My research investigates how identity—people's internalized perceptions of who they are, were, and will become—guides motivation and achievement, from three perspectives. First, I demonstrate how people's assumptions about their ability to reach desired future identities influence motivation and persistence in important domains like education and financial decision-making, drawing particular focus on youth and young adults from diverse socioeconomic backgrounds. Examining similar populations, my second line of my research investigates how real-world institutions and policies interact with central and salient aspects of people's identities to influence academic motivation and achievement. In my third line of research, I examine the variability and organization of motivation across people's various identities—that is, the dynamics of how people's motivational intentions vary as they navigate the various roles and aspects of their lives—and the implications for our understanding of the broader self-concept. In addition, in each stream of research, I apply my theoretical findings to develop subtle social psychological interventions for enhancing motivation and achievement among youth and young adults.
(1) SEEING DESIRED FUTURE IDENTITIES AS WITHIN REACH: In this line of research, I demonstrate how for youth and young adults from low SES backgrounds, the motivation to engage in behaviors that are connected to future success—like investing effort in education and avoiding shortsighted financial decisions—hinges in part on the belief that it is in fact possible for them to reach the socioeconomic future identities they desire, or that people can and do in fact ascend the socioeconomic ladder in their society. Some insights gained from this work include how such beliefs influence low SES students' academic persistence and performance (Browman, Destin, Carswell, & Svoboda, 2017) and their propensity to save for the future (work in preparation). In ongoing research, I am developing in-school interventions to strengthen these beliefs and thereby improve outcomes among at-risk youth and young adults.
(2) PERCEIVING INSTITUTIONAL SUPPORT FOR "PEOPLE LIKE ME": In this line of research, I demonstrate that even when sufficiently motivated and qualified, people frequently interact with institutions and policies that are psychologically incompatible with central and salient aspects of who they are—that is, for people "like them"—which can stifle motivation and performance. For example, I find that when contextual cues suggest that a university is unconcerned with the financial circumstances of low SES students, this reduces 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. This resulted 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 financial needs of low SES students (Browman & Destin, 2016). 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 underprivileged students.
(3) THE VARIABILITY AND ORGANIZATION OF MOTIVATION ACROSS IDENTITIES: People have very multifaceted lives and must juggle multiple identities every day. In this line of research, I investigate the broader question of how people manage motivation as they switch between identities. Some insights gained from this work include how distinct identities are associated with unique motivational orientations—or preferences regarding how goals should be framed and pursued—for promotion and prevention (Browman, Destin, & Molden, invited resubmission) [and for independence and interdependence (Baldwin, Bagust, Docherty, Browman, & Jackson, 2014)] and that the motivations associated with a specific identity (e.g., being a student) predict people's identity-relevant cognitions and sentitivities (e.g., reasons for attending college, levels of academic optimism and confidence) better than both more domain-general measures of motivation and motivations relevant to other identities (e.g., being a healthy and fit person).
- Pre-print PDF
- Data and materials will be available shortly
- 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. Last offered: Winter 2016.
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.