Biomedical subjects

Ken Kelley

Publications and source records attributed to Ken Kelley.

5 recordsLinked to original sources

PubMed2006

Sample size planning for the standardized mean difference: accuracy in parameter estimation via narrow confidence intervals.

Methods for planning sample size (SS) for the standardized mean difference so that a narrow confidence interval (CI) can be obtained via the accuracy in parameter estimation (AIPE) approach are developed. One method plans SS so that the expected width of the CI is sufficiently narrow. A modification adjusts the SS so that the obtained CI is no wider than desired with some specified degree of certainty (e.g., 99% certain the 95% CI will be no wider than omega). The rationale of the AIPE approach to SS planning is given, as is a discussion of the analytic approach to CI formation for the population standardized mean difference. Tables with values of necessary SS are provided. The freely available Methods for the Behavioral, Educational, and Social Sciences (K. Kelley, 2006a) R (R Development Core Team, 2006) software package easily implements the methods discussed.

Confidence Intervals↗

PubMed2004

Assessing the assumption of symmetric proximity measures in the context of multidimensional scaling.

Applications of multidimensional scaling often make the assumption of symmetry for the population matrix of proximity measures. Although the likelihood of such an assumption holding true varies from one area of research to another, formal assessment of such an assumption has received little attention. The present article develops a nonparametric procedure that can be used in a confirmatory fashion or in an exploratory fashion in order to probabilistically assess the assumption of population symmetry for proximity measures in a multidimensional scaling context. The proposed procedure makes use of the bootstrap technique and alleviates the assumptions of parametric statistical procedures. Computer code for R and S-Plus is included in an appendix in order to carry out the proposed procedures.

Models, Statistical↗

PubMed2003

Sample size for multiple regression: obtaining regression coefficients that are accurate, not simply significant.

An approach to sample size planning for multiple regression is presented that emphasizes accuracy in parameter estimation (AIPE). The AIPE approach yields precise estimates of population parameters by providing necessary sample sizes in order for the likely widths of confidence intervals to be sufficiently narrow. One AIPE method yields a sample size such that the expected width of the confidence interval around the standardized population regression coefficient is equal to the width specified. An enhanced formulation ensures, with some stipulated probability, that the width of the confidence interval will be no larger than the width specified. Issues involving standardized regression coefficients and random predictors are discussed, as are the philosophical differences between AIPE and the power analytic approaches to sample size planning.

Humans↗

PubMed2003

Obtaining power or obtaining precision. Delineating methods of sample-size planning.

Sample-size planning historically has been approached from a power analytic perspective in order to have some reasonable probability of correctly rejecting the null hypothesis. Another approach that is not as well-known is one that emphasizes accuracy in parameter estimation (AIPE). From the AIPE perspective, sample size is chosen such that the expected width of a confidence interval will be sufficiently narrow. The rationales of both approaches are delineated and two procedures are given for estimating the sample size from the AIPE perspective for a two-group mean comparison. One method yields the required sample size, such that the expected width of the computed confidence interval will be the value specified. A modification allows for a defined degree of probabilistic assurance that the width of the computed confidence interval will be no larger than specified. The authors emphasize that the correct conceptualization of sample-size planning depends on the research questions and particular goals of the study.

Analysis of Variance↗

PubMed2003

Analytic methods for questions pertaining to a randomized pretest, posttest, follow-up design.

Delineates 5 questions regarding group differences that are likely to be of interest to researchers within the framework of a randomized pretest, posttest, follow-up (PPF) design. These 5 questions are examined from a methodological perspective by comparing and discussing analysis of variance (ANOVA) and analysis of covariance (ANCOVA) methods and briefly discussing hierarchical linear modeling (HLM) for these questions. This article demonstrates that the pretest should be utilized as a covariate in the model rather than as a level of the time factor or as part of the dependent variable within the analysis of group differences. It is also demonstrated that how the posttest and the follow-up are utilized in the analysis of group differences is determined by the specific question asked by the researcher.

Analysis of Variance↗