Longitudinal Gratitude Analyses
Dani Grant
d <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/longitudinal gratitude/analyses/long_grat_w1w5.csv", header = T)
names(d)[names(d) == 'irrirated5'] <- 'irritated5'
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$thankful1 <- NA
thankful <- d[c("subID", "thankful1", "thankful2", "thankful3", "thankful4", "thankful5")]
d$irritated1 <- NA
irritated <- d[c("subID", "irritated1", "irritated2", "irritated3", "irritated4", "irritated5")]
d$anger1 <- NA
anger <- d[c("subID", "anger1", "anger2", "anger3", "anger4", "anger5")]
d$grat1 <- NA
grat <- d[c("subID", "grat1", "grat2", "grat3", "grat4", "grat5")]
#create grat construct
x <- cbind(d$thankful2, d$grat2, d$appreciative2)
d$gratScale2 <- rowMeans(x, na.rm = T)
remove(x)
x <- cbind(d$thankful3, d$grat3, d$appreciative3)
d$gratScale3 <- rowMeans(x, na.rm = T)
remove(x)
x <- cbind(d$thankful4, d$grat4, d$appreciative4)
d$gratScale4 <- rowMeans(x, na.rm = T)
remove(x)
x <- cbind(d$thankful5, d$grat5, d$appreciative5)
d$gratScale5 <- rowMeans(x, na.rm = T)
remove(x)
d$gratScale1 <- NA
gratScale <- d[c("subID", "gratScale1", "gratScale2", "gratScale3", "gratScale4", "gratScale5")]
# pivot long
rank_long <- rank %>% pivot_longer(cols = contains("rank"), names_to = "delete", values_to = "rank")
week_long <- week %>% pivot_longer(cols = contains("week"), names_to = "delete", values_to = "week")
wtr_long <- wtr %>% pivot_longer(cols = contains("wtr"), names_to = "delete", values_to = "wtr")
close_long <- close %>% pivot_longer(cols = contains("close"), names_to = "delete", values_to = "close")
commit_long <- commit %>% pivot_longer(cols = contains("commit"), names_to = "delete", values_to = "commit")
ios_long <- ios %>% pivot_longer(cols = contains("ios"), names_to = "delete", values_to = "ios")
thankful_long <- thankful %>% pivot_longer(cols = contains("thankful"), names_to = "delete", values_to = "thankful")
irritated_long <- irritated %>% pivot_longer(cols = contains("irritated"), names_to = "delete", values_to = "irritated")
anger_long <- anger %>% pivot_longer(cols = contains("anger"), names_to = "delete", values_to = "anger")
grat_long <- grat %>% pivot_longer(cols = contains("grat"), names_to = "delete", values_to = "grat")
gratScale_long <- gratScale %>% pivot_longer(cols = contains("gratScale"), names_to = "delete", values_to = "gratScale")
#append to single data frame
long_final <- data.frame(week_long$week)
long_final$subID <- week_long$subID
long_final$rank <- rank_long$rank
long_final$gratScale <- gratScale_long$gratScale
long_final$grat <- grat_long$grat
long_final$close <- close_long$close
long_final$wtr <- wtr_long$wtr
long_final$close <- close_long$close
long_final$commit <- commit_long$commit
long_final$ios <- ios_long$ios
long_final$thankful <- thankful_long$thankful
long_final$irritated <- irritated_long$irritated
long_final$anger <- anger_long$anger
names(long_final)[names(long_final) == 'week_long.week'] <- 'week'descriptive stats
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 0.69describeBy(d[c("w1close", "w2closeAvg", "w3closeAvg", "w4closeAvg", "w5closeAvg")])## Warning in describeBy(d[c("w1close", "w2closeAvg", "w3closeAvg", "w4closeAvg", :
## no grouping variable requestedcommit
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 0.69describeBy(d[c("w1commit", "w2commitAvg", "w3commitAvg", "w4commitAvg", "w5commitAvg")])## Warning in describeBy(d[c("w1commit", "w2commitAvg", "w3commitAvg",
## "w4commitAvg", : no grouping variable requestedios
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 0.69describeBy(d[c("w1ios", "w2iosAvg", "w3iosAvg", "w4iosAvg", "w5iosAvg")])## Warning in describeBy(d[c("w1ios", "w2iosAvg", "w3iosAvg", "w4iosAvg",
## "w5iosAvg")]): no grouping variable requestedgratitude week 2-5
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 0.71describeBy(d[c("grat2", "grat3", "grat4", "grat5")])## Warning in describeBy(d[c("grat2", "grat3", "grat4", "grat5")]): no grouping
## variable requestedanger
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 0.70describeBy(d[c("anger2", "anger3", "anger4", "anger5")])## Warning in describeBy(d[c("anger2", "anger3", "anger4", "anger5")]): no grouping
## variable requestedirritated
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 0.70describeBy(d[c("irritated2", "irritated3", "irritated4", "irritated5")])## Warning in describeBy(d[c("irritated2", "irritated3", "irritated4",
## "irritated5")]): no grouping variable requestedhappy
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 0.70describeBy(d[c("happy2", "happy3", "happy4", "happy5")])## Warning in describeBy(d[c("happy2", "happy3", "happy4", "happy5")]): no grouping
## variable requestedthankful
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 0.70describeBy(d[c("thankful2", "thankful3", "thankful4", "thankful5")])## Warning in describeBy(d[c("thankful2", "thankful3", "thankful4", "thankful5")]):
## no grouping variable requestedappreciative
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 0.70describeBy(d[c("appreciative2", "appreciative3", "appreciative4", "appreciative5")])## Warning in describeBy(d[c("appreciative2", "appreciative3", "appreciative4", :
## no grouping variable requestedgratitude 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 0.71describeBy(d[c("appreciative2", "appreciative3", "appreciative4", "appreciative5",
"thankful2", "thankful3", "thankful4", "thankful5",
"grat2", "grat3", "grat4", "grat5")])## Warning in describeBy(d[c("appreciative2", "appreciative3", "appreciative4", :
## no grouping variable requestedneed 1-9 for week 1-5
psych::alpha(d[c("w1need1",
"w1need2",
"w1need3",
"w1need4",
"w1need5",
"w1need6",
"w1need7",
"w1need8",
"w1need9")]) #.56## Warning in psych::alpha(d[c("w1need1", "w1need2", "w1need3", "w1need4", : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( w1need2 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = d[c("w1need1", "w1need2", "w1need3", "w1need4",
## "w1need5", "w1need6", "w1need7", "w1need8", "w1need9")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.56 0.5 0.61 0.098 0.98 0.028 3.4 0.84 0.14
##
## lower alpha upper 95% confidence boundaries
## 0.5 0.56 0.61
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## w1need1 0.54 0.48 0.56 0.103 0.92 0.029 0.046 0.14
## w1need2 0.66 0.67 0.68 0.199 1.99 0.023 0.014 0.24
## w1need3 0.47 0.40 0.53 0.077 0.67 0.032 0.069 0.14
## w1need4 0.48 0.40 0.54 0.077 0.67 0.032 0.062 0.12
## w1need5 0.50 0.43 0.56 0.086 0.76 0.031 0.058 0.11
## w1need6 0.50 0.42 0.56 0.083 0.72 0.031 0.066 0.12
## w1need7 0.47 0.39 0.54 0.074 0.64 0.033 0.063 0.11
## w1need8 0.57 0.50 0.62 0.110 0.98 0.027 0.073 0.24
## w1need9 0.47 0.39 0.53 0.074 0.64 0.033 0.068 0.12
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w1need1 486 0.38 0.41 0.37 0.21 2.0 1.3
## w1need2 486 -0.28 -0.26 -0.48 -0.42 5.7 1.3
## w1need3 486 0.62 0.59 0.53 0.41 3.7 2.0
## w1need4 486 0.60 0.59 0.53 0.39 3.4 2.0
## w1need5 486 0.53 0.53 0.46 0.33 3.5 1.8
## w1need6 486 0.52 0.56 0.46 0.34 2.3 1.6
## w1need7 486 0.61 0.61 0.54 0.42 4.4 1.9
## w1need8 486 0.38 0.37 0.18 0.13 3.3 2.0
## w1need9 486 0.63 0.61 0.55 0.42 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 0.01 0.02 0.07 0.05 0.17 0.41 0.27 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",
"w2need3",
"w2need4",
"w2need5",
"w2need6",
"w2need7",
"w2need8",
"w2need9")]) #.62## Warning in psych::alpha(d[c("w2need1", "w2need2", "w2need3", "w2need4", : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( w2need2 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = d[c("w2need1", "w2need2", "w2need3", "w2need4",
## "w2need5", "w2need6", "w2need7", "w2need8", "w2need9")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.62 0.57 0.67 0.13 1.3 0.024 3.6 0.91 0.18
##
## lower alpha upper 95% confidence boundaries
## 0.57 0.62 0.67
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## w2need1 0.61 0.57 0.62 0.14 1.30 0.025 0.043 0.19
## w2need2 0.70 0.70 0.72 0.23 2.38 0.020 0.014 0.20
## w2need3 0.54 0.48 0.60 0.10 0.91 0.029 0.065 0.16
## w2need4 0.56 0.50 0.62 0.11 0.99 0.028 0.062 0.16
## w2need5 0.57 0.50 0.62 0.11 1.01 0.027 0.059 0.15
## w2need6 0.55 0.48 0.58 0.10 0.94 0.028 0.059 0.18
## w2need7 0.56 0.50 0.62 0.11 1.00 0.027 0.065 0.16
## w2need8 0.62 0.56 0.66 0.14 1.27 0.023 0.072 0.20
## w2need9 0.56 0.50 0.61 0.11 1.01 0.027 0.064 0.17
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w2need1 468 0.39 0.40 0.36 0.23 1.9 1.3
## w2need2 468 -0.21 -0.18 -0.37 -0.35 5.6 1.3
## w2need3 468 0.67 0.65 0.59 0.48 3.7 2.2
## w2need4 468 0.61 0.60 0.52 0.42 3.9 2.0
## w2need5 468 0.58 0.58 0.51 0.39 3.6 1.9
## w2need6 468 0.61 0.63 0.60 0.46 2.2 1.6
## w2need7 468 0.58 0.59 0.50 0.40 4.9 1.9
## w2need8 468 0.44 0.42 0.26 0.21 3.6 2.1
## w2need9 468 0.59 0.58 0.51 0.39 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 0.00 0.05 0.03 0.07 0.22 0.37 0.26 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",
"w3need3",
"w3need4",
"w3need5",
"w3need6",
"w3need7",
"w3need8",
"w3need9")]) #.60## Warning in psych::alpha(d[c("w3need1", "w3need2", "w3need3", "w3need4", : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( w3need2 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = d[c("w3need1", "w3need2", "w3need3", "w3need4",
## "w3need5", "w3need6", "w3need7", "w3need8", "w3need9")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.6 0.56 0.68 0.12 1.3 0.024 3.4 0.88 0.24
##
## lower alpha upper 95% confidence boundaries
## 0.55 0.6 0.65
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## w3need1 0.57 0.52 0.61 0.121 1.10 0.026 0.072 0.21
## w3need2 0.72 0.73 0.74 0.254 2.72 0.019 0.017 0.26
## w3need3 0.51 0.46 0.61 0.097 0.86 0.029 0.099 0.24
## w3need4 0.50 0.45 0.59 0.091 0.80 0.031 0.085 0.19
## w3need5 0.50 0.44 0.59 0.089 0.78 0.031 0.080 0.19
## w3need6 0.53 0.47 0.62 0.098 0.87 0.028 0.093 0.19
## w3need7 0.54 0.49 0.63 0.107 0.96 0.027 0.094 0.19
## w3need8 0.62 0.57 0.68 0.141 1.32 0.023 0.100 0.26
## w3need9 0.55 0.49 0.62 0.108 0.97 0.027 0.090 0.21
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w3need1 450 0.44 0.48 0.47 0.30 1.9 1.3
## w3need2 450 -0.39 -0.40 -0.59 -0.53 5.4 1.5
## w3need3 450 0.66 0.64 0.57 0.47 3.5 2.0
## w3need4 450 0.69 0.68 0.65 0.53 3.4 2.0
## w3need5 450 0.69 0.70 0.67 0.54 3.7 1.9
## w3need6 450 0.61 0.63 0.56 0.45 2.5 1.7
## w3need7 450 0.57 0.57 0.48 0.38 4.5 1.9
## w3need8 445 0.36 0.35 0.18 0.12 3.3 2.0
## w3need9 446 0.58 0.57 0.50 0.37 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 0.00 0.08 0.08 0.04 0.21 0.36 0.24 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",
"w4need3",
"w4need4",
"w4need5",
"w4need6",
"w4need7",
"w4need8",
"w4need9")]) #.66## Warning in psych::alpha(d[c("w4need1", "w4need2", "w4need3", "w4need4", : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( w4need2 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = d[c("w4need1", "w4need2", "w4need3", "w4need4",
## "w4need5", "w4need6", "w4need7", "w4need8", "w4need9")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.66 0.63 0.73 0.16 1.7 0.021 3.3 0.93 0.25
##
## lower alpha upper 95% confidence boundaries
## 0.62 0.66 0.7
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## w4need1 0.65 0.62 0.69 0.17 1.6 0.022 0.065 0.26
## w4need2 0.75 0.76 0.76 0.28 3.1 0.017 0.015 0.29
## w4need3 0.56 0.52 0.64 0.12 1.1 0.027 0.080 0.21
## w4need4 0.61 0.58 0.70 0.14 1.4 0.023 0.083 0.21
## w4need5 0.58 0.54 0.67 0.13 1.2 0.025 0.079 0.19
## w4need6 0.58 0.53 0.65 0.12 1.1 0.026 0.072 0.21
## w4need7 0.62 0.58 0.70 0.15 1.4 0.023 0.086 0.26
## w4need8 0.66 0.62 0.72 0.17 1.7 0.021 0.089 0.29
## w4need9 0.59 0.55 0.67 0.13 1.2 0.025 0.084 0.25
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w4need1 401 0.38 0.42 0.38 0.24 1.9 1.3
## w4need2 401 -0.27 -0.27 -0.43 -0.42 5.5 1.5
## w4need3 401 0.74 0.73 0.71 0.59 3.5 2.1
## w4need4 401 0.59 0.58 0.49 0.40 3.5 2.0
## w4need5 401 0.68 0.69 0.64 0.54 3.0 1.7
## w4need6 401 0.69 0.71 0.69 0.57 2.2 1.6
## w4need7 401 0.57 0.56 0.46 0.38 4.5 1.9
## w4need8 401 0.45 0.42 0.29 0.23 3.3 2.1
## w4need9 401 0.67 0.67 0.62 0.51 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 0.00 0.07 0.09 0.02 0.22 0.31 0.28 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",
"w5need3",
"w5need4",
"w5need5",
"w5need6",
"w5need7",
"w5need8",
"w5need9")]) #.44## Warning in psych::alpha(d[c("w5need1", "w5need2", "w5need3", "w5need4", : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( w5need2 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = d[c("w5need1", "w5need2", "w5need3", "w5need4",
## "w5need5", "w5need6", "w5need7", "w5need8", "w5need9")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.48 0.44 0.7 0.081 0.8 0.032 3.4 0.82 0.1
##
## lower alpha upper 95% confidence boundaries
## 0.42 0.48 0.55
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## w5need1 0.43 0.37 0.62 0.068 0.59 0.036 0.078 0.078
## w5need2 0.65 0.67 0.76 0.201 2.01 0.024 0.042 0.217
## w5need3 0.40 0.36 0.61 0.065 0.55 0.036 0.122 0.114
## w5need4 0.46 0.41 0.64 0.080 0.70 0.034 0.111 0.078
## w5need5 0.38 0.31 0.64 0.052 0.44 0.039 0.116 0.058
## w5need6 0.31 0.25 0.58 0.039 0.33 0.044 0.086 0.058
## w5need7 0.39 0.33 0.63 0.058 0.49 0.038 0.110 0.078
## w5need8 0.48 0.44 0.69 0.088 0.77 0.031 0.127 0.159
## w5need9 0.47 0.41 0.68 0.080 0.70 0.033 0.115 0.130
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w5need1 194 0.48 0.52 0.55 0.33 1.8 1.3
## w5need2 194 -0.43 -0.44 -0.60 -0.58 5.5 1.5
## w5need3 194 0.59 0.55 0.50 0.35 3.5 2.1
## w5need4 190 0.46 0.44 0.37 0.20 3.5 1.9
## w5need5 194 0.61 0.64 0.55 0.43 3.0 1.7
## w5need6 194 0.72 0.73 0.75 0.54 2.7 2.0
## w5need7 194 0.59 0.60 0.54 0.38 4.5 1.8
## w5need8 194 0.40 0.38 0.23 0.13 3.4 2.0
## w5need9 194 0.43 0.44 0.32 0.18 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 0.03 0.04 0.07 0.02 0.21 0.37 0.26 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 0.60psych::alpha(d[c("w1needAvg",
"w2needAvg",
"w3needAvg",
"w4needAvg",
"w5needAvg")]) #.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")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.88 0.61 7.9 0.0078 3.6 0.76 0.64
##
## 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 0.85 0.83 0.59 5.6 0.0109 0.012 0.60
## w2needAvg 0.86 0.85 0.83 0.59 5.9 0.0105 0.020 0.61
## w3needAvg 0.85 0.85 0.83 0.59 5.7 0.0107 0.016 0.60
## w4needAvg 0.86 0.86 0.84 0.61 6.2 0.0100 0.011 0.61
## w5needAvg 0.90 0.90 0.88 0.69 9.0 0.0075 0.002 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## w1needAvg 486 0.88 0.87 0.84 0.80 3.7 0.84
## w2needAvg 468 0.86 0.86 0.82 0.77 3.7 0.92
## w3needAvg 450 0.86 0.87 0.83 0.78 3.6 0.88
## w4needAvg 401 0.86 0.84 0.80 0.75 3.5 0.88
## w5needAvg 194 0.75 0.72 0.60 0.57 3.5 0.79H1. Do friend ranks predict WTRs?
conclusion: yes, friend ranks predict WTR, B = -.07, t(96.27) = -10.22, p < .001
1. run model
h1 <- lmer(wtr ~ rank + (rank|subID), data = long_final)
summary(h1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: wtr ~ rank + (rank | subID)
## Data: long_final
##
## REML criterion at convergence: -608.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6748 -0.4594 0.0023 0.4245 5.5443
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.050945 0.2257
## rank 0.003025 0.0550 -0.24
## Residual 0.027067 0.1645
## Number of obs: 1349, groups: subID, 99
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.791306 0.025422 95.509076 31.13 <2e-16 ***
## rank -0.070829 0.006933 96.268621 -10.22 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## rank -0.415tab_model(h1)| wtr | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.79 | 0.74 – 0.84 | <0.001 |
| rank | -0.07 | -0.08 – -0.06 | <0.001 |
| 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
## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1106 rows containing non-finite values (stat_smooth).## Warning: Removed 1106 rows containing missing values (geom_point).
H2. Do WTRs predict gratitude?
Level 1: grat = Bo + B1wtr + Eij Level 2: Bo = a00 + uoj
conclusion: yes, WTR predicts gratitude, B = 2.84, t(67.49) = 9.28, p < .001
1. run model
h2<- lmer(grat ~ wtr + (wtr|subID), data = long_final)
summary(h2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: grat ~ wtr + (wtr | subID)
## Data: long_final
##
## REML criterion at convergence: 3965.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5940 -0.5511 0.0469 0.6110 3.0688
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 1.847 1.359
## wtr 3.890 1.972 -0.60
## Residual 1.573 1.254
## Number of obs: 1118, groups: subID, 99
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.8537 0.2009 66.9036 9.227 1.52e-13 ***
## wtr 2.8354 0.3056 67.4881 9.279 1.14e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## wtr -0.7792. plot
## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1326 rows containing non-finite values (stat_smooth).## Warning: Removed 1326 rows containing missing values (geom_point).
H3. Do changes in WTRs predict gratitude each week?
conclusion: yes, change in WTR predicts gratitude at week 5, B = 1.07, t(106.31) = 2.05, p = .043
1. prep data
2. run model
h4 <- lmer(grat ~ wtrDiff + (1 | subID), data = WTR)
summary(h4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: grat ~ wtrDiff + (1 | subID)
## Data: WTR
##
## REML criterion at convergence: 2597.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.00044 -0.52833 0.07145 0.58912 2.76486
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 1.083 1.041
## Residual 1.954 1.398
## Number of obs: 699, groups: subID, 93
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.5120 0.1235 90.1867 28.43 <2e-16 ***
## wtrDiff -0.5104 0.2917 661.2395 -1.75 0.0806 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## wtrDiff 0.0353. plot
## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1265 rows containing non-finite values (stat_smooth).## Warning: Removed 1265 rows containing missing values (geom_point).