## mydata$past.week.f n percent
## yes 198 0.90000000
## no 18 0.08181818
## 4 0.01818182
## mydata$pain.persist.f n percent valid_percent
## yes 181 0.82272727 0.89603960
## no 16 0.07272727 0.07920792
## 5 0.02272727 0.02475248
## <NA> 18 0.08181818 NA
## mydata$pain.reduce.f n percent valid_percent
## yes 130 0.59090909 0.69892473
## no 50 0.22727273 0.26881720
## 6 0.02727273 0.03225806
## <NA> 34 0.15454545 NA
## mydata$pain.worsen.f n percent valid_percent
## yes 152 0.69090909 0.81720430
## no 29 0.13181818 0.15591398
## 5 0.02272727 0.02688172
## <NA> 34 0.15454545 NA
## mydata$episodes.f n percent
## yes 130 0.59090909
## no 85 0.38636364
## 5 0.02272727
## mydata$episodes.know.f n percent valid_percent
## yes 80 0.36363636 0.59259259
## no 51 0.23181818 0.37777778
## 4 0.01818182 0.02962963
## <NA> 85 0.38636364 NA
## mydata$episodes.certain.f n percent valid_percent
## yes 97 0.440909091 0.71851852
## no 36 0.163636364 0.26666667
## 2 0.009090909 0.01481481
## <NA> 85 0.386363636 NA
## mydata$episodes.body.f n percent valid_percent
## yes 39 0.177272727 0.288888889
## no 95 0.431818182 0.703703704
## 1 0.004545455 0.007407407
## <NA> 85 0.386363636 NA
## mydata$past.weekR n percent valid_percent
## 0 18 0.08181818 0.08333333
## 1 198 0.90000000 0.91666667
## NA 4 0.01818182 NA
## mydata$pain.persistR n percent valid_percent
## 0 16 0.07272727 0.08121827
## 1 181 0.82272727 0.91878173
## NA 23 0.10454545 NA
## mydata$pain.reduceR n percent valid_percent
## 0 50 0.2272727 0.2777778
## 1 130 0.5909091 0.7222222
## NA 40 0.1818182 NA
## mydata$pain.worsenR n percent valid_percent
## 0 29 0.1318182 0.160221
## 1 152 0.6909091 0.839779
## NA 39 0.1772727 NA
## mydata$episodes.occurR n percent valid_percent
## 0 85 0.38636364 0.3953488
## 1 130 0.59090909 0.6046512
## NA 5 0.02272727 NA
## mydata$episodes.knowR n percent valid_percent
## 0 51 0.2318182 0.389313
## 1 80 0.3636364 0.610687
## NA 89 0.4045455 NA
## mydata$episodes.certainR n percent valid_percent
## 0 36 0.1636364 0.2706767
## 1 97 0.4409091 0.7293233
## NA 87 0.3954545 NA
## mydata$episodes.bodyR n percent valid_percent
## 0 95 0.4318182 0.7089552
## 1 39 0.1772727 0.2910448
## NA 86 0.3909091 NA
##
## DESCRIPTIVES
##
## Descriptives
## ────────────────────────────────────────────────────────────────────────────────────────────
## pwb ewb pfunction role.limit inflexibility
## ────────────────────────────────────────────────────────────────────────────────────────────
## N 220 211 202 220 199
## Missing 0 9 18 0 21
## Mean 4.997931 3.752804 1.783324 2.804545 4.302210
## Median 4.944444 3.722222 1.700000 3.500000 4.375000
## Standard deviation 0.9500698 0.5367026 0.4394205 1.487536 1.109033
## Minimum 2.555556 2.333333 1.100000 0 1.687500
## Maximum 7.000000 5.000000 2.800000 4 6.937500
## ────────────────────────────────────────────────────────────────────────────────────────────
##
## DESCRIPTIVES
##
## Descriptives
## ────────────────────────────────────────────────────────────────────────
## masteryR acceptR autonomyR avoidance
## ────────────────────────────────────────────────────────────────────────
## N 218 220 220 199
## Mean 5.231651 4.593939 5.545455 4.043096
## Median 6.000000 4.500000 6.000000 4.000000
## Standard deviation 1.404489 1.106952 1.320795 1.291764
## Minimum 1.000000 2.000000 1.000000 1.000000
## Maximum 7.000000 7.000000 7.000000 6.900000
## ────────────────────────────────────────────────────────────────────────
##
## Reliability analysis
## Call: psych::alpha(x = HELP, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.86 0.88 0.51 6.3 0.01 2.6 0.96 0.6
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.85 0.88 0.9
## Duhachek 0.86 0.88 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pcs1R 0.87 0.85 0.87 0.52 5.5 0.0115 0.0982 0.72
## pcs2R 0.84 0.81 0.83 0.46 4.3 0.0143 0.0931 0.59
## pcs3R 0.84 0.82 0.84 0.48 4.5 0.0141 0.0854 0.57
## pcs4R 0.83 0.81 0.83 0.46 4.3 0.0152 0.0756 0.56
## pcs5R 0.83 0.80 0.83 0.45 4.1 0.0150 0.0885 0.56
## pcs12R 0.92 0.92 0.91 0.69 11.4 0.0086 0.0093 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pcs1R 198 0.76 0.74 0.67 0.64 3.0 1.22
## pcs2R 196 0.88 0.88 0.87 0.81 2.3 1.24
## pcs3R 197 0.87 0.85 0.83 0.79 2.6 1.31
## pcs4R 198 0.90 0.88 0.88 0.84 2.6 1.30
## pcs5R 194 0.90 0.90 0.90 0.85 2.4 1.29
## pcs12R 198 0.29 0.37 0.19 0.17 2.3 0.76
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## pcs1R 0.11 0.29 0.24 0.23 0.13 0.10
## pcs2R 0.34 0.29 0.14 0.18 0.05 0.11
## pcs3R 0.25 0.23 0.23 0.18 0.10 0.10
## pcs4R 0.24 0.33 0.15 0.18 0.10 0.10
## pcs5R 0.33 0.23 0.18 0.22 0.05 0.12
## pcs12R 0.01 0.81 0.09 0.05 0.04 0.10
##
## Reliability analysis
## Call: psych::alpha(x = MANG, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.74 0.59 4.3 0.022 2.7 1 0.61
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.76 0.81 0.85
## Duhachek 0.77 0.81 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pcs6R 0.70 0.70 0.54 0.54 2.3 0.041 NA 0.54
## pcs7R 0.77 0.77 0.62 0.62 3.3 0.031 NA 0.62
## pcs13R 0.76 0.76 0.61 0.61 3.1 0.033 NA 0.61
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pcs6R 198 0.88 0.87 0.78 0.70 3.0 1.2
## pcs7R 195 0.85 0.84 0.71 0.64 2.3 1.2
## pcs13R 195 0.84 0.84 0.72 0.64 2.7 1.2
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## pcs6R 0.06 0.37 0.20 0.25 0.12 0.10
## pcs7R 0.34 0.30 0.18 0.11 0.07 0.11
## pcs13R 0.16 0.30 0.27 0.19 0.08 0.11
##
## Reliability analysis
## Call: psych::alpha(x = RUMIN, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.88 0.7 9.1 0.011 2.8 1.2 0.68
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.9 0.92
## Duhachek 0.88 0.9 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pcs8R 0.88 0.88 0.84 0.71 7.4 0.014 0.0031 0.70
## pcs9R 0.87 0.87 0.82 0.68 6.5 0.016 0.0033 0.66
## pcs10R 0.87 0.87 0.83 0.70 7.0 0.015 0.0022 0.70
## pcs11R 0.87 0.87 0.82 0.69 6.6 0.016 0.0055 0.65
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pcs8R 199 0.88 0.86 0.80 0.76 2.9 1.4
## pcs9R 195 0.89 0.89 0.84 0.79 2.5 1.3
## pcs10R 192 0.88 0.88 0.82 0.77 2.6 1.3
## pcs11R 199 0.90 0.88 0.83 0.79 3.0 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## pcs8R 0.17 0.28 0.18 0.20 0.18 0.10
## pcs9R 0.25 0.32 0.18 0.13 0.11 0.11
## pcs10R 0.20 0.36 0.19 0.14 0.11 0.13
## pcs11R 0.14 0.34 0.17 0.14 0.22 0.10
##
## Reliability analysis
## Call: psych::alpha(x = AVOID, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.92 0.53 11 0.008 4 1.3 0.5
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.92 0.93
## Duhachek 0.9 0.92 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## inflex2r 0.92 0.92 0.92 0.56 11.5 0.0080 0.0110 0.57
## inflex3r 0.91 0.91 0.92 0.53 10.2 0.0088 0.0128 0.50
## inflex7r 0.91 0.91 0.92 0.53 10.3 0.0087 0.0151 0.51
## inflex8r 0.92 0.92 0.92 0.55 10.8 0.0083 0.0139 0.56
## inflex9r 0.90 0.90 0.91 0.51 9.3 0.0095 0.0115 0.50
## inflex11r 0.91 0.91 0.92 0.54 10.4 0.0086 0.0138 0.52
## inflex13r 0.91 0.91 0.91 0.52 9.9 0.0091 0.0130 0.50
## inflex14r 0.90 0.90 0.90 0.50 9.1 0.0097 0.0096 0.50
## inflex15r 0.91 0.90 0.91 0.51 9.5 0.0094 0.0130 0.50
## inflex16r 0.91 0.91 0.92 0.53 10.3 0.0087 0.0148 0.51
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## inflex2r 198 0.60 0.61 0.54 0.52 4.0 1.5
## inflex3r 198 0.75 0.75 0.72 0.68 4.1 1.7
## inflex7r 198 0.73 0.74 0.69 0.67 3.9 1.6
## inflex8r 199 0.67 0.68 0.63 0.59 4.6 1.6
## inflex9r 198 0.85 0.85 0.85 0.81 4.2 1.7
## inflex11r 199 0.73 0.73 0.69 0.66 3.6 1.8
## inflex13r 198 0.80 0.79 0.77 0.73 3.3 1.9
## inflex14r 199 0.88 0.88 0.88 0.85 4.0 1.7
## inflex15r 198 0.84 0.83 0.82 0.79 4.2 1.8
## inflex16r 199 0.73 0.74 0.70 0.67 4.5 1.6
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## inflex2r 0.06 0.14 0.12 0.32 0.22 0.09 0.06 0.1
## inflex3r 0.10 0.11 0.11 0.25 0.20 0.16 0.08 0.1
## inflex7r 0.09 0.11 0.16 0.29 0.20 0.08 0.08 0.1
## inflex8r 0.04 0.08 0.09 0.30 0.19 0.19 0.12 0.1
## inflex9r 0.07 0.11 0.16 0.25 0.15 0.14 0.12 0.1
## inflex11r 0.16 0.17 0.15 0.24 0.11 0.09 0.08 0.1
## inflex13r 0.28 0.12 0.11 0.20 0.13 0.09 0.07 0.1
## inflex14r 0.10 0.13 0.12 0.27 0.17 0.12 0.09 0.1
## inflex15r 0.07 0.15 0.12 0.26 0.15 0.15 0.12 0.1
## inflex16r 0.02 0.12 0.09 0.29 0.20 0.14 0.15 0.1
##
## Reliability analysis
## Call: psych::alpha(x = THOUGHTS, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.7 0.7 0.73 0.32 2.4 0.032 4.8 1.1 0.3
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.64 0.7 0.76
## Duhachek 0.64 0.7 0.77
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## inflex1r 0.62 0.62 0.63 0.29 1.6 0.043 0.040 0.26
## inflex4r 0.70 0.70 0.68 0.36 2.3 0.033 0.024 0.37
## inflex5r 0.63 0.63 0.61 0.30 1.7 0.040 0.035 0.26
## inflex6r 0.68 0.69 0.67 0.35 2.2 0.036 0.024 0.35
## inflex10r 0.64 0.64 0.67 0.31 1.8 0.041 0.051 0.24
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## inflex1r 199 0.75 0.73 0.65 0.54 4.2 1.8
## inflex4r 196 0.60 0.60 0.48 0.35 5.4 1.6
## inflex5r 197 0.71 0.72 0.66 0.52 5.6 1.5
## inflex6r 199 0.63 0.62 0.50 0.40 3.7 1.6
## inflex10r 199 0.70 0.71 0.59 0.50 5.1 1.6
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## inflex1r 0.08 0.16 0.11 0.22 0.20 0.12 0.13 0.10
## inflex4r 0.03 0.04 0.09 0.11 0.24 0.12 0.37 0.11
## inflex5r 0.02 0.03 0.05 0.13 0.18 0.16 0.43 0.10
## inflex6r 0.12 0.14 0.11 0.34 0.17 0.07 0.06 0.10
## inflex10r 0.03 0.04 0.10 0.17 0.21 0.23 0.23 0.10
##
## Reliability analysis
## Call: psych::alpha(x = PWB, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.77 0.78 0.81 0.28 3.5 0.023 5 0.95 0.29
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.72 0.77 0.81
## Duhachek 0.73 0.77 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pwb1r 0.74 0.75 0.78 0.27 2.9 0.027 0.030 0.27
## pwb2r 0.73 0.74 0.77 0.26 2.8 0.028 0.031 0.24
## pwb3R 0.75 0.76 0.79 0.29 3.2 0.026 0.035 0.30
## pwb4R 0.78 0.79 0.81 0.32 3.7 0.022 0.030 0.32
## pwb5r 0.75 0.76 0.79 0.28 3.2 0.026 0.027 0.27
## pwb6r 0.74 0.75 0.78 0.27 3.0 0.026 0.030 0.27
## pwb7R 0.78 0.78 0.81 0.31 3.7 0.023 0.028 0.32
## pwb8r 0.74 0.75 0.78 0.27 2.9 0.027 0.031 0.26
## pwb9r 0.74 0.74 0.78 0.27 2.9 0.027 0.029 0.27
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pwb1r 220 0.65 0.67 0.63 0.53 5.6 1.4
## pwb2r 219 0.71 0.72 0.69 0.60 5.1 1.6
## pwb3R 220 0.60 0.58 0.51 0.45 4.6 1.7
## pwb4R 220 0.44 0.42 0.30 0.26 4.4 1.8
## pwb5r 220 0.58 0.60 0.55 0.44 5.5 1.5
## pwb6r 219 0.63 0.65 0.61 0.50 5.6 1.5
## pwb7R 216 0.47 0.44 0.34 0.29 3.8 1.8
## pwb8r 217 0.66 0.67 0.63 0.54 5.1 1.6
## pwb9r 215 0.66 0.68 0.64 0.55 5.3 1.5
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## pwb1r 0.01 0.02 0.12 0.07 0.08 0.44 0.26 0.00
## pwb2r 0.02 0.05 0.15 0.11 0.16 0.34 0.18 0.00
## pwb3R 0.04 0.10 0.19 0.10 0.22 0.23 0.13 0.00
## pwb4R 0.05 0.13 0.19 0.12 0.16 0.22 0.13 0.00
## pwb5r 0.01 0.05 0.06 0.12 0.11 0.37 0.28 0.00
## pwb6r 0.02 0.04 0.06 0.08 0.13 0.37 0.30 0.00
## pwb7R 0.11 0.15 0.21 0.17 0.13 0.17 0.06 0.02
## pwb8r 0.03 0.05 0.11 0.09 0.15 0.40 0.17 0.01
## pwb9r 0.01 0.06 0.10 0.08 0.15 0.38 0.22 0.02
## Warning in psych::alpha(EWB, check.keys = TRUE): Some items were negatively correlated with total scale and were automatically reversed.
## This is indicated by a negative sign for the variable name.
##
## Reliability analysis
## Call: psych::alpha(x = EWB, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.79 0.81 0.86 0.19 4.1 0.021 3.9 0.53 0.19
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.75 0.79 0.83
## Duhachek 0.75 0.79 0.83
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## ewb1r 0.78 0.79 0.85 0.18 3.8 0.022 0.046 0.19
## ewb2r 0.78 0.79 0.85 0.18 3.7 0.022 0.048 0.18
## ewb3R- 0.81 0.82 0.87 0.21 4.5 0.019 0.040 0.23
## ewb4r 0.78 0.79 0.85 0.18 3.8 0.022 0.046 0.19
## ewb6r 0.78 0.79 0.86 0.18 3.8 0.022 0.048 0.19
## ewb7R 0.79 0.80 0.86 0.20 4.1 0.021 0.041 0.21
## ewb8r 0.78 0.79 0.85 0.18 3.8 0.022 0.046 0.19
## ewb10r 0.79 0.80 0.86 0.19 4.0 0.021 0.048 0.19
## ewb11R 0.78 0.80 0.85 0.19 3.9 0.022 0.044 0.21
## ewb12R 0.79 0.81 0.86 0.20 4.2 0.021 0.042 0.21
## ewb13r 0.77 0.79 0.85 0.18 3.7 0.023 0.046 0.18
## ewb15r 0.78 0.79 0.85 0.18 3.7 0.022 0.048 0.19
## ewb16R 0.78 0.80 0.86 0.19 4.0 0.022 0.044 0.19
## ewb17 0.78 0.80 0.86 0.19 3.9 0.022 0.047 0.19
## ewb18r 0.77 0.78 0.85 0.18 3.6 0.023 0.047 0.19
## ewb19R 0.79 0.80 0.86 0.19 4.1 0.021 0.042 0.21
## ewb20R 0.77 0.79 0.85 0.18 3.8 0.023 0.044 0.19
## ewb21r 0.78 0.80 0.86 0.19 3.9 0.021 0.047 0.19
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## ewb1r 210 0.497 0.54 0.515 0.405 3.9 1.03
## ewb2r 204 0.564 0.59 0.565 0.482 4.0 0.96
## ewb3R- 206 0.089 0.13 0.061 -0.038 4.6 1.15
## ewb4r 210 0.530 0.57 0.544 0.447 4.1 1.01
## ewb6r 208 0.516 0.57 0.530 0.454 4.2 0.81
## ewb7R 210 0.429 0.35 0.320 0.301 3.8 1.33
## ewb8r 209 0.481 0.53 0.495 0.392 4.0 1.02
## ewb10r 208 0.426 0.45 0.385 0.313 3.8 1.19
## ewb11R 209 0.550 0.48 0.462 0.439 3.4 1.35
## ewb12R 207 0.391 0.31 0.268 0.263 3.4 1.28
## ewb13r 205 0.595 0.63 0.611 0.513 4.0 1.02
## ewb15r 210 0.562 0.60 0.576 0.488 4.4 0.92
## ewb16R 209 0.464 0.40 0.366 0.350 3.4 1.31
## ewb17 211 0.443 0.47 0.420 0.341 3.9 1.06
## ewb18r 210 0.630 0.66 0.657 0.561 4.2 0.98
## ewb19R 209 0.438 0.37 0.332 0.320 3.5 1.29
## ewb20R 209 0.618 0.55 0.537 0.517 3.3 1.27
## ewb21r 203 0.432 0.47 0.424 0.334 3.8 1.06
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 miss
## ewb1r 0.01 0.10 0.22 0.33 0.34 0.00 0.05
## ewb2r 0.01 0.08 0.15 0.39 0.37 0.00 0.07
## ewb3R 0.27 0.27 0.27 0.15 0.04 0.00 0.06
## ewb4r 0.02 0.08 0.12 0.38 0.40 0.00 0.05
## ewb6r 0.00 0.05 0.11 0.46 0.38 0.00 0.05
## ewb7R 0.08 0.14 0.13 0.24 0.41 0.00 0.05
## ewb8r 0.02 0.08 0.16 0.36 0.38 0.00 0.05
## ewb10r 0.04 0.13 0.17 0.27 0.38 0.00 0.05
## ewb11R 0.11 0.15 0.20 0.25 0.29 0.00 0.05
## ewb12R 0.08 0.19 0.18 0.29 0.25 0.00 0.06
## ewb13r 0.02 0.07 0.18 0.36 0.37 0.00 0.07
## ewb15r 0.02 0.03 0.08 0.31 0.56 0.00 0.05
## ewb16R 0.10 0.17 0.24 0.22 0.26 0.00 0.05
## ewb17 0.02 0.07 0.23 0.34 0.31 0.02 0.04
## ewb18r 0.02 0.04 0.15 0.32 0.46 0.00 0.05
## ewb19R 0.09 0.16 0.15 0.33 0.27 0.00 0.05
## ewb20R 0.10 0.18 0.22 0.29 0.21 0.00 0.05
## ewb21r 0.03 0.08 0.22 0.36 0.31 0.00 0.08
##
## Reliability analysis
## Call: psych::alpha(x = PF, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.85 0.87 0.36 5.5 0.013 1.8 0.44 0.34
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.86 0.88
## Duhachek 0.83 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pf1R 0.86 0.85 0.86 0.38 5.5 0.014 0.031 0.37
## pf2R 0.84 0.83 0.85 0.35 4.8 0.015 0.035 0.32
## pf3R 0.85 0.84 0.86 0.36 5.1 0.014 0.035 0.37
## pf4R 0.83 0.82 0.84 0.34 4.6 0.016 0.031 0.34
## pf5R 0.83 0.82 0.84 0.34 4.6 0.016 0.028 0.32
## pf6R 0.87 0.86 0.88 0.41 6.3 0.013 0.021 0.40
## pf7R 0.83 0.82 0.84 0.33 4.5 0.016 0.030 0.33
## pf8R 0.83 0.82 0.84 0.34 4.6 0.016 0.028 0.34
## pf9R 0.83 0.82 0.84 0.33 4.5 0.016 0.031 0.32
## pf10R 0.85 0.84 0.86 0.37 5.4 0.014 0.032 0.38
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pf1R 200 0.49 0.53 0.46 0.40 1.3 0.47
## pf2R 201 0.67 0.68 0.64 0.58 1.6 0.68
## pf3R 201 0.60 0.61 0.55 0.50 1.7 0.67
## pf4R 199 0.75 0.75 0.74 0.67 1.7 0.73
## pf5R 199 0.77 0.75 0.75 0.69 2.1 0.73
## pf6R 201 0.28 0.33 0.20 0.18 1.9 0.44
## pf7R 202 0.78 0.77 0.75 0.70 1.6 0.73
## pf8R 201 0.76 0.74 0.72 0.67 1.6 0.72
## pf9R 199 0.78 0.78 0.76 0.71 2.1 0.74
## pf10R 198 0.56 0.55 0.47 0.44 2.4 0.68
##
## Non missing response frequency for each item
## 1 2 3 miss
## pf1R 0.76 0.23 0.01 0.09
## pf2R 0.50 0.39 0.11 0.09
## pf3R 0.46 0.43 0.11 0.09
## pf4R 0.50 0.35 0.15 0.10
## pf5R 0.24 0.46 0.30 0.10
## pf6R 0.13 0.80 0.06 0.09
## pf7R 0.55 0.31 0.14 0.08
## pf8R 0.51 0.35 0.14 0.09
## pf9R 0.22 0.44 0.34 0.10
## pf10R 0.11 0.41 0.48 0.10
##
## Reliability analysis
## Call: psych::alpha(x = INFLEX, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.93 0.39 10 0.0086 4.3 1.1 0.41
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.89 0.91 0.93
## Duhachek 0.89 0.91 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## inflex1r 0.90 0.90 0.93 0.39 9.4 0.0092 0.036 0.41
## inflex2r 0.91 0.91 0.93 0.40 9.9 0.0089 0.035 0.42
## inflex3r 0.90 0.90 0.92 0.38 9.1 0.0095 0.034 0.40
## inflex4r 0.92 0.92 0.93 0.43 11.1 0.0079 0.023 0.43
## inflex5r 0.91 0.91 0.93 0.41 10.6 0.0083 0.029 0.43
## inflex6r 0.91 0.91 0.93 0.39 9.5 0.0091 0.035 0.41
## inflex7r 0.90 0.90 0.93 0.38 9.4 0.0093 0.034 0.41
## inflex8r 0.90 0.90 0.93 0.39 9.4 0.0092 0.036 0.41
## inflex9r 0.90 0.90 0.92 0.37 9.0 0.0097 0.031 0.41
## inflex10r 0.91 0.90 0.93 0.39 9.5 0.0091 0.036 0.41
## inflex11r 0.90 0.90 0.93 0.38 9.3 0.0093 0.033 0.41
## inflex12r 0.91 0.91 0.93 0.40 10.0 0.0086 0.036 0.43
## inflex13r 0.90 0.90 0.92 0.38 9.3 0.0094 0.030 0.41
## inflex14r 0.90 0.90 0.92 0.37 8.9 0.0098 0.029 0.39
## inflex15r 0.90 0.90 0.92 0.37 9.0 0.0097 0.030 0.39
## inflex16r 0.90 0.90 0.93 0.38 9.2 0.0094 0.035 0.39
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## inflex1r 199 0.68 0.67 0.65 0.62 4.2 1.8
## inflex2r 198 0.56 0.57 0.52 0.50 4.0 1.5
## inflex3r 198 0.75 0.75 0.74 0.71 4.1 1.7
## inflex4r 196 0.27 0.28 0.23 0.18 5.4 1.6
## inflex5r 197 0.39 0.41 0.37 0.32 5.6 1.5
## inflex6r 199 0.65 0.65 0.62 0.59 3.7 1.6
## inflex7r 198 0.69 0.70 0.67 0.64 3.9 1.6
## inflex8r 199 0.67 0.68 0.65 0.62 4.6 1.6
## inflex9r 198 0.79 0.79 0.80 0.75 4.2 1.7
## inflex10r 199 0.66 0.66 0.63 0.60 5.1 1.6
## inflex11r 199 0.71 0.70 0.68 0.65 3.6 1.8
## inflex12r 199 0.56 0.55 0.50 0.47 4.4 1.9
## inflex13r 198 0.72 0.71 0.70 0.66 3.3 1.9
## inflex14r 199 0.82 0.81 0.82 0.78 4.0 1.7
## inflex15r 198 0.79 0.79 0.78 0.75 4.2 1.8
## inflex16r 199 0.73 0.74 0.72 0.69 4.5 1.6
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## inflex1r 0.08 0.16 0.11 0.22 0.20 0.12 0.13 0.10
## inflex2r 0.06 0.14 0.12 0.32 0.22 0.09 0.06 0.10
## inflex3r 0.10 0.11 0.11 0.25 0.20 0.16 0.08 0.10
## inflex4r 0.03 0.04 0.09 0.11 0.24 0.12 0.37 0.11
## inflex5r 0.02 0.03 0.05 0.13 0.18 0.16 0.43 0.10
## inflex6r 0.12 0.14 0.11 0.34 0.17 0.07 0.06 0.10
## inflex7r 0.09 0.11 0.16 0.29 0.20 0.08 0.08 0.10
## inflex8r 0.04 0.08 0.09 0.30 0.19 0.19 0.12 0.10
## inflex9r 0.07 0.11 0.16 0.25 0.15 0.14 0.12 0.10
## inflex10r 0.03 0.04 0.10 0.17 0.21 0.23 0.23 0.10
## inflex11r 0.16 0.17 0.15 0.24 0.11 0.09 0.08 0.10
## inflex12r 0.10 0.10 0.11 0.21 0.14 0.15 0.20 0.10
## inflex13r 0.28 0.12 0.11 0.20 0.13 0.09 0.07 0.10
## inflex14r 0.10 0.13 0.12 0.27 0.17 0.12 0.09 0.10
## inflex15r 0.07 0.15 0.12 0.26 0.15 0.15 0.12 0.10
## inflex16r 0.02 0.12 0.09 0.29 0.20 0.14 0.15 0.10
##
## Reliability analysis
## Call: psych::alpha(x = MASTERY, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.59 0.61 0.57 0.34 1.5 0.049 4.8 1.2 0.23
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.49 0.59 0.68
## Duhachek 0.50 0.59 0.69
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pwb7R 0.76 0.76 0.61 0.61 3.11 0.033 NA 0.61
## pwb8r 0.31 0.31 0.18 0.18 0.45 0.092 NA 0.18
## pwb9r 0.37 0.37 0.23 0.23 0.59 0.084 NA 0.23
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pwb7R 216 0.66 0.63 0.28 0.23 3.8 1.8
## pwb8r 217 0.81 0.82 0.72 0.53 5.1 1.6
## pwb9r 215 0.78 0.80 0.69 0.49 5.3 1.5
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## pwb7R 0.11 0.15 0.21 0.17 0.13 0.17 0.06 0.02
## pwb8r 0.03 0.05 0.11 0.09 0.15 0.40 0.17 0.01
## pwb9r 0.01 0.06 0.10 0.08 0.15 0.38 0.22 0.02
##
## Reliability analysis
## Call: psych::alpha(x = MASTERY2, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.76 0.61 0.61 3.1 0.033 5.2 1.4 0.61
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.68 0.76 0.81
## Duhachek 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
## pwb8r 0.65 0.61 0.37 0.61 1.6 NA 0 0.61
## pwb9r 0.57 0.61 0.37 0.61 1.6 NA 0 0.61
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pwb8r 217 0.91 0.9 0.7 0.61 5.1 1.6
## pwb9r 215 0.89 0.9 0.7 0.61 5.3 1.5
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## pwb8r 0.03 0.05 0.11 0.09 0.15 0.40 0.17 0.01
## pwb9r 0.01 0.06 0.10 0.08 0.15 0.38 0.22 0.02
##
## Reliability analysis
## Call: psych::alpha(x = AUTONOMY, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.57 0.59 0.56 0.33 1.5 0.052 5.2 1.2 0.23
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.46 0.57 0.66
## Duhachek 0.47 0.57 0.67
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pwb4R 0.75 0.75 0.60 0.60 3.03 0.033 NA 0.60
## pwb5r 0.36 0.37 0.23 0.23 0.59 0.084 NA 0.23
## pwb6r 0.27 0.27 0.16 0.16 0.37 0.097 NA 0.16
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pwb4R 220 0.67 0.62 0.26 0.21 4.4 1.8
## pwb5r 220 0.76 0.79 0.67 0.46 5.5 1.5
## pwb6r 219 0.79 0.82 0.73 0.52 5.6 1.5
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## pwb4R 0.05 0.13 0.19 0.12 0.16 0.22 0.13 0
## pwb5r 0.01 0.05 0.06 0.12 0.11 0.37 0.28 0
## pwb6r 0.02 0.04 0.06 0.08 0.13 0.37 0.30 0
##
## Reliability analysis
## Call: psych::alpha(x = AUTONOMY2, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.75 0.6 0.6 3 0.033 5.5 1.3 0.6
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.68 0.75 0.81
## Duhachek 0.69 0.75 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pwb5r 0.6 0.6 0.36 0.6 1.5 NA 0 0.6
## pwb6r 0.6 0.6 0.36 0.6 1.5 NA 0 0.6
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pwb5r 220 0.9 0.9 0.69 0.6 5.5 1.5
## pwb6r 219 0.9 0.9 0.69 0.6 5.6 1.5
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## pwb5r 0.01 0.05 0.06 0.12 0.11 0.37 0.28 0
## pwb6r 0.02 0.04 0.06 0.08 0.13 0.37 0.30 0
##
## Reliability analysis
## Call: psych::alpha(x = SA, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.64 0.65 0.58 0.38 1.8 0.042 5.1 1.2 0.38
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.55 0.64 0.72
## Duhachek 0.56 0.64 0.73
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pwb1r 0.55 0.55 0.38 0.38 1.23 0.060 NA 0.38
## pwb2r 0.36 0.37 0.22 0.22 0.58 0.085 NA 0.22
## pwb3R 0.70 0.70 0.54 0.54 2.33 0.040 NA 0.54
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pwb1r 220 0.74 0.77 0.60 0.45 5.6 1.4
## pwb2r 219 0.83 0.83 0.73 0.58 5.1 1.6
## pwb3R 220 0.73 0.70 0.43 0.35 4.6 1.7
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## pwb1r 0.01 0.02 0.12 0.07 0.08 0.44 0.26 0
## pwb2r 0.02 0.05 0.15 0.11 0.16 0.34 0.18 0
## pwb3R 0.04 0.10 0.19 0.10 0.22 0.23 0.13 0
##
## Reliability analysis
## Call: psych::alpha(x = SA2, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.7 0.7 0.54 0.54 2.3 0.04 5.3 1.3 0.54
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.61 0.7 0.77
## Duhachek 0.62 0.7 0.78
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pwb1r 0.49 0.54 0.29 0.54 1.2 NA 0 0.54
## pwb2r 0.60 0.54 0.29 0.54 1.2 NA 0 0.54
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pwb1r 220 0.86 0.88 0.64 0.54 5.6 1.4
## pwb2r 219 0.89 0.88 0.64 0.54 5.1 1.6
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## pwb1r 0.01 0.02 0.12 0.07 0.08 0.44 0.26 0
## pwb2r 0.02 0.05 0.15 0.11 0.16 0.34 0.18 0
## $p
## age pwb ewb inflexibility pcs
## age 0.000000e+00 2.343541e-03 2.094214e-03 7.154178e-01 6.594529e-03
## pwb 2.343541e-03 0.000000e+00 4.894320e-30 2.850826e-08 5.576585e-11
## ewb 2.094214e-03 4.894320e-30 0.000000e+00 6.850910e-03 1.314972e-05
## inflexibility 7.154178e-01 2.850826e-08 6.850910e-03 0.000000e+00 1.244674e-26
## pcs 6.594529e-03 5.576585e-11 1.314972e-05 1.244674e-26 0.000000e+00
## pfunction 4.566295e-02 5.269481e-06 4.056374e-02 3.152992e-09 1.627835e-07
## masteryR 7.508578e-01 1.043928e-38 1.381784e-10 1.512896e-05 1.303235e-05
## acceptR 6.015675e-05 9.535413e-52 6.408168e-18 5.241762e-09 3.103329e-13
## autonomyR 1.071353e-01 2.629433e-30 1.022852e-19 1.598699e-01 1.949821e-02
## age.onset 8.455764e-01 4.548195e-01 8.738515e-01 3.470524e-01 4.794951e-01
## pain.rating 6.025797e-01 6.159504e-01 7.526506e-01 7.233952e-04 1.260404e-06
## pfunction masteryR acceptR autonomyR age.onset
## age 4.566295e-02 7.508578e-01 6.015675e-05 1.071353e-01 0.8455764
## pwb 5.269481e-06 1.043928e-38 9.535413e-52 2.629433e-30 0.4548195
## ewb 4.056374e-02 1.381784e-10 6.408168e-18 1.022852e-19 0.8738515
## inflexibility 3.152992e-09 1.512896e-05 5.241762e-09 1.598699e-01 0.3470524
## pcs 1.627835e-07 1.303235e-05 3.103329e-13 1.949821e-02 0.4794951
## pfunction 0.000000e+00 2.688629e-05 2.082439e-03 1.920804e-03 0.9021173
## masteryR 2.688629e-05 0.000000e+00 2.659598e-07 3.474103e-11 0.5173175
## acceptR 2.082439e-03 2.659598e-07 0.000000e+00 5.010644e-06 0.2278763
## autonomyR 1.920804e-03 3.474103e-11 5.010644e-06 0.000000e+00 0.4249271
## age.onset 9.021173e-01 5.173175e-01 2.278763e-01 4.249271e-01 0.0000000
## pain.rating 3.112469e-04 8.455509e-01 3.831038e-01 5.284056e-01 0.5123154
## pain.rating
## age 6.025797e-01
## pwb 6.159504e-01
## ewb 7.526506e-01
## inflexibility 7.233952e-04
## pcs 1.260404e-06
## pfunction 3.112469e-04
## masteryR 8.455509e-01
## acceptR 3.831038e-01
## autonomyR 5.284056e-01
## age.onset 5.123154e-01
## pain.rating 0.000000e+00
##
## $lowCI
## age pwb ewb inflexibility pcs
## age 1.00000000 0.07421596 0.078192980 -0.16532943 -0.32324043
## pwb 0.07421596 1.00000000 0.600556296 -0.49384446 -0.54731371
## ewb 0.07819298 0.60055630 1.000000000 -0.32165537 -0.42370649
## inflexibility -0.16532943 -0.49384446 -0.321655367 1.00000000 0.57788132
## pcs -0.32324043 -0.54731371 -0.423706489 0.57788132 1.00000000
## pfunction -0.27483597 0.18413590 0.006309487 -0.51452642 -0.47510719
## masteryR -0.11183072 0.67050080 0.307249861 -0.42473452 -0.42573777
## acceptR 0.14041165 0.75524074 0.445686786 -0.50990415 -0.58501413
## autonomyR -0.02379836 0.59284596 0.473295229 -0.23582337 -0.29700954
## age.onset -0.11982233 -0.18170037 -0.145847012 -0.07276324 -0.08907836
## pain.rating -0.10604151 -0.17993692 -0.170753312 0.11193269 0.22534533
## pfunction masteryR acceptR autonomyR age.onset
## age -0.274835968 -0.1118307 0.14041165 -0.02379836 -0.11982233
## pwb 0.184135898 0.6705008 0.75524074 0.59284596 -0.18170037
## ewb 0.006309487 0.3072499 0.44568679 0.47329523 -0.14584701
## inflexibility -0.514526422 -0.4247345 -0.50990415 -0.23582337 -0.07276324
## pcs -0.475107191 -0.4257378 -0.58501413 -0.29700954 -0.08907836
## pfunction 1.000000000 0.1599380 0.07968999 0.08139807 -0.14658202
## masteryR 0.159938010 1.0000000 0.21697655 0.31430087 -0.17593278
## acceptR 0.079689993 0.2169766 1.00000000 0.17702832 -0.21161413
## autonomyR 0.081398073 0.3143009 0.17702832 1.00000000 -0.07877153
## age.onset -0.146582021 -0.1759328 -0.21161413 -0.07877153 1.00000000
## pain.rating -0.408143393 -0.1583580 -0.20628985 -0.18912839 -0.09620645
## pain.rating
## age -0.10604151
## pwb -0.17993692
## ewb -0.17075331
## inflexibility 0.11193269
## pcs 0.22534533
## pfunction -0.40814339
## masteryR -0.15835802
## acceptR -0.20628985
## autonomyR -0.18912839
## age.onset -0.09620645
## pain.rating 1.00000000
##
## $uppCI
## age pwb ewb inflexibility pcs
## age 1.000000000 0.32898491 0.33767614 0.11407472 -0.05449549
## pwb 0.328984913 1.00000000 0.74685943 -0.25535895 -0.32323454
## ewb 0.337676135 0.74685943 1.00000000 -0.05344658 -0.17121455
## inflexibility 0.114074715 -0.25535895 -0.05344658 1.00000000 0.73488702
## pcs -0.054495488 -0.32323454 -0.17121455 0.73488702 1.00000000
## pfunction -0.002804147 0.43344975 0.27677478 -0.28109604 -0.23300310
## masteryR 0.154415162 0.79282398 0.53034142 -0.17034324 -0.17223337
## acceptR 0.387353306 0.84860444 0.63580655 -0.27531470 -0.37202278
## autonomyR 0.238830981 0.73894213 0.65619767 0.03962721 -0.02694678
## age.onset 0.145887690 0.08217576 0.12425806 0.20419174 0.18771554
## pain.rating 0.181301140 0.10743567 0.12408370 0.39645532 0.48947924
## pfunction masteryR acceptR autonomyR age.onset
## age -0.002804147 0.15441516 0.38735331 0.23883098 0.14588769
## pwb 0.433449746 0.79282398 0.84860444 0.73894213 0.08217576
## ewb 0.276774781 0.53034142 0.63580655 0.65619767 0.12425806
## inflexibility -0.281096043 -0.17034324 -0.27531470 0.03962721 0.20419174
## pcs -0.233003102 -0.17223337 -0.37202278 -0.02694678 0.18771554
## pfunction 1.000000000 0.41414694 0.34321798 0.34473380 0.12949945
## masteryR 0.414146945 1.00000000 0.45248286 0.53179703 0.08931579
## acceptR 0.343217980 0.45248286 1.00000000 0.41779293 0.05119670
## autonomyR 0.344733795 0.53179703 0.41779293 1.00000000 0.18501160
## age.onset 0.129499447 0.08931579 0.05119670 0.18501160 1.00000000
## pain.rating -0.128423004 0.13012044 0.08030296 0.09802059 0.19089369
## pain.rating
## age 0.18130114
## pwb 0.10743567
## ewb 0.12408370
## inflexibility 0.39645532
## pcs 0.48947924
## pfunction -0.12842300
## masteryR 0.13012044
## acceptR 0.08030296
## autonomyR 0.09802059
## age.onset 0.19089369
## pain.rating 1.00000000
## Warning in corrplot(cor1b, method = "color", type = "upper", p.mat = cor1$p, :
## p.mat and corr may be not paired, their rownames and colnames are not totally
## same!
# Does the age at which an individual develops a chronic pain condition influence the well-being paradox?
# Hypothesis: Older adults who developed chronic pain later in life will report greater levels of
# eudaimonic well-being than older adults who developed chronic pain earlier in life
# Analysis: Age of pain onset as a moderator of the relationship between age and well-being
#########################################################################################################
# PSYCH WELLBEING
mydata$agec <- c(scale(mydata$age, center=TRUE, scale=FALSE))
mydata$age.onsetc <- c(scale(mydata$age.onset, center=TRUE, scale=FALSE))
i1 <- mydata$agec * mydata$age.onsetc
summary(m1 <- lm(pwb ~ agec + age.onsetc + i1, data = mydata))
##
## Call:
## lm(formula = pwb ~ agec + age.onsetc + i1, data = mydata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.79542 -0.64881 -0.00369 0.68053 2.09564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.003e+00 6.353e-02 78.748 < 2e-16 ***
## agec 2.066e-02 7.702e-03 2.682 0.00789 **
## age.onsetc -3.997e-04 3.938e-04 -1.015 0.31123
## i1 -6.624e-05 1.068e-04 -0.620 0.53583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9374 on 214 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.04671, Adjusted R-squared: 0.03335
## F-statistic: 3.495 on 3 and 214 DF, p-value: 0.01648
tab_model(m1, show.std = TRUE)
|
|
pwb
|
|
Predictors
|
Estimates
|
std. Beta
|
CI
|
standardized CI
|
p
|
|
(Intercept)
|
5.00
|
0.00
|
4.88 – 5.13
|
-0.13 – 0.13
|
<0.001
|
|
agec
|
0.02
|
0.19
|
0.01 – 0.04
|
0.05 – 0.33
|
0.008
|
|
age onsetc
|
-0.00
|
-0.08
|
-0.00 – 0.00
|
-0.23 – 0.07
|
0.311
|
|
i1
|
-0.00
|
-0.05
|
-0.00 – 0.00
|
-0.21 – 0.11
|
0.536
|
|
Observations
|
218
|
|
R2 / R2 adjusted
|
0.047 / 0.033
|
calc.relimp(m1)
## Response variable: pwb
## Total response variance: 0.9090668
## Analysis based on 218 observations
##
## 3 Regressors:
## agec age.onsetc i1
## Proportion of variance explained by model: 4.67%
## Metrics are not normalized (rela=FALSE).
##
## Relative importance metrics:
##
## lmg
## agec 0.037947730
## age.onsetc 0.004519115
## i1 0.004242579
##
## Average coefficients for different model sizes:
##
## 1X 2Xs 3Xs
## agec 2.222218e-02 2.212007e-02 2.065863e-02
## age.onsetc -2.617539e-04 -4.242169e-04 -3.997052e-04
## i1 -9.256936e-05 -8.792773e-05 -6.624158e-05
mod1 <- lm(pwb ~ agec + age.onsetc + i1, data=mydata)
summary(mod1)
##
## Call:
## lm(formula = pwb ~ agec + age.onsetc + i1, data = mydata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.79542 -0.64881 -0.00369 0.68053 2.09564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.003e+00 6.353e-02 78.748 < 2e-16 ***
## agec 2.066e-02 7.702e-03 2.682 0.00789 **
## age.onsetc -3.997e-04 3.938e-04 -1.015 0.31123
## i1 -6.624e-05 1.068e-04 -0.620 0.53583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9374 on 214 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.04671, Adjusted R-squared: 0.03335
## F-statistic: 3.495 on 3 and 214 DF, p-value: 0.01648
coef(summary(mod1))
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.003243e+00 0.0635349077 78.747939 5.321463e-160
## agec 2.065863e-02 0.0077022671 2.682149 7.885996e-03
## age.onsetc -3.997052e-04 0.0003937824 -1.015041 3.112326e-01
## i1 -6.624158e-05 0.0001068187 -0.620131 5.358313e-01
stargazer(mod1,type="text", title = "Hypothesis 1 - PWB")
##
## Hypothesis 1 - PWB
## ===============================================
## Dependent variable:
## ---------------------------
## pwb
## -----------------------------------------------
## agec 0.021***
## (0.008)
##
## age.onsetc -0.0004
## (0.0004)
##
## i1 -0.0001
## (0.0001)
##
## Constant 5.003***
## (0.064)
##
## -----------------------------------------------
## Observations 218
## R2 0.047
## Adjusted R2 0.033
## Residual Std. Error 0.937 (df = 214)
## F Statistic 3.495** (df = 3; 214)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
m1 <- rockchalk::plotSlopes(mod1, plotx="agec", modx="age.onsetc", xlab = "Age", ylab = "Psychological Wellbeing", modxVals = "std.dev")
# EUDAIMONIC WELLBEING
summary(m2 <- lm(ewb ~ agec + age.onsetc + i1, data = mydata))
##
## Call:
## lm(formula = ewb ~ agec + age.onsetc + i1, data = mydata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4323 -0.3751 0.0161 0.3970 1.1747
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.758e+00 3.652e-02 102.900 < 2e-16 ***
## agec 1.299e-02 4.538e-03 2.862 0.00465 **
## age.onsetc -6.459e-05 2.218e-04 -0.291 0.77118
## i1 -1.204e-05 6.027e-05 -0.200 0.84189
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5277 on 205 degrees of freedom
## (11 observations deleted due to missingness)
## Multiple R-squared: 0.04521, Adjusted R-squared: 0.03124
## F-statistic: 3.236 on 3 and 205 DF, p-value: 0.02325
tab_model(m2, show.std = TRUE)
|
|
ewb
|
|
Predictors
|
Estimates
|
std. Beta
|
CI
|
standardized CI
|
p
|
|
(Intercept)
|
3.76
|
0.00
|
3.69 – 3.83
|
-0.13 – 0.13
|
<0.001
|
|
agec
|
0.01
|
0.21
|
0.00 – 0.02
|
0.06 – 0.35
|
0.005
|
|
age onsetc
|
-0.00
|
-0.02
|
-0.00 – 0.00
|
-0.18 – 0.13
|
0.771
|
|
i1
|
-0.00
|
-0.02
|
-0.00 – 0.00
|
-0.18 – 0.15
|
0.842
|
|
Observations
|
209
|
|
R2 / R2 adjusted
|
0.045 / 0.031
|
mod2 <- lm(ewb ~ agec + age.onsetc + i1, data=mydata)
coef(summary(mod2))
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.758075e+00 3.652158e-02 102.9000998 2.021058e-178
## agec 1.298778e-02 4.538469e-03 2.8617094 4.650593e-03
## age.onsetc -6.458995e-05 2.217884e-04 -0.2912232 7.711750e-01
## i1 -1.203692e-05 6.026558e-05 -0.1997313 8.418887e-01
calc.relimp(m2)
## Response variable: ewb
## Total response variance: 0.2874547
## Analysis based on 209 observations
##
## 3 Regressors:
## agec age.onsetc i1
## Proportion of variance explained by model: 4.52%
## Metrics are not normalized (rela=FALSE).
##
## Relative importance metrics:
##
## lmg
## agec 0.0418641901
## age.onsetc 0.0006736711
## i1 0.0026744498
##
## Average coefficients for different model sizes:
##
## 1X 2Xs 3Xs
## agec 1.327888e-02 1.324413e-02 1.298778e-02
## age.onsetc -3.611092e-05 -1.042587e-04 -6.458995e-05
## i1 -4.807792e-05 -3.616583e-05 -1.203692e-05
stargazer(mod2,type="text", title = "Hypothesis 2 - EWB")
##
## Hypothesis 2 - EWB
## ===============================================
## Dependent variable:
## ---------------------------
## ewb
## -----------------------------------------------
## agec 0.013***
## (0.005)
##
## age.onsetc -0.0001
## (0.0002)
##
## i1 -0.00001
## (0.0001)
##
## Constant 3.758***
## (0.037)
##
## -----------------------------------------------
## Observations 209
## R2 0.045
## Adjusted R2 0.031
## Residual Std. Error 0.528 (df = 205)
## F Statistic 3.236** (df = 3; 205)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
m2 <- rockchalk::plotSlopes(mod2, plotx="agec", modx="age.onsetc", xlab = "Age", ylab = "Eudaimonic Wellbeing", modxVals = "std.dev")
# Does pain level, catastrophizing, flexibility, and health-related functioning influence well-being?
# Hypothesis: Pain- and health-related functioning will positively predict while catastrophizing
# and inflexibility will negatively predict well-being for older adults above and beyond age
# Analysis: Regression model predicting well-being, I included all of the main variables
# so I could use the relative importance analysis for more robust effect sizes
#########################################################################################################
# eudaimonic well-being
summary(m3 <- lm(ewb ~ age + age.onset + pfunction + pcs + inflexibility, data = mydata))
##
## Call:
## lm(formula = ewb ~ age + age.onset + pfunction + pcs + inflexibility,
## data = mydata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.38513 -0.28470 0.03462 0.34832 1.06821
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.980e+00 4.356e-01 6.841 1.04e-10 ***
## age 1.400e-02 4.461e-03 3.139 0.00197 **
## age.onset -4.908e-06 1.833e-04 -0.027 0.97866
## pfunction 1.049e-01 9.180e-02 1.143 0.25447
## pcs -1.358e-01 5.153e-02 -2.635 0.00910 **
## inflexibility 6.426e-03 4.464e-02 0.144 0.88569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5049 on 191 degrees of freedom
## (23 observations deleted due to missingness)
## Multiple R-squared: 0.1385, Adjusted R-squared: 0.116
## F-statistic: 6.142 on 5 and 191 DF, p-value: 2.658e-05
tab_model(m3, show.std = TRUE)
|
|
ewb
|
|
Predictors
|
Estimates
|
std. Beta
|
CI
|
standardized CI
|
p
|
|
(Intercept)
|
2.98
|
-0.00
|
2.12 – 3.84
|
-0.13 – 0.13
|
<0.001
|
|
age
|
0.01
|
0.22
|
0.01 – 0.02
|
0.08 – 0.36
|
0.002
|
|
age onset
|
-0.00
|
-0.00
|
-0.00 – 0.00
|
-0.13 – 0.13
|
0.979
|
|
pfunction
|
0.10
|
0.09
|
-0.08 – 0.29
|
-0.06 – 0.24
|
0.254
|
|
pcs
|
-0.14
|
-0.25
|
-0.24 – -0.03
|
-0.43 – -0.06
|
0.009
|
|
inflexibility
|
0.01
|
0.01
|
-0.08 – 0.09
|
-0.17 – 0.20
|
0.886
|
|
Observations
|
197
|
|
R2 / R2 adjusted
|
0.139 / 0.116
|
coef(summary(m3))
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.979708e+00 0.4355722170 6.84090490 1.037302e-10
## age 1.400095e-02 0.0044607023 3.13873156 1.966045e-03
## age.onset -4.907607e-06 0.0001832719 -0.02677774 9.786650e-01
## pfunction 1.049277e-01 0.0918004078 1.14299792 2.544701e-01
## pcs -1.357933e-01 0.0515292581 -2.63526546 9.097568e-03
## inflexibility 6.426049e-03 0.0446417874 0.14394696 8.856941e-01
calc.relimp(m3)
## Response variable: ewb
## Total response variance: 0.2883815
## Analysis based on 197 observations
##
## 5 Regressors:
## age age.onset pfunction pcs inflexibility
## Proportion of variance explained by model: 13.85%
## Metrics are not normalized (rela=FALSE).
##
## Relative importance metrics:
##
## lmg
## age 5.317228e-02
## age.onset 6.380677e-05
## pfunction 1.107486e-02
## pcs 5.919249e-02
## inflexibility 1.500986e-02
##
## Average coefficients for different model sizes:
##
## 1X 2Xs 3Xs 4Xs
## age 1.574905e-02 1.537247e-02 1.489897e-02 1.441446e-02
## age.onset -3.596447e-05 -1.655111e-05 -5.888329e-06 -3.121386e-06
## pfunction 1.697966e-01 1.293690e-01 1.076173e-01 1.007553e-01
## pcs -1.682992e-01 -1.639689e-01 -1.562595e-01 -1.464393e-01
## inflexibility -9.187423e-02 -6.199292e-02 -3.497975e-02 -1.183620e-02
## 5Xs
## age 1.400095e-02
## age.onset -4.907607e-06
## pfunction 1.049277e-01
## pcs -1.357933e-01
## inflexibility 6.426049e-03
stargazer(m3,type="text", title = "Hypothesis 3 - EWB")
##
## Hypothesis 3 - EWB
## ===============================================
## Dependent variable:
## ---------------------------
## ewb
## -----------------------------------------------
## age 0.014***
## (0.004)
##
## age.onset -0.00000
## (0.0002)
##
## pfunction 0.105
## (0.092)
##
## pcs -0.136***
## (0.052)
##
## inflexibility 0.006
## (0.045)
##
## Constant 2.980***
## (0.436)
##
## -----------------------------------------------
## Observations 197
## R2 0.139
## Adjusted R2 0.116
## Residual Std. Error 0.505 (df = 191)
## F Statistic 6.142*** (df = 5; 191)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
# psych well-being
summary(m4 <- lm(pwb ~ age + age.onset + pfunction + pcs + inflexibility, data = mydata))
##
## Call:
## lm(formula = pwb ~ age + age.onset + pfunction + pcs + inflexibility,
## data = mydata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.96220 -0.53869 0.03438 0.55213 2.34329
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.1742528 0.7238932 5.766 3.21e-08 ***
## age 0.0184374 0.0074134 2.487 0.01374 *
## age.onset -0.0001591 0.0003046 -0.522 0.60205
## pfunction 0.4202801 0.1525664 2.755 0.00644 **
## pcs -0.2524467 0.0856383 -2.948 0.00360 **
## inflexibility -0.1083894 0.0741918 -1.461 0.14568
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8391 on 191 degrees of freedom
## (23 observations deleted due to missingness)
## Multiple R-squared: 0.2527, Adjusted R-squared: 0.2331
## F-statistic: 12.92 on 5 and 191 DF, p-value: 7.911e-11
tab_model(m4, show.std = TRUE)
|
|
pwb
|
|
Predictors
|
Estimates
|
std. Beta
|
CI
|
standardized CI
|
p
|
|
(Intercept)
|
4.17
|
0.00
|
2.75 – 5.60
|
-0.12 – 0.12
|
<0.001
|
|
age
|
0.02
|
0.16
|
0.00 – 0.03
|
0.03 – 0.29
|
0.014
|
|
age onset
|
-0.00
|
-0.03
|
-0.00 – 0.00
|
-0.16 – 0.09
|
0.602
|
|
pfunction
|
0.42
|
0.19
|
0.12 – 0.72
|
0.06 – 0.33
|
0.006
|
|
pcs
|
-0.25
|
-0.26
|
-0.42 – -0.08
|
-0.43 – -0.08
|
0.004
|
|
inflexibility
|
-0.11
|
-0.13
|
-0.25 – 0.04
|
-0.30 – 0.04
|
0.146
|
|
Observations
|
197
|
|
R2 / R2 adjusted
|
0.253 / 0.233
|
coef(summary(m4))
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.1742527878 0.7238931545 5.7663935 3.211638e-08
## age 0.0184374018 0.0074134018 2.4870366 1.373852e-02
## age.onset -0.0001590914 0.0003045861 -0.5223199 6.020529e-01
## pfunction 0.4202800800 0.1525664039 2.7547354 6.442030e-03
## pcs -0.2524466650 0.0856383299 -2.9478233 3.599306e-03
## inflexibility -0.1083893698 0.0741917942 -1.4609347 1.456764e-01
calc.relimp(m4)
## Response variable: pwb
## Total response variance: 0.9182046
## Analysis based on 197 observations
##
## 5 Regressors:
## age age.onset pfunction pcs inflexibility
## Proportion of variance explained by model: 25.27%
## Metrics are not normalized (rela=FALSE).
##
## Relative importance metrics:
##
## lmg
## age 0.028272842
## age.onset 0.001710332
## pfunction 0.055907117
## pcs 0.101822274
## inflexibility 0.064969947
##
## Average coefficients for different model sizes:
##
## 1X 2Xs 3Xs 4Xs
## age 0.0203963668 0.0195820609 0.0188795194 0.0184520224
## age.onset -0.0002691214 -0.0002079986 -0.0001730202 -0.0001597285
## pfunction 0.6842606113 0.5614431553 0.4789200041 0.4331448331
## pcs -0.4332250274 -0.3858625534 -0.3391032513 -0.2941950531
## inflexibility -0.3276596663 -0.2618956681 -0.2018086605 -0.1498190492
## 5Xs
## age 0.0184374018
## age.onset -0.0001590914
## pfunction 0.4202800800
## pcs -0.2524466650
## inflexibility -0.1083893698
stargazer(m4,type="text", title = "Hypothesis 4 - PWB")
##
## Hypothesis 4 - PWB
## ===============================================
## Dependent variable:
## ---------------------------
## pwb
## -----------------------------------------------
## age 0.018**
## (0.007)
##
## age.onset -0.0002
## (0.0003)
##
## pfunction 0.420***
## (0.153)
##
## pcs -0.252***
## (0.086)
##
## inflexibility -0.108
## (0.074)
##
## Constant 4.174***
## (0.724)
##
## -----------------------------------------------
## Observations 197
## R2 0.253
## Adjusted R2 0.233
## Residual Std. Error 0.839 (df = 191)
## F Statistic 12.916*** (df = 5; 191)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01