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Recent documents in DigitalCommons@WayneStateen-usFri, 19 Sep 2014 01:32:19 PDT3600Using the Inverse Transform to Specify Contrasts in Regression and Latent Curve Structural Equation Models
http://digitalcommons.wayne.edu/nursingfrp/11
http://digitalcommons.wayne.edu/nursingfrp/11Tue, 16 Sep 2014 06:30:18 PDT
A simple yet general method for specifying contrasts to test hypotheses in regression and latent curve structural equation models is presented. The traditional qualitative variable coding schemes used in multiple regression (e.g., dummy coding) have a more general formulation. Five matrices are involved: The coding scheme, A. The matrix which gives the distribution and ordering of cases, W; WA = X; X is the design matrix. The contrast coefficient matrix C; and C^{-1} = A. In practice, only C, C^{-1}, and A are necessary because the statistical software generates the design matrix. This method has great generality because the same coding matrix, A, is used in multiple regression, multilevel modeling, and latent curve structural equal models. Starting with the contrasts allows one to compute the coding matrix, A, for a wide variety specific hypothesis.
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Thomas N. TemplinNursing Science Research Consulting: A Multidisciplinary Framework
http://digitalcommons.wayne.edu/nursingfrp/10
http://digitalcommons.wayne.edu/nursingfrp/10Tue, 16 Sep 2014 06:30:17 PDT
Nursing science research is at the intersection of the social and medical sciences and statistical developments in many different disciplines are relevant. A framework for nursing science statistics which recognizes and builds upon the statistical contributions from biostatistics, quantitative psychology, epidemiology, econometrics, survey research, computer science and statistics is presented. A broad eclectic framework is necessary to take advantage of new developments in statistical and research design methodology addressing specific problems common to a given area. This framework recognizes that awareness of differences in established expectations (conventions, guidelines, regulations, etc.) with regard to statistical methodology across different research areas is an important aspect of successful consulting. It is hoped that this framework will facilitate interdisciplinary collaboration so nurse scientist statisticians will take a leading role in advancing methodology and research design.
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Thomas N. Templin