Technology and Social Work: Odd Bedfellows No More

The first Global Health and Well-Being Conference hosted by NYU’s Silver School of Social Work took place last week, where I attended a session entitled “Trauma and Technology”. Both presenters gave excellent examples of how social work can use technology in research methods, proving that social work and technology don’t have to be odd bedfellows.

Current sampling methods in crisis environments tend to use cluster sampling methods (most notably Les Robert’s approach of cluster sampling to determine the civilian mortality rate in Iraq after the US invasion). Building upon these sampling techniques, which certainly have a risk of inflated numbers, Professor Royce Hutson from Boise State University presented his work on random grid sampling, which uses statistical properties of estimators to determine population density and randomly select a particular building within that population. This results in a bigger sample size leading to better estimates.

Marion Lok from University of Melbourne presented her doctoral research on use of crisis informatics in social work. She used technology to actually determine how people use technology is disaster. She found that people not directly affected by particular natural disasters in Australia and New Zealand had higher perceived risk than those who were directly affected. This may be because those who were directly affected did not have access to the internet. Lok’s presentation made me wonder, what are the implications for media creating more problems during emergencies?

So, I really like the idea of integrating technology into social work, especially through research methods. In fact, I tried to do this with my use of GPS with Palestinian children and families. I think this is a promising direction for social work research. What do others think?

Unintentional Sampling Bias

I am reading Thomas D. Seeley’s book Honeybee Democracy, which explains the collective wisdom and effective decision-making of the hardest working insect known to man: honeybees. I could write an extremely long post about how amazing honeybees are (don’t get me started!), but I wanted to highlight a great example of unintentional sampling bias detailed by Seeley himself.

In 1975, Seeley worked as a research assistant at the Dyce Laboratory for Honey Bee Studies at Cornell in Ithaca, NY. Under the guidance of Professor Roger A. Morse, Seeley focused his research on determining the ideal honeybee hive, based on observation of existing wild beehives found in the local Ithaca area. Seeley put an ad in the local newspaper, The Ithaca Journal, asking community members to contact him if they saw a tree housing a live colony of honeybees. Within one week, he had secured the rights to 18 accessible bee trees in the woods around Ithaca. He ultimately collected and sampled 21 bee tree nests by killing the hive with calcium cyanide powder, cutting down the tree, and carefully dissecting the dead hive. He also located another 18 nests in trees to gather information about hive openings.

So what does this all have to do with research methods? Well, Seeley observed trends towards small size, floor level, southern orientaton indicating elements that were favorable to honeybees. But what perplexed him was the preponderance of nest entrances that were just a few feet from the ground. Being that close to the ground makes honeybee nests vulnerable to detection by predators, such as bears, whose attacks can be fatal to the hive. But, as Seeley later determined, honeybees actually have a strong preference for nesting cavities¬† with entrances located high above the ground. Seely’s initial findings were affected by unintentional sampling bias. The nests that he sampled were noticed inadvertently by a person walking past a bee tree, people are more likely to notice honeybees trafficking from a ground-level nest entrance than a tree-top one. Seeley determined this when he learned to “line” bees (locating bee trees by baiting foragers from flowers and observing their flights back to their nests). He found that every hunt ended with him straining to spy the bees zipping in and out of a nest entrance high in a tree. He has since determined that the average height of a honey bee nest entrance is 6.5 meters (21 feet). Seeley reports in his book: “Needless to say, I’m now alert to the hidden danger of unintentional sampling bias” (p. 52).

This is an excellent illustration of sampling bias, and why we need to be aware of its effects in our own research.

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