math_gender_iat_ma: Meta-Analysis Example - Replication of gender difference in...
In gitrman/itns: Datasets from the book Introduction to the New Statistics

Description Usage Format Details Source References

An example dataset used in Chapter 9 of the book Introduction to the New Statistics.

1	math_gender_iat_ma

A data frame with 30 rows and 9 variables:

location: Name of the lab
m_male: Mean IAT score for males
sd_male: Standard deviation for males on IAT
n_male: Sample size for males
m_female: Mean IAT score for females
sd_female: Standard deviation for females on IAT
n_female: Sample size for females on IAT
subset: Factor indicating whether the study was conducted in the USA or not
country: Factor with 12 levels indicating the country where the study was conducted

To what extent is gender related to implicit attitudes about bias? To find out, Nosek and colleagues as male and female students to complete an Implicit Association Test (IAT) that measured how easily negative ideas could be connected to art or to mathematics. Higher scores indicate higher levels of bias against mathematics.

Note that all participants collected who had to be excluded due to high error rates or slow responses were excluded prior to creating this summary. Data were omitted from these sites:uva vcu wisc wku wl wpi

This is data is available online at https://osf.io/wx7ck from this study: Klein, R. A., Ratliff, K. A., Vianello, M., Adams ., R. B., Bahnik, S., Bernstein, M. J., ... & Nosek, B. A. (2014). Investigating Variation in Replicability. Social Psychology, 45, 142-152. http://doi.org/10.1027/1864-9335/a000178

The original study exploring this effect is: Nosek, B. a, Banaji, M. R., & Greenwald, A. G. (2002). Math = male, me = female, therefore math not = me. Journal of Personality and Social Psychology, 83(1), 44-59. http://doi.org/10.1037/0022-3514.83.1.44

Cumming, G., & Calin-Jageman, R. (2017). Introduction to the New Statistics. New York; Routledge.

gitrman/itns documentation built on May 17, 2019, 5:29 a.m.