library(haven)## Warning: package 'haven' was built under R version 3.4.3Value_Coding <- read_sav("Data/STEM-IE/Final Aggregated Value Coding Dataset Updated 2-1-2018.sav")Descriptives for all sum measures
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionValue_Coding %>%
select(V01.01.HighUtility_sum: V08.03.PsychologicalCost_sum) %>%
psych::describe()## vars n mean sd
## V01.01.HighUtility_sum 1 237 1.39 2.81
## V01.02.LowUtility_sum 2 237 0.00 0.00
## V01.03.HighIntrinsic_sum 3 237 0.76 2.13
## V01.03a.selftranscendentintrinsic_sum 4 237 0.00 0.06
## V01.04.LowIntrinsic_sum 5 237 0.01 0.11
## V01.05.HighAttainment_sum 6 237 0.04 0.36
## V01.05a.selftranscendentattainment_sum 7 237 0.07 0.34
## V01.06.LowAttainment_sum 8 237 0.00 0.00
## V01.07.LowCost_sum 9 237 0.00 0.06
## V01.08.HighCost_sum 10 237 0.02 0.13
## V02.01.Unsolicited_sum 11 237 1.56 2.81
## V02.02.ResponsetoYouth_sum 12 237 0.65 1.55
## V02.03.ResponsetoOther_sum 13 237 0.06 0.35
## V02.04.Unclear_sum 14 237 0.03 0.18
## V03.01.IndividualYouth_sum 15 237 0.38 1.14
## V03.02.SmallGroup_sum 16 237 0.81 2.33
## V03.03.WholeGroup_sum 17 237 1.05 2.51
## V03.04.OtherLeader_sum 18 237 0.03 0.18
## V03.05.OtherSTEM_IE_Team_sum 19 237 0.01 0.09
## V03.06.Unclear_sum 20 237 0.01 0.09
## V04.01.WholeClass_sum 21 237 1.19 2.64
## V04.02.Leaderself_sum 22 237 0.04 0.30
## V04.03.IndividualYouth_sum 23 237 1.05 2.82
## V04.04.Other_sum 24 237 0.02 0.13
## V05.01.Specific_sum 25 237 2.26 3.91
## V05.02.General_sum 26 237 0.04 0.27
## V06.01.RealLifeNoGoal_sum 27 237 0.56 1.72
## V06.02.ImpactsForPassiveOutcome_sum 28 237 0.22 0.99
## V06.03.UsefulForSpecificGoal_sum 29 237 0.62 1.62
## V07A.01.NaturalPhenomena_sum 30 237 0.03 0.16
## V07A.02.ConceptInSummerProgram_sum 31 237 0.17 1.10
## V07A.03.AcademicYearClass_sum 32 237 0.00 0.00
## V07A.04.Routine_sum 33 237 0.20 0.87
## V07A.05.AdvancesInScienceHealthTech_sum 34 237 0.02 0.27
## V07A.06.GeneralKnowledgeUnderstandingOfPhenomena_sum 35 237 0.06 0.53
## V07B.07.Job_sum 36 237 0.05 0.35
## V07B.08.FutureEducation_sum 37 237 0.00 0.06
## V07B.09.HealthSafetyWellbeing_sum 38 237 0.01 0.19
## V07B.10.Hobby_sum 39 237 0.00 0.06
## V07B.11.Relationships_sum 40 237 0.00 0.00
## V07C.12.LocalProblemInProgram_sum 41 237 0.16 0.69
## V07C.13.ProgramGoalToHelpOthersORCommunity_sum 42 237 0.09 0.50
## V07C.14.GlobalProblemInSpecificEvent_sum 43 237 0.01 0.11
## V07C.15.AdvancingSocietyWorld_sum 44 237 0.00 0.00
## V07D.16.Grades_sum 45 237 0.00 0.06
## V07D.17.FutureSuccessRecognition_sum 46 237 0.00 0.06
## V08.01.EffortCost_sum 47 237 0.02 0.13
## V08.02.OpportunityCost_sum 48 237 0.00 0.00
## V08.03.PsychologicalCost_sum 49 237 0.00 0.06
## median trimmed mad
## V01.01.HighUtility_sum 0 0.69 0
## V01.02.LowUtility_sum 0 0.00 0
## V01.03.HighIntrinsic_sum 0 0.22 0
## V01.03a.selftranscendentintrinsic_sum 0 0.00 0
## V01.04.LowIntrinsic_sum 0 0.00 0
## V01.05.HighAttainment_sum 0 0.00 0
## V01.05a.selftranscendentattainment_sum 0 0.00 0
## V01.06.LowAttainment_sum 0 0.00 0
## V01.07.LowCost_sum 0 0.00 0
## V01.08.HighCost_sum 0 0.00 0
## V02.01.Unsolicited_sum 0 0.91 0
## V02.02.ResponsetoYouth_sum 0 0.26 0
## V02.03.ResponsetoOther_sum 0 0.00 0
## V02.04.Unclear_sum 0 0.00 0
## V03.01.IndividualYouth_sum 0 0.07 0
## V03.02.SmallGroup_sum 0 0.21 0
## V03.03.WholeGroup_sum 0 0.39 0
## V03.04.OtherLeader_sum 0 0.00 0
## V03.05.OtherSTEM_IE_Team_sum 0 0.00 0
## V03.06.Unclear_sum 0 0.00 0
## V04.01.WholeClass_sum 0 0.52 0
## V04.02.Leaderself_sum 0 0.00 0
## V04.03.IndividualYouth_sum 0 0.34 0
## V04.04.Other_sum 0 0.00 0
## V05.01.Specific_sum 0 1.34 0
## V05.02.General_sum 0 0.00 0
## V06.01.RealLifeNoGoal_sum 0 0.11 0
## V06.02.ImpactsForPassiveOutcome_sum 0 0.00 0
## V06.03.UsefulForSpecificGoal_sum 0 0.23 0
## V07A.01.NaturalPhenomena_sum 0 0.00 0
## V07A.02.ConceptInSummerProgram_sum 0 0.00 0
## V07A.03.AcademicYearClass_sum 0 0.00 0
## V07A.04.Routine_sum 0 0.00 0
## V07A.05.AdvancesInScienceHealthTech_sum 0 0.00 0
## V07A.06.GeneralKnowledgeUnderstandingOfPhenomena_sum 0 0.00 0
## V07B.07.Job_sum 0 0.00 0
## V07B.08.FutureEducation_sum 0 0.00 0
## V07B.09.HealthSafetyWellbeing_sum 0 0.00 0
## V07B.10.Hobby_sum 0 0.00 0
## V07B.11.Relationships_sum 0 0.00 0
## V07C.12.LocalProblemInProgram_sum 0 0.00 0
## V07C.13.ProgramGoalToHelpOthersORCommunity_sum 0 0.00 0
## V07C.14.GlobalProblemInSpecificEvent_sum 0 0.00 0
## V07C.15.AdvancingSocietyWorld_sum 0 0.00 0
## V07D.16.Grades_sum 0 0.00 0
## V07D.17.FutureSuccessRecognition_sum 0 0.00 0
## V08.01.EffortCost_sum 0 0.00 0
## V08.02.OpportunityCost_sum 0 0.00 0
## V08.03.PsychologicalCost_sum 0 0.00 0
## min max range skew
## V01.01.HighUtility_sum 0 16 16 2.75
## V01.02.LowUtility_sum 0 0 0 NaN
## V01.03.HighIntrinsic_sum 0 17 17 4.24
## V01.03a.selftranscendentintrinsic_sum 0 1 1 15.20
## V01.04.LowIntrinsic_sum 0 1 1 8.66
## V01.05.HighAttainment_sum 0 5 5 11.85
## V01.05a.selftranscendentattainment_sum 0 3 3 5.53
## V01.06.LowAttainment_sum 0 0 0 NaN
## V01.07.LowCost_sum 0 1 1 15.20
## V01.08.HighCost_sum 0 1 1 7.45
## V02.01.Unsolicited_sum 0 17 17 2.61
## V02.02.ResponsetoYouth_sum 0 11 11 3.52
## V02.03.ResponsetoOther_sum 0 4 4 7.84
## V02.04.Unclear_sum 0 1 1 5.13
## V03.01.IndividualYouth_sum 0 8 8 3.96
## V03.02.SmallGroup_sum 0 17 17 4.22
## V03.03.WholeGroup_sum 0 16 16 3.34
## V03.04.OtherLeader_sum 0 1 1 5.13
## V03.05.OtherSTEM_IE_Team_sum 0 1 1 10.68
## V03.06.Unclear_sum 0 1 1 10.68
## V04.01.WholeClass_sum 0 16 16 3.04
## V04.02.Leaderself_sum 0 4 4 10.86
## V04.03.IndividualYouth_sum 0 21 21 3.97
## V04.04.Other_sum 0 1 1 7.45
## V05.01.Specific_sum 0 22 22 2.21
## V05.02.General_sum 0 3 3 8.34
## V06.01.RealLifeNoGoal_sum 0 13 13 4.21
## V06.02.ImpactsForPassiveOutcome_sum 0 8 8 5.85
## V06.03.UsefulForSpecificGoal_sum 0 16 16 4.88
## V07A.01.NaturalPhenomena_sum 0 1 1 6.01
## V07A.02.ConceptInSummerProgram_sum 0 15 15 10.98
## V07A.03.AcademicYearClass_sum 0 0 0 NaN
## V07A.04.Routine_sum 0 8 8 5.85
## V07A.05.AdvancesInScienceHealthTech_sum 0 4 4 14.08
## V07A.06.GeneralKnowledgeUnderstandingOfPhenomena_sum 0 7 7 10.89
## V07B.07.Job_sum 0 4 4 8.17
## V07B.08.FutureEducation_sum 0 1 1 15.20
## V07B.09.HealthSafetyWellbeing_sum 0 3 3 15.20
## V07B.10.Hobby_sum 0 1 1 15.20
## V07B.11.Relationships_sum 0 0 0 NaN
## V07C.12.LocalProblemInProgram_sum 0 7 7 6.30
## V07C.13.ProgramGoalToHelpOthersORCommunity_sum 0 4 4 5.70
## V07C.14.GlobalProblemInSpecificEvent_sum 0 1 1 8.66
## V07C.15.AdvancingSocietyWorld_sum 0 0 0 NaN
## V07D.16.Grades_sum 0 1 1 15.20
## V07D.17.FutureSuccessRecognition_sum 0 1 1 15.20
## V08.01.EffortCost_sum 0 1 1 7.45
## V08.02.OpportunityCost_sum 0 0 0 NaN
## V08.03.PsychologicalCost_sum 0 1 1 15.20
## kurtosis se
## V01.01.HighUtility_sum 8.06 0.18
## V01.02.LowUtility_sum NaN 0.00
## V01.03.HighIntrinsic_sum 21.82 0.14
## V01.03a.selftranscendentintrinsic_sum 230.03 0.00
## V01.04.LowIntrinsic_sum 73.36 0.01
## V01.05.HighAttainment_sum 152.71 0.02
## V01.05a.selftranscendentattainment_sum 33.83 0.02
## V01.06.LowAttainment_sum NaN 0.00
## V01.07.LowCost_sum 230.03 0.00
## V01.08.HighCost_sum 53.78 0.01
## V02.01.Unsolicited_sum 8.11 0.18
## V02.02.ResponsetoYouth_sum 15.25 0.10
## V02.03.ResponsetoOther_sum 72.74 0.02
## V02.04.Unclear_sum 24.43 0.01
## V03.01.IndividualYouth_sum 18.16 0.07
## V03.02.SmallGroup_sum 20.76 0.15
## V03.03.WholeGroup_sum 12.78 0.16
## V03.04.OtherLeader_sum 24.43 0.01
## V03.05.OtherSTEM_IE_Team_sum 112.53 0.01
## V03.06.Unclear_sum 112.53 0.01
## V04.01.WholeClass_sum 10.21 0.17
## V04.02.Leaderself_sum 135.05 0.02
## V04.03.IndividualYouth_sum 18.52 0.18
## V04.04.Other_sum 53.78 0.01
## V05.01.Specific_sum 4.89 0.25
## V05.02.General_sum 77.64 0.02
## V06.01.RealLifeNoGoal_sum 19.93 0.11
## V06.02.ImpactsForPassiveOutcome_sum 37.02 0.06
## V06.03.UsefulForSpecificGoal_sum 35.08 0.11
## V07A.01.NaturalPhenomena_sum 34.21 0.01
## V07A.02.ConceptInSummerProgram_sum 139.61 0.07
## V07A.03.AcademicYearClass_sum NaN 0.00
## V07A.04.Routine_sum 39.46 0.06
## V07A.05.AdvancesInScienceHealthTech_sum 204.12 0.02
## V07A.06.GeneralKnowledgeUnderstandingOfPhenomena_sum 131.12 0.03
## V07B.07.Job_sum 77.73 0.02
## V07B.08.FutureEducation_sum 230.03 0.00
## V07B.09.HealthSafetyWellbeing_sum 230.03 0.01
## V07B.10.Hobby_sum 230.03 0.00
## V07B.11.Relationships_sum NaN 0.00
## V07C.12.LocalProblemInProgram_sum 48.42 0.04
## V07C.13.ProgramGoalToHelpOthersORCommunity_sum 32.73 0.03
## V07C.14.GlobalProblemInSpecificEvent_sum 73.36 0.01
## V07C.15.AdvancingSocietyWorld_sum NaN 0.00
## V07D.16.Grades_sum 230.03 0.00
## V07D.17.FutureSuccessRecognition_sum 230.03 0.00
## V08.01.EffortCost_sum 53.78 0.01
## V08.02.OpportunityCost_sum NaN 0.00
## V08.03.PsychologicalCost_sum 230.03 0.00Manova on main value statements, including post-hocs for significant differences
fit<-manova(cbind(V01.01.HighUtility_sum, V01.03.HighIntrinsic_sum, V01.05.HighAttainment_sum, V01.04.LowIntrinsic_sum, V01.07.LowCost_sum, V01.08.HighCost_sum ) ~ as.factor(Value_Coding$SiteIDNumeric), data=Value_Coding)
summary(fit, test="Pillai")## Df Pillai approx F num Df den Df
## as.factor(Value_Coding$SiteIDNumeric) 8 0.45922 2.3621 48 1368
## Residuals 228
## Pr(>F)
## as.factor(Value_Coding$SiteIDNumeric) 7.254e-07 ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary.aov(fit)## Response V01.01.HighUtility_sum :
## Df Sum Sq Mean Sq F value
## as.factor(Value_Coding$SiteIDNumeric) 8 224.91 28.1134 3.9094
## Residuals 228 1639.60 7.1912
## Pr(>F)
## as.factor(Value_Coding$SiteIDNumeric) 0.0002399 ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response V01.03.HighIntrinsic_sum :
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(Value_Coding$SiteIDNumeric) 8 164.58 20.5731 5.1962 5.636e-06
## Residuals 228 902.71 3.9592
##
## as.factor(Value_Coding$SiteIDNumeric) ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response V01.05.HighAttainment_sum :
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(Value_Coding$SiteIDNumeric) 8 2.1916 0.27394 2.1941 0.02878
## Residuals 228 28.4667 0.12485
##
## as.factor(Value_Coding$SiteIDNumeric) *
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response V01.04.LowIntrinsic_sum :
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(Value_Coding$SiteIDNumeric) 8 0.17869 0.022336 1.8297 0.07257
## Residuals 228 2.78333 0.012208
##
## as.factor(Value_Coding$SiteIDNumeric) .
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response V01.07.LowCost_sum :
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(Value_Coding$SiteIDNumeric) 8 0.03745 0.0046809 1.1136 0.3548
## Residuals 228 0.95833 0.0042032
##
## Response V01.08.HighCost_sum :
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(Value_Coding$SiteIDNumeric) 8 0.0996 0.012453 0.7408 0.6554
## Residuals 228 3.8329 0.016811
##
## 21 observations deleted due to missingnessTukeyHSD(aov(Value_Coding$V01.01.HighUtility_sum ~ as.factor(Value_Coding$SiteIDNumeric)))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Value_Coding$V01.01.HighUtility_sum ~ as.factor(Value_Coding$SiteIDNumeric))
##
## $`as.factor(Value_Coding$SiteIDNumeric)`
## diff lwr upr p adj
## 2-1 -0.55681818 -3.0357520 1.92211563 0.9987081
## 4-1 0.24271012 -1.8873131 2.37273332 0.9999923
## 5-1 1.16433566 -1.2685717 3.59724308 0.8552165
## 6-1 0.22294372 -2.3392751 2.78516254 0.9999991
## 7-1 0.60984848 -1.8690853 3.08878230 0.9975308
## 8-1 1.41818182 -1.1766026 4.01296625 0.7388307
## 9-1 3.15151515 0.6725813 5.63044897 0.0029235
## 10-1 0.66600791 -1.8385667 3.17058253 0.9957539
## 4-2 0.79952830 -1.2668260 2.86588258 0.9532795
## 5-2 1.72115385 -0.6562102 4.09851786 0.3664493
## 6-2 0.77976190 -1.7297772 3.28930100 0.9879293
## 7-2 1.16666667 -1.2577784 3.59111177 0.8513586
## 8-2 1.97500000 -0.5677795 4.51777947 0.2715821
## 9-2 3.70833333 1.2838882 6.13277843 0.0001034
## 10-2 1.22282609 -1.2278300 3.67348216 0.8238479
## 5-4 0.92162554 -1.0892811 2.93253221 0.8829824
## 6-4 -0.01976640 -2.1853316 2.14579876 1.0000000
## 7-4 0.36713836 -1.6992159 2.43349264 0.9997696
## 8-4 1.17547170 -1.0285277 3.37947111 0.7639438
## 9-4 2.90880503 0.8424508 4.97515931 0.0005365
## 10-4 0.42329779 -1.6737480 2.52034361 0.9994062
## 6-5 -0.94139194 -3.4054764 1.52269249 0.9566016
## 7-5 -0.55448718 -2.9318512 1.82287683 0.9983082
## 8-5 0.25384615 -2.2440836 2.75177591 0.9999969
## 9-5 1.98717949 -0.3901845 4.36454350 0.1850310
## 10-5 -0.49832776 -2.9024161 1.90576060 0.9992787
## 7-6 0.38690476 -2.1226343 2.89644386 0.9999211
## 8-6 1.19523810 -1.4288008 3.81927694 0.8865682
## 9-6 2.92857143 0.4190323 5.43811052 0.0095308
## 10-6 0.44306418 -2.0918062 2.97793452 0.9997962
## 8-7 0.80833333 -1.7344461 3.35111280 0.9860046
## 9-7 2.54166667 0.1172216 4.96611177 0.0319343
## 10-7 0.05615942 -2.3944967 2.50681550 1.0000000
## 9-8 1.73333333 -0.8094461 4.27611280 0.4522984
## 10-8 -0.75217391 -3.3199567 1.81560891 0.9918166
## 10-9 -2.48550725 -4.9361633 -0.03485117 0.0439458TukeyHSD(aov(Value_Coding$V01.03.HighIntrinsic_sum ~ as.factor(Value_Coding$SiteIDNumeric)))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Value_Coding$V01.03.HighIntrinsic_sum ~ as.factor(Value_Coding$SiteIDNumeric))
##
## $`as.factor(Value_Coding$SiteIDNumeric)`
## diff lwr upr p adj
## 2-1 1.250000e-01 -1.71437093 1.9643709 0.9999999
## 4-1 2.245678e-15 -1.58047897 1.5804790 1.0000000
## 5-1 1.000000e+00 -0.80521931 2.8052193 0.7243399
## 6-1 2.523810e+00 0.62264105 4.4249780 0.0014831
## 7-1 4.166667e-01 -1.42270427 2.2560376 0.9986272
## 8-1 4.500000e-01 -1.47533219 2.3753322 0.9982827
## 9-1 1.625000e+00 -0.21437093 3.4643709 0.1312273
## 10-1 1.739130e+00 -0.11926600 3.5975269 0.0868639
## 4-2 -1.250000e-01 -1.65823658 1.4082366 0.9999994
## 5-2 8.750000e-01 -0.88900606 2.6390061 0.8285868
## 6-2 2.398810e+00 0.53672945 4.2608896 0.0023902
## 7-2 2.916667e-01 -1.50727360 2.0906069 0.9998844
## 8-2 3.250000e-01 -1.56174446 2.2117445 0.9998174
## 9-2 1.500000e+00 -0.29894026 3.2989403 0.1876623
## 10-2 1.614130e+00 -0.20425840 3.4325193 0.1271064
## 5-4 1.000000e+00 -0.49209441 2.4920944 0.4766331
## 6-4 2.523810e+00 0.91695839 4.1306607 0.0000578
## 7-4 4.166667e-01 -1.11656991 1.9499032 0.9950708
## 8-4 4.500000e-01 -1.18536938 2.0853694 0.9946325
## 9-4 1.625000e+00 0.09176342 3.1582366 0.0285920
## 10-4 1.739130e+00 0.18312070 3.2951402 0.0160401
## 6-5 1.523810e+00 -0.30454316 3.3521622 0.1881672
## 7-5 -5.833333e-01 -2.34733939 1.1806727 0.9819909
## 8-5 -5.500000e-01 -2.40346594 1.3034659 0.9910813
## 9-5 6.250000e-01 -1.13900606 2.3890061 0.9723353
## 10-5 7.391304e-01 -1.04470511 2.5229660 0.9312329
## 7-6 -2.107143e+00 -3.96922294 -0.2450628 0.0138824
## 8-6 -2.073810e+00 -4.02084851 -0.1267705 0.0271688
## 9-6 -8.988095e-01 -2.76088960 0.9632706 0.8491623
## 10-6 -7.846791e-01 -2.66555497 1.0961968 0.9286077
## 8-7 3.333333e-02 -1.85341113 1.9200778 1.0000000
## 9-7 1.208333e+00 -0.59060693 3.0072736 0.4734467
## 10-7 1.322464e+00 -0.49592506 3.1408526 0.3600823
## 9-8 1.175000e+00 -0.71174446 3.0617445 0.5795205
## 10-8 1.289130e+00 -0.61616654 3.1944274 0.4630021
## 10-9 1.141304e-01 -1.70425840 1.9325193 0.9999999TukeyHSD(aov(Value_Coding$V01.05.HighAttainment_sum ~ as.factor(Value_Coding$SiteIDNumeric)))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Value_Coding$V01.05.HighAttainment_sum ~ as.factor(Value_Coding$SiteIDNumeric))
##
## $`as.factor(Value_Coding$SiteIDNumeric)`
## diff lwr upr p adj
## 2-1 2.838638e-17 -0.326636274 0.3266362735 1.0000000
## 4-1 7.569702e-18 -0.280662128 0.2806621283 1.0000000
## 5-1 5.614196e-17 -0.320571613 0.3205716126 1.0000000
## 6-1 3.333333e-01 -0.004276974 0.6709436410 0.0559798
## 7-1 6.308085e-17 -0.326636274 0.3266362735 1.0000000
## 8-1 1.000000e-01 -0.241901310 0.4419013098 0.9918992
## 9-1 -5.488034e-17 -0.326636274 0.3266362735 1.0000000
## 10-1 -5.488034e-17 -0.330014830 0.3300148298 1.0000000
## 4-2 -2.081668e-17 -0.272272804 0.2722728044 1.0000000
## 5-2 2.775558e-17 -0.313252947 0.3132529467 1.0000000
## 6-2 3.333333e-01 0.002664360 0.6640023062 0.0464876
## 7-2 3.469447e-17 -0.319456578 0.3194565776 1.0000000
## 8-2 1.000000e-01 -0.235048885 0.4350488852 0.9907325
## 9-2 -8.326673e-17 -0.319456578 0.3194565776 1.0000000
## 10-2 -8.326673e-17 -0.322910263 0.3229102626 1.0000000
## 5-4 4.857226e-17 -0.264966760 0.2649667596 1.0000000
## 6-4 3.333333e-01 0.047988026 0.6186786411 0.0094079
## 7-4 5.551115e-17 -0.272272804 0.2722728044 1.0000000
## 8-4 1.000000e-01 -0.190409591 0.3904095909 0.9767917
## 9-4 -6.245005e-17 -0.272272804 0.2722728044 1.0000000
## 10-4 -6.245005e-17 -0.276316870 0.2763168702 1.0000000
## 6-5 3.333333e-01 0.008653686 0.6580129804 0.0391814
## 7-5 6.938894e-18 -0.313252947 0.3132529467 1.0000000
## 8-5 1.000000e-01 -0.229139270 0.4291392701 0.9895781
## 9-5 -1.110223e-16 -0.313252947 0.3132529467 1.0000000
## 10-5 -1.110223e-16 -0.316774275 0.3167742750 1.0000000
## 7-6 -3.333333e-01 -0.664002306 -0.0026643605 0.0464876
## 8-6 -2.333333e-01 -0.579089346 0.1124226790 0.4667243
## 9-6 -3.333333e-01 -0.664002306 -0.0026643605 0.0464876
## 10-6 -3.333333e-01 -0.667340073 0.0006734066 0.0509135
## 8-7 1.000000e-01 -0.235048885 0.4350488852 0.9907325
## 9-7 -1.179612e-16 -0.319456578 0.3194565776 1.0000000
## 10-7 -1.179612e-16 -0.322910263 0.3229102626 1.0000000
## 9-8 -1.000000e-01 -0.435048885 0.2350488852 0.9907325
## 10-8 -1.000000e-01 -0.438343447 0.2383434471 0.9913149
## 10-9 0.000000e+00 -0.322910263 0.3229102626 1.0000000TukeyHSD(aov(Value_Coding$V01.04.LowIntrinsic_sum ~ as.factor(Value_Coding$SiteIDNumeric)))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Value_Coding$V01.04.LowIntrinsic_sum ~ as.factor(Value_Coding$SiteIDNumeric))
##
## $`as.factor(Value_Coding$SiteIDNumeric)`
## diff lwr upr p adj
## 2-1 8.333333e-02 -0.01880264 0.185469303 0.2117781
## 4-1 6.457902e-17 -0.08776030 0.087760304 1.0000000
## 5-1 1.947621e-17 -0.10023961 0.100239609 1.0000000
## 6-1 3.682345e-17 -0.10556744 0.105567442 1.0000000
## 7-1 3.682345e-17 -0.10213597 0.102135969 1.0000000
## 8-1 5.000000e-02 -0.05690920 0.156909197 0.8705693
## 9-1 2.988455e-17 -0.10213597 0.102135969 1.0000000
## 10-1 2.988455e-17 -0.10319241 0.103192411 1.0000000
## 4-2 -8.333333e-02 -0.16847038 0.001803711 0.0603324
## 5-2 -8.333333e-02 -0.18128447 0.014617800 0.1667147
## 6-2 -8.333333e-02 -0.18673029 0.020063622 0.2260465
## 7-2 -8.333333e-02 -0.18322428 0.016557615 0.1871227
## 8-2 -3.333333e-02 -0.13809984 0.071433177 0.9859266
## 9-2 -8.333333e-02 -0.18322428 0.016557615 0.1871227
## 10-2 -8.333333e-02 -0.18430422 0.017637549 0.1988546
## 5-4 -4.510281e-17 -0.08285252 0.082852515 1.0000000
## 6-4 -2.775558e-17 -0.08922469 0.089224688 1.0000000
## 7-4 -2.775558e-17 -0.08513704 0.085137044 1.0000000
## 8-4 5.000000e-02 -0.04080824 0.140808240 0.7309150
## 9-4 -3.469447e-17 -0.08513704 0.085137044 1.0000000
## 10-4 -3.469447e-17 -0.08640158 0.086401584 1.0000000
## 6-5 1.734723e-17 -0.10152415 0.101524151 1.0000000
## 7-5 1.734723e-17 -0.09795113 0.097951134 1.0000000
## 8-5 5.000000e-02 -0.05291863 0.152918632 0.8444632
## 9-5 1.040834e-17 -0.09795113 0.097951134 1.0000000
## 10-5 1.040834e-17 -0.09905222 0.099052219 1.0000000
## 7-6 0.000000e+00 -0.10339695 0.103396955 1.0000000
## 8-6 5.000000e-02 -0.05811452 0.158114525 0.8775664
## 9-6 -6.938894e-18 -0.10339695 0.103396955 1.0000000
## 10-6 -6.938894e-18 -0.10444064 0.104440642 1.0000000
## 8-7 5.000000e-02 -0.05476651 0.154766511 0.8571387
## 9-7 -6.938894e-18 -0.09989095 0.099890949 1.0000000
## 10-7 -6.938894e-18 -0.10097088 0.100970882 1.0000000
## 9-8 -5.000000e-02 -0.15476651 0.054766511 0.8571387
## 10-8 -5.000000e-02 -0.15579669 0.055796688 0.8637594
## 10-9 0.000000e+00 -0.10097088 0.100970882 1.0000000