66 27 -4.99 1.83
## Medium -0.50 1.82 27 -4.24 3.24
## High 3.12 2.04 27 -1.05 7.30
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -1.08 2.47 27 -0.439 0.8994
## Low - High -4.71 2.63 27 -1.792 0.1914
## Medium - High -3.62 2.73 27 -1.327 0.3927
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: tutorial_data_curiosity
##
## REML criterion at convergence: 309.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7455 -0.8113 0.0295 0.9096 1.7947
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.00
## Residual 11.49 3.39
## Number of obs: 62, groups: subject, 31
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 39.88889 1.12987 56.00000 35.304
## game.experience.thirdsMedium 0.01111 1.55742 56.00000 0.007
## game.experience.thirdsHigh -0.13889 1.49468 56.00000 -0.093
## timepost -0.11111 1.59788 56.00000 -0.070
## game.experience.thirdsMedium:timepost 0.11111 2.20252 56.00000 0.050
## game.experience.thirdsHigh:timepost -0.72222 2.11380 56.00000 -0.342
## Pr(>|t|)
## (Intercept) <2e-16 ***
## game.experience.thirdsMedium 0.994
## game.experience.thirdsHigh 0.926
## timepost 0.945
## game.experience.thirdsMedium:timepost 0.960
## game.experience.thirdsHigh:timepost 0.734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.725
## gm.xprnc.tH -0.756 0.548
## timepost -0.707 0.513 0.535
## gm.xprnc.M: 0.513 -0.707 -0.388 -0.725
## gm.xprnc.H: 0.535 -0.388 -0.707 -0.756 0.548
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## contrast game.experience.thirds estimate SE df t.ratio
## 1 pre - post Low 1.111111e-01 1.597879 28 6.953660e-02
## 2 pre - post Medium -4.163336e-17 1.515882 28 -2.746479e-17
## 3 pre - post High 8.333333e-01 1.383804 28 6.022047e-01
## p.value cohens_d
## 1 0.9450567 6.953660e-02
## 2 1.0000000 -2.746479e-17
## 3 0.5518839 6.022047e-01
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium post -0.1222222 1.557420 56 -0.07847736 0.9966105 -0.07847736
## 2 Low - High post 0.8611111 1.494679 56 0.57611762 0.8333793 0.57611762
## 3 Medium - High post 0.9833333 1.451346 56 0.67753195 0.7774763 0.67753195
## contrast time estimate SE df t.ratio p.value
## 1 Low - Medium pre -0.01111111 1.557420 56 -0.007134305 0.9999719
## 2 Low - High pre 0.13888889 1.494679 56 0.092922197 0.9952513
## 3 Medium - High pre 0.15000000 1.451346 56 0.103352331 0.9941289
## cohens_d
## 1 -0.007134305
## 2 0.092922197
## 3 0.103352331
## boundary (singular) fit: see help('isSingular')
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low -0.111 1.60 28 -3.38 3.16
## Medium 0.000 1.52 28 -3.11 3.11
## High -0.833 1.38 28 -3.67 2.00
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -0.111 2.20 28 -0.050 0.9986
## Low - High 0.722 2.11 28 0.342 0.9378
## Medium - High 0.833 2.05 28 0.406 0.9134
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: original_data_curiosity
##
## REML criterion at convergence: 305.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.11216 -0.62838 0.06909 0.76766 2.64513
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 13.09 3.619
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 39.1429 1.3677 54.0000 28.620
## game.experience.thirdsMedium 3.0794 1.8236 54.0000 1.689
## game.experience.thirdsHigh -0.2857 1.6750 54.0000 -0.171
## timepost -3.7143 1.9342 54.0000 -1.920
## game.experience.thirdsMedium:timepost 1.4921 2.5789 54.0000 0.579
## game.experience.thirdsHigh:timepost 6.5000 2.3689 54.0000 2.744
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## game.experience.thirdsMedium 0.09705 .
## game.experience.thirdsHigh 0.86520
## timepost 0.06010 .
## game.experience.thirdsMedium:timepost 0.56529
## game.experience.thirdsHigh:timepost 0.00822 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.750
## gm.xprnc.tH -0.816 0.612
## timepost -0.707 0.530 0.577
## gm.xprnc.M: 0.530 -0.707 -0.433 -0.750
## gm.xprnc.H: 0.577 -0.433 -0.707 -0.816 0.612
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## contrast game.experience.thirds estimate SE df t.ratio p.value
## 1 pre - post Low 3.714286 1.934175 27 1.920346 0.06543507
## 2 pre - post Medium 2.222222 1.705782 27 1.302758 0.20365964
## 3 pre - post High -2.785714 1.367669 27 -2.036834 0.05158080
## cohens_d
## 1 1.920346
## 2 1.302758
## 3 -2.036834
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium post -4.571429 1.823558 54 -2.506873 0.03975021 -2.506873
## 2 Low - High post -6.214286 1.675045 54 -3.709921 0.00140515 -3.709921
## 3 Medium - High post -1.642857 1.545997 54 -1.062652 0.54115069 -1.062652
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium pre -3.0793651 1.823558 54 -1.6886575 0.21879423 -1.6886575
## 2 Low - High pre 0.2857143 1.675045 54 0.1705711 0.98409302 0.1705711
## 3 Medium - High pre 3.3650794 1.545997 54 2.1766409 0.08440962 2.1766409
## boundary (singular) fit: see help('isSingular')
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low -3.71 1.93 27 -7.6829 0.254
## Medium -2.22 1.71 27 -5.7222 1.278
## High 2.79 1.37 27 -0.0205 5.592
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -1.49 2.58 27 -0.579 0.8326
## Low - High -6.50 2.37 27 -2.744 0.0278
## Medium - High -5.01 2.19 27 -2.291 0.0744
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: safe_data_curiosity
##
## REML criterion at convergence: 307.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.19274 -0.56399 -0.03854 0.63457 1.91887
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.5787 0.7607
## Residual 13.0768 3.6162
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 40.1250 0.9238 53.9032 43.433
## game.experience.thirdsMedium -0.7500 1.6001 53.9032 -0.469
## game.experience.thirdsHigh -0.6250 1.7690 53.9032 -0.353
## timepost -2.4375 1.2785 27.0000 -1.907
## game.experience.thirdsMedium:timepost 2.5625 2.2145 27.0000 1.157
## game.experience.thirdsHigh:timepost 4.1042 2.4482 27.0000 1.676
## Pr(>|t|)
## (Intercept) <2e-16 ***
## game.experience.thirdsMedium 0.6412
## game.experience.thirdsHigh 0.7252
## timepost 0.0673 .
## game.experience.thirdsMedium:timepost 0.2573
## game.experience.thirdsHigh:timepost 0.1052
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.577
## gm.xprnc.tH -0.522 0.302
## timepost -0.692 0.400 0.361
## gm.xprnc.M: 0.400 -0.692 -0.209 -0.577
## gm.xprnc.H: 0.361 -0.209 -0.692 -0.522 0.302
## contrast game.experience.thirds estimate SE df t.ratio
## 1 pre - post Low 2.437500 1.278514 27 1.90651092
## 2 pre - post Medium -0.125000 1.808091 27 -0.06913368
## 3 pre - post High -1.666667 2.087804 27 -0.79828700
## p.value cohens_d
## 1 0.06727901 1.90651092
## 2 0.94539269 -0.06913368
## 3 0.43166983 -0.79828700
## contrast time estimate SE df t.ratio p.value
## 1 Low - Medium post -1.812500 1.600126 53.90319 -1.1327235 0.4983722
## 2 Low - High post -3.479167 1.769006 53.90319 -1.9667359 0.1303386
## 3 Medium - High post -1.666667 1.995707 53.90319 -0.8351258 0.6830188
## cohens_d
## 1 -1.1327235
## 2 -1.9667359
## 3 -0.8351258
## contrast time estimate SE df t.ratio p.value
## 1 Low - Medium pre 0.750 1.600126 53.90319 0.46871318 0.8862169
## 2 Low - High pre 0.625 1.769006 53.90319 0.35330585 0.9335944
## 3 Medium - High pre -0.125 1.995707 53.90319 -0.06263443 0.9978395
## cohens_d
## 1 0.46871318
## 2 0.35330585
## 3 -0.06263443
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low -2.438 1.28 27 -5.06 0.186
## Medium 0.125 1.81 27 -3.58 3.835
## High 1.667 2.09 27 -2.62 5.950
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -2.56 2.21 27 -1.157 0.4883
## Low - High -4.10 2.45 27 -1.676 0.2324
## Medium - High -1.54 2.76 27 -0.558 0.8432
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
Tables for
curiosity
Curiosity Impasse
Contrasts
Curiosity
Tutorial Contrasts
Curiosity
Original Contrasts
Difficulty
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ group * game.experience.thirds * time + (1 | subject)
## Data: filter(long_data.thirds, construct == "difficulty")
##
## REML criterion at convergence: 1212.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4650 -0.5261 0.1088 0.4769 2.3403
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 11.87 3.445
## Number of obs: 242, groups: subject, 121
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 21.57143 1.30214 218.00000
## groupI 2.26190 1.63849 218.00000
## groupS -3.63393 1.56121 218.00000
## groupT -2.01587 1.73618 218.00000
## game.experience.thirdsMedium -0.46032 1.73618 218.00000
## game.experience.thirdsHigh -0.71429 1.59479 218.00000
## timepost 3.42857 1.84150 218.00000
## groupI:game.experience.thirdsMedium 0.32698 2.27822 218.00000
## groupS:game.experience.thirdsMedium -1.85218 2.28905 218.00000
## groupT:game.experience.thirdsMedium 0.30476 2.34947 218.00000
## groupI:game.experience.thirdsHigh 0.50595 2.23965 218.00000
## groupS:game.experience.thirdsHigh 0.77679 2.29419 218.00000
## groupT:game.experience.thirdsHigh -1.84127 2.20254 218.00000
## groupI:timepost -3.59524 2.31717 218.00000
## groupS:timepost 4.00893 2.20788 218.00000
## groupT:timepost 1.57143 2.45533 218.00000
## game.experience.thirdsMedium:timepost 0.01587 2.45533 218.00000
## game.experience.thirdsHigh:timepost 1.07143 2.25537 218.00000
## groupI:game.experience.thirdsMedium:timepost 2.15079 3.22189 218.00000
## groupS:game.experience.thirdsMedium:timepost 0.54663 3.23721 218.00000
## groupT:game.experience.thirdsMedium:timepost -0.31587 3.32265 218.00000
## groupI:game.experience.thirdsHigh:timepost 0.72024 3.16735 218.00000
## groupS:game.experience.thirdsHigh:timepost -0.84226 3.24447 218.00000
## groupT:game.experience.thirdsHigh:timepost 1.67857 3.11486 218.00000
## t value Pr(>|t|)
## (Intercept) 16.566 <2e-16 ***
## groupI 1.380 0.1689
## groupS -2.328 0.0208 *
## groupT -1.161 0.2469
## game.experience.thirdsMedium -0.265 0.7912
## game.experience.thirdsHigh -0.448 0.6547
## timepost 1.862 0.0640 .
## groupI:game.experience.thirdsMedium 0.144 0.8860
## groupS:game.experience.thirdsMedium -0.809 0.4193
## groupT:game.experience.thirdsMedium 0.130 0.8969
## groupI:game.experience.thirdsHigh 0.226 0.8215
## groupS:game.experience.thirdsHigh 0.339 0.7352
## groupT:game.experience.thirdsHigh -0.836 0.4041
## groupI:timepost -1.552 0.1222
## groupS:timepost 1.816 0.0708 .
## groupT:timepost 0.640 0.5228
## game.experience.thirdsMedium:timepost 0.006 0.9948
## game.experience.thirdsHigh:timepost 0.475 0.6352
## groupI:game.experience.thirdsMedium:timepost 0.668 0.5051
## groupS:game.experience.thirdsMedium:timepost 0.169 0.8661
## groupT:game.experience.thirdsMedium:timepost -0.095 0.9243
## groupI:game.experience.thirdsHigh:timepost 0.227 0.8203
## groupS:game.experience.thirdsHigh:timepost -0.260 0.7954
## groupT:game.experience.thirdsHigh:timepost 0.539 0.5905
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 24 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: impasse_data_difficulty
##
## REML criterion at convergence: 266.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6627 -0.6657 0.1258 0.5299 2.1361
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.000 0.000
## Residual 6.332 2.516
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 23.8333 0.7264 54.0000 32.811
## game.experience.thirdsMedium -0.1333 1.0774 54.0000 -0.124
## game.experience.thirdsHigh -0.2083 1.1485 54.0000 -0.181
## timepost -0.1667 1.0273 54.0000 -0.162
## game.experience.thirdsMedium:timepost 2.1667 1.5237 54.0000 1.422
## game.experience.thirdsHigh:timepost 1.7917 1.6242 54.0000 1.103
## Pr(>|t|)
## (Intercept) <2e-16 ***
## game.experience.thirdsMedium 0.902
## game.experience.thirdsHigh 0.857
## timepost 0.872
## game.experience.thirdsMedium:timepost 0.161
## game.experience.thirdsHigh:timepost 0.275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.674
## gm.xprnc.tH -0.632 0.426
## timepost -0.707 0.477 0.447
## gm.xprnc.M: 0.477 -0.707 -0.302 -0.674
## gm.xprnc.H: 0.447 -0.302 -0.707 -0.632 0.426
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## contrast game.experience.thirds estimate SE df t.ratio p.value
## 1 pre - post Low 0.1666667 1.027265 27 0.1622432 0.8723225
## 2 pre - post Medium -2.0000000 1.125312 27 -1.7772848 0.0867895
## 3 pre - post High -1.6250000 1.258137 27 -1.2915922 0.2074462
## cohens_d
## 1 0.1622432
## 2 -1.7772848
## 3 -1.2915922
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium post -2.033333 1.077404 54 -1.887252 0.1521594 -1.887252
## 2 Low - High post -1.583333 1.148517 54 -1.378590 0.3591411 -1.378590
## 3 Medium - High post 0.450000 1.193574 54 0.377019 0.9247517 0.377019
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium pre 0.1333333 1.077404 54 0.12375423 0.9915935 0.12375423
## 2 Low - High pre 0.2083333 1.148517 54 0.18139337 0.9820301 0.18139337
## 3 Medium - High pre 0.0750000 1.193574 54 0.06283651 0.9978256 0.06283651
## boundary (singular) fit: see help('isSingular')
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low -0.167 1.03 27 -2.274 1.94
## Medium 2.000 1.13 27 -0.309 4.31
## High 1.625 1.26 27 -0.956 4.21
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -2.167 1.52 27 -1.422 0.3441
## Low - High -1.792 1.62 27 -1.103 0.5205
## Medium - High 0.375 1.69 27 0.222 0.9732
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: tutorial_data_difficulty
##
## REML criterion at convergence: 313
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5752 -0.6581 0.1272 0.5723 2.1301
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 1.100e-19 3.316e-10
## Residual 1.221e+01 3.495e+00
## Number of obs: 62, groups: subject, 31
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 19.5556 1.1650 56.0000 16.786
## game.experience.thirdsMedium -0.1556 1.6058 56.0000 -0.097
## game.experience.thirdsHigh -2.5556 1.5411 56.0000 -1.658
## timepost 5.0000 1.6475 56.0000 3.035
## game.experience.thirdsMedium:timepost -0.3000 2.2709 56.0000 -0.132
## game.experience.thirdsHigh:timepost 2.7500 2.1794 56.0000 1.262
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## game.experience.thirdsMedium 0.92317
## game.experience.thirdsHigh 0.10285
## timepost 0.00365 **
## game.experience.thirdsMedium:timepost 0.89537
## game.experience.thirdsHigh:timepost 0.21225
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.725
## gm.xprnc.tH -0.756 0.548
## timepost -0.707 0.513 0.535
## gm.xprnc.M: 0.513 -0.707 -0.388 -0.725
## gm.xprnc.H: 0.535 -0.388 -0.707 -0.756 0.548
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## contrast game.experience.thirds estimate SE df t.ratio p.value
## 1 pre - post Low -5.00 1.647502 28 -3.034897 5.151824e-03
## 2 pre - post Medium -4.70 1.562958 28 -3.007119 5.519291e-03
## 3 pre - post High -7.75 1.426779 28 -5.431816 8.529335e-06
## cohens_d
## 1 -3.034897
## 2 -3.007119
## 3 -5.431816
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium post 0.4555556 1.605787 56 0.2836962 0.9566382 0.2836962
## 2 Low - High post -0.1944444 1.541097 56 -0.1261727 0.9912632 -0.1261727
## 3 Medium - High post -0.6500000 1.496418 56 -0.4343705 0.9014162 -0.4343705
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium pre 0.1555556 1.605787 56 0.09687188 0.9948402 0.09687188
## 2 Low - High pre 2.5555556 1.541097 56 1.65827015 0.2302635 1.65827015
## 3 Medium - High pre 2.4000000 1.496418 56 1.60382970 0.2524799 1.60382970
## boundary (singular) fit: see help('isSingular')
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low 5.00 1.65 28 1.63 8.37
## Medium 4.70 1.56 28 1.50 7.90
## High 7.75 1.43 28 4.83 10.67
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium 0.30 2.27 28 0.132 0.9904
## Low - High -2.75 2.18 28 -1.262 0.4280
## Medium - High -3.05 2.12 28 -1.441 0.3341
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: original_data_difficulty
##
## REML criterion at convergence: 278.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4307 -0.5514 0.1139 0.5296 2.1775
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.000 0.000
## Residual 7.958 2.821
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 21.57143 1.06626 54.00000 20.231
## game.experience.thirdsMedium -0.46032 1.42168 54.00000 -0.324
## game.experience.thirdsHigh -0.71429 1.30590 54.00000 -0.547
## timepost 3.42857 1.50792 54.00000 2.274
## game.experience.thirdsMedium:timepost 0.01587 2.01056 54.00000 0.008
## game.experience.thirdsHigh:timepost 1.07143 1.84682 54.00000 0.580
## Pr(>|t|)
## (Intercept) <2e-16 ***
## game.experience.thirdsMedium 0.747
## game.experience.thirdsHigh 0.587
## timepost 0.027 *
## game.experience.thirdsMedium:timepost 0.994
## game.experience.thirdsHigh:timepost 0.564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.750
## gm.xprnc.tH -0.816 0.612
## timepost -0.707 0.530 0.577
## gm.xprnc.M: 0.530 -0.707 -0.433 -0.750
## gm.xprnc.H: 0.577 -0.433 -0.707 -0.816 0.612
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## contrast game.experience.thirds estimate SE df t.ratio
## 1 pre - post Low -3.428571 1.507923 27 -2.273705
## 2 pre - post Medium -3.444444 1.329863 27 -2.590075
## 3 pre - post High -4.500000 1.066262 27 -4.220350
## p.value cohens_d
## 1 0.0311510696 -2.273705
## 2 0.0152809350 -2.590075
## 3 0.0002465001 -4.220350
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium post 0.4444444 1.421683 54 0.3126185 0.9476061 0.3126185
## 2 Low - High post -0.3571429 1.305899 54 -0.2734842 0.9596394 -0.2734842
## 3 Medium - High post -0.8015873 1.205290 54 -0.6650574 0.7846417 -0.6650574
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium pre 0.4603175 1.421683 54 0.3237835 0.9439104 0.3237835
## 2 Low - High pre 0.7142857 1.305899 54 0.5469685 0.8484574 0.5469685
## 3 Medium - High pre 0.2539683 1.205290 54 0.2107112 0.9758317 0.2107112
## boundary (singular) fit: see help('isSingular')
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low 3.43 1.51 27 0.335 6.52
## Medium 3.44 1.33 27 0.716 6.17
## High 4.50 1.07 27 2.312 6.69
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -0.0159 2.01 27 -0.008 1.0000
## Low - High -1.0714 1.85 27 -0.580 0.8318
## Medium - High -1.0556 1.70 27 -0.619 0.8109
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: safe_data_difficulty
##
## REML criterion at convergence: 330.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6075 -0.5734 0.1365 0.4676 1.7611
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 20.96 4.578
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 17.9375 1.1445 54.0000 15.673
## game.experience.thirdsMedium -2.3125 1.9824 54.0000 -1.167
## game.experience.thirdsHigh 0.0625 2.1916 54.0000 0.029
## timepost 7.4375 1.6186 54.0000 4.595
## game.experience.thirdsMedium:timepost 0.5625 2.8035 54.0000 0.201
## game.experience.thirdsHigh:timepost 0.2292 3.0994 54.0000 0.074
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## game.experience.thirdsMedium 0.249
## game.experience.thirdsHigh 0.977
## timepost 2.64e-05 ***
## game.experience.thirdsMedium:timepost 0.842
## game.experience.thirdsHigh:timepost 0.941
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.577
## gm.xprnc.tH -0.522 0.302
## timepost -0.707 0.408 0.369
## gm.xprnc.M: 0.408 -0.707 -0.213 -0.577
## gm.xprnc.H: 0.369 -0.213 -0.707 -0.522 0.302
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## contrast game.experience.thirds estimate SE df t.ratio
## 1 pre - post Low -7.437500 1.618592 27 -4.595043
## 2 pre - post Medium -8.000000 2.289035 27 -3.494923
## 3 pre - post High -7.666667 2.643150 27 -2.900580
## p.value cohens_d
## 1 9.042398e-05 -4.595043
## 2 1.654948e-03 -3.494923
## 3 7.320781e-03 -2.900580
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium post 1.7500000 1.982362 54 0.8827852 0.6533976 0.8827852
## 2 Low - High post -0.2916667 2.191584 54 -0.1330849 0.9902847 -0.1330849
## 3 Medium - High post -2.0416667 2.472440 54 -0.8257699 0.6887933 -0.8257699
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium pre 2.3125 1.982362 54 1.16653759 0.4780985 1.16653759
## 2 Low - High pre -0.0625 2.191584 54 -0.02851819 0.9995517 -0.02851819
## 3 Medium - High pre -2.3750 2.472440 54 -0.96058952 0.6047038 -0.96058952
## boundary (singular) fit: see help('isSingular')
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low 7.44 1.62 27 4.12 10.8
## Medium 8.00 2.29 27 3.30 12.7
## High 7.67 2.64 27 2.24 13.1
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -0.562 2.8 27 -0.201 0.9781
## Low - High -0.229 3.1 27 -0.074 0.9970
## Medium - High 0.333 3.5 27 0.095 0.9950
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
Tables for
difficulty
Difficulty
Impasse Contrasts
Difficulty
Tutorial Contrasts
Difficulty
Original Contrasts
Difficulty Safe
Contrasts
Leveldifficulty
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ group * game.experience.thirds * time + (1 | subject)
## Data: filter(long_data.thirds, construct == "leveldifficulty")
##
## REML criterion at convergence: 1677
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2275 -0.4369 0.0299 0.5626 3.0006
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 2.173 1.474
## Residual 97.782 9.888
## Number of obs: 242, groups: subject, 121
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 15.28571 3.77879 217.89704
## groupI 1.88095 4.75488 217.89704
## groupS -2.84821 4.53061 217.89704
## groupT -2.17460 5.03839 217.89704
## game.experience.thirdsMedium 2.93651 5.03839 217.89704
## game.experience.thirdsHigh 1.92857 4.62806 217.89704
## timepost 16.42857 5.28562 109.00000
## groupI:game.experience.thirdsMedium -4.60317 6.61139 217.89704
## groupS:game.experience.thirdsMedium -1.12401 6.64281 217.89704
## groupT:game.experience.thirdsMedium -0.04762 6.81813 217.89704
## groupI:game.experience.thirdsHigh -0.59524 6.49945 217.89704
## groupS:game.experience.thirdsHigh 1.46726 6.65772 217.89704
## groupT:game.experience.thirdsHigh -2.12302 6.39176 217.89704
## groupI:timepost 0.07143 6.65092 109.00000
## groupS:timepost 0.50893 6.33723 109.00000
## groupT:timepost 4.01587 7.04749 109.00000
## game.experience.thirdsMedium:timepost -6.53968 7.04749 109.00000
## game.experience.thirdsHigh:timepost -2.64286 6.47353 109.00000
## groupI:game.experience.thirdsMedium:timepost 7.43968 9.24773 109.00000
## groupS:game.experience.thirdsMedium:timepost 6.72718 9.29168 109.00000
## groupT:game.experience.thirdsMedium:timepost -2.70476 9.53692 109.00000
## groupI:game.experience.thirdsHigh:timepost 0.39286 9.09116 109.00000
## groupS:game.experience.thirdsHigh:timepost -6.96131 9.31254 109.00000
## groupT:game.experience.thirdsHigh:timepost -0.63492 8.94052 109.00000
## t value Pr(>|t|)
## (Intercept) 4.045 7.26e-05 ***
## groupI 0.396 0.6928
## groupS -0.629 0.5302
## groupT -0.432 0.6665
## game.experience.thirdsMedium 0.583 0.5606
## game.experience.thirdsHigh 0.417 0.6773
## timepost 3.108 0.0024 **
## groupI:game.experience.thirdsMedium -0.696 0.4870
## groupS:game.experience.thirdsMedium -0.169 0.8658
## groupT:game.experience.thirdsMedium -0.007 0.9944
## groupI:game.experience.thirdsHigh -0.092 0.9271
## groupS:game.experience.thirdsHigh 0.220 0.8258
## groupT:game.experience.thirdsHigh -0.332 0.7401
## groupI:timepost 0.011 0.9915
## groupS:timepost 0.080 0.9361
## groupT:timepost 0.570 0.5700
## game.experience.thirdsMedium:timepost -0.928 0.3555
## game.experience.thirdsHigh:timepost -0.408 0.6839
## groupI:game.experience.thirdsMedium:timepost 0.804 0.4229
## groupS:game.experience.thirdsMedium:timepost 0.724 0.4706
## groupT:game.experience.thirdsMedium:timepost -0.284 0.7772
## groupI:game.experience.thirdsHigh:timepost 0.043 0.9656
## groupS:game.experience.thirdsHigh:timepost -0.748 0.4564
## groupT:game.experience.thirdsHigh:timepost -0.071 0.9435
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 24 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: impasse_data_leveldifficulty
##
## REML criterion at convergence: 414.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1782 -0.3207 0.0309 0.4050 2.2294
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 6.414 2.533
## Residual 91.054 9.542
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 17.167 2.850 53.767 6.023
## game.experience.thirdsMedium -1.667 4.227 53.767 -0.394
## game.experience.thirdsHigh 1.333 4.506 53.767 0.296
## timepost 16.500 3.896 27.000 4.236
## game.experience.thirdsMedium:timepost 0.900 5.778 27.000 0.156
## game.experience.thirdsHigh:timepost -2.250 6.160 27.000 -0.365
## Pr(>|t|)
## (Intercept) 1.59e-07 ***
## game.experience.thirdsMedium 0.694938
## game.experience.thirdsHigh 0.768454
## timepost 0.000237 ***
## game.experience.thirdsMedium:timepost 0.877380
## game.experience.thirdsHigh:timepost 0.717740
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.674
## gm.xprnc.tH -0.632 0.426
## timepost -0.683 0.461 0.432
## gm.xprnc.M: 0.461 -0.683 -0.291 -0.674
## gm.xprnc.H: 0.432 -0.291 -0.683 -0.632 0.426
## contrast game.experience.thirds estimate SE df t.ratio p.value
## 1 pre - post Low -16.50 3.895590 27 -4.235559 0.0002367051
## 2 pre - post Medium -17.40 4.267404 27 -4.077420 0.0003604987
## 3 pre - post High -14.25 4.771103 27 -2.986731 0.0059355428
## cohens_d
## 1 -4.235559
## 2 -4.077420
## 3 -2.986731
## contrast time estimate SE df t.ratio p.value
## 1 Low - Medium post 0.7666667 4.227174 53.76719 0.18136623 0.9820355
## 2 Low - High post 0.9166667 4.506183 53.76719 0.20342420 0.9774550
## 3 Medium - High post 0.1500000 4.682963 53.76719 0.03203101 0.9994345
## cohens_d
## 1 0.18136623
## 2 0.20342420
## 3 0.03203101
## contrast time estimate SE df t.ratio p.value
## 1 Low - Medium pre 1.666667 4.227174 53.76719 0.3942744 0.9180164
## 2 Low - High pre -1.333333 4.506183 53.76719 -0.2958898 0.9529272
## 3 Medium - High pre -3.000000 4.682963 53.76719 -0.6406201 0.7984234
## cohens_d
## 1 0.3942744
## 2 -0.2958898
## 3 -0.6406201
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low 16.5 3.90 27 8.51 24.5
## Medium 17.4 4.27 27 8.64 26.2
## High 14.2 4.77 27 4.46 24.0
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -0.90 5.78 27 -0.156 0.9867
## Low - High 2.25 6.16 27 0.365 0.9293
## Medium - High 3.15 6.40 27 0.492 0.8757
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: tutorial_data_leveldifficulty
##
## REML criterion at convergence: 444.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5970 -0.3546 0.0706 0.4795 2.6252
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 4.769 2.184
## Residual 123.205 11.100
## Number of obs: 62, groups: subject, 31
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 13.1111 3.7709 55.9223 3.477
## game.experience.thirdsMedium 2.8889 5.1978 55.9223 0.556
## game.experience.thirdsHigh -0.1944 4.9884 55.9223 -0.039
## timepost 20.4444 5.2325 28.0000 3.907
## game.experience.thirdsMedium:timepost -9.2444 7.2125 28.0000 -1.282
## game.experience.thirdsHigh:timepost -3.2778 6.9219 28.0000 -0.474
## Pr(>|t|)
## (Intercept) 0.000989 ***
## game.experience.thirdsMedium 0.580568
## game.experience.thirdsHigh 0.969046
## timepost 0.000539 ***
## game.experience.thirdsMedium:timepost 0.210451
## game.experience.thirdsHigh:timepost 0.639503
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.725
## gm.xprnc.tH -0.756 0.548
## timepost -0.694 0.503 0.524
## gm.xprnc.M: 0.503 -0.694 -0.380 -0.725
## gm.xprnc.H: 0.524 -0.380 -0.694 -0.756 0.548
## contrast game.experience.thirds estimate SE df t.ratio
## 1 pre - post Low -20.44444 5.232487 28 -3.907213
## 2 pre - post Medium -11.20000 4.963973 28 -2.256257
## 3 pre - post High -17.16667 4.531467 28 -3.788324
## p.value cohens_d
## 1 0.0005386079 -3.907213
## 2 0.0320516869 -2.256257
## 3 0.0007390373 -3.788324
## contrast time estimate SE df t.ratio p.value
## 1 Low - Medium post 6.355556 5.197762 55.92235 1.2227484 0.4449085
## 2 Low - High post 3.472222 4.988370 55.92235 0.6960635 0.7667632
## 3 Medium - High post -2.883333 4.843749 55.92235 -0.5952690 0.8232193
## cohens_d
## 1 1.2227484
## 2 0.6960635
## 3 -0.5952690
## contrast time estimate SE df t.ratio p.value
## 1 Low - Medium pre -2.8888889 5.197762 55.92235 -0.55579471 0.8439349
## 2 Low - High pre 0.1944444 4.988370 55.92235 0.03897956 0.9991627
## 3 Medium - High pre 3.0833333 4.843749 55.92235 0.63655931 0.8006583
## cohens_d
## 1 -0.55579471
## 2 0.03897956
## 3 0.63655931
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low 20.4 5.23 28 9.73 31.2
## Medium 11.2 4.96 28 1.03 21.4
## High 17.2 4.53 28 7.88 26.4
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium 9.24 7.21 28 1.282 0.4170
## Low - High 3.28 6.92 28 0.474 0.8842
## Medium - High -5.97 6.72 28 -0.888 0.6524
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: original_data_leveldifficulty
##
## REML criterion at convergence: 406.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3640 -0.3514 0.0267 0.5852 1.7673
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 3.117e-18 1.766e-09
## Residual 8.492e+01 9.215e+00
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 15.286 3.483 54.000 4.389
## game.experience.thirdsMedium 2.937 4.644 54.000 0.632
## game.experience.thirdsHigh 1.929 4.266 54.000 0.452
## timepost 16.429 4.926 54.000 3.335
## game.experience.thirdsMedium:timepost -6.540 6.568 54.000 -0.996
## game.experience.thirdsHigh:timepost -2.643 6.033 54.000 -0.438
## Pr(>|t|)
## (Intercept) 5.34e-05 ***
## game.experience.thirdsMedium 0.52985
## game.experience.thirdsHigh 0.65301
## timepost 0.00155 **
## game.experience.thirdsMedium:timepost 0.32382
## game.experience.thirdsHigh:timepost 0.66307
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.750
## gm.xprnc.tH -0.816 0.612
## timepost -0.707 0.530 0.577
## gm.xprnc.M: 0.530 -0.707 -0.433 -0.750
## gm.xprnc.H: 0.577 -0.433 -0.707 -0.816 0.612
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## contrast game.experience.thirds estimate SE df t.ratio
## 1 pre - post Low -16.428571 4.925723 27 -3.335261
## 2 pre - post Medium -9.888889 4.344079 27 -2.276406
## 3 pre - post High -13.785714 3.483012 27 -3.957986
## p.value cohens_d
## 1 0.0024882141 -3.335261
## 2 0.0309677883 -2.276406
## 3 0.0004945448 -3.957986
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium post 3.6031746 4.644016 54 0.7758747 0.7193245 0.7758747
## 2 Low - High post 0.7142857 4.265801 54 0.1674447 0.9846661 0.1674447
## 3 Medium - High post -2.8888889 3.937156 54 -0.7337502 0.7446287 -0.7337502
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium pre -2.936508 4.644016 54 -0.6323208 0.8030377 -0.6323208
## 2 Low - High pre -1.928571 4.265801 54 -0.4521007 0.8936827 -0.4521007
## 3 Medium - High pre 1.007937 3.937156 54 0.2560062 0.9645397 0.2560062
## boundary (singular) fit: see help('isSingular')
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low 16.43 4.93 27 6.322 26.5
## Medium 9.89 4.34 27 0.976 18.8
## High 13.79 3.48 27 6.639 20.9
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium 6.54 6.57 27 0.996 0.5858
## Low - High 2.64 6.03 27 0.438 0.9000
## Medium - High -3.90 5.57 27 -0.700 0.7656
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: safe_data_leveldifficulty
##
## REML criterion at convergence: 408.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.39271 -0.59303 -0.08869 0.65476 1.84815
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 3.177 1.783
## Residual 85.243 9.233
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 12.4375 2.3508 53.9304 5.291
## game.experience.thirdsMedium 1.8125 4.0717 53.9304 0.445
## game.experience.thirdsHigh 3.3958 4.5015 53.9304 0.754
## timepost 16.9375 3.2643 27.0000 5.189
## game.experience.thirdsMedium:timepost 0.1875 5.6539 27.0000 0.033
## game.experience.thirdsHigh:timepost -9.6042 6.2506 27.0000 -1.537
## Pr(>|t|)
## (Intercept) 2.28e-06 ***
## game.experience.thirdsMedium 0.658
## game.experience.thirdsHigh 0.454
## timepost 1.83e-05 ***
## game.experience.thirdsMedium:timepost 0.974
## game.experience.thirdsHigh:timepost 0.136
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.577
## gm.xprnc.tH -0.522 0.302
## timepost -0.694 0.401 0.363
## gm.xprnc.M: 0.401 -0.694 -0.209 -0.577
## gm.xprnc.H: 0.363 -0.209 -0.694 -0.522 0.302
## contrast game.experience.thirds estimate SE df t.ratio
## 1 pre - post Low -16.937500 3.264266 27 -5.188763
## 2 pre - post Medium -17.125000 4.616369 27 -3.709626
## 3 pre - post High -7.333333 5.330524 27 -1.375725
## p.value cohens_d
## 1 1.834537e-05 -5.188763
## 2 9.491338e-04 -3.709626
## 3 1.802130e-01 -1.375725
## contrast time estimate SE df t.ratio p.value
## 1 Low - Medium post -2.000000 4.071722 53.93036 -0.4911927 0.8757954
## 2 Low - High post 6.208333 4.501458 53.93036 1.3791827 0.3588407
## 3 Medium - High post 8.208333 5.078330 53.93036 1.6163451 0.2475736
## cohens_d
## 1 -0.4911927
## 2 1.3791827
## 3 1.6163451
## contrast time estimate SE df t.ratio p.value
## 1 Low - Medium pre -1.812500 4.071722 53.93036 -0.4451433 0.8967487
## 2 Low - High pre -3.395833 4.501458 53.93036 -0.7543852 0.7322973
## 3 Medium - High pre -1.583333 5.078330 53.93036 -0.3117823 0.9478783
## cohens_d
## 1 -0.4451433
## 2 -0.7543852
## 3 -0.3117823
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low 16.94 3.26 27 10.24 23.6
## Medium 17.12 4.62 27 7.65 26.6
## High 7.33 5.33 27 -3.60 18.3
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -0.188 5.65 27 -0.033 0.9994
## Low - High 9.604 6.25 27 1.537 0.2902
## Medium - High 9.792 7.05 27 1.389 0.3608
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
Tables for
leveldifficulty
Leveldifficulty
Impasse Contrasts
Leveldifficulty
Tutorial Contrasts
Leveldifficulty
Original Contrasts
Leveldifficulty
Safe Contrasts
Engagement
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ group * game.experience.thirds * time + (1 | subject)
## Data: filter(long_data.thirds, construct == "engagement")
##
## REML criterion at convergence: 1293.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3418 -0.6610 0.0721 0.6086 2.6930
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 17.19 4.147
## Number of obs: 242, groups: subject, 121
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 36.0000 1.5673 218.0000
## groupI -3.1667 1.9721 218.0000
## groupS -2.3125 1.8791 218.0000
## groupT -3.5556 2.0897 218.0000
## game.experience.thirdsMedium -3.1111 2.0897 218.0000
## game.experience.thirdsHigh -5.4286 1.9195 218.0000
## timepost -2.2857 2.2165 218.0000
## groupI:game.experience.thirdsMedium 2.0778 2.7421 218.0000
## groupS:game.experience.thirdsMedium 0.7986 2.7551 218.0000
## groupT:game.experience.thirdsMedium 5.2667 2.8279 218.0000
## groupI:game.experience.thirdsHigh 5.8452 2.6957 218.0000
## groupS:game.experience.thirdsHigh 4.7411 2.7613 218.0000
## groupT:game.experience.thirdsHigh 5.8175 2.6510 218.0000
## groupI:timepost 3.1190 2.7890 218.0000
## groupS:timepost 0.8482 2.6574 218.0000
## groupT:timepost 3.0635 2.9553 218.0000
## game.experience.thirdsMedium:timepost 3.1746 2.9553 218.0000
## game.experience.thirdsHigh:timepost 4.5714 2.7146 218.0000
## groupI:game.experience.thirdsMedium:timepost -2.8079 3.8779 218.0000
## groupS:game.experience.thirdsMedium:timepost 0.6379 3.8963 218.0000
## groupT:game.experience.thirdsMedium:timepost -4.8524 3.9992 218.0000
## groupI:game.experience.thirdsHigh:timepost -5.9048 3.8123 218.0000
## groupS:game.experience.thirdsHigh:timepost -4.1339 3.9051 218.0000
## groupT:game.experience.thirdsHigh:timepost -3.5159 3.7491 218.0000
## t value Pr(>|t|)
## (Intercept) 22.970 < 2e-16 ***
## groupI -1.606 0.10978
## groupS -1.231 0.21978
## groupT -1.701 0.09028 .
## game.experience.thirdsMedium -1.489 0.13799
## game.experience.thirdsHigh -2.828 0.00512 **
## timepost -1.031 0.30357
## groupI:game.experience.thirdsMedium 0.758 0.44943
## groupS:game.experience.thirdsMedium 0.290 0.77220
## groupT:game.experience.thirdsMedium 1.862 0.06389 .
## groupI:game.experience.thirdsHigh 2.168 0.03121 *
## groupS:game.experience.thirdsHigh 1.717 0.08741 .
## groupT:game.experience.thirdsHigh 2.194 0.02926 *
## groupI:timepost 1.118 0.26465
## groupS:timepost 0.319 0.74989
## groupT:timepost 1.037 0.30106
## game.experience.thirdsMedium:timepost 1.074 0.28391
## game.experience.thirdsHigh:timepost 1.684 0.09361 .
## groupI:game.experience.thirdsMedium:timepost -0.724 0.46979
## groupS:game.experience.thirdsMedium:timepost 0.164 0.87011
## groupT:game.experience.thirdsMedium:timepost -1.213 0.22631
## groupI:game.experience.thirdsHigh:timepost -1.549 0.12286
## groupS:game.experience.thirdsHigh:timepost -1.059 0.29095
## groupT:game.experience.thirdsHigh:timepost -0.938 0.34939
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 24 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: impasse_data_engagement
##
## REML criterion at convergence: 310.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3758 -0.5390 0.0484 0.5995 1.7818
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 14.35 3.788
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 32.8333 1.0936 54.0000 30.024
## game.experience.thirdsMedium -1.0333 1.6220 54.0000 -0.637
## game.experience.thirdsHigh 0.4167 1.7291 54.0000 0.241
## timepost 0.8333 1.5465 54.0000 0.539
## game.experience.thirdsMedium:timepost 0.3667 2.2939 54.0000 0.160
## game.experience.thirdsHigh:timepost -1.3333 2.4453 54.0000 -0.545
## Pr(>|t|)
## (Intercept) <2e-16 ***
## game.experience.thirdsMedium 0.527
## game.experience.thirdsHigh 0.810
## timepost 0.592
## game.experience.thirdsMedium:timepost 0.874
## game.experience.thirdsHigh:timepost 0.588
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.674
## gm.xprnc.tH -0.632 0.426
## timepost -0.707 0.477 0.447
## gm.xprnc.M: 0.477 -0.707 -0.302 -0.674
## gm.xprnc.H: 0.447 -0.302 -0.707 -0.632 0.426
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## contrast game.experience.thirds estimate SE df t.ratio p.value
## 1 pre - post Low -0.8333333 1.546535 27 -0.5388391 0.5944133
## 2 pre - post Medium -1.2000000 1.694144 27 -0.7083224 0.4848159
## 3 pre - post High 0.5000000 1.894110 27 0.2639762 0.7938027
## cohens_d
## 1 -0.5388391
## 2 -0.7083224
## 3 0.2639762
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium post 0.6666667 1.622019 54 0.4110103 0.9112470 0.4110103
## 2 Low - High post 0.9166667 1.729078 54 0.5301476 0.8569141 0.5301476
## 3 Medium - High post 0.2500000 1.796911 54 0.1391277 0.9893875 0.1391277
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium pre 1.0333333 1.622019 54 0.6370660 0.8004016 0.6370660
## 2 Low - High pre -0.4166667 1.729078 54 -0.2409762 0.9685140 -0.2409762
## 3 Medium - High pre -1.4500000 1.796911 54 -0.8069404 0.7003753 -0.8069404
## boundary (singular) fit: see help('isSingular')
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low 0.833 1.55 27 -2.34 4.01
## Medium 1.200 1.69 27 -2.28 4.68
## High -0.500 1.89 27 -4.39 3.39
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -0.367 2.29 27 -0.160 0.9860
## Low - High 1.333 2.45 27 0.545 0.8497
## Medium - High 1.700 2.54 27 0.669 0.7833
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: tutorial_data_engagement
##
## REML criterion at convergence: 345.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.44980 -0.58388 0.01015 0.60495 2.35396
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.7587 0.8711
## Residual 20.9665 4.5789
## Number of obs: 62, groups: subject, 31
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 32.4444 1.5537 55.9318 20.882
## game.experience.thirdsMedium 2.1556 2.1416 55.9318 1.007
## game.experience.thirdsHigh 0.3889 2.0553 55.9318 0.189
## timepost 0.7778 2.1585 28.0000 0.360
## game.experience.thirdsMedium:timepost -1.6778 2.9753 28.0000 -0.564
## game.experience.thirdsHigh:timepost 1.0556 2.8555 28.0000 0.370
## Pr(>|t|)
## (Intercept) <2e-16 ***
## game.experience.thirdsMedium 0.319
## game.experience.thirdsHigh 0.851
## timepost 0.721
## game.experience.thirdsMedium:timepost 0.577
## game.experience.thirdsHigh:timepost 0.714
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.725
## gm.xprnc.tH -0.756 0.548
## timepost -0.695 0.504 0.525
## gm.xprnc.M: 0.504 -0.695 -0.381 -0.725
## gm.xprnc.H: 0.525 -0.381 -0.695 -0.756 0.548
## contrast game.experience.thirds estimate SE df t.ratio p.value
## 1 pre - post Low -0.7777778 2.158522 28 -0.3603289 0.7213052
## 2 pre - post Medium 0.9000000 2.047753 28 0.4395061 0.6636684
## 3 pre - post High -1.8333333 1.869335 28 -0.9807412 0.3351191
## cohens_d
## 1 -0.3603289
## 2 0.4395061
## 3 -0.9807412
## contrast time estimate SE df t.ratio p.value
## 1 Low - Medium post -0.4777778 2.141595 55.93178 -0.2230943 0.9729488
## 2 Low - High post -1.4444444 2.055321 55.93178 -0.7027829 0.7628458
## 3 Medium - High post -0.9666667 1.995734 55.93178 -0.4843666 0.8789880
## cohens_d
## 1 -0.2230943
## 2 -0.7027829
## 3 -0.4843666
## contrast time estimate SE df t.ratio p.value
## 1 Low - Medium pre -2.1555556 2.141595 55.93178 -1.0065186 0.5758772
## 2 Low - High pre -0.3888889 2.055321 55.93178 -0.1892108 0.9804639
## 3 Medium - High pre 1.7666667 1.995734 55.93178 0.8852216 0.6518007
## cohens_d
## 1 -1.0065186
## 2 -0.1892108
## 3 0.8852216
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low 0.778 2.16 28 -3.64 5.20
## Medium -0.900 2.05 28 -5.09 3.29
## High 1.833 1.87 28 -2.00 5.66
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium 1.68 2.98 28 0.564 0.8402
## Low - High -1.06 2.86 28 -0.370 0.9276
## Medium - High -2.73 2.77 28 -0.986 0.5917
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: original_data_engagement
##
## REML criterion at convergence: 328.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.10862 -0.58487 0.09614 0.57686 2.08310
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.00 0.000
## Residual 19.87 4.458
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 36.000 1.685 54.000 21.367
## game.experience.thirdsMedium -3.111 2.246 54.000 -1.385
## game.experience.thirdsHigh -5.429 2.063 54.000 -2.631
## timepost -2.286 2.383 54.000 -0.959
## game.experience.thirdsMedium:timepost 3.175 3.177 54.000 0.999
## game.experience.thirdsHigh:timepost 4.571 2.918 54.000 1.567
## Pr(>|t|)
## (Intercept) <2e-16 ***
## game.experience.thirdsMedium 0.1718
## game.experience.thirdsHigh 0.0111 *
## timepost 0.3417
## game.experience.thirdsMedium:timepost 0.3221
## game.experience.thirdsHigh:timepost 0.1231
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.750
## gm.xprnc.tH -0.816 0.612
## timepost -0.707 0.530 0.577
## gm.xprnc.M: 0.530 -0.707 -0.433 -0.750
## gm.xprnc.H: 0.577 -0.433 -0.707 -0.816 0.612
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## contrast game.experience.thirds estimate SE df t.ratio p.value
## 1 pre - post Low 2.2857143 2.382715 27 0.9592897 0.3459250
## 2 pre - post Medium -0.8888889 2.101357 27 -0.4230070 0.6756404
## 3 pre - post High -2.2857143 1.684834 27 -1.3566405 0.1861312
## cohens_d
## 1 0.9592897
## 2 -0.4230070
## 3 -1.3566405
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium post -0.06349206 2.246446 54 -0.02826334 0.9995597 -0.02826334
## 2 Low - High post 0.85714286 2.063492 54 0.41538462 0.9094403 0.41538462
## 3 Medium - High post 0.92063492 1.904517 54 0.48339550 0.8794504 0.48339550
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium pre 3.111111 2.246446 54 1.384904 0.35585564 1.384904
## 2 Low - High pre 5.428571 2.063492 54 2.630769 0.02935618 2.630769
## 3 Medium - High pre 2.317460 1.904517 54 1.216823 0.44852784 1.216823
## boundary (singular) fit: see help('isSingular')
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low -2.286 2.38 27 -7.17 2.60
## Medium 0.889 2.10 27 -3.42 5.20
## High 2.286 1.68 27 -1.17 5.74
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -3.17 3.18 27 -0.999 0.5837
## Low - High -4.57 2.92 27 -1.567 0.2771
## Medium - High -1.40 2.69 27 -0.519 0.8630
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: score ~ game.experience.thirds * time + (1 | subject)
## Data: safe_data_engagement
##
## REML criterion at convergence: 303.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.93598 -0.76511 0.07468 0.72704 1.96329
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.6296 0.7935
## Residual 12.0336 3.4689
## Number of obs: 60, groups: subject, 30
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 33.6875 0.8896 53.8668 37.867
## game.experience.thirdsMedium -2.3125 1.5409 53.8668 -1.501
## game.experience.thirdsHigh -0.6875 1.7035 53.8668 -0.404
## timepost -1.4375 1.2265 27.0000 -1.172
## game.experience.thirdsMedium:timepost 3.8125 2.1243 27.0000 1.795
## game.experience.thirdsHigh:timepost 0.4375 2.3485 27.0000 0.186
## Pr(>|t|)
## (Intercept) <2e-16 ***
## game.experience.thirdsMedium 0.1393
## game.experience.thirdsHigh 0.6881
## timepost 0.2514
## game.experience.thirdsMedium:timepost 0.0839 .
## game.experience.thirdsHigh:timepost 0.8536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gm.x.M gm.x.H timpst gm..M:
## gm.xprnc.tM -0.577
## gm.xprnc.tH -0.522 0.302
## timepost -0.689 0.398 0.360
## gm.xprnc.M: 0.398 -0.689 -0.208 -0.577
## gm.xprnc.H: 0.360 -0.208 -0.689 -0.522 0.302
## contrast game.experience.thirds estimate SE df t.ratio p.value
## 1 pre - post Low 1.4375 1.226457 27 1.1720758 0.2514053
## 2 pre - post Medium -2.3750 1.734471 27 -1.3692932 0.1821908
## 3 pre - post High 1.0000 2.002795 27 0.4993022 0.6216108
## cohens_d
## 1 1.1720758
## 2 -1.3692932
## 3 0.4993022
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium post -1.50 1.540892 53.86683 -0.9734620 0.5966523 -0.9734620
## 2 Low - High post 0.25 1.703520 53.86683 0.1467549 0.9881994 0.1467549
## 3 Medium - High post 1.75 1.921830 53.86683 0.9105903 0.6360252 0.9105903
## contrast time estimate SE df t.ratio p.value cohens_d
## 1 Low - Medium pre 2.3125 1.540892 53.86683 1.5007539 0.2986381 1.5007539
## 2 Low - High pre 0.6875 1.703520 53.86683 0.4035760 0.9142824 0.4035760
## 3 Medium - High pre -1.6250 1.921830 53.86683 -0.8455481 0.6765678 -0.8455481
## $emtrends
## game.experience.thirds time_num.trend SE df lower.CL upper.CL
## Low -1.44 1.23 27 -3.95 1.08
## Medium 2.38 1.73 27 -1.18 5.93
## High -1.00 2.00 27 -5.11 3.11
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Low - Medium -3.812 2.12 27 -1.795 0.1904
## Low - High -0.438 2.35 27 -0.186 0.9811
## Medium - High 3.375 2.65 27 1.274 0.4217
##
## Results are averaged over the levels of: time_num
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
Tables for
engagement
Engagement
Impasse Contrasts
Engagement
Tutorial Contrasts
Engagement
Original Contrasts
Engagement Safe
Contrasts
