Some words on Lab 3

Nicky Wakim

2024-03-11

Overall

  • Great work! I know there was a lot of plotting, but you made some great plots!

  • I also appreciate your thoughtful approaches to the relationships between variables

  • Just a reminder: when visualizing, making tables, displaying information from the data: always keep in the back of your mind:

    • What can a reader get from this if they have never seen the data?

    • Is it easy for the reader to understand the plot?

    • Does everything in the plot have a purpose?

    • Is the main thing I’m trying to communicate also the thing that stands out?

A few catches

  • I said glimpse, but that was a bad choice of words

    • head() might be better to show each row with their observations and variables

  • When we were looking into observations that might be suspicious…

    • We should look at the intersection of multiple suspicious observations

    • A few combinations that people came up with that I thought were good ideas:

      • Someone who chose that they felt extremely similar to fat and thin people

      • Someone who reported they were 11-14 years old with education above high school (or other age/education combos)

  • If the variable is an explanatory variable and originally categorical, it’s good to keep it categorical

    • Lots of lm() functions with variable treated as continuous

Multi-selection variables 1

  • Splitting gender identities is NOT mutually exclusive

    • Can identify as all genders!

  • If you are trying pinpoint one group and make it mutually exclusive, we need to take extra steps

  • Someone wanted to identify three groups:

    • Identifies as trans man, trans woman, genderqueer/non-conforming, and/or different identity
    • Identifies as man only
    • Identifies as woman only

  • Point on data equity:

    • Always write out all identities within a grouped category

    • I did not call the group something like non-normative genders, instead I said “Identifies as trans man, trans woman, genderqueer/non-conforming, and/or other”

    • Your coding names in R can be different, but when you write it out, make sure you define the group

      • I would maybe call the first group “Trans and non-binary genders” with an exact make up of the group.

Multi-selection variables 2

  • Create indicators for each identity: don’t any lose information on the individual
iat_new = iat_old %>%
  mutate(ind_m = grepl(1, genderIdentity), 
         ind_f = grepl(2, genderIdentity),
         ind_tmm = grepl(3, genderIdentity),
         ind_twf = grepl(4, genderIdentity),
         ind_gqnc = grepl(5, genderIdentity),
         ind_diff = grepl(6, genderIdentity))
  • Create mutually exclusive groups: lose some information on the individual
iat_new = iat_old %>% # iat_old should not have any NA's in genderIdentity
  mutate(genderID = case_match(genderIdentity,
                               "[1]" ~ "Male/Man only",
                               "[2]" ~ "Female/Woman only",
                               .default = "Trans male/man, trans female/woman, 
                               genderqueer/non-conforming, or differnt identity"))

  • Example : if someone identifies as a man and a trans man, their data are like:

    • \(I(Gender = \text{Male/Man only}) = 0\)
    • \(I(Gender = \text{Female/Woman only}) = 0\)
    • \(I(Gender = \text{Trans male/man, trans female/woman, genderqueer/non-conforming, or different identity}) = 1\)

About confounders and effect modifiers

  • When hypothesizing whether a variable a confounder or effect modifier

    • Make sure to back any claims in your final report with sources

    • We can speculate what’s at play, but we can’t actually know

    • Our own identity may bias how we perceive specific dynamics

  • Education: I saw many of us speculate that higher education may be associated with lower IAT (as a potential confounder)

    • Data showed the opposite: higher education, higher mean IAT score

    • We can think about people’s perception of controllability of weight: do people assume certain behaviors about fat people?

      • Does that align with or go against people’s assumptions about behavior needed for higher education?

      • We also need to think about how education might be linked to socio-economic status, and how that might change what food is affordable

    • Heavily discussed in Maintenance Phase podcast, but I don’t have direct sources

Potential effect modifier from plots

Notes on plotting

  • scale_x_discrete(labels = function(x) str_wrap(x, width = 10)): use this to wrap the text on x-axis

    • Student used this and I loved it!

  • Keep your explanatory variable on the x-axis when you are plottinf three variables at once

  • hjust and vjust will move your text on the x axis so it does not cover your plot

  • Plotting age vs. IAT

    • geom_smooth() to show moving mean value

    • Boxplots and plotting each mean not exactly right for continuous variables

    • See Lab 4 for how I plot this!

  • In geom_smooth(), when to use method = lm

    • Do not use if trying to see how data look

As we go into Lab 4 and Project Report

  • If IAT score ranges from -2 to 2, what changes in mean IAT is a lot?

    • 0.05: about 1.25% change
    • 0.5: about 12.5% change

  • A lot of the coefficients may be significant, but are they clinically meaningful?

Quiz and Lab 2