Exploring the Effects of Threat-Based Climate Change Content in Canadian Post-Secondary Environmental Education
2024-05-1
- Section 1: Importing the data
- Section 2: Tidying and Wrangling the Data
- Section 3:
Pro-Environmental Behaviour (Waterloo)
- 3.1: Removing Unecessary Columns and Renaming the Keepers
- 3.2: Joining the Three Datasets
- 3.3: Designating Pages as Complete or Incomplete
- 3.4 : Creating the Count Variable
- 3.5: Creating the Binary Complete (all 10 pages) or Incomplete (9 and under pages)
- 3.6: Descriptive Statistics and Data Visualization
- 3.7: Checking the Fit of Various Model Options
- 3.8: Hurdle Model
- Section 4: Pro-Environmental Behaviour (Royal Roads University)
- Section 5: Death Thought Accessibility
- Section 6: Environmental
Identity
- 6.1: Removing unecessary columns and renaming
- 6.2: Joining all Three Datasets
- 6.3: Creating a Column for Change in EID Average
- 6.4: Descriptive Statistics and Data Visualization
- 6.5: CFA for EIDQS & BEIDQS
- 6.6: Calculting the Change by Subtracting Latent Factor Scores from one Another
- 6.7: Multiple Imputation
- 6.8: Fitting the Model Without Imputation for the Model Check and Plot
- 6.9: Descriptives and Data Visualization
- 6.10: Tidying the Data for the Plot
- 6.11: Boxplot of Environmental Identity by Measurement and Condition (greyscale)
- 6.12 PLotting Change in Environmental Identity by Condition
- Section 7: Environmental Identity (Royal Roads University)
Section 1: Importing the data
fdf_c <- read_csv("data/feb14.csv")## New names:
## Rows: 200 Columns: 206
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (206): StartDate, EndDate, Status, IPAddress, Progress, Duration (in sec...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `6.2_1` -> `6.2_1...28`
## • `6.2_2` -> `6.2_2...29`
## • `6.2_3` -> `6.2_3...30`
## • `6.2_4` -> `6.2_4...31`
## • `6.2_5` -> `6.2_5...32`
## • `6.2_6` -> `6.2_6...33`
## • `6.2_7` -> `6.2_7...34`
## • `6.2_8` -> `6.2_8...35`
## • `6.2_9` -> `6.2_9...36`
## • `6.2_10` -> `6.2_10...37`
## • `6.2_11` -> `6.2_11...38`
## • `6.2_1` -> `6.2_1...103`
## • `6.2_2` -> `6.2_2...104`
## • `6.2_3` -> `6.2_3...105`
## • `6.2_4` -> `6.2_4...106`
## • `6.2_5` -> `6.2_5...107`
## • `6.2_6` -> `6.2_6...108`
## • `6.2_7` -> `6.2_7...109`
## • `6.2_8` -> `6.2_8...110`
## • `6.2_9` -> `6.2_9...111`
## • `6.2_10` -> `6.2_10...112`
## • `6.2_11` -> `6.2_11...113`fdf_p1 <- read_csv("data/feb14_p1.csv")## Rows: 56 Columns: 37
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (37): StartDate, EndDate, Status, IPAddress, Progress, Duration (in seco...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.fdf_p2 <- read_csv("data/feb14_p2.csv")## Rows: 26 Columns: 157
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (157): StartDate, EndDate, Status, IPAddress, Progress, Duration (in sec...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Section 2: Tidying and Wrangling the Data
2.1: Keeping Only Those who Agreed to the Re-Consent & Only Waterloo
2.1.1: Conjoined
df_c <- fdf_c |>
rename( #The filter fxn doesn't work when it's called 4
University = '4'
) |>
filter( #only keeping rows with "" in the university column
University %in% "I am currently enrolled as an undergraduate or master’s student in Faculty of Environment or Faculty of Science at the University of Waterloo."
) |>
filter ( #this is the final consent
Q109 %in% "Yes")2.1.2: Part 2
df_p2 <- fdf_p2 |>
rename( #The filter fxn doesn't work when it's called 4
University = '4'
) |>
filter( #only keeping rows with "" in the university column
University %in% "I am currently enrolled as an undergraduate or master’s student in The School of Environment, Resources, and Sustainability at the University of Waterloo."
) |>
filter ( #this is the final consent
Q109 %in% "Yes")2.2: Keeping Only Those who Agreed to the Re-Consent & Only Royal Roads
Note: RRU students only received the conjoined version.
df_rru <- fdf_c |>
rename( #The filter fxn doesn't work when it's called 4
University = '4'
) |>
filter( #only keeping rows with "" in the university column
University %in% "I am currently enrolled as an undergraduate or master’s student in The School of Environment and Sustainability at Royal Roads University."
) |>
filter ( #this is the final consent
Q109 %in% "Yes")Section 3: Pro-Environmental Behaviour (Waterloo)
3.1: Removing Unecessary Columns and Renaming the Keepers
3.1.1: Conjoined
wdf_c <- df_c |> rename( #The original name is on the right of the = sign
TP1 = 'Q112_Page Submit',
TP2 = 'Q113_Page Submit',
TP3 = 'Q114_Page Submit',
TP4 = 'Q115_Page Submit',
TP5 = 'Q116_Page Submit',
TP6 = 'Q117_Page Submit',
TP7 = 'Q118_Page Submit',
TP8 = 'Q119_Page Submit',
TP9 = 'Q120_Page Submit',
TP10 = 'Q121_Page Submit',
condition = 'FL_18_DO',
id = '3.2',
time = 'EndDate'
) |>
select(P1.2,P2.2,P3.2,P4.2,P5.2,P6.2,P7.2,P8.2,Q110,P10.2,TP1,TP2,TP3,TP4,TP5,TP6,TP7,TP8,TP9,TP10,condition,id) |> #the columns listed are the ones I keep
mutate_at(c('TP1','TP2','TP3','TP4','TP5','TP6','TP7','TP8','TP9','TP10'), as.numeric)
glimpse(wdf_c)## Rows: 72
## Columns: 22
## $ P1.2 <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes…
## $ P2.2 <chr> "Yes", "Yes", "Yes", "No", "No", "Yes", "Yes", "Yes", "No", …
## $ P3.2 <chr> "No", "Yes", "No", "No", "No", "No", "Yes", "Yes", "No", "No…
## $ P4.2 <chr> "No", "Yes", NA, "No", "No", "No", "No", "No", "No", "No", "…
## $ P5.2 <chr> "No", "Yes", "No", "No", "No", "No", "No", "No", "No", "No",…
## $ P6.2 <chr> "No", "Yes", "No", "No", "No", "No", "No", "No", "No", "No",…
## $ P7.2 <chr> "No", "Yes", "No", "No", "No", "No", "No", "No", "No", "No",…
## $ P8.2 <chr> "No", "Yes", "No", "No", "No", "No", "No", "Yes", "No", "No"…
## $ Q110 <chr> "No", "Yes", "No", "Yes", "No", "No", "No", "No", "No", "No"…
## $ P10.2 <chr> "No", "Yes", "No", "No", "No", "No", "No", "No", "No", "No",…
## $ TP1 <dbl> 130.586, 143.192, 140.081, 148.901, 172.592, 103.180, 87.137…
## $ TP2 <dbl> 80.726, 71.483, 3.853, NA, NA, 99.449, 67.951, 68.852, NA, N…
## $ TP3 <dbl> NA, 322.283, NA, NA, NA, NA, 78.400, 68.792, NA, NA, NA, 184…
## $ TP4 <dbl> NA, 153.117, 1.933, NA, NA, NA, NA, NA, NA, NA, NA, 155.416,…
## $ TP5 <dbl> NA, 196.209, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 84.126,…
## $ TP6 <dbl> NA, 58.927, NA, NA, NA, NA, NA, NA, NA, NA, NA, 161.376, 65.…
## $ TP7 <dbl> NA, 112.231, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 74.856,…
## $ TP8 <dbl> NA, 74.616, NA, NA, NA, NA, NA, 70.729, NA, NA, NA, NA, 70.0…
## $ TP9 <dbl> NA, 65.591, NA, 75.567, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ TP10 <dbl> NA, 51.218, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.66…
## $ condition <chr> "INFO", "INFO", "THREAT", "THREAT", "INFO", "CONTROL", "CONT…
## $ id <chr> "11KatK", "04MaiA", "04JolA", "02SteY", "01RilC", "04AidB", …3.1.2: Part 2
wdf_p2 <- df_p2 |> rename( #The original name is on the right of the = sign
TP1 = 'Q112_Page Submit',
TP2 = 'Q113_Page Submit',
TP3 = 'Q114_Page Submit',
TP4 = 'Q115_Page Submit',
TP5 = 'Q116_Page Submit',
TP6 = 'Q117_Page Submit',
TP7 = 'Q118_Page Submit',
TP8 = 'Q119_Page Submit',
TP9 = 'Q120_Page Submit',
TP10 = 'Q121_Page Submit',
condition = 'FL_10_DO',
id = '3.2'
) |>
select(P1.2,P2.2,P3.2,P4.2,P5.2,P6.2,P7.2,P8.2,Q110,P10.2,TP1,TP2,TP3,TP4,TP5,TP6,TP7,TP8,TP9,TP10,condition,id) |> #the columns listed are the ones I keep
mutate_at(c('TP1','TP2','TP3','TP4','TP5','TP6','TP7','TP8','TP9','TP10'), as.numeric)
glimpse(wdf_p2)## Rows: 11
## Columns: 22
## $ P1.2 <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes"…
## $ P2.2 <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes"…
## $ P3.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "No", "No", …
## $ P4.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "No", "No", …
## $ P5.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "No", "No", "No", "…
## $ P6.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "Y…
## $ P7.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "Y…
## $ P8.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "Y…
## $ Q110 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "Y…
## $ P10.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "Y…
## $ TP1 <dbl> 65.422, 98.405, 71.297, 81.752, 75.001, 78.073, 77.644, NA, …
## $ TP2 <dbl> 61.166, 74.700, 73.268, 69.632, 72.519, 75.818, 78.179, NA, …
## $ TP3 <dbl> 64.005, 60.131, 77.752, NA, 59.277, 137.535, 77.711, NA, NA,…
## $ TP4 <dbl> 62.586, 60.655, 87.240, NA, 65.855, 73.233, 68.493, NA, NA, …
## $ TP5 <dbl> 53.378, 59.649, 110.554, NA, 57.355, 69.250, NA, NA, NA, 79.…
## $ TP6 <dbl> 58.470, 62.261, 65.562, NA, 57.298, NA, NA, NA, NA, 85.115, …
## $ TP7 <dbl> 53.158, 63.614, 84.533, NA, 57.929, NA, NA, NA, NA, 76.761, …
## $ TP8 <dbl> 55.825, 61.244, 66.925, NA, 59.101, NA, NA, NA, NA, 85.001, …
## $ TP9 <dbl> 80.019, 65.553, 87.743, NA, 64.486, NA, NA, NA, NA, 95.039, …
## $ TP10 <dbl> 53.705, 56.225, 79.199, NA, 102.938, NA, NA, NA, NA, 80.262,…
## $ condition <chr> "CONTROL", "INFO", "CONTROL", "THREAT", "THREAT", "INFO", "T…
## $ id <chr> "04MayH", "12EmmP", "09mayh", "01TimC", "04ValF", "08BriK", …3.1.3: Part 1
wdf_p1 <- fdf_p1 |> rename( #The original name is on the right of the = sign
id = '3.2'
) |>
select(id) |> #the columns listed are the ones I keep
filter(!is.na(id)) 3.2: Joining the Three Datasets
wdf_p1p2 <- left_join(wdf_p2, wdf_p1, by = 'id')
wdf_all <- full_join(wdf_p2,wdf_c)## Joining with `by = join_by(P1.2, P2.2, P3.2, P4.2, P5.2, P6.2, P7.2, P8.2,
## Q110, P10.2, TP1, TP2, TP3, TP4, TP5, TP6, TP7, TP8, TP9, TP10, condition, id)`glimpse(wdf_all)## Rows: 83
## Columns: 22
## $ P1.2 <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes"…
## $ P2.2 <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes"…
## $ P3.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "No", "No", …
## $ P4.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "No", "No", …
## $ P5.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "No", "No", "No", "…
## $ P6.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "Y…
## $ P7.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "Y…
## $ P8.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "Y…
## $ Q110 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "Y…
## $ P10.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "Y…
## $ TP1 <dbl> 65.422, 98.405, 71.297, 81.752, 75.001, 78.073, 77.644, NA, …
## $ TP2 <dbl> 61.166, 74.700, 73.268, 69.632, 72.519, 75.818, 78.179, NA, …
## $ TP3 <dbl> 64.005, 60.131, 77.752, NA, 59.277, 137.535, 77.711, NA, NA,…
## $ TP4 <dbl> 62.586, 60.655, 87.240, NA, 65.855, 73.233, 68.493, NA, NA, …
## $ TP5 <dbl> 53.378, 59.649, 110.554, NA, 57.355, 69.250, NA, NA, NA, 79.…
## $ TP6 <dbl> 58.470, 62.261, 65.562, NA, 57.298, NA, NA, NA, NA, 85.115, …
## $ TP7 <dbl> 53.158, 63.614, 84.533, NA, 57.929, NA, NA, NA, NA, 76.761, …
## $ TP8 <dbl> 55.825, 61.244, 66.925, NA, 59.101, NA, NA, NA, NA, 85.001, …
## $ TP9 <dbl> 80.019, 65.553, 87.743, NA, 64.486, NA, NA, NA, NA, 95.039, …
## $ TP10 <dbl> 53.705, 56.225, 79.199, NA, 102.938, NA, NA, NA, NA, 80.262,…
## $ condition <chr> "CONTROL", "INFO", "CONTROL", "THREAT", "THREAT", "INFO", "T…
## $ id <chr> "04MayH", "12EmmP", "09mayh", "01TimC", "04ValF", "08BriK", …3.3: Designating Pages as Complete or Incomplete
This is based on guidelines from Lange et al. 2020. If the time spent on the page was 2 standard deviations below the mean for that page, it is incomplete. Also, if the value is NA it is incomplete. Otherwise, it’s marked as complete.
wdf_all2 <- wdf_all |>
mutate(TP1_mean = mean(TP1, na.rm = TRUE),
TP1_sd = sd(TP1, na.rm = TRUE),
cutoff = TP1_mean - (2*TP1_sd),
P1 = ifelse(TP1 > cutoff, 1, 0),
P1 = ifelse(is.na(TP1),0,1),
TP2_mean = mean(TP2, na.rm = TRUE),
TP2_sd = sd(TP2, na.rm = TRUE),
cutoff = TP2_mean - (2*TP2_sd),
P2 = ifelse(TP2 > cutoff, 1, 0),
P2 = ifelse(is.na(TP2),0,1),
TP3_mean = mean(TP3, na.rm = TRUE),
TP3_sd = sd(TP3, na.rm = TRUE),
cutoff = TP3_mean - (2*TP3_sd),
P3 = ifelse(TP3 > cutoff, 1, 0),
P3 = ifelse(is.na(TP3),0,1),
TP4_mean = mean(TP4, na.rm = TRUE),
TP4_sd = sd(TP4, na.rm = TRUE),
cutoff = TP4_mean - (2*TP4_sd),
P4 = ifelse(TP4 > cutoff, 1, 0),
P4 = ifelse(is.na(TP4),0,1),
TP5_mean = mean(TP5, na.rm = TRUE),
TP5_sd = sd(TP5, na.rm = TRUE),
cutoff = TP5_mean - (2*TP5_sd),
P5 = ifelse(TP5 > cutoff, 1, 0),
P5 = ifelse(is.na(TP5),0,1),
TP6_mean = mean(TP6, na.rm = TRUE),
TP6_sd = sd(TP6, na.rm = TRUE),
cutoff = TP6_mean - (2*TP6_sd),
P6 = ifelse(TP6 > cutoff, 1, 0),
P6 = ifelse(is.na(TP6),0,1),
TP7_mean = mean(TP7, na.rm = TRUE),
TP7_sd = sd(TP7, na.rm = TRUE),
cutoff = TP7_mean - (2*TP7_sd),
P7 = ifelse(TP7 > cutoff, 1, 0),
P7 = ifelse(is.na(TP7),0,1),
TP8_mean = mean(TP8, na.rm = TRUE),
TP8_sd = sd(TP8, na.rm = TRUE),
cutoff = TP8_mean - (2*TP8_sd),
P8 = ifelse(TP8 > cutoff, 1, 0),
P8 = ifelse(is.na(TP8),0,1),
TP9_mean = mean(TP9, na.rm = TRUE),
TP9_sd = sd(TP9, na.rm = TRUE),
cutoff = TP9_mean - (2*TP9_sd),
P9 = ifelse(TP9 > cutoff, 1, 0),
P9 = ifelse(is.na(TP9),0,1),
TP10_mean = mean(TP10, na.rm = TRUE),
TP10_sd = sd(TP10, na.rm = TRUE),
cutoff = TP10_mean - (2*TP10_sd),
P10 = ifelse(TP10 > cutoff, 1, 0),
P10 = ifelse(is.na(TP10),0,1))3.4 : Creating the Count Variable
(sum of pages completed per participant)
c_to_sum <- c('P1', 'P2', 'P3','P4','P5','P6','P7','P8','P9','P10')
wdf_all3 <- wdf_all2 |>
mutate(page_count = rowSums(select(wdf_all2, c_to_sum) == 1, na.rm = TRUE))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `page_count = rowSums(select(wdf_all2, c_to_sum) == 1, na.rm =
## TRUE)`.
## Caused by warning:
## ! Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(c_to_sum)
##
## # Now:
## data %>% select(all_of(c_to_sum))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.glimpse(wdf_all3)## Rows: 83
## Columns: 54
## $ P1.2 <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes…
## $ P2.2 <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes…
## $ P3.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "No", "No",…
## $ P4.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "No", "No",…
## $ P5.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "No", "No", "No", …
## $ P6.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "…
## $ P7.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "…
## $ P8.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "…
## $ Q110 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "…
## $ P10.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", "…
## $ TP1 <dbl> 65.422, 98.405, 71.297, 81.752, 75.001, 78.073, 77.644, NA,…
## $ TP2 <dbl> 61.166, 74.700, 73.268, 69.632, 72.519, 75.818, 78.179, NA,…
## $ TP3 <dbl> 64.005, 60.131, 77.752, NA, 59.277, 137.535, 77.711, NA, NA…
## $ TP4 <dbl> 62.586, 60.655, 87.240, NA, 65.855, 73.233, 68.493, NA, NA,…
## $ TP5 <dbl> 53.378, 59.649, 110.554, NA, 57.355, 69.250, NA, NA, NA, 79…
## $ TP6 <dbl> 58.470, 62.261, 65.562, NA, 57.298, NA, NA, NA, NA, 85.115,…
## $ TP7 <dbl> 53.158, 63.614, 84.533, NA, 57.929, NA, NA, NA, NA, 76.761,…
## $ TP8 <dbl> 55.825, 61.244, 66.925, NA, 59.101, NA, NA, NA, NA, 85.001,…
## $ TP9 <dbl> 80.019, 65.553, 87.743, NA, 64.486, NA, NA, NA, NA, 95.039,…
## $ TP10 <dbl> 53.705, 56.225, 79.199, NA, 102.938, NA, NA, NA, NA, 80.262…
## $ condition <chr> "CONTROL", "INFO", "CONTROL", "THREAT", "THREAT", "INFO", "…
## $ id <chr> "04MayH", "12EmmP", "09mayh", "01TimC", "04ValF", "08BriK",…
## $ TP1_mean <dbl> 117.3524, 117.3524, 117.3524, 117.3524, 117.3524, 117.3524,…
## $ TP1_sd <dbl> 61.2256, 61.2256, 61.2256, 61.2256, 61.2256, 61.2256, 61.22…
## $ cutoff <dbl> 6.784365, 6.784365, 6.784365, 6.784365, 6.784365, 6.784365,…
## $ P1 <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ TP2_mean <dbl> 98.98881, 98.98881, 98.98881, 98.98881, 98.98881, 98.98881,…
## $ TP2_sd <dbl> 54.39919, 54.39919, 54.39919, 54.39919, 54.39919, 54.39919,…
## $ P2 <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0,…
## $ TP3_mean <dbl> 103.48, 103.48, 103.48, 103.48, 103.48, 103.48, 103.48, 103…
## $ TP3_sd <dbl> 80.16526, 80.16526, 80.16526, 80.16526, 80.16526, 80.16526,…
## $ P3 <dbl> 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0,…
## $ TP4_mean <dbl> 88.61337, 88.61337, 88.61337, 88.61337, 88.61337, 88.61337,…
## $ TP4_sd <dbl> 48.23742, 48.23742, 48.23742, 48.23742, 48.23742, 48.23742,…
## $ P4 <dbl> 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,…
## $ TP5_mean <dbl> 97.45468, 97.45468, 97.45468, 97.45468, 97.45468, 97.45468,…
## $ TP5_sd <dbl> 87.09504, 87.09504, 87.09504, 87.09504, 87.09504, 87.09504,…
## $ P5 <dbl> 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,…
## $ TP6_mean <dbl> 95.52426, 95.52426, 95.52426, 95.52426, 95.52426, 95.52426,…
## $ TP6_sd <dbl> 108.3888, 108.3888, 108.3888, 108.3888, 108.3888, 108.3888,…
## $ P6 <dbl> 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,…
## $ TP7_mean <dbl> 85.82197, 85.82197, 85.82197, 85.82197, 85.82197, 85.82197,…
## $ TP7_sd <dbl> 59.46157, 59.46157, 59.46157, 59.46157, 59.46157, 59.46157,…
## $ P7 <dbl> 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,…
## $ TP8_mean <dbl> 88.83827, 88.83827, 88.83827, 88.83827, 88.83827, 88.83827,…
## $ TP8_sd <dbl> 86.38976, 86.38976, 86.38976, 86.38976, 86.38976, 86.38976,…
## $ P8 <dbl> 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0,…
## $ TP9_mean <dbl> 95.85375, 95.85375, 95.85375, 95.85375, 95.85375, 95.85375,…
## $ TP9_sd <dbl> 68.73305, 68.73305, 68.73305, 68.73305, 68.73305, 68.73305,…
## $ P9 <dbl> 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,…
## $ TP10_mean <dbl> 68.94396, 68.94396, 68.94396, 68.94396, 68.94396, 68.94396,…
## $ TP10_sd <dbl> 31.0798, 31.0798, 31.0798, 31.0798, 31.0798, 31.0798, 31.07…
## $ P10 <dbl> 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,…
## $ page_count <dbl> 10, 10, 10, 2, 10, 5, 4, 0, 2, 10, 2, 2, 10, 3, 2, 1, 2, 3,…3.5: Creating the Binary Complete (all 10 pages) or Incomplete (9 and under pages)
wdf_all4 <- wdf_all3 |>
mutate(
cutoff == 9,
binary = ifelse(page_count > cutoff, 1, 0)
) |>
mutate_at(c('page_count','condition'), as.factor)
glimpse(wdf_all4)## Rows: 83
## Columns: 56
## $ P1.2 <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "…
## $ P2.2 <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "…
## $ P3.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "No", "N…
## $ P4.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "No", "N…
## $ P5.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "No", "No", "No…
## $ P6.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No"…
## $ P7.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No"…
## $ P8.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No"…
## $ Q110 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No"…
## $ P10.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No"…
## $ TP1 <dbl> 65.422, 98.405, 71.297, 81.752, 75.001, 78.073, 77.644, …
## $ TP2 <dbl> 61.166, 74.700, 73.268, 69.632, 72.519, 75.818, 78.179, …
## $ TP3 <dbl> 64.005, 60.131, 77.752, NA, 59.277, 137.535, 77.711, NA,…
## $ TP4 <dbl> 62.586, 60.655, 87.240, NA, 65.855, 73.233, 68.493, NA, …
## $ TP5 <dbl> 53.378, 59.649, 110.554, NA, 57.355, 69.250, NA, NA, NA,…
## $ TP6 <dbl> 58.470, 62.261, 65.562, NA, 57.298, NA, NA, NA, NA, 85.1…
## $ TP7 <dbl> 53.158, 63.614, 84.533, NA, 57.929, NA, NA, NA, NA, 76.7…
## $ TP8 <dbl> 55.825, 61.244, 66.925, NA, 59.101, NA, NA, NA, NA, 85.0…
## $ TP9 <dbl> 80.019, 65.553, 87.743, NA, 64.486, NA, NA, NA, NA, 95.0…
## $ TP10 <dbl> 53.705, 56.225, 79.199, NA, 102.938, NA, NA, NA, NA, 80.…
## $ condition <fct> CONTROL, INFO, CONTROL, THREAT, THREAT, INFO, THREAT, IN…
## $ id <chr> "04MayH", "12EmmP", "09mayh", "01TimC", "04ValF", "08Bri…
## $ TP1_mean <dbl> 117.3524, 117.3524, 117.3524, 117.3524, 117.3524, 117.35…
## $ TP1_sd <dbl> 61.2256, 61.2256, 61.2256, 61.2256, 61.2256, 61.2256, 61…
## $ cutoff <dbl> 6.784365, 6.784365, 6.784365, 6.784365, 6.784365, 6.7843…
## $ P1 <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ TP2_mean <dbl> 98.98881, 98.98881, 98.98881, 98.98881, 98.98881, 98.988…
## $ TP2_sd <dbl> 54.39919, 54.39919, 54.39919, 54.39919, 54.39919, 54.399…
## $ P2 <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1,…
## $ TP3_mean <dbl> 103.48, 103.48, 103.48, 103.48, 103.48, 103.48, 103.48, …
## $ TP3_sd <dbl> 80.16526, 80.16526, 80.16526, 80.16526, 80.16526, 80.165…
## $ P3 <dbl> 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1,…
## $ TP4_mean <dbl> 88.61337, 88.61337, 88.61337, 88.61337, 88.61337, 88.613…
## $ TP4_sd <dbl> 48.23742, 48.23742, 48.23742, 48.23742, 48.23742, 48.237…
## $ P4 <dbl> 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0,…
## $ TP5_mean <dbl> 97.45468, 97.45468, 97.45468, 97.45468, 97.45468, 97.454…
## $ TP5_sd <dbl> 87.09504, 87.09504, 87.09504, 87.09504, 87.09504, 87.095…
## $ P5 <dbl> 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
## $ TP6_mean <dbl> 95.52426, 95.52426, 95.52426, 95.52426, 95.52426, 95.524…
## $ TP6_sd <dbl> 108.3888, 108.3888, 108.3888, 108.3888, 108.3888, 108.38…
## $ P6 <dbl> 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
## $ TP7_mean <dbl> 85.82197, 85.82197, 85.82197, 85.82197, 85.82197, 85.821…
## $ TP7_sd <dbl> 59.46157, 59.46157, 59.46157, 59.46157, 59.46157, 59.461…
## $ P7 <dbl> 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
## $ TP8_mean <dbl> 88.83827, 88.83827, 88.83827, 88.83827, 88.83827, 88.838…
## $ TP8_sd <dbl> 86.38976, 86.38976, 86.38976, 86.38976, 86.38976, 86.389…
## $ P8 <dbl> 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1,…
## $ TP9_mean <dbl> 95.85375, 95.85375, 95.85375, 95.85375, 95.85375, 95.853…
## $ TP9_sd <dbl> 68.73305, 68.73305, 68.73305, 68.73305, 68.73305, 68.733…
## $ P9 <dbl> 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,…
## $ TP10_mean <dbl> 68.94396, 68.94396, 68.94396, 68.94396, 68.94396, 68.943…
## $ TP10_sd <dbl> 31.0798, 31.0798, 31.0798, 31.0798, 31.0798, 31.0798, 31…
## $ P10 <dbl> 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
## $ page_count <fct> 10, 10, 10, 2, 10, 5, 4, 0, 2, 10, 2, 2, 10, 3, 2, 1, 2,…
## $ `cutoff == 9` <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
## $ binary <dbl> 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,…3.6: Descriptive Statistics and Data Visualization
3.6.1: Descriptives for PEB by Condition
library(DescTools)## Warning: package 'DescTools' was built under R version 4.2.3wdf_all3_summary <- wdf_all3 |>
group_by(condition) |>
summarise(
n = n(),
page_count_mean = mean(page_count, na.rm = TRUE),
page_count_sd = sd(page_count, na.rm = TRUE),
page_count_median = median(page_count, na.rm = TRUE),
page_count_mode = Mode(page_count, na.rm = TRUE))
wdf_all3_summary| condition | n | page_count_mean | page_count_sd | page_count_median | page_count_mode |
|---|---|---|---|---|---|
| CONTROL | 26 | 6.038462 | 3.883100 | 6.5 | 10 |
| INFO | 26 | 3.576923 | 3.214630 | 2.0 | 1 |
| THREAT | 31 | 4.903226 | 3.360107 | 4.0 | 10 |
3.6.2: Plotting Distribution by Condition (in colour)
library("viridis")## Warning: package 'viridis' was built under R version 4.2.3## Loading required package: viridisLitelibrary("viridisLite")
vis_2 <- ggplot(wdf_all4, aes(x=page_count, fill=condition)) +
geom_histogram(stat = "count", position = position_dodge(preserve = "single"), binwidth = .5, bins = 11) +
scale_x_discrete(limits = c("0","1", "2","3","4","5","6","7","8","9","10"), drop = FALSE) +
scale_y_discrete(limits = c("0","1","2","3","4","5","6","7","8","9","10","11")) +
theme_minimal() +
theme(
plot.title=element_text(hjust=.5, vjust=.5, size=18, lineheight = 0.9),
legend.position = c(0.03, .97),
legend.justification = c("left", "top"),
legend.title = element_text(size=14),
legend.text = element_text(size=11),
axis.text = element_text(face = "bold", size = 10),
axis.title = element_text(size=14.5),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
axis.text.x = element_text(vjust=+1.5),
axis.title.x = element_text(vjust=+0.5),
axis.text.y = element_text(vjust=+7)) +
labs(title="Distribution of Completed Pages by News Article Condition.", x = "Number of Completed Pages", y = "Participant Count", fill="Condition") +
scale_color_viridis(discrete = TRUE, begin = .25, end = 1) +
scale_fill_viridis(discrete = TRUE, labels=c("Control","Informational","Threat-Based"), begin = .25, end = 1)## Warning in geom_histogram(stat = "count", position = position_dodge(preserve =
## "single"), : Ignoring unknown parameters: `binwidth`, `bins`, and `pad`vis_2
#### Saving the Graph
ggsave("figures/wept_distribution.png", plot=vis_2, units='in', width=8, height=6, dpi=600)3.6.2: Plotting Distribution by Condition (in greyscale)
wept_plot_grey <- ggplot(wdf_all4, aes(x=page_count, fill=condition)) +
geom_histogram(stat = "count", position = position_dodge(preserve = "single"), binwidth = .5, bins = 11) +
scale_x_discrete(limits = c("0","1", "2","3","4","5","6","7","8","9","10"), drop = FALSE) +
scale_y_discrete(limits = c("0","1","2","3","4","5","6","7","8","9","10","11")) +
theme_minimal() +
theme(
plot.title=element_text(hjust=.5, vjust=.5, size=18, lineheight = 0.9),
legend.position = c(0.03, .97),
legend.justification = c("left", "top"),
legend.title = element_text(size=14),
legend.text = element_text(size=11),
axis.text = element_text(face = "bold", size = 10),
axis.title = element_text(size=14.5),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
axis.text.x = element_text(vjust=+1.5),
axis.title.x = element_text(vjust=+0.5),
axis.text.y = element_text(vjust=+4.5)) +
labs(title="", x = "Number of Completed Pages", y = "Participant Count", fill="Condition") +
scale_fill_grey(labels=c("Control","Informational","Threat-Based"))## Warning in geom_histogram(stat = "count", position = position_dodge(preserve =
## "single"), : Ignoring unknown parameters: `binwidth`, `bins`, and `pad`wept_plot_grey
Saving the Graph
ggsave("figures/wept_distribution_grey.png", plot=wept_plot_grey, units='in', width=8, height=6, dpi=600)3.7: Checking the Fit of Various Model Options
3.7.1: Poisson Regression
Notes - Look at the bottom of the output to check whether the model has over-dispersion or under-dispersion. If the Residual Deviance is greater than the degrees of freedom, then over-dispersion exists. This means that the estimates are correct, but the standard errors (standard deviation) are wrong and unaccounted for by the model. - The regression coefficients will give you the difference in means between each other category and the reference category.
poisson_model <- glm(page_count ~ condition, data = wdf_all3, family = "poisson")
summary(poisson_model)##
## Call:
## glm(formula = page_count ~ condition, family = "poisson", data = wdf_all3)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4752 -1.4898 -0.4215 1.4716 2.7777
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.79815 0.07981 22.531 < 2e-16 ***
## conditionINFO -0.52365 0.13085 -4.002 6.29e-05 ***
## conditionTHREAT -0.20826 0.11379 -1.830 0.0672 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 239.93 on 82 degrees of freedom
## Residual deviance: 223.32 on 80 degrees of freedom
## AIC: 481.61
##
## Number of Fisher Scoring iterations: 5Visual Inspection of Poisson Regression
check_model(poisson_model)
3.7.2: Negative Binomial Regression
library(MASS)## Warning: package 'MASS' was built under R version 4.2.3##
## Attaching package: 'MASS'## The following object is masked from 'package:patchwork':
##
## area## The following object is masked from 'package:dplyr':
##
## selectnb_model <- glm.nb(page_count ~ condition, data = wdf_all3)
summary(nb_model)##
## Call:
## glm.nb(formula = page_count ~ condition, data = wdf_all3, init.theta = 2.640077772,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5067 -0.9605 -0.2548 0.7635 1.6196
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.7981 0.1447 12.427 <2e-16 ***
## conditionINFO -0.5236 0.2151 -2.435 0.0149 *
## conditionTHREAT -0.2083 0.1993 -1.045 0.2961
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(2.6401) family taken to be 1)
##
## Null deviance: 99.220 on 82 degrees of freedom
## Residual deviance: 93.264 on 80 degrees of freedom
## AIC: 432.82
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 2.640
## Std. Err.: 0.689
##
## 2 x log-likelihood: -424.818Visual Inspection of Negative Binomial Regression
check_model(nb_model)
3.7.3: Quasi-Poisson
quasipoisson_model <- glm(page_count ~ condition, data = wdf_all3, family = "quasipoisson")
summary(quasipoisson_model)##
## Call:
## glm(formula = page_count ~ condition, family = "quasipoisson",
## data = wdf_all3)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4752 -1.4898 -0.4215 1.4716 2.7777
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7981 0.1274 14.119 <2e-16 ***
## conditionINFO -0.5236 0.2088 -2.508 0.0142 *
## conditionTHREAT -0.2083 0.1816 -1.147 0.2549
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 2.546645)
##
## Null deviance: 239.93 on 82 degrees of freedom
## Residual deviance: 223.32 on 80 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5Visual Inspection of Quasi-Poisson Regression
check_model(quasipoisson_model)
3.8: Hurdle Model
3.8.1: Logistic Regression
Notes : This is following a Hurdle model that is recommended for zero-inflated data, but mine is inversed with the inflation at the max count of 10.
Interpreting results: - “being in the info condition is associated with a statistically significant decrease in the log odds of the binary outcome compared to the control condition” - “being in the”threat” condition may have a somewhat negative effect on the log odds of the binary outcome compared to the control”
logreg_model <- glm(binary ~ condition, data = wdf_all4, family = binomial)
summary(logreg_model)##
## Call:
## glm(formula = binary ~ condition, family = binomial, data = wdf_all4)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1774 -0.7726 -0.5780 1.1774 1.9348
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.351e-17 3.922e-01 0.000 1.0000
## conditionINFO -1.705e+00 6.703e-01 -2.543 0.0110 *
## conditionTHREAT -1.056e+00 5.677e-01 -1.860 0.0629 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 101.572 on 82 degrees of freedom
## Residual deviance: 93.772 on 80 degrees of freedom
## AIC: 99.772
##
## Number of Fisher Scoring iterations: 4Visual Inspection of Logistic Regression
check_model(logreg_model)
3.8.2 Second Phase of Hurdle Model
Fitting a model to the subset who did not complete all 10 pages. Again - I tested the various model options to see which had the best fit based on visual inspection and ratio of null deviance to degrees of freedom (as an overdispersion estimate).
3.8.2.1 Truncated Poisson Regression
#This creates a data frame with only the participants who did not complete all 10
wdf_all5 <- wdf_all3 |>
filter(page_count < 9)
#Running a poisson regression on these
poisson_model_sub <- glm(page_count ~ condition, data = wdf_all5, family = "poisson")
summary(poisson_model_sub)##
## Call:
## glm(formula = page_count ~ condition, family = "poisson", data = wdf_all5)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6141 -1.0294 -0.2716 0.9352 2.2944
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.14210 0.14586 7.830 4.89e-15 ***
## conditionINFO -0.26285 0.20036 -1.312 0.190
## conditionTHREAT 0.08657 0.18295 0.473 0.636
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 97.289 on 60 degrees of freedom
## Residual deviance: 93.131 on 58 degrees of freedom
## AIC: 259.96
##
## Number of Fisher Scoring iterations: 5Visual Inspection of Truncated Poisson Regression
check_model(poisson_model_sub)
3.8.2.2: Truncated Quasi-Poisson
#This creates a data frame with only the participants who did not complete all 10
wdf_all5 <- wdf_all3 |>
filter(page_count < 9)
#Run a poisson regression on these
quasipoisson_model_sub <- glm(page_count ~ condition, data = wdf_all5, family = "quasipoisson")
summary(quasipoisson_model_sub)##
## Call:
## glm(formula = page_count ~ condition, family = "quasipoisson",
## data = wdf_all5)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6141 -1.0294 -0.2716 0.9352 2.2944
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.14210 0.17289 6.606 1.34e-08 ***
## conditionINFO -0.26285 0.23748 -1.107 0.273
## conditionTHREAT 0.08657 0.21685 0.399 0.691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 1.404858)
##
## Null deviance: 97.289 on 60 degrees of freedom
## Residual deviance: 93.131 on 58 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 5Visual Inspection of Truncated Quasi-Poisson Regression
check_model(quasipoisson_model_sub)
3.8.2.3: Truncated Negative Binomial Regression
library(MASS)
nb_model_sub <- glm.nb(page_count ~ condition, data = wdf_all5)
summary(nb_model_sub)##
## Call:
## glm.nb(formula = page_count ~ condition, data = wdf_all5, init.theta = 7.435270808,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3712 -0.9207 -0.2378 0.7939 1.8179
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.14210 0.17390 6.567 5.12e-11 ***
## conditionINFO -0.26285 0.23500 -1.119 0.263
## conditionTHREAT 0.08657 0.21918 0.395 0.693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(7.4353) family taken to be 1)
##
## Null deviance: 73.045 on 60 degrees of freedom
## Residual deviance: 70.055 on 58 degrees of freedom
## AIC: 258.66
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 7.44
## Std. Err.: 5.14
##
## 2 x log-likelihood: -250.663Visual Inspection of Truncated Negative Binomial Regression
check_model(nb_model_sub)
Section 4: Pro-Environmental Behaviour (Royal Roads University)
4.1: Removing Unecessary Columns and Renaming the Keepers
detach("package:MASS", unload=TRUE) #The select function was not working when the MASS package was loaded ## Warning: 'MASS' namespace cannot be unloaded:
## namespace 'MASS' is imported by 'DescTools' so cannot be unloadedwdf_rru <- df_rru |> rename( #The original name is on the right of the = sign
TP1 = 'Q112_Page Submit',
TP2 = 'Q113_Page Submit',
TP3 = 'Q114_Page Submit',
TP4 = 'Q115_Page Submit',
TP5 = 'Q116_Page Submit',
TP6 = 'Q117_Page Submit',
TP7 = 'Q118_Page Submit',
TP8 = 'Q119_Page Submit',
TP9 = 'Q120_Page Submit',
TP10 = 'Q121_Page Submit',
condition = 'FL_18_DO',
id = '3.2',
time = 'EndDate'
) |>
select(P1.2,P2.2,P3.2,P4.2,P5.2,P6.2,P7.2,P8.2,Q110,P10.2,TP1,TP2,TP3,TP4,TP5,TP6,TP7,TP8,TP9,TP10,condition,id) |> #the columns listed are the ones I keep
mutate_at(c('TP1','TP2','TP3','TP4','TP5','TP6','TP7','TP8','TP9','TP10'), as.numeric)
glimpse(wdf_rru)## Rows: 11
## Columns: 22
## $ P1.2 <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes…
## $ P2.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "Yes", "Yes"…
## $ P3.2 <chr> "Yes", "Yes", "No", "No", "Yes", "Yes", "Yes", "Yes", "Yes",…
## $ P4.2 <chr> "Yes", "Yes", "No", "No", "Yes", "Yes", "Yes", "Yes", "Yes",…
## $ P5.2 <chr> "No", "Yes", "No", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes",…
## $ P6.2 <chr> "Yes", "Yes", "No", "No", "Yes", "Yes", "Yes", "Yes", "No", …
## $ P7.2 <chr> "No", "No", "No", "No", "Yes", "Yes", "Yes", "Yes", "No", "Y…
## $ P8.2 <chr> "No", "No", "No", "No", "Yes", "Yes", "Yes", "No", "No", "Ye…
## $ Q110 <chr> "Yes", "No", "Yes", "No", "Yes", "Yes", "Yes", "No", "No", "…
## $ P10.2 <chr> "No", "No", "No", "No", "No", "Yes", "Yes", "No", "No", "Yes…
## $ TP1 <dbl> 92.614, 74.656, 127.249, 134.318, 73.376, 74.178, 128.301, 9…
## $ TP2 <dbl> 72.548, 58.775, 132.299, NA, 78.092, 78.913, 202.501, 99.488…
## $ TP3 <dbl> 66.203, 68.332, NA, NA, 79.661, 85.253, 205.443, 75.857, 99.…
## $ TP4 <dbl> 60.523, 70.291, NA, NA, 74.010, 66.910, 217.254, 80.097, 136…
## $ TP5 <dbl> NA, 55.296, NA, 128.669, 77.175, 73.377, 261.437, 90.727, 25…
## $ TP6 <dbl> 62.773, 53.854, NA, NA, 64.132, 59.184, 217.546, 316.344, NA…
## $ TP7 <dbl> NA, NA, NA, NA, 65.059, 74.378, 253.157, 85.718, NA, 61.937,…
## $ TP8 <dbl> NA, NA, NA, NA, 88.668, 76.608, 233.029, NA, NA, 64.334, 62.…
## $ TP9 <dbl> 69.227, NA, 99.901, NA, 61.355, 95.095, 186.440, NA, NA, 65.…
## $ TP10 <dbl> NA, NA, NA, NA, NA, 84.545, 185.184, NA, NA, 66.368, 51.600
## $ condition <chr> "CONTROL", "INFO", "THREAT", "THREAT", "INFO", "CONTROL", "T…
## $ id <chr> "11-Ala-G", "03SueM", "09DamO", "10JusS", "11-Elo-Q", "11bre…4.2: Designating Pages as Complete or Incomplete
This is based on guidelines from Lange et al. 2020. If the time spent on the page was 2 standard deviations below the mean for that page, it is incomplete. Also, if the value is NA it is incomplete. Otherwise, it’s marked as complete.
wdf_rru2 <- wdf_rru |>
mutate(TP1_mean = mean(TP1, na.rm = TRUE),
TP1_sd = sd(TP1, na.rm = TRUE),
cutoff = TP1_mean - (2*TP1_sd),
P1 = ifelse(TP1 > cutoff, 1, 0),
P1 = ifelse(is.na(TP1),0,1),
TP2_mean = mean(TP2, na.rm = TRUE),
TP2_sd = sd(TP2, na.rm = TRUE),
cutoff = TP2_mean - (2*TP2_sd),
P2 = ifelse(TP2 > cutoff, 1, 0),
P2 = ifelse(is.na(TP2),0,1),
TP3_mean = mean(TP3, na.rm = TRUE),
TP3_sd = sd(TP3, na.rm = TRUE),
cutoff = TP3_mean - (2*TP3_sd),
P3 = ifelse(TP3 > cutoff, 1, 0),
P3 = ifelse(is.na(TP3),0,1),
TP4_mean = mean(TP4, na.rm = TRUE),
TP4_sd = sd(TP4, na.rm = TRUE),
cutoff = TP4_mean - (2*TP4_sd),
P4 = ifelse(TP4 > cutoff, 1, 0),
P4 = ifelse(is.na(TP4),0,1),
TP5_mean = mean(TP5, na.rm = TRUE),
TP5_sd = sd(TP5, na.rm = TRUE),
cutoff = TP5_mean - (2*TP5_sd),
P5 = ifelse(TP5 > cutoff, 1, 0),
P5 = ifelse(is.na(TP5),0,1),
TP6_mean = mean(TP6, na.rm = TRUE),
TP6_sd = sd(TP6, na.rm = TRUE),
cutoff = TP6_mean - (2*TP6_sd),
P6 = ifelse(TP6 > cutoff, 1, 0),
P6 = ifelse(is.na(TP6),0,1),
TP7_mean = mean(TP7, na.rm = TRUE),
TP7_sd = sd(TP7, na.rm = TRUE),
cutoff = TP7_mean - (2*TP7_sd),
P7 = ifelse(TP7 > cutoff, 1, 0),
P7 = ifelse(is.na(TP7),0,1),
TP8_mean = mean(TP8, na.rm = TRUE),
TP8_sd = sd(TP8, na.rm = TRUE),
cutoff = TP8_mean - (2*TP8_sd),
P8 = ifelse(TP8 > cutoff, 1, 0),
P8 = ifelse(is.na(TP8),0,1),
TP9_mean = mean(TP9, na.rm = TRUE),
TP9_sd = sd(TP9, na.rm = TRUE),
cutoff = TP9_mean - (2*TP9_sd),
P9 = ifelse(TP9 > cutoff, 1, 0),
P9 = ifelse(is.na(TP9),0,1),
TP10_mean = mean(TP10, na.rm = TRUE),
TP10_sd = sd(TP10, na.rm = TRUE),
cutoff = TP10_mean - (2*TP10_sd),
P10 = ifelse(TP10 > cutoff, 1, 0),
P10 = ifelse(is.na(TP10),0,1))4.3: Creating the Count Variable
(sum of pages completed per participant)
c_to_sum <- c('P1', 'P2', 'P3','P4','P5','P6','P7','P8','P9','P10')
wdf_rru3 <- wdf_rru2 |>
mutate(page_count = rowSums(select(wdf_rru2, c_to_sum) == 1, na.rm = TRUE))
glimpse(wdf_rru3)## Rows: 11
## Columns: 54
## $ P1.2 <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Ye…
## $ P2.2 <chr> "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "Yes", "Yes…
## $ P3.2 <chr> "Yes", "Yes", "No", "No", "Yes", "Yes", "Yes", "Yes", "Yes"…
## $ P4.2 <chr> "Yes", "Yes", "No", "No", "Yes", "Yes", "Yes", "Yes", "Yes"…
## $ P5.2 <chr> "No", "Yes", "No", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes"…
## $ P6.2 <chr> "Yes", "Yes", "No", "No", "Yes", "Yes", "Yes", "Yes", "No",…
## $ P7.2 <chr> "No", "No", "No", "No", "Yes", "Yes", "Yes", "Yes", "No", "…
## $ P8.2 <chr> "No", "No", "No", "No", "Yes", "Yes", "Yes", "No", "No", "Y…
## $ Q110 <chr> "Yes", "No", "Yes", "No", "Yes", "Yes", "Yes", "No", "No", …
## $ P10.2 <chr> "No", "No", "No", "No", "No", "Yes", "Yes", "No", "No", "Ye…
## $ TP1 <dbl> 92.614, 74.656, 127.249, 134.318, 73.376, 74.178, 128.301, …
## $ TP2 <dbl> 72.548, 58.775, 132.299, NA, 78.092, 78.913, 202.501, 99.48…
## $ TP3 <dbl> 66.203, 68.332, NA, NA, 79.661, 85.253, 205.443, 75.857, 99…
## $ TP4 <dbl> 60.523, 70.291, NA, NA, 74.010, 66.910, 217.254, 80.097, 13…
## $ TP5 <dbl> NA, 55.296, NA, 128.669, 77.175, 73.377, 261.437, 90.727, 2…
## $ TP6 <dbl> 62.773, 53.854, NA, NA, 64.132, 59.184, 217.546, 316.344, N…
## $ TP7 <dbl> NA, NA, NA, NA, 65.059, 74.378, 253.157, 85.718, NA, 61.937…
## $ TP8 <dbl> NA, NA, NA, NA, 88.668, 76.608, 233.029, NA, NA, 64.334, 62…
## $ TP9 <dbl> 69.227, NA, 99.901, NA, 61.355, 95.095, 186.440, NA, NA, 65…
## $ TP10 <dbl> NA, NA, NA, NA, NA, 84.545, 185.184, NA, NA, 66.368, 51.600
## $ condition <chr> "CONTROL", "INFO", "THREAT", "THREAT", "INFO", "CONTROL", "…
## $ id <chr> "11-Ala-G", "03SueM", "09DamO", "10JusS", "11-Elo-Q", "11br…
## $ TP1_mean <dbl> 95.26091, 95.26091, 95.26091, 95.26091, 95.26091, 95.26091,…
## $ TP1_sd <dbl> 23.60147, 23.60147, 23.60147, 23.60147, 23.60147, 23.60147,…
## $ cutoff <dbl> -23.80134, -23.80134, -23.80134, -23.80134, -23.80134, -23.…
## $ P1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
## $ TP2_mean <dbl> 96.6296, 96.6296, 96.6296, 96.6296, 96.6296, 96.6296, 96.62…
## $ TP2_sd <dbl> 42.81128, 42.81128, 42.81128, 42.81128, 42.81128, 42.81128,…
## $ P2 <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1
## $ TP3_mean <dbl> 94.61025, 94.61025, 94.61025, 94.61025, 94.61025, 94.61025,…
## $ TP3_sd <dbl> 45.96059, 45.96059, 45.96059, 45.96059, 45.96059, 45.96059,…
## $ P3 <dbl> 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0
## $ TP4_mean <dbl> 96.7405, 96.7405, 96.7405, 96.7405, 96.7405, 96.7405, 96.74…
## $ TP4_sd <dbl> 54.31901, 54.31901, 54.31901, 54.31901, 54.31901, 54.31901,…
## $ P4 <dbl> 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0
## $ TP5_mean <dbl> 127.4925, 127.4925, 127.4925, 127.4925, 127.4925, 127.4925,…
## $ TP5_sd <dbl> 84.36753, 84.36753, 84.36753, 84.36753, 84.36753, 84.36753,…
## $ P5 <dbl> 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0
## $ TP6_mean <dbl> 120.5434, 120.5434, 120.5434, 120.5434, 120.5434, 120.5434,…
## $ TP6_sd <dbl> 104.113, 104.113, 104.113, 104.113, 104.113, 104.113, 104.1…
## $ P6 <dbl> 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0
## $ TP7_mean <dbl> 99.89983, 99.89983, 99.89983, 99.89983, 99.89983, 99.89983,…
## $ TP7_sd <dbl> 75.7045, 75.7045, 75.7045, 75.7045, 75.7045, 75.7045, 75.70…
## $ P7 <dbl> 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1
## $ TP8_mean <dbl> 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105
## $ TP8_sd <dbl> 72.34651, 72.34651, 72.34651, 72.34651, 72.34651, 72.34651,…
## $ P8 <dbl> 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1
## $ TP9_mean <dbl> 92.24814, 92.24814, 92.24814, 92.24814, 92.24814, 92.24814,…
## $ TP9_sd <dbl> 44.19504, 44.19504, 44.19504, 44.19504, 44.19504, 44.19504,…
## $ P9 <dbl> 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1
## $ TP10_mean <dbl> 96.92425, 96.92425, 96.92425, 96.92425, 96.92425, 96.92425,…
## $ TP10_sd <dbl> 60.3628, 60.3628, 60.3628, 60.3628, 60.3628, 60.3628, 60.36…
## $ P10 <dbl> 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1
## $ page_count <dbl> 6, 6, 3, 2, 9, 10, 10, 7, 5, 10, 64.4: Descriptive Statistics and Data Visualization
4.4.1 - PEB by Condition
library(DescTools)
wdf_rru3_summary <- wdf_rru3 |>
group_by(condition) |>
summarise(
n = n(),
page_count_mean = mean(page_count, na.rm = TRUE),
page_count_sd = sd(page_count, na.rm = TRUE),
page_count_median = median(page_count, na.rm = TRUE),
page_count_mode = Mode(page_count, na.rm = TRUE))
wdf_rru3_summary| condition | n | page_count_mean | page_count_sd | page_count_median | page_count_mode |
|---|---|---|---|---|---|
| CONTROL | 3 | 7.333333 | 2.309401 | 6 | 6 |
| INFO | 3 | 6.666667 | 2.081666 | 6 | NA |
| THREAT | 5 | 6.400000 | 3.781534 | 7 | 10 |
4.4.2: Distribution
vis_rru_peb <- ggplot(wdf_all3, aes(x=page_count)) +
geom_histogram()
vis_rru_peb## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
4.4.3: Plotting the Distribution by Condition (colour)
library("viridis")
library("viridisLite")
rru_peb_colour <- ggplot(wdf_rru3, aes(x=factor(page_count), fill=condition)) +
geom_histogram(stat = "count", position = position_dodge(preserve = "single"), binwidth = .5, bins = 11) +
scale_x_discrete(limits = c("0","1", "2","3","4","5","6","7","8","9","10"), drop = FALSE) +
scale_y_discrete(limits = c("0","1","2","3","4","5","6")) +
theme_minimal() +
theme(
plot.title=element_text(hjust=.5, vjust=.5, size=18, lineheight = 0.9),
legend.position = c(0.03, .97),
legend.justification = c("left", "top"),
legend.title = element_text(size=14),
legend.text = element_text(size=11),
axis.text = element_text(face = "bold", size = 10),
axis.title = element_text(size=14.5),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
axis.text.x = element_text(vjust=+1.5),
axis.title.x = element_text(vjust=+0.5),
axis.text.y = element_text(vjust=+10.5)) +
labs(title="", x = "Number of Completed Pages", y = "Participant Count", fill="Condition") +
scale_color_viridis(discrete = TRUE, begin = .25, end = 1) +
scale_fill_viridis(discrete = TRUE, labels=c("Control","Informational","Threat-Based"), begin = .25, end = 1)## Warning in geom_histogram(stat = "count", position = position_dodge(preserve =
## "single"), : Ignoring unknown parameters: `binwidth`, `bins`, and `pad`rru_peb_colour
Saving the Graph
ggsave("figures/rru_wept_distribution.png", plot=vis_2, units='in', width=8, height=6, dpi=600)4.4.4: Plotting the Distribution by Condition (greyscale)
rru_peb_grey <- ggplot(wdf_rru3, aes(x=factor(page_count), fill=condition)) +
geom_histogram(stat = "count", position = position_dodge(preserve = "single"), binwidth = .5, bins = 11) +
scale_x_discrete(limits = c("0","1", "2","3","4","5","6","7","8","9","10"), drop = FALSE) +
scale_y_discrete(limits = c("0","1","2","3","4","5","6")) +
theme_minimal() +
theme(
plot.title=element_text(hjust=.5, vjust=.5, size=18, lineheight = 0.9),
legend.position = c(0.03, .97),
legend.justification = c("left", "top"),
legend.title = element_text(size=14),
legend.text = element_text(size=11),
axis.text = element_text(face = "bold", size = 10),
axis.title = element_text(size=14.5),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
axis.text.x = element_text(vjust=+1.5),
axis.title.x = element_text(vjust=+0.5),
axis.text.y = element_text(vjust=+7)) +
labs(title="", x = "Number of Completed Pages", y = "Participant Count", fill="Condition") +
scale_fill_grey(labels=c("Control","Informational","Threat-Based"))## Warning in geom_histogram(stat = "count", position = position_dodge(preserve =
## "single"), : Ignoring unknown parameters: `binwidth`, `bins`, and `pad`rru_peb_grey
Save the Graph
ggsave("figures/rru_wept_distribution_grey.png", plot=rru_peb_grey, units='in', width=8, height=6, dpi=600)Section 5: Death Thought Accessibility
5.1: Removing Unecessary Columns and Renaming
5.1.1: Part 2
adf_p2 <- df_p2 |> rename( #The original name is on the right of the = sign
death_h = 'Q29_3',
corpse_p = 'Q35_2',
stiff_f = 'Q41_1',
widdow_d = 'Q43_2',
grave_v = 'Q25_1',
condition = 'FL_10_DO',
id = '3.2'
) |>
select(death_h,corpse_p,stiff_f,widdow_d,grave_v,condition,id) #the columns listed are the ones I keep 5.1.2: Conjoined
adf_c <- df_c |> rename( #The original name is on the right of the = sign
death_h = 'Q29_3',
corpse_p = 'Q35_2',
stiff_f = 'Q41_1',
widdow_d = 'Q43_2',
grave_v = 'Q25_1',
condition = 'FL_18_DO',
id = '3.2',
) |>
select(death_h,corpse_p,stiff_f,widdow_d,grave_v,condition,id)5.2: Joining Part 2 and Conjoined
#first joining p1 and p2 using a left join because we only want to keep users that completed p2
#adf_p1p2 <- left_join(adf_p2, adf_p1, by = 'id')
#next using full join to combine the conjoined dataset because we want all of the participants from that one and all from p2
df_all <- full_join(adf_p2,adf_c)## Joining with `by = join_by(death_h, corpse_p, stiff_f, widdow_d, grave_v,
## condition, id)`glimpse(df_all)## Rows: 83
## Columns: 7
## $ death_h <chr> "h", "H", "s", "death", "S", NA, "H", "a", "Y", "s", NA, "h"…
## $ corpse_p <chr> "r", "R", "r", "coarse", "R", "COURSE", "P", "p", "p", "p", …
## $ stiff_f <chr> "l", "L", "n", "stick", "N", "STICH", "L", "n", "L", "a", "l…
## $ widdow_d <chr> "n", "N", "n", "Widdow", "N", "WIDDOW", "N", "n", "n", "n", …
## $ grave_v <chr> "n", "I", "d", "grate", "T", "GRAPE", "P", "p", "E", "p", "s…
## $ condition <chr> "CONTROL", "INFO", "CONTROL", "THREAT", "THREAT", "INFO", "T…
## $ id <chr> "04MayH", "12EmmP", "09mayh", "01TimC", "04ValF", "08BriK", …5.3: Coding pages as 1 or 0 depending on word choices
wdf_all <- df_all |>
mutate(
death = case_when(str_detect(death_h, "h|H|death|DEATH|Death") ~ 1, TRUE ~ 0),
corpse = case_when(str_detect(corpse_p, "p|P|CORPSE|corpse|Corpse") ~ 1, TRUE ~ 0),
stiff = case_when(str_detect(stiff_f, "f|F|STIFF|stiff|Stiff") ~ 1, TRUE ~ 0),
widdow = case_when(str_detect(widdow_d, "d|D|WIDDOW|widdow|Widdow") ~ 1, TRUE ~ 0),
grave = case_when(str_detect(grave_v, "v|V|GRAVE|grave|Grave") ~ 1, TRUE ~ 0),
) |>
mutate_at(c('death', 'corpse', 'stiff','widdow','grave'), as.logical) |>
mutate_at('condition', as.factor)5.4: Creating the Count Variable
(sum of words completed as death option per participant)
c_to_sum <- c('death', 'corpse', 'stiff','widdow','grave')
wdf_all2 <- wdf_all |>
mutate(death_count = rowSums(select(wdf_all, c_to_sum) == 1, na.rm = TRUE))
#|>
#mutate_at(('death_count'), as.factor)
glimpse(wdf_all2)## Rows: 83
## Columns: 13
## $ death_h <chr> "h", "H", "s", "death", "S", NA, "H", "a", "Y", "s", NA, "…
## $ corpse_p <chr> "r", "R", "r", "coarse", "R", "COURSE", "P", "p", "p", "p"…
## $ stiff_f <chr> "l", "L", "n", "stick", "N", "STICH", "L", "n", "L", "a", …
## $ widdow_d <chr> "n", "N", "n", "Widdow", "N", "WIDDOW", "N", "n", "n", "n"…
## $ grave_v <chr> "n", "I", "d", "grate", "T", "GRAPE", "P", "p", "E", "p", …
## $ condition <fct> CONTROL, INFO, CONTROL, THREAT, THREAT, INFO, THREAT, INFO…
## $ id <chr> "04MayH", "12EmmP", "09mayh", "01TimC", "04ValF", "08BriK"…
## $ death <lgl> TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE,…
## $ corpse <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE…
## $ stiff <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
## $ widdow <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALS…
## $ grave <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
## $ death_count <dbl> 1, 1, 0, 2, 0, 1, 2, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0…5.5: Descriptive Statistics and Data Visualization
5.5.1: DTA Descriptives by Condition
library(DescTools)
wdf_all3_summary <- wdf_all2 |>
group_by(condition) |>
summarise(
n = n(),
page_count_mean = mean(death_count, na.rm = TRUE),
page_count_sd = sd(death_count, na.rm = TRUE),
page_count_median = median(death_count, na.rm = TRUE),
page_count_mode = Mode(death_count, na.rm = TRUE))
wdf_all3_summary| condition | n | page_count_mean | page_count_sd | page_count_median | page_count_mode |
|---|---|---|---|---|---|
| CONTROL | 26 | 0.3846154 | 0.4961389 | 0 | 0 |
| INFO | 26 | 0.6538462 | 0.6894814 | 1 | 0 |
| THREAT | 31 | 0.4516129 | 0.7676245 | 0 | 0 |
5.5.2: Plotting the DTA Distribution by Condition (colour)
library("viridis")
library("viridisLite")
#In order for the x axis to recognize zeros as zero, I need to set death_count as a factor variable
vis_df <- wdf_all2 |>
mutate_at(('death_count'), as.factor)
vis_dta <- ggplot(vis_df, aes(x=death_count, fill=condition)) +
geom_histogram(stat = "count", position = position_dodge(preserve = "single"), binwidth = .5, bins = 6) +
scale_x_discrete(limits = c("0","1", "2","3","4","5"), drop = FALSE) +
theme_minimal() +
theme(
plot.title=element_text(hjust=.5, vjust=.5, size=13, lineheight = 0.9),
legend.position = c(.95, .95),
legend.justification = c("right", "top"),
axis.text = element_text(face="bold",size=8),
axis.title = element_text(size=11.5),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
axis.text.x = element_text(vjust=+8),
axis.title.x = element_text(vjust=+5),
axis.title.y = element_text(vjust=+2),
axis.text.y = element_text(margin = unit(c(0,-0.2,0,0), "cm"))) +
labs(title = "", x = "Number of Words Completed as Death Option", y = "Participant Count", fill="Condition") +
scale_color_viridis(discrete = TRUE, begin = .25, end = 1) +
scale_fill_viridis(discrete = TRUE, labels=c("Control","Informational","Threat-Based"), begin = .25, end = 1)## Warning in geom_histogram(stat = "count", position = position_dodge(preserve =
## "single"), : Ignoring unknown parameters: `binwidth`, `bins`, and `pad`vis_dta
Saving the Graph
ggsave("figures/dta_distribution.png", plot=vis_dta, units='in', width=8, height=6, dpi=600)5.5.3: Plotting the DTA Distribution by Condition (greyscale)
vis_dta_grey <- ggplot(vis_df, aes(x=death_count, fill=condition)) +
geom_histogram(stat = "count", position = position_dodge(preserve = "single"), binwidth = .5, bins = 6) +
scale_x_discrete(limits = c("0","1", "2","3","4","5"), drop = FALSE) +
theme_minimal() +
theme(
plot.title=element_text(hjust=.5, vjust=.5, size=13, lineheight = 0.9),
legend.position = c(.95, .95),
legend.justification = c("right", "top"),
axis.text = element_text(face="bold",size=8),
axis.title = element_text(size=11.5),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
axis.text.x = element_text(vjust=+8),
axis.title.x = element_text(vjust=+5),
axis.title.y = element_text(vjust=+2),
axis.text.y = element_text(margin = unit(c(0,-0.2,0,0), "cm"))) +
labs(title = "", x = "Number of Words Completed as Death Option", y = "Participant Count", fill="Condition") +
scale_fill_grey(labels=c("Control","Informational","Threat-Based"))## Warning in geom_histogram(stat = "count", position = position_dodge(preserve =
## "single"), : Ignoring unknown parameters: `binwidth`, `bins`, and `pad`vis_dta_grey
Saving the Graph
ggsave("figures/dta_distribution_greyscale.png", plot=vis_dta_grey, units='in', width=8, height=6, dpi=600)5.6: Fitting a Poisson Regression
Notes - Look at the bottom of the ouput to check whether the model has over-dispersion or under-dispersion. If the Residual Deviance is greater than the degrees of freedom, then over-dispersion exists. This means that the estimates are correct, but the standard errors (standard deviation) are wrong and unaccounted for by the model. - The regression coefficients will give you the difference in means between each other category and the reference category.
poisson_model <- glm(death_count ~ condition, data = wdf_all2, family = "poisson")
summary(poisson_model)##
## Call:
## glm(formula = death_count ~ condition, family = "poisson", data = wdf_all2)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1435 -0.9504 -0.8771 0.7022 1.6898
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.9555 0.3162 -3.022 0.00251 **
## conditionINFO 0.5306 0.3985 1.331 0.18303
## conditionTHREAT 0.1606 0.4140 0.388 0.69813
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 80.013 on 82 degrees of freedom
## Residual deviance: 77.995 on 80 degrees of freedom
## AIC: 154.9
##
## Number of Fisher Scoring iterations: 65.6.1: Visual Inspection of the Poisson Regression
check_model(poisson_model)
Section 6: Environmental Identity
6.1: Removing unecessary columns and renaming
6.1.1: Part 1
adf_p1 <- fdf_p1 |> rename( #The original name is on the right of the = sign
BEIDQ1 = '6.2_1',
BEIDQ2 = '6.2_2',
BEIDQ3 = '6.2_3',
BEIDQ4 = '6.2_4',
BEIDQ5 = '6.2_5',
BEIDQ6 = '6.2_6',
BEIDQ7 = '6.2_7',
BEIDQ8 = '6.2_8',
BEIDQ9 = '6.2_9',
BEIDQ10 = '6.2_10',
BEIDQ11 = '6.2_11',
id = '3.2'
) |>
select(BEIDQ1:BEIDQ11,id) |> #the columns listed are the ones I keep
filter(!is.na(id)) |>
mutate_at(c('BEIDQ1','BEIDQ2','BEIDQ3','BEIDQ4','BEIDQ5','BEIDQ6','BEIDQ7','BEIDQ8','BEIDQ9','BEIDQ10','BEIDQ11'), as.numeric)## Warning: There were 11 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `BEIDQ1 = .Primitive("as.double")(BEIDQ1)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 10 remaining warnings.6.1.2: Part 2
adf_p2 <- df_p2 |> rename( #The original name is on the right of the = sign
EIDQ1 = '6.2_1',
EIDQ2 = '6.2_2',
EIDQ3 = '6.2_3',
EIDQ4 = '6.2_4',
EIDQ5 = '6.2_5',
EIDQ6 = '6.2_6',
EIDQ7 = '6.2_7',
EIDQ8 = '6.2_8',
EIDQ9 = '6.2_9',
EIDQ10 = '6.2_10',
EIDQ11 = '6.2_11',
condition = 'FL_10_DO',
id = '3.2'
) |>
select(EIDQ1:EIDQ11,condition,id) |> #the columns listed are the ones I keep
mutate_at(c('EIDQ1','EIDQ2','EIDQ3','EIDQ4','EIDQ5','EIDQ6','EIDQ7','EIDQ8','EIDQ9','EIDQ10','EIDQ11'), as.numeric)6.1.3: Conjoined
adf_c <- df_c |> rename( #The original name is on the right of the = sign
EIDQ1 = 'Q143_1',
EIDQ2 = 'Q143_2',
EIDQ3 = 'Q143_3',
EIDQ4 = 'Q143_4',
EIDQ5 = 'Q143_5',
EIDQ6 = 'Q143_6',
EIDQ7 = 'Q143_7',
EIDQ8 = 'Q143_8',
EIDQ9 = 'Q143_9',
EIDQ10 = 'Q143_10',
EIDQ11 = 'Q143_11',
BEIDQ1 = '6.2_1...28',
BEIDQ2 = '6.2_2...29',
BEIDQ3 = '6.2_3...30',
BEIDQ4 = '6.2_4...31',
BEIDQ5 = '6.2_5...32',
BEIDQ6 = '6.2_6...33',
BEIDQ7 = '6.2_7...34',
BEIDQ8 = '6.2_8...35',
BEIDQ9 = '6.2_9...36',
BEIDQ10 = '6.2_10...37',
BEIDQ11 = '6.2_11...38',
condition = 'FL_18_DO',
id = '3.2'
) |>
select(EIDQ1:EIDQ11,BEIDQ1:BEIDQ11,condition,id) |>
mutate_at(c('EIDQ1','EIDQ2','EIDQ3','EIDQ4','EIDQ5','EIDQ6','EIDQ7','EIDQ8','EIDQ9','EIDQ10','EIDQ11','BEIDQ1','BEIDQ2','BEIDQ3','BEIDQ4','BEIDQ5','BEIDQ6','BEIDQ7','BEIDQ8','BEIDQ9','BEIDQ10','BEIDQ11'), as.numeric)6.2: Joining all Three Datasets
#first joining p1 and p2 using a left join because we only want to keep users that completed p2
adf_p1p2 <- left_join(adf_p2, adf_p1, by = 'id')
#next using full join to combine the conjoined dataset because we want all of the participants from that one and all from p2
df_all <- full_join(adf_p1p2,adf_c)## Joining with `by = join_by(EIDQ1, EIDQ2, EIDQ3, EIDQ4, EIDQ5, EIDQ6, EIDQ7,
## EIDQ8, EIDQ9, EIDQ10, EIDQ11, condition, id, BEIDQ1, BEIDQ2, BEIDQ3, BEIDQ4,
## BEIDQ5, BEIDQ6, BEIDQ7, BEIDQ8, BEIDQ9, BEIDQ10, BEIDQ11)`glimpse(df_all)## Rows: 83
## Columns: 24
## $ EIDQ1 <dbl> 7.00, 7.00, 6.00, 6.00, 6.00, 7.00, 6.00, 5.00, 4.00, 5.00, …
## $ EIDQ2 <dbl> 7.00, 7.00, 6.00, 5.00, 7.00, 6.00, 6.00, 7.00, 3.00, 5.00, …
## $ EIDQ3 <dbl> 7.00, 7.00, 7.00, 7.00, 7.00, 7.00, 7.00, 7.00, 7.00, 7.00, …
## $ EIDQ4 <dbl> 5.00, 7.00, 5.00, 5.00, 6.00, 6.00, 7.00, 5.00, 6.00, 7.00, …
## $ EIDQ5 <dbl> 5.00, 6.00, 5.00, 2.00, 6.00, 5.00, 6.00, 4.00, 2.00, 6.00, …
## $ EIDQ6 <dbl> 7.00, 7.00, 7.00, 6.00, 6.00, 5.00, 7.00, 6.00, 5.00, 7.00, …
## $ EIDQ7 <dbl> 6.00, 7.00, 7.00, 6.00, 7.00, 6.00, 7.00, 7.00, 7.00, 7.00, …
## $ EIDQ8 <dbl> 7.00, 7.00, 7.00, 7.00, 7.00, 7.00, 7.00, 4.00, 7.00, 7.00, …
## $ EIDQ9 <dbl> 7.00, 7.00, 7.00, 6.00, 7.00, 7.00, 7.00, 5.00, 7.00, 7.00, …
## $ EIDQ10 <dbl> 4.00, 5.00, 6.00, 6.00, 7.00, 5.00, 6.00, 3.00, 3.00, 7.00, …
## $ EIDQ11 <dbl> 7.00, 7.00, 6.00, 2.00, 7.00, 5.00, 7.00, 7.00, 6.00, 7.00, …
## $ condition <chr> "CONTROL", "INFO", "CONTROL", "THREAT", "THREAT", "INFO", "T…
## $ id <chr> "04MayH", "12EmmP", "09mayh", "01TimC", "04ValF", "08BriK", …
## $ BEIDQ1 <dbl> 6, NA, NA, NA, NA, 7, 5, 5, 5, 5, 3, 5, 4, 7, 6, 6, 2, 6, 4,…
## $ BEIDQ2 <dbl> 6, NA, NA, NA, NA, 7, 5, 7, 2, 4, 6, 4, 6, 7, 4, 5, 5, 6, 2,…
## $ BEIDQ3 <dbl> 7, NA, NA, NA, NA, 7, 5, 7, 7, 6, 7, 6, 6, 7, 6, 7, 7, 7, 5,…
## $ BEIDQ4 <dbl> 7, NA, NA, NA, NA, 7, 5, 6, 7, 6, 5, 4, 5, 7, 7, 6, 4, 7, 1,…
## $ BEIDQ5 <dbl> 6, NA, NA, NA, NA, 5, 5, 3, 3, 5, 3, 2, 3, 3, 6, 7, 7, 5, 1,…
## $ BEIDQ6 <dbl> 7, NA, NA, NA, NA, 6, 5, 7, 4, 5, 7, 6, 5, 7, 5, 6, 7, 7, 5,…
## $ BEIDQ7 <dbl> 6, NA, NA, NA, NA, 7, 5, 7, 6, 7, 5, 6, 7, 7, 5, 7, 7, 7, 7,…
## $ BEIDQ8 <dbl> 7, NA, NA, NA, NA, 7, 5, 4, 7, 7, 4, 7, 7, 7, 7, 7, 7, 7, 2,…
## $ BEIDQ9 <dbl> 7, NA, NA, NA, NA, 7, 5, 6, 7, 7, 5, 6, 5, 7, 7, 7, 4, 7, 4,…
## $ BEIDQ10 <dbl> 4, NA, NA, NA, NA, 6, 5, 4, 5, 7, 7, 4, 6, 7, 7, 7, 4, 7, 4,…
## $ BEIDQ11 <dbl> 7, NA, NA, NA, NA, 4, 5, 7, 7, 7, 4, 1, 5, 7, 4, 7, 4, 4, 1,…6.3: Creating a Column for Change in EID Average
Notes: - I used the row means function here instead of creating a sum and then dividing by 11 because that was not processing the NA values correctly.
EIDQS <- c('EIDQ1', 'EIDQ2', 'EIDQ3', 'EIDQ4', 'EIDQ5', 'EIDQ6', 'EIDQ7', 'EIDQ8', 'EIDQ9', 'EIDQ10', 'EIDQ11')
BEIDQS <- c('BEIDQ1', 'BEIDQ2', 'BEIDQ3', 'BEIDQ4', 'BEIDQ5', 'BEIDQ6', 'BEIDQ7', 'BEIDQ8', 'BEIDQ9', 'BEIDQ10', 'BEIDQ11')
df_all2 <- df_all %>%
#Round to whole numbers
mutate_at(vars(all_of(EIDQS)), ~ round(., digits = 0)) %>%
#EID scores and changes for descriptive statistics
mutate(EID_score = rowMeans(select(., all_of(EIDQS)), na.rm = TRUE)) %>%
mutate(BEID_score = rowMeans(select(., all_of(BEIDQS)), na.rm = TRUE)) %>%
mutate(EID_change = EID_score - BEID_score) %>%
#Count non-NA values in EIDQS and BEIDQS columns
mutate(EIDQS_non_na = rowSums(!is.na(select(., all_of(EIDQS)))),
BEIDQS_non_na = rowSums(!is.na(select(., all_of(BEIDQS))))) %>%
#Filter rows where more than half of the values are NA in either EIDQS or BEIDQS
filter(EIDQS_non_na >= (length(EIDQS) / 2), BEIDQS_non_na >= (length(BEIDQS) / 2)) %>%
select(-EIDQS_non_na, -BEIDQS_non_na)
glimpse(df_all2)## Rows: 75
## Columns: 27
## $ EIDQ1 <dbl> 7, 7, 6, 5, 4, 5, 4, 6, 5, 7, 5, 6, 1, 6, 1, 3, 2, 5, 5, 5,…
## $ EIDQ2 <dbl> 7, 6, 6, 7, 3, 5, 6, 4, 5, 5, 5, 6, 6, 5, 3, 5, 2, 5, 7, 3,…
## $ EIDQ3 <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 6, 7, 7, 7, 7, 7, 5, 4, 6, 5, 7, 7,…
## $ EIDQ4 <dbl> 5, 6, 7, 5, 6, 7, 6, 5, 5, 7, 7, 6, 5, 6, 1, 4, 2, 5, 5, 5,…
## $ EIDQ5 <dbl> 5, 5, 6, 4, 2, 6, 4, 2, NA, 3, 5, 7, 6, 4, 1, 5, 4, 5, 4, 5…
## $ EIDQ6 <dbl> 7, 5, 7, 6, 5, 7, NA, 6, 5, 7, 5, 6, 7, 7, 5, 4, 6, 6, 7, 5…
## $ EIDQ7 <dbl> 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 5, 7, 7, 7, 7, 6, 6, 5, 7, 5,…
## $ EIDQ8 <dbl> 7, 7, 7, 4, 7, 7, 2, 6, 6, 7, 7, 7, 6, 5, 2, 3, 2, 4, 7, 7,…
## $ EIDQ9 <dbl> 7, 7, 7, 5, 7, 7, 7, 5, 5, 7, 6, 7, 4, 6, 2, 3, 5, 7, 7, 3,…
## $ EIDQ10 <dbl> 4, 5, 6, 3, 3, 7, 7, 4, 6, 7, 7, 7, 2, 6, 3, 6, 4, 5, 7, 6,…
## $ EIDQ11 <dbl> 7, 5, 7, 7, 6, 7, 5, 1, 5, 7, 4, 7, 3, 3, 1, 4, NA, 7, 7, 3…
## $ condition <chr> "CONTROL", "INFO", "THREAT", "INFO", "INFO", "CONTROL", "TH…
## $ id <chr> "04MayH", "08BriK", "04NadM", "01DeaG", "07DanB", "07EriS",…
## $ BEIDQ1 <dbl> 6, 7, 5, 5, 5, 5, 3, 5, 4, 7, 6, 6, 2, 6, 4, 2, 2, 6, 5, 5,…
## $ BEIDQ2 <dbl> 6, 7, 5, 7, 2, 4, 6, 4, 6, 7, 4, 5, 5, 6, 2, 3, 2, 6, 7, 4,…
## $ BEIDQ3 <dbl> 7, 7, 5, 7, 7, 6, 7, 6, 6, 7, 6, 7, 7, 7, 5, 4, 6, 6, 7, 7,…
## $ BEIDQ4 <dbl> 7, 7, 5, 6, 7, 6, 5, 4, 5, 7, 7, 6, 4, 7, 1, 5, 3, 7, 4, 6,…
## $ BEIDQ5 <dbl> 6, 5, 5, 3, 3, 5, 3, 2, 3, 3, 6, 7, 7, 5, 1, 6, 4, 5, 4, 3,…
## $ BEIDQ6 <dbl> 7, 6, 5, 7, 4, 5, 7, 6, 5, 7, 5, 6, 7, 7, 5, 5, 6, 7, 7, 5,…
## $ BEIDQ7 <dbl> 6, 7, 5, 7, 6, 7, 5, 6, 7, 7, 5, 7, 7, 7, 7, 7, 5, 6, 7, 7,…
## $ BEIDQ8 <dbl> 7, 7, 5, 4, 7, 7, 4, 7, 7, 7, 7, 7, 7, 7, 2, 3, 2, 5, 7, 7,…
## $ BEIDQ9 <dbl> 7, 7, 5, 6, 7, 7, 5, 6, 5, 7, 7, 7, 4, 7, 4, 5, 3, 7, 7, 7,…
## $ BEIDQ10 <dbl> 4, 6, 5, 4, 5, 7, 7, 4, 6, 7, 7, 7, 4, 7, 4, 6, 5, 7, 7, 7,…
## $ BEIDQ11 <dbl> 7, 4, 5, 7, 7, 7, 4, 1, 5, 7, 4, 7, 4, 4, 1, 4, 1, 7, 7, 2,…
## $ EID_score <dbl> 6.272727, 6.000000, 6.636364, 5.454545, 5.181818, 6.545455,…
## $ BEID_score <dbl> 6.363636, 6.363636, 5.000000, 5.727273, 5.454545, 6.000000,…
## $ EID_change <dbl> -0.09090909, -0.36363636, 1.63636364, -0.27272727, -0.27272…6.4: Descriptive Statistics and Data Visualization
6.4.1: EID by Condition
library(DescTools)
df_all_summary <- df_all2 |>
group_by(condition) |>
summarise(
n = n(),
EID_mean = mean(EID_score, na.rm = TRUE),
BEID_mean = mean(BEID_score, na.rm = TRUE),
EID_sd = sd(EID_score, na.rm = TRUE),
EID_median = median(EID_score, na.rm = TRUE),
EID_mode = Mode(EID_score, na.rm = TRUE))## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
## always returns an ungrouped data frame and adjust accordingly.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `summarise()` has grouped output by 'condition'. You can override using the
## `.groups` argument.df_all_summary| condition | n | EID_mean | BEID_mean | EID_sd | EID_median | EID_mode |
|---|---|---|---|---|---|---|
| CONTROL | 25 | 5.562899 | 5.731273 | 0.7582787 | 5.454546 | 4.909091 |
| INFO | 23 | 5.347299 | 5.486166 | 0.9721340 | 5.363636 | 5.181818 |
| INFO | 23 | 5.347299 | 5.486166 | 0.9721340 | 5.363636 | 6.363636 |
| THREAT | 27 | 5.445501 | 5.430976 | 0.9335670 | 5.333333 | 5.000000 |
6.4.2: Distribution
vis_3 <- ggplot(df_all2, aes(x=EID_score)) +
geom_histogram()
vis_3## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
6.4.3: EID & EID Change by Condition
A negative change value means EID decreased after the intervention
summary <- df_all2 |>
group_by(condition) |>
summarise(
n = n(),
EID_mean = mean(EID_score, na.rm = TRUE),
BEID_mean = mean(BEID_score, na.rm = TRUE),
EID_change = mean(EID_change, na.rm = TRUE),
EID_sd = sd(EID_score, na.rm = TRUE),
BEID_sd = sd(BEID_score, na.rm = TRUE))
summary| condition | n | EID_mean | BEID_mean | EID_change | EID_sd | BEID_sd |
|---|---|---|---|---|---|---|
| CONTROL | 25 | 5.562899 | 5.731273 | -0.1683737 | 0.7582787 | 0.7342848 |
| INFO | 23 | 5.347299 | 5.486166 | -0.1388669 | 0.9721340 | 0.8786681 |
| THREAT | 27 | 5.445501 | 5.430976 | 0.0145249 | 0.9335670 | 0.9022796 |
6.4.4: Initial Visualization
vis_1 <- ggplot(df_all2, aes(x=condition, y=EID_change)) +
geom_violin()
vis_1 +
stat_summary(fun.data = "mean_sdl",
geom = "crossbar",
position = position_dodge(width = 0.75),
width = 0.2)
vis_1
6.5: CFA for EIDQS & BEIDQS
This doesn’t work when I put in missing = “ML” and ordered = TRUE at the same time. “lavaan ERROR: missing = “ml” not available in the categorical setting”
6.5.1: CFA (Post-Test) with ordered=TRUE and Pairwise Deletion
library(lavaan)## Warning: package 'lavaan' was built under R version 4.2.3## This is lavaan 0.6-17
## lavaan is FREE software! Please report any bugs.eid <- '
eid =~ EIDQ1 + EIDQ2 + EIDQ3 + EIDQ4 + EIDQ5 + EIDQ6 + EIDQ7 + EIDQ8 + EIDQ9 + EIDQ10 + EIDQ11
'
model_cfa_o <- cfa(eid, data = df_all2, ordered="TRUE", missing = "pairwise") ## Warning in lavaan::lavaan(model = eid, data = df_all2, ordered = "TRUE", : lavaan WARNING: ordered variable(s): TRUE
## could not be found in the data and will be ignored## Warning in lav_partable_check(lavpartable, categorical =
## lavoptions$.categorical, : lavaan WARNING: parameter table does not contain
## thresholds## Warning in lav_samplestats_from_data(lavdata = lavdata, lavoptions = lavoptions, : lavaan WARNING: number of observations (75) too small to compute Gamma## Warning in lav_partable_check(lavpartable, categorical =
## lavoptions$.categorical, : lavaan WARNING: parameter table does not contain
## thresholdssummary(model_cfa_o, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6.17 ended normally after 40 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 33
##
## Number of observations 75
## Number of missing patterns 13
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 62.042 100.494
## Degrees of freedom 44 44
## P-value (Chi-square) 0.038 0.000
## Scaling correction factor 0.756
## Shift parameter 18.415
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 604.375 276.716
## Degrees of freedom 55 55
## P-value 0.000 0.000
## Scaling correction factor 2.478
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.967 0.745
## Tucker-Lewis Index (TLI) 0.959 0.681
##
## Robust Comparative Fit Index (CFI) 0.922
## Robust Tucker-Lewis Index (TLI) 0.903
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.074 0.132
## 90 Percent confidence interval - lower 0.019 0.098
## 90 Percent confidence interval - upper 0.115 0.166
## P-value H_0: RMSEA <= 0.050 0.179 0.000
## P-value H_0: RMSEA >= 0.080 0.439 0.992
##
## Robust RMSEA 0.115
## 90 Percent confidence interval - lower 0.085
## 90 Percent confidence interval - upper 0.144
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.971
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.103 0.103
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## eid =~
## EIDQ1 1.000 0.922 0.559
## EIDQ2 0.984 0.227 4.342 0.000 0.907 0.627
## EIDQ3 0.625 0.144 4.346 0.000 0.576 0.618
## EIDQ4 1.106 0.203 5.458 0.000 1.019 0.718
## EIDQ5 0.737 0.208 3.540 0.000 0.679 0.458
## EIDQ6 0.604 0.170 3.551 0.000 0.557 0.539
## EIDQ7 0.508 0.177 2.860 0.004 0.468 0.524
## EIDQ8 0.849 0.170 4.996 0.000 0.783 0.543
## EIDQ9 1.074 0.210 5.105 0.000 0.989 0.792
## EIDQ10 0.827 0.210 3.941 0.000 0.762 0.473
## EIDQ11 1.398 0.319 4.378 0.000 1.288 0.666
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .EIDQ1 4.730 0.189 25.003 0.000 4.730 2.868
## .EIDQ2 5.135 0.166 30.964 0.000 5.135 3.551
## .EIDQ3 6.253 0.108 58.148 0.000 6.253 6.714
## .EIDQ4 5.230 0.163 32.124 0.000 5.230 3.684
## .EIDQ5 4.549 0.166 27.348 0.000 4.549 3.071
## .EIDQ6 5.958 0.116 51.294 0.000 5.958 5.761
## .EIDQ7 6.347 0.103 61.562 0.000 6.347 7.109
## .EIDQ8 5.917 0.163 36.293 0.000 5.917 4.105
## .EIDQ9 5.813 0.144 40.318 0.000 5.813 4.655
## .EIDQ10 5.137 0.183 28.013 0.000 5.137 3.191
## .EIDQ11 4.955 0.211 23.495 0.000 4.955 2.562
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .EIDQ1 1.871 0.349 5.360 0.000 1.871 0.688
## .EIDQ2 1.269 0.184 6.912 0.000 1.269 0.607
## .EIDQ3 0.536 0.081 6.606 0.000 0.536 0.618
## .EIDQ4 0.977 0.229 4.274 0.000 0.977 0.485
## .EIDQ5 1.733 0.227 7.648 0.000 1.733 0.790
## .EIDQ6 0.759 0.109 6.965 0.000 0.759 0.710
## .EIDQ7 0.578 0.099 5.826 0.000 0.578 0.726
## .EIDQ8 1.465 0.334 4.389 0.000 1.465 0.705
## .EIDQ9 0.580 0.166 3.503 0.000 0.580 0.372
## .EIDQ10 2.011 0.306 6.575 0.000 2.011 0.776
## .EIDQ11 2.081 0.444 4.691 0.000 2.081 0.556
## eid 0.849 0.343 2.475 0.013 1.000 1.0006.5.2: CFA for BEIDQS (Baseline)
library(lavaan)
beid <- '
beid =~ BEIDQ1 + BEIDQ2 + BEIDQ3 + BEIDQ4 + BEIDQ5 + BEIDQ6 + BEIDQ7 + BEIDQ8 + BEIDQ9 + BEIDQ10 + BEIDQ11
'
model_cfa_b <- cfa(beid, data = df_all2, ordered=TRUE, missing = "pairwise") ## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 7.002562e-18) is close to zero. This may be a symptom that the
## model is not identified.summary(model_cfa_b, standardized = TRUE, fit.measures = TRUE)## lavaan 0.6.17 ended normally after 23 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 67
##
## Number of observations 75
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 85.826 97.737
## Degrees of freedom 44 44
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.043
## Shift parameter 15.429
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 1047.552 598.995
## Degrees of freedom 55 55
## P-value 0.000 0.000
## Scaling correction factor 1.825
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.958 0.901
## Tucker-Lewis Index (TLI) 0.947 0.877
##
## Robust Comparative Fit Index (CFI) 0.781
## Robust Tucker-Lewis Index (TLI) 0.726
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.113 0.128
## 90 Percent confidence interval - lower 0.077 0.094
## 90 Percent confidence interval - upper 0.149 0.163
## P-value H_0: RMSEA <= 0.050 0.004 0.000
## P-value H_0: RMSEA >= 0.080 0.936 0.988
##
## Robust RMSEA 0.156
## 90 Percent confidence interval - lower 0.107
## 90 Percent confidence interval - upper 0.203
## P-value H_0: Robust RMSEA <= 0.050 0.001
## P-value H_0: Robust RMSEA >= 0.080 0.992
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.121 0.121
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## beid =~
## BEIDQ1 1.000 0.732 0.732
## BEIDQ2 0.894 0.111 8.030 0.000 0.654 0.654
## BEIDQ3 0.986 0.123 8.010 0.000 0.721 0.721
## BEIDQ4 1.091 0.109 9.966 0.000 0.799 0.799
## BEIDQ5 0.541 0.133 4.072 0.000 0.396 0.396
## BEIDQ6 0.676 0.138 4.907 0.000 0.495 0.495
## BEIDQ7 0.805 0.124 6.511 0.000 0.589 0.589
## BEIDQ8 0.801 0.112 7.146 0.000 0.586 0.586
## BEIDQ9 1.098 0.114 9.588 0.000 0.803 0.803
## BEIDQ10 0.577 0.131 4.389 0.000 0.422 0.422
## BEIDQ11 0.898 0.122 7.362 0.000 0.657 0.657
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## BEIDQ1|t1 -1.932 0.304 -6.364 0.000 -1.932 -1.932
## BEIDQ1|t2 -1.111 0.184 -6.051 0.000 -1.111 -1.111
## BEIDQ1|t3 -0.664 0.158 -4.203 0.000 -0.664 -0.664
## BEIDQ1|t4 -0.358 0.149 -2.403 0.016 -0.358 -0.358
## BEIDQ1|t5 0.544 0.154 3.535 0.000 0.544 0.544
## BEIDQ1|t6 1.051 0.179 5.869 0.000 1.051 1.051
## BEIDQ2|t1 -1.321 0.203 -6.514 0.000 -1.321 -1.321
## BEIDQ2|t2 -1.051 0.179 -5.869 0.000 -1.051 -1.051
## BEIDQ2|t3 -0.583 0.155 -3.759 0.000 -0.583 -0.583
## BEIDQ2|t4 -0.017 0.146 -0.115 0.909 -0.017 -0.017
## BEIDQ2|t5 0.664 0.158 4.203 0.000 0.664 0.664
## BEIDQ3|t1 -2.211 0.390 -5.664 0.000 -2.211 -2.211
## BEIDQ3|t2 -1.607 0.241 -6.662 0.000 -1.607 -1.607
## BEIDQ3|t3 -1.042 0.180 -5.802 0.000 -1.042 -1.042
## BEIDQ3|t4 -0.102 0.147 -0.693 0.488 -0.102 -0.102
## BEIDQ4|t1 -1.932 0.304 -6.364 0.000 -1.932 -1.932
## BEIDQ4|t2 -1.405 0.212 -6.624 0.000 -1.405 -1.405
## BEIDQ4|t3 -1.051 0.179 -5.869 0.000 -1.051 -1.051
## BEIDQ4|t4 -0.664 0.158 -4.203 0.000 -0.664 -0.664
## BEIDQ4|t5 0.050 0.146 0.344 0.731 0.050 0.050
## BEIDQ4|t6 0.505 0.153 3.310 0.001 0.505 0.505
## BEIDQ5|t1 -1.932 0.304 -6.364 0.000 -1.932 -1.932
## BEIDQ5|t2 -1.175 0.189 -6.222 0.000 -1.175 -1.175
## BEIDQ5|t3 -0.505 0.153 -3.310 0.001 -0.505 -0.505
## BEIDQ5|t4 -0.050 0.146 -0.344 0.731 -0.050 -0.050
## BEIDQ5|t5 0.544 0.154 3.535 0.000 0.544 0.544
## BEIDQ5|t6 0.994 0.175 5.678 0.000 0.994 0.994
## BEIDQ6|t1 -2.216 0.390 -5.688 0.000 -2.216 -2.216
## BEIDQ6|t2 -1.244 0.195 -6.378 0.000 -1.244 -1.244
## BEIDQ6|t3 -0.394 0.150 -2.631 0.009 -0.394 -0.394
## BEIDQ6|t4 0.050 0.146 0.344 0.731 0.050 0.050
## BEIDQ7|t1 -1.932 0.304 -6.364 0.000 -1.932 -1.932
## BEIDQ7|t2 -1.111 0.184 -6.051 0.000 -1.111 -1.111
## BEIDQ7|t3 -0.431 0.151 -2.858 0.004 -0.431 -0.431
## BEIDQ8|t1 -1.932 0.304 -6.364 0.000 -1.932 -1.932
## BEIDQ8|t2 -1.613 0.241 -6.706 0.000 -1.613 -1.613
## BEIDQ8|t3 -1.051 0.179 -5.869 0.000 -1.051 -1.051
## BEIDQ8|t4 -0.583 0.155 -3.759 0.000 -0.583 -0.583
## BEIDQ8|t5 -0.151 0.146 -1.032 0.302 -0.151 -0.151
## BEIDQ9|t1 -2.216 0.390 -5.688 0.000 -2.216 -2.216
## BEIDQ9|t2 -1.932 0.304 -6.364 0.000 -1.932 -1.932
## BEIDQ9|t3 -1.405 0.212 -6.624 0.000 -1.405 -1.405
## BEIDQ9|t4 -0.706 0.160 -4.423 0.000 -0.706 -0.706
## BEIDQ9|t5 -0.050 0.146 -0.344 0.731 -0.050 -0.050
## BEIDQ10|t1 -1.932 0.304 -6.364 0.000 -1.932 -1.932
## BEIDQ10|t2 -1.751 0.264 -6.623 0.000 -1.751 -1.751
## BEIDQ10|t3 -1.321 0.203 -6.514 0.000 -1.321 -1.321
## BEIDQ10|t4 -0.583 0.155 -3.759 0.000 -0.583 -0.583
## BEIDQ10|t5 -0.084 0.146 -0.573 0.566 -0.084 -0.084
## BEIDQ10|t6 0.431 0.151 2.858 0.004 0.431 0.431
## BEIDQ11|t1 -1.321 0.203 -6.514 0.000 -1.321 -1.321
## BEIDQ11|t2 -0.994 0.175 -5.678 0.000 -0.994 -0.994
## BEIDQ11|t3 -0.795 0.164 -4.855 0.000 -0.795 -0.795
## BEIDQ11|t4 -0.151 0.146 -1.032 0.302 -0.151 -0.151
## BEIDQ11|t5 0.219 0.147 1.490 0.136 0.219 0.219
## BEIDQ11|t6 0.431 0.151 2.858 0.004 0.431 0.431
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .BEIDQ1 0.464 0.464 0.464
## .BEIDQ2 0.572 0.572 0.572
## .BEIDQ3 0.480 0.480 0.480
## .BEIDQ4 0.362 0.362 0.362
## .BEIDQ5 0.843 0.843 0.843
## .BEIDQ6 0.755 0.755 0.755
## .BEIDQ7 0.653 0.653 0.653
## .BEIDQ8 0.656 0.656 0.656
## .BEIDQ9 0.355 0.355 0.355
## .BEIDQ10 0.822 0.822 0.822
## .BEIDQ11 0.568 0.568 0.568
## beid 0.536 0.081 6.596 0.000 1.000 1.0006.6: Calculting the Change by Subtracting Latent Factor Scores from one Another
note: - Due to limitations in the CFA function, any eid or beid score that had an NA value in its components comes out as NA in this dataset.
df_cfa_b <- as.data.frame(predict(model_cfa_b))
df_bind <- cbind(df_all2, df_cfa_b)
df_cfa <- as.data.frame(predict(model_cfa_o))
df_binded <- cbind(df_bind, df_cfa)
df_binded_change <- df_binded %>%
mutate(
change = eid - beid
)
glimpse(df_binded_change)## Rows: 75
## Columns: 30
## $ EIDQ1 <dbl> 7, 7, 6, 5, 4, 5, 4, 6, 5, 7, 5, 6, 1, 6, 1, 3, 2, 5, 5, 5,…
## $ EIDQ2 <dbl> 7, 6, 6, 7, 3, 5, 6, 4, 5, 5, 5, 6, 6, 5, 3, 5, 2, 5, 7, 3,…
## $ EIDQ3 <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 6, 7, 7, 7, 7, 7, 5, 4, 6, 5, 7, 7,…
## $ EIDQ4 <dbl> 5, 6, 7, 5, 6, 7, 6, 5, 5, 7, 7, 6, 5, 6, 1, 4, 2, 5, 5, 5,…
## $ EIDQ5 <dbl> 5, 5, 6, 4, 2, 6, 4, 2, NA, 3, 5, 7, 6, 4, 1, 5, 4, 5, 4, 5…
## $ EIDQ6 <dbl> 7, 5, 7, 6, 5, 7, NA, 6, 5, 7, 5, 6, 7, 7, 5, 4, 6, 6, 7, 5…
## $ EIDQ7 <dbl> 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 5, 7, 7, 7, 7, 6, 6, 5, 7, 5,…
## $ EIDQ8 <dbl> 7, 7, 7, 4, 7, 7, 2, 6, 6, 7, 7, 7, 6, 5, 2, 3, 2, 4, 7, 7,…
## $ EIDQ9 <dbl> 7, 7, 7, 5, 7, 7, 7, 5, 5, 7, 6, 7, 4, 6, 2, 3, 5, 7, 7, 3,…
## $ EIDQ10 <dbl> 4, 5, 6, 3, 3, 7, 7, 4, 6, 7, 7, 7, 2, 6, 3, 6, 4, 5, 7, 6,…
## $ EIDQ11 <dbl> 7, 5, 7, 7, 6, 7, 5, 1, 5, 7, 4, 7, 3, 3, 1, 4, NA, 7, 7, 3…
## $ condition <chr> "CONTROL", "INFO", "THREAT", "INFO", "INFO", "CONTROL", "TH…
## $ id <chr> "04MayH", "08BriK", "04NadM", "01DeaG", "07DanB", "07EriS",…
## $ BEIDQ1 <dbl> 6, 7, 5, 5, 5, 5, 3, 5, 4, 7, 6, 6, 2, 6, 4, 2, 2, 6, 5, 5,…
## $ BEIDQ2 <dbl> 6, 7, 5, 7, 2, 4, 6, 4, 6, 7, 4, 5, 5, 6, 2, 3, 2, 6, 7, 4,…
## $ BEIDQ3 <dbl> 7, 7, 5, 7, 7, 6, 7, 6, 6, 7, 6, 7, 7, 7, 5, 4, 6, 6, 7, 7,…
## $ BEIDQ4 <dbl> 7, 7, 5, 6, 7, 6, 5, 4, 5, 7, 7, 6, 4, 7, 1, 5, 3, 7, 4, 6,…
## $ BEIDQ5 <dbl> 6, 5, 5, 3, 3, 5, 3, 2, 3, 3, 6, 7, 7, 5, 1, 6, 4, 5, 4, 3,…
## $ BEIDQ6 <dbl> 7, 6, 5, 7, 4, 5, 7, 6, 5, 7, 5, 6, 7, 7, 5, 5, 6, 7, 7, 5,…
## $ BEIDQ7 <dbl> 6, 7, 5, 7, 6, 7, 5, 6, 7, 7, 5, 7, 7, 7, 7, 7, 5, 6, 7, 7,…
## $ BEIDQ8 <dbl> 7, 7, 5, 4, 7, 7, 4, 7, 7, 7, 7, 7, 7, 7, 2, 3, 2, 5, 7, 7,…
## $ BEIDQ9 <dbl> 7, 7, 5, 6, 7, 7, 5, 6, 5, 7, 7, 7, 4, 7, 4, 5, 3, 7, 7, 7,…
## $ BEIDQ10 <dbl> 4, 6, 5, 4, 5, 7, 7, 4, 6, 7, 7, 7, 4, 7, 4, 6, 5, 7, 7, 7,…
## $ BEIDQ11 <dbl> 7, 4, 5, 7, 7, 7, 4, 1, 5, 7, 4, 7, 4, 4, 1, 4, 1, 7, 7, 2,…
## $ EID_score <dbl> 6.272727, 6.000000, 6.636364, 5.454545, 5.181818, 6.545455,…
## $ BEID_score <dbl> 6.363636, 6.363636, 5.000000, 5.727273, 5.454545, 6.000000,…
## $ EID_change <dbl> -0.09090909, -0.36363636, 1.63636364, -0.27272727, -0.27272…
## $ beid <dbl> 0.64751671, 0.80268740, -0.57111294, 0.15598431, 0.11026599…
## $ eid <dbl> 0.81637533, 0.58703930, 1.14215449, 0.04797683, 0.11320022,…
## $ change <dbl> 0.168858618, -0.215648104, 1.713267428, -0.108007478, 0.002…6.7: Multiple Imputation
library(mice)##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbindlibrary(mitml)## *** This is beta software. Please report any bugs!
## *** See the NEWS file for recent changes.# run MI
imp <- mice(data = df_binded_change, method = "norm", m = 100)##
## iter imp variable
## 1 1 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 2 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 3 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 4 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 5 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 6 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 7 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 8 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 9 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change
## 1 10 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 11 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 12 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 13 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 14 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 15 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 16 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid change*
## 1 17 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 18 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 19 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 20 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 21 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 22 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 23 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 24 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 25 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 26 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 27 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 28 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 29 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 30 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 31 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 32 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 33 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 34 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 35 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 36 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 37 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 38 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 39 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid change*
## 1 40 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 41 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 42 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 43 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 44 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 45 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 46 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 47 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 48 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 49 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 50 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 51 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 52 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 53 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 54 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change
## 1 55 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 56 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 57 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 58 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 59 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 60 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 61 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 62 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 63 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 64 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 65 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 66 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 67 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 68 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid change*
## 1 69 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 70 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 71 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 72 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 73 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 74 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 75 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 76 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 77 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 78 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 79 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 80 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 81 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 82 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid change*
## 1 83 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 84 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 85 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 86 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 87 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 88 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 89 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 90 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 91 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 92 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 93 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 94 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 95 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 96 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 97 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 98 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 99 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 1 100 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 1 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 2 2 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 2 3 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 2 4 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 5 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 2 6 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid change*
## 2 7 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 8 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 9 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 10 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid change*
## 2 11 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 2 12 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change
## 2 13 EIDQ1 EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 14 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 2 15 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 2 16 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 2 17 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 18 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 19 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid change
## 2 20 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 2 21 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 2 22 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 23 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 2 24 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 25 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 26 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 27 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change
## 2 28 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 2 29 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 30 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change
## 2 31 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6 EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 2 32 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 33 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 34 EIDQ1 EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 35 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 36 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 37 EIDQ1* EIDQ2 EIDQ4 EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 38 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 2 39 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 2 40 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 41 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 42 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 2 43 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid change*
## 2 44 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 45 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 2 46 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 47 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 48 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 49 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 2 50 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 51 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 2 52 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 2 53 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 54 EIDQ1 EIDQ2 EIDQ4 EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 2 55 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 2 56 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid change*
## 2 57 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 2 58 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 2 59 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 60 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 61 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 2 62 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 2 63 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 64 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 65 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 66 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 67 EIDQ1 EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 2 68 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 69 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 2 70 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 71 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 72 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid change*
## 2 73 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change
## 2 74 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 2 75 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 2 76 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid change*
## 2 77 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 78 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 79 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 2 80 EIDQ1 EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 2 81 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 82 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change
## 2 83 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 2 84 EIDQ1 EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 85 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid change*
## 2 86 EIDQ1 EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 87 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 88 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 89 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 90 EIDQ1* EIDQ2 EIDQ4 EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 91 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 92 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 93 EIDQ1* EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 2 94 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change
## 2 95 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 96 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 2 97 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 2 98 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 2 99 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 2 100 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 1 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 3 2 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 3 3 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 3 4 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 5 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 3 6 EIDQ1* EIDQ2 EIDQ4 EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 7 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 3 8 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 9 EIDQ1 EIDQ2* EIDQ4 EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 10 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3 eid* change
## 3 11 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 12 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid change*
## 3 13 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 3 14 EIDQ1 EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 3 15 EIDQ1* EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 3 16 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 17 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 18 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 3 19 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 20 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 3 21 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 3 22 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 23 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 3 24 EIDQ1* EIDQ2 EIDQ4 EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid change*
## 3 25 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 3 26 EIDQ1* EIDQ2 EIDQ4 EIDQ5 EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 3 27 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 3 28 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 29 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 3 30 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 3 31 EIDQ1 EIDQ2* EIDQ4 EIDQ5 EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 32 EIDQ1 EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 3 33 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 3 34 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 3 35 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 36 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 37 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 38 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 3 39 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 3 40 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 3 41 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 3 42 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 3 43 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 44 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 45 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 3 46 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 3 47 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11* BEIDQ3 eid change*
## 3 48 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 49 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid change*
## 3 50 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 3 51 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 3 52 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 3 53 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 3 54 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 3 55 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 56 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 57 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid change*
## 3 58 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 59 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 3 60 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 61 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 3 62 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 63 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 64 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 3 65 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 3 66 EIDQ1* EIDQ2 EIDQ4 EIDQ5 EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid change*
## 3 67 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 68 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 3 69 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid change*
## 3 70 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 71 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 3 72 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 3 73 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid change*
## 3 74 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 3 75 EIDQ1* EIDQ2 EIDQ4 EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 76 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 3 77 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 78 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 79 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6* EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid change*
## 3 80 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 81 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid change*
## 3 82 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 3 83 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 3 84 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 85 EIDQ1 EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 3 86 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 3 87 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 3 88 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 89 EIDQ1 EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 3 90 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 3 91 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 3 92 EIDQ1 EIDQ2* EIDQ4 EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 3 93 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 94 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 95 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 3 96 EIDQ1 EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 3 97 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 98 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 99 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 3 100 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 1 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid change*
## 4 2 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 3 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 4 4 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 4 5 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 6 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 7 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change
## 4 8 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 4 9 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 4 10 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 4 11 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid change*
## 4 12 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 4 13 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 4 14 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 4 15 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 4 16 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 17 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 4 18 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 19 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 4 20 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid change*
## 4 21 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid change*
## 4 22 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 23 EIDQ1 EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 24 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change
## 4 25 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 4 26 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 4 27 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 28 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 4 29 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid change*
## 4 30 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 4 31 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 32 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 4 33 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 4 34 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid change*
## 4 35 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 36 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 37 EIDQ1 EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 38 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 39 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 40 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 4 41 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid change*
## 4 42 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 43 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 4 44 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 45 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 4 46 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 47 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 4 48 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 49 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 4 50 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 51 EIDQ1* EIDQ2 EIDQ4 EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 4 52 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 4 53 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 54 EIDQ1* EIDQ2 EIDQ4 EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 55 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 4 56 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 4 57 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6 EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 4 58 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change
## 4 59 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 4 60 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid change*
## 4 61 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 4 62 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 63 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 4 64 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 4 65 EIDQ1 EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid change*
## 4 66 EIDQ1* EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 67 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid change*
## 4 68 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid change*
## 4 69 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 70 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 4 71 EIDQ1 EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 4 72 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 73 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 4 74 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 4 75 EIDQ1 EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid change*
## 4 76 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 4 77 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 78 EIDQ1* EIDQ2 EIDQ4 EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 4 79 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 4 80 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid change*
## 4 81 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 82 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 4 83 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 4 84 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8* EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 4 85 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 4 86 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 4 87 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3 eid* change
## 4 88 EIDQ1 EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 4 89 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 90 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 4 91 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 92 EIDQ1* EIDQ2 EIDQ4 EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 93 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 4 94 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 4 95 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 96 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 4 97 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 4 98 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 4 99 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 4 100 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 5 1 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 5 2 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 5 3 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 4 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 5 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 6 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 5 7 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 5 8 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 5 9 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 5 10 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 5 11 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6* EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 5 12 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 5 13 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 14 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 5 15 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 5 16 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 17 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 5 18 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 5 19 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 5 20 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 5 21 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 22 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 23 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid change*
## 5 24 EIDQ1 EIDQ2 EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 25 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 5 26 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid change*
## 5 27 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid change*
## 5 28 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 29 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 30 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 5 31 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 5 32 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 5 33 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change
## 5 34 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 35 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid change*
## 5 36 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 5 37 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid change*
## 5 38 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 5 39 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 40 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 41 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 5 42 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 43 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 44 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 45 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 5 46 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 5 47 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 48 EIDQ1 EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid change*
## 5 49 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 50 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid change*
## 5 51 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 52 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 5 53 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 5 54 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 55 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 56 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 57 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 5 58 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 5 59 EIDQ1* EIDQ2 EIDQ4 EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 5 60 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 61 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 5 62 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 63 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 5 64 EIDQ1 EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 65 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 5 66 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10* EIDQ11 BEIDQ3 eid* change*
## 5 67 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 5 68 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 5 69 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 5 70 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 71 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 5 72 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 73 EIDQ1* EIDQ2* EIDQ4 EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 5 74 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid change*
## 5 75 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 5 76 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 5 77 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 5 78 EIDQ1 EIDQ2 EIDQ4 EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 79 EIDQ1* EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 5 80 EIDQ1* EIDQ2* EIDQ4 EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid change*
## 5 81 EIDQ1 EIDQ2 EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 82 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 5 83 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 5 84 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 85 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 5 86 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 5 87 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10 EIDQ11* BEIDQ3* eid change*
## 5 88 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10 EIDQ11 BEIDQ3* eid* change*
## 5 89 EIDQ1 EIDQ2 EIDQ4 EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change*
## 5 90 EIDQ1* EIDQ2* EIDQ4* EIDQ5 EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 91 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6* EIDQ8 EIDQ10* EIDQ11 BEIDQ3* eid* change*
## 5 92 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8* EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 93 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 5 94 EIDQ1* EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 95 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3 eid* change*
## 5 96 EIDQ1* EIDQ2* EIDQ4* EIDQ5* EIDQ6 EIDQ8* EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 5 97 EIDQ1* EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11* BEIDQ3 eid* change*
## 5 98 EIDQ1 EIDQ2 EIDQ4 EIDQ5* EIDQ6* EIDQ8 EIDQ10* EIDQ11* BEIDQ3* eid* change*
## 5 99 EIDQ1* EIDQ2 EIDQ4 EIDQ5 EIDQ6 EIDQ8 EIDQ10 EIDQ11 BEIDQ3 eid* change*
## 5 100 EIDQ1 EIDQ2 EIDQ4* EIDQ5 EIDQ6 EIDQ8* EIDQ10 EIDQ11* BEIDQ3* eid* change## Warning: Number of logged events: 8516# create a list of completed data sets
implist <- mids2mitml.list(imp)
# fit the ANOVA model to the imputed data sets
fit2 <- with(implist, aov(change ~ condition))
# Assess fit using pooled parameter estimates
Estimates <- testEstimates(fit2)6.8: Fitting the Model Without Imputation for the Model Check and Plot
6.8.1: Renaming for the Plot
df_binded_change_2 <- df_binded_change |>
rename("Change" = "change") |>
mutate(Condition = case_when(
str_detect(condition, "INFO") ~ 'Informational',
str_detect(condition, "CONTROL") ~ 'Control',
str_detect(condition, "THREAT") ~ 'Threat-Based'))6.8.2: Fitting the Model
model_lm_change <- lm(Change ~ Condition, data=df_binded_change_2)
summary(model_lm_change)##
## Call:
## lm(formula = Change ~ Condition, data = df_binded_change_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.11142 -0.27722 -0.01077 0.27941 1.65766
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01774 0.11505 -0.154 0.878
## ConditionInformational -0.09482 0.16271 -0.583 0.562
## ConditionThreat-Based 0.07334 0.16271 0.451 0.654
##
## Residual standard error: 0.5145 on 57 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.01849, Adjusted R-squared: -0.01595
## F-statistic: 0.537 on 2 and 57 DF, p-value: 0.5874Visual Inspection of the Model (without imputation)
check_model(model_lm_change)
6.9: Descriptives and Data Visualization
6.9.1: Creating a Summary
summary <- df_binded_change |>
group_by(condition) |>
summarise(
n = n(),
eid_mean = mean(eid, na.rm = TRUE),
eid_sd = sd(eid, na.rm = TRUE),
beid_mean = mean(beid, na.rm = TRUE),
beid_sd = sd(beid, na.rm = TRUE))
summary| condition | n | eid_mean | eid_sd | beid_mean | beid_sd |
|---|---|---|---|---|---|
| CONTROL | 25 | 0.2230186 | 0.6600302 | 0.1081528 | 0.5851068 |
| INFO | 23 | -0.1992025 | 1.0241788 | -0.0767696 | 0.6663329 |
| THREAT | 27 | 0.1345312 | 0.9961528 | -0.0595342 | 0.7151255 |
6.10: Tidying the Data for the Plot
6.10.1: Pivot Data
df_plot <- df_binded_change %>%
pivot_longer(cols = c(eid, beid), names_to = "eid_measurement", values_to = "eid_mean")
glimpse(df_plot)## Rows: 150
## Columns: 30
## $ EIDQ1 <dbl> 7, 7, 7, 7, 6, 6, 5, 5, 4, 4, 5, 5, 4, 4, 6, 6, 5, 5, …
## $ EIDQ2 <dbl> 7, 7, 6, 6, 6, 6, 7, 7, 3, 3, 5, 5, 6, 6, 4, 4, 5, 5, …
## $ EIDQ3 <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, …
## $ EIDQ4 <dbl> 5, 5, 6, 6, 7, 7, 5, 5, 6, 6, 7, 7, 6, 6, 5, 5, 5, 5, …
## $ EIDQ5 <dbl> 5, 5, 5, 5, 6, 6, 4, 4, 2, 2, 6, 6, 4, 4, 2, 2, NA, NA…
## $ EIDQ6 <dbl> 7, 7, 5, 5, 7, 7, 6, 6, 5, 5, 7, 7, NA, NA, 6, 6, 5, 5…
## $ EIDQ7 <dbl> 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, …
## $ EIDQ8 <dbl> 7, 7, 7, 7, 7, 7, 4, 4, 7, 7, 7, 7, 2, 2, 6, 6, 6, 6, …
## $ EIDQ9 <dbl> 7, 7, 7, 7, 7, 7, 5, 5, 7, 7, 7, 7, 7, 7, 5, 5, 5, 5, …
## $ EIDQ10 <dbl> 4, 4, 5, 5, 6, 6, 3, 3, 3, 3, 7, 7, 7, 7, 4, 4, 6, 6, …
## $ EIDQ11 <dbl> 7, 7, 5, 5, 7, 7, 7, 7, 6, 6, 7, 7, 5, 5, 1, 1, 5, 5, …
## $ condition <chr> "CONTROL", "CONTROL", "INFO", "INFO", "THREAT", "THREA…
## $ id <chr> "04MayH", "04MayH", "08BriK", "08BriK", "04NadM", "04N…
## $ BEIDQ1 <dbl> 6, 6, 7, 7, 5, 5, 5, 5, 5, 5, 5, 5, 3, 3, 5, 5, 4, 4, …
## $ BEIDQ2 <dbl> 6, 6, 7, 7, 5, 5, 7, 7, 2, 2, 4, 4, 6, 6, 4, 4, 6, 6, …
## $ BEIDQ3 <dbl> 7, 7, 7, 7, 5, 5, 7, 7, 7, 7, 6, 6, 7, 7, 6, 6, 6, 6, …
## $ BEIDQ4 <dbl> 7, 7, 7, 7, 5, 5, 6, 6, 7, 7, 6, 6, 5, 5, 4, 4, 5, 5, …
## $ BEIDQ5 <dbl> 6, 6, 5, 5, 5, 5, 3, 3, 3, 3, 5, 5, 3, 3, 2, 2, 3, 3, …
## $ BEIDQ6 <dbl> 7, 7, 6, 6, 5, 5, 7, 7, 4, 4, 5, 5, 7, 7, 6, 6, 5, 5, …
## $ BEIDQ7 <dbl> 6, 6, 7, 7, 5, 5, 7, 7, 6, 6, 7, 7, 5, 5, 6, 6, 7, 7, …
## $ BEIDQ8 <dbl> 7, 7, 7, 7, 5, 5, 4, 4, 7, 7, 7, 7, 4, 4, 7, 7, 7, 7, …
## $ BEIDQ9 <dbl> 7, 7, 7, 7, 5, 5, 6, 6, 7, 7, 7, 7, 5, 5, 6, 6, 5, 5, …
## $ BEIDQ10 <dbl> 4, 4, 6, 6, 5, 5, 4, 4, 5, 5, 7, 7, 7, 7, 4, 4, 6, 6, …
## $ BEIDQ11 <dbl> 7, 7, 4, 4, 5, 5, 7, 7, 7, 7, 7, 7, 4, 4, 1, 1, 5, 5, …
## $ EID_score <dbl> 6.272727, 6.272727, 6.000000, 6.000000, 6.636364, 6.63…
## $ BEID_score <dbl> 6.363636, 6.363636, 6.363636, 6.363636, 5.000000, 5.00…
## $ EID_change <dbl> -0.09090909, -0.09090909, -0.36363636, -0.36363636, 1.…
## $ change <dbl> 0.168858618, 0.168858618, -0.215648104, -0.215648104, …
## $ eid_measurement <chr> "eid", "beid", "eid", "beid", "eid", "beid", "eid", "b…
## $ eid_mean <dbl> 0.81637533, 0.64751671, 0.58703930, 0.80268740, 1.1421…6.11: Boxplot of Environmental Identity by Measurement and Condition (greyscale)
vis_boxplot_grey <- ggplot(df_plot, aes(x=condition, y=eid_mean, fill=eid_measurement)) +
geom_boxplot() +
scale_x_discrete(labels=c("Control","Informational","Threat-Based")) +
theme_minimal() +
theme(
plot.title=element_text(hjust=.7, vjust=1.4, size=13, lineheight=0.9),
axis.text = element_text(face="bold"),
axis.title = element_text(size=11.5),
panel.grid.major.x = element_blank(),
axis.text.x = element_text(vjust=+6),
axis.title.x = element_text(vjust=+4),
axis.title.y = element_text(vjust=+2)) +
labs(title = "", x = "Condition", y = "Environmental Identity", fill="Measurement") +
#scale_color_discrete(labels = c("beid" = "Baseline", "eid" = "Post-Test")) +
scale_fill_grey(labels = c("beid" = "Baseline", "eid" = "Post-Test"),start=0.35)
vis_boxplot_grey## Warning: Removed 15 rows containing non-finite values (`stat_boxplot()`).
Saving the Graph
ggsave("figures/eid_means_boxplot_greyscale.png", plot=vis_boxplot_grey, units='in', width=8, height=6, dpi=600)## Warning: Removed 15 rows containing non-finite values (`stat_boxplot()`).6.12 PLotting Change in Environmental Identity by Condition
change_plot <- ggpredict(model_lm_change, terms = c("Condition")) |> plot(use.theme = T) +
theme_minimal() +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank())
change_plot
Saving the Graph
ggsave("figures/eid_change.png", plot=change_plot, units='in', width=8, height=6, dpi=600)Section 7: Environmental Identity (Royal Roads University)
7.1: Removing Unnecessary Columns and Renaming thr Keepers
7.1.1: First Chunk (Likert responses at both measurement times)
chunk_one <- df_rru %>%
rename(
EIDQ1 = '6.2_1...103',
EIDQ2 = '6.2_2...104',
EIDQ3 = '6.2_3...105',
EIDQ4 = '6.2_4...106',
EIDQ5 = '6.2_5...107',
EIDQ6 = '6.2_6...108',
EIDQ7 = '6.2_7...109',
EIDQ8 = '6.2_8...110',
EIDQ9 = '6.2_9...111',
EIDQ10 = '6.2_10...112',
EIDQ11 = '6.2_11...113',
BEIDQ1 = '6.2_1...28',
BEIDQ2 = '6.2_2...29',
BEIDQ3 = '6.2_3...30',
BEIDQ4 = '6.2_4...31',
BEIDQ5 = '6.2_5...32',
BEIDQ6 = '6.2_6...33',
BEIDQ7 = '6.2_7...34',
BEIDQ8 = '6.2_8...35',
BEIDQ9 = '6.2_9...36',
BEIDQ10 = '6.2_10...37',
BEIDQ11 = '6.2_11...38',
condition = 'FL_18_DO',
id = '3.2'
) %>%
select(EIDQ1:EIDQ11,BEIDQ1:BEIDQ11,condition,id) |>
mutate_at(c('EIDQ1','EIDQ2','EIDQ3','EIDQ4','EIDQ5','EIDQ6','EIDQ7','EIDQ8','EIDQ9','EIDQ10','EIDQ11','BEIDQ1','BEIDQ2','BEIDQ3','BEIDQ4','BEIDQ5','BEIDQ6','BEIDQ7','BEIDQ8','BEIDQ9','BEIDQ10','BEIDQ11'), as.numeric) %>%
drop_na(EIDQ1)7.1.2: Second Chunk (Slider responses at Post-Test)
chunk_two <- df_rru %>%
rename(
EIDQ1 = 'Q143_1',
EIDQ2 = 'Q143_2',
EIDQ3 = 'Q143_3',
EIDQ4 = 'Q143_4',
EIDQ5 = 'Q143_5',
EIDQ6 = 'Q143_6',
EIDQ7 = 'Q143_7',
EIDQ8 = 'Q143_8',
EIDQ9 = 'Q143_9',
EIDQ10 = 'Q143_10',
EIDQ11 = 'Q143_11',
BEIDQ1 = '6.2_1...28',
BEIDQ2 = '6.2_2...29',
BEIDQ3 = '6.2_3...30',
BEIDQ4 = '6.2_4...31',
BEIDQ5 = '6.2_5...32',
BEIDQ6 = '6.2_6...33',
BEIDQ7 = '6.2_7...34',
BEIDQ8 = '6.2_8...35',
BEIDQ9 = '6.2_9...36',
BEIDQ10 = '6.2_10...37',
BEIDQ11 = '6.2_11...38',
condition = 'FL_18_DO',
id = '3.2'
) |>
select(EIDQ1:EIDQ11,BEIDQ1:BEIDQ11,condition,id) |>
mutate_at(c('EIDQ1','EIDQ2','EIDQ3','EIDQ4','EIDQ5','EIDQ6','EIDQ7','EIDQ8','EIDQ9','EIDQ10','EIDQ11','BEIDQ1','BEIDQ2','BEIDQ3','BEIDQ4','BEIDQ5','BEIDQ6','BEIDQ7','BEIDQ8','BEIDQ9','BEIDQ10','BEIDQ11'), as.numeric) %>%
drop_na(EIDQ1)7.2: Combine chunks
df_rru_eid <- full_join(chunk_one,chunk_two)## Joining with `by = join_by(EIDQ1, EIDQ2, EIDQ3, EIDQ4, EIDQ5, EIDQ6, EIDQ7,
## EIDQ8, EIDQ9, EIDQ10, EIDQ11, BEIDQ1, BEIDQ2, BEIDQ3, BEIDQ4, BEIDQ5, BEIDQ6,
## BEIDQ7, BEIDQ8, BEIDQ9, BEIDQ10, BEIDQ11, condition, id)`7.3: Round to whole numbers
EIDQS <- c('EIDQ1', 'EIDQ2', 'EIDQ3', 'EIDQ4', 'EIDQ5', 'EIDQ6', 'EIDQ7', 'EIDQ8', 'EIDQ9', 'EIDQ10', 'EIDQ11')
BEIDQS <- c('BEIDQ1', 'BEIDQ2', 'BEIDQ3', 'BEIDQ4', 'BEIDQ5', 'BEIDQ6', 'BEIDQ7', 'BEIDQ8', 'BEIDQ9', 'BEIDQ10', 'BEIDQ11')
df_rru_eid <- df_rru_eid %>%
#Round to whole numbers
mutate_at(vars(all_of(EIDQS)), ~ round(., digits = 0)) %>%
#EID scores and changes for descriptive statistics
mutate(EID_score = rowMeans(select(., all_of(EIDQS)), na.rm = TRUE)) %>%
mutate(BEID_score = rowMeans(select(., all_of(BEIDQS)), na.rm = TRUE))7.4: Pivot
df_rru_eid <- df_rru_eid %>%
pivot_longer(cols = c(EID_score, BEID_score), names_to = "eid_measurement", values_to = "eid_mean")7.5: Descriptives and Data Vsiaulization
7.5.1: EID by Condition Desctiptives
library(DescTools)
df_rru_summary <- df_rru_eid |>
group_by(condition, eid_measurement) |>
summarise(
n = n(),
EID_mean = mean(eid_mean, na.rm = TRUE),
EID_sd = sd(eid_mean, na.rm = TRUE),
EID_median = median(eid_mean, na.rm = TRUE),
EID_mode = Mode(eid_mean, na.rm = TRUE))## `summarise()` has grouped output by 'condition'. You can override using the
## `.groups` argument.df_rru_summary| condition | eid_measurement | n | EID_mean | EID_sd | EID_median | EID_mode |
|---|---|---|---|---|---|---|
| CONTROL | BEID_score | 3 | 6.333333 | 0.1892424 | 6.272727 | NA |
| CONTROL | EID_score | 3 | 6.181818 | 0.1574592 | 6.272727 | 6.272727 |
| INFO | BEID_score | 3 | 6.393939 | 0.2287829 | 6.363636 | NA |
| INFO | EID_score | 3 | 6.454546 | 0.1818182 | 6.454546 | NA |
| THREAT | BEID_score | 5 | 6.109091 | 0.7111857 | 6.000000 | 6.818182 |
| THREAT | EID_score | 5 | 6.109091 | 0.7935692 | 6.090909 | NA |
7.5.2: Environmental Identity Boxplot (colour)
library("viridis")
library("viridisLite")
vis_rru <- ggplot(df_rru_eid, aes(x=condition, y=eid_mean, fill=eid_measurement)) +
geom_boxplot() +
scale_x_discrete(labels=c("Control","Informational","Threat-Based")) +
theme_minimal() +
theme(
plot.title=element_text(hjust=.7, vjust=1.4, size=13, lineheight=0.9),
axis.text = element_text(face="bold"),
axis.title = element_text(size=11.5),
panel.grid.major.x = element_blank(),
axis.text.x = element_text(vjust=+6),
axis.title.x = element_text(vjust=+4),
axis.title.y = element_text(vjust=+2)) +
labs(title = "Environmental Identity by News Article Condition and Measurement Time", x = "Condition", y = "Environmental Identity", fill="Measurement") +
#scale_color_discrete(labels = c("beid" = "Baseline", "eid" = "Post-Test")) +
scale_fill_viridis(discrete = TRUE, labels = c("BEID_score" = "Baseline", "EID_score" = "Post-Test"), begin = 0.25)
vis_rru
Saving the Graph
ggsave("figures/eid_rru.png", plot=vis_rru, units='in', width=8, height=6, dpi=600)7.5.3: Environmental Identity Boxplot (colour)
library("viridis")
library("viridisLite")
vis_rru_grey <- ggplot(df_rru_eid, aes(x=condition, y=eid_mean, fill=eid_measurement)) +
geom_boxplot() +
scale_x_discrete(labels=c("Control","Informational","Threat-Based")) +
theme_minimal() +
theme(
plot.title=element_text(hjust=.7, vjust=1.4, size=13, lineheight=0.9),
axis.text = element_text(face="bold"),
axis.title = element_text(size=11.5),
panel.grid.major.x = element_blank(),
axis.text.x = element_text(vjust=+6),
axis.title.x = element_text(vjust=+4),
axis.title.y = element_text(vjust=+2)) +
labs(title = "", x = "Condition", y = "Environmental Identity", fill="Measurement") +
#scale_color_discrete(labels = c("beid" = "Baseline", "eid" = "Post-Test")) +
scale_fill_grey(labels = c("BEID_score" = "Baseline", "EID_score" = "Post-Test"),start=0.35)
vis_rru_grey
Saving the Graph
ggsave("figures/eid_rru_greyscale.png", plot=vis_rru_grey, units='in', width=8, height=6, dpi=600)The End