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Computing Variances from Data with Complex Sampling Designs: A Comparison of Stata and SPSS

Listed author(s):
  • Alicia C. Dowd


    (Univ. Mass. Boston, Graduate College of Education)

  • Michael B. Duggan


    (Suffolk University)

Registered author(s):

    Most of the data sets available through the National Center for Education Statistics (NCES) are based on complex sampling designs involving multi-stage sampling, stratification, and clustering. These complex designs require appropriate statistical techniques to calculate the variance. Stata employs specialized methods that appropriately adjust for the complex designs, while SPSS does not. Researchers using SPSS must obtain the design effects through NCES and adjust the standard errors generated by SPSS with these values. This presentation addresses the pros and cons of recommending Stata or SPSS to novice researchers. The first presenter teaches research methods to doctoral students and uses Stata to conduct research with NCES data. She uses SPSS to teach her research methods course, due to its user-friendly interface. The second presenter is a doctoral student conducting dissertation research with NCES data. In his professional life as an institutional researcher, he uses SPSS. NCES data sets are a rich resource, but the complex sampling designs create conceptual issues beyond the immediate grasp of most doctoral candidates in the field. The session considers and invites comment on the best approaches to introducing new researchers to complex sampling designs in order to enable them to use NCES data.

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    Paper provided by Stata Users Group in its series North American Stata Users' Group Meetings 2001 with number 3.1.

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    Date of creation: 15 Jan 2001
    Handle: RePEc:boc:asug01:3.1
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