## Data Frame Summary
## df
## Dimensions: 452 x 5
## Duplicates: 394
##
## --------------------------------------------------------------------------------------------------------
## No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
## ---- -------------- --------------------- -------------------- --------------------- --------- ---------
## 1 Sex 1. female 327 (72.5%) IIIIIIIIIIIIII 451 1
## [factor] 2. male 122 (27.1%) IIIII (99.8%) (0.2%)
## 3. intersex 2 ( 0.4%)
##
## 2 Orientation 1. heterosexual 325 (72.2%) IIIIIIIIIIIIII 450 2
## [factor] 2. gay/lesbian 16 ( 3.6%) (99.6%) (0.4%)
## 3. bisexual 69 (15.3%) III
## 4. asexual 3 ( 0.7%)
## 5. queer 21 ( 4.7%)
## 6. questioning 12 ( 2.7%)
## 7. not listed 1 ( 0.2%)
## 8. private 3 ( 0.7%)
##
## 3 ParentDegree 1. parent degree 354 (78.5%) IIIIIIIIIIIIIII 451 1
## [factor] 2. no parent degree 97 (21.5%) IIII (99.8%) (0.2%)
##
## 4 CSUResidency 1. in state 301 (66.7%) IIIIIIIIIIIII 451 1
## [factor] 2. out of state 150 (33.3%) IIIIII (99.8%) (0.2%)
##
## 5 Living 1. alone 26 ( 5.8%) I 451 1
## [factor] 2. roommate(s) 397 (88.0%) IIIIIIIIIIIIIIIII (99.8%) (0.2%)
## 3. parents 28 ( 6.2%) I
## --------------------------------------------------------------------------------------------------------
## Sex Orientation ParentDegree
## 1 451 (99.8%) 450 (99.6%) 451 (99.8%)
## EndDate ResponseId CAS1 CAS2
## Length:452 Length:452 Min. :1.000 Min. :1.000
## Class :character Class :character 1st Qu.:1.000 1st Qu.:1.000
## Mode :character Mode :character Median :1.000 Median :1.000
## Mean :1.781 Mean :1.418
## 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :5.000 Max. :4.000
## NA's :2
## CAS3 CAS4 CAS5 CAS6
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :1.223 Mean :1.297 Mean :1.504 Mean :1.235
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:1.000
## Max. :4.000 Max. :4.000 Max. :5.000 Max. :4.000
## NA's :1
## CAS7 CAS8 CAS9 CAS10 CAS11
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.00 Median :1.000
## Mean :1.146 Mean :1.235 Mean :1.199 Mean :1.29 Mean :1.148
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.00 3rd Qu.:1.000
## Max. :4.000 Max. :5.000 Max. :5.000 Max. :5.00 Max. :4.000
##
## CAS12 CAS13 CAS14 CAS15
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000 Median :1.000
## Mean :1.119 Mean :1.135 Mean :1.812 Mean :1.898
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :5.000 Max. :5.000
## NA's :1 NA's :1
## CAS16 CAS17 CAS18 CAS19
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:3.000 1st Qu.:4.000
## Median :2.000 Median :3.000 Median :4.000 Median :4.000
## Mean :2.268 Mean :2.732 Mean :3.841 Mean :4.208
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :1
## CAS20 CAS21 CAS22 CAPT1 CAPT2
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.00 Median :3.000 Median :2.000 Median :3.000
## Mean :3.285 Mean :2.77 Mean :2.792 Mean :2.491 Mean :2.666
## 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.00 Max. :5.000 Max. :4.000 Max. :5.000
## NA's :12
## CAPT3 CAPT4 CAPT5_1 CAPT5_2 CAPT5_3
## Min. :1.00 Min. :1.000 Min. :1 Min. :1 Min. :1
## 1st Qu.:2.00 1st Qu.:1.000 1st Qu.:1 1st Qu.:1 1st Qu.:1
## Median :2.00 Median :2.000 Median :1 Median :1 Median :1
## Mean :2.43 Mean :1.701 Mean :1 Mean :1 Mean :1
## 3rd Qu.:3.00 3rd Qu.:2.000 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1
## Max. :5.00 Max. :4.000 Max. :1 Max. :1 Max. :1
## NA's :1 NA's :211 NA's :196 NA's :252
## CAPT5_4 CAPT5_5 CAPT5_6 CAPT5_6_TEXT CAPT6
## Min. :1 Min. :1 Min. :1 Length:452 Yes : 77
## 1st Qu.:1 1st Qu.:1 1st Qu.:1 Class :character No :169
## Median :1 Median :1 Median :1 Mode :character Don't know:180
## Mean :1 Mean :1 Mean :1 Yes- text : 26
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1 Max. :1
## NA's :333 NA's :397 NA's :434
## CAPT6_4_TEXT CAPT7_1 CAPT7_2 CAPT7_3 CAPT7_4
## Length:452 Mode:logical Mode:logical Mode:logical Mode:logical
## Class :character TRUE:323 TRUE:222 TRUE:302 TRUE:103
## Mode :character NA's:129 NA's:230 NA's:150 NA's:349
##
##
##
##
## CAPT7_5 CAPT7_6 CAPT7_6_TEXT RACE
## Mode:logical Min. :1 Length:452 White :327
## TRUE:210 1st Qu.:1 Class :character Multiracial: 61
## NA's:242 Median :1 Mode :character Latina(o) : 31
## Mean :1 Asian : 13
## 3rd Qu.:1 Black : 9
## Max. :1 (Other) : 10
## NA's :435 NA's : 1
## CSUStatus CSUResidency Living Sex
## attend :450 in state :301 alone : 26 female :327
## do not attend: 0 out of state:150 roommate(s):397 male :122
## NA's : 2 NA's : 1 parents : 28 intersex: 2
## NA's : 1 NA's : 1
##
##
##
## Orientation ParentDegree Employ
## heterosexual:325 parent degree :354 employed :169
## bisexual : 69 no parent degree: 97 not employed:282
## queer : 21 NA's : 1 NA's : 1
## gay/lesbian : 16
## questioning : 12
## (Other) : 7
## NA's : 2
## Income Income_r Age Fac1
## Length:452 Min. : 0 < 18 y : 0 Min. :1.000
## Class :character 1st Qu.: 0 18-22 y:435 1st Qu.:1.000
## Mode :character Median : 4000 > 23 y : 16 Median :1.125
## Mean : 10402 NA's : 1 Mean :1.355
## 3rd Qu.: 12000 3rd Qu.:1.500
## Max. :160000 Max. :3.250
## NA's :173
## Fac4 Fac3 Fac2 sumAct
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.667 1st Qu.:1.000
## Median :1.000 Median :1.667 Median :3.333 Median :2.000
## Mean :1.178 Mean :1.992 Mean :3.271 Mean :1.927
## 3rd Qu.:1.000 3rd Qu.:3.000 3rd Qu.:3.833 3rd Qu.:3.000
## Max. :4.200 Max. :5.000 Max. :5.000 Max. :5.000
##
Table 1. Summary of Demographic Variables
|
Variable
|
Category
|
Count
|
Percent.Var1
|
Percent.Freq
|
|
Sex
|
|
Sex
|
female
|
327
|
female
|
72.5
|
|
Sex
|
male
|
122
|
male
|
27.1
|
|
Sex
|
intersex
|
2
|
intersex
|
0.4
|
|
RACE
|
|
RACE
|
Asian
|
13
|
Asian
|
2.9
|
|
RACE
|
Black
|
9
|
Black
|
2.0
|
|
RACE
|
Indigenous
|
4
|
Indigenous
|
0.9
|
|
RACE
|
Latina(o)
|
31
|
Latina(o)
|
6.9
|
|
RACE
|
Middle Eastern
|
4
|
Middle Eastern
|
0.9
|
|
RACE
|
White
|
327
|
White
|
72.5
|
|
RACE
|
Multiracial
|
61
|
Multiracial
|
13.5
|
|
RACE
|
private
|
2
|
private
|
0.4
|
|
Orientation
|
|
Orientation
|
heterosexual
|
325
|
heterosexual
|
72.2
|
|
Orientation
|
gay/lesbian
|
16
|
gay/lesbian
|
3.6
|
|
Orientation
|
bisexual
|
69
|
bisexual
|
15.3
|
|
Orientation
|
asexual
|
3
|
asexual
|
0.7
|
|
Orientation
|
queer
|
21
|
queer
|
4.7
|
|
Orientation
|
questioning
|
12
|
questioning
|
2.7
|
|
Orientation
|
not listed
|
1
|
not listed
|
0.2
|
|
Orientation
|
private
|
3
|
private
|
0.7
|
|
ParentDegree
|
|
ParentDegree
|
parent degree
|
354
|
parent degree
|
78.5
|
|
ParentDegree
|
no parent degree
|
97
|
no parent degree
|
21.5
|
|
CSUResidency
|
|
CSUResidency
|
in state
|
301
|
in state
|
66.7
|
|
CSUResidency
|
out of state
|
150
|
out of state
|
33.3
|
|
Living
|
|
Living
|
alone
|
26
|
alone
|
5.8
|
|
Living
|
roommate(s)
|
397
|
roommate(s)
|
88.0
|
|
Living
|
parents
|
28
|
parents
|
6.2
|
Table 1. Summary of Demographic Variables
|
Variable
|
Category
|
Count
|
Percent.Var1
|
Percent.Freq
|
|
CSUResidency
|
|
Sex
|
female
|
327
|
female
|
72.5
|
|
Sex
|
male
|
122
|
male
|
27.1
|
|
Living
|
|
Sex
|
intersex
|
2
|
intersex
|
0.4
|
|
RACE
|
Asian
|
13
|
Asian
|
2.9
|
|
RACE
|
Black
|
9
|
Black
|
2.0
|
|
Orientation
|
|
RACE
|
Indigenous
|
4
|
Indigenous
|
0.9
|
|
RACE
|
Latina(o)
|
31
|
Latina(o)
|
6.9
|
|
RACE
|
Middle Eastern
|
4
|
Middle Eastern
|
0.9
|
|
RACE
|
White
|
327
|
White
|
72.5
|
|
RACE
|
Multiracial
|
61
|
Multiracial
|
13.5
|
|
RACE
|
private
|
2
|
private
|
0.4
|
|
Orientation
|
heterosexual
|
325
|
heterosexual
|
72.2
|
|
Orientation
|
gay/lesbian
|
16
|
gay/lesbian
|
3.6
|
|
ParentDegree
|
|
Orientation
|
bisexual
|
69
|
bisexual
|
15.3
|
|
Orientation
|
asexual
|
3
|
asexual
|
0.7
|
|
RACE
|
|
Orientation
|
queer
|
21
|
queer
|
4.7
|
|
Orientation
|
questioning
|
12
|
questioning
|
2.7
|
|
Orientation
|
not listed
|
1
|
not listed
|
0.2
|
|
Orientation
|
private
|
3
|
private
|
0.7
|
|
ParentDegree
|
parent degree
|
354
|
parent degree
|
78.5
|
|
ParentDegree
|
no parent degree
|
97
|
no parent degree
|
21.5
|
|
CSUResidency
|
in state
|
301
|
in state
|
66.7
|
|
CSUResidency
|
out of state
|
150
|
out of state
|
33.3
|
|
Sex
|
|
Living
|
alone
|
26
|
alone
|
5.8
|
|
Living
|
roommate(s)
|
397
|
roommate(s)
|
88.0
|
|
Living
|
parents
|
28
|
parents
|
6.2
|
Table 2. Summary of Climate Behavior and Psychological Measures
|
Variable
|
Mean
|
SD
|
Min
|
Median
|
Max
|
|
sumAct
|
1.93
|
1.22
|
0
|
2.000000
|
5.00
|
|
Fac1
|
1.36
|
0.51
|
1
|
1.125000
|
3.25
|
|
Fac2
|
3.27
|
0.83
|
1
|
3.333333
|
5.00
|
|
Fac3
|
1.99
|
1.10
|
1
|
1.666667
|
5.00
|
|
Fac4
|
1.18
|
0.43
|
1
|
1.000000
|
4.20
|
Frequency data for qualitative data
## CAPT5_1 CAPT5_2 CAPT5_3 CAPT5_4 CAPT5_5 CAPT5_6
## 241 256 200 119 55 18
Histograms
Factor Analysis
## Factor Analysis using method = minres
## Call: fa(r = df %>% dplyr::select(CAS1:CAS8), nfactors = 1, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## CAS1 0.70 0.49 0.51 1
## CAS2 0.72 0.51 0.49 1
## CAS3 0.64 0.41 0.59 1
## CAS4 0.75 0.56 0.44 1
## CAS5 0.68 0.47 0.53 1
## CAS6 0.82 0.68 0.32 1
## CAS7 0.60 0.36 0.64 1
## CAS8 0.73 0.53 0.47 1
##
## MR1
## SS loadings 4.02
## Proportion Var 0.50
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 28 with the objective function = 4.12 with Chi Square = 1843.82
## df of the model are 20 and the objective function was 0.67
##
## The root mean square of the residuals (RMSR) is 0.08
## The df corrected root mean square of the residuals is 0.09
##
## The harmonic n.obs is 451 with the empirical chi square 145.76 with prob < 4.1e-21
## The total n.obs was 452 with Likelihood Chi Square = 301.22 with prob < 4.5e-52
##
## Tucker Lewis Index of factoring reliability = 0.783
## RMSEA index = 0.176 and the 90 % confidence intervals are 0.159 0.194
## BIC = 178.95
## Fit based upon off diagonal values = 0.98
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.95
## Multiple R square of scores with factors 0.90
## Minimum correlation of possible factor scores 0.80
##
## Reliability analysis
## Call: alpha(x = df %>% dplyr::select(CAS1:CAS8))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.89 0.9 0.5 7.9 0.0082 1.4 0.51 0.5
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.86 0.88 0.89
## Duhachek 0.86 0.88 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CAS1 0.86 0.87 0.87 0.50 7.0 0.0095 0.0092 0.50
## CAS2 0.86 0.87 0.87 0.50 6.9 0.0096 0.0096 0.50
## CAS3 0.87 0.88 0.88 0.51 7.3 0.0089 0.0089 0.51
## CAS4 0.86 0.87 0.87 0.49 6.7 0.0096 0.0113 0.47
## CAS5 0.87 0.88 0.88 0.50 7.1 0.0092 0.0098 0.50
## CAS6 0.85 0.86 0.87 0.47 6.3 0.0098 0.0088 0.47
## CAS7 0.87 0.88 0.89 0.52 7.6 0.0088 0.0084 0.51
## CAS8 0.86 0.87 0.88 0.49 6.8 0.0094 0.0106 0.51
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CAS1 452 0.79 0.75 0.71 0.68 1.8 0.93
## CAS2 450 0.77 0.76 0.73 0.68 1.4 0.73
## CAS3 452 0.68 0.70 0.66 0.59 1.2 0.59
## CAS4 451 0.78 0.78 0.75 0.70 1.3 0.65
## CAS5 452 0.76 0.73 0.68 0.64 1.5 0.88
## CAS6 452 0.81 0.83 0.82 0.76 1.2 0.57
## CAS7 452 0.62 0.67 0.60 0.55 1.1 0.45
## CAS8 452 0.75 0.76 0.72 0.67 1.2 0.62
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## CAS1 0.51 0.25 0.20 0.04 0.00 0
## CAS2 0.71 0.19 0.08 0.02 0.00 0
## CAS3 0.85 0.09 0.05 0.01 0.00 0
## CAS4 0.80 0.12 0.07 0.01 0.00 0
## CAS5 0.70 0.16 0.10 0.04 0.01 0
## CAS6 0.83 0.12 0.05 0.01 0.00 0
## CAS7 0.89 0.09 0.02 0.01 0.00 0
## CAS8 0.85 0.09 0.05 0.01 0.00 0
## Factor Analysis using method = minres
## Call: fa(r = df %>% dplyr::select(CAS9:CAS13), nfactors = 1, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## CAS9 0.86 0.75 0.25 1
## CAS10 0.63 0.39 0.61 1
## CAS11 0.88 0.77 0.23 1
## CAS12 0.85 0.72 0.28 1
## CAS13 0.72 0.52 0.48 1
##
## MR1
## SS loadings 3.16
## Proportion Var 0.63
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 10 with the objective function = 2.96 with Chi Square = 1327.66
## df of the model are 5 and the objective function was 0.05
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic n.obs is 452 with the empirical chi square 7.22 with prob < 0.2
## The total n.obs was 452 with Likelihood Chi Square = 22.97 with prob < 0.00034
##
## Tucker Lewis Index of factoring reliability = 0.973
## RMSEA index = 0.089 and the 90 % confidence intervals are 0.054 0.128
## BIC = -7.6
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.96
## Multiple R square of scores with factors 0.92
## Minimum correlation of possible factor scores 0.83
##
## Reliability analysis
## Call: alpha(x = df %>% dplyr::select(CAS9:CAS13))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.89 0.88 0.62 8.1 0.0092 1.2 0.43 0.62
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.86 0.88 0.89
## Duhachek 0.86 0.88 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CAS9 0.83 0.85 0.82 0.59 5.7 0.014 0.0139 0.59
## CAS10 0.89 0.90 0.87 0.69 8.8 0.008 0.0043 0.68
## CAS11 0.83 0.85 0.82 0.58 5.6 0.013 0.0106 0.60
## CAS12 0.84 0.85 0.83 0.59 5.9 0.012 0.0118 0.60
## CAS13 0.86 0.88 0.86 0.64 7.3 0.011 0.0126 0.66
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CAS9 452 0.88 0.88 0.85 0.80 1.2 0.55
## CAS10 452 0.78 0.74 0.63 0.60 1.3 0.67
## CAS11 452 0.87 0.89 0.86 0.80 1.1 0.50
## CAS12 452 0.85 0.87 0.84 0.78 1.1 0.41
## CAS13 451 0.78 0.80 0.72 0.66 1.1 0.46
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## CAS9 0.86 0.09 0.04 0.01 0 0
## CAS10 0.81 0.10 0.07 0.01 0 0
## CAS11 0.90 0.06 0.03 0.01 0 0
## CAS12 0.91 0.07 0.02 0.00 0 0
## CAS13 0.91 0.06 0.03 0.01 0 0
## Factor Analysis using method = minres
## Call: fa(r = df %>% dplyr::select(CAS14:CAS16), nfactors = 1, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## CAS14 0.91 0.83 0.17 1
## CAS15 0.93 0.86 0.14 1
## CAS16 0.74 0.54 0.46 1
##
## MR1
## SS loadings 2.23
## Proportion Var 0.74
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 3 with the objective function = 1.92 with Chi Square = 863.62
## df of the model are 0 and the objective function was 0
##
## The root mean square of the residuals (RMSR) is 0
## The df corrected root mean square of the residuals is NA
##
## The harmonic n.obs is 451 with the empirical chi square 0 with prob < NA
## The total n.obs was 452 with Likelihood Chi Square = 0 with prob < NA
##
## Tucker Lewis Index of factoring reliability = -Inf
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.96
## Multiple R square of scores with factors 0.92
## Minimum correlation of possible factor scores 0.85
##
## Reliability analysis
## Call: alpha(x = df %>% dplyr::select(CAS14:CAS16))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.89 0.86 0.73 8.2 0.0097 2 1.1 0.68
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.86 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
## CAS14 0.81 0.81 0.68 0.68 4.3 0.0178 NA 0.68
## CAS15 0.79 0.80 0.67 0.67 4.1 0.0189 NA 0.67
## CAS16 0.91 0.91 0.84 0.84 10.7 0.0081 NA 0.84
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CAS14 451 0.91 0.92 0.89 0.82 1.8 1.1
## CAS15 452 0.92 0.93 0.90 0.82 1.9 1.2
## CAS16 452 0.88 0.87 0.73 0.71 2.3 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## CAS14 0.57 0.15 0.18 0.07 0.02 0
## CAS15 0.59 0.09 0.19 0.10 0.03 0
## CAS16 0.46 0.10 0.20 0.16 0.07 0
## Factor Analysis using method = minres
## Call: fa(r = df %>% dplyr::select(CAS17:CAS22), nfactors = 1, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## CAS17 0.55 0.30 0.70 1
## CAS18 0.65 0.43 0.57 1
## CAS19 0.55 0.31 0.69 1
## CAS20 0.86 0.73 0.27 1
## CAS21 0.69 0.48 0.52 1
## CAS22 0.65 0.42 0.58 1
##
## MR1
## SS loadings 2.67
## Proportion Var 0.44
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 15 with the objective function = 2.15 with Chi Square = 962.83
## df of the model are 9 and the objective function was 0.3
##
## The root mean square of the residuals (RMSR) is 0.09
## The df corrected root mean square of the residuals is 0.11
##
## The harmonic n.obs is 452 with the empirical chi square 102.88 with prob < 4.1e-18
## The total n.obs was 452 with Likelihood Chi Square = 133.78 with prob < 2e-24
##
## Tucker Lewis Index of factoring reliability = 0.78
## RMSEA index = 0.175 and the 90 % confidence intervals are 0.15 0.202
## BIC = 78.75
## Fit based upon off diagonal values = 0.96
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.93
## Multiple R square of scores with factors 0.86
## Minimum correlation of possible factor scores 0.72
##
## Reliability analysis
## Call: alpha(x = df %>% dplyr::select(CAS17:CAS22))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.82 0.43 4.5 0.013 3.3 0.83 0.46
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.79 0.82 0.84
## Duhachek 0.79 0.82 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## CAS17 0.81 0.81 0.80 0.46 4.3 0.014 0.014 0.50
## CAS18 0.79 0.79 0.77 0.43 3.8 0.015 0.014 0.46
## CAS19 0.81 0.81 0.80 0.46 4.2 0.014 0.014 0.50
## CAS20 0.75 0.76 0.75 0.38 3.1 0.018 0.015 0.35
## CAS21 0.78 0.78 0.77 0.42 3.6 0.017 0.019 0.43
## CAS22 0.79 0.79 0.79 0.43 3.8 0.016 0.018 0.46
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## CAS17 451 0.68 0.65 0.55 0.50 2.7 1.27
## CAS18 452 0.71 0.73 0.67 0.57 3.8 1.08
## CAS19 452 0.63 0.66 0.57 0.49 4.2 0.96
## CAS20 452 0.84 0.84 0.82 0.74 3.3 1.15
## CAS21 452 0.77 0.75 0.70 0.63 2.8 1.24
## CAS22 452 0.72 0.72 0.64 0.58 2.8 1.15
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## CAS17 0.25 0.13 0.32 0.22 0.08 0
## CAS18 0.05 0.06 0.20 0.38 0.31 0
## CAS19 0.04 0.01 0.10 0.39 0.45 0
## CAS20 0.10 0.11 0.33 0.31 0.14 0
## CAS21 0.20 0.21 0.30 0.20 0.09 0
## CAS22 0.16 0.23 0.37 0.16 0.09 0
## Factor Analysis using method = minres
## Call: fa(r = df_male %>% dplyr::select(CAS1:CAS8), nfactors = 1, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## CAS1 0.79 0.62 0.38 1
## CAS2 0.73 0.54 0.46 1
## CAS3 0.64 0.42 0.58 1
## CAS4 0.73 0.54 0.46 1
## CAS5 0.65 0.42 0.58 1
## CAS6 0.82 0.67 0.33 1
## CAS7 0.63 0.40 0.60 1
## CAS8 0.86 0.75 0.25 1
##
## MR1
## SS loadings 4.36
## Proportion Var 0.54
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 28 with the objective function = 5.94 with Chi Square = 697.8
## df of the model are 20 and the objective function was 1.8
##
## The root mean square of the residuals (RMSR) is 0.11
## The df corrected root mean square of the residuals is 0.14
##
## The harmonic n.obs is 122 with the empirical chi square 89.2 with prob < 1e-10
## The total n.obs was 122 with Likelihood Chi Square = 210.85 with prob < 7.9e-34
##
## Tucker Lewis Index of factoring reliability = 0.599
## RMSEA index = 0.28 and the 90 % confidence intervals are 0.247 0.316
## BIC = 114.77
## Fit based upon off diagonal values = 0.96
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.96
## Multiple R square of scores with factors 0.92
## Minimum correlation of possible factor scores 0.83
## Factor Analysis using method = minres
## Call: fa(r = df_female %>% dplyr::select(CAS1:CAS8), nfactors = 1,
## rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## CAS1 0.67 0.45 0.55 1
## CAS2 0.73 0.53 0.47 1
## CAS3 0.66 0.43 0.57 1
## CAS4 0.76 0.58 0.42 1
## CAS5 0.68 0.46 0.54 1
## CAS6 0.82 0.68 0.32 1
## CAS7 0.59 0.34 0.66 1
## CAS8 0.68 0.46 0.54 1
##
## MR1
## SS loadings 3.94
## Proportion Var 0.49
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 28 with the objective function = 3.97 with Chi Square = 1281.01
## df of the model are 20 and the objective function was 0.64
##
## The root mean square of the residuals (RMSR) is 0.08
## The df corrected root mean square of the residuals is 0.09
##
## The harmonic n.obs is 326 with the empirical chi square 106.45 with prob < 8.7e-14
## The total n.obs was 327 with Likelihood Chi Square = 205.07 with prob < 1.1e-32
##
## Tucker Lewis Index of factoring reliability = 0.793
## RMSEA index = 0.168 and the 90 % confidence intervals are 0.148 0.19
## BIC = 89.27
## Fit based upon off diagonal values = 0.98
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.95
## Multiple R square of scores with factors 0.90
## Minimum correlation of possible factor scores 0.79
## Factor Analysis using method = minres
## Call: fa(r = df_male %>% dplyr::select(CAS17:CAS22), nfactors = 1,
## rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## CAS17 0.49 0.24 0.76 1
## CAS18 0.61 0.37 0.63 1
## CAS19 0.62 0.39 0.61 1
## CAS20 0.80 0.64 0.36 1
## CAS21 0.65 0.42 0.58 1
## CAS22 0.63 0.39 0.61 1
##
## MR1
## SS loadings 2.46
## Proportion Var 0.41
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 15 with the objective function = 1.94 with Chi Square = 229.23
## df of the model are 9 and the objective function was 0.38
##
## The root mean square of the residuals (RMSR) is 0.1
## The df corrected root mean square of the residuals is 0.13
##
## The harmonic n.obs is 122 with the empirical chi square 37.5 with prob < 2.1e-05
## The total n.obs was 122 with Likelihood Chi Square = 44.56 with prob < 1.1e-06
##
## Tucker Lewis Index of factoring reliability = 0.722
## RMSEA index = 0.18 and the 90 % confidence intervals are 0.13 0.235
## BIC = 1.32
## Fit based upon off diagonal values = 0.94
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.91
## Multiple R square of scores with factors 0.83
## Minimum correlation of possible factor scores 0.65
## Factor Analysis using method = minres
## Call: fa(r = df_female %>% dplyr::select(CAS17:CAS22), nfactors = 1,
## rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## CAS17 0.52 0.27 0.73 1
## CAS18 0.68 0.46 0.54 1
## CAS19 0.52 0.28 0.72 1
## CAS20 0.88 0.78 0.22 1
## CAS21 0.68 0.46 0.54 1
## CAS22 0.64 0.41 0.59 1
##
## MR1
## SS loadings 2.66
## Proportion Var 0.44
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 15 with the objective function = 2.15 with Chi Square = 695.67
## df of the model are 9 and the objective function was 0.29
##
## The root mean square of the residuals (RMSR) is 0.08
## The df corrected root mean square of the residuals is 0.11
##
## The harmonic n.obs is 327 with the empirical chi square 66.5 with prob < 7.4e-11
## The total n.obs was 327 with Likelihood Chi Square = 92.33 with prob < 5.5e-16
##
## Tucker Lewis Index of factoring reliability = 0.796
## RMSEA index = 0.168 and the 90 % confidence intervals are 0.138 0.201
## BIC = 40.22
## Fit based upon off diagonal values = 0.97
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.93
## Multiple R square of scores with factors 0.87
## Minimum correlation of possible factor scores 0.74
## Factor Analysis using method = minres
## Call: fa(r = df_male %>% dplyr::select(CAS14:CAS16), nfactors = 1,
## rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## CAS14 0.90 0.81 0.19 1
## CAS15 0.95 0.90 0.10 1
## CAS16 0.82 0.67 0.33 1
##
## MR1
## SS loadings 2.38
## Proportion Var 0.79
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 3 with the objective function = 2.27 with Chi Square = 270.9
## df of the model are 0 and the objective function was 0
##
## The root mean square of the residuals (RMSR) is 0
## The df corrected root mean square of the residuals is NA
##
## The harmonic n.obs is 122 with the empirical chi square 0 with prob < NA
## The total n.obs was 122 with Likelihood Chi Square = 0 with prob < NA
##
## Tucker Lewis Index of factoring reliability = -Inf
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.97
## Multiple R square of scores with factors 0.94
## Minimum correlation of possible factor scores 0.87
## Factor Analysis using method = minres
## Call: fa(r = df_female %>% dplyr::select(CAS14:CAS16), nfactors = 1,
## rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## CAS14 0.91 0.82 0.18 1
## CAS15 0.92 0.85 0.15 1
## CAS16 0.71 0.50 0.50 1
##
## MR1
## SS loadings 2.17
## Proportion Var 0.72
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 3 with the objective function = 1.81 with Chi Square = 587.06
## df of the model are 0 and the objective function was 0
##
## The root mean square of the residuals (RMSR) is 0
## The df corrected root mean square of the residuals is NA
##
## The harmonic n.obs is 326 with the empirical chi square 0 with prob < NA
## The total n.obs was 327 with Likelihood Chi Square = 0 with prob < NA
##
## Tucker Lewis Index of factoring reliability = -Inf
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.96
## Multiple R square of scores with factors 0.92
## Minimum correlation of possible factor scores 0.84
## Factor Analysis using method = minres
## Call: fa(r = df_male %>% dplyr::select(CAS9:CAS13), nfactors = 1, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## CAS9 0.80 0.64 0.36 1
## CAS10 0.67 0.45 0.55 1
## CAS11 0.83 0.69 0.31 1
## CAS12 0.72 0.52 0.48 1
## CAS13 0.90 0.81 0.19 1
##
## MR1
## SS loadings 3.13
## Proportion Var 0.63
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 10 with the objective function = 3.09 with Chi Square = 365.75
## df of the model are 5 and the objective function was 0.26
##
## The root mean square of the residuals (RMSR) is 0.05
## The df corrected root mean square of the residuals is 0.07
##
## The harmonic n.obs is 122 with the empirical chi square 6.81 with prob < 0.24
## The total n.obs was 122 with Likelihood Chi Square = 30.1 with prob < 1.4e-05
##
## Tucker Lewis Index of factoring reliability = 0.858
## RMSEA index = 0.203 and the 90 % confidence intervals are 0.137 0.277
## BIC = 6.08
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.96
## Multiple R square of scores with factors 0.91
## Minimum correlation of possible factor scores 0.82
## Factor Analysis using method = minres
## Call: fa(r = df_female %>% dplyr::select(CAS9:CAS13), nfactors = 1,
## rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## CAS9 0.89 0.79 0.21 1
## CAS10 0.61 0.37 0.63 1
## CAS11 0.90 0.82 0.18 1
## CAS12 0.90 0.81 0.19 1
## CAS13 0.67 0.44 0.56 1
##
## MR1
## SS loadings 3.24
## Proportion Var 0.65
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 10 with the objective function = 3.38 with Chi Square = 1092.66
## df of the model are 5 and the objective function was 0.1
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.05
##
## The harmonic n.obs is 327 with the empirical chi square 6.93 with prob < 0.23
## The total n.obs was 327 with Likelihood Chi Square = 31.36 with prob < 8e-06
##
## Tucker Lewis Index of factoring reliability = 0.951
## RMSEA index = 0.127 and the 90 % confidence intervals are 0.087 0.171
## BIC = 2.41
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.97
## Multiple R square of scores with factors 0.93
## Minimum correlation of possible factor scores 0.87
Table. Summary of Factor Analysis Results by Gender and Full Sample
|
|
Variance Explained
|
RMSEA
|
TLI
|
|
Factor
|
Variance Explained (Full)
|
Variance Explained (Male)
|
Variance Explained (Female)
|
RMSEA (Full)
|
RMSEA (Male)
|
RMSEA (Female)
|
TLI (Full)
|
TLI (Male)
|
TLI (Female)
|
|
F1: Cognitive/Emotional Impairment
|
0.50
|
0.54
|
0.49
|
0.176
|
0.280
|
0.168
|
0.783
|
0.599
|
0.793
|
|
F2: Behavioral Engagement
|
0.44
|
0.41
|
0.44
|
0.175
|
0.180
|
0.168
|
0.780
|
0.722
|
0.796
|
|
F3: Climate Change Experience
|
0.74
|
0.79
|
0.72
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
F4: Functional Impairment
|
0.63
|
0.63
|
0.65
|
0.089
|
0.203
|
0.127
|
0.973
|
0.858
|
0.951
|
## Fac1 Fac2 Fac3 Fac4
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.667 1st Qu.:1.000 1st Qu.:1.000
## Median :1.125 Median :3.333 Median :1.667 Median :1.000
## Mean :1.355 Mean :3.271 Mean :1.992 Mean :1.178
## 3rd Qu.:1.500 3rd Qu.:3.833 3rd Qu.:3.000 3rd Qu.:1.000
## Max. :3.250 Max. :5.000 Max. :5.000 Max. :4.200
## Fac1 Fac2 Fac3 Fac4
## Fac1 1.0000000 0.3988640 0.5215330 0.7472871
## Fac2 0.3988640 1.0000000 0.5117046 0.2398682
## Fac3 0.5215330 0.5117046 1.0000000 0.3652623
## Fac4 0.7472871 0.2398682 0.3652623 1.0000000
## Fac1 Fac2 Fac3 Fac4
## Fac1 1.00 0.40 0.52 0.75
## Fac2 0.40 1.00 0.51 0.24
## Fac3 0.52 0.51 1.00 0.37
## Fac4 0.75 0.24 0.37 1.00
## Fac1 Fac2 Fac3 Fac4
## Fac1 0 0.00e+00 0 0.00e+00
## Fac2 0 0.00e+00 0 2.46e-07
## Fac3 0 0.00e+00 0 0.00e+00
## Fac4 0 2.46e-07 0 0.00e+00
Linear Models of CCA factors on sum of behaviors
##
## Call:
## lm(formula = sumAct ~ Fac1 + Fac2 + Fac3 + Fac4, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3125 -0.7431 -0.1486 0.6461 3.9319
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01059 0.23713 -0.045 0.964
## Fac1 0.39614 0.17097 2.317 0.021 *
## Fac2 0.48816 0.07475 6.531 1.78e-10 ***
## Fac3 0.06659 0.06011 1.108 0.269
## Fac4 -0.27905 0.18334 -1.522 0.129
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.108 on 447 degrees of freedom
## Multiple R-squared: 0.1814, Adjusted R-squared: 0.1741
## F-statistic: 24.77 on 4 and 447 DF, p-value: < 2.2e-16
##
## Call:
## glm(formula = sumAct ~ Fac1, family = quasipoisson, data = df)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.26397 0.07855 3.360 0.000845 ***
## Fac1 0.28105 0.05075 5.538 5.21e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0.7385551)
##
## Null deviance: 400.89 on 451 degrees of freedom
## Residual deviance: 379.89 on 450 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
## 2.5 % 97.5 %
## (Intercept) 1.116 1.519
## Fac1 1.197 1.461
## [1] "For every 1.0 unit increase in Factor 1, Cognitive/Emotional Impairment, there is a 1.3245 increase in the sum of pro-environmental behaviors."
##
## Call:
## glm(formula = sumAct ~ Fac2, family = quasipoisson, data = df)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.48401 0.13274 -3.646 0.000297 ***
## Fac2 0.33721 0.03723 9.057 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0.6958887)
##
## Null deviance: 400.89 on 451 degrees of freedom
## Residual deviance: 340.39 on 450 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
## [1] "For every 1.0 unit increase in Factor 2, Behavioral Engagement, there is a 1.401 increase in the sum of pro-environmental behaviors."
## [1] 0.6958887
##
## Call:
## glm(formula = sumAct ~ Fac3, family = quasipoisson, data = df)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.33955 0.06182 5.492 6.65e-08 ***
## Fac3 0.15155 0.02494 6.077 2.61e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0.7371004)
##
## Null deviance: 400.89 on 451 degrees of freedom
## Residual deviance: 374.59 on 450 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
## [1] "For every 1.0 unit increase in Factor 3, Climate Change Experience, there is a 1.1636 increase in the sum of pro-environmental behaviors."
##
## Call:
## glm(formula = sumAct ~ Fac4, family = quasipoisson, data = df)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.45756 0.08002 5.718 1.96e-08 ***
## Fac4 0.16604 0.06129 2.709 0.007 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0.7638753)
##
## Null deviance: 400.89 on 451 degrees of freedom
## Residual deviance: 395.70 on 450 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
## [1] "For every 1.0 unit increase in Factor 4, Functional Impairment, there is a 1.1806 increase in the sum of pro-environmental behaviors."
Table 3. Quasi-Poisson Regression Results: Climate Anxiety Factors and
Pro-Environmental Behavior
|
Model
|
IRR
|
CI Lower
|
CI Upper
|
p-value
|
|
Cognitive Emotional Impairment
|
1.325
|
1.197
|
1.461
|
1e-07
|
|
Behavioral Engagement
|
1.401
|
1.303
|
1.508
|
0e+00
|
|
Climate Change Experience
|
1.164
|
1.108
|
1.222
|
0e+00
|
|
Functional Impairment
|
1.181
|
1.043
|
1.327
|
7e-03
|
##
## Call:
## glm.nb(formula = df$sumAct ~ df$Fac2, data = df, init.theta = 40594.24551,
## link = log)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.48400 0.15913 -3.042 0.00235 **
## df$Fac2 0.33721 0.04463 7.555 4.18e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(40594.25) family taken to be 1)
##
## Null deviance: 400.88 on 451 degrees of freedom
## Residual deviance: 340.38 on 450 degrees of freedom
## AIC: 1391.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 40594
## Std. Err.: 247111
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -1385.121
##
## Call:
## glm.nb(formula = sumAct ~ df$Fac3, data = df, init.theta = 40114.34058,
## link = log)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.33955 0.07201 4.715 2.41e-06 ***
## df$Fac3 0.15155 0.02905 5.217 1.82e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(40114.34) family taken to be 1)
##
## Null deviance: 400.88 on 451 degrees of freedom
## Residual deviance: 374.57 on 450 degrees of freedom
## AIC: 1425.3
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 40114
## Std. Err.: 275437
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -1419.318
##
## Call:
## glm.nb(formula = sumAct ~ df$Fac4, data = df, init.theta = 38320.4072,
## link = log)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.45756 0.09156 4.998 5.8e-07 ***
## df$Fac4 0.16604 0.07013 2.368 0.0179 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(38320.41) family taken to be 1)
##
## Null deviance: 400.88 on 451 degrees of freedom
## Residual deviance: 395.69 on 450 degrees of freedom
## AIC: 1446.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 38320
## Std. Err.: 281256
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -1440.434
Logistic regressions of CCA factor effects on taking adaptive
measures
## # weights: 9 (4 variable)
## initial value 496.572754
## final value 476.654950
## converged
## Call:
## multinom(formula = CAPT6_multi ~ Fac1, data = df)
##
## Coefficients:
## (Intercept) Fac1
## No 1.7263159 -0.9214387
## Don't know 0.8169519 -0.1809020
##
## Std. Errors:
## (Intercept) Fac1
## No 0.3746080 0.2637671
## Don't know 0.3372041 0.2183467
##
## Residual Deviance: 953.3099
## AIC: 961.3099
## [1] "Yes" "No" "Don't know" "Yes- text"
## (Intercept) Fac1
## No 5.619912 0.3979461
## Don't know 2.263590 0.8345171
##
## Call:
## glm(formula = CAPT6_binary ~ Fac1, family = binomial, data = df)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.8782 0.3094 -6.071 1.27e-09 ***
## Fac1 0.4742 0.2032 2.334 0.0196 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 485.17 on 451 degrees of freedom
## Residual deviance: 479.94 on 450 degrees of freedom
## AIC: 483.94
##
## Number of Fisher Scoring iterations: 4
## (Intercept) Fac1
## 0.152863 1.606698
##
## Call:
## glm(formula = CAPT6_binary ~ Fac2, family = binomial, data = df)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.5186 0.5134 -4.906 9.29e-07 ***
## Fac2 0.3884 0.1468 2.646 0.00814 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 485.17 on 451 degrees of freedom
## Residual deviance: 477.72 on 450 degrees of freedom
## AIC: 481.72
##
## Number of Fisher Scoring iterations: 4
## (Intercept) Fac2
## 0.08056993 1.47457221
##
## Call:
## glm(formula = CAPT6_binary ~ Fac3, family = binomial, data = df)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.70275 0.23897 -7.125 1.04e-12 ***
## Fac3 0.23328 0.09832 2.373 0.0177 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 485.17 on 451 degrees of freedom
## Residual deviance: 479.63 on 450 degrees of freedom
## AIC: 483.63
##
## Number of Fisher Scoring iterations: 4
## (Intercept) Fac3
## 0.182181 1.262735
##
## Call:
## glm(formula = CAPT6_binary ~ Fac4, family = binomial, data = df)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.4207 0.3152 -4.507 6.58e-06 ***
## Fac4 0.1689 0.2466 0.685 0.493
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 485.17 on 451 degrees of freedom
## Residual deviance: 484.72 on 450 degrees of freedom
## AIC: 488.72
##
## Number of Fisher Scoring iterations: 4
## (Intercept) Fac4
## 0.2415409 1.1839453
## [1] "For every 1.0 unit increase in Factor 1, Cognitive/Emotional Impairment, there odds of reporting adaptive measures increases by 60.7%."
## [1] "For every 1.0 unit increase in Factor 2, Behavioral engagement, there odds of reporting adaptive measures increases by 47.5%."
## [1] "For every 1.0 unit increase in Factor 3, Climate change experience, there odds of reporting adaptive measures increases by 26.3%."
## [1] "For every 1.0 unit increase in Factor 4, Functional Impairment, there odds of reporting adaptive measures increases by 18.4%."
Table. Logistic Regression Results: Predicting CAPT6 Binary Outcome from
Climate Anxiety (Fac1)
|
Term
|
Odds_Ratio
|
std.error
|
statistic
|
p-value
|
95% CI Lower
|
95% CI Upper
|
|
(Intercept)
|
0.153
|
0.309
|
-6.071
|
0.00
|
0.083
|
0.280
|
|
Fac1
|
1.607
|
0.203
|
2.334
|
0.02
|
1.072
|
2.385
|
Table. Logistic Regression Results: Predicting CAPT6 Binary from Climate
Anxiety Factors
|
Model
|
Odds_Ratio
|
CI Lower
|
CI Upper
|
p-value
|
|
Cognitive Emotional Impairment
|
1.607
|
1.072
|
2.385
|
0.01960
|
|
Behavioral Engagement
|
1.475
|
1.113
|
1.981
|
0.00814
|
|
Climate Change Experience
|
1.263
|
1.040
|
1.531
|
0.01770
|
|
Functional Impairment
|
1.184
|
0.709
|
1.889
|
0.49300
|
## R version 4.4.3 (2025-02-28)
## Platform: aarch64-apple-darwin20
## Running under: macOS 26.2
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: US/Pacific
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] MASS_7.3-64 kableExtra_1.4.0 broom_1.0.7 nnet_7.3-20
## [5] stargazer_5.2.3 tm_0.7-15 NLP_0.3-2 wordcloud_2.6
## [9] RColorBrewer_1.1-3 stringr_1.5.1 knitr_1.49 psych_2.4.12
## [13] lavaan_0.6-19 summarytools_1.0.1 ggplot2_3.5.1 tibble_3.2.1
## [17] tidyr_1.3.1 dplyr_1.1.4
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 xfun_0.49 bslib_0.8.0 lattice_0.22-6
## [5] quadprog_1.5-8 vctrs_0.6.5 tools_4.4.3 generics_0.1.3
## [9] stats4_4.4.3 parallel_4.4.3 fansi_1.0.6 pkgconfig_2.0.3
## [13] Matrix_1.7-2 checkmate_2.3.2 pryr_0.1.6 lifecycle_1.0.4
## [17] farver_2.1.2 compiler_4.4.3 textshaping_0.4.0 rapportools_1.1
## [21] munsell_0.5.1 mnormt_2.1.1 codetools_0.2-20 htmltools_0.5.8.1
## [25] sass_0.4.9 yaml_2.3.10 pillar_1.9.0 jquerylib_0.1.4
## [29] cachem_1.1.0 magick_2.8.5 nlme_3.1-167 tidyselect_1.2.1
## [33] digest_0.6.37 stringi_1.8.4 slam_0.1-55 reshape2_1.4.4
## [37] pander_0.6.5 purrr_1.0.2 splines_4.4.3 labeling_0.4.3
## [41] fastmap_1.2.0 grid_4.4.3 colorspace_2.1-1 cli_3.6.3
## [45] magrittr_2.0.3 base64enc_0.1-3 utf8_1.2.4 pbivnorm_0.6.0
## [49] withr_3.0.2 scales_1.3.0 backports_1.5.0 lubridate_1.9.3
## [53] timechange_0.3.0 rmarkdown_2.29 matrixStats_1.5.0 ragg_1.3.3
## [57] evaluate_1.0.1 tcltk_4.4.3 viridisLite_0.4.2 mgcv_1.9-1
## [61] rlang_1.1.4 Rcpp_1.0.13-1 glue_1.8.0 xml2_1.3.6
## [65] svglite_2.1.3 rstudioapi_0.17.1 jsonlite_1.8.9 R6_2.5.1
## [69] plyr_1.8.9 systemfonts_1.1.0
