title: Conceptual and technical considerations when combining large data sets.
abstract: Secondary analysis provides a useful method for the development of new knowledge. Larger samples can be constructed, and secondary analysis can be enhanced when data sets are combined. A standardized method for combining large data sets is crucial, yet literature on methods for combining large data sets for secondary analysis is lacking. The purpose of this article is to outline and explain the process of combining two or more large data sets (n = 276, n = 125) for secondary analysis by using these authors' previous work with large oncology and AIDS caregiver data sets.
authors.affiliation: Temple University, USA.
authors.forename: A J
authors.initials: AJ
authors.lastname: Orsi
authors.forename: M
authors.initials: M
authors.lastname: Grey
authors.forename: M M
authors.initials: MM
authors.lastname: Mahon
authors.forename: H J
authors.initials: HJ
authors.lastname: Moriarty
authors.forename: M P
authors.initials: MP
authors.lastname: Shepard
authors.forename: R M
authors.initials: RM
authors.lastname: Carroll
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000451:nursing; D000704:Analysis of Variance; D016009:Chi-Square Distribution; D003627:Data Interpretation, Statistical; D006801:Humans; D015201:Meta-Analysis as Topic; D016294:Models, Nursing; D009369:Neoplasms / Q000451:nursing; D015399:Nursing Research / Q000379:methods*; D009742:Nursing Theory; D015203:Reproducibility of Results; D012107:Research Design / Q000592:standards; D013997:Time Factors