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PROJECT PAGE for: How to activate threat perceptions in behavior research: A simple technique for inducing health and resource scarcity threats #660

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ben-domingue opened this issue Oct 31, 2024 · 12 comments
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@ben-domingue
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https://osf.io/grafm/
https://link.springer.com/article/10.3758/s13428-024-02481-6#availability-of-data-and-materials

panas items, for example, might be good (even for RCTs @joshgilbert1994 ?)

@joshgilbert1994
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joshgilbert1994 commented Oct 31, 2024 via email

@saviranadela saviranadela self-assigned this Nov 17, 2024
@saviranadela
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[WIP: this might require some additional work]

processed data:
ThreatPerceptions_Isler_2024.csv

code:

library(readxl)
library(dplyr)
library(tidyr)

# ---------- EXPERIMENT 1 ----------

df1 <- read_xlsx("Experiment 1 Data.xlsx")

df1 <- df1 %>%
  select(ID, 
         interested:repulsed, 
         P_Risk_You, 
         P_Risk_Average, 
         P_Severity_You, 
         P_Severity_Average, 
         CRT1,
         P_intuition, 
         P_scarcity, 
         BiG, 
         Conservatism_economic, 
         Conservatism_social,
         SES_ladder)

colnames(df1) <- tolower(colnames(df1))

df1 <- df1 %>%
  pivot_longer(-id,
               names_to = "item",
               values_to = "resp")

df1$experiment <- "1"

# ---------- EXPERIMENT 2 ----------

df2 <- read_xlsx("Experiment 2 Data.xlsx")

df2 <- df2 %>%
  select(ID, 
         P_Risk_Health,
         P_Risk_Scarcity,
         interested:repulsed,
         CRT1,
         BiG, 
         Conservatism_economic, 
         Conservatism_social,
         SES_ladder)

colnames(df2) <- tolower(colnames(df2))

df2 <- df2 %>%
  pivot_longer(-id,
               names_to = "item",
               values_to = "resp")

df2$experiment <- "2"

# ---------- EXPERIMENT 3 ----------

df3 <- read_xlsx("Experiment 3 Data.xlsx")

df3 <- df3 %>%
  select(ID, 
         P_Risk_Health,
         P_Risk_Scarcity,
         P_Food_Myself:P_Dying_Society,
         interested:repulsed,
         BiG, 
         Conservatism_economic, 
         Conservatism_social,
         SES_ladder) %>%
  rename("inspired" = "inspiredâBelowarewordsth")

colnames(df3) <- tolower(colnames(df3))

df3 <- df3 %>%
  pivot_longer(-id,
               names_to = "item",
               values_to = "resp")

df3$experiment <- "3"

# ---------- EXPERIMENT 4 ----------

df4 <- read_xlsx("Experiment 4 Data.xlsx")

df4 <- df4 %>%
  select(ID, 
         P_Risk_Health,
         P_Risk_Scarcity,
         P_Food_Myself:P_Dying_Myself,
         CRT1,
         CRT2,
         CRT3,
         interested:repulsed,
         BiG, 
         Conservatism_economic, 
         Conservatism_social,
         SES_ladder)

colnames(df4) <- tolower(colnames(df4))

df4 <- df4 %>%
  pivot_longer(-id,
               names_to = "item",
               values_to = "resp")

df4$experiment <- "4"


combined <- bind_rows(df1, df2, df3, df4)

write.csv(combined, "ThreatPerceptions_Isler_2024.csv", row.names=FALSE)

@saviranadela
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i need some feedback here.

what i'm currently doing:

  • selecting all relevant items responses data
  • binding rows for data from experiment 1-4 with a new "experiment" column added to differentiate each experiment

what i'm unsure of:

  • since participants differ across experiments, instead of adding a new "experiment" column, I was also considering appending a suffix (e.g., "_[experiment #]") to the IDs, as ID 1 from Experiment 1 and ID 1 from Experiment 2 refer to different individuals.
  • there are columns recording the time taken by participants to respond to each topic group, rather than each individual item. i’ve currently left this out.
  • how should the data split be structured? should we divide it into categories like PANAS, CRT, SES ladder (which has only one item), manipulation checks, etc.?
  • variables s1:s4 are in text format but also have corresponding count data in another column. i’m uncertain whether we should keep or exclude this information.

sorry that's quite a lot of questions :)) please let me know your feedback or if i'm on the right track

@ben-domingue @KingArthur0205

@ben-domingue
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hi! some notes:

  • since participants differ across experiments, instead of adding a new "experiment" column, I was also considering appending a suffix (e.g., "_[experiment #]") to the IDs, as ID 1 from Experiment 1 and ID 1 from Experiment 2 refer to different individuals. SOUNDS REASONABLE (BOTH THE APPENDIX TO THE ID AND THE NEW COLUMN)
  • LEAVE OUT TOPIC GROUP TIME, GOOD CALL

@ben-domingue
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  • s1: s4: there might be something we can do with this but i also think it is fine to leave out (as i can't readily see what it would be).
  • i think just panas and crt would be fine. the others seems out-of-scope.

@saviranadela
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saviranadela commented Nov 20, 2024

updated file:
ThreatPerceptions_Isler_2024_CRT.csv
ThreatPerceptions_Isler_2024_PANAS.csv

code:

library(readxl)
library(dplyr)
library(tidyr)

# ---------- EXPERIMENT 1 ----------

df1 <- read_xlsx("Experiment 1 Data.xlsx")

df1 <- df1 %>%
  select(ID, 
         interested:repulsed,
         CRT1)

colnames(df1) <- tolower(colnames(df1))

df1 <- df1 %>%
  pivot_longer(-id,
               names_to = "item",
               values_to = "resp")

df1$experiment <- "1"
df1$id <- paste0(df1$id, "_1")

# ---------- EXPERIMENT 2 ----------

df2 <- read_xlsx("Experiment 2 Data.xlsx")

df2 <- df2 %>%
  select(ID,
         interested:repulsed,
         CRT1)

colnames(df2) <- tolower(colnames(df2))

df2 <- df2 %>%
  pivot_longer(-id,
               names_to = "item",
               values_to = "resp")

df2$experiment <- "2"
df2$id <- paste0(df2$id, "_2")

# ---------- EXPERIMENT 3 ----------

df3 <- read_xlsx("Experiment 3 Data.xlsx")

df3 <- df3 %>%
  select(ID,
         interested:repulsed) %>%
  rename("inspired" = "inspiredâBelowarewordsth")

colnames(df3) <- tolower(colnames(df3))

df3 <- df3 %>%
  pivot_longer(-id,
               names_to = "item",
               values_to = "resp")

df3$experiment <- "3"
df3$id <- paste0(df3$id, "_3")

# ---------- EXPERIMENT 4 ----------

df4 <- read_xlsx("Experiment 4 Data.xlsx")

df4 <- df4 %>%
  select(ID,
         CRT1,
         CRT2,
         CRT3,
         interested:repulsed)

colnames(df4) <- tolower(colnames(df4))

df4 <- df4 %>%
  pivot_longer(-id,
               names_to = "item",
               values_to = "resp")

df4$experiment <- "4"
df4$id <- paste0(df4$id, "_4")


combined <- bind_rows(df1, df2, df3, df4)

df_panas <- combined %>%
  filter(!grepl("crt", item))

df_crt <- combined %>%
  filter(grepl("crt", item))

write.csv(df_panas, "ThreatPerceptions_Isler_2024_PANAS.csv", row.names=FALSE)
write.csv(df_crt, "ThreatPerceptions_Isler_2024_CRT.csv", row.names=FALSE)

please let me know if you have further input. thanks!

@saviranadela
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PR: #700

@ben-domingue
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hiya! one quesiton: are the crt & panas items mixed in this one csv? i think we should have them separate. i may have confused things earlier; i was trying to say i think we can have just those tables (but we do want them as separate tables i think)

@saviranadela
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ooops got it got it! updated!

@ben-domingue
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added. confirming it is also just 3 items in crt scale @saviranadela

@ben-domingue
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and ship PR?

@saviranadela
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yes it's only 3 items in crt

and yes i shiped and updated the PR here : #700
@ben-domingue

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