to do:

model time as a fixed effect (try polynomial)
fix all the NAs

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$anger <- anger_long$anger

demographics

draw <- read.csv("C:/Users/Marri/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     34

table(draw$class)

## 
##  freshman    junior    senior sophomore 
##        72        10         5        28

descriptive 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
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt      0.9  0.91  0.92
## Duhachek   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   0.0089 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 394  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 0.18
## 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 0.19
## w4closeAvg 0.02 0.02 0.04   0 0.17 0.26 0.27 0.22 0.31
## w5closeAvg 0.03 0.03 0.03   0 0.10 0.26 0.25 0.30 0.69

describe(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
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt      0.9  0.91  0.92
## Duhachek   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 0.0127  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 398  0.90  0.87  0.82   0.79  4.3 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 0.18
## 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 0.69

describe(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
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.91  0.92  0.93
## Duhachek  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 10.0   0.0068 0.0039  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   0.87  4.2 1.6
## w3iosAvg 400  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 0.18
## 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 0.30
## w5iosAvg 0.05 0.07 0.01 0.10 0.25 0.23   0 0.20 0.10 0.69

describe(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
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.82  0.85  0.87
## Duhachek  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 365  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 0.15 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("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
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.78  0.81  0.83
## Duhachek  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    0.017 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 0.02629  0.56
## anger5      0.78      0.78    0.71      0.55 3.6    0.017 0.00137  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 368  0.89  0.84  0.78   0.70 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 0.22
## 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 0.70

describe(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      0.51 4.2 0.014  1.4 1.5     0.54
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.78  0.81  0.84
## Duhachek  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 0.01175  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 370  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 0.70

describe(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
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.83  0.85  0.87
## Duhachek  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 4.7    0.014 0.00282  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      0.55 3.6    0.017 0.00063  0.55
## happy5      0.82      0.82    0.75      0.60 4.5    0.014 0.00121  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 368  0.88  0.83  0.75   0.69  3.8 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 0.70

describe(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
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.85  0.87  0.89
## Duhachek  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 0.0035  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 0.0041  0.60
## thankful5      0.84      0.84    0.78      0.64 5.3    0.012 0.0024  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 370  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 0.22
## 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

describe(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 6.3 0.01  3.5 1.6     0.61
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.84  0.86  0.88
## Duhachek  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    0.72      0.54 3.6    0.017 0.0071  0.56
## appreciative5      0.84      0.84    0.78      0.63 5.2    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 368  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 0.21
## appreciative3 0.10 0.05 0.10 0.18 0.25 0.15 0.15 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

describe(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

## In smc, smcs < 0 were set to .0

## 
## 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  3.4 1.6     0.62
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.95  0.96  0.96
## Duhachek  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 368  0.88  0.85  0.86   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     370  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         365  0.87  0.84  0.83   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 0.21
## appreciative3 0.10 0.05 0.10 0.18 0.25 0.15 0.15 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 0.22
## 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 0.15 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     0.24
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.65  0.69  0.73
## Duhachek  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  0.23
## w1need2.R      0.66      0.66    0.68      0.20 2.0    0.023 0.014  0.23
## w1need3        0.68      0.70    0.72      0.23 2.3    0.022 0.021  0.25
## w1need4        0.65      0.67    0.70      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  0.23
## 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 2.2    0.023 0.024  0.25
## 
##  Item statistics 
##             n raw.r std.r r.cor r.drop mean  sd
## w1need1   484  0.53  0.60  0.58   0.40  2.0 1.3
## w1need2.R 484  0.54  0.62  0.61   0.42  2.3 1.3
## w1need3   484  0.52  0.46  0.36   0.32  3.7 2.0
## w1need4   484  0.61  0.60  0.53   0.44  3.5 2.0
## w1need5   484  0.61  0.62  0.56   0.45  3.5 1.8
## w1need6   484  0.53  0.56  0.46   0.38  2.3 1.6
## w1need7   484  0.61  0.60  0.52   0.45  4.4 1.9
## w1need8   484  0.38  0.36  0.20   0.17  3.3 2.0
## w1need9   484  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 0.06 0.24 0.12 0.09 0.01
## w1need4   0.25 0.18 0.07 0.06 0.29 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 0.11 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 0.10 0.06 0.08 0.08 0.01

psych::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
##       0.71      0.73    0.76      0.23 2.6 0.019  3.2  1     0.19
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.67  0.71  0.75
## Duhachek  0.68  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
## 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    0.021 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 2.2    0.022 0.025  0.18
## w2need5        0.67      0.68    0.73      0.21 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 2.3    0.021 0.024  0.19
## w2need8        0.73      0.74    0.77      0.26 2.9    0.018 0.019  0.24
## w2need9        0.69      0.70    0.73      0.23 2.3    0.021 0.023  0.19
## 
##  Item statistics 
##             n raw.r std.r r.cor r.drop mean  sd
## w2need1   466  0.55  0.62  0.61   0.43  2.0 1.4
## w2need2.R 466  0.47  0.55  0.52   0.35  2.4 1.3
## w2need3   466  0.62  0.57  0.49   0.43  3.7 2.2
## w2need4   466  0.62  0.60  0.52   0.45  3.9 2.0
## w2need5   466  0.62  0.63  0.56   0.46  3.7 1.9
## w2need6   466  0.58  0.58  0.53   0.44  2.2 1.6
## w2need7   466  0.57  0.56  0.46   0.40  4.9 1.9
## w2need8   466  0.40  0.36  0.21   0.18  3.6 2.1
## w2need9   466  0.58  0.56  0.48   0.40  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   0.17 0.19 0.05 0.09 0.23 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 0.17 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 0.05

psych::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 3.3 0.017  3.1 1.1     0.28
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.72  0.76  0.79
## Duhachek  0.72  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      0.73    0.74      0.26 2.7    0.018 0.018  0.27
## w3need2.R      0.72      0.73    0.73      0.25 2.7    0.019 0.017  0.26
## w3need3        0.74      0.76    0.78      0.28 3.1    0.018 0.025  0.29
## w3need4        0.71      0.73    0.76      0.25 2.7    0.020 0.026  0.26
## w3need5        0.70      0.72    0.75      0.24 2.6    0.020 0.025  0.26
## w3need6        0.73      0.75    0.78      0.27 2.9    0.018 0.027  0.27
## w3need7        0.74      0.75    0.78      0.28 3.0    0.018 0.026  0.30
## w3need8        0.78      0.79    0.80      0.32 3.7    0.015 0.016  0.31
## w3need9        0.74      0.75    0.77      0.27 3.0    0.018 0.022  0.28
## 
##  Item statistics 
##             n raw.r std.r r.cor r.drop mean  sd
## w3need1   448  0.60  0.66  0.65   0.51  1.9 1.3
## w3need2.R 448  0.64  0.69  0.69   0.54  2.6 1.5
## w3need3   448  0.57  0.53  0.44   0.39  3.5 2.0
## w3need4   448  0.69  0.67  0.62   0.56  3.4 2.0
## w3need5   448  0.72  0.72  0.68   0.60  3.7 1.8
## w3need6   448  0.59  0.60  0.51   0.46  2.5 1.7
## w3need7   448  0.56  0.56  0.46   0.40  4.5 1.9
## w3need8   443  0.35  0.34  0.20   0.16  3.3 2.0
## w3need9   444  0.57  0.56  0.48   0.40  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 0.21 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 0.13 0.04 0.08
## w3need6   0.40 0.25 0.10 0.09 0.08 0.06 0.01 0.08
## w3need7   0.09 0.12 0.10 0.08 0.21 0.29 0.12 0.08
## w3need8   0.26 0.23 0.04 0.17 0.13 0.14 0.05 0.09
## w3need9   0.40 0.23 0.03 0.07 0.11 0.12 0.04 0.09

psych::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
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.74  0.77   0.8
## Duhachek  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    0.019 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 0.018  0.28
## 
##  Item statistics 
##             n raw.r std.r r.cor r.drop mean  sd
## w4need1   400  0.54  0.61  0.58   0.44  1.9 1.3
## w4need2.R 400  0.54  0.60  0.57   0.42  2.5 1.5
## w4need3   400  0.68  0.64  0.60   0.53  3.5 2.1
## w4need4   400  0.61  0.60  0.51   0.45  3.5 2.0
## w4need5   400  0.67  0.68  0.62   0.56  3.0 1.7
## w4need6   400  0.70  0.70  0.68   0.60  2.2 1.6
## w4need7   400  0.57  0.56  0.46   0.42  4.5 1.9
## w4need8   400  0.47  0.44  0.32   0.28  3.3 2.1
## w4need9   400  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   0.50 0.31 0.08 0.03 0.08 0.01 0.00 0.18
## w4need2.R 0.28 0.31 0.22 0.03 0.09 0.07 0.00 0.18
## w4need3   0.27 0.16 0.09 0.07 0.17 0.16 0.09 0.18
## w4need4   0.24 0.17 0.07 0.11 0.24 0.11 0.06 0.18
## w4need5   0.22 0.30 0.07 0.10 0.24 0.05 0.01 0.18
## w4need6   0.54 0.18 0.08 0.09 0.06 0.05 0.01 0.18
## w4need7   0.11 0.12 0.08 0.06 0.23 0.30 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 0.18

psych::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
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.67  0.71  0.74
## Duhachek  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    0.024 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    0.020 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 0.60

x <- 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      0.91     0.9      0.66 9.6 0.0083  3.4 0.79     0.68
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.87  0.89   0.9
## Duhachek  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  7.2   0.0108 0.011  0.62
## w2needAvg      0.91      0.92    0.90      0.73 10.9   0.0064 0.003  0.73
## w3needAvg      0.84      0.87    0.85      0.63  6.8   0.0115 0.013  0.62
## w4needAvg      0.85      0.88    0.86      0.64  7.2   0.0115 0.016  0.64
## w5needAvg      0.85      0.88    0.86      0.64  7.2   0.0108 0.011  0.65
## 
##  Item statistics 
##             n raw.r std.r r.cor r.drop mean   sd
## w1needAvg 484  0.88  0.87  0.85   0.78  3.5 0.84
## w2needAvg 167  0.79  0.75  0.64   0.61  2.5 1.32
## w3needAvg 448  0.87  0.89  0.87   0.81  3.4 0.88
## w4needAvg 400  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)

graphs: friendship/time

i = 1
for(i in unique(d1$subID)){
  print(ggplot(data = d1[d1$subID == i,], aes(x = week, y = rank, color = friend)) + 
          theme_minimal() +
          ylab("rank") +
          geom_point() + 
          geom_line() +
          ggtitle(paste0("participant ", i)) + 
          scale_x_continuous(breaks = c(1, 2, 3, 4, 5),
                     limits = c(1, 5)))
  }

H1. Do friend ranks predict WTRs?

1. run model

h1 <- lmer(wtr ~ rank + (rank | subID) + (1 | week), data = d1)

## boundary (singular) fit: see help('isSingular')

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)	0.789	0.026	0.738 – 0.840	30.468	<0.001	1561.000
rank	-0.071	0.007	-0.085 – -0.058	-10.508	<0.001	1561.000
Random Effects
σ²	0.03
τ₀₀ _subID	0.06
τ₀₀ _week	0.00
τ₁₁ _subID.rank	0.00
ρ₀₁ _subID	-0.32
N _subID	99
N _week	5
Observations	1568
Marginal R² / Conditional R²	0.213 / NA

plot_model(h1, type = "pred", terms = c("rank")) +
  ggtitle("") + 
  ylab("WTR") +
  xlab("Friend Ranking") +
  theme_minimal() +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black"),
        legend.position = "none", 
        legend.background = element_rect(fill = "white", color = "white"),
        legend.title = element_blank())

H2. Do WTRs predict gratitude?

1. run model

h2<- lmer(gratScale ~ wtr + (wtr | subID) + (1 | week), data = d1)

## boundary (singular) fit: see help('isSingular')

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 – 2.284	9.575	<0.001	1121.000
wtr	2.853	0.294	2.277 – 3.429	9.714	<0.001	1121.000
Random Effects
σ²	1.35
τ₀₀ _subID	1.92
τ₀₀ _week	0.00
τ₁₁ _subID.wtr	3.72
ρ₀₁ _subID	-0.60
N _subID	99
N _week	4
Observations	1128
Marginal R² / Conditional R²	0.376 / NA

plot_model(h2, type = "pred", terms = c("wtr")) +
  ggtitle("") + 
  ylab("Gratitude Scale") +
  xlab("WTR") +
  theme_minimal() +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black"),
        legend.position = "none", 
        legend.background = element_rect(fill = "white", color = "white"),
        legend.title = element_blank()) +
  scale_x_continuous(breaks = c(0, .25, .5, .75, 1, 1.25), 
                     limits = c(0, 1.25))

H3. Do changes in WTRs predict gratitude each week?

2. run model

originally included random int for week but was singular

summary(h3 <- lmer(gratScale ~ wtrDiff + (wtrDiff | subID), data = d1))

## Linear mixed model fit by REML ['lmerMod']
## Formula: gratScale ~ wtrDiff + (wtrDiff | subID)
##    Data: d1
## 
## REML criterion at convergence: 3738.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.7791 -0.5352  0.0773  0.6280  2.9205 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr 
##  subID    (Intercept) 1.263    1.124         
##           wtrDiff     0.252    0.502    -0.05
##  Residual             1.731    1.316         
## Number of obs: 1042, groups:  subID, 98
## 
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)   3.5088     0.1228   28.58
## wtrDiff       1.3747     0.2420    5.68
## 
## Correlation of Fixed Effects:
##         (Intr)
## wtrDiff 0.003

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 – 3.750	28.578	<0.001	1036.000
wtrDiff	1.375	0.242	0.900 – 1.850	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

plot_model(h3, type = "pred", terms = c("wtrDiff")) +
  ggtitle("") + 
  ylab("Gratitude Scale") +
  xlab("Change in WTR") +
  theme_minimal() +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black"),
        legend.position = "none", 
        legend.background = element_rect(fill = "white", color = "white"),
        legend.title = element_blank())

# Q1. How do relationships change over time?

2. run model

# make codes for weeks
d1$lin <- NA
d1$lin[d1$week == 1] <- -2
d1$lin[d1$week == 2] <- -1
d1$lin[d1$week == 3] <- 0
d1$lin[d1$week == 4] <- 1
d1$lin[d1$week == 5] <- 2

d1$quad <- NA
d1$quad[d1$week == 1] <- 2
d1$quad[d1$week == 2] <- -1
d1$quad[d1$week == 3] <- -2
d1$quad[d1$week == 4] <- -1
d1$quad[d1$week == 5] <- 2
  
d1$cubic <- NA
d1$cubic[d1$week == 1] <- -1
d1$cubic[d1$week == 2] <- 2
d1$cubic[d1$week == 3] <- 0
d1$cubic[d1$week == 4] <- -2
d1$cubic[d1$week == 5] <- 1

d1$quart <- NA
d1$quart[d1$week == 1] <- 1
d1$quart[d1$week == 2] <- -4
d1$quart[d1$week == 3] <- 6
d1$quart[d1$week == 4] <- -4
d1$quart[d1$week == 5] <- 1

summary(h3 <- lmer(rank ~ wtrDiff + (lin + quad + cubic + quart) + (1 | subID) + (1|week), data = d1))

## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient

## Warning in optwrap(optimizer, devfun, getStart(start, rho$pp), lower =
## rho$lower, : convergence code -4 from nloptwrap: NLOPT_ROUNDOFF_LIMITED:
## Roundoff errors led to a breakdown of the optimization algorithm. In this case,
## the returned minimum may still be useful. (e.g. this error occurs in NEWUOA if
## one tries to achieve a tolerance too close to machine precision.)

## boundary (singular) fit: see help('isSingular')

## Linear mixed model fit by REML ['lmerMod']
## Formula: rank ~ wtrDiff + (lin + quad + cubic + quart) + (1 | subID) +  
##     (1 | week)
##    Data: d1
## 
## REML criterion at convergence: 3450.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.5485 -0.6126  0.2266  1.0531  1.9933 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.000000 0.00000 
##  week     (Intercept) 0.002407 0.04907 
##  Residual             1.573993 1.25459 
## Number of obs: 1044, groups:  subID, 98; week, 4
## 
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)  2.67686    0.15054  17.782
## wtrDiff     -0.33658    0.20065  -1.677
## lin         -0.08428    0.15178  -0.555
## quad         0.02149    0.10639   0.202
## cubic       -0.04990    0.08077  -0.618
## 
## Correlation of Fixed Effects:
##         (Intr) wtrDff lin    quad  
## wtrDiff -0.038                     
## lin     -0.921  0.050              
## quad     0.943 -0.055 -0.932       
## cubic   -0.886  0.050  0.925 -0.890
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
## optimizer (nloptwrap) convergence code: -4 (NLOPT_ROUNDOFF_LIMITED: Roundoff errors led to a breakdown of the optimization algorithm. In this case, the returned minimum may still be useful. (e.g. this error occurs in NEWUOA if one tries to achieve a tolerance too close to machine precision.))
## boundary (singular) fit: see help('isSingular')

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)

	rank
Predictors	Est	SE	CI	t	p	df
(Intercept)	2.677	0.151	2.381 – 2.972	17.782	<0.001	1036.000
wtrDiff	-0.337	0.201	-0.730 – 0.057	-1.677	0.094	1036.000
lin	-0.084	0.152	-0.382 – 0.214	-0.555	0.579	1036.000
quad	0.021	0.106	-0.187 – 0.230	0.202	0.840	1036.000
cubic	-0.050	0.081	-0.208 – 0.109	-0.618	0.537	1036.000
Random Effects
σ²	1.57
τ₀₀ _subID	0.00
τ₀₀ _week	0.00
N _subID	98
N _week	4
Observations	1044
Marginal R² / Conditional R²	0.004 / NA

Longitudinal Gratitude Analyses

Dani Grant

demographics

descriptive stats

- close

- commit

- ios

- gratitude

- anger

- irritated

- happy

- thankful

- appreciative

- gratitude scale

- need

- weight (NA)

- height (NA)

graphs: friendship/time

H1. Do friend ranks predict WTRs?

1. run model

H2. Do WTRs predict gratitude?

1. run model

H3. Do changes in WTRs predict gratitude each week?

2. run model

2. run model