Our group focused on a dataset known as the Immigrant and Intergenerational Mobility in Metropolitan Los Angeles (IIMMLA) study from 2004. This study is the third initiative by the Russell Sage Foundation’s research program to see how well young adult offspring of recent immigrants are assimilating when going through the American education system and labor force. This study focuses on 1.5-3rd generation immigrants in Los Angeles in their young adult years, with the goal of gathering information about how successful assimilation strategies differ among groups. The data was collected through conducted 35-minute long structured telephone interviews with random samples of first-generation immigrants who arrived before age 13 (the 1.5 generation) plus second- and third-generation adults, age 20-39, from as many ethnic subgroups. Interviewees provided basic demographic information as well as extensive data about socio-cultural orientation and mobility, economic mobility geographic, and civic engagement and politics.
How do education level affects income and occupation–what we’re defining as traditional measures of “success”–in young adult children of immigrants in metropolitan Los Angeles?
How do these statistics and trends vary and compare across the four generational groups (1.5, 2, 3, 4+) and ethnic groups represented in the study?
Our dataset was obtained as a Google Sheet with a codebook, which we used to identify different variables and the questions they represented. In Google sheets, we were able to identify which variables we wanted to use, transpose coded variables with the codebook, and clean up our data for program usage. We exported this data and used Tableau to create a variety of data visualizations and maps.Data visualizations were important to clearly understand and identify the trends between the variables we were most interested in, which was the highest education level, total personal income, occupation, and generation. With the maps, we were able to visualize the way these variables were distributed across the metropolitan Los Angeles area. Because the majority of our data was ordinal data, we mainly utilized bar charts, pie charts, histograms, and symbol plots to easily display comparisons between each variable (Yau, 146).
Contributed to data visualizations, designing the website, and creating the site's unique images.