This compilation dissertation explores school results (or alterably, educational or academic achievement) at the Swedish lower-secondary level (Grades 7–9), particularly Grade 9 and to lesser extent Grade 8, using both aggregated data at the school and municipality level from Swedish National Agency for Education (Skolverket) and similar databases (Sweden Statistics, Kolada), and individual data from Education through follow-up (UGU, utvärdering genom uppföljning), and the Programme for international student assessment, PISA (2018). Data were obtained from the years 2013, 2018, and 2019. The outcome variables consisted either of the sum score grades at least E (where E is the lowest pass grade and A is the highest) in all subjects or grade point average (either in all 17 subjects or a sum of English, Mathematics and Swedish), national test results (English, Mathematics, Swedish, Swedish as a second language), or PISA scores in mathematics and reading. The theoretical framework hinged on a socio-ecological model which covers the micro- (individuals, families), meso- (schools), macro levels (social factors such as political systems and social changes) of school results in different contexts. This rather comprehensive approach to school results was in turn related to six main variables that aimed to explain the variation in academic achievement, mostly by using linear regression models: socioeconomic status (SES, such as average parental education or resources within municipalities, schools or families), migration background (i.e., differences between native-born students and first- and second-generation migrant students), cognitive ability (i.e., the scores obtained from cognitive ability tests), non-cognitive abilities (e.g., the degree to which students regard themselves as being able to handle their school situation), teacher competence or teaching quality (i.e., mostly formal teacher competence such as the degree to which municipalities have teachers with a formal degree in teaching), as well as the geographical position of municipalities and students. The findings, which are related to four different studies, indicate that when all six variables were included in the same regression models (only in the UGU study), cognitive ability was the strongest factor, followed by non-cognitive abilities, SES, teaching quality, migration background, and geographical position. In some regression models, migration background was not even statistically significant, which was also the case with geographical position. When exploring the aggregated Skolverket data, the SES variables were the strongest, followed by migration background, and teacher competence, while geographical position was only statistically significant when the municipalities whose school results were the highest were compared with their lowest counterparts. The study which was built on PISA data and used a multi-level model approach, found a much stronger effect for migration background at the within-school level, which may be because students with a migration background have difficulties in understanding the long and reading-intense PISA tasks. It might also be because PISA does not include cognitive ability indicators. Moreover, for reading achievement, some non-cognitive abilities were also important such as self-assessed reading capabilities. At the between-school level, differences were associated with reading motivation. These results reflect upon recent phenomena in the Swedish context such as individualisation (the emphasis on individual level factors), socioeconomic disparities, and migration (social change).