Altmetrics are metrics and qualitative data that are complementary to citation-based metrics. Some argue that these metrics should be considered in tenure decisions, along with the more traditional metrics of publishing in a high impact journal with many citations. Almetrics cover a wider range of materials than just those in professional journals – websites, blogs, materials in repositories like figshare or GitHub. It also covers more than citations, such as views and downloads.
How to Use Altmetrics to Showcase Engagement Efforts for Promotion and Tenure
Stacy Konkiel | Altmetric Blog
October 18, 2016
This blogpost from the Altmetric site, shows how Altmetrics can be incorporated into a traditional tenure document.
And an even more informative article on Altmetrics is a summary written by Yan Fu in a PSC news report:
Altmetrics: New Ways to Measure the Impact of Research Products
Yan Fu | PSC Center News
[Link to Undercover Historian blog]
The Undercover Historian
Beatrice Cherrier | blog
This is a blog by Beatrice Cherrier, an historian of economics. It has been in existence since 2011 and has a wealth of information about the history of the field of economics. And, no I don’t know what her quote about “pig-headed” is referencing.
How One 19-Year-Old Illinois Man Is Distorting National Polling Averages
New York Times | Nate Cohn @Nate_Cohn
October 12, 2016
[Link to NYT article]
This is a nice illustration of the decisions polls make when they weight their respondents. The authors disagree with the decisions of the USC/LA-Times pollsters, but applaud them for transparency:
It’s worth noting that this analysis is possible only because the poll is extremely and admirably transparent: It has published a data set and the documentation necessary to replicate the survey.
The article has multiple illustrations of what the trend of national Trump support would have been with different weighting decisions. Check it out.
Nathan Yau at Flowing Data points out two tools for making cartograms. One from Pitch Interactive, which allows you to upload state-by-state data and the tool creates the map. Another from Bhaskar V. Karambelkar at RPubs lets you create the map in R.
Gary King, Director for the Institute for Quantitative Social Science at Harvard University spoke at a recent Michigan Institute for Data Science (MIDAS) symposium. Below are links to the slides and a video of the presentation.
Slides | Video
For those who don’t want to watch the entire presentation, here are links to specific papers and/or software he mentions in the presentation.
Automated Text Analysis
VA: Verbal Autopsy [software]
Evaluating U.S. Social Security Administration Forecasts
Learning Catalytics [commercial start-up]
Crimson Hexagon: Social Media Insights [commercial start-up]
Perusall [commercial start-up, e-book platform to increase student engagement]
And, it might be more productive to just go through King’s personal website to find the content yourself. The above is just a fraction of his productivity.
Nathan Yau at Flowing Data has an interactive graphic showing the growth of obesity rates by state, year (since 1985) and gender.
A post by Sunmoo Yoon in the NIH OBSSR blog looks at the potential of data mining to offer insights into predictors of physical activity in older urban adults:
Only two out of ten older adults meet the national guidelines for physical activity in the United States. Little is known about interrelationships of many socio-ecological factors to improve physical activity behavior among Hispanic older adults. As we move towards a precision medicine approach, we need innovative strategies to discover precisely tailored targets and accurate interventions. Data mining has the potential to offer such insights.
The Census Bureau gathered data on fertility by asking a “children ever born” question from 1940 to 1990 in the decennial census. The 2000 Census did not ask a fertility question at all. With the advent of the American Community Survey, fertility was covered but with a different question. It asked if a woman had given birth to a child in the past year. This allows researchers to compute a total fertility rate. It performs reasonably well against the measure produced from the vital statistics system. And, given that geography is not readily available with the natality detail files anymore, this is a welcome solution. The main drawback to the ACS question is that the reference year will not span the calendar year that the vital statistics system is based on. Only the December respondents are referencing a January to December calendar year. See the Background section below for a further discussion of this.
However, recently, the Census Bureau noticed some anomalies in the data for selected areas and determined that some interviewers had been sloppy and asked “Have you given birth” rather than “Have you given birth in the last year.” Many more women will answer yes to the former and inflate the numerator. This is a good illustration of how much effort the Census Bureau goes to for producing accurate and robust statistics.
Addressing Data Collection Errors in the Fertility Question in the American Community Survey
Tavia Simmons | Census Bureau
In recent years, a few geographic areas in the American Community Survey (ACS) data had unusually high percentages of women reported as giving birth in the past year, quite unlike what was seen in previous years for those areas. This paper describes the issue that was discovered, and the measures taken to address it.
Indicators of Marriage and Fertility in the United States from the American Community Survey: 2000 to 2004
T. Johnson and J. Dye | Census Bureau
Slides 23 to 26 discuss and illustrate how the ACS and Vital Statistics estimates diverge from each other.