====================================================== === backfitting fixed effects === ====================================================== processing model terms of interaction level 1 all terms of interaction level 1 significant pruning random effects structure ... nothing to prune ====================================================== === forwardfitting random effects === ====================================================== evaluating addition of (as.factor(Rater)|Artifact) to model Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 ====================================================== === backfitting fixed effects === ====================================================== setting REML to FALSE processing model terms of interaction level 1 all terms of interaction level 1 significant pruning random effects structure ... nothing to prune ====================================================== === forwardfitting random effects === ====================================================== evaluating addition of (as.factor(Rater)|Artifact) to model Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 ====================================================== === backfitting fixed effects === ====================================================== processing model terms of interaction level 1 all terms of interaction level 1 significant pruning random effects structure ... nothing to prune ====================================================== === forwardfitting random effects === ====================================================== evaluating addition of (Semester|Artifact) to model Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 [1] 3 cept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 [1] 3 ====================================================== === backfitting fixed effects === ====================================================== processing model terms of interaction level 1 all terms of interaction level 1 significant pruning random effects structure ... nothing to prune ====================================================== === forwardfitting random effects === ====================================================== evaluating addition of (as.factor(Rater)|Artifact) to model Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 ====================================================== === backfitting fixed effects === ====================================================== setting REML to FALSE processing model terms of interaction level 1 all terms of interaction level 1 significant pruning random effects structure ... nothing to prune ====================================================== === forwardfitting random effects === ====================================================== evaluating addition of (as.factor(Rater)|Artifact) to model Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 ====================================================== === backfitting fixed effects === ====================================================== processing model terms of interaction level 1 all terms of interaction level 1 significant pruning random effects structure ... nothing to prune ====================================================== === forwardfitting random effects === ====================================================== evaluating addition of (Semester|Artifact) to model Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 [1] 3 n level 1 all terms of interaction level 1 significant pruning random effects structure ... nothing to prune ====================================================== === forwardfitting random effects === ====================================================== evaluating addition of (Semester|Artifact) to model Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 [1] 3 ====================================================== === backfitting fixed effects === ====================================================== processing model terms of interaction level 1 all terms of interaction level 1 significant pruning random effects structure ... nothing to prune ====================================================== === forwardfitting random effects === ====================================================== evaluating addition of (as.factor(Rater)|Artifact) to model Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 ====================================================== === backfitting fixed effects === ====================================================== setting REML to FALSE processing model terms of interaction level 1 all terms of interaction level 1 significant pruning random effects structure ... nothing to prune ====================================================== === forwardfitting random effects === ====================================================== evaluating addition of (as.factor(Rater)|Artifact) to model Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 ====================================================== === backfitting fixed effects === ====================================================== processing model terms of interaction level 1 all terms of interaction level 1 significant pruning random effects structure ... nothing to prune ====================================================== === forwardfitting random effects === ====================================================== evaluating addition of (Semester|Artifact) to model Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 Linear mixed model fit by REML ['lmerMod'] Formula: as.numeric(Rating) ~ as.factor(Rater) + Semester + (1 | Artifact) - 1 Data: tall.nonmissing[tall.nonmissing$Rubric == "SelMeth", ] REML criterion at convergence: 143.6 Scaled residuals: Min 1Q Median 3Q Max -2.0480 -0.3923 -0.0551 0.2674 2.5827 Random effects: Groups Name Variance Std.Dev. Artifact (Intercept) 0.08973 0.2996 Residual 0.10842 0.3293 Number of obs: 116, groups: Artifact, 90 Fixed effects: Estimate Std. Error t value as.factor(Rater)1 2.25037 0.07503 29.992 as.factor(Rater)2 2.22653 0.07424 29.991 as.factor(Rater)3 2.03316 0.07521 27.033 SemesterS19 -0.35860 0.09796 -3.661 Correlation of Fixed Effects: a.(R)1 a.(R)2 a.(R)3 as.fctr(R)2 0.285 as.fctr(R)3 0.287 0.280 SemesterS19 -0.413 -0.391 -0.394 [1] 3