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Senior Lecturer School of Mental Health & Learning Disabilities UWE Bristol |
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Publications |
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Ingram, R. (1989). Self-Inflicted Injury. Nursing Times. January 4, Vol 85, No 1, pp 47-49.
Ingram.
R. (1991). Learning difficulties and communication. Nursing
Standard. April 24, Vol 5, No 31, pp 36-39
Ingram,
R. (1991). Why does nursing need theory? Journal of Advanced
Nursing. 16, 350-353. (Abstract)
Ingram.
R. (1998). Power analysis and sample size estimation. NT Research.
3(2) March/April, 132-139. (Abstract)
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Moule, P., Gilchrist, M., Ingram, R. & Pontin, D. (2003). Data Analysis Web Site. Bristol: UWE Bristol. Available online http://learntech.uwe.ac.uk/dataanalysis/ |
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Why does nursing need theory?
The last 25 years have witnessed a growing support for and
recognition of the importance of nursing theory, both in education
and practice. This paper seeks to explore this movement, and also
the issues in theory development, study and application. Definitions
of theory are reviewed, and linked to the purpose of theory and
theory development in nursing. The origins and motivation for
developing nursing theory are identified, and arguments for the use
of nursing theory in nursing are raised. Further questions from the
discussion of these issues are identified, questions likely to
provided continued debate and investigation within the profession
for decades to come.
This paper has been re-published in:
Tjallinks, J.E. (Editor) (1993). Reader for Nursing Education.
Pretoria.
University of South Africa.
and in the course readers for the
University of Dundee Diploma in Advanced Nursing Studies
and the
Open University of Hong Kong Advanced Nursing distance learning
module
Power analysis and sample size estimation
Determining the appropriate sample size is often a difficult
decision in the process of developing quantitative research
proposals. The novice researcher may well understand that the need
for an adequate sample size is an important issue, but may lack the
knowledge to make an informed decision. Often the sample size will
be based on the constraints of practical considerations such as time
or cost, but with little confidence that the sample is adequate in
any statistical sense. This paper explores power analysis as an
approach to sample size estimation that can be used even by novice
researchers to provide a more rational basis for such decisions. The
principles underpinning power analysis and the factors that
contribute to statistical power are discussed, with an example of
power analysis applied to a simple experimental design. Some
arguments against a perceived overemphasis on power analysis are
raised. Finally, relevant literature, computer software and World
Wide Web resources are included in a bibliography.
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