THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research

https://research.vu.nl/ws/files/2901670/267479.pdf

Quality criteria were proposed for measurement properties of health status questionnaires

Preface Chapter 1 The challenge Chapter 2 The nature of the evidence: A synthesis of meta-analyses Chapter 3 The argument: Visible teaching and visible learning Chapter 4: The contributions from the student Chapter 5 The contributions from the home Chapter 6 The contributions from the school Chapter 7 The contributions from the teacher Chapter 8 The contributions from the curricula Chapter 9 The contributions from teaching approaches - I Chapter 10 The contributions from teaching approaches - II Chapter 11: Bringing it all together Appendix A: The 800 meta-analyses Appendix B: The meta-analyses by rank order References

Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement

Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA’s such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.

/pdf/calculating-and-reporting-effect-sizes-to-facilitate-4a429kama3.pdf

Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

Although intraclass correlation coefficients (ICCs) are commonly used in behavioral measurement, psychometrics, and behavioral genetics, procedures available for forming inferences about ICCs are not widely known. Following a review of the distinction between various forms of the ICC, this article p

https://psycnet.apa.org/journals/met/1/1/30.pdf

Forming inferences about some intraclass correlation coefficients.

Some of the shortcomings in interpretability and generalizability of the effect size statistics currently available to researchers can be overcome by a statistic that expresses how often a score sampled from one distribution will be greater than a score sampled from another distribution. The statistic, the common language effect size indicator, is easily calculated from sample means and variances (or from proportions in the case of nominal-level data). It can be used for expressing the effect observed in both independent and related sample designs and in both 2-group and n-group designs

A common language effect size statistic.

Objective. This study was performed to determine prospectively whether, in the presence of proved or presumed bacterial infection, the sensitivity of serum C-reactive protein (CRP) response could be enhanced by serial rather than single determinations. We also sought to assess CRP responses to clinically identified noninfectious disorders. Design. The CRP responses of 491 infants on 691 occasions of suspected infection were assessed. CRP levels were measured initially and twice again at 12-hour intervals (rate immunonephelometry). Assessments also included a blood culture, complete blood cell count, and chest radiograph and culture of spinal fluid when appropriate. CRP responses were correlated with four designated clinical groups: (1) positive blood or cerebrospinal fluid cultures (n = 190); (2) negative blood culture-definite infection (necrotizing enterocolitis stages 2 and 3, pneumonia, subcutaneous abscess) (n = 52); (3) negative blood culture-possible infection (antenatal risk factors, meconium aspiration, positive urine group B streptococcus antigen, necrotizing enterocolitis stage 1, febrile infants) (n = 287); and (4) negative blood culture-no infection (respiratory distress syndrome, transient tachypnea of the newborn, patent ductus arteriosus, tissue trauma) (n = 160). Diagnoses were made before CRP results were known. Results. In all, 187 (27%) of the blood cultures were positive. A single organism was recovered from 174 of these; two organisms from 13. Among the single-organism cultures, 50 (29%) were Gram-negative, 120 (69%) were Gram-positive, and 4 (2%) were budding yeasts. CRP levels were elevated in various groups as follows: in the positive blood culture group (by organism), Gram-negative rods, 92% (46/50); group B streptococcus, 92% (12/13); Staphylococcus aureus, 89% (8/9); group D streptococcus, 71% (10/14); Streptococcus viridans, 60% (6/10); Staphylococcus epidermidis, 55% (40/73). In the negative blood culture-definite infection group, CRP levels were abnormal in 88%; in the negative culture-possible infection group, CRP was elevated in 33%; and in the negative blood culture-no infection group, CRP was elevated in 9%. Serial determinations of CRP resulted in enhanced sensitivity in the positive blood culture group, the negative blood culture-definite infection group, and the negative blood culture-possible infection group. Initial determinations by themselves were inadequatey sensitive. Serial determinations did not enhance sensitivity of the negative blood culture-no infection group. High specificity (91%) is suggested by the low incidence of abnormal CRP levels among infants who were not infected. Conclusions. These data suggest that it would be appropriate to conduct a cautious, controlled trial to assess the safety of discontinuing antibiotic therapy if three serial CRP measurements are normal and if there are no other clinical factors suggestive of infection. The data also indicate the necessity for serial determinations of CRP for optimal sensitivity.

S. P. Wong

Papers

Forming inferences about some intraclass correlation coefficients.

A common language effect size statistic.

Significance of Serial C-Reactive Protein Responses in Neonatal Infection and Other Disorders

"Forming inferences about some intraclass correlations coefficients": Correction.

A method for exploring the effects of attrition in randomized experiments with dichotomous outcomes