R Notebook

upload data

library(readxl)
game_report <- read_excel("specific-user-all-game-report.xlsx", 
    col_types = c("text", "numeric", "date", 
        "date", "numeric"))
print(game_report)

library(haven)
phase_3 <- read_dta("FDIC HMSRU Phase 3_Final_10_14.dta")
View(phase_3)

add packages

library(psych)
library(tidyverse)

create variable to capture games completed at pre/post/follow up

clean up game report data

game_report_2 <- game_report %>%
  select(.,
         RecipientEmail,
         game_id,
         complete_game_date)

# Transform data from long to wide
game_report_wide <- game_report_2 %>%
  pivot_wider(names_from = game_id, values_from = complete_game_date)

# View the wide data
print(game_report_wide)

library(dplyr)
game_report_3 <- game_report_wide %>% rename(game_1_date = 3, game_2_date = 4, game_3_date = 5, game_4_date = 15, game_5_date = 6, game_6_date = 7, game_7_date = 8, game_8_date = 9, game_9_date = 10, game_10_date = 11, game_11_date = 2, game_12_date = 12, game_13_date = 13, game_14_date = 14)

game_report_3 <- game_report_3 %>%
select(.,
       RecipientEmail,
       game_1_date,
       game_2_date,
       game_3_date,
       game_4_date,
       game_5_date,
       game_6_date,
       game_7_date,
       game_8_date,
       game_9_date,
       game_10_date,
       game_11_date,
       game_12_date,
       game_13_date,
       game_14_date)

merge the two datasets to capture a date for pre

phase_3_merge <- merge(phase_3, game_report_3, by = "RecipientEmail", all.x = TRUE)

phase_3_4 <- phase_3_merge %>%
  rowwise() %>%
  mutate(games_before_pretest = sum(c_across(starts_with("game_")) < RecordedDate, na.rm = TRUE))

post-test

phase_3_4 <- phase_3_4 %>%
  rowwise() %>%
  mutate(games_before_post_test = sum(c_across(starts_with("game_")) < RecordedDate_post, na.rm = TRUE))

follow up

phase_3_4 <- phase_3_4 %>%
  rowwise() %>%
  mutate(games_before_follow = sum(c_across(starts_with("game_")) < RecordedDate_follow, na.rm = TRUE))

Outcome: Self-Report Knowledge

phase_3_4$game_4_SelfTotalScore_Pre <- phase_3_4$financial_know_1_7 + phase_3_4$financial_know_1_8 + phase_3_4$financial_know_1_9


phase_3_4$game_4_SelfScaleScore_Pre <- ((phase_3_4$game_4_SelfTotalScore_Pre / 12)*4)

phase_3_4$game_10_SelfTotalScore_Pre <- phase_3_4$financial_know_1_4 + phase_3_4$financial_know_1_5 + phase_3_4$financial_know_1_6


phase_3_4$game_10_SelfScaleScore_Pre <- ((phase_3_4$game_10_SelfTotalScore_Pre / 12)*4)

phase_3_4$game_14_SelfTotalScore_Pre <- phase_3_4$financial_know_1_1 + phase_3_4$financial_know_1_2 + phase_3_4$financial_know_1_3


phase_3_4$game_14_SelfScaleScore_Pre <- ((phase_3_4$game_14_SelfTotalScore_Pre / 12)*4)

phase_3_4$game_4_SelfTotalScore_Post <- phase_3_4$financial_know_1_7_post + phase_3_4$financial_know_1_8_post + phase_3_4$financial_know_1_9_post


phase_3_4$game_4_SelfScaleScore_Post <- ((phase_3_4$game_4_SelfTotalScore_Post / 12)*4)

phase_3_4$game_10_SelfTotalScore_Post <- phase_3_4$financial_know_1_4 + phase_3_4$financial_know_1_5 + phase_3_4$financial_know_1_6


phase_3_4$game_10_SelfScaleScore_Post <- ((phase_3_4$game_10_SelfTotalScore_Post / 12)*4)

phase_3_4$game_14_SelfTotalScore_Post <- phase_3_4$financial_know_1_1 + phase_3_4$financial_know_1_2 + phase_3_4$financial_know_1_3


phase_3_4$game_14_SelfScaleScore_Post <- ((phase_3_4$game_14_SelfTotalScore_Post / 12)*4)

phase_3_4$game_4_SelfTotalScore_Follow <- phase_3_4$financial_know_1_7_follow + phase_3_4$financial_know_1_8_follow + phase_3_4$financial_know_1_9_follow


phase_3_4$game_4_SelfScaleScore_Follow <- ((phase_3_4$game_4_SelfTotalScore_Follow / 12)*4)

phase_3_4$game_10_SelfTotalScore_Follow <- phase_3_4$financial_know_1_4 + phase_3_4$financial_know_1_5 + phase_3_4$financial_know_1_6


phase_3_4$game_10_SelfScaleScore_Follow <- ((phase_3_4$game_10_SelfTotalScore_Follow / 12)*4)

phase_3_4$game_14_SelfTotalScore_Follow <- phase_3_4$financial_know_1_1 + phase_3_4$financial_know_1_2 + phase_3_4$financial_know_1_3


phase_3_4$game_14_SelfScaleScore_Follow <- ((phase_3_4$game_14_SelfTotalScore_Follow / 12)*4)

phase_3_all_outcomes<- phase_3_4

Outcome: Objective Knowledge

phase_3_all_outcomes$game_4_ObjTotalScore_Pre <- phase_3_all_outcomes$general_know_7 + phase_3_all_outcomes$general_know_8 + phase_3_all_outcomes$general_know_9


phase_3_all_outcomes$game_4_ObjScaleScore_Pre <- ((phase_3_all_outcomes$game_4_ObjTotalScore_Pre / 12)*4)

phase_3_all_outcomes$game_10_ObjTotalScore_Pre <- phase_3_all_outcomes$general_know_3 + phase_3_all_outcomes$general_know_4 + phase_3_all_outcomes$general_know_5 + phase_3_all_outcomes$general_know_6


phase_3_all_outcomes$game_10_ObjScaleScore_Pre <- ((phase_3_all_outcomes$game_10_ObjTotalScore_Pre / 12)*4)

phase_3_all_outcomes$game_14_ObjTotalScore_Pre <- phase_3_all_outcomes$general_know_1 + phase_3_all_outcomes$general_know_2


phase_3_all_outcomes$game_14_ObjScaleScore_Pre <- ((phase_3_all_outcomes$game_14_ObjTotalScore_Pre / 12)*4)

phase_3_all_outcomes$game_4_ObjTotalScore_Post <- phase_3_all_outcomes$general_know_7_post + phase_3_all_outcomes$general_know_8_post + phase_3_all_outcomes$general_know_9_post


phase_3_all_outcomes$game_4_ObjScaleScore_Post <- ((phase_3_all_outcomes$game_4_ObjTotalScore_Post / 12)*4)

phase_3_all_outcomes$game_10_ObjTotalScore_Post <- phase_3_all_outcomes$general_know_3_post + phase_3_all_outcomes$general_know_4_post + phase_3_all_outcomes$general_know_5_post + phase_3_all_outcomes$general_know_6_post


phase_3_all_outcomes$game_10_ObjScaleScore_Post <- ((phase_3_all_outcomes$game_10_ObjTotalScore_Post / 12)*4)

phase_3_all_outcomes$game_14_ObjTotalScore_Post <- phase_3_all_outcomes$general_know_1_post + phase_3_all_outcomes$general_know_2_post


phase_3_all_outcomes$game_14_ObjScaleScore_Post <- ((phase_3_all_outcomes$game_14_ObjTotalScore_Post / 12)*4)

phase_3_all_outcomes$game_4_ObjTotalScore_Follow <- phase_3_all_outcomes$general_know_7_follow + phase_3_all_outcomes$general_know_8_follow + phase_3_4$general_know_9_follow


phase_3_all_outcomes$game_4_ObjScaleScore_Follow <- ((phase_3_all_outcomes$game_4_ObjTotalScore_Follow / 12)*4)

phase_3_all_outcomes$game_10_ObjTotalScore_Follow <- phase_3_all_outcomes$general_know_3_follow + phase_3_all_outcomes$general_know_4_follow + phase_3_all_outcomes$general_know_5_follow + phase_3_all_outcomes$general_know_6_follow


phase_3_all_outcomes$game_10_ObjScaleScore_Follow <- ((phase_3_all_outcomes$game_10_ObjTotalScore_Follow / 12)*4)

phase_3_all_outcomes$game_14_ObjTotalScore_Follow <- phase_3_all_outcomes$general_know_1_follow + phase_3_all_outcomes$general_know_2_follow


phase_3_all_outcomes$game_14_ObjScaleScore_Follow <- ((phase_3_all_outcomes$game_14_ObjTotalScore_Follow / 12)*4)

library(Hmisc)

label(phase_3_4$game_4_SelfTotalScore_Pre) <- "Game 4 Self-Report Knowledge Total Score - Follow-up"

label(phase_3_4$game_10_SelfTotalScore_Pre) <- "Game 10 Self-Report Knowledge Total Score - Baseline"

label(phase_3_4$game_14_SelfTotalScore_Pre) <- "Game 14 Self-Report Knowledge Total Score - Baseline"

label(phase_3_4$game_4_SelfTotalScore_Post) <- "Game 4 Self-Report Knowledge Total Score - Post"

label(phase_3_4$game_10_SelfTotalScore_Post) <- "Game 10 Self-Report Knowledge Total Score - Post"

label(phase_3_4$game_14_SelfTotalScore_Post) <- "Game 14 Self-Report Knowledge Total Score - Post"

label(phase_3_4$game_4_SelfTotalScore_Follow) <- "Game 4 Self-Report Knowledge Total Score- Follow"

label(phase_3_4$game_10_SelfTotalScore_Follow) <- "Game 10 Self-Report Knowledge Total Score- Follow"

label(phase_3_4$game_14_SelfTotalScore_Follow) <- "Game 14 Self-Report Knowledge Total Score- Follow"


label(phase_3_all_outcomes$game_4_ObjTotalScore_Pre) <- "Game 4 Objective Knowledge Total Score - Baseline"

label(phase_3_all_outcomes$game_10_ObjTotalScore_Pre) <- "Game 10 Objective Knowledge Total Score - Baseline"

label(phase_3_all_outcomes$game_14_ObjTotalScore_Pre) <- "Game 14 Objective Knowledge Total Score - Baseline"

label(phase_3_all_outcomes$game_4_ObjTotalScore_Post) <- "Game 4 Objective Knowledge Total Score - Post"

label(phase_3_all_outcomes$game_10_ObjTotalScore_Post) <- "Game 10 Objective Knowledge Total Score - Post"

label(phase_3_all_outcomes$game_14_ObjTotalScore_Post) <- "Game 14 Objective Knowledge Total Score - Post"

label(phase_3_all_outcomes$game_4_ObjTotalScore_Follow) <- "Game 4 Objective Knowledge Total Score- Follow"

label(phase_3_all_outcomes$game_10_ObjTotalScore_Follow) <- "Game 10 Objective Knowledge Total Score- Follow"

label(phase_3_all_outcomes$game_14_ObjTotalScore_Follow) <- "Game 14 Objective Knowledge Total Score- Follow"

phase_3_4$SelfKnow_OverallScore_pre <- ((phase_3_4$game_4_SelfScaleScore_Pre + phase_3_4$game_10_SelfScaleScore_Pre + phase_3_4$game_14_SelfScaleScore_Pre) / 3)

phase_3_all_outcomes$Obj_OverallScore_pre <- ((phase_3_all_outcomes$game_4_ObjScaleScore_Pre + phase_3_all_outcomes$game_10_ObjScaleScore_Pre + phase_3_all_outcomes$game_14_ObjScaleScore_Pre))

describe(phase_3_all_outcomes$Obj_OverallScore_pre)

label(phase_3_4$SelfKnow_OverallScore_pre) <- "Self-Reported Knowledge Overall Score - Baseline"

label(phase_3_all_outcomes$Obj_OverallScore_pre) <- "Objective Knowledge Overall Score - Baseline"

phase_3_all_outcomes$SelfKnow_OverallScore_post <- ((phase_3_all_outcomes$game_4_SelfScaleScore_Post + phase_3_all_outcomes$game_10_SelfScaleScore_Post + phase_3_all_outcomes$game_14_SelfScaleScore_Post) / 3)

phase_3_all_outcomes$Obj_OverallScore_post <- ((phase_3_all_outcomes$game_4_ObjScaleScore_Post + phase_3_all_outcomes$game_10_ObjScaleScore_Post + phase_3_all_outcomes$game_14_ObjScaleScore_Post))

label(phase_3_4$SelfKnow_OverallScore_post) <- "Self-Reported Knowledge Overall Score - Post"

label(phase_3_all_outcomes$Obj_OverallScore_post) <- "Objective Knowledge Overall Score - Post"

phase_3_4$SelfKnow_OverallScore_follow <- (phase_3_4$game_4_SelfScaleScore_Follow + phase_3_4$game_10_SelfScaleScore_Follow + phase_3_4$game_14_SelfScaleScore_Follow) / 3

phase_3_all_outcomes$Obj_OverallScore_follow <- ((phase_3_all_outcomes$game_4_ObjScaleScore_Follow + phase_3_all_outcomes$game_10_ObjScaleScore_Follow + phase_3_all_outcomes$game_14_ObjScaleScore_Follow))

label(phase_3_4$SelfKnow_OverallScore_follow) <- "Self-Reported Knowledge Overall Score - Follow"

label(phase_3_all_outcomes$Obj_OverallScore_follow) <- "Objective Knowledge Overall Score - Follow"

phase_3_all_outcomes <- phase_3_all_outcomes %>% select(-'_merge')

phase_3_all_outcomes$SelfKnow_OverallScore_follow <- (phase_3_all_outcomes$game_4_SelfScaleScore_Follow + phase_3_all_outcomes$game_10_SelfScaleScore_Follow + phase_3_all_outcomes$game_14_SelfScaleScore_Follow) / 3

phase_3_all_outcomes$Obj_OverallScore_follow <- ((phase_3_all_outcomes$game_4_ObjScaleScore_Follow + phase_3_all_outcomes$game_10_ObjScaleScore_Follow + phase_3_all_outcomes$game_14_ObjScaleScore_Follow))

label(phase_3_all_outcomes$SelfKnow_OverallScore_follow) <- "Self-Reported Knowledge Overall Score - Follow"

label(phase_3_all_outcomes$Obj_OverallScore_follow) <- "Objective Knowledge Overall Score - Follow"

save as SPSS file and merge in STATA outcomes

write_sav(phase_3_all_outcomes, "phase_3_knowledge_outcome.sav")

library(haven)
phase_3_all_outcomes <- read_dta("phase_3_all_outcomes.dta")
print(phase_3_all_outcomes)

Clean variables

phase_3_all_outcomes$organizational_affiliation.fac <- factor(phase_3_4$Organization_Affiliation)

Make grouping variable dichotomous

phase_3_all_outcomes$participant_type.fac <- factor(phase_3_4$participant_type)

phase_3_all_outcomes <- phase_3_all_outcomes %>%
  mutate(participant_type_2 = case_when(
    participant_type.fac == "3" ~ "2",
    TRUE ~ participant_type.fac
  ))

label(phase_3_all_outcomes$participant_type_2) <- "Combined Comparison Groups"
levels(phase_3_all_outcomes$participant_type.fac) <- c("Treatment", "Non-Completer Comparison Group", "WinCo Comparison Group")
phase_3_all_outcomes$participant_type_2 <- factor(phase_3_all_outcomes$participant_type_2, 
                           levels = c("1", "2"),
                           labels = c("Treatment", "Control"))

age variable

phase_3_all_outcomes$age <- cut(phase_3_all_outcomes$demographics_1,
                    breaks = c(0, 24, 34, 44, 64, 1000),  # Define the breaks
                    labels = c("24 and younger", "25-34", "35-44", "45-64", "65+"),  # Define the labels
                    right = TRUE)  # If TRUE, the right endpoint is included

organization variable

phase_3_all_outcomes$organization <- ifelse(phase_3_all_outcomes$organizational_affiliation.fac != "None", "Yes", "No")

outcome 3: attitudes toward banks

phase_3_all_outcomes$att_toward_banks_pre <- (phase_3_all_outcomes$financial_att_1_1 + phase_3_all_outcomes$financial_att_1_2 + phase_3_all_outcomes$financial_att_1_3 + phase_3_all_outcomes$financial_att_1_4 + phase_3_all_outcomes$financial_att_1_5) / 5

label(phase_3_all_outcomes$att_toward_banks_pre) <- "Attitudes toward Banks- Baseline"

phase_3_all_outcomes$att_toward_banks_post <- (phase_3_all_outcomes$financial_att_1_1_post + phase_3_all_outcomes$financial_att_1_2_post + phase_3_all_outcomes$financial_att_1_3_post + phase_3_all_outcomes$financial_att_1_4_post + phase_3_all_outcomes$financial_att_1_5_post) / 5

label(phase_3_all_outcomes$att_toward_banks_post) <- "Attitudes toward Banks- Post"

phase_3_all_outcomes$att_toward_banks_follow <- (phase_3_all_outcomes$financial_att_1_1_follow + phase_3_all_outcomes$financial_att_1_2_follow + phase_3_all_outcomes$financial_att_1_3_follow + phase_3_all_outcomes$financial_att_1_4_follow + phase_3_all_outcomes$financial_att_1_5_follow) / 5

label(phase_3_all_outcomes$financial_att_1_1) <- "I find dealing with banks to be stressful: Pre"
label(phase_3_all_outcomes$financial_att_1_1_post) <- "I find dealing with banks to be stressful: Post"
label(phase_3_all_outcomes$financial_att_1_1_follow) <- "I find dealing with banks to be stressful: Follow"
label(phase_3_all_outcomes$financial_att_1_2) <- "Banks make it easy for their customers to learn about the financial services they provide: Pre"
label(phase_3_all_outcomes$financial_att_1_2_post) <- "Banks make it easy for their customers to learn about the financial services they provide: Post"
label(phase_3_all_outcomes$financial_att_1_2_follow) <- "Banks make it easy for their customers to learn about the financial services they provide: Follow-up"
label(phase_3_all_outcomes$financial_att_1_3) <- "Banks protect the privacy and personal information of their customers: Pre"
label(phase_3_all_outcomes$financial_att_1_3_post) <- "Banks protect the privacy and personal information of their customers: Post"
label(phase_3_all_outcomes$financial_att_1_3_follow) <- "Banks protect the privacy and personal information of their customers: Follow-up"
label(phase_3_all_outcomes$financial_att_1_4) <- "Banks generally have their customers’ best interests in mind: Pre"
label(phase_3_all_outcomes$financial_att_1_4_post) <- "Banks generally have their customers’ best interests in mind: Post"
label(phase_3_all_outcomes$financial_att_1_4_follow) <- "Banks generally have their customers’ best interests in mind: Folow-up"
label(phase_3_all_outcomes$financial_att_1_5) <- "Banks offer accounts and other products that fit my needs well: Pre"
label(phase_3_all_outcomes$financial_att_1_5_post) <- "Banks offer accounts and other products that fit my needs well: Post"
label(phase_3_all_outcomes$financial_att_1_5_follow) <- "Banks offer accounts and other products that fit my needs well: Follow-up"

label(phase_3_all_outcomes$att_toward_banks_follow) <- "Attitudes toward Banks- Follow"

explore the data

install.packages("summarytools")
library(summarytools)

# Create a detailed descriptive table for the factor variable
freq(phase_3_all_outcomes$race)

class(phase_3_all_outcomes$race)
library(haven)

phase_3_all_outcomes$race <- as_factor(phase_3_all_outcomes$race)

class(phase_3_all_outcomes$demographics_4)

phase_3_all_outcomes$demographics_4 <- as_factor(phase_3_all_outcomes$demographics_4)

class(phase_3_all_outcomes$demographics_5)

phase_3_all_outcomes$demographics_5 <- as_factor(phase_3_all_outcomes$demographics_5)

3 Groups Demographics

library(table1)
table1(~ organization + race + age + demographics_4 + demographics_5|participant_type.fac, data=phase_3_all_outcomes)

2 Groups Demographics

table1(~ organization + race + age + demographics_4 + demographics_5|participant_type_2, data=phase_3_all_outcomes)

Outcome 1: Subject Financial Knowledge

My understanding is that this captures “Does using HMSRU have any impact on participants’ financial knowledge?”

This covers Game 4 (Credit Reports and Scores): Q27, 28, 29, Game 10 (You Can Bank On It): Q24, 25, 26, and Game 14 (Your Spending and Saving Plan): Q21, 22, 23

Baseline Equivalence

The WWC calculates effect sizes with Hedge’s G for continuous variables

install.packages("effsize")

library(effsize)

library(boot) 

library(table1)

library(MatchIt)

# Calculate means and standard deviations for each group
summary_stats_self_know_overall_pre <- phase_3_all_outcomes %>%
  group_by(participant_type_2) %>%
  summarise(
    mean = mean(SelfKnow_OverallScore_pre, na.rm = TRUE),
    sd = sd(SelfKnow_OverallScore_pre, na.rm = TRUE),
    n = n()
  )

# View the summary statistics
print(summary_stats_self_know_overall_pre)

library(effectsize)

Attaching package: ‘effectsize’

The following object is masked from ‘package:psych’:

    phi

library(esvis)
hedg_g(phase_3_all_outcomes, SelfKnow_OverallScore_pre ~ participant_type_2) 

cohensD(SelfKnow_OverallScore_pre ~ participant_type_2, data = phase_3_all_outcomes)

# Calculate means and standard deviations for each group
summary_stats_self_know_overall_pre_org <- phase_3_all_outcomes %>%
  group_by(organization) %>%
  summarise(
    mean = mean(SelfKnow_OverallScore_pre, na.rm = TRUE),
    sd = sd(SelfKnow_OverallScore_pre, na.rm = TRUE),
    n = n()
  )

# View the summary statistics
print(summary_stats_self_know_overall_pre_org)

library(lsr)

cohensD(SelfKnow_OverallScore_pre ~ organization, data = phase_3_all_outcomes)

hedg_g(phase_3_all_outcomes, SelfKnow_OverallScore_pre ~ organization) 

# Calculate means and standard deviations for each group
summary_stats_self_know_overall_pre_race <- phase_3_all_outcomes %>%
  group_by(race) %>%
  summarise(
    mean = mean(SelfKnow_OverallScore_pre, na.rm = TRUE),
    sd = sd(SelfKnow_OverallScore_pre, na.rm = TRUE),
    n = n()
  )

# View the summary statistics
print(summary_stats_self_know_overall_pre_race)

# Calculate means and standard deviations for each group
summary_stats_obj_know_overall <- phase_3_all_outcomes %>%
  group_by(participant_type_2) %>%
  summarise(
    mean = mean(Obj_OverallScore_pre, na.rm = TRUE),
    sd = sd(Obj_OverallScore_pre, na.rm = TRUE),
    n = n()
  )

# View the summary statistics
print(summary_stats_obj_know_overall)

NA

library(lsr)

cohensD(Obj_OverallScore_pre ~ participant_type_2, data = phase_3_all_outcomes)

hedg_g(phase_3_all_outcomes, Obj_OverallScore_pre ~ participant_type_2) 

hedg_g(phase_3_all_outcomes, SelfKnow_OverallScore_pre ~ organization) 

# Calculate means and standard deviations for each group
summary_stats_att_overall <- phase_3_all_outcomes %>%
  group_by(participant_type_2) %>%
  summarise(
    mean = mean(att_toward_banks_pre, na.rm = TRUE),
    sd = sd(att_toward_banks_pre, na.rm = TRUE),
    n = n()
  )

# View the summary statistics
print(summary_stats_att_overall)

library(lsr)

cohensD(att_toward_banks_pre ~ participant_type_2, data = phase_3_all_outcomes)

[1] 0.1027145

hedg_g(phase_3_all_outcomes, att_toward_banks_pre ~ participant_type_2) 

# Calculate means and standard deviations for each group
summary_stats_fs_overall <- phase_3_all_outcomes %>%
  group_by(participant_type_2) %>%
  summarise(
    mean = mean(fs_pre, na.rm = TRUE),
    sd = sd(fs_pre, na.rm = TRUE),
    n = n()
  )

# View the summary statistics
print(summary_stats_fs_overall)

library(lsr)

cohensD(fs_pre ~ participant_type_2, data = phase_3_all_outcomes)

[1] 0.1002707

hedg_g(phase_3_all_outcomes, fs_pre ~ participant_type_2) 

# Calculate means and standard deviations for each group
summary_stats_fwb_overall <- phase_3_all_outcomes %>%
  group_by(participant_type_2) %>%
  summarise(
    mean = mean(fwb_pre, na.rm = TRUE),
    sd = sd(fwb_pre, na.rm = TRUE),
    n = n()
  )

# View the summary statistics
print(summary_stats_fwb_overall)

library(lsr)

cohensD(fwb_pre ~ participant_type_2, data = phase_3_all_outcomes)

[1] 0.1829208

hedg_g(phase_3_all_outcomes, fwb_pre ~ participant_type_2) 

#Pre/Post/Follow up Exploration

Outcome 1

table1(~ SelfKnow_OverallScore_pre + SelfKnow_OverallScore_post * SelfKnow_OverallScore_follow|participant_type.fac, data=phase_3_all_outcomes)

install.packages("ggplot2")  # Uncomment if you haven't installed ggplot2
install.packages("zoo")
library(zoo)
library(ggplot2)

# Create a data frame
self_know <- data.frame(
  Group = rep(c("Treatment", "Non-Completer Comparison Group", "WinCo Comparison Group"), each = 3),
  Timepoint = rep(c("1:Pre", "2:Post", "3: Follow-Up"), times = 3),
  Score = c(2.98, 3.15, 3.12, 2.96, NA, 2.98, 3.10, NA, 3.12)
)
self_know <- self_know %>%
  group_by(Group) %>%
  mutate(Score = na.approx(Score, na.rm = FALSE)) %>%
  ungroup()

# Create the plot
ggplot(self_know, aes(x = Timepoint, y = Score, group = Group, color = Group)) +
  geom_line() +
  geom_point(size = 4) +
  labs(title = "Pre, Post, and Follow-Up Results",
       x = "Timepoint",
       y = "Score") +
  theme_minimal()

Outcome 2

table1(~ Obj_OverallScore_pre + Obj_OverallScore_post + Obj_OverallScore_follow|participant_type.fac, data=phase_3_all_outcomes)

install.packages("ggplot2")  # Uncomment if you haven't installed ggplot2
install.packages("zoo")
library(zoo)
library(ggplot2)

# Create a data frame
obj_know <- data.frame(
  Group = rep(c("Treatment", "Non-Completer Comparison Group", "WinCo Comparison Group"), each = 3),
  Timepoint = rep(c("1: Pre", "2: Post", "3: Follow-Up"), times = 3),
  Score = c(7.21, 7.22, 7.18, 7.20, NA, 7.20, 7.23, NA, 7.21)
)
obj_know <- obj_know %>%
  group_by(Group) %>%
  mutate(Score = na.approx(Score, na.rm = FALSE)) %>%
  ungroup()

# Create the plot
ggplot(obj_know, aes(x = Timepoint, y = Score, group = Group, color = Group)) +
  geom_line() +
  geom_point(size = 4) +
  labs(title =  "Pre, Post, and Follow-Up Results",
       x = "Timepoint",
       y = "Score") +
  theme_minimal()

write_sav(phase_3_all_outcomes, "phase_3_final_10_16.sav")

Outcome 3: Atitudes Toward Banks

table1(~ financial_att_1_1 + financial_att_1_1_post + financial_att_1_1_follow + financial_att_1_2 + financial_att_1_2_post + financial_att_1_2_follow + financial_att_1_3 + financial_att_1_3_post + financial_att_1_3_follow + financial_att_1_4 + financial_att_1_4_post + financial_att_1_4_follow + financial_att_1_5 + financial_att_1_5_post + financial_att_1_5_follow + att_toward_banks_pre + att_toward_banks_post + att_toward_banks_follow|participant_type.fac, data=phase_3_all_outcomes)

library(zoo)
library(ggplot2)

# Create a data frame
att_banks <- data.frame(
  Group = rep(c("Treatment", "Non-Completer Comparison Group", "WinCo Comparison Group"), each = 3),
  Timepoint = rep(c("1: Pre", "2: Post", "3: Follow-Up"), times = 3),
  Score = c(2.81, 2.88, 2.83, 2.77, NA, 2.77, 2.74, NA, 2.78)
)
att_banks <- att_banks %>%
  group_by(Group) %>%
  mutate(Score = na.approx(Score, na.rm = FALSE)) %>%
  ungroup()

# Create the plot
ggplot(att_banks, aes(x = Timepoint, y = Score, group = Group, color = Group)) +
  geom_line() +
  geom_point(size = 4) +
  labs(title =  "Pre, Post, and Follow-Up Results: Attitudes Towards Banks",
       x = "Timepoint",
       y = "Score") +
  theme_minimal()

Outcome 4: Finanical Skill

library(haven)
phase_3_all_outcomes$fs1_complexdecision_pre <- as.numeric(phase_3_all_outcomes$fs1_complexdecision_pre)
phase_3_all_outcomes$fs1_complexdecision_post <- as.numeric(phase_3_all_outcomes$fs1_complexdecision_post)
phase_3_all_outcomes$fs1_complexdecision_follow_fol <- as.numeric(phase_3_all_outcomes$fs1_complexdecision_follow_fol)

phase_3_all_outcomes$fs2_goodnewdecision_pre <- as.numeric(phase_3_all_outcomes$fs2_goodnewdecision_pre)
phase_3_all_outcomes$fs2_goodnewdecision_post <- as.numeric(phase_3_all_outcomes$fs2_goodnewdecision_post)
phase_3_all_outcomes$fs2_goodnewdecision_follow_fol <- as.numeric(phase_3_all_outcomes$fs2_goodnewdecision_follow_fol)
phase_3_all_outcomes$fs2_goodnewdecision_pre <- as.numeric(phase_3_all_outcomes$fs2_goodnewdecision_pre)

phase_3_all_outcomes$fs2_goodnewdecision_pre <- as.numeric(phase_3_all_outcomes$fs2_goodnewdecision_pre)
phase_3_all_outcomes$fs2_goodnewdecision_post <- as.numeric(phase_3_all_outcomes$fs2_goodnewdecision_post)
phase_3_all_outcomes$fs2_goodnewdecision_follow_fol <- as.numeric(phase_3_all_outcomes$fs2_goodnewdecision_follow_fol)
phase_3_all_outcomes$fs2_goodnewdecision_pre <- as.numeric(phase_3_all_outcomes$fs2_goodnewdecision_pre)

table1(~ fs1_complexdecision_pre + fs1_complexdecision_post + fs1_complexdecision_follow_fol + fs2_goodnewdecision_pre + fs2_goodnewdecision_post + fs2_goodnewdecision_follow_fol + fs3_followthrough_pre + fs3_followthrough_post + fs3_followthrough_follow_fol + fs4_recognizegoodinvestment_pre + fs4_recognizegoodinvestment_post + fs4_recognizegoodinvestment_fol + fs5_keepfromspending_pre  + fs5_keepfromspending_post  + fs5_keepfromspending_fol + fs6_howtosave_pre + fs6_howtosave_post + fs6_howtosave_fol + fs7_findadvice_pre + fs7_findadvice_post + fs7_findadvice_fol + fs8_notenoughinfo_pre + fs8_notenoughinfo_post + fs8_notenoughinfo_fol + fs9_whenadvice_pre + fs9_whenadvice_post + fs9_whenadvice_fol + fs10_struggleunderstand_pre + fs10_struggleunderstand_post + fs10_struggleunderstand_fol|participant_type.fac, data=phase_3_all_outcomes)

table1(~ fs_pre + fs_post + fs_fol|participant_type.fac, data=phase_3_all_outcomes)

library(zoo)
library(ggplot2)

# Create a data frame
fs <- data.frame(
  Group = rep(c("Treatment", "Non-Completer Comparison Group", "WinCo Comparison Group"), each = 3),
  Timepoint = rep(c("1: Pre", "2: Post", "3: Follow-Up"), times = 3),
  Score = c(50.8, 53.9, 54.8, 49.8, NA, 49.8, 49.4, NA, 49.6)
)
fs <- fs %>%
  group_by(Group) %>%
  mutate(Score = na.approx(Score, na.rm = FALSE)) %>%
  ungroup()

# Create the plot
ggplot(fs, aes(x = Timepoint, y = Score, group = Group, color = Group)) +
  geom_line() +
  geom_point(size = 4) +
  labs(title =  "Pre, Post, and Follow-Up Results: Financial Skill",
       x = "Timepoint",
       y = "Score") +
  theme_minimal()

Outcome 5

label(phase_3_all_outcomes$financial_behave_1) <- "Do you have a spending and saving plan or budget that you use to help manage your finances?"
label(phase_3_all_outcomes$financial_behave_2) <- "How often do you check to see if you are following your spending and saving plan/budget?"
label(phase_3_all_outcomes$financial_behave_3) <- "How often do you review your spending and saving plan/budget to see if you need to make changes to it?"
label(phase_3_all_outcomes$financial_behave_1_post) <- "When you started to play the How Money Smart Are You games, did you have a spending and saving plan or budget that you used to help manage your finances?"
label(phase_3_all_outcomes$financial_behave_2_post) <- "Since starting to play the How Money Smart Are You games, have you reviewed your spending and saving plan/budget to see if you need to make changes to it?"
label(phase_3_all_outcomes$financial_behave_3_post) <- "Since starting to play the How Money Smart Are You games, have you created a spending and saving plan/budget?"
label(phase_3_all_outcomes$financial_behave_4_post) <- "How likely do you think it is that you will create a spending and saving plan or budget in the next 3 months?"
label(phase_3_all_outcomes$financial_behave_1_follow) <- "Do you have a spending and saving plan or budget that you use to help manage your finances?"
label(phase_3_all_outcomes$financial_behave_2_follow) <- "How often do you check to see if you are following your spending and saving plan/budget?"
label(phase_3_all_outcomes$financial_behave_3_follow) <- "How often do you review your spending and saving plan/budget to see if you need to make changes to it?"

table1(~ factor(financial_behave_1) + financial_behave_2 + financial_behave_3 + factor(financial_behave_1_post) + factor(financial_behave_2_post) + factor(financial_behave_3_post) + financial_behave_4_post + factor(financial_behave_1_follow) + financial_behave_2_follow + financial_behave_3_follow|participant_type.fac, data=phase_3_all_outcomes)

table1(~ factor(financial_behave_4) + factor(financial_behave_6) + factor(financial_behave_7) + factor(financial_behave_8) + factor(financial_behave_9) + factor(financial_behave_3_post) + factor(financial_behave_5_post) + factor(financial_behave_6_post) + factor(financial_behave_7_post) + factor(financial_behave_8_post) + factor(financial_behave_9_post) + factor(financial_behave_4_follow) + factor(financial_behave_6_follow) + factor(financial_behave_7_follow) + factor(financial_behave_8_follow) + factor(financial_behave_9_follow) |participant_type.fac, data=phase_3_all_outcomes)

Outcome 6

table1(~ fwb1_exp_pre +  fwb3_secure_pre + fwb5_never_pre + fwb6_enjoy_pre + fwb2_getby_pre + fwb4_concern_pre + fwb9_strain_pre + fwb10_left_pre +  fwb7_behind_pre + fwb8_control_pre + fwb1_exp_post +  fwb3_secure_post + fwb5_never_post + fwb6_enjoy_post + fwb2_getby_post + fwb4_concern_post + fwb9_strain_post + fwb10_left_post +  fwb7_behind_post + fwb8_control_post + fwb1_exp_follow +  fwb3_secure_follow + fwb5_never_follow + fwb6_enjoy_follow + fwb2_getby_follow + fwb4_concern_follow + fwb9_strain_follow + fwb10_left_follow +  fwb7_behind_follow + fwb8_control_follow|participant_type.fac, data=phase_3_all_outcomes)

table1(~ fwb_pre + fwb_post + fwb_follow|participant_type.fac, data=phase_3_all_outcomes)

library(zoo)
library(ggplot2)

# Create a data frame
fwb <- data.frame(
  Group = rep(c("Treatment", "Non-Completer Comparison Group", "WinCo Comparison Group"), each = 3),
  Timepoint = rep(c("1: Pre", "2: Post", "3: Follow-Up"), times = 3),
  Score = c(53.0, 53.8, 55.4, 51.2, NA, 50.6, 50.4, NA, 51.0)
)
fwb <- fwb %>%
  group_by(Group) %>%
  mutate(Score = na.approx(Score, na.rm = FALSE)) %>%
  ungroup()

# Create the plot
ggplot(fwb, aes(x = Timepoint, y = Score, group = Group, color = Group)) +
  geom_line() +
  geom_point(size = 4) +
  labs(title =  "Pre, Post, and Follow-Up Results: Financial Well-being",
       x = "Timepoint",
       y = "Score") +
  theme_minimal()

table1(~ games_before_pretest + games_before_post_test + games_before_follow|participant_type.fac, data=phase_3_all_outcomes)