Are Srishti Dads weird?

Author

Diya Bijoy

Published

November 4, 2025

R Packages Setup

library(ggformula)
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    learnr::run_tutorial("introduction", package = "ggformula")
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library(janitor)

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library(mosaic)
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library(skimr)

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library(correlation)

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library(vcd)
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library(visStatistics)

Reading the file

weird_dads <- read.csv("3-weird_dads.csv")
glimpse(weird_dads)
Rows: 65
Columns: 4
$ gender       <chr> "F", "F", "M", "F", "M", "M", "M", "M", "F", "M", "F", "M…
$ college      <chr> "SMI", "SMI", "SMI", "SMI", "MIT", "MIT", "MIT", "SMI", "…
$ is_dad_weird <chr> "No", "No", "No", "Yes", "No", "No", "No", "No", "Yes", "…
$ name         <chr> "Manya", "Sradha", "Arun", "Nidhi", "Shaurya", "Pratham",…

Munging

weird_dads_modified <- weird_dads %>% 
  mutate(across(where(is.character), as.factor))%>%
  dplyr::relocate("gender", "college", "is_dad_weird", .after = name)
glimpse(weird_dads_modified)
Rows: 65
Columns: 4
$ name         <fct> Manya, Sradha, Arun, Nidhi, Shaurya, Pratham, Jeevan, Dhr…
$ gender       <fct> F, F, M, F, M, M, M, M, F, M, F, M, F, F, M, M, F, M, M, …
$ college      <fct> SMI, SMI, SMI, SMI, MIT, MIT, MIT, SMI, SMI, SMI, SMI, SM…
$ is_dad_weird <fct> No, No, No, Yes, No, No, No, No, Yes, Yes, Yes, Yes, No, …

Removing NA Values

weird_dads_modified2 <- weird_dads_modified %>% drop_na()

Examining the Data

weird_dads_modified2 %>% 
  group_by(college) %>% 
  count(is_dad_weird)
# A tibble: 4 × 3
# Groups:   college [2]
  college is_dad_weird     n
  <fct>   <fct>        <int>
1 MIT     No              28
2 MIT     Yes              5
3 SMI     No              19
4 SMI     Yes             12
weird_dads_modified2 %>% 
  count(is_dad_weird)
  is_dad_weird  n
1           No 47
2          Yes 17
weird_dads_modified2 %>% 
  group_by(gender) %>% 
  count(is_dad_weird)
# A tibble: 5 × 3
# Groups:   gender [3]
  gender is_dad_weird     n
  <fct>  <fct>        <int>
1 F      No              23
2 F      Yes              9
3 M      No              24
4 M      Yes              7
5 NB     Yes              1

Visualising the Data

weird_dads_modified2 %>% 
  gf_bar(~is_dad_weird | college, fill = ~ is_dad_weird) %>% 
  gf_labs(
    title = "Count of Yes or No to whether dad is weird or not",
    subtitle = "Faceted by College",
    x = "Is your dad weird?",
    y = "Count"
  ) %>% 
  gf_refine(scale_fill_brewer(palette = "Set2")) %>% 
  gf_theme(theme_minimal)

weird_dads_modified2 %>% 
  gf_bar(~is_dad_weird, fill = ~ college, position = "fill") %>% 
  gf_labs(
    title = "Count of Yes or No to whether dad is weird or not",
    subtitle = "Filled by college",
    x = "Is your dad weird?",
    y = "Proportion"
  ) %>% 
  gf_refine(scale_fill_brewer(palette = "Set1")) %>% 
  gf_theme(theme_minimal)

weird_dads_modified2 %>% 
  gf_bar(~is_dad_weird | college, fill = ~ gender, position = "dodge") %>% 
  gf_labs(
    title = "Count of Yes or No to whether dad is weird or not",
    subtitle = "Faceted by college",
    x = "Is your dad weird?",
    y = "Count"
  ) %>% 
  gf_refine(scale_fill_brewer(palette = "Set1")) %>% 
  gf_theme(theme_minimal)

Chisq.test:

Hunch: Are Srishti Dads weird?

Test 1: Dad Weirdness & College

  • H0: Weirdness of dads does not depend on college
  • H1: Weirdness of dads depends on college
contingency_table_college <- vcd::structable(is_dad_weird~ college,
  data = weird_dads_modified2
)
contingency_table_college
        is_dad_weird No Yes
college                    
MIT                  28   5
SMI                  19  12
gss_vcd_table_college <- vcd::structable(college~is_dad_weird,
  data = weird_dads_modified2)

gss_vcd_table_college %>%
  vcd::mosaic(
    gp = shading_max, direction = "v", # Changed shading function
    main = "Dad Weirdness vs College",
    legend = TRUE,
    labeling = labeling_border(
      varnames = c("F", "F"),
      rot_labels = c(90, 0, 0, 0),
      just_labels = c("left", "left", "left", "right")
    )
  )

xq_test_object_college <- chisq.test(contingency_table_college)
xq_test_object_college

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table_college
X-squared = 3.4202, df = 1, p-value = 0.0644

  • P=0.06 which is >0.05, we fail to reject the Null Hypothesis.

  • Therefore, weirdness of dads do not depend on college. Srishti dads are not significantly weirder or less weird than dads from other colleges

xq_test_object_college %>%
  broom::tidy() %>%
  select(statistic) %>%
  as.numeric() -> X_squared_observed_college
X_squared_observed_college
[1] 3.420189
# Determine the Chi-Square critical value
X_squared_critical_college <- qchisq(
  p = .05,
  df = (2 - 1) * (2 - 1), # (nrows-1) * (ncols-1)
  lower.tail = FALSE
)
X_squared_critical_college
[1] 3.841459
mosaic::xqchisq(
  p = 0.95, df = 1,
  return = c("plot"), verbose = F,
  system = "gg"
) %>%
  gf_labs(
    x = "Chi-Square Value", 
    title = "Critical and Observed Chi-Square Values"
  ) %>%
  gf_vline(
    xintercept = X_squared_observed_college,
    color = "red", linewidth = 1
  ) %>%
  gf_vline(
    xintercept = X_squared_critical_college,
    color = "dodgerblue", linewidth = 1
  ) %>%
  gf_annotate(
    x = X_squared_observed_college + 0.2, y = 0.05,
    geom = "label", label = "Observed\nChi-Square",
    fill = "red", alpha = 0.3
  ) %>%
  gf_annotate(
    x = X_squared_critical_college - 0.5, y = 0.05,
    geom = "label", label = "Critical\nChi-Square",
    fill = "lightblue"
  )

Test 2: Dad Weirdness & Gender

  • H0: Perception of dad weirdness is the same for all genders
  • H1: Perception of dad weirdness differs by gender.
contingency_table_gender<- vcd::structable(is_dad_weird~ gender,
  data = weird_dads_modified2
)
contingency_table_gender
       is_dad_weird No Yes
gender                    
F                   23   9
M                   24   7
NB                   0   1
gss_vcd_table_gender <- vcd::structable(gender~is_dad_weird,
  data = weird_dads_modified2)

gss_vcd_table_gender %>%
  vcd::mosaic(
    gp = shading_max, direction = "v", # Changed shading function
    main = "Dad Weirdness vs Gender",
    legend = TRUE,
    labeling = labeling_border(
      varnames = c("F", "F"),
      rot_labels = c(90, 0, 0, 0),
      just_labels = c("left", "left", "left", "right")
    )
  )

xq_test_object_gender<- chisq.test(contingency_table_gender)
Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
incorrect
xq_test_object_gender

    Pearson's Chi-squared test

data:  contingency_table_gender
X-squared = 3.0567, df = 2, p-value = 0.2169

Inference

  • P=0.21 which is >0.05, we fail to reject the Null Hypothesis. Therefore, weirdness of dads do not depend on gender.

  • This shows that gender does not influence perceptions of dad weirdness, both males and females responded similarly when asked if their dads are weird.

xq_test_object_gender %>%
  broom::tidy() %>%
  select(statistic) %>%
  as.numeric() -> X_squared_observed_gender
X_squared_observed_gender
[1] 3.056724
# Determine the Chi-Square critical value
X_squared_critical_gender <- qchisq(
  p = .05,
  df = (2 - 1) * (2 - 1), # (nrows-1) * (ncols-1)
  lower.tail = FALSE
)
X_squared_critical_gender
[1] 3.841459
mosaic::xqchisq(
  p = 0.95, df = 1,
  return = c("plot"), verbose = F,
  system = "gg"
) %>%
  gf_labs(
    x = "Chi-Square Value", 
    title = "Critical and Observed Chi-Square Values"
  ) %>%
  gf_vline(
    xintercept = X_squared_observed_gender,
    color = "red", linewidth = 1
  ) %>%
  gf_vline(
    xintercept = X_squared_critical_gender,
    color = "dodgerblue", linewidth = 1
  ) %>%
  gf_annotate(
    x = X_squared_observed_gender + 0.2, y = 0.05,
    geom = "label", label = "Observed\nChi-Square",
    fill = "red", alpha = 0.3
  ) %>%
  gf_annotate(
    x = X_squared_critical_gender - 0.5, y = 0.05,
    geom = "label", label = "Critical\nChi-Square",
    fill = "lightblue"
  )

Conclusion:

To conclude, chi-square tests indicate that neither college nor gender significantly affect perceptions of dad weirdness. Overall, the hunch that “Srishti dads are weird” is not statistically supported - dads in MIT & SMI seem to have a similar level of weirdness.