the relative strength of each predictor variable in predicting achievement (i.e., to find the most robust predictors of achievement) in each model. Also, although fit indices are not intrinsically interpretable (i.e., their values cannot be interpreted as being large or small in themselves), they can be compared across different steps of each model to check whether changes in the model lead to better fit; for all three indices presented in the tables, smaller values indicate better model fit regardless of the absolute number.

# 4. Results

## 4.1 Descriptive statistics

### _4.1.1 Descriptive statistics of outcome variables_

Table 2 shows the mean scores in mathematics and science by student gender in the TIMSS 2019 and NALO 2018 assessments. Results from both data sets show that boys consistently underperform compared to girls in both subject domains and at both grade levels in Saudi Arabia. The differences are larger for science than for mathematics at both grade levels, in both TIMSS and NALO.

**Table 2. Mean mathematics and science achievement in Saudi Arabia, by grade level and student gender, TIMSS**

||||**2019 and NALO**|**2018**||||
|---|---|---|---|---|---|---|---|
||||**TIMSS 2019**|||**NALO 2018**||
|||Male<br>_Mean(SD)_|Female<br>_Mean(SD)_|_M-F_|Male<br>_Mean(SD)_|Female<br>_Mean(SD)_|_M-F_|
|**Gd 4**|Mathematics|385 (108)|412 (91)|_-26_|496 (103)|504 (97)|_-8_|
|**rae**|Science|373 (116)|434 (97)|_-60_|483 (101)|518 (96)|_-35_|
|**Grade 8**|Mathematics|385 (80)|403 (74)|_-17_|493 (101)|507 (99)|_-14_|
||Science|408(91)|455(79)|_-47_|480(100)|521(96)|_-42_|

_Source:_ TIMSS 2019 data reported by Mullis et al. (2020). NALO data are authors’ own calculations. All differences between genders have been rounded.

### _4.1.2 Descriptive statistics of predictor variables (TIMSS 2019)_

Table 3 shows the percentages of boys and girls in grades 4 and 8 in Saudi Arabia across the TIMSS 2019 contextual categorical variables of interest to this paper (i.e., categorical variables that were included in the models). Table 4 shows the means ( _M_ ), standard deviations ( _SD_ ), minimum ( _min_ ), and maximum scores ( _max)_ for boys and girls in grades 4 and 8 in Saudi Arabia across the TIMSS contextual continuous variables of interest to this study (i.e., continuous variables that were included in the models). The tables also include the effect sizes ( _φ_ / _φc_ and _d_ ) for the gender differences in each of the contextual variables (see section 3.2 for further information). Given that some of the estimates presented in table 4 are not intrinsically interpretable (i.e., their values cannot be interpreted as being large or small per se), cut-scores and their corresponding interpretation along the continuum of each of these variables as set by TIMSS are provided in table A2.1 in appendix A2.

Across most of the TIMSS 2019 contextual variables, the differences between boys and girls are statistically significant; however, most of the differences yielded small to moderate effect sizes. Among these differences the most noticeable at grade 4 are observed in teachers’ absenteeism, with girls being more likely to attend schools where teacher absenteeism is a minor problem compared to boys who are more likely to attend schools where teacher absenteeism is either not a problem or a moderate to serious problem. Another considerable difference can be found in teachers’ major area of study, with more teachers in boys’ schools having education and mathematics or science as their major area of study, compared to teachers in girls’ schools who tend to primarily have mathematics or science but not education as their major area of study. Differences in teachers’ professional development are also noticeable, with teachers in boys’ schools reporting having attended fewer hours of professional development on mathematics and science compared to teachers in girls’ schools. Also, in comparison to grade 4 girls, grade 4 boys are less likely to have positive attitudes (liking/confidence) toward mathematics 10