Literature Review

The project’s research activities started with a systematic review of grey and academic literature on inequalities in education, focusing on studies with a longitudinal or repeated cross-sectional research design. 

Three research questions were addressed through the review:

1

What are the studies with a longitudinal or repeated cross-sectional research design on inequalities in primary and secondary school education in the countries covered by LINEup? What are the datasets they are based on?

2
Which analytical methods and techniques1 are used in the identified studies to assess inequalities in primary and secondary school education through longitudinal and repeated cross-sectional data?
3
Which variables are identified as factors/predictors2 of educational inequalities in the analysed studies?

The systematic literature review followed:

  • The PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and workflow steps (Identification, Screening, Inclusion) to increase the dependability and reliability of the collected data. 
  • An opendata approach, developing a Zotero library to collect and organise the identified publications, which will be available as open source at the project’s end as reference for future studies, particularly for researchers and policymakers interested in educational inequalities in Europe.

Overall, the systematic literature review identified:

datasets
0

 of longitudinal or repeated
cross-sectional data,

methods and techniques
0

for analysing such data,

variables,
0

which are factors and/or predictors
of educational inequalities.

1In this review, methods refer to the overarching strategies employed by researchers to answer their research question(s) and, consequently, the methodology chosen. Techniques refer to the data collection instruments and/or to the type of analysis done on the collected data.

2In this review, term factor is used when describing the relationship between two or more variables – hence factors associated with an outcome of interest. The term predictor is used when a variable reliably predicts an outcome – hence predictors have an impact on the outcome of interest. Studies with a longitudinal or repeated cross-sectional design are useful in understanding what factors are predictors of educational inequalities.

The variables were categorised into four clusters (student, family, teacher and school/system) and ten sub-clusters leading to the development of the LINEup’s conceptual model of educational inequalities.

Fancy to learn more? Read the Report’s Executive Summary