I study how individuals psychologically experience and internalize social and economic inequality in their environments. In particular, I examine the consequences of these experiences for motivation and performance in education and other important future-oriented endeavors, especially for individuals from less advantaged, lower-opportunity backgrounds. One of my lines of research examines how individuals interpret and react to environmental inequality. Another line explores how influential figures and leaders in these environments contribute to this inequality. Across my research, I examine how these elements drive motivation and performance in both educational and broader societal institutions, and I apply my empirical findings to develop novel interventions that inoculate individuals from less advantaged backgrounds against the negative psychological effects of this inequality.
Here, I provide some information about each of these lines of research.
How internalizing inequality impacts motivation and performance: For people to feel motivated to persist in important domains, they must perceive their immediate environments as providing support and opportunities for them to achieve success. My research explores how for youth and young adults from less advantaged backgrounds in particular, their motivation and performance are sensitive to whether they perceive the educational and broader social institutions they inhabit as both (a) affording them equal opportunities for social and economic advancement (Browman, Destin, Carswell, & Svoboda, 2017; Browman, Svoboda, & Destin, in press; Browman & Destin, under review; for review, see Browman, Destin, Kearney, & Levine, 2019), and (b) supporting the needs of people with their background, including their financial needs (Browman & Destin, 2016) and preferred academic goal-pursuit strategies (Browman, in preparation, based on Browman, Destin, & Molden, 2017). Harnessing these insights, I develop interventions designed to providing young people with the psychological support and resources necessary for strengthening these respective beliefs, including (a) highlighting multiple viable school-based paths to future mobility (Browman, Svoboda, & Destin, in press), and (b) introducing tailored programs that provide these individuals with the institutional resources and support they need (Browman, Destin, Kockrell, & Rivera, in prep.).
How influential figures’ beliefs contribute to unequal treatment: People’s motivation to persist in important domains also hinges on whether influential figures within that environment have confidence in their capabilities and thus support their academic efforts. Specifically, my research demonstrates that (a) leaders’ beliefs about whether intelligence is improvable influences how helpful they are when working with lower-performing individuals (Browman, Miele, May, & O’Dwyer, in prep.; Miele, Perez, Butler, Browman, O’Dwyer, & McNeish, under review), and that (b) both educators and members of the general public tend to rate people's ability levels using very subjective information, such as their beliefs about why a person is working hard (because the task requires it vs. because they are personally motivated to do so; Miele, Browman, & Vasilyeva, 2019), and even solely based on arbitrary facial information Browman & Miele, under review).
For links to my publications: Please see my CV (below). Links to both open-access pre-prints and the official articles are provided.
For materials and data: All available materials, data, and analytic syntax associated with my publications can be found on my Open Science Framework page.
If you are having issues with the PDF viewer (below), click here to access my CV directly.
My teaching philosophy is based on three foci that draw from several areas of cognitive and motivational research on learning. First, I aim to evoke interest in course materials by showcasing its relevance for understanding human experiences in the real-world. Second, I seek to promote deep critical thinking about course materials by having students “problematize” or explore conflicting explanations for core concepts. Finally, I use application to help scaffold knowledge transfer from the abstract concepts we cover in class to more concrete real-world situations, especially those that connect to my students’ own lives. I have had the opportunity to refine and put this philosophy into practice at College of the Holy Cross, Boston College, Northwestern University, and in workshops with teachers in local school districts.
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.