Ashley’s Civility Climate Study
Robert W. Walker
March 15, 2017
Loading the Data
Directly from Excel, here is a look at the data.
AN.data <- read_excel("Civility data_2012.xlsx", sheet="Civility data_2012")
AN.data$N <- seq(1,dim(AN.data)[[1]])
summary(AN.data)## Gender Age Job_type Direct_indirect
## Min. :1.000 Min. :19.00 Min. :0.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:29.00 1st Qu.:5.000 1st Qu.:1.000
## Median :2.000 Median :39.00 Median :5.000 Median :1.000
## Mean :1.898 Mean :39.91 Mean :5.031 Mean :1.102
## 3rd Qu.:2.000 3rd Qu.:50.00 3rd Qu.:6.000 3rd Qu.:1.000
## Max. :2.000 Max. :67.00 Max. :8.000 Max. :2.000
## NA's :2 NA's :17 NA's :3
## Unit Hrs Ten_yr Ten_m
## Min. : 1.00 Min. :16.00 Min. : 0.000 Min. : 0.000
## 1st Qu.: 3.00 1st Qu.:36.00 1st Qu.: 2.000 1st Qu.: 0.000
## Median :12.00 Median :36.00 Median : 4.000 Median : 2.000
## Mean :10.35 Mean :37.82 Mean : 6.825 Mean : 2.954
## 3rd Qu.:14.75 3rd Qu.:40.00 3rd Qu.: 9.000 3rd Qu.: 5.000
## Max. :31.00 Max. :90.00 Max. :40.000 Max. :11.000
## NA's :2 NA's :63 NA's :28 NA's :29
## VAgEx VAg_cust VAg_co Vag_sup
## Min. :1.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000
## 1st Qu.:1.000 1st Qu.: 1.000 1st Qu.: 0.000 1st Qu.: 0.0000
## Median :2.000 Median : 1.000 Median : 1.000 Median : 0.0000
## Mean :2.713 Mean : 3.169 Mean : 1.677 Mean : 0.3174
## 3rd Qu.:4.000 3rd Qu.: 3.000 3rd Qu.: 2.000 3rd Qu.: 0.0000
## Max. :6.000 Max. :80.000 Max. :40.000 Max. :15.0000
## NA's :137 NA's :164
## IntentVA IntensVA PerInVA PerpowVA
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.632 1st Qu.:1.000
## Median :3.000 Median :3.000 Median :5.000 Median :2.000
## Mean :3.258 Mean :3.315 Mean :4.226 Mean :2.127
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:3.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :5.000
## NA's :73 NA's :71 NA's :62 NA's :48
## IntimEx Inti_cus Inti_co Inti_sup
## Min. :1.000 Min. : 0.000 Min. : 0.000 Min. :0.0000
## 1st Qu.:1.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.0000
## Median :1.000 Median : 1.000 Median : 1.000 Median :0.0000
## Mean :2.115 Mean : 1.775 Mean : 1.354 Mean :0.1798
## 3rd Qu.:3.000 3rd Qu.: 2.000 3rd Qu.: 1.750 3rd Qu.:0.0000
## Max. :6.000 Max. :51.000 Max. :21.000 Max. :6.0000
## NA's :214 NA's :198
## IntentIn IntensIn PerInInt PerpoInt
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:1.000
## Median :3.000 Median :3.000 Median :4.000 Median :2.000
## Mean :3.112 Mean :3.146 Mean :3.792 Mean :2.031
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:2.933
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :5.000
## NA's :105 NA's :104 NA's :87 NA's :70
## ExclusEx Excl_cus Excl_co Excl_sup
## Min. :1.000 Min. :0.0000 Min. : 0.000 Min. :0.0000
## 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.: 0.000 1st Qu.:0.0000
## Median :1.000 Median :0.0000 Median : 1.000 Median :0.0000
## Mean :1.789 Mean :0.1236 Mean : 3.316 Mean :0.1236
## 3rd Qu.:2.000 3rd Qu.:0.0000 3rd Qu.: 3.000 3rd Qu.:0.0000
## Max. :6.000 Max. :3.0000 Max. :101.000 Max. :6.0000
## NA's :267 NA's :220
## IntentEx IntensEx PerInEx PerpoEx
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000
## Median :2.890 Median :2.970 Median :3.275 Median :1.625
## Mean :2.885 Mean :2.809 Mean :3.185 Mean :1.841
## 3rd Qu.:3.990 3rd Qu.:3.610 3rd Qu.:4.000 3rd Qu.:2.095
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :5.000
## NA's :105 NA's :105 NA's :74 NA's :66
## UnderEx Und_cus Und_co Und_sup
## Min. :1.000 Min. : 0.0000 Min. : 0.0 Min. :0.0000
## 1st Qu.:1.000 1st Qu.: 0.0000 1st Qu.: 0.0 1st Qu.:0.0000
## Median :1.000 Median : 0.0000 Median : 1.0 Median :0.0000
## Mean :1.888 Mean : 0.5372 Mean : 1.7 Mean :0.1208
## 3rd Qu.:2.000 3rd Qu.: 1.0000 3rd Qu.: 2.0 3rd Qu.:0.0000
## Max. :6.000 Max. :10.0000 Max. :25.0 Max. :4.0000
## NA's :235 NA's :196
## IntentUn IntensUn PerInUnd PerpoUnd
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.350 1st Qu.:1.000
## Median :3.660 Median :3.000 Median :3.850 Median :2.000
## Mean :3.403 Mean :3.217 Mean :3.536 Mean :2.068
## 3rd Qu.:4.330 3rd Qu.:4.000 3rd Qu.:4.490 3rd Qu.:2.480
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :5.000
## NA's :107 NA's :106 NA's :85 NA's :70
## RudeEx Rude_cus Rude_co Rude_sup
## Min. :1.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000
## 1st Qu.:2.000 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 0.0000
## Median :3.000 Median : 2.000 Median : 2.000 Median : 0.0000
## Mean :3.593 Mean : 2.112 Mean : 2.552 Mean : 0.3343
## 3rd Qu.:6.000 3rd Qu.: 2.000 3rd Qu.: 3.000 3rd Qu.: 0.0000
## Max. :6.000 Max. :20.000 Max. :51.000 Max. :20.0000
## NA's :151 NA's :115
## IntentR IntensR PerInR PerpoR
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:1.000
## Median :3.000 Median :3.000 Median :4.000 Median :2.000
## Mean :2.964 Mean :3.007 Mean :3.902 Mean :2.015
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:3.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :5.000
## NA's :43 NA's :42 NA's :37 NA's :30
## ICEx IC_cust IC_co IC_sup
## Min. :1.00 Min. : 0.000 Min. : 0.000 Min. :0.00000
## 1st Qu.:1.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.00000
## Median :1.00 Median : 0.000 Median : 1.000 Median :0.00000
## Mean :1.57 Mean : 2.023 Mean : 1.475 Mean :0.08427
## 3rd Qu.:2.00 3rd Qu.: 1.000 3rd Qu.: 2.000 3rd Qu.:0.00000
## Max. :6.00 Max. :100.000 Max. :25.000 Max. :6.00000
## NA's :268 NA's :236
## IntentIC IntensIC PerInIC PerpoIC
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.00
## Median :2.950 Median :3.000 Median :3.980 Median :1.93
## Mean :2.881 Mean :2.923 Mean :3.462 Mean :1.85
## 3rd Qu.:3.650 3rd Qu.:3.915 3rd Qu.:5.000 3rd Qu.:2.13
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :5.00
## NA's :111 NA's :109 NA's :80 NA's :70
## PhAgEX Injured PhAg_cus PhAg_co
## Min. :1.000 Min. :1.000 Min. :0.0000 Min. :0.00000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :1.000 Median :1.000 Median :0.0000 Median :0.00000
## Mean :1.197 Mean :1.128 Mean :0.6808 Mean :0.06173
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:0.00000
## Max. :6.000 Max. :6.000 Max. :6.0000 Max. :3.00000
## NA's :1 NA's :200 NA's :262 NA's :275
## PhAg_sup IntentPA IntensPA PerInPA
## Min. :0.00000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:0.00000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :0.00000 Median :2.425 Median :2.500 Median :3.530
## Mean :0.01685 Mean :2.523 Mean :2.484 Mean :3.196
## 3rd Qu.:0.00000 3rd Qu.:3.240 3rd Qu.:3.260 3rd Qu.:4.000
## Max. :5.00000 Max. :6.000 Max. :6.000 Max. :6.000
## NA's :114 NA's :109 NA's :89
## PerpoPA PIC1 PIC2 PIC3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.410 Median :2.000 Median :2.000 Median :1.000
## Mean :1.583 Mean :1.603 Mean :1.551 Mean :1.143
## 3rd Qu.:1.940 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:1.000
## Max. :5.000 Max. :2.000 Max. :2.000 Max. :2.000
## NA's :73 NA's :39 NA's :42 NA's :48
## PIC4 PIC5 PIC6 PIC7
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :2.000 Median :1.000
## Mean :1.342 Mean :1.395 Mean :1.625 Mean :1.282
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
## NA's :46 NA's :47 NA's :36 NA's :47
## PIC8 PIC9 PIC1a PIC2a
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :1.372 Mean :1.401 Mean :1.111 Mean :1.121
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
## NA's :44 NA's :42 NA's :94 NA's :91
## PIC3a PIC4a PIC5a PIC6a
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :1.023 Mean :1.103 Mean :1.077 Mean :1.223
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
## NA's :97 NA's :94 NA's :95 NA's :83
## PIC7a PIC8a PIC9a PIC1b
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :1.073 Mean :1.079 Mean :1.079 Mean :1.284
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
## NA's :97 NA's :91 NA's :91 NA's :92
## PIC2b PIC3b PIC4b PIC5b
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.00 Median :1.000 Median :1.000
## Mean :1.259 Mean :1.07 Mean :1.128 Mean :1.208
## 3rd Qu.:2.000 3rd Qu.:1.00 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :2.000 Max. :2.00 Max. :2.000 Max. :2.000
## NA's :93 NA's :98 NA's :99 NA's :97
## PIC6b PIC7b PIC8b PIC9b
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :1.362 Mean :1.144 Mean :1.181 Mean :1.194
## 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
## NA's :88 NA's :99 NA's :91 NA's :93
## NS1 NS2 NS3 NS4
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :1.135 Mean :1.072 Mean :1.009 Mean :1.032
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :5.000 Max. :4.000 Max. :3.000 Max. :3.000
## NA's :8 NA's :9 NA's :11 NA's :10
## NS5 ACO1 ACO2R ACO3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:4.000
## Median :1.000 Median :5.000 Median :5.000 Median :5.000
## Mean :1.049 Mean :4.467 Mean :4.204 Mean :4.475
## 3rd Qu.:1.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :4.000 Max. :6.000 Max. :6.000 Max. :6.000
## NA's :10 NA's :11 NA's :11 NA's :11
## ACO4R CCO1 CCO2 CCO3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000
## Median :5.000 Median :4.000 Median :5.000 Median :2.000
## Mean :4.304 Mean :3.999 Mean :4.133 Mean :2.743
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
## NA's :12 NA's :12 NA's :12 NA's :12
## CCO4 ACP1 ACP2 ACP3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:5.000 1st Qu.:4.000 1st Qu.:4.000
## Median :2.000 Median :5.000 Median :5.000 Median :5.000
## Mean :2.727 Mean :4.855 Mean :4.742 Mean :4.637
## 3rd Qu.:4.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
## NA's :12 NA's :17 NA's :17 NA's :17
## ACP4 CCP1 CCP2 CCP3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:5.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:2.000
## Median :5.000 Median :4.000 Median :5.000 Median :3.000
## Mean :4.975 Mean :3.819 Mean :4.218 Mean :3.171
## 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
## NA's :17 NA's :17 NA's :17 NA's :17
## CCP4 TI1 TI2 TI3
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000
## Median :3.000 Median :2.000 Median :2.00 Median :2.000
## Mean :3.025 Mean :2.458 Mean :2.48 Mean :2.584
## 3rd Qu.:4.670 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.00 Max. :6.000
## NA's :17 NA's :15 NA's :15 NA's :15
## Dep1 Dep2 Dep3r Dep4
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.00 Median :2.000 Median :1.000 Median :1.000
## Mean :1.93 Mean :1.872 Mean :1.803 Mean :1.729
## 3rd Qu.:2.00 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :19 NA's :19 NA's :19 NA's :19
## Dep5 Dep6r Anx1 Anx2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.00
## Median :1.000 Median :2.000 Median :2.000 Median :1.00
## Mean :1.774 Mean :2.021 Mean :1.911 Mean :1.54
## 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
## NA's :19 NA's :19 NA's :20 NA's :20
## Anx3r Anx4 Irr1 Irr2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :1.000 Median :2.000 Median :2.000
## Mean :2.079 Mean :1.704 Mean :2.134 Mean :2.423
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :20 NA's :20 NA's :21 NA's :21
## Irr3 SS1_CO SS2_CO SS3_CO
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :2.000 Median :3.000 Median :4.000 Median :4.000
## Mean :2.551 Mean :3.385 Mean :3.538 Mean :3.598
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :19 NA's :18 NA's :18 NA's :18
## SS4_COr SS1_Man SS2_Man SS3_Man
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.652 Mean :3.601 Mean :3.629 Mean :3.566
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :18 NA's :18 NA's :18 NA's :18
## SS4_Manr SS1_Org SS2_Org SS3_Org
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000
## Median :4.000 Median :4.000 Median :4.000 Median :2.000
## Mean :3.817 Mean :3.446 Mean :3.443 Mean :2.423
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :18 NA's :18 NA's :18 NA's :18
## SS4_Orgr BO1 BO2 BO3
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.00 Median :3.000 Median :3.000 Median :3.000
## Mean :2.64 Mean :3.021 Mean :3.134 Mean :3.317
## 3rd Qu.:3.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :18 NA's :18 NA's :18 NA's :18
## BO4 SB1 SB2 SB3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :3.000 Median :2.000 Median :2.000 Median :2.000
## Mean :2.807 Mean :2.122 Mean :2.183 Mean :2.537
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :18 NA's :20 NA's :21 NA's :21
## SB4 SB5 SB6 SB7
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :1.000 Median :2.000
## Mean :2.104 Mean :1.743 Mean :1.291 Mean :1.809
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:2.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :21 NA's :21 NA's :21 NA's :21
## InC1 InC2r InC3 InC4
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.00
## Median :4.000 Median :5.000 Median :5.000 Median :5.00
## Mean :4.091 Mean :4.501 Mean :4.917 Mean :4.65
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.00
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.00
## NA's :27 NA's :27 NA's :27 NA's :27
## InC5r InC6 InC7r InC8
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:5.00 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000
## Median :6.00 Median :5.000 Median :6.000 Median :5.000
## Mean :5.24 Mean :4.088 Mean :5.053 Mean :4.133
## 3rd Qu.:6.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :6.00 Max. :6.000 Max. :6.000 Max. :6.000
## NA's :27 NA's :27 NA's :27 NA's :27
## InC9 InC10r InC11r InC12
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.00 1st Qu.:3.000
## Median :4.000 Median :5.000 Median :5.00 Median :4.000
## Mean :3.878 Mean :4.758 Mean :4.46 Mean :3.972
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:6.00 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.00 Max. :6.000
## NA's :27 NA's :27 NA's :27 NA's :27
## N
## Min. : 1.00
## 1st Qu.: 89.75
## Median :178.50
## Mean :178.50
## 3rd Qu.:267.25
## Max. :356.00
## is.wholenumber <- function(x, tol = .Machine$double.eps^0.5) abs(x - round(x)) < tolCleaning the Constructs
Civility Climate
Civ.Clime <- subset(AN.data,select=c(InC1,InC2r,InC3,InC4,InC5r,InC6,InC7r,InC8,InC9,InC10r,InC11r,InC12,N))
Civ.Clime.NA <- apply(Civ.Clime, 2, is.wholenumber)
Civ.Clime[Civ.Clime.NA==FALSE] <- NA
Civ.Clime$CC.Miss <- apply(Civ.Clime, 1, function(x) { sum(is.na(x))})Some basic plots about the factor structure and the results of simple scaling exercises follow.
Civ.Clime.Clean <- Civ.Clime[Civ.Clime$CC.Miss<12,c(1:12)]
Civ.Clime.Clean.F <- sapply(Civ.Clime.Clean, as.factor)
scree(Civ.Clime.Clean)
I have my doubts about the application of a simple scree plot but it does provide useful validation. Another useful thing to look at before turning to the approach that was actually taken is to look at a different taken on the scree plot; item cluster analysis. The idea is to cluster items instead of clustering subjects. What items group together by response patterns.
iclust(Civ.Clime.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = Civ.Clime.Clean)
##
## Purified Alpha:
## C9 C10
## 0.83 0.84
##
## G6* reliability:
## C9 C10
## 1 1
##
## Original Beta:
## C9 C10
## 0.83 0.72
##
## Cluster size:
## C9 C10
## 7 5
##
## Item by Cluster Structure matrix:
## O P C9 C10
## InC1 C9 C9 0.54 0.29
## InC2r C10 C10 0.57 0.72
## InC3 C9 C9 0.70 0.48
## InC4 C9 C9 0.76 0.56
## InC5r C10 C10 0.41 0.70
## InC6 C9 C9 0.49 0.24
## InC7r C10 C10 0.51 0.81
## InC8 C9 C9 0.56 0.39
## InC9 C9 C9 0.70 0.47
## InC10r C10 C10 0.53 0.75
## InC11r C10 C10 0.29 0.58
## InC12 C9 C9 0.72 0.46
##
## With eigenvalues of:
## C9 C10
## 3.0 2.7
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## C9 C10
## C9 0.83 0.65
## C10 0.54 0.84
##
## Cluster fit = 0.86 Pattern fit = 0.99 RMSR = 0.05Lastly, for some details about the items themselves I will turn to tools from the Graded Repsonse Model. The following graphic displays the distribution of each item and how it falls in the recovered latent space of a single factor model like the one that we have deployed.
library(ltm)
fit <- grm(Civ.Clime.Clean)
plot(fit, type="IIC", legend = TRUE)
Burnout
Burnout <- subset(AN.data,select=c(BO1,BO2,BO3,BO4,N))
Burnout.NA <- apply(Burnout, 2, is.wholenumber)
Burnout[Burnout.NA==FALSE] <- NA
Burnout$BO.Miss <- apply(Burnout, 1, function(x) { sum(is.na(x))})Burnout.Clean <- Social.Burden[Burnout$BO.Miss<4,c(1:4)]
scree(Burnout.Clean)
iclust(Burnout.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = Burnout.Clean)
##
## Purified Alpha:
## [1] 0.85
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.74
##
## Cluster size:
## [1] 4
##
## Item by Cluster Structure matrix:
## [,1]
## SB1 0.62
## SB2 0.78
## SB3 0.80
## SB4 0.79
##
## With eigenvalues of:
## [1] 2.3
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.85
##
## Cluster fit = 0.9 Pattern fit = 1 RMSR = 0.03fit <- grm(Burnout.Clean)
plot(fit, type="IIC", legend = TRUE)
Support: Organization
Org.Support <- subset(AN.data,select=c(SS1_Org,SS2_Org,SS3_Org,SS4_Orgr,N))
Org.Support.NA <- apply(Org.Support, 2, is.wholenumber)
Org.Support[Org.Support.NA==FALSE] <- NA
Org.Support$OS.Miss <- apply(Org.Support, 1, function(x) { sum(is.na(x))})OrgSupp.Clean <- Org.Support[Org.Support$OS.Miss<4,c(1:4)]
scree(OrgSupp.Clean)
iclust(OrgSupp.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = OrgSupp.Clean)
##
## Purified Alpha:
## [1] 0.87
##
## G6* reliability:
## [1] 0.66
##
## Original Beta:
## [1] 0.64
##
## Cluster size:
## [1] 4
##
## Item by Cluster Structure matrix:
## [,1]
## SS1_Org -0.86
## SS2_Org -0.89
## SS3_Org 0.52
## SS4_Orgr 0.90
##
## With eigenvalues of:
## [1] 2.6
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.87
##
## Cluster fit = 0.93 Pattern fit = 1 RMSR = 0.03fit <- grm(OrgSupp.Clean)
plot(fit, type="IIC", legend = TRUE)
Support: Manager
Mgr.Support <- subset(AN.data,select=c(SS1_Man,SS2_Man,SS3_Man,SS4_Manr,N))
Mgr.Support.NA <- apply(Mgr.Support, 2, is.wholenumber)
Mgr.Support[Mgr.Support.NA==FALSE] <- NA
Mgr.Support$MS.Miss <- apply(Mgr.Support, 1, function(x) { sum(is.na(x))})MgrSupp.Clean <- Mgr.Support[Mgr.Support$MS.Miss<4,c(1:4)]
scree(MgrSupp.Clean)
iclust(MgrSupp.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = MgrSupp.Clean)
##
## Purified Alpha:
## [1] 0.91
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.74
##
## Cluster size:
## [1] 4
##
## Item by Cluster Structure matrix:
## [,1]
## SS1_Man 0.91
## SS2_Man 0.93
## SS3_Man 0.92
## SS4_Manr 0.64
##
## With eigenvalues of:
## [1] 3
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.91
##
## Cluster fit = 0.96 Pattern fit = 1 RMSR = 0.02fit <- grm(MgrSupp.Clean)
plot(fit, type="IIC", legend = TRUE)
Support: Coworker
CO.Support <- subset(AN.data,select=c(SS1_CO,SS2_CO,SS3_CO,SS4_COr,N))
CO.Support.NA <- apply(CO.Support, 2, is.wholenumber)
CO.Support[CO.Support.NA==FALSE] <- NA
CO.Support$CW.Miss <- apply(CO.Support, 1, function(x) { sum(is.na(x))})CWSupp.Clean <- CO.Support[CO.Support$CW.Miss<4,c(1:4)]
scree(CWSupp.Clean)
iclust(CWSupp.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = CWSupp.Clean)
##
## Purified Alpha:
## [1] 0.86
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.65
##
## Cluster size:
## [1] 4
##
## Item by Cluster Structure matrix:
## [,1]
## SS1_CO 0.82
## SS2_CO 0.84
## SS3_CO 0.89
## SS4_COr 0.53
##
## With eigenvalues of:
## [1] 2.5
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.86
##
## Cluster fit = 0.91 Pattern fit = 1 RMSR = 0.03fit <- grm(CWSupp.Clean)
plot(fit, type="IIC", legend = TRUE)
Irritation
Irritation <- subset(AN.data,select=c(Irr1,Irr2,Irr3,N))
Irritation.NA <- apply(Irritation, 2, is.wholenumber)
Irritation[Irritation.NA==FALSE] <- NA
Irritation$Irr.Miss <- apply(Irritation, 1, function(x) { sum(is.na(x))})Irr.Clean <- Irritation[Irritation$Irr.Miss<3,c(1:3)]
scree(Irr.Clean)
iclust(Irr.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = Irr.Clean)
##
## Purified Alpha:
## [1] 0.91
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.86
##
## Cluster size:
## [1] 3
##
## Item by Cluster Structure matrix:
## [,1]
## Irr1 0.79
## Irr2 0.90
## Irr3 0.90
##
## With eigenvalues of:
## [1] 2.3
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.91
##
## Cluster fit = 0.97 Pattern fit = 1 RMSR = 0.03fit <- grm(Irr.Clean)
plot(fit, type="IIC", legend = TRUE)
Anxiety
Anxiety <- subset(AN.data,select=c(Anx1,Anx2,Anx3r,Anx4,N))
Anxiety.NA <- apply(Anxiety, 2, is.wholenumber)
Anxiety[Anxiety.NA==FALSE] <- NA
Anxiety$Anx1[Anxiety$Anx1==99] <- NA
Anxiety$Anx.Miss <- apply(Anxiety, 1, function(x) { sum(is.na(x))})Anx.Clean <- Anxiety[Anxiety$Anx.Miss<4,c(1:4)]
scree(Anx.Clean)
iclust(Anx.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = Anx.Clean)
##
## Purified Alpha:
## [1] 0.72
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.3
##
## Cluster size:
## [1] 4
##
## Item by Cluster Structure matrix:
## [,1]
## Anx1 0.80
## Anx2 0.81
## Anx3r 0.22
## Anx4 0.69
##
## With eigenvalues of:
## [1] 1.8
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.72
##
## Cluster fit = 0.78 Pattern fit = 1 RMSR = 0.05fit <- grm(Anx.Clean)
plot(fit, type="IIC", legend = TRUE)
Depression
# NB: The 99 values were omitted -- replaced as missing.
Depression <- subset(AN.data,select=c(Dep1,Dep2,Dep3r,Dep4,Dep5,Dep6r,N))
Depression.NA <- apply(Depression, 2, is.wholenumber)
Depression[Depression.NA==FALSE] <- NA
Depression$Dep.Miss <- apply(Depression, 1, function(x) { sum(is.na(x))})Dep.Clean <- Depression[Depression$Dep.Miss<6,c(1:6)]
scree(Dep.Clean)
iclust(Dep.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = Dep.Clean)
##
## Purified Alpha:
## [1] 0.87
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.51
##
## Cluster size:
## [1] 6
##
## Item by Cluster Structure matrix:
## [,1]
## Dep1 0.82
## Dep2 0.84
## Dep3r 0.56
## Dep4 0.83
## Dep5 0.84
## Dep6r 0.53
##
## With eigenvalues of:
## [1] 3.4
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.87
##
## Cluster fit = 0.86 Pattern fit = 0.95 RMSR = 0.16fit <- grm(Dep.Clean)
plot(fit, type="IIC", legend = TRUE)
Turnover Intentions
Turnover.Int <- subset(AN.data,select=c(TI1,TI2,TI3,N))
Turnover.Int.NA <- apply(Turnover.Int, 2, is.wholenumber)
Turnover.Int[Turnover.Int.NA==FALSE] <- NA
Turnover.Int$TI.Miss <- apply(Turnover.Int, 1, function(x) { sum(is.na(x))})TI.Clean <- Turnover.Int[Turnover.Int$TI.Miss<3,c(1:3)]
scree(TI.Clean)
iclust(TI.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = TI.Clean)
##
## Purified Alpha:
## [1] 0.91
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.89
##
## Cluster size:
## [1] 3
##
## Item by Cluster Structure matrix:
## [,1]
## TI1 0.83
## TI2 0.87
## TI3 0.89
##
## With eigenvalues of:
## [1] 2.2
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.91
##
## Cluster fit = 0.97 Pattern fit = 1 RMSR = 0.03fit <- grm(TI.Clean)
plot(fit, type="IIC", legend = TRUE)
Affective Commitment to the Profession
Affective.Comm.Prof <- subset(AN.data,select=c(ACP1,ACP2,ACP3,ACP4,N))
Affective.Comm.Prof.NA <- apply(Affective.Comm.Prof, 2, is.wholenumber)
Affective.Comm.Prof[Affective.Comm.Prof.NA==FALSE] <- NA
Affective.Comm.Prof$ACP.Miss <- apply(Affective.Comm.Prof, 1, function(x) { sum(is.na(x))})ACP.Clean <- Affective.Comm.Prof[Affective.Comm.Prof$ACP.Miss<4,c(1:4)]
scree(ACP.Clean)
iclust(ACP.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = ACP.Clean)
##
## Purified Alpha:
## [1] 0.91
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.82
##
## Cluster size:
## [1] 4
##
## Item by Cluster Structure matrix:
## [,1]
## ACP1 0.86
## ACP2 0.89
## ACP3 0.89
## ACP4 0.72
##
## With eigenvalues of:
## [1] 2.9
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.91
##
## Cluster fit = 0.96 Pattern fit = 1 RMSR = 0.03fit <- grm(ACP.Clean)
plot(fit, type="IIC", legend = TRUE)
Continuance Commitment to the Profession
Continuance.Comm.Prof <- subset(AN.data,select=c(CCP1,CCP2,CCP3,CCP4,N))
Continuance.Comm.Prof.NA <- apply(Continuance.Comm.Prof, 2, is.wholenumber)
Continuance.Comm.Prof[Continuance.Comm.Prof.NA==FALSE] <- NA
Continuance.Comm.Prof$CCP.Miss <- apply(Continuance.Comm.Prof, 1, function(x) { sum(is.na(x))})CCP.Clean <- Continuance.Comm.Prof[Continuance.Comm.Prof$CCP.Miss<4,c(1:4)]
scree(CCP.Clean)
iclust(CCP.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = CCP.Clean)
##
## Purified Alpha:
## [1] 0.8
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.72
##
## Cluster size:
## [1] 4
##
## Item by Cluster Structure matrix:
## [,1]
## CCP1 0.64
## CCP2 0.65
## CCP3 0.78
## CCP4 0.70
##
## With eigenvalues of:
## [1] 1.9
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.8
##
## Cluster fit = 0.83 Pattern fit = 0.99 RMSR = 0.07fit <- grm(CCP.Clean)
plot(fit, type="IIC", legend = TRUE)
Affective Commitment to the Organization
Affective.Comm.Org <- subset(AN.data,select=c(ACO1,ACO2R,ACO3,ACO4R,N))
Affective.Comm.Org.NA <- apply(Affective.Comm.Org, 2, is.wholenumber)
Affective.Comm.Org[Affective.Comm.Org.NA==FALSE] <- NA
Affective.Comm.Org$ACO.Miss <- apply(Affective.Comm.Org, 1, function(x) { sum(is.na(x))})ACO.Clean <- Affective.Comm.Org[Affective.Comm.Org$ACO.Miss<4,c(1:4)]
scree(ACO.Clean)
iclust(ACO.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = ACO.Clean)
##
## Purified Alpha:
## [1] 0.81
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.74
##
## Cluster size:
## [1] 4
##
## Item by Cluster Structure matrix:
## [,1]
## ACO1 0.75
## ACO2R 0.67
## ACO3 0.69
## ACO4R 0.71
##
## With eigenvalues of:
## [1] 2
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.81
##
## Cluster fit = 0.85 Pattern fit = 0.99 RMSR = 0.07fit <- grm(ACO.Clean)
plot(fit, type="IIC", legend = TRUE)
Continuance Commitment to the Organization
# NB: There was a value 9 in the dataset that was deleted -- replaced as missing.
Continuance.Comm.Org <- subset(AN.data,select=c(CCO1,CCO2,CCO3,CCO4,N))
Continuance.Comm.Org.NA <- apply(Continuance.Comm.Org, 2, is.wholenumber)
Continuance.Comm.Org[Continuance.Comm.Org.NA==FALSE] <- NA
Continuance.Comm.Org$CCO.Miss <- apply(Continuance.Comm.Org, 1, function(x) { sum(is.na(x))})CCO.Clean <- Continuance.Comm.Org[Continuance.Comm.Org$CCO.Miss<4,c(1:4)]
scree(CCO.Clean)
iclust(CCO.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = CCO.Clean)
##
## Purified Alpha:
## [1] 0.68
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.55
##
## Cluster size:
## [1] 4
##
## Item by Cluster Structure matrix:
## [,1]
## CCO1 0.64
## CCO2 0.53
## CCO3 0.62
## CCO4 0.49
##
## With eigenvalues of:
## [1] 1.3
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.68
##
## Cluster fit = 0.65 Pattern fit = 0.98 RMSR = 0.1fit <- grm(CCO.Clean)
plot(fit, type="IIC", legend = TRUE)
Injuries
Injuries <- subset(AN.data,select=c(PIC1,PIC2,PIC3,PIC4,PIC5,PIC6,PIC7,PIC8,PIC9,N))
Injuries.NA <- apply(Injuries, 2, is.wholenumber)
Injuries[Injuries.NA==FALSE] <- NA
Injuries$PIC.Miss <- apply(Injuries, 1, function(x) { sum(is.na(x))})Injuries: Work
InjuriesW <- subset(AN.data,select=c(PIC1a,PIC2a,PIC3a,PIC4a,PIC5a,PIC6a,PIC7a,PIC8a,PIC9a,N))
InjuriesW.NA <- apply(InjuriesW, 2, is.wholenumber)
InjuriesW[InjuriesW.NA==FALSE] <- NA
InjuriesW$PICa.Miss <- apply(InjuriesW, 1, function(x) { sum(is.na(x))})Injuries: Month
InjuriesM <- subset(AN.data,select=c(PIC1b,PIC2b,PIC3b,PIC4b,PIC5b,PIC6b,PIC7b,PIC8b,PIC9b,N))
InjuriesM.NA <- apply(InjuriesM, 2, is.wholenumber)
InjuriesM[InjuriesM.NA==FALSE] <- NA
InjuriesM$PICb.Miss <- apply(InjuriesM, 1, function(x) { sum(is.na(x))})Perceived Power
Perc.Power <- subset(AN.data,select=c(PerpowVA,PerpoInt,PerpoEx,PerpoUnd,PerpoR,PerpoIC,PerpoPA,N))
Perc.Power.NA <- apply(Perc.Power, 2, is.wholenumber)
Perc.Power[Perc.Power.NA==FALSE] <- NA
Perc.Power$PIC.Miss <- apply(Perc.Power, 1, function(x) { sum(is.na(x))})PPOW.Clean <- Perc.Power[Perc.Power$PIC.Miss<7,c(1:7)]
scree(PPOW.Clean)
iclust(PPOW.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = PPOW.Clean)
##
## Purified Alpha:
## [1] 0.88
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.63
##
## Cluster size:
## [1] 7
##
## Item by Cluster Structure matrix:
## [,1]
## PerpowVA 0.66
## PerpoInt 0.78
## PerpoEx 0.76
## PerpoUnd 0.82
## PerpoR 0.78
## PerpoIC 0.73
## PerpoPA 0.49
##
## With eigenvalues of:
## [1] 3.7
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.88
##
## Cluster fit = 0.9 Pattern fit = 0.99 RMSR = 0.07fit <- grm(PPOW.Clean)
plot(fit, type="IIC", legend = TRUE)
Perceived Visiblity
Perc.Visibility <- subset(AN.data,select=c(PerInVA,PerInInt,PerInEx,PerInUnd,PerInR,PerInIC,PerInPA,N))
Perc.Visibility.NA <- apply(Perc.Visibility, 2, is.wholenumber)
Perc.Visibility[Perc.Visibility.NA==FALSE] <- NA
Perc.Visibility$PVis.Miss <- apply(Perc.Visibility, 1, function(x) { sum(is.na(x))})PVIS.Clean <- Perc.Visibility[Perc.Visibility$PVis.Miss<7,c(1:7)]
scree(PVIS.Clean)
iclust(PVIS.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = PVIS.Clean)
##
## Purified Alpha:
## [1] 0.85
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.59
##
## Cluster size:
## [1] 7
##
## Item by Cluster Structure matrix:
## [,1]
## PerInVA 0.67
## PerInInt 0.76
## PerInEx 0.64
## PerInUnd 0.75
## PerInR 0.67
## PerInIC 0.72
## PerInPA 0.44
##
## With eigenvalues of:
## [1] 3.2
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.85
##
## Cluster fit = 0.85 Pattern fit = 0.99 RMSR = 0.06fit <- grm(PVIS.Clean)
plot(fit, type="IIC", legend = TRUE)
Intention
Intention <- subset(AN.data,select=c(IntentVA,IntentIn,IntentEx,IntentUn,IntentR,IntentIC,IntentPA,N))
Intention.NA <- apply(Intention, 2, is.wholenumber)
Intention[Intention.NA==FALSE] <- NA
Intention$Intn.Miss <- apply(Intention, 1, function(x) { sum(is.na(x))})INTENT.Clean <- Intention[Intention$Intn.Miss<7,c(1:7)]
scree(INTENT.Clean)
iclust(INTENT.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = INTENT.Clean)
##
## Purified Alpha:
## [1] 0.87
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.57
##
## Cluster size:
## [1] 7
##
## Item by Cluster Structure matrix:
## [,1]
## IntentVA 0.71
## IntentIn 0.83
## IntentEx 0.68
## IntentUn 0.80
## IntentR 0.72
## IntentIC 0.75
## IntentPA 0.44
##
## With eigenvalues of:
## [1] 3.6
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.87
##
## Cluster fit = 0.89 Pattern fit = 0.99 RMSR = 0.06fit <- grm(INTENT.Clean)
plot(fit, type="IIC", legend = TRUE)
Intensity
Intensity <- subset(AN.data,select=c(IntensVA,IntensIn,IntensEx,IntensUn,IntensR,IntensIC,IntensPA,N))
Intensity.NA <- apply(Intensity, 2, is.wholenumber)
Intensity[Intensity.NA==FALSE] <- NA
Intensity$Ints.Miss <- apply(Intensity, 1, function(x) { sum(is.na(x))})INTENSE.Clean <- Intensity[Intensity$Ints.Miss<7,c(1:7)]
scree(INTENSE.Clean)
iclust(INTENSE.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = INTENSE.Clean)
##
## Purified Alpha:
## [1] 0.88
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.54
##
## Cluster size:
## [1] 7
##
## Item by Cluster Structure matrix:
## [,1]
## IntensVA 0.73
## IntensIn 0.80
## IntensEx 0.79
## IntensUn 0.77
## IntensR 0.80
## IntensIC 0.72
## IntensPA 0.42
##
## With eigenvalues of:
## [1] 3.7
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.88
##
## Cluster fit = 0.9 Pattern fit = 0.99 RMSR = 0.06fit <- grm(INTENSE.Clean)
plot(fit, type="IIC", legend = TRUE)
Relationship Power
Workplace Aggression Frequency
# NB: There are no value 5 in physical aggression; I will change the lone six to a five.
Workplace.Agg.Freq <- subset(AN.data,select=c(VAgEx,IntimEx,ExclusEx,UnderEx,RudeEx,ICEx,PhAgEX,N))
Workplace.Agg.Freq.NA <- apply(Workplace.Agg.Freq, 2, is.wholenumber)
Workplace.Agg.Freq[Workplace.Agg.Freq.NA==FALSE] <- NA
Workplace.Agg.Freq$WAF.Miss <- apply(Workplace.Agg.Freq, 1, function(x) { sum(is.na(x))})WAF.Clean <- Workplace.Agg.Freq[Workplace.Agg.Freq$WAF.Miss<7,c(1:7)]
WAF.Clean[(!is.na(WAF.Clean$PhAgEX) & WAF.Clean$PhAgEX>5)==TRUE,"PhAgEX"] <- 5
scree(WAF.Clean)
iclust(WAF.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = WAF.Clean)
##
## Purified Alpha:
## [1] 0.77
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.39
##
## Cluster size:
## [1] 7
##
## Item by Cluster Structure matrix:
## [,1]
## VAgEx 0.69
## IntimEx 0.71
## ExclusEx 0.46
## UnderEx 0.69
## RudeEx 0.63
## ICEx 0.54
## PhAgEX 0.28
##
## With eigenvalues of:
## [1] 2.4
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.77
##
## Cluster fit = 0.73 Pattern fit = 0.99 RMSR = 0.06fit <- grm(WAF.Clean)
plot(fit, type="IIC", legend = TRUE)
Taking the PA parts
# NB: There are no value 5 in physical aggression; I will change the lone six to a five.
Phys.Agg <- subset(AN.data,select=c(PerpoPA,PerInPA,IntentPA,IntensPA,PhAgEX,N))
Phys.Agg.NA <- apply(Phys.Agg, 2, is.wholenumber)
Phys.Agg[Phys.Agg.NA==FALSE] <- NA
Phys.Agg$PAg.Miss <- apply(Phys.Agg, 1, function(x) { sum(is.na(x))})PAgg.Clean <- Phys.Agg[Phys.Agg$PAg.Miss<5,c(1:5)]
PAgg.Clean[(!is.na(PAgg.Clean$PhAgEX) & PAgg.Clean$PhAgEX>5)==TRUE,"PhAgEX"] <- 5
scree(PAgg.Clean)
iclust(PAgg.Clean)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = PAgg.Clean)
##
## Purified Alpha:
## [1] 0.77
##
## G6* reliability:
## [1] 1
##
## Original Beta:
## [1] 0.6
##
## Cluster size:
## [1] 5
##
## Item by Cluster Structure matrix:
## [,1]
## PerpoPA 0.42
## PerInPA 0.76
## IntentPA 0.77
## IntensPA 0.62
## PhAgEX 0.60
##
## With eigenvalues of:
## [1] 2.1
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## [,1]
## [1,] 0.77
##
## Cluster fit = 0.77 Pattern fit = 0.98 RMSR = 0.09fit <- grm(PAgg.Clean)
plot(fit, type="IIC", legend = TRUE)
Cleaning Up
save.image("Ashley.Data.Cleaned.RData")
Social Burden