DH Lab 6: Assessing Data

We’ve somehow made it to mid-semester already. And while the workload certainly supports that observation, the time itself has flown by. Getting halfway through an upper-level course often means the focus shifts towards a final project, which is exactly where my Digital Humanities course is headed.

For our lab this week, the class was asked to evaluate a data set that might be used as a source for our final projects. The goal of the project is to formulate an argument based on the comparisons of two different datasets. One of the datasets must be the Longitudinal Religious Congregations and Membership File discussed in a previous blog post. The other source can be one of our choosing. Which is great unless you have a brain like mine that basically runs like an internet browser with too many tabs open. I’ve found one rabbit hole after another and (as often happens) have been slightly derailed from my long-term goal. This is where small goals become especially handy; as this blog post will hopefully help move me in a step closer towards finalizing my ideas for my final project.

This lab, as stated earlier, asks various questions about a dataset of our choosing. One dataset I stumbled on while exploring rabbit holes did pique my interest, especially considering what was being asked of it. Though it may not end up in my final project, it will nevertheless function as a stepping stone to getting me closer to that endeavor.

So, without further ado . . .

  1. Are there changes needed to make the data tidy?
    • yes, the Excel sheet combines all the data to comb for similarities, but in doing so, makes the data more confusing than easier to understand.
  2. Are there over- or under-represented groups?
    • Over-represented: African Americans and white Americans, members of major ‘world religions’ (Catholic, Protestant, Jewish, None, or Other), middle-age and older individuals (no young folks).
  3. Is there missing data, i.e., “null” values?
    • The ‘nones’ and ‘others’
  4. Does the data pass the “sniff test”? Does anything look wonky?
    • Data collection changes over time (Survey questions change, some questions removed entirely) 
  5. Where did the data come from?
    • The continental United States’ household population aged 25 and older, exclusive of residents of Alaska and Hawaii.
  6. Who collected it?
    • James S. House, University of Michigan. Institute for Social Research. Survey Research Center
    • US Department of Health and Human Services (funded)
  7. When?
    • 1986, 1989, 1994, 2002, and 2011
  8. How was it collected?
    • “Wave I of the study began in 1986 with a nation face-to-face survey of 3,617 adults ages 25 and up, with Black Americans and people aged 60 and over over-sampled at twice the rate of the others. Wave II constitutes face-to-face re-interviews in 1989 of those still alive. Survivors have been re-interviewed by telephone, and when necessary face-to-face, in 1994, 2001/02, and 2011, making up Waves III, IV, and V of the data.”
  9. Why was it collected?
    • “ACL was designed and sought out to investigate the following: (1) the ways in which a wide range of activities and social relationships that people engage in are broadly ‘productive,’ (2) how individuals adapt to acute life events and chronic stresses that threaten the maintenance of health, effective functioning, and productive activity, and (3) sociocultural variations in nature, meaning, determinants, and consequences of productive activity and relationships.”
  10. Whose goals are prioritized? 
    • Those who want to show a relationship between race and lifestyle.
  11. Who benefits and who is overlooked?
    • Focuses on African Americans and white Americans in middle and late-life 
    • Includes (in 1986): Protestant, Roman Catholic, Jewish, None, and Other (World Religions Paradigm) 
      • These measurements disappear a
    • Those who do not define productivity in the same way as a researcher 

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