How do children respond to parents’ use of gender generics and specifics?

As a part of Yijun’s independent project (which she is submitting to EPA 2026), she has created a Python-based workflow to: 1) isolate pairs of parents’ use of gender generics and specifics, and 2) code children’s responses. We are now running statistical analyses in preparation for her EPA abstract submission (due 12/5).

Multinominal logistic regression: Child Response ~ Parental Linguistic Scope * Stimuli Stereotype Consistency * Stimuli Gender

Multinomial Logistic Regression (Disagree = Baseline)
outcome predictor estimate std_error z_value p_value OR
Neutral (Intercept) 0.380 0.208 1.826 0.068 1.462
Neutral gendered_scopegender_specific 0.648 0.254 2.546 0.011 1.911
Neutral stimuli_coninconsistent 0.314 0.318 0.988 0.323 1.368
Neutral stimuli_gendermale_stim -0.054 0.331 -0.164 0.870 0.947
Neutral gendered_scopegender_specific:stimuli_coninconsistent -0.689 0.371 -1.856 0.063 0.502
Neutral gendered_scopegender_specific:stimuli_gendermale_stim 0.002 0.389 0.006 0.996 1.002
Neutral stimuli_coninconsistent:stimuli_gendermale_stim -0.240 0.447 -0.537 0.591 0.787
Neutral gendered_scopegender_specific:stimuli_coninconsistent:stimuli_gendermale_stim 0.223 0.523 0.427 0.670 1.250
Agree (Intercept) 0.325 0.210 1.549 0.121 1.385
Agree gendered_scopegender_specific 0.719 0.256 2.806 0.005 2.052
Agree stimuli_coninconsistent 0.348 0.320 1.089 0.276 1.417
Agree stimuli_gendermale_stim 0.328 0.320 1.025 0.305 1.389
Agree gendered_scopegender_specific:stimuli_coninconsistent -0.763 0.373 -2.043 0.041 0.466
Agree gendered_scopegender_specific:stimuli_gendermale_stim -0.436 0.380 -1.149 0.251 0.646
Agree stimuli_coninconsistent:stimuli_gendermale_stim -0.577 0.439 -1.313 0.189 0.561
Agree gendered_scopegender_specific:stimuli_coninconsistent:stimuli_gendermale_stim 0.995 0.515 1.931 0.053 2.705

Plotting predicted probability of child responses by scope x stim consistency

When we look at the predicted probabilities for each response type (Figure 1), children generally disagree less and agree slightly more when parents use specific language compared to generic language.

However, this pattern shifts on counter-stereotypical pages: disagreement increases and agreement decreases, specifically when parents used specific language. Because these shifts occur in opposite directions across the three response categories, the interaction is somewhat difficult to see in Figure 1, because we have this third code, neutral.

Focusing on scope x consistency interaction

The contrast plot (Figure 2), which focuses on responses as P(Agree) – P(Disagree), makes this interaction clearer to see: children were more likely to agree when parents use specific language on stereotype-consistent pages, but this preference drops on stereotype-inconsistent pages.

In contrast, responses to generic language remain relatively stable across page types.

This pattern matches the multinomial model, which revealed a significant interaction between parent linguistic scope and stimuli consistency (β = –0.763, z = –2.043, p = .041), suggesting that specific language heightens children’s sensitivity to stereotype violations, leading them to push back more when the content contradicts their expectations.

Session Info

## R version 4.4.2 (2024-10-31)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.6.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] lmtest_0.9-40    zoo_1.8-12       sandwich_3.1-1   nnet_7.3-20     
##  [5] corrr_0.4.4      emmeans_1.10.7   knitr_1.49       kableExtra_1.4.0
##  [9] lubridate_1.9.4  forcats_1.0.0    stringr_1.5.1    purrr_1.0.2     
## [13] tibble_3.2.1     tidyverse_2.0.0  readr_2.1.5      tidyr_1.3.1     
## [17] lme4_1.1-36      Matrix_1.7-1     dplyr_1.1.4      ggplot2_3.5.2   
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6       xfun_0.50          bslib_0.9.0        lattice_0.22-6    
##  [5] tzdb_0.4.0         vctrs_0.6.5        tools_4.4.2        Rdpack_2.6.2      
##  [9] generics_0.1.3     pkgconfig_2.0.3    RColorBrewer_1.1-3 lifecycle_1.0.4   
## [13] compiler_4.4.2     farver_2.1.2       codetools_0.2-20   htmltools_0.5.8.1 
## [17] sass_0.4.9         yaml_2.3.10        pillar_1.10.2      nloptr_2.1.1      
## [21] jquerylib_0.1.4    MASS_7.3-61        cachem_1.1.0       reformulas_0.4.0  
## [25] boot_1.3-31        multcomp_1.4-28    nlme_3.1-166       tidyselect_1.2.1  
## [29] digest_0.6.37      mvtnorm_1.3-2      stringi_1.8.4      labeling_0.4.3    
## [33] splines_4.4.2      fastmap_1.2.0      grid_4.4.2         cli_3.6.5         
## [37] magrittr_2.0.3     survival_3.7-0     TH.data_1.1-3      withr_3.0.2       
## [41] scales_1.4.0       timechange_0.3.0   estimability_1.5.1 rmarkdown_2.29    
## [45] hms_1.1.3          coda_0.19-4.1      evaluate_1.0.3     rbibutils_2.3     
## [49] viridisLite_0.4.2  rlang_1.1.6        Rcpp_1.0.13-1      xtable_1.8-4      
## [53] glue_1.8.0         xml2_1.3.6         svglite_2.1.3      rstudioapi_0.17.1 
## [57] minqa_1.2.8        jsonlite_1.8.9     R6_2.6.1           systemfonts_1.2.1