1 = male, 2 = female
##
## 1 2
## 30 73M
## [1] 40.45631SD
## [1] 11.405Min
## [1] 20Max
## [1] 63## Questionnaire Time N M SD SE CI
## 1 CES T1 103 28.94175 4.618431 0.4550675 0.8919324
## 2 CES T2 103 25.60194 6.337470 0.6244495 1.2239210
## 5 PTCI T1 103 149.18447 34.315362 3.3811931 6.6271385
## 6 PTCI T2 103 114.89320 45.660493 4.4990621 8.8181617
## 3 DTS T1 103 83.31068 22.127540 2.1802913 4.2733709
## 4 DTS T2 103 61.59223 29.271694 2.8842258 5.6530825##
## One-sample Kolmogorov-Smirnov test
##
## data: data$CES_T1
## D = 0.099263, p-value = 0.2621
## alternative hypothesis: two-sided##
## One-sample Kolmogorov-Smirnov test
##
## data: data$CES_T2
## D = 0.1464, p-value = 0.02418
## alternative hypothesis: two-sided##
## One-sample Kolmogorov-Smirnov test
##
## data: data$PTCI_T1
## D = 0.10196, p-value = 0.2346
## alternative hypothesis: two-sided##
## One-sample Kolmogorov-Smirnov test
##
## data: data$PTCI_T2
## D = 0.07468, p-value = 0.6139
## alternative hypothesis: two-sided##
## One-sample Kolmogorov-Smirnov test
##
## data: data$DTS_T1
## D = 0.050097, p-value = 0.9583
## alternative hypothesis: two-sided##
## One-sample Kolmogorov-Smirnov test
##
## data: data$DTS_T2
## D = 0.075085, p-value = 0.607
## alternative hypothesis: two-sided##
## Reliability analysis
## Call: alpha(x = data[c("CES1_T1", "CES2_T1", "CES3_T1", "CES4_T1",
## "CES5_T1", "CES6_T1", "CES7_T1")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.8 0.8 0.36 4 0.033 4.1 0.66 0.35
##
## lower alpha upper 95% confidence boundaries
## 0.72 0.78 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CES1_T1 0.78 0.79 0.79 0.39 3.9 0.034 0.0090 0.39
## CES2_T1 0.76 0.78 0.76 0.37 3.5 0.037 0.0093 0.37
## CES3_T1 0.75 0.77 0.77 0.36 3.3 0.039 0.0131 0.33
## CES4_T1 0.74 0.76 0.74 0.34 3.1 0.040 0.0086 0.34
## CES5_T1 0.74 0.76 0.75 0.35 3.2 0.039 0.0089 0.33
## CES6_T1 0.77 0.78 0.78 0.38 3.7 0.036 0.0103 0.36
## CES7_T1 0.75 0.77 0.75 0.36 3.3 0.038 0.0083 0.35
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CES1_T1 103 0.61 0.58 0.47 0.41 3.9 1.15
## CES2_T1 103 0.68 0.65 0.58 0.50 3.7 1.19
## CES3_T1 103 0.67 0.69 0.61 0.55 4.3 0.85
## CES4_T1 103 0.72 0.74 0.71 0.62 4.3 0.79
## CES5_T1 103 0.69 0.72 0.68 0.58 4.5 0.80
## CES6_T1 103 0.65 0.62 0.53 0.46 4.0 1.17
## CES7_T1 103 0.68 0.70 0.64 0.54 4.3 0.96
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## CES1_T1 0.04 0.11 0.17 0.30 0.39 0
## CES2_T1 0.08 0.05 0.25 0.29 0.33 0
## CES3_T1 0.01 0.01 0.17 0.31 0.50 0
## CES4_T1 0.00 0.03 0.12 0.39 0.47 0
## CES5_T1 0.00 0.04 0.08 0.25 0.63 0
## CES6_T1 0.04 0.10 0.15 0.25 0.47 0
## CES7_T1 0.00 0.06 0.18 0.20 0.55 0##
## Reliability analysis
## Call: alpha(x = data[c("CES1_T2", "CES2_T2", "CES3_T2", "CES4_T2",
## "CES5_T2", "CES6_T2", "CES7_T2")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.88 0.51 7.4 0.018 3.7 0.91 0.52
##
## lower alpha upper 95% confidence boundaries
## 0.84 0.88 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CES1_T2 0.89 0.89 0.88 0.57 8.0 0.018 0.0068 0.55
## CES2_T2 0.85 0.86 0.85 0.50 5.9 0.022 0.0198 0.52
## CES3_T2 0.85 0.85 0.84 0.48 5.6 0.023 0.0165 0.50
## CES4_T2 0.85 0.85 0.84 0.49 5.7 0.023 0.0161 0.51
## CES5_T2 0.85 0.85 0.85 0.49 5.8 0.023 0.0173 0.50
## CES6_T2 0.87 0.87 0.86 0.54 7.0 0.019 0.0150 0.54
## CES7_T2 0.87 0.87 0.86 0.52 6.6 0.020 0.0209 0.52
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CES1_T2 103 0.59 0.60 0.50 0.45 3.7 1.1
## CES2_T2 103 0.80 0.81 0.77 0.72 3.4 1.2
## CES3_T2 103 0.85 0.85 0.83 0.78 3.7 1.2
## CES4_T2 103 0.83 0.84 0.82 0.76 3.8 1.1
## CES5_T2 103 0.82 0.82 0.80 0.75 3.9 1.2
## CES6_T2 103 0.71 0.70 0.62 0.58 3.3 1.3
## CES7_T2 103 0.74 0.73 0.66 0.63 3.8 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## CES1_T2 0.06 0.05 0.29 0.31 0.29 0
## CES2_T2 0.08 0.14 0.31 0.26 0.21 0
## CES3_T2 0.06 0.11 0.22 0.34 0.27 0
## CES4_T2 0.04 0.10 0.21 0.37 0.28 0
## CES5_T2 0.05 0.09 0.16 0.30 0.41 0
## CES6_T2 0.15 0.10 0.27 0.29 0.19 0
## CES7_T2 0.08 0.10 0.17 0.21 0.44 0##
## Reliability analysis
## Call: alpha(x = data[c("PTCI1_T1", "PTCI2_T1", "PTCI3_T1", "PTCI4_T1",
## "PTCI5_T1", "PTCI6_T1", "PTCI7_T1", "PTCI8_T1", "PTCI9_T1",
## "PTCI10_T1", "PTCI11_T1", "PTCI12_T1", "PTCI13_T1", "PTCI14_T1",
## "PTCI15_T1", "PTCI16_T1", "PTCI17_T1", "PTCI18_T1", "PTCI19_T1",
## "PTCI20_T1", "PTCI21_T1", "PTCI22_T1", "PTCI23_T1", "PTCI24_T1",
## "PTCI25_T1", "PTCI26_T1", "PTCI27_T1", "PTCI28_T1", "PTCI29_T1",
## "PTCI30_T1", "PTCI31_T1", "PTCI32_T1", "PTCI33_T1")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.97 0.34 17 0.0082 4.5 1 0.35
##
## lower alpha upper 95% confidence boundaries
## 0.93 0.94 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PTCI1_T1 0.94 0.94 0.97 0.34 16 0.0085 0.023 0.34
## PTCI2_T1 0.94 0.95 0.97 0.35 18 0.0078 0.020 0.36
## PTCI3_T1 0.94 0.94 0.97 0.34 16 0.0086 0.023 0.34
## PTCI4_T1 0.94 0.94 0.97 0.34 16 0.0084 0.022 0.35
## PTCI5_T1 0.94 0.94 0.97 0.33 16 0.0087 0.022 0.34
## PTCI6_T1 0.94 0.94 0.97 0.34 17 0.0083 0.023 0.35
## PTCI7_T1 0.94 0.94 0.97 0.34 17 0.0082 0.022 0.36
## PTCI8_T1 0.94 0.94 0.97 0.34 17 0.0083 0.023 0.35
## PTCI9_T1 0.94 0.94 0.97 0.34 16 0.0085 0.023 0.34
## PTCI10_T1 0.94 0.94 0.97 0.34 16 0.0085 0.023 0.35
## PTCI11_T1 0.94 0.94 0.97 0.34 16 0.0084 0.023 0.35
## PTCI12_T1 0.94 0.94 0.97 0.34 16 0.0085 0.023 0.35
## PTCI13_T1 0.94 0.94 0.97 0.34 17 0.0082 0.023 0.35
## PTCI14_T1 0.94 0.94 0.97 0.34 16 0.0084 0.023 0.34
## PTCI15_T1 0.94 0.94 0.97 0.34 16 0.0084 0.023 0.35
## PTCI16_T1 0.94 0.94 0.97 0.34 17 0.0083 0.023 0.35
## PTCI17_T1 0.94 0.94 0.97 0.34 17 0.0083 0.023 0.35
## PTCI18_T1 0.94 0.94 0.97 0.34 16 0.0085 0.023 0.34
## PTCI19_T1 0.94 0.94 0.97 0.34 16 0.0085 0.023 0.35
## PTCI20_T1 0.94 0.94 0.97 0.33 16 0.0085 0.022 0.34
## PTCI21_T1 0.94 0.94 0.97 0.33 16 0.0086 0.023 0.34
## PTCI22_T1 0.94 0.94 0.97 0.33 16 0.0086 0.023 0.34
## PTCI23_T1 0.94 0.94 0.97 0.34 17 0.0083 0.022 0.35
## PTCI24_T1 0.94 0.94 0.97 0.34 16 0.0085 0.023 0.34
## PTCI25_T1 0.94 0.94 0.97 0.33 16 0.0086 0.022 0.34
## PTCI26_T1 0.94 0.94 0.97 0.34 17 0.0083 0.023 0.35
## PTCI27_T1 0.94 0.94 0.97 0.33 16 0.0086 0.023 0.34
## PTCI28_T1 0.94 0.94 0.97 0.34 16 0.0085 0.023 0.34
## PTCI29_T1 0.94 0.94 0.97 0.33 16 0.0086 0.023 0.34
## PTCI30_T1 0.94 0.94 0.97 0.34 16 0.0085 0.023 0.35
## PTCI31_T1 0.94 0.95 0.97 0.35 17 0.0080 0.020 0.36
## PTCI32_T1 0.94 0.94 0.97 0.34 17 0.0083 0.023 0.35
## PTCI33_T1 0.94 0.94 0.97 0.35 17 0.0081 0.022 0.36
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PTCI1_T1 103 0.69 0.68 0.67 0.66 4.1 1.7
## PTCI2_T1 103 0.23 0.21 0.18 0.17 3.9 2.1
## PTCI3_T1 103 0.70 0.69 0.69 0.67 4.0 1.9
## PTCI4_T1 103 0.60 0.62 0.61 0.57 5.5 1.4
## PTCI5_T1 103 0.77 0.77 0.77 0.75 3.8 1.9
## PTCI6_T1 103 0.49 0.48 0.47 0.45 2.7 1.8
## PTCI7_T1 103 0.48 0.46 0.45 0.43 4.2 2.1
## PTCI8_T1 103 0.51 0.52 0.51 0.47 5.3 1.6
## PTCI9_T1 103 0.66 0.67 0.66 0.64 4.9 1.7
## PTCI10_T1 103 0.65 0.65 0.64 0.62 5.1 1.6
## PTCI11_T1 103 0.62 0.63 0.63 0.59 5.1 1.6
## PTCI12_T1 103 0.64 0.64 0.63 0.61 5.4 1.6
## PTCI13_T1 103 0.49 0.47 0.45 0.44 3.6 2.0
## PTCI14_T1 103 0.62 0.63 0.62 0.59 4.2 1.6
## PTCI15_T1 103 0.60 0.60 0.58 0.57 4.3 1.8
## PTCI16_T1 103 0.52 0.54 0.53 0.49 5.1 1.3
## PTCI17_T1 103 0.54 0.54 0.52 0.50 4.4 1.8
## PTCI18_T1 103 0.65 0.66 0.65 0.62 5.2 1.6
## PTCI19_T1 103 0.65 0.65 0.64 0.62 4.3 1.7
## PTCI20_T1 103 0.70 0.72 0.72 0.67 5.1 1.6
## PTCI21_T1 103 0.71 0.71 0.70 0.68 4.7 1.8
## PTCI22_T1 103 0.73 0.72 0.72 0.70 4.2 1.7
## PTCI23_T1 103 0.48 0.50 0.49 0.45 5.4 1.4
## PTCI24_T1 103 0.69 0.69 0.69 0.66 5.0 1.6
## PTCI25_T1 103 0.73 0.73 0.72 0.70 3.9 1.9
## PTCI26_T1 103 0.57 0.55 0.54 0.53 4.2 2.2
## PTCI27_T1 103 0.73 0.72 0.72 0.70 4.1 1.9
## PTCI28_T1 103 0.67 0.68 0.68 0.64 4.9 1.6
## PTCI29_T1 103 0.71 0.71 0.71 0.68 4.7 1.7
## PTCI30_T1 103 0.65 0.64 0.64 0.62 4.8 1.7
## PTCI31_T1 103 0.37 0.33 0.32 0.31 3.7 2.2
## PTCI32_T1 103 0.54 0.54 0.53 0.50 4.7 1.8
## PTCI33_T1 103 0.38 0.39 0.37 0.33 4.9 1.7
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## PTCI1_T1 0.17 0.03 0.09 0.25 0.26 0.16 0.05 0
## PTCI2_T1 0.22 0.08 0.09 0.27 0.07 0.12 0.16 0
## PTCI3_T1 0.20 0.04 0.12 0.10 0.28 0.21 0.05 0
## PTCI4_T1 0.03 0.03 0.01 0.12 0.25 0.33 0.23 0
## PTCI5_T1 0.19 0.09 0.16 0.13 0.23 0.15 0.06 0
## PTCI6_T1 0.42 0.09 0.13 0.17 0.14 0.05 0.02 0
## PTCI7_T1 0.20 0.05 0.10 0.16 0.17 0.17 0.15 0
## PTCI8_T1 0.05 0.03 0.06 0.08 0.24 0.28 0.26 0
## PTCI9_T1 0.10 0.01 0.04 0.18 0.25 0.29 0.13 0
## PTCI10_T1 0.04 0.07 0.06 0.08 0.29 0.31 0.16 0
## PTCI11_T1 0.04 0.07 0.05 0.15 0.23 0.26 0.20 0
## PTCI12_T1 0.03 0.05 0.05 0.06 0.26 0.24 0.31 0
## PTCI13_T1 0.23 0.08 0.17 0.19 0.15 0.08 0.11 0
## PTCI14_T1 0.10 0.08 0.11 0.19 0.34 0.13 0.06 0
## PTCI15_T1 0.10 0.08 0.15 0.13 0.28 0.17 0.11 0
## PTCI16_T1 0.01 0.03 0.06 0.19 0.30 0.28 0.13 0
## PTCI17_T1 0.11 0.09 0.12 0.14 0.24 0.20 0.11 0
## PTCI18_T1 0.07 0.01 0.06 0.15 0.17 0.34 0.20 0
## PTCI19_T1 0.11 0.05 0.16 0.17 0.27 0.17 0.08 0
## PTCI20_T1 0.06 0.02 0.11 0.10 0.23 0.29 0.19 0
## PTCI21_T1 0.10 0.06 0.09 0.13 0.21 0.25 0.17 0
## PTCI22_T1 0.14 0.03 0.09 0.30 0.20 0.17 0.08 0
## PTCI23_T1 0.02 0.02 0.07 0.11 0.28 0.24 0.26 0
## PTCI24_T1 0.09 0.02 0.03 0.10 0.34 0.27 0.16 0
## PTCI25_T1 0.17 0.06 0.17 0.20 0.17 0.12 0.11 0
## PTCI26_T1 0.21 0.05 0.08 0.16 0.17 0.14 0.19 0
## PTCI27_T1 0.16 0.04 0.19 0.15 0.22 0.12 0.13 0
## PTCI28_T1 0.03 0.07 0.10 0.15 0.29 0.19 0.17 0
## PTCI29_T1 0.08 0.07 0.08 0.17 0.22 0.27 0.12 0
## PTCI30_T1 0.07 0.03 0.14 0.13 0.27 0.21 0.16 0
## PTCI31_T1 0.29 0.04 0.13 0.15 0.14 0.12 0.15 0
## PTCI32_T1 0.10 0.02 0.10 0.23 0.17 0.22 0.16 0
## PTCI33_T1 0.04 0.08 0.10 0.17 0.20 0.20 0.20 0##
## Reliability analysis
## Call: alpha(x = data[c("PTCI1_T2", "PTCI2_T2", "PTCI3_T2", "PTCI4_T2",
## "PTCI5_T2", "PTCI6_T2", "PTCI7_T2", "PTCI8_T2", "PTCI9_T2",
## "PTCI10_T2", "PTCI11_T2", "PTCI12_T2", "PTCI13_T2", "PTCI14_T2",
## "PTCI15_T2", "PTCI16_T2", "PTCI17_T2", "PTCI18_T2", "PTCI19_T2",
## "PTCI20_T2", "PTCI21_T2", "PTCI22_T2", "PTCI23_T2", "PTCI24_T2",
## "PTCI25_T2", "PTCI26_T2", "PTCI27_T2", "PTCI28_T2", "PTCI29_T2",
## "PTCI30_T2", "PTCI31_T2", "PTCI32_T2", "PTCI33_T2")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.98 0.98 0.99 0.55 41 0.0034 3.5 1.4 0.57
##
## lower alpha upper 95% confidence boundaries
## 0.97 0.98 0.98
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PTCI1_T2 0.97 0.98 0.99 0.55 39 0.0035 0.018 0.57
## PTCI2_T2 0.98 0.98 0.99 0.56 41 0.0034 0.016 0.59
## PTCI3_T2 0.97 0.97 0.99 0.55 39 0.0036 0.018 0.57
## PTCI4_T2 0.97 0.97 0.99 0.55 39 0.0036 0.018 0.57
## PTCI5_T2 0.97 0.97 0.99 0.55 39 0.0036 0.017 0.57
## PTCI6_T2 0.98 0.98 0.99 0.56 41 0.0034 0.017 0.59
## PTCI7_T2 0.98 0.98 0.99 0.56 41 0.0034 0.016 0.58
## PTCI8_T2 0.97 0.98 0.99 0.55 39 0.0035 0.018 0.57
## PTCI9_T2 0.97 0.98 0.99 0.55 39 0.0036 0.018 0.57
## PTCI10_T2 0.97 0.97 0.99 0.55 39 0.0036 0.017 0.57
## PTCI11_T2 0.97 0.98 0.99 0.55 39 0.0036 0.018 0.57
## PTCI12_T2 0.97 0.97 0.99 0.55 39 0.0036 0.018 0.57
## PTCI13_T2 0.98 0.98 0.99 0.56 40 0.0035 0.018 0.58
## PTCI14_T2 0.97 0.97 0.99 0.55 39 0.0036 0.018 0.57
## PTCI15_T2 0.97 0.98 0.99 0.55 39 0.0036 0.018 0.57
## PTCI16_T2 0.98 0.98 0.99 0.56 40 0.0035 0.018 0.58
## PTCI17_T2 0.97 0.98 0.99 0.55 39 0.0036 0.018 0.57
## PTCI18_T2 0.97 0.98 0.99 0.55 39 0.0036 0.018 0.57
## PTCI19_T2 0.97 0.97 0.99 0.55 39 0.0036 0.017 0.57
## PTCI20_T2 0.97 0.97 0.99 0.55 38 0.0036 0.017 0.57
## PTCI21_T2 0.97 0.97 0.99 0.55 39 0.0036 0.018 0.57
## PTCI22_T2 0.97 0.97 0.99 0.55 39 0.0036 0.018 0.57
## PTCI23_T2 0.98 0.98 0.99 0.56 40 0.0035 0.018 0.58
## PTCI24_T2 0.97 0.97 0.99 0.55 39 0.0036 0.018 0.57
## PTCI25_T2 0.97 0.97 0.99 0.55 39 0.0036 0.017 0.57
## PTCI26_T2 0.98 0.98 0.99 0.56 40 0.0035 0.018 0.58
## PTCI27_T2 0.97 0.98 0.99 0.55 39 0.0036 0.018 0.57
## PTCI28_T2 0.97 0.98 0.99 0.55 39 0.0035 0.018 0.57
## PTCI29_T2 0.97 0.97 0.99 0.55 39 0.0036 0.018 0.57
## PTCI30_T2 0.97 0.97 0.99 0.55 39 0.0036 0.018 0.57
## PTCI31_T2 0.98 0.98 0.99 0.56 40 0.0034 0.017 0.58
## PTCI32_T2 0.97 0.97 0.99 0.55 39 0.0036 0.017 0.57
## PTCI33_T2 0.98 0.98 0.99 0.56 41 0.0034 0.015 0.59
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PTCI1_T2 103 0.74 0.74 0.73 0.72 2.7 1.8
## PTCI2_T2 103 0.54 0.54 0.52 0.51 3.2 2.1
## PTCI3_T2 103 0.80 0.80 0.79 0.78 3.0 2.0
## PTCI4_T2 103 0.82 0.82 0.81 0.80 4.0 2.0
## PTCI5_T2 103 0.81 0.81 0.81 0.80 2.8 1.8
## PTCI6_T2 103 0.58 0.58 0.57 0.55 2.3 1.6
## PTCI7_T2 103 0.56 0.56 0.55 0.53 3.1 2.0
## PTCI8_T2 103 0.76 0.76 0.76 0.75 4.5 1.8
## PTCI9_T2 103 0.79 0.79 0.78 0.77 3.6 1.9
## PTCI10_T2 103 0.82 0.82 0.82 0.81 3.2 1.7
## PTCI11_T2 103 0.78 0.79 0.78 0.77 4.0 1.8
## PTCI12_T2 103 0.81 0.81 0.80 0.79 3.9 2.0
## PTCI13_T2 103 0.68 0.67 0.66 0.65 3.0 1.9
## PTCI14_T2 103 0.84 0.84 0.84 0.83 3.2 1.8
## PTCI15_T2 103 0.79 0.79 0.79 0.77 2.9 1.8
## PTCI16_T2 103 0.66 0.67 0.66 0.64 4.2 1.7
## PTCI17_T2 103 0.79 0.79 0.79 0.78 3.4 1.8
## PTCI18_T2 103 0.79 0.79 0.79 0.78 3.6 1.9
## PTCI19_T2 103 0.85 0.85 0.85 0.83 3.1 1.8
## PTCI20_T2 103 0.88 0.88 0.88 0.87 3.9 1.9
## PTCI21_T2 103 0.80 0.80 0.80 0.79 3.6 1.9
## PTCI22_T2 103 0.81 0.81 0.81 0.79 3.4 1.7
## PTCI23_T2 103 0.65 0.65 0.65 0.63 4.9 1.7
## PTCI24_T2 103 0.80 0.80 0.80 0.78 3.7 1.9
## PTCI25_T2 103 0.83 0.83 0.83 0.81 2.9 1.8
## PTCI26_T2 103 0.67 0.67 0.66 0.65 3.2 2.0
## PTCI27_T2 103 0.79 0.79 0.78 0.77 2.9 1.8
## PTCI28_T2 103 0.77 0.77 0.77 0.75 4.0 1.8
## PTCI29_T2 103 0.83 0.83 0.83 0.82 3.8 1.7
## PTCI30_T2 103 0.83 0.83 0.83 0.82 3.7 2.0
## PTCI31_T2 103 0.63 0.63 0.62 0.61 3.1 2.0
## PTCI32_T2 103 0.79 0.80 0.79 0.78 3.4 1.9
## PTCI33_T2 103 0.52 0.52 0.51 0.49 4.7 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## PTCI1_T2 0.39 0.12 0.20 0.09 0.13 0.03 0.05 0
## PTCI2_T2 0.34 0.14 0.02 0.18 0.18 0.05 0.09 0
## PTCI3_T2 0.39 0.08 0.12 0.15 0.12 0.13 0.03 0
## PTCI4_T2 0.17 0.11 0.15 0.06 0.23 0.22 0.06 0
## PTCI5_T2 0.40 0.05 0.24 0.12 0.10 0.08 0.02 0
## PTCI6_T2 0.46 0.17 0.17 0.07 0.09 0.03 0.02 0
## PTCI7_T2 0.33 0.16 0.11 0.07 0.17 0.13 0.04 0
## PTCI8_T2 0.11 0.08 0.05 0.17 0.24 0.27 0.09 0
## PTCI9_T2 0.23 0.10 0.10 0.24 0.13 0.15 0.06 0
## PTCI10_T2 0.26 0.12 0.21 0.13 0.17 0.10 0.01 0
## PTCI11_T2 0.14 0.12 0.14 0.13 0.26 0.16 0.07 0
## PTCI12_T2 0.18 0.11 0.15 0.10 0.25 0.12 0.10 0
## PTCI13_T2 0.35 0.12 0.13 0.15 0.13 0.11 0.03 0
## PTCI14_T2 0.28 0.09 0.18 0.18 0.15 0.10 0.02 0
## PTCI15_T2 0.36 0.14 0.11 0.10 0.21 0.08 0.01 0
## PTCI16_T2 0.09 0.13 0.11 0.16 0.30 0.19 0.03 0
## PTCI17_T2 0.21 0.17 0.12 0.15 0.19 0.16 0.00 0
## PTCI18_T2 0.24 0.07 0.16 0.14 0.25 0.12 0.03 0
## PTCI19_T2 0.28 0.12 0.19 0.13 0.17 0.10 0.01 0
## PTCI20_T2 0.15 0.16 0.10 0.11 0.31 0.11 0.08 0
## PTCI21_T2 0.20 0.14 0.14 0.17 0.17 0.14 0.05 0
## PTCI22_T2 0.23 0.11 0.13 0.21 0.22 0.10 0.00 0
## PTCI23_T2 0.06 0.06 0.09 0.13 0.24 0.26 0.17 0
## PTCI24_T2 0.21 0.10 0.11 0.13 0.26 0.17 0.03 0
## PTCI25_T2 0.36 0.12 0.18 0.10 0.17 0.05 0.03 0
## PTCI26_T2 0.36 0.07 0.14 0.09 0.20 0.12 0.03 0
## PTCI27_T2 0.34 0.15 0.17 0.11 0.13 0.10 0.01 0
## PTCI28_T2 0.14 0.12 0.13 0.11 0.30 0.17 0.05 0
## PTCI29_T2 0.16 0.09 0.17 0.15 0.26 0.16 0.02 0
## PTCI30_T2 0.24 0.11 0.10 0.10 0.25 0.12 0.09 0
## PTCI31_T2 0.35 0.12 0.10 0.11 0.18 0.13 0.02 0
## PTCI32_T2 0.25 0.09 0.17 0.17 0.19 0.07 0.06 0
## PTCI33_T2 0.08 0.10 0.07 0.11 0.23 0.27 0.15 0##
## Reliability analysis
## Call: alpha(x = data[c("DTS1F_T1", "DTS1S_T1", "DTS2F_T1", "DTS2S_T1",
## "DTS3F_T1", "DTS3S_T1", "DTS4F_T1", "DTS4S_T1", "DTS5F_T1",
## "DTS5S_T1", "DTS6F_T1", "DTS6S_T1", "DTS7F_T1", "DTS7S_T1",
## "DTS8F_T1", "DTS8S_T1", "DTS9F_T1", "DTS9S_T1", "DTS10F_T1",
## "DTS10S_T1", "DTS11F_T1", "DTS11S_T1", "DTS12F_T1", "DTS12S_T1",
## "DTS13F_T1", "DTS13S_T1", "DTS14F_T1", "DTS14S_T1", "DTS15F_T1",
## "DTS15S_T1", "DTS16F_T1", "DTS16S_T1", "DTS17F_T1", "DTS17S_T1")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.97 0.25 11 0.012 2.5 0.65 0.24
##
## lower alpha upper 95% confidence boundaries
## 0.89 0.92 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## DTS1F_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS1S_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS2F_T1 0.91 0.92 0.97 0.25 11 0.013 0.020 0.24
## DTS2S_T1 0.91 0.92 0.97 0.25 11 0.013 0.020 0.24
## DTS3F_T1 0.91 0.92 0.97 0.25 11 0.013 0.020 0.24
## DTS3S_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS4F_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS4S_T1 0.91 0.92 0.97 0.25 11 0.013 0.020 0.24
## DTS5F_T1 0.91 0.92 0.97 0.25 11 0.012 0.021 0.24
## DTS5S_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.23
## DTS6F_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS6S_T1 0.91 0.91 0.97 0.25 11 0.013 0.020 0.23
## DTS7F_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS7S_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS8F_T1 0.92 0.92 0.97 0.26 11 0.012 0.020 0.24
## DTS8S_T1 0.91 0.92 0.97 0.25 11 0.013 0.021 0.23
## DTS9F_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS9S_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS10F_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS10S_T1 0.91 0.92 0.97 0.25 11 0.013 0.020 0.24
## DTS11F_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS11S_T1 0.91 0.92 0.97 0.25 11 0.013 0.020 0.24
## DTS12F_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS12S_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS13F_T1 0.92 0.92 0.97 0.26 11 0.012 0.019 0.24
## DTS13S_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS14F_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS14S_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS15F_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS15S_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.23
## DTS16F_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS16S_T1 0.91 0.92 0.97 0.25 11 0.012 0.021 0.23
## DTS17F_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
## DTS17S_T1 0.91 0.92 0.97 0.25 11 0.012 0.020 0.24
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## DTS1F_T1 103 0.41 0.42 0.41 0.37 2.9 1.01
## DTS1S_T1 103 0.58 0.60 0.59 0.56 2.9 0.89
## DTS2F_T1 103 0.58 0.58 0.58 0.54 1.8 1.31
## DTS2S_T1 103 0.60 0.60 0.60 0.56 2.3 1.47
## DTS3F_T1 103 0.59 0.58 0.57 0.54 1.8 1.33
## DTS3S_T1 103 0.58 0.57 0.57 0.53 2.5 1.45
## DTS4F_T1 103 0.50 0.50 0.49 0.46 2.5 1.17
## DTS4S_T1 103 0.62 0.62 0.62 0.59 2.7 1.07
## DTS5F_T1 103 0.47 0.47 0.47 0.43 2.6 1.07
## DTS5S_T1 103 0.59 0.61 0.60 0.56 2.8 0.92
## DTS6F_T1 103 0.44 0.45 0.43 0.39 2.7 1.34
## DTS6S_T1 103 0.64 0.65 0.64 0.61 2.3 1.04
## DTS7F_T1 103 0.44 0.43 0.42 0.38 2.4 1.45
## DTS7S_T1 103 0.56 0.56 0.55 0.51 2.2 1.29
## DTS8F_T1 103 0.37 0.35 0.33 0.31 1.4 1.58
## DTS8S_T1 103 0.58 0.56 0.55 0.53 1.8 1.46
## DTS9F_T1 103 0.51 0.50 0.49 0.46 2.7 1.34
## DTS9S_T1 103 0.57 0.57 0.56 0.54 2.4 1.20
## DTS10F_T1 103 0.48 0.46 0.46 0.43 2.7 1.33
## DTS10S_T1 103 0.60 0.59 0.59 0.56 2.4 1.21
## DTS11F_T1 103 0.51 0.49 0.49 0.46 1.9 1.54
## DTS11S_T1 103 0.59 0.58 0.58 0.55 2.0 1.45
## DTS12F_T1 103 0.55 0.52 0.52 0.49 2.0 1.60
## DTS12S_T1 103 0.55 0.53 0.53 0.50 2.1 1.51
## DTS13F_T1 103 0.33 0.34 0.33 0.28 3.3 1.14
## DTS13S_T1 103 0.48 0.50 0.49 0.44 3.0 1.12
## DTS14F_T1 103 0.40 0.40 0.39 0.35 1.9 1.36
## DTS14S_T1 103 0.45 0.44 0.44 0.40 2.2 1.35
## DTS15F_T1 103 0.50 0.51 0.50 0.46 3.1 1.12
## DTS15S_T1 103 0.57 0.59 0.59 0.54 2.8 1.05
## DTS16F_T1 103 0.41 0.43 0.42 0.37 3.2 1.02
## DTS16S_T1 103 0.60 0.62 0.61 0.57 2.8 0.93
## DTS17F_T1 103 0.49 0.50 0.49 0.45 2.8 1.27
## DTS17S_T1 103 0.57 0.58 0.58 0.53 2.5 1.21
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## DTS1F_T1 0.01 0.09 0.24 0.31 0.35 0
## DTS1S_T1 0.02 0.05 0.21 0.50 0.22 0
## DTS2F_T1 0.22 0.17 0.28 0.20 0.12 0
## DTS2S_T1 0.23 0.01 0.19 0.31 0.25 0
## DTS3F_T1 0.21 0.23 0.25 0.17 0.14 0
## DTS3S_T1 0.19 0.03 0.13 0.34 0.31 0
## DTS4F_T1 0.09 0.08 0.33 0.29 0.21 0
## DTS4S_T1 0.08 0.05 0.18 0.51 0.17 0
## DTS5F_T1 0.02 0.17 0.28 0.31 0.22 0
## DTS5S_T1 0.02 0.07 0.22 0.48 0.21 0
## DTS6F_T1 0.12 0.04 0.23 0.20 0.41 0
## DTS6S_T1 0.08 0.10 0.36 0.37 0.10 0
## DTS7F_T1 0.16 0.16 0.17 0.22 0.30 0
## DTS7S_T1 0.16 0.17 0.17 0.38 0.13 0
## DTS8F_T1 0.50 0.08 0.14 0.12 0.17 0
## DTS8S_T1 0.32 0.10 0.21 0.23 0.14 0
## DTS9F_T1 0.12 0.06 0.23 0.21 0.38 0
## DTS9S_T1 0.13 0.06 0.27 0.39 0.16 0
## DTS10F_T1 0.11 0.07 0.22 0.22 0.38 0
## DTS10S_T1 0.11 0.09 0.25 0.36 0.19 0
## DTS11F_T1 0.31 0.08 0.22 0.17 0.21 0
## DTS11S_T1 0.25 0.08 0.22 0.26 0.18 0
## DTS12F_T1 0.31 0.08 0.20 0.14 0.27 0
## DTS12S_T1 0.30 0.02 0.18 0.31 0.18 0
## DTS13F_T1 0.06 0.03 0.11 0.18 0.62 0
## DTS13S_T1 0.06 0.04 0.17 0.34 0.40 0
## DTS14F_T1 0.22 0.11 0.34 0.17 0.17 0
## DTS14S_T1 0.18 0.10 0.27 0.26 0.18 0
## DTS15F_T1 0.05 0.03 0.19 0.21 0.51 0
## DTS15S_T1 0.06 0.03 0.20 0.43 0.28 0
## DTS16F_T1 0.04 0.00 0.19 0.23 0.53 0
## DTS16S_T1 0.03 0.03 0.25 0.45 0.24 0
## DTS17F_T1 0.10 0.05 0.22 0.26 0.37 0
## DTS17S_T1 0.10 0.08 0.24 0.35 0.23 0##
## Reliability analysis
## Call: alpha(x = data[c("DTS1F_T2", "DTS1S_T2", "DTS2F_T2", "DTS2S_T2",
## "DTS3F_T2", "DTS3S_T2", "DTS4F_T2", "DTS4S_T2", "DTS5F_T2",
## "DTS5S_T2", "DTS6F_T2", "DTS6S_T2", "DTS7F_T2", "DTS7S_T2",
## "DTS8F_T2", "DTS8S_T2", "DTS9F_T2", "DTS9S_T2", "DTS10F_T2",
## "DTS10S_T2", "DTS11F_T2", "DTS11S_T2", "DTS12F_T2", "DTS12S_T2",
## "DTS13F_T2", "DTS13S_T2", "DTS14F_T2", "DTS14S_T2", "DTS15F_T2",
## "DTS15S_T2", "DTS16F_T2", "DTS16S_T2", "DTS17F_T2", "DTS17S_T2")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.96 0.96 0.99 0.45 27 0.005 1.8 0.86 0.45
##
## lower alpha upper 95% confidence boundaries
## 0.95 0.96 0.97
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## DTS1F_T2 0.96 0.96 0.99 0.45 27 0.0051 0.019 0.45
## DTS1S_T2 0.96 0.96 0.99 0.44 26 0.0053 0.018 0.45
## DTS2F_T2 0.96 0.96 0.99 0.45 27 0.0051 0.018 0.46
## DTS2S_T2 0.96 0.96 0.99 0.45 27 0.0051 0.018 0.45
## DTS3F_T2 0.96 0.96 0.99 0.45 27 0.0052 0.019 0.45
## DTS3S_T2 0.96 0.96 0.99 0.44 26 0.0052 0.018 0.45
## DTS4F_T2 0.96 0.96 0.99 0.44 26 0.0052 0.019 0.45
## DTS4S_T2 0.96 0.96 0.99 0.44 26 0.0052 0.019 0.45
## DTS5F_T2 0.96 0.96 0.99 0.44 26 0.0053 0.019 0.45
## DTS5S_T2 0.96 0.96 0.99 0.44 26 0.0053 0.018 0.45
## DTS6F_T2 0.96 0.96 0.99 0.45 27 0.0052 0.019 0.45
## DTS6S_T2 0.96 0.96 0.99 0.44 26 0.0052 0.019 0.45
## DTS7F_T2 0.96 0.96 0.99 0.45 27 0.0051 0.019 0.46
## DTS7S_T2 0.96 0.96 0.99 0.44 26 0.0052 0.019 0.45
## DTS8F_T2 0.97 0.97 0.99 0.46 28 0.0049 0.015 0.46
## DTS8S_T2 0.96 0.96 0.99 0.45 27 0.0051 0.019 0.45
## DTS9F_T2 0.96 0.96 0.99 0.44 26 0.0052 0.019 0.45
## DTS9S_T2 0.96 0.96 0.99 0.44 26 0.0052 0.019 0.45
## DTS10F_T2 0.96 0.96 0.99 0.45 27 0.0052 0.019 0.45
## DTS10S_T2 0.96 0.96 0.99 0.45 27 0.0052 0.019 0.45
## DTS11F_T2 0.96 0.96 0.99 0.45 27 0.0050 0.018 0.46
## DTS11S_T2 0.96 0.96 0.99 0.45 27 0.0052 0.019 0.45
## DTS12F_T2 0.96 0.96 0.99 0.45 27 0.0051 0.019 0.45
## DTS12S_T2 0.96 0.96 0.99 0.45 27 0.0051 0.019 0.45
## DTS13F_T2 0.96 0.96 0.99 0.45 27 0.0051 0.019 0.46
## DTS13S_T2 0.96 0.96 0.99 0.45 27 0.0052 0.019 0.45
## DTS14F_T2 0.96 0.96 0.99 0.45 27 0.0051 0.019 0.46
## DTS14S_T2 0.96 0.96 0.99 0.45 27 0.0051 0.019 0.46
## DTS15F_T2 0.96 0.96 0.99 0.44 26 0.0052 0.019 0.45
## DTS15S_T2 0.96 0.96 0.99 0.44 26 0.0052 0.019 0.45
## DTS16F_T2 0.96 0.96 0.99 0.44 26 0.0053 0.018 0.45
## DTS16S_T2 0.96 0.96 0.99 0.44 26 0.0053 0.018 0.45
## DTS17F_T2 0.96 0.96 0.99 0.45 27 0.0052 0.019 0.45
## DTS17S_T2 0.96 0.96 0.99 0.44 26 0.0052 0.019 0.45
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## DTS1F_T2 103 0.63 0.63 0.62 0.60 2.57 1.2
## DTS1S_T2 103 0.81 0.81 0.81 0.80 2.17 1.0
## DTS2F_T2 103 0.56 0.56 0.56 0.53 1.34 1.3
## DTS2S_T2 103 0.63 0.63 0.63 0.60 1.46 1.4
## DTS3F_T2 103 0.69 0.69 0.69 0.67 1.43 1.3
## DTS3S_T2 103 0.74 0.74 0.74 0.72 1.84 1.4
## DTS4F_T2 103 0.74 0.74 0.73 0.72 2.12 1.3
## DTS4S_T2 103 0.72 0.73 0.72 0.70 2.04 1.1
## DTS5F_T2 103 0.78 0.78 0.78 0.77 2.04 1.3
## DTS5S_T2 103 0.80 0.80 0.80 0.78 1.98 1.3
## DTS6F_T2 103 0.69 0.68 0.68 0.66 1.55 1.3
## DTS6S_T2 103 0.77 0.77 0.77 0.75 1.49 1.1
## DTS7F_T2 103 0.56 0.56 0.55 0.53 1.37 1.3
## DTS7S_T2 103 0.72 0.72 0.71 0.70 1.44 1.2
## DTS8F_T2 103 0.30 0.31 0.29 0.27 0.88 1.2
## DTS8S_T2 103 0.65 0.65 0.64 0.62 1.13 1.2
## DTS9F_T2 103 0.72 0.71 0.71 0.69 2.10 1.4
## DTS9S_T2 103 0.74 0.73 0.73 0.72 1.89 1.2
## DTS10F_T2 103 0.69 0.68 0.68 0.66 1.69 1.4
## DTS10S_T2 103 0.70 0.70 0.70 0.68 1.72 1.3
## DTS11F_T2 103 0.56 0.56 0.55 0.53 1.40 1.4
## DTS11S_T2 103 0.67 0.67 0.67 0.65 1.54 1.3
## DTS12F_T2 103 0.65 0.65 0.64 0.62 1.35 1.4
## DTS12S_T2 103 0.65 0.65 0.64 0.62 1.50 1.4
## DTS13F_T2 103 0.57 0.57 0.57 0.54 2.60 1.2
## DTS13S_T2 103 0.66 0.67 0.66 0.64 2.25 1.2
## DTS14F_T2 103 0.58 0.58 0.57 0.55 1.73 1.2
## DTS14S_T2 103 0.63 0.64 0.63 0.61 1.93 1.2
## DTS15F_T2 103 0.74 0.74 0.74 0.72 2.71 1.2
## DTS15S_T2 103 0.73 0.73 0.73 0.71 2.31 1.2
## DTS16F_T2 103 0.79 0.79 0.79 0.77 2.16 1.4
## DTS16S_T2 103 0.83 0.83 0.83 0.82 2.06 1.2
## DTS17F_T2 103 0.68 0.68 0.68 0.66 2.03 1.3
## DTS17S_T2 103 0.74 0.75 0.74 0.73 1.79 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## DTS1F_T2 0.02 0.20 0.28 0.17 0.32 0
## DTS1S_T2 0.06 0.20 0.33 0.32 0.09 0
## DTS2F_T2 0.37 0.17 0.26 0.14 0.06 0
## DTS2S_T2 0.39 0.15 0.17 0.22 0.08 0
## DTS3F_T2 0.32 0.22 0.27 0.08 0.11 0
## DTS3S_T2 0.27 0.15 0.18 0.26 0.14 0
## DTS4F_T2 0.11 0.23 0.29 0.17 0.19 0
## DTS4S_T2 0.11 0.19 0.34 0.27 0.09 0
## DTS5F_T2 0.16 0.17 0.34 0.17 0.17 0
## DTS5S_T2 0.18 0.17 0.22 0.34 0.09 0
## DTS6F_T2 0.31 0.17 0.25 0.17 0.09 0
## DTS6S_T2 0.22 0.29 0.31 0.13 0.05 0
## DTS7F_T2 0.37 0.15 0.30 0.12 0.07 0
## DTS7S_T2 0.28 0.25 0.25 0.17 0.04 0
## DTS8F_T2 0.55 0.16 0.20 0.03 0.06 0
## DTS8S_T2 0.45 0.17 0.20 0.16 0.02 0
## DTS9F_T2 0.20 0.14 0.26 0.16 0.24 0
## DTS9S_T2 0.21 0.10 0.33 0.30 0.06 0
## DTS10F_T2 0.30 0.13 0.30 0.13 0.15 0
## DTS10S_T2 0.29 0.10 0.27 0.28 0.06 0
## DTS11F_T2 0.41 0.12 0.28 0.06 0.14 0
## DTS11S_T2 0.34 0.12 0.25 0.24 0.05 0
## DTS12F_T2 0.40 0.18 0.20 0.10 0.12 0
## DTS12S_T2 0.39 0.08 0.23 0.25 0.05 0
## DTS13F_T2 0.05 0.12 0.30 0.25 0.28 0
## DTS13S_T2 0.10 0.19 0.25 0.27 0.18 0
## DTS14F_T2 0.20 0.19 0.35 0.17 0.08 0
## DTS14S_T2 0.17 0.17 0.30 0.24 0.11 0
## DTS15F_T2 0.05 0.13 0.26 0.19 0.37 0
## DTS15S_T2 0.09 0.16 0.32 0.23 0.20 0
## DTS16F_T2 0.16 0.17 0.27 0.18 0.22 0
## DTS16S_T2 0.17 0.13 0.30 0.30 0.11 0
## DTS17F_T2 0.16 0.17 0.36 0.14 0.18 0
## DTS17S_T2 0.17 0.21 0.32 0.23 0.06 0##
## Reliability analysis
## Call: alpha(x = data[c("PDS301_T1", "PDS302_T1", "PDS303_T1", "PDS304_T1",
## "PDS305_T1", "PDS306_T1", "PDS307_T1", "PDS308_T1", "PDS309_T1",
## "PDS310_T1", "PDS311_T1", "PDS312_T1", "PDS313_T1", "PDS314_T1",
## "PDS315_T1", "PDS316_T1", "PDS317_T1")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.77 0.82 0.17 3.4 0.035 1.9 0.44 0.15
##
## lower alpha upper 95% confidence boundaries
## 0.69 0.76 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PDS301_T1 0.74 0.75 0.80 0.16 3.0 0.038 0.017 0.14
## PDS302_T1 0.75 0.76 0.81 0.16 3.2 0.037 0.017 0.15
## PDS303_T1 0.73 0.75 0.80 0.16 3.0 0.039 0.017 0.14
## PDS304_T1 0.74 0.75 0.79 0.16 3.0 0.038 0.015 0.15
## PDS305_T1 0.74 0.75 0.80 0.16 3.0 0.038 0.017 0.15
## PDS306_T1 0.74 0.75 0.80 0.16 3.1 0.038 0.019 0.14
## PDS307_T1 0.75 0.76 0.81 0.17 3.2 0.036 0.020 0.15
## PDS308_T1 0.76 0.77 0.82 0.18 3.4 0.034 0.019 0.15
## PDS309_T1 0.76 0.77 0.82 0.17 3.4 0.035 0.019 0.15
## PDS310_T1 0.73 0.75 0.80 0.16 3.0 0.039 0.019 0.14
## PDS311_T1 0.76 0.77 0.82 0.17 3.4 0.035 0.019 0.15
## PDS312_T1 0.74 0.76 0.81 0.17 3.2 0.037 0.020 0.15
## PDS313_T1 0.74 0.76 0.81 0.17 3.2 0.037 0.020 0.15
## PDS314_T1 0.76 0.78 0.82 0.18 3.5 0.035 0.018 0.15
## PDS315_T1 0.73 0.75 0.80 0.16 3.0 0.038 0.019 0.14
## PDS316_T1 0.74 0.76 0.81 0.16 3.1 0.037 0.019 0.14
## PDS317_T1 0.75 0.76 0.81 0.17 3.2 0.037 0.019 0.15
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PDS301_T1 103 0.51 0.54 0.52 0.43 2.3 0.75
## PDS302_T1 103 0.45 0.46 0.43 0.33 1.6 1.06
## PDS303_T1 103 0.57 0.59 0.58 0.48 1.6 0.98
## PDS304_T1 103 0.53 0.57 0.58 0.45 2.3 0.78
## PDS305_T1 103 0.54 0.57 0.55 0.45 2.0 0.80
## PDS306_T1 103 0.52 0.53 0.50 0.43 2.1 0.89
## PDS307_T1 103 0.44 0.42 0.36 0.31 2.1 1.13
## PDS308_T1 103 0.32 0.31 0.23 0.17 1.3 1.20
## PDS309_T1 103 0.36 0.34 0.26 0.23 1.8 1.12
## PDS310_T1 103 0.61 0.59 0.57 0.51 1.8 1.04
## PDS311_T1 103 0.34 0.32 0.24 0.21 1.6 1.07
## PDS312_T1 103 0.46 0.44 0.38 0.34 1.8 1.09
## PDS313_T1 103 0.44 0.45 0.39 0.34 2.4 0.89
## PDS314_T1 103 0.27 0.25 0.17 0.14 1.5 0.99
## PDS315_T1 103 0.56 0.57 0.54 0.47 2.3 0.85
## PDS316_T1 103 0.49 0.49 0.45 0.39 2.2 0.96
## PDS317_T1 103 0.43 0.42 0.37 0.31 2.0 1.00
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## PDS301_T1 0.02 0.12 0.40 0.47 0
## PDS302_T1 0.20 0.23 0.33 0.23 0
## PDS303_T1 0.13 0.37 0.27 0.23 0
## PDS304_T1 0.03 0.12 0.40 0.46 0
## PDS305_T1 0.03 0.22 0.46 0.29 0
## PDS306_T1 0.06 0.17 0.37 0.41 0
## PDS307_T1 0.15 0.14 0.15 0.57 0
## PDS308_T1 0.37 0.19 0.20 0.23 0
## PDS309_T1 0.18 0.22 0.24 0.35 0
## PDS310_T1 0.15 0.21 0.33 0.31 0
## PDS311_T1 0.18 0.27 0.28 0.26 0
## PDS312_T1 0.17 0.17 0.31 0.34 0
## PDS313_T1 0.06 0.10 0.23 0.61 0
## PDS314_T1 0.20 0.28 0.36 0.16 0
## PDS315_T1 0.06 0.08 0.39 0.48 0
## PDS316_T1 0.09 0.11 0.28 0.52 0
## PDS317_T1 0.11 0.16 0.33 0.41 0Paired t-tests
##
## Paired t-test
##
## data: data$CES_T1 and data$CES_T2
## t = 5.7109, df = 102, p-value = 1.115e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.179834 4.499778
## sample estimates:
## mean of the differences
## 3.339806##
## Paired t-test
##
## data: data$PTCI_T1 and data$PTCI_T2
## t = 9.4702, df = 102, p-value = 1.212e-15
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 27.10907 41.47346
## sample estimates:
## mean of the differences
## 34.29126##
## Paired t-test
##
## data: data$DTS_T1 and data$DTS_T2
## t = 8.5716, df = 102, p-value = 1.159e-13
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 16.69271 26.74418
## sample estimates:
## mean of the differences
## 21.71845CES (Event Centrality)
## [1] 0.5627121PTCI (Posttraumatic Cognitions)
## [1] 0.933123DTS (PTSD symptomatology)
## [1] 0.8445816##
## Pearson's product-moment correlation
##
## data: data$CES_T1 and data$PTCI_T1
## t = 3.8692, df = 101, p-value = 0.0001936
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1781583 0.5168799
## sample estimates:
## cor
## 0.3592952##
## Pearson's product-moment correlation
##
## data: data$PTCI_T1 and data$DTS_T1
## t = 4.6726, df = 101, p-value = 9.201e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2483393 0.5687241
## sample estimates:
## cor
## 0.4216018##
## Pearson's product-moment correlation
##
## data: data$DTS_T1 and data$CES_T1
## t = 4.8889, df = 101, p-value = 3.83e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2664803 0.5817236
## sample estimates:
## cor
## 0.4374457##
## Pearson's product-moment correlation
##
## data: data$CES_T2 and data$PTCI_T2
## t = 8.1926, df = 101, p-value = 8.253e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4993888 0.7354447
## sample estimates:
## cor
## 0.631849##
## Pearson's product-moment correlation
##
## data: data$PTCI_T2 and data$DTS_T2
## t = 12.926, df = 101, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7034120 0.8527139
## sample estimates:
## cor
## 0.7894683##
## Pearson's product-moment correlation
##
## data: data$DTS_T2 and data$CES_T2
## t = 6.4992, df = 101, p-value = 3.1e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3905569 0.6665171
## sample estimates:
## cor
## 0.5430375## CES_T1 PTCI_T1 DTS_T1 CES_T2 PTCI_T2 DTS_T2
## CES_T1 1.0000000 0.3592952 0.4374457 0.4487127 0.2219626 0.2785893
## PTCI_T1 0.3592952 1.0000000 0.4216018 0.3504865 0.6101203 0.3773504
## DTS_T1 0.4374457 0.4216018 1.0000000 0.3993181 0.4211240 0.5289376
## CES_T2 0.4487127 0.3504865 0.3993181 1.0000000 0.6318490 0.5430375
## PTCI_T2 0.2219626 0.6101203 0.4211240 0.6318490 1.0000000 0.7894683
## DTS_T2 0.2785893 0.3773504 0.5289376 0.5430375 0.7894683 1.0000000table <- matrix(rep(NA, 30300), ncol=24)
set.seed(1) # same data can be generated first in wide and then in long format
for(i in 1:1010){
# data simulation
data_sim <- mixedDesign(B=NULL, W=c(2, 3), M=mat.mean, SD=mat.sd, R=mat.cor, n=111, long=FALSE, empirical=FALSE)
names(data_sim) <- c("Subj", "CES_T1", "PTCI_T1", "DTS_T1", "CES_T2", "PTCI_T2", "DTS_T2")
data_sim3 <- data_sim[,,i]
# (lavaan) model estimation
model_sim <- '
CES_T1
PTCI_T1 ~ a1*CES_T1
DTS_T1 ~ b1*PTCI_T1 + c1*CES_T1
CES_T2 ~ s11*CES_T1 + clp21*PTCI_T1 + clp31*DTS_T1
PTCI_T2 ~ clp12*CES_T1 + s22*PTCI_T1 + clp32*DTS_T1 + a2*CES_T2
DTS_T2 ~ clp13*CES_T1 + clp23*PTCI_T1 + s33*DTS_T1 + b2*PTCI_T2 + c2*CES_T2
# direct effects
direct_T1 := c1
direct_T2 := c2
# indirect effects (a*b)
indirect_T1 := a1*b1
indirect_T2 := a2*b2
# total effects
total_T1 := c1 + (a1*b1)
total_T2 := c2 + (a2*b2)
'
fit_sim <- sem(model_sim, data=data_sim3)
summary(fit_sim, fit.measures=TRUE, standardized=TRUE, rsq=TRUE)
# coeff extraction
beta_clp21 <- fit_sim@ParTable$est[5]
beta_clp12 <- fit_sim@ParTable$est[7]
beta_clp31 <- fit_sim@ParTable$est[6]
beta_clp13 <- fit_sim@ParTable$est[11]
beta_clp32 <- fit_sim@ParTable$est[9]
beta_clp23 <- fit_sim@ParTable$est[12]
beta_indirect1 <- fit_sim@ParTable$est[24]
beta_indirect2 <- fit_sim@ParTable$est[25]
se_clp21 <- fit_sim@ParTable$se[5]
se_clp12 <- fit_sim@ParTable$se[7]
se_clp31 <- fit_sim@ParTable$se[6]
se_clp13 <- fit_sim@ParTable$se[11]
se_clp32 <- fit_sim@ParTable$se[9]
se_clp23 <- fit_sim@ParTable$se[12]
se_indirect1 <- fit_sim@ParTable$se[24]
se_indirect2 <- fit_sim@ParTable$se[25]
# coeff storage in tables
table[i,1] <- beta_clp21
table[i,2] <- beta_clp12
table[i,3] <- beta_clp31
table[i,4] <- beta_clp13
table[i,5] <- beta_clp32
table[i,6] <- beta_clp23
table[i,7] <- beta_indirect1
table[i,8] <- beta_indirect2
table[i,9] <- se_clp21
table[i,10] <- se_clp12
table[i,11] <- se_clp31
table[i,12] <- se_clp13
table[i,13] <- se_clp32
table[i,14] <- se_clp23
table[i,15] <- se_indirect1
table[i,16] <- se_indirect2
# indirect effects significant (=1)?
table[i,17] <- ifelse(beta_indirect1 > 0 & (abs(beta_indirect1) > abs(1.96*se_indirect1)), 1, 0)
table[i,18] <- ifelse(beta_indirect2 > 0 & (abs(beta_indirect2) > abs(1.96*se_indirect2)), 1, 0)
# CLPs significant (=1)?
table[i,19] <- ifelse(abs(beta_clp21) > abs(1.96*se_clp21), 1, 0)
table[i,20] <- ifelse(abs(beta_clp12) > abs(1.96*se_clp12), 1, 0)
table[i,21] <- ifelse(abs(beta_clp31) > abs(1.96*se_clp31), 1, 0)
table[i,22] <- ifelse(abs(beta_clp13) > abs(1.96*se_clp13), 1, 0)
table[i,23] <- ifelse(abs(beta_clp32) > abs(1.96*se_clp32), 1, 0)
table[i,24] <- ifelse(abs(beta_clp23) > abs(1.96*se_clp23), 1, 0)
}
colnames(table) <- c("beta_clp21", "beta_clp12", "beta_clp31", "beta_clp13", "beta_clp23", "beta_clp23", "beta_ind1", "beta_ind2", "se_clp21", "se_clp12", "se_clp31", "se_clp13", "se_clp32", "se_clp23", "se_ind1", "se_ind2", "sig_ind1", "sig_ind2", "sig_clp21", "sig_clp12", "sig_clp31", "sig_clp13", "sig_clp23", "sig_clp23")
head(table)
table_exNA <- na.omit(table) #exclude NAs
table_complete <- table_exNA[sample(nrow(table_exNA), 1000), ] #randomly extract 1000 rows from table_exNA
# count significances
sum(table_complete[,17]) # indirect effect T1
sum(table_complete[,18]) # indirect effect T2
sum(table_complete[,19]) # CLP clp21
sum(table_complete[,20]) # CLP clp12
sum(table_complete[,21]) # CLP clp31
sum(table_complete[,22]) # CLP clp13
sum(table_complete[,23]) # CLP clp32
sum(table_complete[,24]) # CLP clp23## Power
## ind1 84.3
## ind2 100.0
## clp21 37.4
## clp12 89.3
## clp31 55.4
## clp13 33.6
## clp32 40.7
## clp23 97.3model_MedT1 <- '
PTCI_T1 ~ a1*CES_T1
DTS_T1 ~ b1*PTCI_T1 + c1*CES_T1
direct_T1 := c1
indirect_T1 := a1*b1
total_T1 := c1 + (a1*b1)
'
fit_MedT1 <- sem(model_MedT1, data=data)
summary(fit_MedT1, fit.measures=TRUE, standardized=TRUE, rsq=TRUE)## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 5
##
## Number of observations 103
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 46.884
## Degrees of freedom 3
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -950.993
## Loglikelihood unrestricted model (H1) -950.993
##
## Akaike (AIC) 1911.986
## Bayesian (BIC) 1925.160
## Sample-size adjusted Bayesian (BIC) 1909.366
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PTCI_T1 ~
## CES_T1 (a1) 2.670 0.683 3.907 0.000 2.670 0.359
## DTS_T1 ~
## PTCI_T1 (b1) 0.196 0.058 3.370 0.001 0.196 0.304
## CES_T1 (c1) 1.573 0.432 3.644 0.000 1.573 0.328
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PTCI_T1 1015.575 141.517 7.176 0.000 1015.575 0.871
## .DTS_T1 353.160 49.212 7.176 0.000 353.160 0.728
##
## R-Square:
## Estimate
## PTCI_T1 0.129
## DTS_T1 0.272
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## direct_T1 1.573 0.432 3.644 0.000 1.573 0.328
## indirect_T1 0.523 0.205 2.552 0.011 0.523 0.109
## total_T1 2.096 0.425 4.937 0.000 2.096 0.437model_MedT2 <- '
PTCI_T2 ~ a2*CES_T2
DTS_T2 ~ b2*PTCI_T2 + c2*CES_T2
direct_T2 := c2
indirect_T2 := a2*b2
total_T2 := c2 + (a2*b2)
'
fit_MedT2 <- sem(model_MedT2, data=data)
summary(fit_MedT2, fit.measures=TRUE, standardized=TRUE, rsq=TRUE)## lavaan 0.6-7 ended normally after 24 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 5
##
## Number of observations 103
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 153.926
## Degrees of freedom 3
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -955.713
## Loglikelihood unrestricted model (H1) -955.713
##
## Akaike (AIC) 1921.425
## Bayesian (BIC) 1934.599
## Sample-size adjusted Bayesian (BIC) 1918.805
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PTCI_T2 ~
## CES_T2 (a2) 4.552 0.550 8.273 0.000 4.552 0.632
## DTS_T2 ~
## PTCI_T2 (b2) 0.476 0.050 9.563 0.000 0.476 0.743
## CES_T2 (c2) 0.340 0.359 0.947 0.343 0.340 0.074
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PTCI_T2 1240.367 172.841 7.176 0.000 1240.367 0.601
## .DTS_T2 316.908 44.160 7.176 0.000 316.908 0.373
##
## R-Square:
## Estimate
## PTCI_T2 0.399
## DTS_T2 0.627
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## direct_T2 0.340 0.359 0.947 0.343 0.340 0.074
## indirect_T2 2.168 0.347 6.257 0.000 2.168 0.469
## total_T2 2.508 0.382 6.563 0.000 2.508 0.543model_Max <- '
PTCI_T1 ~ a1*CES_T1
DTS_T1 ~ b1*PTCI_T1 + c1*CES_T1
CES_T2 ~ s11*CES_T1 + clp21*PTCI_T1 + clp31*DTS_T1
PTCI_T2 ~ clp12*CES_T1 + s22*PTCI_T1 + clp32*DTS_T1 + a2*CES_T2
DTS_T2 ~ clp13*CES_T1 + clp23*PTCI_T1 + s33*DTS_T1 + b2*PTCI_T2 + c2*CES_T2
direct_T1 := c1
direct_T2 := c2
indirect_T1 := a1*b1
indirect_T2 := a2*b2
total_T1 := c1 + (a1*b1)
total_T2 := c2 + (a2*b2)
'
fit_Max <- sem(model_Max, data=data)
summary(fit_Max, fit.measures=TRUE, standardized=TRUE, rsq=TRUE)## lavaan 0.6-7 ended normally after 57 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 20
##
## Number of observations 103
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 304.600
## Degrees of freedom 15
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2190.647
## Loglikelihood unrestricted model (H1) -2190.647
##
## Akaike (AIC) 4421.293
## Bayesian (BIC) 4473.988
## Sample-size adjusted Bayesian (BIC) 4410.811
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PTCI_T1 ~
## CES_T1 (a1) 2.670 0.683 3.907 0.000 2.670 0.359
## DTS_T1 ~
## PTCI_T1 (b1) 0.196 0.058 3.370 0.001 0.196 0.304
## CES_T1 (c1) 1.573 0.432 3.644 0.000 1.573 0.328
## CES_T2 ~
## CES_T1 (s11) 0.419 0.131 3.186 0.001 0.419 0.305
## PTCI_T1 (cl21) 0.029 0.018 1.650 0.099 0.029 0.157
## DTS_T1 (cl31) 0.057 0.028 2.028 0.043 0.057 0.200
## PTCI_T2 ~
## CES_T1 (cl12) -2.287 0.724 -3.156 0.002 -2.287 -0.231
## PTCI_T1 (s22) 0.610 0.093 6.526 0.000 0.610 0.458
## DTS_T1 (cl32) 0.244 0.151 1.612 0.107 0.244 0.118
## CES_T2 (a2) 3.802 0.518 7.335 0.000 3.802 0.528
## DTS_T2 ~
## CES_T1 (cl13) 0.592 0.420 1.410 0.159 0.592 0.093
## PTCI_T1 (cl23) -0.231 0.062 -3.757 0.000 -0.231 -0.271
## DTS_T1 (s33) 0.346 0.085 4.081 0.000 0.346 0.262
## PTCI_T2 (b2) 0.548 0.055 10.041 0.000 0.548 0.854
## CES_T2 (c2) -0.223 0.354 -0.629 0.529 -0.223 -0.048
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PTCI_T1 1015.575 141.517 7.176 0.000 1015.575 0.871
## .DTS_T1 353.160 49.212 7.176 0.000 353.160 0.728
## .CES_T2 28.972 4.037 7.176 0.000 28.972 0.728
## .PTCI_T2 801.909 111.743 7.176 0.000 801.909 0.388
## .DTS_T2 245.666 34.233 7.176 0.000 245.666 0.290
##
## R-Square:
## Estimate
## PTCI_T1 0.129
## DTS_T1 0.272
## CES_T2 0.272
## PTCI_T2 0.612
## DTS_T2 0.710
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## direct_T1 1.573 0.432 3.644 0.000 1.573 0.328
## direct_T2 -0.223 0.354 -0.629 0.529 -0.223 -0.048
## indirect_T1 0.523 0.205 2.552 0.011 0.523 0.109
## indirect_T2 2.082 0.352 5.923 0.000 2.082 0.451
## total_T1 2.096 0.425 4.937 0.000 2.096 0.437
## total_T2 1.859 0.404 4.607 0.000 1.859 0.403Equation of clp21 (CES_T2 -> PTCI_T1) and clp12 (PTCI_T2 -> CES_T1)
model_clp2112 <- '
PTCI_T1 ~ a1*CES_T1
DTS_T1 ~ b1*PTCI_T1 + c1*CES_T1
CES_T2 ~ s11*CES_T1 + clp2112*PTCI_T1 + clp31*DTS_T1
PTCI_T2 ~ clp2112*CES_T1 + s22*PTCI_T1 + clp32*DTS_T1 + a2*CES_T2
DTS_T2 ~ clp13*CES_T1 + clp23*PTCI_T1 + s33*DTS_T1 + b2*PTCI_T2 + c2*CES_T2
'
fit_clp2112 <- sem(model_clp2112, data=data)
# Comparison of the constrained Model with the Maximal Model
anova(fit_Max, fit_clp2112)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit_Max 0 4421.3 4474.0 0.0000
## fit_clp2112 1 4429.0 4479.1 9.7354 9.7354 1 0.001808 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Equation of clp31 (DTS_T1 -> CES_T2) and clp13 (CES_T1 -> DTS_T2)
model_clp3113 <- '
PTCI_T1 ~ a1*CES_T1
DTS_T1 ~ b1*PTCI_T1 + c1*CES_T1
CES_T2 ~ s11*CES_T1 + clp21*PTCI_T1 + clp3113*DTS_T1
PTCI_T2 ~ clp12*CES_T1 + s22*PTCI_T1 + clp32*DTS_T1 + a2*CES_T2
DTS_T2 ~ clp3113*CES_T1 + clp23*PTCI_T1 + s33*DTS_T1 + b2*PTCI_T2 + c2*CES_T2
'
fit_clp3113 <- sem(model_clp3113, data=data)
# Comparison of the constrained Model with the Maximal Model
anova(fit_Max, fit_clp3113)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit_Max 0 4421.3 4474 0.0000
## fit_clp3113 1 4420.9 4471 1.6026 1.6026 1 0.2055Eqation of clp32 (DTS_T1 -> PTCI_T2) with clp23 (PTCI_T1 -> DTS_T2)
model_clp3223 <- '
PTCI_T1 ~ a1*CES_T1
DTS_T1 ~ b1*PTCI_T1 + c1*CES_T1
CES_T2 ~ s11*CES_T1 + clp21*PTCI_T1 + clp31*DTS_T1
PTCI_T2 ~ clp12*CES_T1 + s22*PTCI_T1 + clp3223*DTS_T1 + a2*CES_T2
DTS_T2 ~ clp13*CES_T1 + clp3223*PTCI_T1 + s33*DTS_T1 + b2*PTCI_T2 + c2*CES_T2
'
fit_clp3223 <- sem(model_clp3223, data=data)
# Comparison of the constrained Model with the Maximal Model
anova(fit_Max, fit_clp3223)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit_Max 0 4421.3 4474.0 0.0000
## fit_clp3223 1 4427.5 4477.6 8.2001 8.2001 1 0.004189 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Maximal Submodel, including only positive CLPs - excluding negative CLPs (clp12, clp23)
model_SubPosCLP <- '
PTCI_T1 ~ a1*CES_T1
DTS_T1 ~ b1*PTCI_T1 + c1*CES_T1
CES_T2 ~ s11*CES_T1 + clp21*PTCI_T1 + clp31*DTS_T1
PTCI_T2 ~ 0*CES_T1 + s22*PTCI_T1 + clp32*DTS_T1 + a2*CES_T2
DTS_T2 ~ clp13*CES_T1 + 0*PTCI_T1 + s33*DTS_T1 + b2*PTCI_T2 + c2*CES_T2
direct_T1 := c1
direct_T2 := c2
indirect_T1 := a1*b1
indirect_T2 := a2*b2
total_T1 := c1 + (a1*b1)
total_T2 := c2 + (a2*b2)
'
fit_SubPosCLP <- sem(model_SubPosCLP, data=data)
summary(fit_SubPosCLP, fit.measures=TRUE, standardized=TRUE, rsq=TRUE)## lavaan 0.6-7 ended normally after 50 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 18
##
## Number of observations 103
##
## Model Test User Model:
##
## Test statistic 22.735
## Degrees of freedom 2
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 304.600
## Degrees of freedom 15
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.928
## Tucker-Lewis Index (TLI) 0.463
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2202.014
## Loglikelihood unrestricted model (H1) -2190.647
##
## Akaike (AIC) 4440.028
## Bayesian (BIC) 4487.453
## Sample-size adjusted Bayesian (BIC) 4430.594
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.317
## 90 Percent confidence interval - lower 0.208
## 90 Percent confidence interval - upper 0.440
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.054
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PTCI_T1 ~
## CES_T1 (a1) 2.670 0.683 3.907 0.000 2.670 0.359
## DTS_T1 ~
## PTCI_T1 (b1) 0.196 0.058 3.370 0.001 0.196 0.304
## CES_T1 (c1) 1.573 0.432 3.644 0.000 1.573 0.328
## CES_T2 ~
## CES_T1 (s11) 0.419 0.131 3.186 0.001 0.419 0.305
## PTCI_T1 (cl21) 0.029 0.018 1.650 0.099 0.029 0.157
## DTS_T1 (cl31) 0.057 0.028 2.028 0.043 0.057 0.200
## PTCI_T2 ~
## CES_T1 0.000 0.000 0.000
## PTCI_T1 (s22) 0.565 0.097 5.837 0.000 0.565 0.424
## DTS_T1 (cl32) 0.121 0.153 0.790 0.429 0.121 0.059
## CES_T2 (a2) 3.312 0.518 6.395 0.000 3.312 0.460
## DTS_T2 ~
## CES_T1 (cl13) 0.144 0.424 0.339 0.734 0.144 0.023
## PTCI_T1 0.000 0.000 0.000
## DTS_T1 (s33) 0.301 0.088 3.427 0.001 0.301 0.227
## PTCI_T2 (b2) 0.437 0.048 9.048 0.000 0.437 0.680
## CES_T2 (c2) 0.053 0.354 0.150 0.881 0.053 0.011
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PTCI_T1 1015.575 141.517 7.176 0.000 1015.575 0.871
## .DTS_T1 353.160 49.212 7.176 0.000 353.160 0.728
## .CES_T2 28.972 4.037 7.176 0.000 28.972 0.728
## .PTCI_T2 879.469 122.551 7.176 0.000 879.469 0.426
## .DTS_T2 279.325 38.923 7.176 0.000 279.325 0.328
##
## R-Square:
## Estimate
## PTCI_T1 0.129
## DTS_T1 0.272
## CES_T2 0.272
## PTCI_T2 0.574
## DTS_T2 0.672
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## direct_T1 1.573 0.432 3.644 0.000 1.573 0.328
## direct_T2 0.053 0.354 0.150 0.881 0.053 0.011
## indirect_T1 0.523 0.205 2.552 0.011 0.523 0.109
## indirect_T2 1.447 0.277 5.222 0.000 1.447 0.312
## total_T1 2.096 0.425 4.937 0.000 2.096 0.437
## total_T2 1.500 0.378 3.967 0.000 1.500 0.324Comparison: Maximal Model vs. Submodel only PosCLP
anova(fit_Max, fit_SubPosCLP)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit_Max 0 4421.3 4474.0 0.000
## fit_SubPosCLP 2 4440.0 4487.5 22.735 22.735 2 1.157e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Maximal Submodel, including only stabilities (s11, s22, s33)
model_SubStab <- '
PTCI_T1 ~ a1*CES_T1
DTS_T1 ~ c1*CES_T1 + b1*PTCI_T1
CES_T2 ~ s11*CES_T1
PTCI_T2 ~ a2*CES_T2 + s22*PTCI_T1
DTS_T2 ~ c2*CES_T2 + b2*PTCI_T2 + s33*DTS_T1
direct_T1 := c1
direct_T2 := c2
indirect_T1 := a1*b1
indirect_T2 := a2*b2
total_T1 := c1 + (a1*b1)
total_T2 := c2 + (a2*b2)
'
fit_SubStab <- sem(model_SubStab, data=data)
summary(fit_SubStab, standardized=TRUE, fit.measures=TRUE, rsq=TRUE)## lavaan 0.6-7 ended normally after 40 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 14
##
## Number of observations 103
##
## Model Test User Model:
##
## Test statistic 32.949
## Degrees of freedom 6
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 304.600
## Degrees of freedom 15
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.907
## Tucker-Lewis Index (TLI) 0.767
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -2207.121
## Loglikelihood unrestricted model (H1) -2190.647
##
## Akaike (AIC) 4442.242
## Bayesian (BIC) 4479.129
## Sample-size adjusted Bayesian (BIC) 4434.905
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.209
## 90 Percent confidence interval - lower 0.143
## 90 Percent confidence interval - upper 0.281
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.095
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PTCI_T1 ~
## CES_T1 (a1) 2.670 0.683 3.907 0.000 2.670 0.359
## DTS_T1 ~
## CES_T1 (c1) 1.573 0.432 3.644 0.000 1.573 0.328
## PTCI_T1 (b1) 0.196 0.058 3.370 0.001 0.196 0.304
## CES_T2 ~
## CES_T1 (s11) 0.616 0.121 5.096 0.000 0.616 0.449
## PTCI_T2 ~
## CES_T2 (a2) 3.433 0.471 7.291 0.000 3.433 0.497
## PTCI_T1 (s22) 0.590 0.087 6.779 0.000 0.590 0.462
## DTS_T2 ~
## CES_T2 (c2) 0.099 0.319 0.310 0.756 0.099 0.022
## PTCI_T2 (b2) 0.434 0.047 9.181 0.000 0.434 0.679
## DTS_T1 (s33) 0.311 0.078 3.977 0.000 0.311 0.246
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PTCI_T1 1015.575 141.517 7.176 0.000 1015.575 0.871
## .DTS_T1 353.160 49.212 7.176 0.000 353.160 0.728
## .CES_T2 31.765 4.426 7.176 0.000 31.765 0.799
## .PTCI_T2 884.802 123.294 7.176 0.000 884.802 0.466
## .DTS_T2 279.628 38.965 7.176 0.000 279.628 0.361
##
## R-Square:
## Estimate
## PTCI_T1 0.129
## DTS_T1 0.272
## CES_T2 0.201
## PTCI_T2 0.534
## DTS_T2 0.639
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## direct_T1 1.573 0.432 3.644 0.000 1.573 0.328
## direct_T2 0.099 0.319 0.310 0.756 0.099 0.022
## indirect_T1 0.523 0.205 2.552 0.011 0.523 0.109
## indirect_T2 1.490 0.261 5.710 0.000 1.490 0.337
## total_T1 2.096 0.425 4.937 0.000 2.096 0.437
## total_T2 1.589 0.336 4.728 0.000 1.589 0.360Comparison: Maximal Model vs. Submodel only Stabilites
anova(fit_Max, fit_SubStab)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit_Max 0 4421.3 4474.0 0.000
## fit_SubStab 6 4442.2 4479.1 32.949 32.949 6 1.073e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sessionInfo()## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] de_DE.UTF-8/de_DE.UTF-8/de_DE.UTF-8/C/de_DE.UTF-8/de_DE.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] afex_0.28-1 lme4_1.1-26 Matrix_1.3-2 hypr_0.1.11
## [5] MASS_7.3-53.1 lsr_0.5 car_3.0-10 carData_3.0-4
## [9] reshape2_1.4.4 plyr_1.8.6 semPlot_1.1.2 qgraph_1.6.9
## [13] lavaan_0.6-7 psych_2.0.12 haven_2.3.1 forcats_0.5.1
## [17] stringr_1.4.0 dplyr_1.0.4 purrr_0.3.4 readr_1.4.0
## [21] tidyr_1.1.2 tibble_3.0.6 ggplot2_3.3.3 tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] minqa_1.2.4 colorspace_2.0-0 rio_0.5.16
## [4] ellipsis_0.3.1 htmlTable_2.1.0 corpcor_1.6.9
## [7] base64enc_0.1-3 fs_1.5.0 rstudioapi_0.13
## [10] fansi_0.4.2 lubridate_1.7.9.2 xml2_1.3.2
## [13] splines_3.6.3 mnormt_2.0.2 knitr_1.31
## [16] glasso_1.11 Formula_1.2-4 jsonlite_1.7.2
## [19] nloptr_1.2.2.2 broom_0.7.5 cluster_2.1.1
## [22] dbplyr_2.1.0 png_0.1-7 regsem_1.6.2
## [25] compiler_3.6.3 httr_1.4.2 backports_1.2.1
## [28] assertthat_0.2.1 cli_2.3.1 htmltools_0.5.1.1
## [31] tools_3.6.3 lmerTest_3.1-3 OpenMx_2.18.1
## [34] igraph_1.2.6 coda_0.19-4 gtable_0.3.0
## [37] glue_1.4.2 Rcpp_1.0.6 cellranger_1.1.0
## [40] vctrs_0.3.6 nlme_3.1-152 lisrelToR_0.1.4
## [43] xfun_0.21 openxlsx_4.2.3 rvest_0.3.6
## [46] lifecycle_1.0.0 gtools_3.8.2 XML_3.99-0.3
## [49] statmod_1.4.35 scales_1.1.1 kutils_1.70
## [52] hms_1.0.0 parallel_3.6.3 RColorBrewer_1.1-2
## [55] curl_4.3 yaml_2.2.1 pbapply_1.4-3
## [58] gridExtra_2.3 rpart_4.1-15 latticeExtra_0.6-29
## [61] stringi_1.5.3 sem_3.1-11 checkmate_2.0.0
## [64] zip_2.1.1 boot_1.3-27 truncnorm_1.0-8
## [67] rlang_0.4.10 pkgconfig_2.0.3 Rsolnp_1.16
## [70] arm_1.11-2 evaluate_0.14 lattice_0.20-41
## [73] htmlwidgets_1.5.3 tidyselect_1.1.0 magrittr_2.0.1
## [76] R6_2.5.0 generics_0.1.0 Hmisc_4.4-2
## [79] DBI_1.1.1 pillar_1.5.0 foreign_0.8-75
## [82] withr_2.4.1 rockchalk_1.8.144 survival_3.2-7
## [85] abind_1.4-5 nnet_7.3-15 modelr_0.1.8
## [88] crayon_1.4.1 fdrtool_1.2.16 utf8_1.1.4
## [91] tmvnsim_1.0-2 rmarkdown_2.7 jpeg_0.1-8.1
## [94] grid_3.6.3 readxl_1.3.1 data.table_1.13.6
## [97] pbivnorm_0.6.0 matrixcalc_1.0-3 reprex_1.0.0
## [100] digest_0.6.27 xtable_1.8-4 mi_1.0
## [103] numDeriv_2016.8-1.1 stats4_3.6.3 munsell_0.5.0