By Peter L. Bonate

How do you examine pretest-posttest facts? distinction rankings? percentage swap rankings? ANOVA? In clinical, mental, sociological, and academic stories, researchers frequently layout experiments during which they acquire baseline (pretest) information sooner than randomization. although, they generally locate it tricky to determine which approach to statistical research is most excellent to take advantage of. before, consulting the on hand literature could end up a protracted and laborious activity, with papers moderately scattered all through journals and textbook references few and much between.

Analysis of Pretest-Posttest Designs brings welcome aid from this conundrum. This one-stop reference - written in particular for researchers - solutions the questions and is helping transparent the confusion approximately reading pretest-posttest facts. retaining derivations to a minimal and delivering genuine lifestyles examples from a number disciplines, the writer gathers and elucidates the ideas and methods most beneficial for experiences incorporating baseline data.

Understand the professionals and cons of other tools - ANOVA, ANCOVA, percentage switch, distinction rankings, and more

Learn to settle on the main acceptable statistical try - a variety of Monte Carlo simulations examine some of the assessments and assist you choose the single most suitable in your data

Tackle tougher analyses - The wide SAS code integrated saves you programming time and effort

Requiring only a uncomplicated historical past in data and experimental layout, this booklet accommodates such a lot, if no longer the entire reference fabric that bargains with pretest-posttest facts. in case you use baseline information on your reviews, research of Pretest-Posttest Designs will prevent time, elevate your figuring out, and eventually enhance the translation and research of your data.

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