data1=import("AIReflectionBW.NoMissingS1.sav")%>%data.table
data2=import("AIReflectionBW.NoMissingS2.sav")%>%data.table #%>%
data2$W.dX=as.factor(data2$W.X)
data2 <- data2 %>%
mutate(
W.X10 = ifelse(W.X == 1, 1, 0),
W.X01 = ifelse(W.X == 2, 1, 0)
)
data2=data2[, W.CheckDummyX := paste(W.Intervention_new,W.X,W.X10,W.X01, sep = "_")]
Freq(data2$W.CheckDummyX)## Frequency Statistics:
## ───────────────────────────
## N %
## ───────────────────────────
## AI_0_0_0 328 33.3
## No_1_1_0 328 33.3
## Traditional_2_0_1 328 33.3
## ───────────────────────────
## Total N = 984#data2=import("AIReflectionBW.NoMissingS2.sav")%>%data.table #%>%
# mutate(
# W.X1 = ifelse(W.X == 1, 1, 0),
# W.X2 = ifelse(W.X == 2, 1, 0)
# )
#head(data2[,.(W.X,W.X1,W.X2)],10)%>%print_table()# 假设 data 是一个 data.table
# 筛选出列名包含 "V.GroC" 的列
selected_columns <- grep("V\\.GroC", names(data1), value = TRUE)
# 提取包含这些列的数据
filtered_data <- data1[, ..selected_columns]
# 查看筛选出的列
print(selected_columns)## [1] "WA.WorkReflectionForManipulationCheckV.GroC"
## [2] "WA.PositiveWorkReflectionForManipulationCheckV.GroC"
## [3] "WA.NegativeWorkReflectionForManipulationCheckV.GroC"
## [4] "WA.ProblemSolvingPonderingV.GroC"
## [5] "WA.LearningFromOperationalFailureV.GroC"
## [6] "WA.SelfReflectionForManipulationCheckV.GroC"
## [7] "WA.RealizingTheNeedForReworkV.GroC"
## [8] "WA.TemperoalReflectionForManipulationCheckV.GroC"
## [9] "WA.LearningFromErrorsV.GroC"
## [10] "WA.ThrivingInLearningV.GroC"
## [11] "WA.CognitiveJobEngagementV.GroC"
## [12] "WA.ErrorStrainV.GroC"
## [13] "WA.ThinkingAboutErrorsV.GroC"
## [14] "WA.AffectiveRuminationV.GroC"
## [15] "WA.DetachmentV.GroC"
## [16] "WA.AffectiveCommitmentForWorkImprovmentV.GroC"
## [17] "WA.DetachmentBasedRecoveryFromWorkV.GroC"
## [18] "WA.GratitudeV.GroC"
## [19] "WA.SegmentationFromWorkV.GroC"
## [20] "WP.TakingChargeBehaviorsForSystemImprovementV.GroC"
## [21] "WP.VoiceForSystemImprovmentV.GroC"
## [22] "WP.AIUsageForFacilitatingWorkV.GroC"
## [23] "WP.LearningBehaviorV.GroC"
## [24] "WP.SystemPerformanceImprovementBehaviorV.GroC"
## [25] "WP.AIEnabledInnovationBehaviorV.GroC"
## [26] "WP.SocialLearningV.GroC"
## [27] "WP.IndependentObservationBasedSocialLearningV.GroC"
## [28] "WP.AdviceThinkingBasedSocialLearningV.GroC"
## [29] "WP.FeedbackSeekingForSystemImprovementV.GroC"
## [30] "WP.ConstructiveChallengingBehaviorForSystemImprovementV.GroC"
## [31] "WP.AIEnabledCreativityV.GroC"
## [32] "WP.EmployeeWorkWellBeingV.GroC"
## [33] "WP.PerceivedWorkGrowthV.GroC"
## [34] "WP.TaskPerformanceImprovementV.GroC"
## [35] "WP.CreativeProcessEngagmentV.GroC"
## [36] "WP.SleepQuantityV.GroC"
## [37] "WP.FamilyMemberUndermingV.GroC"
## [38] "WP.FamilyMemberConflictV.GroC"# 查看筛选后的数据
#print(filtered_data)
# 假设 data 是一个 data.table
# 筛选出列名包含 "V.GroC" 的列
selected_columns <- grep("V\\.GroC", names(data2), value = TRUE)
# 提取包含这些列的数据
filtered_data <- data2[, ..selected_columns]
# 查看筛选出的列
print(selected_columns)## [1] "WA.WorkReflectionForManipulationCheckV.GroC"
## [2] "WA.PositiveWorkReflectionForManipulationCheckV.GroC"
## [3] "WA.NegativeWorkReflectionForManipulationCheckV.GroC"
## [4] "WA.ProblemSolvingPonderingV.GroC"
## [5] "WA.LearningFromOperationalFailureV.GroC"
## [6] "WA.SelfReflectionForManipulationCheckV.GroC"
## [7] "WA.RealizingTheNeedForReworkV.GroC"
## [8] "WA.TemperoalReflectionForManipulationCheckV.GroC"
## [9] "WA.LearningFromErrorsV.GroC"
## [10] "WA.ThrivingInLearningV.GroC"
## [11] "WA.CognitiveJobEngagementV.GroC"
## [12] "WA.ErrorStrainV.GroC"
## [13] "WA.ThinkingAboutErrorsV.GroC"
## [14] "WA.AffectiveRuminationV.GroC"
## [15] "WA.DetachmentV.GroC"
## [16] "WA.AffectiveCommitmentForWorkImprovmentV.GroC"
## [17] "WA.DetachmentBasedRecoveryFromWorkV.GroC"
## [18] "WA.GratitudeV.GroC"
## [19] "WA.SegmentationFromWorkV.GroC"
## [20] "WP.TakingChargeBehaviorsForSystemImprovementV.GroC"
## [21] "WP.VoiceForSystemImprovmentV.GroC"
## [22] "WP.AIUsageForFacilitatingWorkV.GroC"
## [23] "WP.LearningBehaviorV.GroC"
## [24] "WP.SystemPerformanceImprovementBehaviorV.GroC"
## [25] "WP.AIEnabledInnovationBehaviorV.GroC"
## [26] "WP.SocialLearningV.GroC"
## [27] "WP.IndependentObservationBasedSocialLearningV.GroC"
## [28] "WP.AdviceThinkingBasedSocialLearningV.GroC"
## [29] "WP.FeedbackSeekingForSystemImprovementV.GroC"
## [30] "WP.ConstructiveChallengingBehaviorForSystemImprovementV.GroC"
## [31] "WP.AIEnabledCreativityV.GroC"
## [32] "WP.EmployeeWorkWellBeingV.GroC"
## [33] "WP.PerceivedWorkGrowthV.GroC"
## [34] "WP.TaskPerformanceImprovementV.GroC"
## [35] "WP.CreativeProcessEngagmentV.GroC"
## [36] "WP.SleepQuantityV.GroC"
## [37] "WP.FamilyMemberUndermingV.GroC"
## [38] "WP.FamilyMemberConflictV.GroC"WA.WorkReflectionForManipulationCheck.Main=lmer(WA.WorkReflectionForManipulationCheckV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.PositiveWorkReflectionForManipulationCheck.Main=lmer(WA.PositiveWorkReflectionForManipulationCheckV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.NegativeWorkReflectionForManipulationCheck.Main=lmer(WA.NegativeWorkReflectionForManipulationCheckV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.ProblemSolvingPondering.Main=lmer(WA.ProblemSolvingPonderingV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.LearningFromOperationalFailure.Main=lmer(WA.LearningFromOperationalFailureV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.SelfReflectionForManipulationCheck.Main=lmer(WA.SelfReflectionForManipulationCheckV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.RealizingTheNeedForRework.Main=lmer(WA.RealizingTheNeedForReworkV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.TemperoalReflectionForManipulationCheck.Main=lmer(WA.TemperoalReflectionForManipulationCheckV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.LearningFromErrors.Main=lmer(WA.LearningFromErrorsV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.ThrivingInLearning.Main=lmer(WA.ThrivingInLearningV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.CognitiveJobEngagement.Main=lmer(WA.CognitiveJobEngagementV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.ErrorStrain.Main=lmer(WA.ErrorStrainV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.ThinkingAboutErrors.Main=lmer(WA.ThinkingAboutErrorsV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.AffectiveRumination.Main=lmer(WA.AffectiveRuminationV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.Detachment.Main=lmer(WA.DetachmentV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.AffectiveCommitmentForWorkImprovment.Main=lmer(WA.AffectiveCommitmentForWorkImprovmentV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.DetachmentBasedRecoveryFromWork.Main=lmer(WA.DetachmentBasedRecoveryFromWorkV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.Gratitude.Main=lmer(WA.GratitudeV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WA.SegmentationFromWork.Main=lmer(WA.SegmentationFromWorkV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.TakingChargeBehaviorsForSystemImprovement.Main=lmer(WP.TakingChargeBehaviorsForSystemImprovementV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.VoiceForSystemImprovment.Main=lmer(WP.VoiceForSystemImprovmentV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.AIUsageForFacilitatingWork.Main=lmer(WP.AIUsageForFacilitatingWorkV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.LearningBehavior.Main=lmer(WP.LearningBehaviorV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.SystemPerformanceImprovementBehavior.Main=lmer(WP.SystemPerformanceImprovementBehaviorV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.AIEnabledInnovationBehavior.Main=lmer(WP.AIEnabledInnovationBehaviorV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.SocialLearning.Main=lmer(WP.SocialLearningV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.IndependentObservationBasedSocialLearning.Main=lmer(WP.IndependentObservationBasedSocialLearningV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.AdviceThinkingBasedSocialLearning.Main=lmer(WP.AdviceThinkingBasedSocialLearningV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.FeedbackSeekingForSystemImprovement.Main=lmer(WP.FeedbackSeekingForSystemImprovementV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.ConstructiveChallengingBehaviorForSystemImprovement.Main=lmer(WP.ConstructiveChallengingBehaviorForSystemImprovementV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.AIEnabledCreativity.Main=lmer(WP.AIEnabledCreativityV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.EmployeeWorkWellBeing.Main=lmer(WP.EmployeeWorkWellBeingV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.PerceivedWorkGrowth.Main=lmer(WP.PerceivedWorkGrowthV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.TaskPerformanceImprovement.Main=lmer(WP.TaskPerformanceImprovementV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.CreativeProcessEngagment.Main=lmer(WP.CreativeProcessEngagmentV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.SleepQuantity.Main=lmer(WP.SleepQuantityV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.FamilyMemberUnderming.Main=lmer(WP.FamilyMemberUndermingV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))
WP.FamilyMemberConflict.Main=lmer(WP.FamilyMemberConflictV.GroC~W.X + (W.X|B.ID), na.action = na.exclude, data = data1, control=lmerControl(optimizer="bobyqa"))## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.055 (0.039) 304.813 1.398 .163
## W.X -0.110 (0.061) 170.000 -1.816 .071 .
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.047 (0.042) 313.001 1.113 .266
## W.X -0.095 (0.065) 170.000 -1.454 .148
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.063 (0.055) 323.497 1.143 .254
## W.X -0.126 (0.084) 170.000 -1.503 .135
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) -0.060 (0.041) 380.643 -1.459 .145
## W.X 0.120 (0.061) 170.000 1.977 .050 *
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) -0.069 (0.044) 459.107 -1.576 .116
## W.X 0.139 (0.063) 170.000 2.197 .029 *
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.019 (0.044) 409.133 0.438 .662
## W.X -0.039 (0.065) 170.000 -0.600 .549
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.013 (0.045) 682.000 0.279 .780
## W.X -0.025 (0.064) 682.000 -0.395 .693
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) -0.004 (0.047) 682.000 -0.094 .925
## W.X 0.009 (0.066) 682.000 0.133 .894
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) -0.022 (0.047) 271.020 -0.471 .638
## W.X 0.044 (0.074) 170.000 0.596 .552
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.034 (0.040) 333.163 0.845 .399
## W.X -0.067 (0.060) 170.000 -1.118 .265
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.010 (0.050) 302.587 0.203 .839
## W.X -0.020 (0.078) 170.000 -0.264 .792
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) -0.003 (0.046) 337.567 -0.064 .949
## W.X 0.006 (0.069) 170.000 0.085 .932
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) -0.059 (0.047) 495.039 -1.248 .213
## W.X 0.118 (0.067) 170.000 1.758 .081 .
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) -0.017 (0.047) 285.710 -0.362 .718
## W.X 0.034 (0.073) 170.000 0.463 .644
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.044 (0.047) 275.288 0.945 .346
## W.X -0.088 (0.074) 170.000 -1.199 .232
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.019 (0.042) 393.710 0.440 .660
## W.X -0.037 (0.062) 170.000 -0.600 .549
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) -0.013 (0.045) 266.277 -0.278 .781
## W.X 0.025 (0.072) 170.000 0.350 .727
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) -0.059 (0.044) 469.133 -1.351 .177
## W.X 0.119 (0.063) 170.001 1.889 .061 .
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.042 (0.051) 354.775 0.821 .412
## W.X -0.083 (0.076) 170.000 -1.098 .274
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.072 (0.049) 682.000 1.461 .144
## W.X -0.144 (0.070) 682.000 -2.067 .039 *
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.075 (0.048) 682.000 1.564 .118
## W.X -0.150 (0.068) 682.000 -2.211 .027 *
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.079 (0.057) 286.118 1.375 .170
## W.X -0.158 (0.090) 170.000 -1.761 .080 .
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.019 (0.057) 471.958 0.343 .732
## W.X -0.039 (0.081) 170.000 -0.480 .632
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.063 (0.049) 474.888 1.279 .201
## W.X -0.126 (0.070) 170.000 -1.792 .075 .
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.136 (0.052) 682.000 2.615 .009 **
## W.X -0.273 (0.074) 682.000 -3.699 <.001 ***
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.083 (0.048) 682.000 1.745 .081 .
## W.X -0.166 (0.067) 682.000 -2.467 .014 *
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.077 (0.057) 487.953 1.369 .172
## W.X -0.155 (0.081) 170.000 -1.924 .056 .
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.088 (0.055) 482.651 1.617 .107
## W.X -0.177 (0.078) 170.000 -2.270 .024 *
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.071 (0.052) 362.780 1.358 .175
## W.X -0.142 (0.078) 170.000 -1.825 .070 .
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.051 (0.047) 328.710 1.077 .282
## W.X -0.101 (0.071) 170.000 -1.420 .157
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.109 (0.052) 682.000 2.094 .037 *
## W.X -0.219 (0.074) 682.000 -2.962 .003 **
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.045 (0.050) 452.818 0.895 .371
## W.X -0.089 (0.072) 170.000 -1.246 .214
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.084 (0.051) 468.117 1.660 .098 .
## W.X -0.168 (0.072) 170.000 -2.321 .021 *
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.066 (0.047) 682.000 1.415 .157
## W.X -0.133 (0.066) 682.000 -2.001 .046 *
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.009 (0.049) 682.000 0.179 .858
## W.X -0.018 (0.069) 682.000 -0.254 .800
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) 0.105 (0.086) 649.000 1.229 .220
## W.X -0.212 (0.121) 649.000 -1.745 .082 .
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) -0.042 (0.038) 472.042 -1.120 .263
## W.X 0.085 (0.054) 170.000 1.568 .119
## ──────────────────────────────────────────────────────## ──────────────────────────────────────────────────────
## Estimate S.E. df t p
## ──────────────────────────────────────────────────────
## (Intercept) -0.015 (0.038) 338.084 -0.386 .700
## W.X 0.029 (0.057) 170.000 0.512 .609
## ──────────────────────────────────────────────────────WA.WorkReflectionForManipulationCheck.Main=lmer(WA.WorkReflectionForManipulationCheckV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.PositiveWorkReflectionForManipulationCheck.Main=lmer(WA.PositiveWorkReflectionForManipulationCheckV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.NegativeWorkReflectionForManipulationCheck.Main=lmer(WA.NegativeWorkReflectionForManipulationCheckV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.ProblemSolvingPondering.Main=lmer(WA.ProblemSolvingPonderingV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.LearningFromOperationalFailure.Main=lmer(WA.LearningFromOperationalFailureV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.SelfReflectionForManipulationCheck.Main=lmer(WA.SelfReflectionForManipulationCheckV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.RealizingTheNeedForRework.Main=lmer(WA.RealizingTheNeedForReworkV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.TemperoalReflectionForManipulationCheck.Main=lmer(WA.TemperoalReflectionForManipulationCheckV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.LearningFromErrors.Main=lmer(WA.LearningFromErrorsV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.ThrivingInLearning.Main=lmer(WA.ThrivingInLearningV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.CognitiveJobEngagement.Main=lmer(WA.CognitiveJobEngagementV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.ErrorStrain.Main=lmer(WA.ErrorStrainV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.ThinkingAboutErrors.Main=lmer(WA.ThinkingAboutErrorsV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.AffectiveRumination.Main=lmer(WA.AffectiveRuminationV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.Detachment.Main=lmer(WA.DetachmentV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.AffectiveCommitmentForWorkImprovment.Main=lmer(WA.AffectiveCommitmentForWorkImprovmentV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.DetachmentBasedRecoveryFromWork.Main=lmer(WA.DetachmentBasedRecoveryFromWorkV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.Gratitude.Main=lmer(WA.GratitudeV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WA.SegmentationFromWork.Main=lmer(WA.SegmentationFromWorkV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.TakingChargeBehaviorsForSystemImprovement.Main=lmer(WP.TakingChargeBehaviorsForSystemImprovementV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.VoiceForSystemImprovment.Main=lmer(WP.VoiceForSystemImprovmentV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.AIUsageForFacilitatingWork.Main=lmer(WP.AIUsageForFacilitatingWorkV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.LearningBehavior.Main=lmer(WP.LearningBehaviorV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.SystemPerformanceImprovementBehavior.Main=lmer(WP.SystemPerformanceImprovementBehaviorV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.AIEnabledInnovationBehavior.Main=lmer(WP.AIEnabledInnovationBehaviorV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.SocialLearning.Main=lmer(WP.SocialLearningV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.IndependentObservationBasedSocialLearning.Main=lmer(WP.IndependentObservationBasedSocialLearningV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.AdviceThinkingBasedSocialLearning.Main=lmer(WP.AdviceThinkingBasedSocialLearningV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.FeedbackSeekingForSystemImprovement.Main=lmer(WP.FeedbackSeekingForSystemImprovementV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.ConstructiveChallengingBehaviorForSystemImprovement.Main=lmer(WP.ConstructiveChallengingBehaviorForSystemImprovementV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.AIEnabledCreativity.Main=lmer(WP.AIEnabledCreativityV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.EmployeeWorkWellBeing.Main=lmer(WP.EmployeeWorkWellBeingV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.PerceivedWorkGrowth.Main=lmer(WP.PerceivedWorkGrowthV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.TaskPerformanceImprovement.Main=lmer(WP.TaskPerformanceImprovementV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.CreativeProcessEngagment.Main=lmer(WP.CreativeProcessEngagmentV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.SleepQuantity.Main=lmer(WP.SleepQuantityV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.FamilyMemberUnderming.Main=lmer(WP.FamilyMemberUndermingV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))
WP.FamilyMemberConflict.Main=lmer(WP.FamilyMemberConflictV.GroC~factor(W.X) + (factor(W.X)|B.ID), na.action = na.exclude, data = data2, control=lmerControl(optimizer="bobyqa"))## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.043 (0.034) 817.764 -1.250 .212
## factor(W.X)1 0.028 (0.050) 678.788 0.573 .567
## factor(W.X)2 0.099 (0.050) 631.648 1.982 .048 *
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.032 (0.038) 818.000 -0.846 .398
## factor(W.X)1 -0.001 (0.055) 457.142 -0.019 .985
## factor(W.X)2 0.097 (0.055) 456.696 1.766 .078 .
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.053 (0.046) 268.840 -1.153 .250
## factor(W.X)1 0.058 (0.068) 163.000 0.853 .395
## factor(W.X)2 0.102 (0.072) 163.000 1.406 .162
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.020 (0.041) 602.084 -0.482 .630
## factor(W.X)1 0.071 (0.061) 195.326 1.156 .249
## factor(W.X)2 -0.011 (0.058) 618.607 -0.188 .851
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.086 (0.046) 981.000 -1.871 .062 .
## factor(W.X)1 0.112 (0.065) 981.000 1.716 .086 .
## factor(W.X)2 0.147 (0.065) 981.000 2.253 .024 *
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.017 (0.041) 439.661 -0.405 .686
## factor(W.X)1 0.041 (0.056) 783.901 0.734 .463
## factor(W.X)2 0.008 (0.064) 175.620 0.130 .897
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) 0.022 (0.043) 806.192 0.506 .613
## factor(W.X)1 0.050 (0.064) 453.496 0.775 .439
## factor(W.X)2 -0.116 (0.067) 360.951 -1.741 .083 .
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) 0.006 (0.045) 327.415 0.137 .891
## factor(W.X)1 0.045 (0.060) 716.335 0.749 .454
## factor(W.X)2 -0.063 (0.071) 165.485 -0.882 .379
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.019 (0.043) 816.383 -0.453 .651
## factor(W.X)1 0.037 (0.063) 287.468 0.592 .554
## factor(W.X)2 0.021 (0.063) 271.246 0.324 .746
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) 0.005 (0.039) 807.934 0.134 .893
## factor(W.X)1 -0.025 (0.058) 565.387 -0.434 .664
## factor(W.X)2 0.009 (0.060) 427.064 0.153 .878
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.029 (0.047) 374.114 -0.623 .534
## factor(W.X)1 0.028 (0.064) 777.017 0.446 .655
## factor(W.X)2 0.060 (0.075) 172.497 0.795 .427
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) 0.048 (0.050) 295.724 0.962 .337
## factor(W.X)1 0.029 (0.072) 163.000 0.400 .689
## factor(W.X)2 -0.174 (0.075) 163.000 -2.319 .022 *
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.015 (0.052) 299.763 -0.287 .774
## factor(W.X)1 0.018 (0.076) 163.000 0.232 .817
## factor(W.X)2 0.027 (0.079) 163.000 0.347 .729
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.049 (0.045) 331.482 -1.109 .268
## factor(W.X)1 0.093 (0.066) 163.002 1.420 .158
## factor(W.X)2 0.055 (0.067) 162.999 0.816 .415
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) 0.001 (0.046) 816.482 0.018 .986
## factor(W.X)1 0.017 (0.067) 284.811 0.255 .799
## factor(W.X)2 -0.020 (0.067) 269.221 -0.291 .772
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) 0.004 (0.049) 265.159 0.083 .934
## factor(W.X)1 -0.041 (0.069) 163.000 -0.592 .554
## factor(W.X)2 0.028 (0.074) 163.000 0.383 .702
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.038 (0.044) 817.997 -0.873 .383
## factor(W.X)1 0.093 (0.064) 676.220 1.462 .144
## factor(W.X)2 0.021 (0.063) 757.806 0.328 .743
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) 0.015 (0.046) 294.483 0.328 .743
## factor(W.X)1 -0.005 (0.067) 163.000 -0.076 .940
## factor(W.X)2 -0.041 (0.067) 163.000 -0.609 .543
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.002 (0.050) 275.945 -0.036 .972
## factor(W.X)1 0.021 (0.071) 163.000 0.300 .765
## factor(W.X)2 -0.016 (0.073) 163.000 -0.219 .827
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.048 (0.042) 789.317 -1.146 .252
## factor(W.X)1 0.104 (0.061) 519.546 1.711 .088 .
## factor(W.X)2 0.040 (0.063) 361.035 0.629 .530
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.021 (0.040) 747.993 -0.517 .605
## factor(W.X)1 0.057 (0.059) 329.456 0.970 .333
## factor(W.X)2 0.005 (0.064) 240.137 0.076 .940
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.053 (0.045) 279.943 -1.182 .238
## factor(W.X)1 0.129 (0.067) 163.000 1.913 .057 .
## factor(W.X)2 0.029 (0.069) 163.000 0.430 .668
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.048 (0.043) 550.586 -1.111 .267
## factor(W.X)1 0.125 (0.061) 635.464 2.050 .041 *
## factor(W.X)2 0.020 (0.066) 187.145 0.298 .766
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.026 (0.042) 817.997 -0.635 .525
## factor(W.X)1 0.039 (0.065) 439.910 0.604 .546
## factor(W.X)2 0.040 (0.065) 437.311 0.622 .534
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.016 (0.043) 800.195 -0.365 .715
## factor(W.X)1 0.098 (0.062) 599.027 1.589 .113
## factor(W.X)2 -0.051 (0.063) 359.974 -0.806 .421
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.063 (0.042) 774.527 -1.483 .139
## factor(W.X)1 0.117 (0.064) 226.343 1.821 .070 .
## factor(W.X)2 0.072 (0.062) 328.031 1.169 .243
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.083 (0.051) 817.946 -1.649 .099 .
## factor(W.X)1 0.172 (0.074) 687.220 2.332 .020 *
## factor(W.X)2 0.078 (0.073) 791.568 1.064 .288
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.043 (0.054) 254.983 -0.788 .431
## factor(W.X)1 0.061 (0.081) 163.000 0.751 .453
## factor(W.X)2 0.067 (0.082) 163.000 0.821 .413
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.054 (0.043) 663.490 -1.260 .208
## factor(W.X)1 0.106 (0.060) 568.205 1.757 .079 .
## factor(W.X)2 0.055 (0.065) 222.221 0.851 .396
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.028 (0.042) 322.451 -0.666 .506
## factor(W.X)1 0.074 (0.062) 163.000 1.208 .229
## factor(W.X)2 0.009 (0.064) 163.000 0.143 .886
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) 0.020 (0.043) 715.877 0.457 .648
## factor(W.X)1 0.006 (0.062) 394.777 0.099 .921
## factor(W.X)2 -0.065 (0.065) 212.034 -1.000 .319
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.066 (0.042) 753.374 -1.549 .122
## factor(W.X)1 0.129 (0.062) 272.990 2.083 .038 *
## factor(W.X)2 0.068 (0.064) 196.168 1.053 .293
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.058 (0.042) 815.784 -1.398 .163
## factor(W.X)1 0.124 (0.062) 573.872 1.987 .047 *
## factor(W.X)2 0.051 (0.065) 456.131 0.780 .436
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) -0.016 (0.040) 799.812 -0.405 .686
## factor(W.X)1 0.046 (0.059) 287.502 0.773 .440
## factor(W.X)2 0.003 (0.060) 238.424 0.051 .960
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) 0.012 (0.040) 806.735 0.305 .761
## factor(W.X)1 -0.001 (0.059) 302.461 -0.017 .986
## factor(W.X)2 -0.036 (0.060) 259.750 -0.589 .556
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) 0.041 (0.075) 430.408 0.549 .583
## factor(W.X)1 0.002 (0.103) 777.185 0.021 .984
## factor(W.X)2 -0.128 (0.116) 172.328 -1.099 .273
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) 0.011 (0.032) 817.997 0.360 .719
## factor(W.X)1 -0.019 (0.046) 250.728 -0.415 .679
## factor(W.X)2 -0.015 (0.046) 250.101 -0.332 .740
## ───────────────────────────────────────────────────────## ───────────────────────────────────────────────────────
## Estimate S.E. df t p
## ───────────────────────────────────────────────────────
## (Intercept) 0.019 (0.030) 307.037 0.626 .532
## factor(W.X)1 -0.043 (0.043) 162.999 -1.001 .318
## factor(W.X)2 -0.014 (0.045) 163.000 -0.308 .758
## ───────────────────────────────────────────────────────