Demographic information
med_data_recoded %>% summarise_at(vars(Age), list(mean = mean, sd = sd))## mean sd
## 1 40.9951 14.87435med_data_recoded %>% dplyr::count(Gender)## Gender n
## 1 Female 102
## 2 Male 102med_data_recoded %>% dplyr::count(Relstatus)## Relstatus n
## 1 Divorced 12
## 2 Married 88
## 3 Never 38
## 4 Remarried 3
## 5 Separated 3
## 6 Single 56
## 7 Widowed 4med_data_recoded %>% dplyr::count(Ethnicity)## Ethnicity n
## 1 Asian/AsianBritish 6
## 2 Black...Black British 3
## 3 Mixed/Multiple 4
## 4 Other 2
## 5 PNTS 1
## 6 White 188med_data_recoded %>% dplyr::count(Education)## Education n
## 1 A-level 53
## 2 Doctorate 2
## 3 GCSE 26
## 4 Postgrad 26
## 5 Undergrad 97describeBy(med_data$Age, med_data_recoded$Gender, mat = TRUE)## item group1 vars n mean sd median trimmed mad min max
## X11 1 Female 1 102 38.22549 13.28284 36 37.06098 14.8260 19 75
## X12 2 Male 1 102 43.76471 15.89830 40 43.25610 19.2738 19 82
## range skew kurtosis se
## X11 56 0.7048292 -0.2679939 1.315197
## X12 63 0.2531238 -0.9662789 1.574166Internal consistency
med_data_recoded %>% dplyr::select(starts_with("TABS")) %>%
psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.96 0.97 0.98 0.49 28 0.0034 5.5 1.1 0.49
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.96 0.96 0.97
## Duhachek 0.96 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
## TABS_New_1 0.96 0.96 0.98 0.50 27 0.0035 0.017 0.49
## TABS_New_2 0.96 0.96 0.98 0.49 27 0.0036 0.017 0.49
## TABS_New_3 0.96 0.96 0.98 0.49 27 0.0036 0.017 0.49
## TABS_New_4 0.96 0.97 0.98 0.50 28 0.0035 0.017 0.50
## TABS_New_5 0.96 0.96 0.98 0.49 27 0.0036 0.017 0.49
## TABS_New_6 0.96 0.97 0.98 0.50 28 0.0035 0.017 0.50
## TABS_New_7 0.96 0.96 0.98 0.49 27 0.0035 0.018 0.49
## TABS_New_8 0.96 0.96 0.98 0.49 27 0.0036 0.018 0.49
## TABS_New_9 0.96 0.96 0.98 0.49 27 0.0036 0.017 0.49
## TABS_New_10 0.96 0.96 0.98 0.49 27 0.0036 0.017 0.49
## TABS_New_11 0.96 0.96 0.98 0.50 28 0.0035 0.018 0.50
## TABS_New_12 0.96 0.97 0.98 0.50 28 0.0035 0.017 0.50
## TABS_New_13 0.96 0.96 0.98 0.49 27 0.0036 0.016 0.49
## TABS_New_14 0.96 0.96 0.98 0.49 27 0.0036 0.017 0.49
## TABS_New_15 0.96 0.96 0.98 0.49 27 0.0035 0.017 0.49
## TABS_New_16 0.96 0.96 0.98 0.49 27 0.0035 0.017 0.49
## TABS_New_17 0.96 0.96 0.98 0.49 27 0.0036 0.017 0.49
## TABS_New_18 0.96 0.97 0.98 0.50 28 0.0035 0.017 0.50
## TABS_New_19 0.96 0.96 0.98 0.49 27 0.0035 0.017 0.50
## TABS_New_20 0.96 0.96 0.98 0.49 27 0.0035 0.018 0.49
## TABS_New_21 0.96 0.97 0.98 0.50 28 0.0035 0.017 0.50
## TABS_New_22 0.96 0.96 0.98 0.49 27 0.0035 0.017 0.49
## TABS_New_23 0.97 0.97 0.98 0.51 29 0.0034 0.015 0.51
## TABS_New_24 0.96 0.96 0.98 0.49 27 0.0036 0.018 0.49
## TABS_New_25 0.96 0.97 0.98 0.50 28 0.0035 0.018 0.50
## TABS_New_26 0.96 0.96 0.98 0.49 27 0.0035 0.017 0.49
## TABS_New_27 0.96 0.97 0.98 0.50 29 0.0034 0.016 0.50
## TABS_New_28 0.96 0.97 0.98 0.50 28 0.0035 0.017 0.50
## TABS_New_29 0.96 0.97 0.98 0.50 28 0.0035 0.018 0.50
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## TABS_New_1 204 0.70 0.71 0.70 0.68 5.7 1.62
## TABS_New_2 204 0.81 0.81 0.81 0.80 5.4 1.59
## TABS_New_3 204 0.81 0.81 0.81 0.79 5.6 1.74
## TABS_New_4 204 0.69 0.68 0.67 0.66 5.7 1.70
## TABS_New_5 204 0.78 0.78 0.78 0.76 5.9 1.41
## TABS_New_6 204 0.62 0.63 0.61 0.59 5.9 1.60
## TABS_New_7 204 0.71 0.72 0.71 0.69 5.7 1.57
## TABS_New_8 204 0.75 0.75 0.74 0.73 5.5 1.68
## TABS_New_9 204 0.85 0.85 0.85 0.84 5.8 1.52
## TABS_New_10 204 0.83 0.83 0.83 0.81 5.4 1.74
## TABS_New_11 204 0.69 0.69 0.68 0.66 5.9 1.64
## TABS_New_12 204 0.64 0.65 0.64 0.61 6.0 1.49
## TABS_New_13 204 0.86 0.86 0.86 0.85 5.9 1.46
## TABS_New_14 204 0.79 0.79 0.79 0.77 5.8 1.64
## TABS_New_15 204 0.74 0.73 0.72 0.72 5.3 1.71
## TABS_New_16 204 0.74 0.72 0.72 0.71 4.5 1.81
## TABS_New_17 204 0.76 0.74 0.74 0.74 4.6 1.86
## TABS_New_18 204 0.71 0.69 0.68 0.68 4.4 1.86
## TABS_New_19 204 0.74 0.72 0.72 0.71 4.4 1.95
## TABS_New_20 204 0.76 0.77 0.77 0.75 5.8 1.33
## TABS_New_21 204 0.67 0.65 0.64 0.63 4.8 1.72
## TABS_New_22 204 0.75 0.73 0.73 0.73 4.3 1.93
## TABS_New_23 204 0.44 0.44 0.41 0.40 4.3 1.47
## TABS_New_24 204 0.77 0.76 0.76 0.75 5.5 1.56
## TABS_New_25 204 0.62 0.65 0.64 0.60 6.3 1.14
## TABS_New_26 204 0.72 0.75 0.75 0.71 6.4 1.03
## TABS_New_27 204 0.51 0.54 0.51 0.48 6.0 1.27
## TABS_New_28 204 0.61 0.64 0.63 0.59 6.4 0.86
## TABS_New_29 204 0.62 0.65 0.64 0.60 6.4 1.00
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## TABS_New_1 0.04 0.02 0.05 0.06 0.09 0.33 0.40 0
## TABS_New_2 0.03 0.03 0.07 0.09 0.22 0.25 0.30 0
## TABS_New_3 0.05 0.04 0.05 0.08 0.07 0.27 0.43 0
## TABS_New_4 0.05 0.03 0.04 0.07 0.07 0.30 0.44 0
## TABS_New_5 0.04 0.01 0.02 0.03 0.14 0.37 0.39 0
## TABS_New_6 0.05 0.01 0.03 0.06 0.08 0.31 0.46 0
## TABS_New_7 0.04 0.01 0.04 0.09 0.09 0.33 0.39 0
## TABS_New_8 0.04 0.03 0.08 0.09 0.11 0.31 0.33 0
## TABS_New_9 0.03 0.02 0.03 0.06 0.09 0.32 0.43 0
## TABS_New_10 0.05 0.04 0.08 0.12 0.09 0.31 0.31 0
## TABS_New_11 0.04 0.02 0.05 0.06 0.06 0.27 0.50 0
## TABS_New_12 0.03 0.02 0.04 0.04 0.07 0.28 0.50 0
## TABS_New_13 0.03 0.01 0.04 0.07 0.07 0.36 0.42 0
## TABS_New_14 0.04 0.03 0.03 0.09 0.08 0.27 0.46 0
## TABS_New_15 0.02 0.05 0.09 0.15 0.07 0.26 0.34 0
## TABS_New_16 0.10 0.06 0.09 0.23 0.18 0.21 0.14 0
## TABS_New_17 0.09 0.07 0.13 0.15 0.15 0.27 0.14 0
## TABS_New_18 0.08 0.09 0.18 0.21 0.11 0.17 0.17 0
## TABS_New_19 0.10 0.11 0.13 0.16 0.12 0.21 0.17 0
## TABS_New_20 0.02 0.01 0.01 0.14 0.12 0.34 0.35 0
## TABS_New_21 0.04 0.09 0.10 0.13 0.24 0.23 0.18 0
## TABS_New_22 0.10 0.11 0.13 0.24 0.09 0.17 0.17 0
## TABS_New_23 0.05 0.07 0.09 0.44 0.11 0.18 0.06 0
## TABS_New_24 0.02 0.05 0.04 0.10 0.19 0.24 0.36 0
## TABS_New_25 0.01 0.01 0.00 0.03 0.07 0.32 0.54 0
## TABS_New_26 0.00 0.01 0.02 0.02 0.06 0.27 0.61 0
## TABS_New_27 0.01 0.01 0.02 0.08 0.09 0.31 0.48 0
## TABS_New_28 0.00 0.00 0.01 0.02 0.06 0.29 0.60 0
## TABS_New_29 0.01 0.00 0.00 0.05 0.03 0.26 0.63 0med_data_recoded %>% dplyr::select(starts_with("TMFS")) %>%
psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.87 0.86 0.52 6.6 0.015 5.5 0.65 0.54
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.86 0.89
## Duhachek 0.84 0.86 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## TMFS_1 0.83 0.83 0.81 0.50 4.9 0.018 0.0081 0.49
## TMFS_2 0.85 0.86 0.84 0.55 6.0 0.016 0.0065 0.57
## TMFS_3 0.84 0.85 0.83 0.53 5.6 0.017 0.0093 0.55
## TMFS_4 0.84 0.85 0.83 0.53 5.7 0.017 0.0080 0.55
## TMFS_5 0.82 0.83 0.81 0.49 4.8 0.020 0.0074 0.48
## TMFS_6 0.85 0.86 0.84 0.55 6.1 0.016 0.0067 0.56
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## TMFS_1 204 0.82 0.83 0.81 0.74 5.6 0.73
## TMFS_2 204 0.73 0.73 0.65 0.60 5.6 0.86
## TMFS_3 204 0.78 0.77 0.70 0.65 5.2 0.96
## TMFS_4 204 0.78 0.76 0.69 0.65 5.1 0.96
## TMFS_5 204 0.85 0.85 0.83 0.77 5.4 0.81
## TMFS_6 204 0.70 0.72 0.64 0.59 5.8 0.69
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## TMFS_1 0 0 0.00 0.07 0.31 0.54 0.08 0
## TMFS_2 0 0 0.00 0.13 0.23 0.52 0.13 0
## TMFS_3 0 0 0.02 0.18 0.34 0.41 0.04 0
## TMFS_4 0 0 0.02 0.26 0.31 0.34 0.06 0
## TMFS_5 0 0 0.02 0.10 0.36 0.48 0.04 0
## TMFS_6 0 0 0.00 0.02 0.25 0.60 0.12 0med_data_recoded %>% dplyr::select(starts_with("GES")) %>%
psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.92 0.26 8.7 0.0098 3.1 0.52 0.27
##
## 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
## GES_1 0.89 0.89 0.91 0.25 8.1 0.0105 0.021 0.26
## GES_2 0.89 0.89 0.92 0.26 8.3 0.0102 0.022 0.27
## GES_3 0.90 0.89 0.91 0.26 8.3 0.0102 0.021 0.27
## GES_4 0.90 0.90 0.92 0.27 8.9 0.0098 0.021 0.28
## GES_5 0.89 0.89 0.91 0.25 8.1 0.0105 0.021 0.26
## GES_6 0.90 0.89 0.92 0.26 8.5 0.0100 0.021 0.28
## GES_7 0.89 0.89 0.91 0.25 8.2 0.0104 0.022 0.26
## GES_8 0.89 0.89 0.91 0.26 8.2 0.0103 0.022 0.26
## GES_9 0.90 0.90 0.92 0.27 9.0 0.0098 0.020 0.28
## GES_10 0.89 0.89 0.91 0.26 8.2 0.0104 0.021 0.26
## GES_11 0.90 0.90 0.92 0.26 8.6 0.0099 0.022 0.28
## GES_12 0.90 0.90 0.92 0.26 8.6 0.0098 0.022 0.28
## GES_13 0.89 0.89 0.91 0.25 8.0 0.0106 0.020 0.26
## GES_14 0.89 0.89 0.91 0.25 8.2 0.0104 0.021 0.26
## GES_15 0.90 0.90 0.92 0.27 8.9 0.0097 0.020 0.28
## GES_16 0.90 0.90 0.92 0.27 8.7 0.0098 0.021 0.28
## GES_17 0.90 0.90 0.92 0.26 8.6 0.0100 0.021 0.27
## GES_18 0.89 0.89 0.91 0.25 8.1 0.0105 0.021 0.26
## GES_19 0.89 0.89 0.91 0.25 8.0 0.0108 0.020 0.26
## GES_20 0.89 0.89 0.91 0.26 8.3 0.0103 0.021 0.26
## GES_21 0.89 0.89 0.91 0.25 8.1 0.0104 0.021 0.26
## GES_22 0.89 0.89 0.91 0.25 8.1 0.0105 0.021 0.26
## GES_23 0.89 0.89 0.91 0.25 8.2 0.0104 0.021 0.26
## GES_24 0.89 0.89 0.92 0.26 8.3 0.0102 0.022 0.27
## GES_25 0.90 0.90 0.92 0.27 9.1 0.0095 0.019 0.28
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## GES_1 204 0.68 0.68 0.67 0.64 3.2 0.96
## GES_2 204 0.57 0.58 0.56 0.52 3.6 0.95
## GES_3 204 0.56 0.56 0.54 0.50 3.4 0.97
## GES_4 204 0.30 0.31 0.27 0.24 2.8 0.84
## GES_5 204 0.67 0.67 0.66 0.63 3.2 0.89
## GES_6 204 0.48 0.47 0.44 0.41 2.9 1.08
## GES_7 204 0.62 0.62 0.60 0.57 2.4 1.06
## GES_8 204 0.60 0.60 0.58 0.55 3.4 0.96
## GES_9 204 0.23 0.26 0.21 0.18 2.0 0.68
## GES_10 204 0.62 0.61 0.60 0.57 2.7 0.96
## GES_11 204 0.43 0.42 0.38 0.36 3.2 1.07
## GES_12 204 0.43 0.42 0.38 0.36 2.8 1.10
## GES_13 204 0.70 0.71 0.70 0.66 3.6 0.97
## GES_14 204 0.65 0.64 0.64 0.60 3.4 1.02
## GES_15 204 0.31 0.32 0.28 0.25 2.1 0.89
## GES_16 204 0.35 0.37 0.33 0.29 3.2 0.89
## GES_17 204 0.43 0.45 0.42 0.38 4.0 0.70
## GES_18 204 0.68 0.69 0.68 0.64 2.9 1.01
## GES_19 204 0.75 0.74 0.74 0.70 2.7 1.13
## GES_20 204 0.61 0.60 0.59 0.55 3.3 1.06
## GES_21 204 0.66 0.66 0.65 0.61 3.3 0.94
## GES_22 204 0.66 0.66 0.66 0.61 3.4 0.96
## GES_23 204 0.63 0.63 0.62 0.59 3.5 0.95
## GES_24 204 0.57 0.57 0.54 0.52 3.1 1.07
## GES_25 204 0.21 0.21 0.15 0.13 2.5 0.99
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## GES_1 0.04 0.21 0.32 0.39 0.04 0
## GES_2 0.03 0.13 0.20 0.53 0.11 0
## GES_3 0.03 0.15 0.32 0.39 0.11 0
## GES_4 0.04 0.35 0.42 0.17 0.01 0
## GES_5 0.01 0.25 0.32 0.39 0.03 0
## GES_6 0.10 0.26 0.29 0.29 0.05 0
## GES_7 0.19 0.42 0.18 0.19 0.02 0
## GES_8 0.04 0.17 0.21 0.53 0.05 0
## GES_9 0.22 0.63 0.13 0.01 0.00 0
## GES_10 0.10 0.35 0.31 0.23 0.01 0
## GES_11 0.04 0.26 0.25 0.35 0.10 0
## GES_12 0.08 0.42 0.20 0.24 0.06 0
## GES_13 0.03 0.14 0.17 0.54 0.12 0
## GES_14 0.04 0.18 0.14 0.56 0.08 0
## GES_15 0.26 0.50 0.17 0.06 0.01 0
## GES_16 0.01 0.22 0.33 0.40 0.04 0
## GES_17 0.01 0.02 0.11 0.65 0.22 0
## GES_18 0.06 0.34 0.28 0.27 0.04 0
## GES_19 0.14 0.34 0.25 0.22 0.06 0
## GES_20 0.05 0.18 0.27 0.38 0.11 0
## GES_21 0.03 0.19 0.30 0.42 0.06 0
## GES_22 0.03 0.14 0.28 0.46 0.09 0
## GES_23 0.02 0.14 0.28 0.45 0.11 0
## GES_24 0.07 0.24 0.25 0.38 0.06 0
## GES_25 0.17 0.34 0.31 0.17 0.01 0med_data_recoded %>% dplyr::select("TABS_New_15",
"TABS_New_16",
"TABS_New_17",
"TABS_New_18",
"TABS_New_19",
"TABS_New_20",
"TABS_New_21",
"TABS_New_22",
"TABS_New_23",
"TABS_New_24") %>%
psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.93 0.94 0.58 14 0.0065 4.8 1.4 0.62
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.92 0.93 0.95
## Duhachek 0.92 0.93 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## TABS_New_15 0.93 0.93 0.93 0.58 12 0.0070 0.023 0.60
## TABS_New_16 0.92 0.92 0.92 0.56 11 0.0076 0.021 0.60
## TABS_New_17 0.92 0.92 0.93 0.56 12 0.0075 0.023 0.61
## TABS_New_18 0.92 0.92 0.93 0.57 12 0.0074 0.022 0.60
## TABS_New_19 0.92 0.92 0.92 0.55 11 0.0079 0.020 0.60
## TABS_New_20 0.93 0.93 0.93 0.60 13 0.0066 0.021 0.64
## TABS_New_21 0.93 0.92 0.93 0.57 12 0.0072 0.023 0.62
## TABS_New_22 0.92 0.92 0.92 0.56 11 0.0076 0.022 0.60
## TABS_New_23 0.94 0.94 0.94 0.63 16 0.0060 0.009 0.64
## TABS_New_24 0.93 0.92 0.93 0.57 12 0.0072 0.024 0.62
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## TABS_New_15 204 0.77 0.77 0.74 0.72 5.3 1.7
## TABS_New_16 204 0.86 0.86 0.85 0.82 4.5 1.8
## TABS_New_17 204 0.85 0.85 0.84 0.81 4.6 1.9
## TABS_New_18 204 0.84 0.83 0.81 0.79 4.4 1.9
## TABS_New_19 204 0.89 0.89 0.89 0.86 4.4 1.9
## TABS_New_20 204 0.66 0.67 0.63 0.60 5.8 1.3
## TABS_New_21 204 0.80 0.79 0.77 0.75 4.8 1.7
## TABS_New_22 204 0.87 0.86 0.85 0.82 4.3 1.9
## TABS_New_23 204 0.51 0.52 0.43 0.42 4.3 1.5
## TABS_New_24 204 0.81 0.82 0.80 0.77 5.5 1.6
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## TABS_New_15 0.02 0.05 0.09 0.15 0.07 0.26 0.34 0
## TABS_New_16 0.10 0.06 0.09 0.23 0.18 0.21 0.14 0
## TABS_New_17 0.09 0.07 0.13 0.15 0.15 0.27 0.14 0
## TABS_New_18 0.08 0.09 0.18 0.21 0.11 0.17 0.17 0
## TABS_New_19 0.10 0.11 0.13 0.16 0.12 0.21 0.17 0
## TABS_New_20 0.02 0.01 0.01 0.14 0.12 0.34 0.35 0
## TABS_New_21 0.04 0.09 0.10 0.13 0.24 0.23 0.18 0
## TABS_New_22 0.10 0.11 0.13 0.24 0.09 0.17 0.17 0
## TABS_New_23 0.05 0.07 0.09 0.44 0.11 0.18 0.06 0
## TABS_New_24 0.02 0.05 0.04 0.10 0.19 0.24 0.36 0Preliminary analyses
# Descriptives
med_data_total %>% summarise_at(vars(TABS_Total, GES_Total, TMFS_Total, GS_subscale_Total), list(mean = mean, sd = sd, range = range))## TABS_Total_mean GES_Total_mean TMFS_Total_mean GS_subscale_Total_mean
## 1 159.451 76.63235 32.89706 47.83333
## 2 159.451 76.63235 32.89706 47.83333
## TABS_Total_sd GES_Total_sd TMFS_Total_sd GS_subscale_Total_sd
## 1 32.34044 13.10763 3.889381 13.6772
## 2 32.34044 13.10763 3.889381 13.6772
## TABS_Total_range GES_Total_range TMFS_Total_range GS_subscale_Total_range
## 1 52 39 20 14
## 2 203 114 42 70# Correlation
corr <- corr.test(as.matrix(med_numeric_subset))
print(corr, short=FALSE)## Call:corr.test(x = as.matrix(med_numeric_subset))
## Correlation matrix
## TABS_Total GES_Total TMFS_Total GS_subscale_Total
## TABS_Total 1.00 -0.68 -0.34 0.90
## GES_Total -0.68 1.00 0.30 -0.73
## TMFS_Total -0.34 0.30 1.00 -0.34
## GS_subscale_Total 0.90 -0.73 -0.34 1.00
## Sample Size
## [1] 204
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## TABS_Total GES_Total TMFS_Total GS_subscale_Total
## TABS_Total 0 0 0 0
## GES_Total 0 0 0 0
## TMFS_Total 0 0 0 0
## GS_subscale_Total 0 0 0 0
##
## Confidence intervals based upon normal theory. To get bootstrapped values, try cor.ci
## raw.lower raw.r raw.upper raw.p lower.adj upper.adj
## TABS_-GES_T -0.74 -0.68 -0.59 0 -0.76 -0.57
## TABS_-TMFS_ -0.46 -0.34 -0.21 0 -0.47 -0.19
## TABS_-GS__T 0.86 0.90 0.92 0 0.85 0.93
## GES_T-TMFS_ 0.17 0.30 0.42 0 0.17 0.42
## GES_T-GS__T -0.79 -0.73 -0.66 0 -0.80 -0.63
## TMFS_-GS__T -0.46 -0.34 -0.22 0 -0.48 -0.19# Assumptions
mvn(med_numeric_subset)## $multivariateNormality
## Test HZ p value MVN
## 1 Henze-Zirkler 1.58661 8.338628e-09 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling TABS_Total 3.9693 <0.001 NO
## 2 Anderson-Darling GES_Total 0.4533 0.2685 YES
## 3 Anderson-Darling TMFS_Total 1.9545 1e-04 NO
## 4 Anderson-Darling GS_subscale_Total 1.4903 7e-04 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th
## TABS_Total 204 159.45098 32.340439 165 52 203 143.00 184.00
## GES_Total 204 76.63235 13.107631 77 39 114 67.75 86.25
## TMFS_Total 204 32.89706 3.889381 33 20 42 30.00 36.00
## GS_subscale_Total 204 47.83333 13.677196 50 14 70 39.75 58.25
## Skew Kurtosis
## TABS_Total -1.1037014 1.12280572
## GES_Total -0.1976592 -0.13028275
## TMFS_Total -0.4016190 -0.05196759
## GS_subscale_Total -0.4391299 -0.55614049boxplot(med_data_total$TABS)
boxplot(med_data_total$GS_subscale_Total)
# Descriptives by gender
med_data_total %>% group_by(Gender) %>% summarise_at(vars(TABS_Total, GES_Total, TMFS_Total, GS_subscale_Total), list(mean = mean, sd = sd))## # A tibble: 2 × 9
## Gender TABS_Total_mean GES_T…¹ TMFS_…² GS_su…³ TABS_…⁴ GES_T…⁵ TMFS_…⁶ GS_su…⁷
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Female 167. 75.4 32.4 50 26.1 12.6 4.12 12.7
## 2 Male 152. 77.9 33.4 45.7 36.1 13.6 3.59 14.3
## # … with abbreviated variable names ¹GES_Total_mean, ²TMFS_Total_mean,
## # ³GS_subscale_Total_mean, ⁴TABS_Total_sd, ⁵GES_Total_sd, ⁶TMFS_Total_sd,
## # ⁷GS_subscale_Total_sdDifferences by gender
In support of our first hypothesis, independent sample t-tests revealed significantly more positive attitudes toward TGD people in women, relative to men, t(202) = -3.46, p <.001; Men: M = 151.81, SD = 36.10; Women: M = 167.09, SD = 26.10; Cohen’s d = -.49, 95% CI [-.77, -.21]. Women also showed significantly more positive gender and sex beliefs relative to men, t(202) = -2.29, p = .023; Men: M = 45.67, SD = 14.30; Women: M = 50.0, SD = 12.70; Cohen’s d = -.32, 95% CI [-.60, -.05]. There was no significant differences in terms of adhering to traditional gender norms, t(202) = 1.87, p = .064; Men: M = 33.40, SD = 3.59; Women: M = 32.40, SD = 4.12; Cohen’s d = .26, 95% CI [-.01, .54], or having gender essentialist views between men and women, t(202) = 1.40, p = .164; Men: M = 77.91, SD = 13.6; Women: M = 75.35, SD = 12.6; Cohen’s d = .20, 95% CI [-.08, .47].
# Differences in variables by gender
t.test(TABS_Total ~ Gender, med_data_total, var.equal = TRUE) # Men show significantly lower TABS total scores##
## Two Sample t-test
##
## data: TABS_Total by Gender
## t = 3.463, df = 202, p-value = 0.0006519
## alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
## 95 percent confidence interval:
## 6.577554 23.971466
## sample estimates:
## mean in group Female mean in group Male
## 167.0882 151.8137t.test(GS_subscale_Total ~ Gender, med_data_total, var.equal = TRUE) # Men show significantly lower Gender total scores##
## Two Sample t-test
##
## data: GS_subscale_Total by Gender
## t = 2.286, df = 202, p-value = 0.02329
## alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
## 95 percent confidence interval:
## 0.595708 8.070959
## sample estimates:
## mean in group Female mean in group Male
## 50.00000 45.66667t.test(TMFS_Total ~ Gender, med_data_total, var.equal = TRUE) # No significant difference in tendency to promote gender norms##
## Two Sample t-test
##
## data: TMFS_Total by Gender
## t = -1.8654, df = 202, p-value = 0.06357
## alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
## 95 percent confidence interval:
## -2.07717806 0.05757022
## sample estimates:
## mean in group Female mean in group Male
## 32.39216 33.40196t.test(GES_Total ~ Gender, med_data_total, var.equal = TRUE) # No significant differences in views of gender essentialism##
## Two Sample t-test
##
## data: GES_Total by Gender
## t = -1.3974, df = 202, p-value = 0.1638
## alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
## 95 percent confidence interval:
## -6.169431 1.051784
## sample estimates:
## mean in group Female mean in group Male
## 75.35294 77.91176cohen.d(med_data_total$TABS_Total, med_data_total$Gender)## Call: cohen.d(x = med_data_total$TABS_Total, group = med_data_total$Gender)
## Cohen d statistic of difference between two means
## lower effect upper
## [1,] -0.77 -0.49 -0.21
##
## Multivariate (Mahalanobis) distance between groups
## [1] 0.49
## r equivalent of difference between two means
## data
## -0.24cohen.d(med_data_total$GS_subscale_Total, med_data_total$Gender)## Call: cohen.d(x = med_data_total$GS_subscale_Total, group = med_data_total$Gender)
## Cohen d statistic of difference between two means
## lower effect upper
## [1,] -0.6 -0.32 -0.05
##
## Multivariate (Mahalanobis) distance between groups
## [1] 0.32
## r equivalent of difference between two means
## data
## -0.16cohen.d(med_data_total$TMFS_Total, med_data_total$Gender)## Call: cohen.d(x = med_data_total$TMFS_Total, group = med_data_total$Gender)
## Cohen d statistic of difference between two means
## lower effect upper
## [1,] -0.01 0.26 0.54
##
## Multivariate (Mahalanobis) distance between groups
## [1] 0.26
## r equivalent of difference between two means
## data
## 0.13cohen.d(med_data_total$GES_Total, med_data_total$Gender)## Call: cohen.d(x = med_data_total$GES_Total, group = med_data_total$Gender)
## Cohen d statistic of difference between two means
## lower effect upper
## [1,] -0.08 0.2 0.47
##
## Multivariate (Mahalanobis) distance between groups
## [1] 0.2
## r equivalent of difference between two means
## data
## 0.1Mediation models
# TABS Total as outcome
model1 <- '
TMFS_Total ~ a1*Gender
GES_Total ~ a2*Gender + d21*TMFS_Total
TABS_Total ~ cp*Gender + b1*TMFS_Total + b2*GES_Total
# indirect and total effects
ab1 := a1*d21*b2 # Serial indirect
ab2 := a1*b1 # Through TMFS
ab3 := a2*b2 # Through GES
'
fit1 <- sem(model1, med_data_stan, se = "bootstrap", bootstrap = 10000)## Warning in lav_model_nvcov_bootstrap(lavmodel = lavmodel, lavsamplestats =
## lavsamplestats, : lavaan WARNING: 9 bootstrap runs failed or did not converge.summary(fit1)## lavaan 0.6-12 ended normally after 1 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 204
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 10000
## Number of successful bootstrap draws 9991
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## TMFS_Total ~
## Gender (a1) 0.260 0.140 1.860 0.063
## GES_Total ~
## Gender (a2) 0.120 0.133 0.904 0.366
## TMFS_Ttl (d21) 0.291 0.080 3.639 0.000
## TABS_Total ~
## Gender (cp) -0.316 0.095 -3.333 0.001
## TMFS_Ttl (b1) -0.135 0.057 -2.376 0.018
## GES_Totl (b2) -0.621 0.052 -11.925 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .TMFS_Total 0.978 0.094 10.462 0.000
## .GES_Total 0.903 0.098 9.228 0.000
## .TABS_Total 0.494 0.054 9.195 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ab1 -0.047 0.027 -1.704 0.088
## ab2 -0.035 0.025 -1.382 0.167
## ab3 -0.074 0.083 -0.892 0.372parameterEstimates(fit1, boot.ci.type = "bca.simple")## lhs op rhs label est se z pvalue ci.lower ci.upper
## 1 TMFS_Total ~ Gender a1 0.260 0.140 1.860 0.063 -0.011 0.533
## 2 GES_Total ~ Gender a2 0.120 0.133 0.904 0.366 -0.140 0.382
## 3 GES_Total ~ TMFS_Total d21 0.291 0.080 3.639 0.000 0.129 0.441
## 4 TABS_Total ~ Gender cp -0.316 0.095 -3.333 0.001 -0.505 -0.136
## 5 TABS_Total ~ TMFS_Total b1 -0.135 0.057 -2.376 0.018 -0.254 -0.032
## 6 TABS_Total ~ GES_Total b2 -0.621 0.052 -11.925 0.000 -0.722 -0.519
## 7 TMFS_Total ~~ TMFS_Total 0.978 0.094 10.462 0.000 0.817 1.188
## 8 GES_Total ~~ GES_Total 0.903 0.098 9.228 0.000 0.736 1.125
## 9 TABS_Total ~~ TABS_Total 0.494 0.054 9.195 0.000 0.407 0.623
## 10 Gender ~~ Gender 0.250 0.000 NA NA 0.250 0.250
## 11 ab1 := a1*d21*b2 ab1 -0.047 0.027 -1.704 0.088 -0.114 -0.003
## 12 ab2 := a1*b1 ab2 -0.035 0.025 -1.382 0.167 -0.109 -0.001
## 13 ab3 := a2*b2 ab3 -0.074 0.083 -0.892 0.372 -0.243 0.086summary(lm(TABS_Total ~ Gender, med_data_stan))##
## Call:
## lm(formula = TABS_Total ~ Gender, data = med_data_stan)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0863 -0.5529 0.2334 0.6775 1.5827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.23615 0.09644 2.449 0.015188 *
## GenderMale -0.47230 0.13638 -3.463 0.000652 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.974 on 202 degrees of freedom
## Multiple R-squared: 0.05604, Adjusted R-squared: 0.05137
## F-statistic: 11.99 on 1 and 202 DF, p-value: 0.0006519# Gender/Sex Subscale as outcome
model2 <- '
TMFS_Total ~ a1*Gender
GES_Total ~ a2*Gender + d21*TMFS_Total
GS_subscale_Total ~ cp*Gender + b1*TMFS_Total + b2*GES_Total
# indirect and total effects
ab1 := a1*d21*b2 # Serial indirect
ab2 := a1*b1 # Through TMFS
ab3 := a2*b2 # Through GES
'
fit2 <- sem(model2, med_data_stan, se = "bootstrap", bootstrap = 10000)## Warning in lav_model_nvcov_bootstrap(lavmodel = lavmodel, lavsamplestats =
## lavsamplestats, : lavaan WARNING: 7 bootstrap runs failed or did not converge.summary(fit2)## lavaan 0.6-12 ended normally after 1 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 204
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 10000
## Number of successful bootstrap draws 9993
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## TMFS_Total ~
## Gender (a1) 0.260 0.139 1.863 0.062
## GES_Total ~
## Gender (a2) 0.120 0.131 0.914 0.360
## TMFS_Ttl (d21) 0.291 0.081 3.579 0.000
## GS_subscale_Total ~
## Gender (cp) -0.149 0.091 -1.645 0.100
## TMFS_Ttl (b1) -0.131 0.054 -2.429 0.015
## GES_Totl (b2) -0.684 0.047 -14.507 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .TMFS_Total 0.978 0.095 10.296 0.000
## .GES_Total 0.903 0.098 9.186 0.000
## .GS_subscal_Ttl 0.442 0.043 10.376 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ab1 -0.052 0.030 -1.727 0.084
## ab2 -0.034 0.024 -1.421 0.155
## ab3 -0.082 0.090 -0.908 0.364parameterEstimates(fit2, boot.ci.type = "bca.simple")## lhs op rhs label est se z pvalue
## 1 TMFS_Total ~ Gender a1 0.260 0.139 1.863 0.062
## 2 GES_Total ~ Gender a2 0.120 0.131 0.914 0.360
## 3 GES_Total ~ TMFS_Total d21 0.291 0.081 3.579 0.000
## 4 GS_subscale_Total ~ Gender cp -0.149 0.091 -1.645 0.100
## 5 GS_subscale_Total ~ TMFS_Total b1 -0.131 0.054 -2.429 0.015
## 6 GS_subscale_Total ~ GES_Total b2 -0.684 0.047 -14.507 0.000
## 7 TMFS_Total ~~ TMFS_Total 0.978 0.095 10.296 0.000
## 8 GES_Total ~~ GES_Total 0.903 0.098 9.186 0.000
## 9 GS_subscale_Total ~~ GS_subscale_Total 0.442 0.043 10.376 0.000
## 10 Gender ~~ Gender 0.250 0.000 NA NA
## 11 ab1 := a1*d21*b2 ab1 -0.052 0.030 -1.727 0.084
## 12 ab2 := a1*b1 ab2 -0.034 0.024 -1.421 0.155
## 13 ab3 := a2*b2 ab3 -0.082 0.090 -0.908 0.364
## ci.lower ci.upper
## 1 -0.015 0.534
## 2 -0.140 0.378
## 3 0.127 0.444
## 4 -0.321 0.030
## 5 -0.235 -0.022
## 6 -0.777 -0.590
## 7 0.815 1.189
## 8 0.735 1.130
## 9 0.372 0.544
## 10 0.250 0.250
## 11 -0.124 -0.004
## 12 -0.100 -0.001
## 13 -0.261 0.094summary(lm(GS_subscale_Total ~ Gender, med_data_stan))##
## Call:
## lm(formula = GS_subscale_Total ~ Gender, data = med_data_stan)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3397 -0.6580 0.1462 0.6946 1.7791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1584 0.0980 1.616 0.1076
## GenderMale -0.3168 0.1386 -2.286 0.0233 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9898 on 202 degrees of freedom
## Multiple R-squared: 0.02522, Adjusted R-squared: 0.02039
## F-statistic: 5.226 on 1 and 202 DF, p-value: 0.02329