Sentence examples for gender coefficients from inspiring English sources

For example, the Netherlands has one of the largest gender coefficients.

With one of the smallest gender coefficients among the 20 countries, the decline in the U.S. is only from − 0.91 to − 0.78, corresponding to odds ratios of 0.40 and 0.46, respectively.

It bears mentioning that the magnitudes of the gender coefficients, though still very large, are substantially smaller in the set of analyses using the FT sample in comparison to those in the set of preliminary analyses referred to above.

In Model 3, the gender coefficient declines from − 2.16 to − 1.19, corresponding to odds ratios of 0.12 and 0.30, respectively.

For example, if one of these factors (e.g. Gender) is correlated with both hourly earnings and hours worked, then there would be ambiguity in the interpretation of the gender coefficient in the wage equation.

As one of the major methodological decisions, student-level variables were defined as fixed (e.g., gender coefficient does not differ across schools) and the intercept was defined as random (i.e., average performance varies across schools) in all eight HLM models (two for each country).

Prior literature methods are followed to estimate the gender test score gap by regressing test scores (Y i ) of student i in each test grade and each subject on the gender dummy variable (Male i which takes the value of 1 if the student is male and 0 if female); therefore, the sign and magnitude of the gender coefficient estimate indicates the direction and magnitude of the gender test score gap.

There was a trend for the following factors to predict USMLE Step 1 performance (0.1 >  p > 0.05): Gender (Coefficient = -3.389), and Age (Coefficient = -0.615).

Table  6 shows the gender coefficient is 0.036, which indicates that women's scores are lower than that of men's in demographic variables.

The linear regression model revealed that the major determinants of higher motivation were female gender (coefficient: 5.8, P = 0.008) and working in non-clinical areas (for example, pharmacists or laboratory technicians, coefficient: 6.9, P = 0.039).

In both of these cases, the odds ratio estimated for the adjusted gender effect was very extreme and the standard error for the gender coefficient also was quite large.