I study how characteristics of young people’s educational and social environments shape their motivation and achievement. I place particular emphasis how on these opportunity structures perpetuate disparities for those from disadvantaged backgrounds, and I apply my findings to develop evidence-based psychological interventions.
Here is some information regarding some of my primary projects.
(1) INEQUALITY, BELIEFS ABOUT OPPORTUNITY, AND IMPORTANT LIFE PURSUITS: When asked about the kinds of futures they desire for themselves, adolescents and young adults from disadvantaged backgrounds often emphasize the importance of attaining socioeconomic stability—of gaining better and more stable occupations, incomes, and living conditions than they currently experience—as a crucial foundation for those futures. However, a noteworthy trend in today’s world is the rise of economic inequality, which entails increased disparities in lower and higher socioeconomic status (SES) individuals’ ability to access resources and opportunities that contribute to success and well-being in life. In this line of research, I find that living in areas with high levels of economic inequality signals to inhabitants that people are unlikely to be able to change their socioeconomic situation in that society (Browman & Destin, under review). And critically, among students from disadvantaged backgrounds, weakened beliefs about the attainability mobility reduces their motivation to engage in behaviors that contribute to future life success, such as persisting in school (Browman, Destin, Carswell, & Svoboda, 2017). In ongoing research, I am developing role model-based in-school interventions to strengthen these beliefs (and thereby improve outcomes) among disadvantaged youth and young adults by increasing awareness of multiple available post-secondary paths by which they can realistically attain future socioeconomic and life success (Browman, Svoboda, & Destin, in prep.).
(2) PERCEPTIONS OF INSTITUTIONAL SUPPORT FOR DIVERSITY: In this line of research, I demonstrate that even when sufficiently qualified and motivated, individuals from disadvantaged backgrounds frequently encounter institutions and policies that, despite their best intentions, are perceived as being unsupportive of "people like them," which can stifle motivation and performance. Specifically, I demonstrate that despite recent efforts to provide greater support for students from disadvantaged backgrounds, subtle contextual cues can still make students feel like that an institution does not support diversity or acknowledge the specific needs of these students (Browman & Destin, 2016). This can reduce their perceptions of the prevalence of socioeconomic diversity at their university and their sense of match between their own socioeconomic background and the student body at-large, resulting in lower confidence in pursuing academic tasks, lower expectations for academic success, and a weaker (implicit) sense of personal connectedness to academic success compared with when the institution was framed as explicitly dedicated to supporting the needs of students from diverse backgrounds. In ongoing research, I am working with university administrators to develop of effective institution-level interventions to help create a lasting sense of institutional support among disadvantaged students.
(3) THE VARIABILITY OF MOTIVATIONAL PREFERENCES: In this line of research, I explore the importance of considering not just the level or amount of motivation people have for engaging in important life pursuits, but also the manner in which they are motivated to engage in them. Specifically, I establish that people instead have distinct role- or identity-specific motivational preferences (e.g., to pursue gains or to avoid losses), which uniquely influence their current thoughts and actions when the associated identity is salient (e.g., student identity when at school, employee identity when at work; Browman, Destin, & Molden, 2017. Most notably, I demonstrated the independence of academic motivations to pursue gains or avoid losses when enacting one identity (e.g., student) from the motivational preferences associated with a person’s other identities (e.g., being a friend, or being a healthy person), as well as the unique power of academic-specific preferences for predicting students’ motives for persisting in school (e.g., gain-oriented preferences predicting wanting to “expand my knowledge of the world” vs. loss-averse preferences predicting wanting to “be a role model for my community”). In ongoing research, I am exploring the implication of these dynamics equality of academic opportunities, as they suggests the importance of ensuring that educational contexts are conducive to all students’ academic preferences.
- Official article link
- Pre-print PDF
- Data and materials
- Media coverage by the Chronicle of Higher Education
Human Motivation (Northwestern University, PSYCH-314-CN): This course explores the multiple ways in which our motivations—the goals, values, needs, and desires that drive and energize us on a day-to-day basis—can affect our impressions, perceptions, decisions, explanations, and behaviors. Because motivation science focuses on investigating everyday human thought and behaviour, special attention is paid to promoting students' understanding of how motivation science knowledge can be applied within their own lives. Overarching themes include how to succeed at your personal goals, how to become an expert, and how to motivate others, with the domains of education, sports, health, and relationships being highlighted.
Social psychology and related fields often require that researchers include correlation tables, ANOVA tables, and regression tables in their manuscripts. Creating these typically entails running the analyses with a statistical program and then manually copying the values into a table in a Word document. This is both time consuming and can result in transcription errors. In addition, many journals (for example, Psychological Science and Personality and Social Psychology Bulletin) have begun to require that confidence intervals be included when point estimates (e.g., correlation coefficients, regression coefficients) are provided. This can require additional statistical analyses (depending on the software used) and creates more opportunity for transcription errors. I have created a few scripts to automate these processes for correlation, ANOVA, and regression tables and thereby deal with these issues. Please feel free to contact me if you have any questions or feedback.
(1) R-to-Word Correlation Tables: Create correlation tables with 95% confidence intervals and significance stars and export them to a Word document for easy inclusion in a manuscript. This script creates a correlation table with 95% confidence intervals and significance stars and exports it to a Word document. You can then paste the table directly into your manuscript and edit the styling as necessary. Here is an example of the output:
Instructions: First, create a
corlist that contains all of the variables you want to include in the correlation table (e.g.,
corlist <- data.frame(var.1, var.2, ..., var.n)). Then, simply run the script below and follow the few directions included therein. The .doc file will be exported to the working directory, and the unformatted correlation coefficients and confidence intervals will also be shown in your R console.
(2) R-to-Word ANOVA and Regression Tables: Create ANOVA and regression tables with unstandardized regression coefficients, 95% confidence intervals, t-values, and p-values, and export them to a Word document for easy inclusion in a manuscript. This script creates ANOVA and regression summary tables which includes unstandardized regression coefficients, 95% confidence intervals, t-values, and p-values, and exports them to a Word document for easy inclusion in a manuscript. You can then paste the table directly into your manuscript and edit the styling as necessary. This script is made to handle one or multiple dependent variables, as shown in these examples of the outputs it can produce:
Instructions: First, you must create your
lm ANOVA/regression models (e.g.,
regression.1 <- lm(dv.1 ~ var.1:var.2)). Next, create a
models that contains all of the models you want to include in the summary table (e.g.,
models <- list(regression.1, regression.2, ..., regression.n)). (NOTE: All the models you include must include the same number of predictors (including interaction terms); otherwise, the function will throw an error.) Then, simply run the script below and follow the few directions included therein. The .doc file will be exported to the working directory.