THE DECISION TREE FOR STATISTICS - Aurora. The material used in this guide is based upon "A Guide for Selecting Statistical Techniques for Analyzing Social Science Data," Second Edit ion, produced at the Institute for Social Research, The University of Michigan, under the authorship of Frank M. Andrews, Laura Klem, Terrence N. Davidson, Patrick O'Malley, and Willard L. Rodgers, copyright 1981 by The University of Michigan, All Rights Reserved. This electronic version copyright 2014 by Neal Van Eck. The Decision Tree helps select statistics or statistical techniques appropriate for the purpose and conditions of a particular analysis and to select the MicrOsiris commands which produce them or find the corresponding SPSS and SAS commands.
Start with the first question on the next screen and choose one of the alternatives presented there by selecting the appropriate link. GlossaryReferences. Index. Home. 90–Day Cycles and Improvement Research. Printer-friendly version In a recent Education Week article, representatives from private and government organizations concerned with education research lined up behind the 90-day cycle model, developed by the Institute for Healthcare Improvement (IHI) in Cambridge, Mass., as a way to accomplish “deep-dive, quick turnaround” education research.
Carnegie leadership spent a week at IHI last year, and after our own “deep dive” into 90-day cycles, has a team in place, has completed its second round of cycles and will begin another round this month. This work has proceeded with coaching from IHI staff. It is part of Carnegie’s new mission to cull insights from high performing improvement organizations like IHI, as well as to assume a leadership role as thought partners and conveners to assist others interested in similar work.
For example, we’ve worked closely with the Knowledge Alliance for the past two years, leading conference sessions and hosting forums. The Joy of Stats. Online Evaluation Resource Library. Simpson's paradox. Simpson's paradox for continuous data: a positive trend appears for two separate groups (blue and red), a negative trend (black, dashed) appears when the data are combined.
In probability and statistics, Simpson's paradox, or the Yule–Simpson effect, is a paradox in which a trend that appears in different groups of data disappears when these groups are combined, and the reverse trend appears for the aggregate data. This result is often encountered in social-science and medical-science statistics,[1] and is particularly confounding when frequency data are unduly given causal interpretations.[2] Simpson's Paradox disappears when causal relations are brought into consideration.
Many statisticians believe that the mainstream public should be informed of the counter-intuitive results in statistics such as Simpson's paradox.[3][4] Edward H. Examples[edit] Kidney stone treatment[edit] Which treatment is considered better is determined by an inequality between two ratios (successes/total). And . . Martin Tulic, Book indexing - About indexing - Software for indexing. Home > About indexing > Less than twenty years ago, most indexes were created by writing data on index cards and then editing and sorting the cards.
Although cards may still be used occasionally, today most indexes are created using software of one kind or other. There are three software-based approaches to creating indexes: 1. Embedded indexing, in which the program used to create a document is also used to embed index entries in the document itself. 2. 3. The major standalone indexing programs are: There are also special-purpose application programs to assist indexers in their work. These special-purpose programs are used almost exclusively by professional indexers or technical writers. Guidelines for using indexing software: Try the demo version before purchasing the product. To top of page.