Teaching Students to Use Large-Scale, Machine-Readable, Demographic Data Sources
Bibliographic Data
| ID | 11062833 |
|---|---|
| Authors | Robert E Kennedy (0000-0002-5507-474X, corresponding author) |
| Year | 1986 |
| Volume | 14 |
| Issue | 2 |
| Pages | 134 |
| Publication date | 1986-04-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Teaching Sociology (JOURNAL) |
| Journal identifiers | ISSN: 0092-055X • E-ISSN: 1939-862X |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.2307/1317621 |
| OpenAlex | W2139733672 |
| Language | EN |
| References cited | 1 |
than just a few years ago. One result is that machine-readable, federally conducted (or financed) sources of demographic information are now readily available to individual researchersincluding those employed by small private corporations or by local and state governments. Examples of such sources are census samples, the Current Population Surveys, the NORC General Social Surveys, national fertility surveys, national household surveys, and social security surveys. These developments have led me to devote half of my upper division Population Methods course to showing students how they can begin using machine-readable demographic sources. After completing the course, students are able to use the skills they have gained, and the sources they have learned about, to begin their own independent research projects. Several students have told me that the skills they learned in the course helped them compete more effectively for research-related employment. Course Objectives. My goal is to have each student complete his or her own hands-on, elementary analysis of selected variables using a large scale, machine-readable demographic source. Course Procedures. I obtain the data source for
Data science · Geography · Higher education · Mathematics education · Pedagogy · Political science · Scale (ratio · Social science · Sociology · Census and Population Estimation · Computer Science · demographic modeling and climate adaptation · Insurance, Mortality, Demography, Risk Management · Law · Psychology
| Highly cited | No |
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