I study how young people think about the future, how those thoughts can motivate them, and how schools and broader social systems can influence that motivation. I place particular emphasis on the experiences of youth and young adults from disadvantaged backgrounds, applying my theoretical findings to develop evidence-based social-psychological interventions and programs.
Here is some information regarding some of my primary projects.
(1) PERCEPTIONS OF OPPORTUNITY AND FUTURE-FOCUSED BEHAVIOR: For youth and young adults from disadvantaged backgrounds, a key aspect of a desired future is the ability to attain upward socioeconomic mobility—to have better occupations, incomes, and living conditions than they currently experience. In this line of research, I demonstrate that for these young people, the motivation to engage in behaviors that are connected to future financial success—like investing effort in education (Browman, Destin, Carswell, & Svoboda, 2017) and avoiding short-sighted financial decisions (work in preparation)—hinges on the perception that society they live in will afford them the opportunity to reach the socioeconomic futures they desire. 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.
(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 AND ORGANIZATION OF MOTIVATION ACROSS IDENTITIES: Central to both lines of research discussed is the overarching notion that people hold numerous idiosyncratic identities. These include the full range of roles (e.g., student, athlete), relationships (e.g., sister, romantic partner), and social group memberships (e.g., their socioeconomic and racial-ethnic backgrounds) that encapsulate how they see and define themselves across the various situations they encounter in their lives. This overarching idea raises a broader theoretical question: do people's various identities play a role in guiding their motivational experiences as they navigate their daily lives? Undertaking a systematic investigation of this question (Browman, Destin, & Molden, 2017), my research suggests that (a) people have distinct identity-specific motivations that are independent of their domain-general motivations, and (b) these identity-specific motivations uniquely influence their current thoughts and actions when the associated identity is active. Furthermore, I tested competing models regarding how these identity-specific motivations might be situated and coordinated within a person's larger self-concept. I found that (c) the less compatible people's specific identities, the more distinct are the motivations connected to those identities, and (d) such motivations likely develop and are situated at the trait level of the self-concept, such that these traits then cumulatively influence the identities with which they have become associated. Together, this work provides new theoretical and practical insights regarding how people's idiosyncratic identities guide their motivation and behaviors as they navigate their daily lives. In ongoing research, I am examining the implications of these identity-specific (versus domain-general) motivations for persistence and achievement in important real-world domains like education.
- 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.