Longitudinal Gratitude Analyses
Dani Grant
d <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/longitudinal gratitude/data prep/long_grat_w1w5.csv", header = T)
names(d)[names(d) == 'irrirated5'] <- 'irritated5'
d$w1weight[d$w1weight == "175\n175"] <- 175
d$w1weight <- as.numeric(d$w1weight)
dem <- d[,c("subID", "w1weight", "w2weight", "w3weight", "w4weight", "w5weight", "height", "age", "sex", "class", "friend")]
rank <- d[c("subID", "f_rank1", "f_rank2", "f_rank3", "f_rank4", "f_rank5")]
week <- d[c("subID", "week1", "week2", "week3", "week4", "week5")]
wtr <- d[c("subID", "w1wtr", "w2wtrAvg", "w3wtrAvg", "w4wtrAvg", "w5wtrAvg")]
close <- d[c("subID", "w1close", "w2closeAvg", "w3closeAvg", "w4closeAvg", "w5closeAvg")]
commit <- d[c("subID", "w1commit", "w2commitAvg", "w3commitAvg", "w4commitAvg", "w5commitAvg")]
ios <- d[c("subID", "w1ios", "w2iosAvg", "w3iosAvg", "w4iosAvg", "w5iosAvg")]
d$grat1 <- NA
grat <- d[c("subID", "grat1", "grat2", "grat3", "grat4", "grat5")]
d$thankful1 <- NA
thankful <- d[c("subID", "thankful1", "thankful2", "thankful3", "thankful4", "thankful5")]
d$appreciative1 <- NA
appreciative <- d[c("subID", "appreciative1", "appreciative2", "appreciative3", "appreciative4", "appreciative5")]
d$irritated1 <- NA
irritated <- d[c("subID", "irritated1", "irritated2", "irritated3", "irritated4", "irritated5")]
d$anger1 <- NA
anger <- d[c("subID", "anger1", "anger2", "anger3", "anger4", "anger5")]
d$happy1 <- NA
happy <- d[c("subID", "happy1", "happy2", "happy3", "happy4", "happy5")]
#create grat construct
x <- cbind(d$thankful2, d$grat2, d$appreciative2)
d$gratScale2 <- rowMeans(x, na.rm = T)
x <- cbind(d$thankful3, d$grat3, d$appreciative3)
d$gratScale3 <- rowMeans(x, na.rm = T)
x <- cbind(d$thankful4, d$grat4, d$appreciative4)
d$gratScale4 <- rowMeans(x, na.rm = T)
x <- cbind(d$thankful5, d$grat5, d$appreciative5)
d$gratScale5 <- rowMeans(x, na.rm = T)
remove(x)
d$gratScale1 <- NA
gratScale <- d[c("subID", "gratScale1", "gratScale2", "gratScale3", "gratScale4", "gratScale5")]
# change in WTR
wtrDiff <- data.frame(d$subID)
wtrDiff$wtrDiff10 <- NA
wtrDiff$wtrDiff21 <- d$wtr21diff
wtrDiff$wtrDiff32 <- d$wtr32diff
wtrDiff$wtrDiff43 <- d$wtr43diff
wtrDiff$wtrDiff54 <- d$wtr54diff
# pivot long
rank_long <- rank %>% pivot_longer(cols = contains("rank"), names_to = "delete", values_to = "rank")
week_long <- week %>% pivot_longer(cols = contains("week"), names_to = "delete", values_to = "week")
wtr_long <- wtr %>% pivot_longer(cols = contains("wtr"), names_to = "delete", values_to = "wtr")
wtrDiff_long <- wtrDiff %>% pivot_longer(cols = contains("wtrDiff"), names_to = "delete", values_to = "wtrDiff")
dem_long <- dem %>% pivot_longer(cols = contains("weight"), names_to = "delete", values_to = "weight")
close_long <- close %>% pivot_longer(cols = contains("close"), names_to = "delete", values_to = "close")
commit_long <- commit %>% pivot_longer(cols = contains("commit"), names_to = "delete", values_to = "commit")
ios_long <- ios %>% pivot_longer(cols = contains("ios"), names_to = "delete", values_to = "ios")
thankful_long <- thankful %>% pivot_longer(cols = contains("thankful"), names_to = "delete", values_to = "thankful")
appreciative_long <- appreciative %>% pivot_longer(cols = contains("appreciative"), names_to = "delete", values_to = "appreciative")
grat_long <- grat %>% pivot_longer(cols = contains("grat"), names_to = "delete", values_to = "grat")
gratScale_long <- gratScale %>% pivot_longer(cols = contains("gratScale"), names_to = "delete", values_to = "gratScale")
irritated_long <- irritated %>% pivot_longer(cols = contains("irritated"), names_to = "delete", values_to = "irritated")
anger_long <- anger %>% pivot_longer(cols = contains("anger"), names_to = "delete", values_to = "anger")
happy_long <- happy %>% pivot_longer(cols = contains("happy"), names_to = "delete", values_to = "happy")
#append to single data frame
d1 <- data.frame(week_long$week)
names(d1)[names(d1) == 'week_long.week'] <- 'week'
d1$age <- dem_long$age
d1$sex <- dem_long$sex
d1$weight <- dem_long$weight
d1$class <- dem_long$class
d1$subID <- week_long$subID
d1$rank <- rank_long$rank
d1$friend <- dem_long$friend
d1$wtr <- wtr_long$wtr
d1$wtrDiff <- wtrDiff_long$wtrDiff
d1$gratScale <- gratScale_long$gratScale
d1$gratScale[d1$gratScale == "NaN"] <- NA
d1$grat <- grat_long$grat
d1$thankful <- thankful_long$thankful
d1$appreciative <- appreciative_long$appreciative
d1$close <- close_long$close
d1$commit <- commit_long$commit
d1$ios <- ios_long$ios
d1$happy <- happy_long$happy
d1$irritated <- irritated_long$irritated
d1$anger <- anger_long$angerdemographics
draw <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/longitudinal gratitude/data prep/long_grat_raw.csv", header = T)
describe(draw$age, na.rm = T)table(draw$sex)##
## female male
## 81 34table(draw$class)##
## freshman junior senior sophomore
## 72 10 5 28descriptive stats
- close
How close is your friendship with this person? (0 = not at all close, 6 = extremely close)
psych::alpha(d[c("w1close", "w2closeAvg", "w3closeAvg", "w4closeAvg", "w5closeAvg")]) #.91##
## Reliability analysis
## Call: psych::alpha(x = d[c("w1close", "w2closeAvg", "w3closeAvg", "w4closeAvg",
## "w5closeAvg")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.92 0.67 10 0.0066 4.3 1.3 0.68
##
## lower alpha upper 95% confidence boundaries
## 0.9 0.91 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## w1close 0.91 0.91 0.90 0.72 10.1 0.0068 0.0053 0.70
## w2closeAvg 0.88 0.88 0.86 0.64 7.1 0.0092 0.0156 0.63
## w3closeAvg 0.89 0.89 0.89 0.66 7.7 0.0088 0.0167 0.66
## w4closeAvg 0.88 0.88 0.87 0.65 7.5 0.0088 0.0108 0.65
## w5closeAvg 0.89 0.89 0.88 0.67 8.3 0.0081 0.0087 0.70
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w1close 400 0.85 0.79 0.72 0.67 4.5 1.4
## w2closeAvg 406 0.92 0.90 0.87 0.83 4.4 1.4
## w3closeAvg 395 0.90 0.87 0.83 0.79 4.3 1.4
## w4closeAvg 336 0.90 0.88 0.86 0.81 4.3 1.4
## w5closeAvg 151 0.87 0.85 0.82 0.76 4.5 1.4
##
## Non missing response frequency for each item
## 0 1 2 2.5 3 4 5 6 miss
## w1close 0.00 0.03 0.05 0 0.14 0.23 0.23 0.31 0.19
## w2closeAvg 0.01 0.02 0.05 0 0.15 0.26 0.25 0.26 0.17
## w3closeAvg 0.02 0.03 0.04 0 0.15 0.27 0.27 0.21 0.20
## w4closeAvg 0.02 0.02 0.04 0 0.17 0.26 0.27 0.22 0.32
## w5closeAvg 0.03 0.03 0.03 0 0.10 0.26 0.25 0.30 0.69describe(d[c("w1close", "w2closeAvg", "w3closeAvg", "w4closeAvg", "w5closeAvg")])- commit
How committed are you to your friendship with this person? (0 = not at all committed, 6 = extremely committed)
psych::alpha(d[c("w1commit", "w2commitAvg", "w3commitAvg", "w4commitAvg", "w5commitAvg")]) #.91##
## Reliability analysis
## Call: psych::alpha(x = d[c("w1commit", "w2commitAvg", "w3commitAvg",
## "w4commitAvg", "w5commitAvg")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.91 0.67 10 0.0065 4.4 1.3 0.68
##
## lower alpha upper 95% confidence boundaries
## 0.9 0.91 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## w1commit 0.91 0.91 0.89 0.72 10.1 0.0067 0.0027 0.70
## w2commitAvg 0.88 0.88 0.86 0.65 7.3 0.0090 0.0126 0.65
## w3commitAvg 0.89 0.89 0.89 0.66 7.9 0.0086 0.0138 0.68
## w4commitAvg 0.89 0.89 0.87 0.66 7.8 0.0086 0.0084 0.68
## w5commitAvg 0.89 0.89 0.87 0.67 8.3 0.0081 0.0060 0.70
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w1commit 399 0.86 0.80 0.73 0.68 4.6 1.4
## w2commitAvg 406 0.91 0.90 0.87 0.83 4.5 1.4
## w3commitAvg 399 0.90 0.87 0.82 0.79 4.4 1.5
## w4commitAvg 339 0.91 0.87 0.85 0.80 4.5 1.4
## w5commitAvg 152 0.88 0.86 0.82 0.77 4.6 1.5
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## w1commit 0.01 0.03 0.05 0.15 0.14 0.26 0.36 0.19
## w2commitAvg 0.01 0.02 0.04 0.15 0.22 0.27 0.28 0.17
## w3commitAvg 0.03 0.03 0.04 0.13 0.26 0.25 0.26 0.19
## w4commitAvg 0.02 0.02 0.03 0.15 0.24 0.22 0.31 0.31
## w5commitAvg 0.03 0.02 0.03 0.11 0.16 0.32 0.33 0.69describe(d[c("w1commit", "w2commitAvg", "w3commitAvg", "w4commitAvg", "w5commitAvg")])- ios
Using the diagram below, please indicate which picture best described your relationship with this person by selecting a number: 1 = no overlap, 4 = half overlap, 7 = almost complete overlap)
psych::alpha(d[c("w1ios", "w2iosAvg", "w3iosAvg", "w4iosAvg", "w5iosAvg")]) #.92##
## Reliability analysis
## Call: psych::alpha(x = d[c("w1ios", "w2iosAvg", "w3iosAvg", "w4iosAvg",
## "w5iosAvg")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.91 0.7 12 0.0057 4.2 1.4 0.71
##
## lower alpha upper 95% confidence boundaries
## 0.91 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
## w1ios 0.91 0.91 0.89 0.72 10.4 0.0065 0.0013 0.73
## w2iosAvg 0.89 0.89 0.86 0.67 8.1 0.0082 0.0016 0.66
## w3iosAvg 0.90 0.90 0.88 0.70 9.4 0.0071 0.0025 0.71
## w4iosAvg 0.90 0.90 0.88 0.70 9.3 0.0072 0.0036 0.70
## w5iosAvg 0.91 0.91 0.89 0.71 9.9 0.0068 0.0038 0.74
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w1ios 400 0.87 0.84 0.79 0.75 4.3 1.6
## w2iosAvg 412 0.92 0.92 0.90 0.86 4.2 1.6
## w3iosAvg 401 0.90 0.87 0.83 0.79 4.2 1.6
## w4iosAvg 340 0.88 0.87 0.83 0.80 4.4 1.4
## w5iosAvg 153 0.87 0.86 0.80 0.77 4.5 1.6
##
## Non missing response frequency for each item
## 1 2 2.5 3 4 5 5.5 6 7 miss
## w1ios 0.03 0.13 0.00 0.16 0.24 0.18 0 0.16 0.10 0.19
## w2iosAvg 0.04 0.14 0.00 0.14 0.21 0.23 0 0.18 0.07 0.16
## w3iosAvg 0.04 0.12 0.00 0.15 0.25 0.20 0 0.17 0.06 0.18
## w4iosAvg 0.04 0.09 0.00 0.12 0.27 0.25 0 0.18 0.05 0.31
## w5iosAvg 0.05 0.07 0.01 0.10 0.25 0.23 0 0.20 0.10 0.69describe(d[c("w1ios", "w2iosAvg", "w3iosAvg", "w4iosAvg", "w5iosAvg")])The following questions are about this friend: For the questions on this page, please consider the interactions you have had with this person since the last time you filled out this questionnaire.
- gratitude
Over the past two weeks how grateful have you been toward this person?
psych::alpha(d[c("grat2", "grat3", "grat4", "grat5")]) #.85##
## Reliability analysis
## Call: psych::alpha(x = d[c("grat2", "grat3", "grat4", "grat5")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.83 0.58 5.6 0.011 3.4 1.6 0.6
##
## lower alpha upper 95% confidence boundaries
## 0.83 0.85 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## grat2 0.84 0.84 0.79 0.63 5.2 0.013 0.0062 0.68
## grat3 0.79 0.79 0.74 0.55 3.7 0.017 0.0216 0.58
## grat4 0.76 0.76 0.70 0.52 3.2 0.019 0.0141 0.54
## grat5 0.83 0.83 0.77 0.63 5.0 0.013 0.0026 0.63
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## grat2 383 0.86 0.78 0.67 0.61 3.5 1.8
## grat3 366 0.89 0.86 0.80 0.73 3.4 1.8
## grat4 320 0.88 0.89 0.85 0.78 3.5 1.9
## grat5 143 0.87 0.79 0.69 0.62 3.6 1.9
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## grat2 0.09 0.07 0.10 0.22 0.21 0.16 0.14 0.22
## grat3 0.10 0.05 0.13 0.21 0.23 0.13 0.14 0.25
## grat4 0.11 0.05 0.10 0.14 0.25 0.18 0.16 0.35
## grat5 0.11 0.05 0.12 0.13 0.25 0.17 0.17 0.71describe(d[c("grat2", "grat3", "grat4", "grat5")])- anger
Over the past two weeks how angry have you been with this person?
psych::alpha(d[c("anger2", "anger3", "anger4", "anger5")]) #.81##
## Reliability analysis
## Call: psych::alpha(x = d[c("anger2", "anger3", "anger4", "anger5")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.79 0.51 4.2 0.014 1.1 1.4 0.55
##
## lower alpha upper 95% confidence boundaries
## 0.78 0.81 0.83
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## anger2 0.79 0.79 0.72 0.56 3.8 0.016 0.00032 0.56
## anger3 0.72 0.72 0.66 0.46 2.6 0.022 0.02177 0.52
## anger4 0.74 0.74 0.69 0.48 2.8 0.020 0.02637 0.56
## anger5 0.78 0.78 0.71 0.55 3.6 0.017 0.00139 0.54
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## anger2 380 0.85 0.75 0.64 0.57 1.09 1.6
## anger3 369 0.89 0.84 0.78 0.71 1.19 1.7
## anger4 317 0.84 0.83 0.74 0.67 0.92 1.5
## anger5 147 0.79 0.76 0.66 0.57 0.88 1.4
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## anger2 0.57 0.15 0.08 0.07 0.06 0.05 0.01 0.23
## anger3 0.56 0.13 0.10 0.07 0.05 0.04 0.04 0.25
## anger4 0.61 0.16 0.06 0.09 0.04 0.02 0.02 0.35
## anger5 0.59 0.18 0.10 0.07 0.02 0.01 0.02 0.70describe(d[c("anger2", "anger3", "anger4", "anger5")])- irritated
Over the past two weeks how irritated have you been with this person?
psych::alpha(d[c("irritated2", "irritated3", "irritated4", "irritated5")]) #.81##
## Reliability analysis
## Call: psych::alpha(x = d[c("irritated2", "irritated3", "irritated4",
## "irritated5")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.78 0.52 4.2 0.014 1.4 1.5 0.54
##
## lower alpha upper 95% confidence boundaries
## 0.78 0.81 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## irritated2 0.77 0.77 0.69 0.53 3.4 0.018 0.00069 0.53
## irritated3 0.74 0.74 0.66 0.48 2.8 0.021 0.00821 0.53
## irritated4 0.75 0.75 0.69 0.50 3.1 0.019 0.01179 0.56
## irritated5 0.78 0.78 0.70 0.54 3.5 0.017 0.00140 0.54
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## irritated2 383 0.85 0.78 0.68 0.60 1.4 1.7
## irritated3 371 0.87 0.83 0.75 0.67 1.4 1.8
## irritated4 320 0.83 0.81 0.71 0.64 1.3 1.7
## irritated5 146 0.81 0.77 0.66 0.59 1.2 1.7
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## irritated2 0.48 0.15 0.13 0.08 0.09 0.05 0.03 0.22
## irritated3 0.48 0.17 0.11 0.08 0.07 0.05 0.05 0.24
## irritated4 0.48 0.22 0.10 0.08 0.05 0.03 0.04 0.35
## irritated5 0.53 0.16 0.10 0.08 0.04 0.05 0.03 0.70describe(d[c("irritated2", "irritated3", "irritated4", "irritated5")])- happy
Over the past two weeks how happy have you been with this person?
psych::alpha(d[c("happy2", "happy3", "happy4", "happy5")]) #.85##
## Reliability analysis
## Call: psych::alpha(x = d[c("happy2", "happy3", "happy4", "happy5")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.81 0.58 5.6 0.011 3.9 1.5 0.58
##
## lower alpha upper 95% confidence boundaries
## 0.83 0.85 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## happy2 0.82 0.82 0.76 0.61 4.6 0.014 0.00283 0.64
## happy3 0.81 0.81 0.74 0.58 4.2 0.015 0.00371 0.59
## happy4 0.78 0.78 0.71 0.54 3.6 0.017 0.00061 0.55
## happy5 0.82 0.82 0.75 0.60 4.4 0.014 0.00120 0.59
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## happy2 383 0.86 0.81 0.70 0.65 4.0 1.5
## happy3 369 0.88 0.83 0.75 0.69 3.9 1.6
## happy4 319 0.88 0.86 0.81 0.75 4.0 1.6
## happy5 147 0.88 0.82 0.72 0.67 4.1 1.7
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## happy2 0.05 0.02 0.08 0.16 0.27 0.26 0.15 0.22
## happy3 0.06 0.04 0.07 0.18 0.30 0.18 0.17 0.25
## happy4 0.07 0.03 0.07 0.14 0.26 0.25 0.18 0.35
## happy5 0.06 0.03 0.05 0.16 0.19 0.27 0.24 0.70describe(d[c("happy2", "happy3", "happy4", "happy5")])- thankful
Over the past two weeks how thankful have you been toward this person?
psych::alpha(d[c("thankful2", "thankful3", "thankful4", "thankful5")]) #.87##
## Reliability analysis
## Call: psych::alpha(x = d[c("thankful2", "thankful3", "thankful4", "thankful5")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.85 0.63 6.8 0.0094 3.4 1.6 0.62
##
## lower alpha upper 95% confidence boundaries
## 0.85 0.87 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## thankful2 0.87 0.87 0.82 0.68 6.5 0.010 0.0036 0.69
## thankful3 0.83 0.83 0.79 0.62 4.9 0.013 0.0141 0.62
## thankful4 0.80 0.80 0.74 0.57 4.0 0.015 0.0040 0.60
## thankful5 0.84 0.84 0.78 0.64 5.3 0.012 0.0023 0.62
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## thankful2 380 0.85 0.80 0.69 0.65 3.5 1.8
## thankful3 371 0.89 0.86 0.79 0.74 3.4 1.8
## thankful4 319 0.89 0.90 0.87 0.81 3.5 1.9
## thankful5 147 0.89 0.84 0.78 0.72 3.7 1.8
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## thankful2 0.09 0.05 0.10 0.22 0.21 0.20 0.13 0.23
## thankful3 0.10 0.08 0.12 0.17 0.23 0.14 0.16 0.24
## thankful4 0.11 0.08 0.08 0.18 0.18 0.22 0.15 0.35
## thankful5 0.10 0.03 0.12 0.18 0.18 0.20 0.20 0.70describe(d[c("thankful2", "thankful3", "thankful4", "thankful5")])- appreciative
Over the past two weeks how appreciative have you been toward this person?
psych::alpha(d[c("appreciative2", "appreciative3", "appreciative4", "appreciative5")]) #.87##
## Reliability analysis
## Call: psych::alpha(x = d[c("appreciative2", "appreciative3", "appreciative4",
## "appreciative5")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.84 0.61 6.2 0.01 3.5 1.6 0.61
##
## lower alpha upper 95% confidence boundaries
## 0.84 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
## appreciative2 0.86 0.86 0.81 0.66 5.9 0.011 0.0088 0.68
## appreciative3 0.82 0.82 0.78 0.60 4.5 0.014 0.0219 0.60
## appreciative4 0.78 0.78 0.71 0.54 3.6 0.017 0.0070 0.56
## appreciative5 0.84 0.84 0.78 0.63 5.1 0.013 0.0019 0.62
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## appreciative2 385 0.85 0.79 0.68 0.63 3.7 1.7
## appreciative3 369 0.89 0.85 0.78 0.72 3.5 1.8
## appreciative4 319 0.88 0.90 0.88 0.81 3.5 1.9
## appreciative5 148 0.88 0.82 0.75 0.68 3.8 1.8
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## appreciative2 0.08 0.06 0.07 0.21 0.24 0.20 0.15 0.22
## appreciative3 0.10 0.05 0.10 0.18 0.25 0.15 0.16 0.25
## appreciative4 0.11 0.08 0.08 0.18 0.18 0.22 0.15 0.35
## appreciative5 0.10 0.03 0.09 0.16 0.22 0.20 0.20 0.70describe(d[c("appreciative2", "appreciative3", "appreciative4", "appreciative5")])- gratitude scale
psych::alpha(d[c("appreciative2", "appreciative3", "appreciative4", "appreciative5",
"thankful2", "thankful3", "thankful4", "thankful5",
"grat2", "grat3", "grat4", "grat5")]) #alpha = .96## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done##
## Reliability analysis
## Call: psych::alpha(x = d[c("appreciative2", "appreciative3", "appreciative4",
## "appreciative5", "thankful2", "thankful3", "thankful4", "thankful5",
## "grat2", "grat3", "grat4", "grat5")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.96 0.96 0.98 0.66 23 0.0029 3.5 1.6 0.62
##
## lower alpha upper 95% confidence boundaries
## 0.95 0.96 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## appreciative2 0.96 0.96 0.97 0.67 22 0.0030 0.020 0.67
## appreciative3 0.95 0.95 0.97 0.66 21 0.0032 0.022 0.62
## appreciative4 0.95 0.95 0.98 0.65 20 0.0034 0.022 0.62
## appreciative5 0.96 0.96 0.97 0.66 22 0.0031 0.020 0.62
## thankful2 0.96 0.96 0.97 0.67 22 0.0030 0.021 0.67
## thankful3 0.95 0.95 0.97 0.66 21 0.0032 0.022 0.62
## thankful4 0.95 0.95 0.98 0.65 20 0.0034 0.022 0.62
## thankful5 0.95 0.95 0.97 0.66 21 0.0032 0.021 0.62
## grat2 0.96 0.96 0.97 0.67 22 0.0030 0.021 0.67
## grat3 0.96 0.95 0.97 0.66 21 0.0032 0.023 0.62
## grat4 0.95 0.95 0.96 0.65 21 0.0033 0.023 0.62
## grat5 0.96 0.96 0.97 0.67 22 0.0030 0.018 0.62
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## appreciative2 385 0.82 0.77 0.77 0.72 3.7 1.7
## appreciative3 369 0.88 0.85 0.85 0.82 3.5 1.8
## appreciative4 319 0.88 0.90 0.85 0.88 3.5 1.9
## appreciative5 148 0.87 0.81 0.81 0.77 3.8 1.8
## thankful2 380 0.84 0.78 0.78 0.74 3.5 1.8
## thankful3 371 0.87 0.85 0.85 0.81 3.4 1.8
## thankful4 319 0.88 0.90 0.85 0.88 3.5 1.9
## thankful5 147 0.90 0.84 0.85 0.81 3.7 1.8
## grat2 383 0.82 0.77 0.76 0.72 3.5 1.8
## grat3 366 0.87 0.84 0.84 0.80 3.4 1.8
## grat4 320 0.86 0.88 0.87 0.85 3.5 1.9
## grat5 143 0.84 0.77 0.77 0.73 3.6 1.9
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## appreciative2 0.08 0.06 0.07 0.21 0.24 0.20 0.15 0.22
## appreciative3 0.10 0.05 0.10 0.18 0.25 0.15 0.16 0.25
## appreciative4 0.11 0.08 0.08 0.18 0.18 0.22 0.15 0.35
## appreciative5 0.10 0.03 0.09 0.16 0.22 0.20 0.20 0.70
## thankful2 0.09 0.05 0.10 0.22 0.21 0.20 0.13 0.23
## thankful3 0.10 0.08 0.12 0.17 0.23 0.14 0.16 0.24
## thankful4 0.11 0.08 0.08 0.18 0.18 0.22 0.15 0.35
## thankful5 0.10 0.03 0.12 0.18 0.18 0.20 0.20 0.70
## grat2 0.09 0.07 0.10 0.22 0.21 0.16 0.14 0.22
## grat3 0.10 0.05 0.13 0.21 0.23 0.13 0.14 0.25
## grat4 0.11 0.05 0.10 0.14 0.25 0.18 0.16 0.35
## grat5 0.11 0.05 0.12 0.13 0.25 0.17 0.17 0.71
describe(d[c("appreciative2", "appreciative3", "appreciative4", "appreciative5",
"thankful2", "thankful3", "thankful4", "thankful5",
"grat2", "grat3", "grat4", "grat5")])- need
1. when I consider my life right now, I would say I am in a terrible place.
2. hen I consider my life right now, i would say I am in a really good place.
Please indicate how much you agree that each of the following are currently a cause of stress:
1. Financial problems (ex: a lack of money, owe someone money, etc.)
2. Health related issues (ex: poor sleep, sickness, injury, death of someone close, etc.)
3. Friend problems (ex: arguments, conflicts with roommate, not enough friends, etc.)
4. General life problems (ex: victim of a crime, car troubles, traffic ticket, etc.)
5. Academic issues (ex: did poorly on a test, a lot of deadlines, etc.)
6. Relationship issues (ex: breaking up with a boy/girlfriend, fights,long-distance relationship, etc.)
7. Family problems (ex: divorce, arguments, not enough support, etc.)
d$w1need2.R <- 8 - d$w1need2
d$w2need2.R <- 8 - d$w2need2
d$w3need2.R <- 8 - d$w3need2
d$w4need2.R <- 8 - d$w4need2
d$w5need2.R <- 8 - d$w5need2
### need 1-9 for week 1-5
psych::alpha(d[c("w1need1",
"w1need2.R",
"w1need3",
"w1need4",
"w1need5",
"w1need6",
"w1need7",
"w1need8",
"w1need9")], check.keys = TRUE) #.56##
## Reliability analysis
## Call: psych::alpha(x = d[c("w1need1", "w1need2.R", "w1need3", "w1need4",
## "w1need5", "w1need6", "w1need7", "w1need8", "w1need9")],
## check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.69 0.71 0.74 0.21 2.4 0.021 3.1 0.96 0.25
##
## lower alpha upper 95% confidence boundaries
## 0.65 0.69 0.73
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## w1need1 0.66 0.67 0.68 0.20 2.0 0.023 0.015 0.24
## w1need2.R 0.66 0.67 0.68 0.20 2.0 0.023 0.014 0.24
## w1need3 0.68 0.70 0.72 0.23 2.4 0.022 0.021 0.25
## w1need4 0.65 0.67 0.71 0.20 2.0 0.024 0.025 0.23
## w1need5 0.65 0.66 0.70 0.20 2.0 0.024 0.025 0.24
## w1need6 0.66 0.68 0.72 0.21 2.1 0.023 0.027 0.24
## w1need7 0.65 0.67 0.71 0.20 2.0 0.024 0.027 0.19
## w1need8 0.71 0.72 0.75 0.24 2.6 0.020 0.023 0.27
## w1need9 0.67 0.69 0.72 0.22 2.3 0.023 0.024 0.25
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w1need1 486 0.53 0.60 0.58 0.40 2.0 1.3
## w1need2.R 486 0.54 0.62 0.61 0.42 2.3 1.3
## w1need3 486 0.52 0.46 0.36 0.32 3.7 2.0
## w1need4 486 0.61 0.60 0.53 0.44 3.4 2.0
## w1need5 486 0.61 0.62 0.56 0.45 3.5 1.8
## w1need6 486 0.54 0.56 0.46 0.38 2.3 1.6
## w1need7 486 0.62 0.61 0.52 0.45 4.4 1.9
## w1need8 486 0.38 0.36 0.20 0.17 3.3 2.0
## w1need9 486 0.55 0.50 0.40 0.35 2.8 2.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## w1need1 0.51 0.27 0.06 0.08 0.05 0.01 0.01 0.01
## w1need2.R 0.27 0.41 0.17 0.05 0.07 0.02 0.01 0.01
## w1need3 0.19 0.21 0.08 0.07 0.24 0.12 0.09 0.01
## w1need4 0.25 0.18 0.07 0.06 0.28 0.11 0.04 0.01
## w1need5 0.17 0.23 0.08 0.16 0.19 0.12 0.04 0.01
## w1need6 0.43 0.30 0.04 0.09 0.09 0.04 0.02 0.01
## w1need7 0.12 0.10 0.08 0.07 0.31 0.20 0.12 0.01
## w1need8 0.27 0.19 0.07 0.12 0.17 0.13 0.04 0.01
## w1need9 0.42 0.18 0.09 0.09 0.06 0.08 0.08 0.01psych::alpha(d[c("w2need1",
"w2need2.R",
"w2need3",
"w2need4",
"w2need5",
"w2need6",
"w2need7",
"w2need8",
"w2need9")], check.keys = TRUE) #.62##
## Reliability analysis
## Call: psych::alpha(x = d[c("w2need1", "w2need2.R", "w2need3", "w2need4",
## "w2need5", "w2need6", "w2need7", "w2need8", "w2need9")],
## check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.72 0.73 0.76 0.23 2.7 0.019 3.2 1 0.19
##
## lower alpha upper 95% confidence boundaries
## 0.68 0.72 0.75
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## w2need1 0.69 0.69 0.70 0.22 2.2 0.021 0.014 0.20
## w2need2.R 0.70 0.70 0.72 0.23 2.4 0.020 0.014 0.20
## w2need3 0.68 0.70 0.74 0.23 2.3 0.022 0.022 0.19
## w2need4 0.68 0.69 0.73 0.22 2.3 0.022 0.025 0.18
## w2need5 0.68 0.69 0.73 0.22 2.2 0.022 0.024 0.17
## w2need6 0.68 0.70 0.72 0.22 2.3 0.022 0.019 0.19
## w2need7 0.69 0.70 0.74 0.23 2.4 0.021 0.024 0.19
## w2need8 0.73 0.74 0.77 0.26 2.9 0.018 0.018 0.24
## w2need9 0.69 0.70 0.73 0.23 2.4 0.021 0.023 0.19
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w2need1 468 0.55 0.62 0.60 0.43 1.9 1.3
## w2need2.R 468 0.48 0.55 0.52 0.35 2.4 1.3
## w2need3 468 0.62 0.57 0.49 0.43 3.7 2.2
## w2need4 468 0.62 0.60 0.52 0.46 3.9 2.0
## w2need5 468 0.62 0.63 0.56 0.47 3.6 1.9
## w2need6 468 0.58 0.58 0.53 0.44 2.2 1.6
## w2need7 468 0.57 0.56 0.47 0.41 4.9 1.9
## w2need8 468 0.40 0.36 0.21 0.19 3.6 2.1
## w2need9 468 0.58 0.56 0.49 0.41 2.8 2.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## w2need1 0.52 0.27 0.08 0.03 0.07 0.03 0.00 0.05
## w2need2.R 0.26 0.37 0.22 0.07 0.03 0.05 0.00 0.05
## w2need3 0.26 0.16 0.05 0.06 0.21 0.14 0.11 0.05
## w2need4 0.18 0.19 0.05 0.09 0.22 0.20 0.07 0.05
## w2need5 0.17 0.21 0.09 0.08 0.27 0.14 0.04 0.05
## w2need6 0.47 0.25 0.09 0.05 0.06 0.05 0.02 0.05
## w2need7 0.07 0.11 0.05 0.05 0.27 0.25 0.19 0.05
## w2need8 0.24 0.18 0.05 0.14 0.18 0.12 0.10 0.05
## w2need9 0.44 0.12 0.07 0.10 0.15 0.10 0.03 0.05psych::alpha(d[c("w3need1",
"w3need2.R",
"w3need3",
"w3need4",
"w3need5",
"w3need6",
"w3need7",
"w3need8",
"w3need9")], check.keys = TRUE) #.60##
## Reliability analysis
## Call: psych::alpha(x = d[c("w3need1", "w3need2.R", "w3need3", "w3need4",
## "w3need5", "w3need6", "w3need7", "w3need8", "w3need9")],
## check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.77 0.8 0.27 3.4 0.016 3.1 1.1 0.28
##
## lower alpha upper 95% confidence boundaries
## 0.73 0.76 0.79
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## w3need1 0.73 0.74 0.75 0.26 2.8 0.018 0.017 0.27
## w3need2.R 0.72 0.73 0.74 0.25 2.7 0.019 0.017 0.26
## w3need3 0.74 0.76 0.78 0.28 3.2 0.017 0.025 0.29
## w3need4 0.71 0.73 0.76 0.26 2.8 0.020 0.026 0.26
## w3need5 0.71 0.72 0.76 0.25 2.6 0.020 0.024 0.26
## w3need6 0.73 0.75 0.78 0.27 3.0 0.018 0.027 0.27
## w3need7 0.74 0.76 0.78 0.28 3.1 0.018 0.026 0.31
## w3need8 0.78 0.79 0.80 0.32 3.7 0.015 0.016 0.31
## w3need9 0.74 0.75 0.78 0.28 3.1 0.018 0.022 0.28
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w3need1 450 0.60 0.66 0.65 0.51 1.9 1.3
## w3need2.R 450 0.64 0.69 0.69 0.53 2.6 1.5
## w3need3 450 0.57 0.53 0.44 0.39 3.5 2.0
## w3need4 450 0.70 0.68 0.63 0.56 3.4 2.0
## w3need5 450 0.72 0.72 0.68 0.61 3.7 1.9
## w3need6 450 0.59 0.60 0.52 0.46 2.5 1.7
## w3need7 450 0.57 0.56 0.47 0.41 4.5 1.9
## w3need8 445 0.36 0.35 0.21 0.17 3.3 2.0
## w3need9 446 0.57 0.56 0.49 0.41 2.8 2.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## w3need1 0.51 0.27 0.09 0.05 0.07 0.01 0.00 0.08
## w3need2.R 0.24 0.36 0.21 0.04 0.08 0.08 0.00 0.08
## w3need3 0.22 0.20 0.09 0.07 0.22 0.10 0.09 0.08
## w3need4 0.24 0.23 0.05 0.06 0.28 0.06 0.08 0.08
## w3need5 0.18 0.19 0.05 0.15 0.26 0.14 0.04 0.08
## w3need6 0.40 0.25 0.10 0.09 0.08 0.07 0.01 0.08
## w3need7 0.09 0.12 0.10 0.08 0.21 0.29 0.12 0.08
## w3need8 0.26 0.22 0.04 0.17 0.13 0.14 0.04 0.09
## w3need9 0.40 0.23 0.03 0.07 0.11 0.12 0.04 0.09psych::alpha(d[c("w4need1",
"w4need2.R",
"w4need3",
"w4need4",
"w4need5",
"w4need6",
"w4need7",
"w4need8",
"w4need9")], check.keys = TRUE) #.66##
## Reliability analysis
## Call: psych::alpha(x = d[c("w4need1", "w4need2.R", "w4need3", "w4need4",
## "w4need5", "w4need6", "w4need7", "w4need8", "w4need9")],
## 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 0.016 3 1.1 0.28
##
## lower alpha upper 95% confidence boundaries
## 0.74 0.77 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## w4need1 0.75 0.76 0.77 0.28 3.1 0.017 0.014 0.28
## w4need2.R 0.75 0.76 0.76 0.28 3.1 0.017 0.015 0.29
## w4need3 0.73 0.75 0.77 0.27 3.0 0.018 0.018 0.28
## w4need4 0.75 0.76 0.79 0.28 3.1 0.017 0.022 0.27
## w4need5 0.73 0.74 0.78 0.27 2.9 0.018 0.022 0.25
## w4need6 0.73 0.74 0.77 0.26 2.8 0.018 0.018 0.26
## w4need7 0.75 0.76 0.80 0.29 3.3 0.017 0.022 0.30
## w4need8 0.78 0.78 0.81 0.31 3.6 0.015 0.017 0.31
## w4need9 0.75 0.76 0.79 0.28 3.2 0.017 0.017 0.28
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w4need1 401 0.54 0.61 0.58 0.44 1.9 1.3
## w4need2.R 401 0.54 0.60 0.57 0.42 2.5 1.5
## w4need3 401 0.68 0.64 0.60 0.53 3.5 2.1
## w4need4 401 0.61 0.60 0.51 0.45 3.5 2.0
## w4need5 401 0.67 0.68 0.62 0.56 3.0 1.7
## w4need6 401 0.70 0.70 0.67 0.59 2.2 1.6
## w4need7 401 0.57 0.56 0.46 0.42 4.5 1.9
## w4need8 401 0.47 0.44 0.32 0.28 3.3 2.1
## w4need9 401 0.60 0.59 0.52 0.45 2.6 1.9
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## w4need1 0.49 0.31 0.08 0.02 0.08 0.01 0.00 0.18
## w4need2.R 0.28 0.31 0.22 0.02 0.09 0.07 0.00 0.18
## w4need3 0.27 0.16 0.09 0.07 0.17 0.16 0.08 0.18
## w4need4 0.24 0.17 0.07 0.11 0.24 0.11 0.06 0.18
## w4need5 0.22 0.31 0.07 0.10 0.24 0.05 0.01 0.18
## w4need6 0.53 0.18 0.08 0.08 0.06 0.05 0.01 0.18
## w4need7 0.11 0.12 0.08 0.06 0.23 0.29 0.11 0.18
## w4need8 0.28 0.20 0.04 0.14 0.16 0.09 0.09 0.18
## w4need9 0.43 0.24 0.07 0.04 0.08 0.11 0.04 0.18psych::alpha(d[c("w5need1",
"w5need2.R",
"w5need3",
"w5need4",
"w5need5",
"w5need6",
"w5need7",
"w5need8",
"w5need9")], check.keys = TRUE) #.44##
## Reliability analysis
## Call: psych::alpha(x = d[c("w5need1", "w5need2.R", "w5need3", "w5need4",
## "w5need5", "w5need6", "w5need7", "w5need8", "w5need9")],
## check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.71 0.73 0.83 0.23 2.7 0.02 3.1 1 0.26
##
## lower alpha upper 95% confidence boundaries
## 0.67 0.71 0.75
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## w5need1 0.66 0.67 0.77 0.20 2.0 0.023 0.040 0.21
## w5need2.R 0.65 0.67 0.76 0.20 2.0 0.024 0.042 0.22
## w5need3 0.71 0.74 0.80 0.26 2.8 0.019 0.056 0.27
## w5need4 0.69 0.72 0.79 0.24 2.6 0.021 0.056 0.26
## w5need5 0.67 0.70 0.81 0.23 2.4 0.023 0.065 0.27
## w5need6 0.62 0.65 0.77 0.19 1.9 0.027 0.050 0.21
## w5need7 0.67 0.69 0.80 0.22 2.3 0.023 0.062 0.21
## w5need8 0.74 0.76 0.83 0.28 3.2 0.018 0.052 0.31
## w5need9 0.71 0.73 0.82 0.25 2.7 0.020 0.056 0.27
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w5need1 194 0.67 0.73 0.75 0.583 1.8 1.3
## w5need2.R 194 0.68 0.73 0.76 0.575 2.5 1.5
## w5need3 194 0.46 0.40 0.33 0.247 3.5 2.1
## w5need4 190 0.51 0.49 0.44 0.316 3.5 1.9
## w5need5 194 0.57 0.58 0.49 0.427 3.0 1.7
## w5need6 194 0.78 0.79 0.79 0.674 2.7 2.0
## w5need7 194 0.61 0.62 0.55 0.459 4.5 1.8
## w5need8 194 0.31 0.27 0.14 0.092 3.4 2.0
## w5need9 194 0.45 0.45 0.35 0.249 2.8 2.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## w5need1 0.60 0.24 0.05 0.04 0.05 0.03 0.00 0.60
## w5need2.R 0.26 0.37 0.21 0.02 0.07 0.04 0.03 0.60
## w5need3 0.25 0.21 0.06 0.05 0.23 0.11 0.10 0.60
## w5need4 0.17 0.26 0.11 0.00 0.29 0.11 0.05 0.61
## w5need5 0.16 0.41 0.08 0.09 0.18 0.06 0.03 0.60
## w5need6 0.43 0.21 0.07 0.02 0.12 0.10 0.06 0.60
## w5need7 0.07 0.09 0.21 0.03 0.30 0.15 0.16 0.60
## w5need8 0.24 0.17 0.16 0.11 0.10 0.14 0.07 0.60
## w5need9 0.42 0.13 0.09 0.09 0.15 0.06 0.05 0.60x <- d[c("w1need2.R", "w2need2.R", "w3need2.R", "w4need2.R", "w5need2.R")]
d$w2needAvg <- rowMeans(x)
psych::alpha(d[c("w1needAvg",
"w2needAvg",
"w3needAvg",
"w4needAvg",
"w5needAvg")], check.keys = TRUE) #.89## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = d[c("w1needAvg", "w2needAvg", "w3needAvg", "w4needAvg",
## "w5needAvg")], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.9 0.9 0.66 9.5 0.0083 3.4 0.8 0.68
##
## lower alpha upper 95% confidence boundaries
## 0.87 0.89 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## w1needAvg 0.85 0.88 0.86 0.64 7.1 0.0108 0.0101 0.62
## w2needAvg 0.91 0.91 0.90 0.73 10.8 0.0064 0.0029 0.73
## w3needAvg 0.84 0.87 0.85 0.63 6.8 0.0115 0.0128 0.62
## w4needAvg 0.85 0.88 0.86 0.64 7.1 0.0115 0.0162 0.64
## w5needAvg 0.85 0.88 0.85 0.64 7.1 0.0109 0.0105 0.65
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w1needAvg 486 0.88 0.87 0.85 0.78 3.4 0.84
## w2needAvg 167 0.79 0.75 0.64 0.61 2.5 1.32
## w3needAvg 450 0.87 0.89 0.86 0.81 3.4 0.88
## w4needAvg 401 0.90 0.87 0.84 0.79 3.3 0.93
## w5needAvg 194 0.87 0.87 0.85 0.78 3.4 0.82- weight (NA)
Please enter your current weight (pounds/kilograms)
- height (NA)
Please enter your height
head(d1, 10)H1. Do friend ranks predict WTRs?
1. run model
h1 <- lmer(wtr ~ rank + (rank | subID), data = d1)
tab_model(h1,
show.df = T,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "Est",
digits = 3)| wtr | ||||||
|---|---|---|---|---|---|---|
| Predictors | Est | SE | CI | t | p | df |
| (Intercept) | 0.791 | 0.025 | 0.741 – 0.841 | 31.127 | <0.001 | 1343.000 |
| rank | -0.071 | 0.007 | -0.084 – -0.057 | -10.217 | <0.001 | 1343.000 |
| Random Effects | ||||||
| σ2 | 0.03 | |||||
| τ00 subID | 0.05 | |||||
| τ11 subID.rank | 0.00 | |||||
| ρ01 subID | -0.24 | |||||
| ICC | 0.68 | |||||
| N subID | 99 | |||||
| Observations | 1349 | |||||
| Marginal R2 / Conditional R2 | 0.068 / 0.706 | |||||
2. plot
H2. Do WTRs predict gratitude?
1. run model
h2<- lmer(gratScale ~ wtr + (wtr | subID), data = d1)
tab_model(h2,
show.df = T,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "Est",
digits = 3)| gratScale | ||||||
|---|---|---|---|---|---|---|
| Predictors | Est | SE | CI | t | p | df |
| (Intercept) | 1.895 | 0.198 | 1.507 – 2.283 | 9.574 | <0.001 | 1123.000 |
| wtr | 2.854 | 0.294 | 2.278 – 3.430 | 9.718 | <0.001 | 1123.000 |
| Random Effects | ||||||
| σ2 | 1.34 | |||||
| τ00 subID | 1.92 | |||||
| τ11 subID.wtr | 3.72 | |||||
| ρ01 subID | -0.60 | |||||
| ICC | 0.56 | |||||
| N subID | 99 | |||||
| Observations | 1129 | |||||
| Marginal R2 / Conditional R2 | 0.210 / 0.652 | |||||
2. plot
H3. Do changes in WTRs predict gratitude each week?
2. run model
h3 <- lmer(gratScale ~ wtrDiff + (wtrDiff | subID), data = d1)
tab_model(h3,
show.df = T,
show.ci = .95,
show.se = T,
show.stat = T,
string.stat = "t",
string.se="SE",
string.est = "Est",
digits = 3)| gratScale | ||||||
|---|---|---|---|---|---|---|
| Predictors | Est | SE | CI | t | p | df |
| (Intercept) | 3.509 | 0.123 | 3.268 – 3.749 | 28.578 | <0.001 | 1036.000 |
| wtrDiff | 1.375 | 0.242 | 0.900 – 1.849 | 5.680 | <0.001 | 1036.000 |
| Random Effects | ||||||
| σ2 | 1.73 | |||||
| τ00 subID | 1.26 | |||||
| τ11 subID.wtrDiff | 0.25 | |||||
| ρ01 subID | -0.05 | |||||
| ICC | 0.42 | |||||
| N subID | 98 | |||||
| Observations | 1042 | |||||
| Marginal R2 / Conditional R2 | 0.023 / 0.437 | |||||
3. plot
