Appendix A — Overview of GEAM Employee variables

Abstract

Annex I provides an overivew of all GEAM variables as chart and frequency table.

Keywords

Descriptive statistics

A.1 Socio-demographic variables

A.1.1 Gender

df.geam |>
  table_frq(SDEM004)

Table A.1: Frequency table of SDEM004 - Gender

A.1.2 Sexual orienation

df.geam |>
  table_frq(SDEM007)

Table A.2: Frequency table of SDEM007 - Sexual orientation

A.1.3 Socioeconomic status

Socio-economic status is calculated based upon SDEM0017 and SDEM0018 on parents/legal tutor highest educational level.

# create new SES variable based upon the higher value of SDEM017 vs. SDEM018
df.geam <- df.geam |>
  mutate(SDEM017.comp = if_else(SDEM017 == "Prefer not to say" | 
                                SDEM017 == "Other" | 
                                is.na(SDEM017), -99, as.numeric(SDEM017)), 
         SDEM018.comp = if_else(SDEM018 == "Prefer not to say" |
                                SDEM018 == "Other" | 
                                is.na(SDEM018), -99, as.numeric(SDEM018)), 
         higher_ses = if_else(SDEM017.comp >= SDEM018.comp, SDEM017.comp, SDEM018.comp))

# create three SES groups
df.geam <- df.geam |>
  mutate(ses_3g = case_when(
    higher_ses >0 & higher_ses <=3 ~ "Lower SES", 
    higher_ses >3 & higher_ses <=5 ~ "Medium SES",
    higher_ses >5 & higher_ses <=9 ~ "Higher SES",
    .default = NA
  ))


# reconvert to factor
df.geam$ses <- factor(df.geam$ses_3g)

A.2 Working conditions - Work-life balance

A.2.1 Job satisfaction

Job satisfaction is measured via two variables in the GEAM, a global job satisfaction score EWCS88JobSatisfact1 and three additional items used in EWCS89JobSatisfact8 capturing career prospects, motivation, and job security.

cpal <- RColorBrewer::brewer.pal(4, "Spectral")

df.geam |>
  select(starts_with("EWCS89JobSatisfact8.SQ")) |>  
  sjPlot::plot_likert(values="hide", 
              show.prc.sign = F, 
              expand.grid = T, 
              show.n = F, 
              wrap.legend.labels = 10, 
              geom.colors = cpal, 
              catcount = 4, 
              cat.neutral = 3
              )

Aggregations for job satisfaction

df.geam <- df.geam |>
  rowwise() |>
  mutate(jobsatis2 = mean(as.numeric(c_across(c(EWCS89JobSatisfact8.SQ001.,EWCS89JobSatisfact8.SQ002.))), na.rm=T), 
         jobsatis3 = jobsatis2 * as.numeric(EWCS88JobSatisfact1))

A.3 Organisational Culture and Climate - Working Culture

A.3.1 Glick Masculinity Contest Culture

cpal <- RColorBrewer::brewer.pal(4, "Spectral")

df.geam |>
  select(starts_with("GlickMasculCont8.SQ")) |>
  plot_likert(values="hide", 
              show.prc.sign = T, 
              grid.range = c(1,1),
              expand.grid = T, 
              show.n = F, 
              wrap.legend.labels = 20, 
              wrap.labels = 40,
              catcount = 4, 
              cat.neutral = 3,
              geom.colors = cpal
)

A.3.2 Bias against women leadership

Diel leader 1

cpal <- RColorBrewer::brewer.pal(4, "Spectral")

df.geam |>
  select(starts_with("DiehlBiasLeaderAgree.SQ")) |>
  sjPlot::plot_likert(values="hide", 
              show.prc.sign = T, 
              grid.range = c(1,1),
              expand.grid = T, 
              show.n = F, 
              wrap.legend.labels = 20, 
              wrap.labels = 40,
              catcount = 4, 
              cat.neutral = 3,
              geom.colors = cpal
)

Diehl leader 2

cpal <- RColorBrewer::brewer.pal(4, "Spectral")

df.geam |>
  select(starts_with("DiehlBiasLeaderFreq.SQ")) |>
  sjPlot::plot_likert(values="hide", 
              show.prc.sign = T, 
              grid.range = c(1,1),
              expand.grid = T, 
              show.n = F, 
              wrap.legend.labels = 20, 
              wrap.labels = 40,
              catcount = 4, 
              cat.neutral = 3,
              geom.colors = cpal
)

A.4 Bullying, Harassment, Microagressions

A.4.1 Microaggressions

By microaggressions we mean brief and commonplace verbal, behavioral, and environmental indignities, whether intentional or unintentional, that communicate hostile, derogatory, or negative slights and insults to the target person or group. They might be related to race or the colour of one’s skin, gender, sexual orientation, age, ethnic group, or religion.

cpal <- RColorBrewer::brewer.pal(4, "Spectral")

df.geam |>
  select(starts_with("BIMA001.SQ")) |>
  plot_likert(values="hide", 
              show.prc.sign = T, 
              grid.range = c(.4,1.4),
              expand.grid = T, 
              show.n = F, 
              wrap.legend.labels = 20, 
              wrap.labels = 40,
              geom.colors = cpal
)