Best practices in using large, complex samples: The importance of using appropriate weights and design effect compensation

Practical Assessment, Research and Evaluation

Volumn 16, Issue 12, 2011, Pages 1-7

  Osborne, Jason W ^a  

^a OLD DOMINION UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84859041753     PISSN: None     EISSN: 15317714     Source Type: Journal    
DOI: None     Document Type: Article

References (9)

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2. Ingels, S. (1994). National Education Longitudinal Study of 1988: second follow-up: student component data file user's manual: US Dept. of Education, Office of Educational Research and Improvement, National Center for Education Statistics.
3. Johnson, D. R., & Elliott, L. A. (1998). Sampling Design Effects: Do They Affect the Analyses of Data from the National Survey of Families and Households? Journal of Marriage and Family, 60(4), 993-1001.
4. Kish, L. (1965). Selection techniques for rare traits. Genetics and the epidemiology of chronic diseases
5. Lehtonen, R., & Pahkinen, E. (2004). Practical methods for design and analysis of complex surveys: Wiley.
6. Osborne, J. W. (1995). Academics, self-esteem, and race: A look at the assumptions underlying the Disidentification hypothesis. Personality and Social Psychology Bulletin, 21(5), 449-455.
7. Osborne, J. W. (1997). Race and academic disidentification. Journal of Educational Psychology, 89(4), 728-735.
8. Osborne, J. W. (2000). Advantages of Hierarchical Linear Modeling. Practical Assessment, Research & Evaluation, 7(1).
9. Osborne, J. W. (2008). A brief introduction to Hierarchical Linear Modeling. In J. W. Osborne (Ed.), Best Practices in Quantitative Methods. Thousand Oaks, CA: Sage.