We argue that to best comprehend many data sets, plotting judiciously selected sample statistics with associated confidence intervals can usefully supplement, or even replace, standard hypothesis-testing procedures. We note that most social science statistics textbooks limit discussion of confidence intervals to their use in between-subject designs. Our central purpose in this article is to describe how to compute an analogous confidence interval that can be used in within-subject designs. This confidence interval rests on the reasoning that because between-subject variance typically plays no role in statistical analyses of within-subject designs, it can legitimately be ignored; hence, an appropriate confidence interval can be based on the standard within-subject error term-that is, on the variability due to the subject × condition interaction. Computation of such a confidence interval is simple and is embodied in Equation 2 on p. 482 of this article. This confidence interval has two useful properties. First, it is based on the same error term as is the corresponding analysis of variance, and hence leads to comparable conclusions. Second, it is related by a known factor (√2) to a confidence interval of the difference between sample means; accordingly, it can be used to infer the faith one can put in some pattern of sample means as a reflection of the underlying pattern of population means. These two properties correspond to analogous properties of the more widely used between-subject confidence interval.

https://faculty.washington.edu/gloftus/Research/Publications/Manuscript.pdf/Loftus%20&%20Masson%201994.pdf

Using confidence intervals in within-subject designs

Enterprise resource planning (ERP) systems and other complex information systems represent critical organizational resources. For such systems, firms typically use consultants to aid in the implementation process. Client firms expect consultants to transfer their implementation knowledge to their employees so that they can contribute to successful implementations and learn to maintain the systems independent of the consultants. This study examines the antecedents of knowledge transfer in the context of such an interfirm complex information systems implementation environment. Drawing from the knowledge transfer, information systems, and communication literatures, an integrated theoretical model is developed that posits that knowledge transfer is influenced by knowledge-related, motivational, and communication-related factors. Data were collected from consultant-and-client matched-pair samples from 96 ERP implementation projects. Unlike most prior studies, a behavioral measure of knowledge transfer that incorporates the application of knowledge was used. The analysis suggests that all three groups of factors influence knowledge transfer, and provides support for 9 of the 13 hypotheses. The analysis also confirms two mediating relationships. These results (1) adapt prior research, primarily done in non-IS contexts, to the ERP implementation context, (2) enhance prior findings by confirming the significance of an antecedent that has previously shown mixed results, and (3) incorporate new IS-related constructs and measures in developing an integrated model that should be broadly applicable to the interfirm IS implementation context and other IS situations. Managerial and research implications are discussed.

https://www.jstor.org/stable/pdfplus/25148668.pdf

Antecedents of knowledge transfer from consultants to clients in enterprise system implementations

The purpose of this article is to provide a survey of the various stages in the development of response surface methodology RSM. The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s. Part I covers the period, 1951-1975, during which the so-called classical RSM was developed. This includes a review of basic experimental designs for fitting linear response surface models, in addition to a description of methods for the determination of optimum operating conditions. Part II, which covers the period, 1976-1999, discusses more recent modeling techniques in RSM, in addition to a coverage of Taguchi's robust parameter design and its response surface alternative approach. Part III provides a coverage of further extensions and research directions in modern RSM. This includes discussions concerning response surface models with random effects, generalized linear models, and graphical techniques for comparing response surface designs. Copyright © 2010 John Wiley & Sons, Inc.

Response Surface Methodology.

Response surface methodology

Basics of Software Engineering Experimentation is a practical guide to experimentation in a field which has long been underpinned by suppositions, assumptions, speculations and beliefs It demonstrates to software engineers how Experimental Design and Analysis can be used to validate their beliefs and ideas The book does not assume its readers have an in-depth knowledge of mathematics, specifying the conceptual essence of the techniques to use in the design and analysis of experiments and keeping the mathematical calculations clear and simple Basics of Software Engineering Experimentation is practically oriented and is specially written for software engineers, all the examples being based on real and fictitious software engineering experiments

Basics of Software Engineering Experimentation

Review of Some Basic Statistical Concepts Some Intermediate Data Analysis Concepts A Scientific Approach to Experimentation Completely Randomized Design (CRD) Randomized Complete Block Design (RCBD) Nested (Hierarchical) and Nested Factorial Designs Split Plot Type Design Latin Square Type Designs 2n Factorial Experiments (Complete and Incomplete Blocks) Fractional Factorial Experiments for Two-Leveled Factors Three-Level Factorial Experiments Mixed Factorial Experiments and Other Incomplete Block Designs Response Surface Exploration Appendices

Virgil L. Anderson

Papers

Design of Experiments: A Realistic Approach

Applied Factorial and Fractional Designs

Corrigendum: Restriction Errors: Another Dimension in Teaching Experimental Statistics

Restriction Errors: Another Dimension in Teaching Experimental Statistics

Nested (Hierarchical) and Nested Factorial Designs