https://doi.org/10.24310/isl.20.1.2025.20605

questionnaires were used: one for schools, one for homes, and one for students. Hierarchical linear modelling with two

levels was employed: students and schools. The results show that student factors contribute more to the variance in

performance than school factors. After controlling for the socio-economic variable (student context), the student factors

that best predict reading performance are years of early childhood education and early literacy activities at home. As for

school-level factors, school composition and the school's emphasis on academic success are the variables that best predict

reading performance. These findings suggest the need to develop students' reading skills within the family context prior

to beginning primary education, to strengthen family–school cooperation, and to design activities involving teachers,

students, and families, with the aim of creating a positive and productive environment.

The role of early education of students in pre-primary

resources available at home stand out among student

predictors, while school predictors include the school’s

Viramontes Anaya et al. (2019) found a correlation

comprehension variables, such as vocabulary and listening

comprehension (Quezada Gaponov et al., 2024).

In the case of the first student predictor (socio-economic

background), research has found that students having a

higher socio-economic status promotes literacy in the

home, with an impact on the development of linguistic

skills and learning of vocabulary (Liu & Chung, 2022;

Yeung et al., 2022), so that the effect of the efforts of

2021; Toste et al., 2020) as knowledge of phonemes at this

stage prevents difficulties with reading comprehension

(Kaminski & Powell-Smith, 2017). In the 2011 edition of

PIRLS, children who did not attend early childhood

education grew up in homes where they received less

support for learning to read than those who attended early

leads to the grouping of disadvantaged students in schools

located in low socio-economic contexts (Owens, 2018;

Workman, 2022).

Regarding the second school predictor, the percentage of

students with basic skills who attend the school, research

has shown a negative correlation between the percentage of

students without basic literacy skills enrolled at a school

and the mean reading performance of the students who

attend the school (Mickelson et al., 2020; Yarnell &

Bohrnstedt, 2018). Other studies have found that the

taken by teachers, students and families to create a positive

and productive environment, research has found that

cooperation between the home and the school in learning

(Anthony & Ogg, 2019), confirming the association

performance (Li et al., 2023; Nilsen & Gustafsson, 2014),

as students find more positive models to follow in schools

with high expectations. The work by Lopes et al. (2022)

low-performing schools in Florida, found a significant

effect of additional instruction time on performance.

Thompson (2021) found that an increase in teaching of one

hour per week in schools in Oregon led to a small increase

in reading performance. Other studies conclude that

instruction time in primary education does not influence

reading performance (Brill & Wang, 2021; Lavy, 2020).

that is valuable and necessary for understanding the impact

of the predictors described above on the reading

principals and covers the characteristics of the

school. It includes questions about the

characteristics of the school, such as the school

environment, statistical information about the

technological resources. It takes 30 minutes to

complete.

university education).

worker, craft or trade worker or service or sales worker, 1

= Clerical worker, 2 = Small business owner, 3 = Corporate

manager or senior official, technical professional).

than 25 books at home and neither of their parents has

completed secondary education or has a professional

occupation. Students from a high background have more

than 25 books at home and at least one of their parents or

guardians has completed university and has a professional

meet the criteria for either high or low backgrounds.

calculated based on the percentage of students from

different socio-economic backgrounds.

Disadvantaged context: Schools where more than 25% of

students come from a low socio-economic background and

these categories).

The analytical procedure comprised three stages.

First, multivariate normality tests were performed.

Additionally, independent estimates were made for the

plausible values from the PIRLS study. Average risk values

were also calculated to measure reading performance (the

dependent variable).

In the second stage, the mean, standard deviation,

minimum and maximum were calculated for each variable

in order to identify the profile of the average student in

performance to be the dependent variable and student