Research

Grounded in a commitment to justice and accountability as a scientist, my research demonstrates systematic development and refinement of theory and method to produce knowledge that is useful and purposeful. Consistent with the American Psychological Association’s strategic goal to “Utilize psychology to make a positive impact on critical societal issues,” I understand my skillset in psychological science as essential to my public service.

Why STEM? STEM innovation is increasingly critical to macro-level economic development, affirming the place of STEM education as a gateway to the economic empowerment of individuals and communities. We need to grow our STEM workforce in order to retain American standing as a leader in innovation and creativity. Much of my research is guided by the situated expectancy-value model (Eccles & Wigfield, 2020), which broadly posits that aspects of STEM motivation are rooted in our sociocultural context and have real consequences for STEM achievement.

But, most importantly, STEM is for everyone. It needs to meet the interests and needs of all people, especially if it’s publicly funded. That goal is only achievable if our scientific practices, education, and workforce engage people from a wide range of communities and perspectives.

My research program engages with three major, interconnected research aims and questions:

The study of psychological gender differences and the deployment of meta-analysis to synthesize large bodies of quantitative research on men and women have been integral to psychology and have transformed our understanding of how gender/sex relate to human behavior. Often, the findings have challenged prevailing ideas about gender and encouraged a more rigorous and nuanced approach that incorporates the examination of effect sizes (e.g., see Hyde’s 2005 Gender Similarities Hypothesis). For example, my meta-analysis of gender differences in self-conscious emotions challenged blanket stereotypes about women’s greater emotionality. And, our meta-analysis of gender differences in child temperament provided important insights about gender differences in self-regulation and has been especially relevant to research questions about gender development and disparities in self-regulation and mood disorders, as well as gendered masking of childhood disorders.

Research attention to psychological gender differences can reinforce the tendency to essentialize gender, but it can also be grounded in a critique of a binary framework and the aim to challenge gender stereotypes. Indeed, decades of data from psychology and neuroscience point to the multidimensionality of gender/sex and demonstrate how gender/sex comprises multiple interrelated but separate biopsychosocial dimensions (Hyde, Bigler, Joel, Tate, & van Anders, 2019), including gender identity, gender typicality, and gender-typing, in addition to neuroendocrine dimensions. Thus, one of my current NSF-funded interdisciplinary projects builds upon previous findings of gender differences in student learning outcomes with math video games, deploying the multidimensional gender framework to explain gendered patterns of learning outcomes and develop educational tools that help all students.

Exploring the heterogeneity of psychological gender differences requires that we contextualize and socially situate gender/sex in ways that psychologists traditionally have not done. My interest in applying intersectionality in psychology began in earnest with my NSF-funded Philadelphia Adolescent Life Study (PALS), a longitudinal study of n = 460 high schoolers and their parents. A central aim of PALS was to contextualize and deepen our understanding gender differences in the development of STEM motivation and achievement among high schoolers in African American, Latine, Asian American, and European American youth from low-income households (Else-Quest et al., 2013; Else-Quest & Morse, 2014; Else-Quest & Peterca, 2015; Telfer & Else-Quest, 2022). Likewise, in our cross-national meta-analysis, I placed the situated expectancy-value model in a global context and applied an intersectional approach to gender gaps in math, examining patterns of gender differences in math motivation and achievement and their links to indicators of macro-level gender equality across 69 nations (Else-Quest et al., 2010).

Making STEM more innovative requires retooling and interdisciplinarity. For example, we can incorporate measures from the emergent literature on gender development and macro-level gender equality. My work on research methods for intersectional approaches in social sciences includes several projects that describe the challenges and importance of measuring and analyzing societal gender equality in psychological research (e.g., Else-Quest & Grabe, 2012; Else-Quest & Hamilton, 2018; Grabe & Else-Quest, 2012). Working from the theoretical orientation that a country’s gender equality is a dimension of our developmental milieu, I have examined and promoted critical analysis and use of these variables within psychology (i.e., to connect the micro- and macro-levels), including in current collaborative projects.

An intersectional approach to science goes beyond simply describing differences associated with social categories (e.g., gender, race, class) to examining how those categories and differences are linked to power and inequality. Thus, it faces considerable hurdles related to epistemology and assumptions about the nature of social categories like gender/sex, race/ethnicity, and social class; as such, it has the potential to transform psychological science. To promote that effort, I contributed an accessible synthesis of intersectionality theorizing and application of quantitative techniques for researchers and journal editors in an exciting and provocative series of papers (Else-Quest & Hyde, 2016a, 2016b, 2016c, 2016d) in Psychology of Women Quarterly. Presenting a set of techniques to implement intersectionality with quantitative methods, my collaborators and I have called for psychologists to engage with intersectionality and explored ways to implement an intersectional approach in research using quantitative methods and included commentaries from other intersectionality scholars. Subsequent papers (e.g., Abrams et al., 2020; Else-Quest et al., 2023; Else-Quest & Hyde, 2020) have extended this work to qualitative and mixed-methods research. You can learn more about interdisciplinary social science methods within an intersectional approach here.

Given what we know about the factors contributing to disparities among Americans, how can we improve outcomes? Most of my current research efforts are focused on this third aim of boosting engagement and persistence among students from groups that have been historically excluded from STEM.

The American STEM workforce does not reflect the US population. For example, women’s completion of engineering PhDs remains alarmingly low: in 2017 women earned 26.7% of engineering PhDs, with only 3.1% being earned by Black, Latina, or Indigenous women (ASEE, 2019). Yet, most research on this problem focuses on individual psychological mechanisms of these students, ignoring organizational climate as a contributing meso-level factor. Multiply-marginalized students are hurt most by organizational climate problems but are so underrepresented in traditional survey designs that intersectional invisibility results. Thus, one of my current projects on doctoral engineering programs uses an intersectional approach to connect students’ lived experiences to organization-level factors. This project includes a meta-synthesis and a mixed-methods climate scale development project (Aldridge, Else-Quest, Roy, & Yoon, 2023).

Several of my collaborative intervention projects are developing and/or evaluating individualized instruction in STEM to reduce student disparities. For example, I am part of a collaborative NIGMS-funded randomized, controlled field study of a utility-value intervention with undergraduate students in gateway undergraduate STEM courses. Derived from the situated expectancy-value model (Eccles & Wigfield, 2020), this intervention leverages students’ values and goals to bolster engagement, persistence, and achievement in STEM, particularly among students from historically excluded groups (Asher et al., 2023; Harackiewicz et al., 2023). And, in a recently completed NSF-funded project we meta-analyzed data on the effectiveness of instructional technologies–like adaptive learning systems (or virtual tutors) and simulations–in STEM education (Sun, Else-Quest, Hodges, French, & Dowling, 2021). Another in-progress collaboration evaluates the gendered effects of digital learning games on math learning outcomes. Rejecting a deficit approach to expanding participation in STEM, each of these projects adapts STEM instruction to meet the wide range of student needs and interests.