Social support is often referred to as a near-panacea in the psychological literature. The vast majority of research is correlational. Research that experimentally tests the effects of social support on life outcomes is, in comparison, underwhelming. Reviews, on the whole, have provided some support for a positive effect of social support on a wide range of outcomes, but it is still unknown which types of social support interventions work best for which problem (Hogan, Linden, Najarian, 2002).
Often, when presented with evidence of racism, or the existence of racism, critics will say neither exists, at least not in a meaningful way because many immigrant groups have higher household incomes than White Americans. These critics often point to the median household income of Nigerian Americans are irrefutable proof that racism is a thing of the past. However, comparing the income of Nigerian Americans with White Americans is not the right comparison.
At work recently, I ran into a situation where the wonderful tabulizer package was just not picking up a table. I thinkt it was because the table was just text, and the cells were quite large, much like in the table below: the full pdf can be found here In order to get the text out in a reliable way, whle maintaining the table structure, I used a combination of the magick and imager packages in R, and the cv package in Python.
Exploring Explanations Of the Black-White wealth gap The black-white wealth gap is nearly 15:1; the mean of black household wealth is $138,200; for white houesholds it is $933,700. A frequent explanation has been single-parent families in the Black community (1, 2 3) or culture (1, 2, 3, 4, 5) Fortunately, we can test these claims. I am going to use the Add Health to investigate them. The National Longitudinal Study of Adolescent to Adult Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-95 school year.
Tips on Standford NLP There are several pacakges in R that use the Stanford CoreNLP Software (e.g. cleanNLP, coreNLP). These packages are great for using CoreNLP, but for large projects they are slowww. For a recent project, I had to employ Named Enity Recognition on hundreds of thousands of document, and the aforementioned wrappers around Stanford CoreNLP were just too slow. What significantly sped things up was using the Stanford CoreNLP Software from the command line.