1 Poster Abstract

Title: Gender and race stereotypes mold perceptions of science and influence sense of belonging regarding scientific interest and confidence. Authors: Sophia Sullivan, Ronen Fisher, Emily Thomas, Tessa Wilson, & Heather Perkins

Utilizing multiple perspectives in science expands meaningful scientific enrichment and enhances the work science provides. However, improvements can be made regarding the scientific environment concerning diversity and an overall sense of belonging for all races and genders (Trujillo & Tanner, 2014). In regards to gender, previous research emphasizes persistent stereotypes associating men with agentic careers and women with communal careers (Cai et al., 2016; Stout et al., 2016). Research shows that these perceptions develop early, with parents and K-12 environments working to instill and reinforce them (Blažev et al., 2017; Kollmayer et al., 2018). These stereotypes not only impact women’s interest in science classes and careers, but also their confidence in scientific ability in classrooms and workplaces as they face sexism fueled by a presumed lack of belonging (Banchefsky & Park, 2018).

Additionally, there are racial stereotypes in science, often tied to assumptions about intelligence and academic achievement (Williams et al., 2019; Okeke et al., 2009), which also emerge at an early age (Copping et al., 2013). Stereotype threat (Beasley & Fischer, 2012), a lack of role models (Gladstone & Cimpian, 2021), and competitive climates (Edman & Brazil, 2009) all work to alienate students of color and lead to decreased interest and belonging in science (White et al., 2019). The present work aims for a better understanding of how diversity of gender and race impacts interest in science, confidence to excel in the sciences, and perceptions of scientists.

A survey was conducted to further understand the variability in science interest, science confidence, and perceptions of scientists between gender and race. Data was collected from 291 students at a diverse community college in Northern California. Our participant pool was diverse, with a majority of students identifying as Asian (n = 96), Latino/a/x (n = 51), or White (n = 38). The majority of our participants were women (n = 177), followed by men (n = 46), and non-binary or genderqueer individuals (n = 3). Participants were asked about their science interest and science confidence using items from the Student Assessment of Learning Gains (Seymour et al., 2000) and perceptions of scientists using items from the Stereotypes of Scientists Scale (Wyer et al., 2010). Lastly, participants were asked about their demographics and to write-in their gender and racial/ethnic identities. We hypothesized that gender and race would impact scientific interest and confidence, as well as perceptions of warmth and competence. Specifically, we predicted that perceptions of scientists would vary between men and women, with men perceiving scientists as competent and women perceiving scientists as warm. We predicted men would have greater scientific interest and confidence when compared to women. We predicted that race would impact perceptions of scientists, as well as interest and confidence, based on previous work assessing perceptions of scientists among racial minorities and students of Color (Miles et al., 2021).

The survey results support the idea that gender and race have an impact on science interest, F(2,235) = 3.31, p = .021, ή2 = .04, but only gender impacts science confidence, F(1,235) = 15.94, p < .001, ή2 = .06. Further, the collected data shows the importance of warmth and competence roles. There was no gender difference for perceptions of scientists as competent (p = .965), but there was a gender difference in perceptions of warmth, F(1,106) = 5.36, p = .022, ή2 = .05. There was also a gender by race interaction regarding perceptions of warmth, F(3,106) = 3.65, p = .015, ή2 = .09.

Many gender stereotypes and the prevalence of white social norms have molded the scientific domain and left more to be desired (White et al., 2019; Stout et al., 2016). Our results replicate previous work identifying race and gender differences in science interest and confidence as well as perceptions of scientists. We argue that gender and race stereotypes mold overall perceptions of scientists and directly impact students’ interest and confidence in science.

2 References

TBA.

3 Load Libraries

library(naniar) # for the gg_miss-upset() command
library(psych) # for the describe() command
library(kableExtra) # for tables
library(corrplot) # for correlation plots
library(afex) # for ANOVA
library(emmeans)
library(ggplot2) # for plots
library(dplyr)

4 Load Data

df <- read.csv(file="data/data_9-16.csv")

df <- df %>%
  mutate(raceeth = na_if(raceeth, "") %>% na_if(" "))

5 Notes

Variables of interest: * Race * Gender (self-reported demographic) * SOS warmth * SOS competence * Science interest * Science confidence

We came up with two hypotheses to test in our next meeting:

  • Hypothesis 1: we will see significant differences in science interest and confidence by gender and race.
  • Hypothesis 2: we will see significant differences in perceptions of warmth and competence by gender and race.

SOS warmth:

  • They have fun with colleagues at work
  • They have happy marriages
  • They were committed to their hobbies (like sports or art) as children.
  • They struggled to do well in school.
  • They defy expectations about what they’re ‘supposed’ to be like.
  • They had parents who struggled to make ends meet.
  • They enjoy literature and writing.
  • They are warm and caring people.
  • They are devoted to their families.
  • They are determined to have fun with their work.
  • They work with a lot of people everyday.

SOS competence:

  • They know a lot about the latest discoveries
  • They are the ones who know how equipment works
  • They are careful with expensive instruments
  • They are competitive
  • They are cooperative
  • They are independent
  • They are work oriented
  • They are technically competent
  • They are competent
  • They are collaborative
  • They are highly focused
  • They have imagination.
  • They think outside the box.
  • They succeed in spite of failure.
  • They are forward thinkers who want to improve the world.

Interest:

  • Enthusiastic about this subject
  • Interested in discussing this subject area with friends or family
  • Interested in taking or planning to take additional classes in this subject
  • Interested in pursuing a science career

Confidence:

  • Confident that I understand this subject
  • Confident that I can do this subject
  • Comfortable working with complex ideas

6 Create Variables

df2 <- subset(df, select=c(UID))
df2$sos_comp <- (df$sos4 + df$sos8 + df$sos11 + df$sos16 + df$sos19 + df$sos20 + df$sos21 + df$sos28 + df$sos30 + df$sos39 + df$sos7 + df$sos10 + df$sos12 + df$sos13 + df$sos15 + df$sos33)/15
df2$sos_warm <- (df$sos9 + df$sos24 + df$sos26 + df$sos29 + df$sos31 + df$sos32 + df$sos37 + df$sos38 + df$sos2 + df$sos27 + df$sos25)/11
df2$int <- (df$salg1 + df$salg2 + df$salg3 + df$salg4)/4
df2$con <- (df$salg5 + df$salg6 + df$salg7)/3

df2$gender2 <- "N"
# Recode 'f' or 'F' as 'F'
df2$gender2[df$gender %in% c("f", "F")] <- "F"
# Recode 'm' or 'M' as 'M'
df2$gender2[df$gender %in% c("m", "M")] <- "M"
# Recode 'female' or 'Female' as 'F'
df2$gender2[df$gender %in% c("female", "Female", "female ", "Female ", "Woman")] <- "F"
# Recode 'male' or 'Male' as 'M'
df2$gender2[df$gender %in% c("male", "Male", "male ", "Male ")] <- "M"
df2$gender2 <- as.factor(df2$gender2)

race <- read.csv(file="racesort2.csv", header=T, na.strings = c("", " "))
df2$race_fin <- race$newrace

df2$race_fin2[df2$race_fin == "asian"] <- "asian"
df2$race_fin2[df2$race_fin == "black"] <- "other"
df2$race_fin2[df2$race_fin == "latino"] <- "latino"
df2$race_fin2[df2$race_fin == "mena"] <- "other"
df2$race_fin2[df2$race_fin == "multi"] <- "other"
df2$race_fin2[df2$race_fin == "pi"] <- "other"
df2$race_fin2[df2$race_fin == "white"] <- "white"

df4 <- distinct(df2)

6.1 View Missing Data

gg_miss_upset(df4, nsets = 8)

6.2 View Item Normality

desc <- describe(df4[-1])
kable(round(desc, digits = 2)) %>%
  kable_styling() %>%
  row_spec(which(desc$kurtosis > 2), bold = T) %>%
  row_spec(which(desc$kurtosis < -2), bold = T) %>%
  row_spec(which(desc$skew > 2), italic = T) %>%
  row_spec(which(desc$skew < -2), italic = T)
vars n mean sd median trimmed mad min max range skew kurtosis se
sos_comp 1 122 5.58 0.59 5.67 5.63 0.64 3.40 6.4 3.00 -0.87 1.14 0.05
sos_warm 2 122 4.44 0.76 4.41 4.45 0.74 2.55 6.0 3.45 -0.07 -0.54 0.07
int 3 264 3.59 0.96 3.75 3.64 1.11 1.25 5.0 3.75 -0.40 -0.64 0.06
con 4 264 3.40 0.92 3.33 3.40 0.99 1.00 5.0 4.00 -0.02 -0.46 0.06
gender2* 5 291 1.55 0.75 1.00 1.44 0.00 1.00 3.0 2.00 0.95 -0.61 0.04
race_fin* 6 247 3.40 2.32 3.00 3.26 2.97 1.00 7.0 6.00 0.33 -1.45 0.15
race_fin2* 7 247 2.17 1.11 2.00 2.09 1.48 1.00 4.0 3.00 0.34 -1.30 0.07 
table(df4$gender2, useNA = "always")

F M N 177 68 46 0

table(df4$race_fin2, useNA = "always")

asian latino other white 96 51 62 38 44

6.3 Check Outliers

Using Mahalanobis’ distance. One outlier dropped.

d <- na.omit(subset(df4, select=-c(6:8)))

m_dist <- mahalanobis(d[-1], colMeans(d[-1]), cov(d[-1]))
d$MD <- round(m_dist, 1)
plot(d$MD)
describe(m_dist)
##    vars   n mean   sd median trimmed  mad  min   max range skew kurtosis   se
## X1    1 122 3.97 2.92   3.29    3.53 2.01 0.25 15.25 14.99 1.65     3.16 0.26
cut <- qchisq(.99, df=(ncol(d)-1))
abline(a=cut, b=0, col="red")

d$outlier <- F
d$outlier[d$MD > cut] <- T
table(d$outlier)
## 
## FALSE  TRUE 
##   121     1
outs <- subset(d, select=c(UID, outlier), outlier == T)
df3 <- subset(df2, !(UID %in% outs$UID))

7 H1

Hypothesis 1: we will see significant differences in science interest and confidence by gender and race.

df5 <- subset(df4, gender2 != "N")
table(df5$gender2, df5$race_fin2, useNA = "always")
##       
##        asian latino other white <NA>
##   F       66     39    41    30    1
##   M       28     11    20     8    1
##   N        0      0     0     0    0
##   <NA>     0      0     0     0    0
df5$gender2 <- droplevels(df5$gender2)

aov_out <- aov_ez(id = "UID", dv = "int", data = df5, between = c("gender2","race_fin2"))
nice(aov_out)
## Anova Table (Type 3 tests)
## 
## Response: int
##              Effect     df  MSE      F  ges p.value
## 1           gender2 1, 235 0.90   0.62 .003    .431
## 2         race_fin2 3, 235 0.90   0.67 .009    .570
## 3 gender2:race_fin2 3, 235 0.90 3.31 * .041    .021
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
afex_plot(aov_out, x = "gender2") +
  ylim(1,5)

afex_plot(aov_out, x = "race_fin2") +
  ylim(1,5)

afex_plot(aov_out, x = "race_fin2", trace = "gender2", legend_title="Gender", factor_levels = list(gender2 = c("Women","Men"), race_fin2 = c("Asian","Latino","Other","White"))) +
  ylab("Science Interest") + xlab("Race/Ethnicity") +
  ylim(1,5)

emmeans(aov_out, specs = "gender2", by = "race_fin2")
## race_fin2 = asian:
##  gender2 emmean    SE  df lower.CL upper.CL
##  F         3.51 0.117 235     3.28     3.74
##  M         3.60 0.179 235     3.24     3.95
## 
## race_fin2 = latino:
##  gender2 emmean    SE  df lower.CL upper.CL
##  F         3.37 0.152 235     3.07     3.66
##  M         4.07 0.286 235     3.50     4.63
## 
## race_fin2 = other:
##  gender2 emmean    SE  df lower.CL upper.CL
##  F         3.52 0.148 235     3.23     3.82
##  M         3.96 0.212 235     3.54     4.38
## 
## race_fin2 = white:
##  gender2 emmean    SE  df lower.CL upper.CL
##  F         3.88 0.173 235     3.54     4.22
##  M         3.12 0.336 235     2.46     3.79
## 
## Confidence level used: 0.95
pairs(emmeans(aov_out, specs = "gender2", by = "race_fin2"))
## race_fin2 = asian:
##  contrast estimate    SE  df t.ratio p.value
##  F - M     -0.0906 0.214 235  -0.423  0.6724
## 
## race_fin2 = latino:
##  contrast estimate    SE  df t.ratio p.value
##  F - M     -0.7028 0.324 235  -2.169  0.0311
## 
## race_fin2 = other:
##  contrast estimate    SE  df t.ratio p.value
##  F - M     -0.4381 0.259 235  -1.692  0.0919
## 
## race_fin2 = white:
##  contrast estimate    SE  df t.ratio p.value
##  F - M      0.7583 0.378 235   2.008  0.0458
pairs(emmeans(aov_out, specs = "race_fin2", by = "gender2"))
## gender2 = F:
##  contrast       estimate    SE  df t.ratio p.value
##  asian - latino   0.1422 0.192 235   0.742  0.8801
##  asian - other   -0.0168 0.189 235  -0.089  0.9997
##  asian - white   -0.3758 0.209 235  -1.798  0.2770
##  latino - other  -0.1590 0.212 235  -0.749  0.8771
##  latino - white  -0.5179 0.231 235  -2.247  0.1138
##  other - white   -0.3589 0.228 235  -1.574  0.3956
## 
## gender2 = M:
##  contrast       estimate    SE  df t.ratio p.value
##  asian - latino  -0.4700 0.338 235  -1.391  0.5060
##  asian - other   -0.3643 0.278 235  -1.311  0.5570
##  asian - white    0.4732 0.381 235   1.243  0.5998
##  latino - other   0.1057 0.356 235   0.297  0.9909
##  latino - white   0.9432 0.441 235   2.138  0.1441
##  other - white    0.8375 0.397 235   2.109  0.1532
## 
## P value adjustment: tukey method for comparing a family of 4 estimates
aov_out <- aov_ez(id = "UID", dv = "con", data = df5, between = c("gender2","race_fin2"))
nice(aov_out)
## Anova Table (Type 3 tests)
## 
## Response: con
##              Effect     df  MSE         F  ges p.value
## 1           gender2 1, 235 0.81 15.94 *** .064   <.001
## 2         race_fin2 3, 235 0.81      0.44 .006    .723
## 3 gender2:race_fin2 3, 235 0.81      1.00 .013    .392
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
afex_plot(aov_out, x = "gender2", factor_levels = list(gender2 = c("Women","Men"))) +
  ylab("Science Confidence") + xlab("Gender") +
  ylim(1,5)

emmeans(aov_out, specs = "gender2", by = "race_fin2")
## race_fin2 = asian:
##  gender2 emmean    SE  df lower.CL upper.CL
##  F         3.34 0.111 235     3.13     3.56
##  M         3.61 0.170 235     3.27     3.94
## 
## race_fin2 = latino:
##  gender2 emmean    SE  df lower.CL upper.CL
##  F         3.13 0.144 235     2.84     3.41
##  M         3.79 0.271 235     3.25     4.32
## 
## race_fin2 = other:
##  gender2 emmean    SE  df lower.CL upper.CL
##  F         3.18 0.140 235     2.90     3.46
##  M         3.97 0.201 235     3.57     4.36
## 
## race_fin2 = white:
##  gender2 emmean    SE  df lower.CL upper.CL
##  F         3.40 0.164 235     3.08     3.72
##  M         3.96 0.318 235     3.33     4.58
## 
## Confidence level used: 0.95
pairs(emmeans(aov_out, specs = "gender2"))
##  contrast estimate    SE  df t.ratio p.value
##  F - M      -0.567 0.142 235  -3.993  0.0001
## 
## Results are averaged over the levels of: race_fin2

8 H2

Hypothesis 2: we will see significant differences in perceptions of warmth and competence by gender and race.

# gender and perceptions of scientists
aov_out <- aov_ez(id = "UID", dv = "sos_comp", data = df5, between = c("gender2","race_fin2"))
nice(aov_out)
## Anova Table (Type 3 tests)
## 
## Response: sos_comp
##              Effect     df  MSE    F   ges p.value
## 1           gender2 1, 106 0.32 0.00 <.001    .965
## 2         race_fin2 3, 106 0.32 0.32  .009    .809
## 3 gender2:race_fin2 3, 106 0.32 1.07  .030    .363
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
afex_plot(aov_out, x = "gender2") +
  ylim(1,7)

afex_plot(aov_out, x = "race_fin2") +
  ylim(1,7)

afex_plot(aov_out, x = "race_fin2", trace = "gender2", legend_title="Gender", factor_levels = list(gender2 = c("Women","Men"), race_fin2 = c("Asian","Latino","Other","White"))) +
  ylab("Perceptions of Scientist Competence") + xlab("Race/Ethnicity") +
  ylim(1,7)

aov_out <- aov_ez(id = "UID", dv = "sos_warm", data = df5, between = c("gender2","race_fin2"))
nice(aov_out)
## Anova Table (Type 3 tests)
## 
## Response: sos_warm
##              Effect     df  MSE      F  ges p.value
## 1           gender2 1, 106 0.53 5.36 * .048    .022
## 2         race_fin2 3, 106 0.53 2.25 + .060    .087
## 3 gender2:race_fin2 3, 106 0.53 3.65 * .094    .015
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
afex_plot(aov_out, x = "gender2") +
  ylim(1,7)

afex_plot(aov_out, x = "race_fin2") +
  ylim(1,7)

afex_plot(aov_out, x = "race_fin2", trace = "gender2", legend_title="Gender", factor_levels = list(gender2 = c("Women","Men"), race_fin2 = c("Asian","Latino","Other","White"))) +
  ylab("Perceptions of Warmth") + xlab("Race/Ethnicity") +
  ylim(1,7)

emmeans(aov_out, specs = "gender2", by = "race_fin2")
## race_fin2 = asian:
##  gender2 emmean    SE  df lower.CL upper.CL
##  F         4.66 0.133 106     4.39     4.92
##  M         4.04 0.195 106     3.65     4.42
## 
## race_fin2 = latino:
##  gender2 emmean    SE  df lower.CL upper.CL
##  F         4.51 0.146 106     4.22     4.80
##  M         4.56 0.297 106     3.97     5.15
## 
## race_fin2 = other:
##  gender2 emmean    SE  df lower.CL upper.CL
##  F         4.48 0.163 106     4.15     4.80
##  M         4.74 0.243 106     4.26     5.22
## 
## race_fin2 = white:
##  gender2 emmean    SE  df lower.CL upper.CL
##  F         4.58 0.297 106     3.99     5.17
##  M         3.30 0.364 106     2.57     4.02
## 
## Confidence level used: 0.95
pairs(emmeans(aov_out, specs = "gender2", by = "race_fin2"))
## race_fin2 = asian:
##  contrast estimate    SE  df t.ratio p.value
##  F - M      0.6186 0.236 106   2.624  0.0100
## 
## race_fin2 = latino:
##  contrast estimate    SE  df t.ratio p.value
##  F - M     -0.0479 0.331 106  -0.145  0.8853
## 
## race_fin2 = other:
##  contrast estimate    SE  df t.ratio p.value
##  F - M     -0.2601 0.292 106  -0.890  0.3757
## 
## race_fin2 = white:
##  contrast estimate    SE  df t.ratio p.value
##  F - M      1.2803 0.470 106   2.723  0.0076
pairs(emmeans(aov_out, specs = "race_fin2", by = "gender2"))
## gender2 = F:
##  contrast       estimate    SE  df t.ratio p.value
##  asian - latino   0.1448 0.197 106   0.734  0.8831
##  asian - other    0.1803 0.210 106   0.857  0.8267
##  asian - white    0.0818 0.326 106   0.251  0.9944
##  latino - other   0.0355 0.219 106   0.162  0.9985
##  latino - white  -0.0630 0.331 106  -0.190  0.9975
##  other - white   -0.0985 0.339 106  -0.290  0.9914
## 
## gender2 = M:
##  contrast       estimate    SE  df t.ratio p.value
##  asian - latino  -0.5216 0.355 106  -1.468  0.4607
##  asian - other   -0.6984 0.311 106  -2.244  0.1182
##  asian - white    0.7435 0.413 106   1.800  0.2790
##  latino - other  -0.1768 0.384 106  -0.460  0.9674
##  latino - white   1.2652 0.470 106   2.691  0.0407
##  other - white    1.4419 0.438 106   3.294  0.0072
## 
## P value adjustment: tukey method for comparing a family of 4 estimates