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The increasing availability of individual participant data (IPD) in the social sciences offers new possibilities to synthesize research evidence across primary studies. Two-stage IPD meta-analysis represents a framework that can utilize these possibilities. While most of the methodological research on two-stage IPD meta-analysis focused on its performance compared with other approaches, dealing with the complexities of the primary data has received little attention, particularly when IPD are drawn from complex sampling surveys. Complex sampling surveys often feature clustering, stratification, disproportionate sampling, and multiple stages of sample selection to obtain nationally or internationally representative data from a target population. Furthermore, IPD from these studies are likely to provide more than one effect size. To address the complexities of the primary and meta-analytic data obtained from complex surveys, we propose a two-stage IPD meta-analytic approach and illustrate its utility. To aid meta-analysts who wish to utilize complex survey data, we present a sequence of steps and discuss the methodological decisions and options within. Given its flexibility and ability to deal with the complex nature of the primary data, the proposed two-stage approach opens up new analytical possibilities for synthesizing knowledge meta-analytically.

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Studying digital gender inequalities is a critical issue in education today. The so-called “digital gender divides” can exist not only in students’ access to and usage of ICT but also in their attitudes toward technology, digital knowledge, skills, and learning outcomes. Given that the extant body of research has mainly studied these divides in isolation and largely ignored their dependencies, we investigate how they relate to each other using large-scale data from multiple studies. Specifically, we analyzed the student data from the International Computer and Information Literacy Study (ICILS) in 2013 and 2018 and synthesized the links between digital gender divides in digital skills and attitudes toward technology after controlling for divides in ICT access across studies. Our findings suggested that (a) girls had a higher performance than boys in digital skills (\beta = -0.11 to -0.29); (b) gender divides in attitudes toward technology partly explained the gender divides in digital skills; (c) significant variation across countries and the types of digital skills and attitudes existed. We conclude that attitudes toward technology are key predictors of the gender divides in digital skills and that the first- and second-level gender digital divides are connected.

Fixed and growth mindsets represent implicit theories about the nature of one’s abilities or traits. The existing body of research on academic achievement and the effectiveness of mindset interventions for student learning largely relies on the premise that fixed and growth mindsets are mutually exclusive. This premise has led to the common practice in which measures of one mindset are reversed and then assumed to represent the other mindset. Focusing on K-12 and university students (N = 27328), we tested the validity of this practice via a comprehensive item-level meta-analysis of the Implicit Theories of Intelligence Scale (ITIS). By means of meta-analytic structural equation modeling and network analysis, we examined (a) the ITIS item-item correlations and their heterogeneity across 32 primary studies; (b) the factor structure of the ITIS, including the distinction between fixed and growth mindset; and (c) moderator effects of sample, study, and measurement characteristics. We found positive item-item correlations within the sets of fixed and mindset items, with substantial between-study heterogeneity. The ITIS factor structure comprised two moderately correlated mindset factors (ρ = .63–.65), even after reversing one mindset scale. This structure was moderated by the educational level and origin of the student sample, the assessment mode, and scale modifications. Overall, we argue that fixed and growth mindsets are not mutually exclusive but correlated constructs. We discuss the implications for the assessment of implicit theories of intelligence in education.