I agree with with Rich Ulrich's comments, but bear in mind I was only answering the original query, which was for a good text. I find Diekhoff useful as an additional reference in my introductory stats course (it's not the text I use, which is Runyon, Haber, Pittenger and Coleman.) Diekhoff's actual decision trees occupy less than four pages of a 23 page chapter. The decision trees are elaborated with extensive discussion of the purpose of an analysis, the nature of the research question, the number of variables involved, the kind of data collected... Very few statistics texts contain this kind of material. As I teach my course (one semester, 13 3-hour sessions) I continually link new statistical tests to the Diekhoff decision tree. I also give homework and run a workshop in which students are given a wide variety of research scenarios (sample data, explicit or implicit research questions) and ask them to consider which statistical test or tests would be appropriate. Obviously this doesn't instantly turn all my students into expert designers and statisticians, but they certainly display good competence in getting the majority of tasks right. This is in marked contrast to students who have never been asked to do such things, and learn stats by simply doing exercises from textbooks and who have never been asked to decide on appropriate procedures when given an unfamiliar scenario. Since my course is only an introduction, we cover only a limited number of statistical procedures, and obviously there are dozens or hundreds of others. But I think the procedure I use encourages the students to read and reflect on research situations, and frame the question, "How might this research question be answered?" Paul Gardner Rich Ulrich wrote: > > [rearranging this note, to put the posts into order, earliest first. ] > > > > At 07:44 AM 02/29/2000 -0800, Ward Soper wrote: > > > >After one learns to do the textbook problems, as in Freund's > > > >Mathematical Statistics, where should one turn to learn what tests to > > > >use in various situations and how to design studies? Can anyone suggest > > > >some good texts or other resources? > > =============== > > dennis roberts wrote: > > > > > > william trochim's research methods knowledge base is a good place to start > > > ... to get ideas > > > > > > http://trochim.human.cornell.edu/kb/ > > ================ > On 29 Feb 2000 17:48:07 -0800, [EMAIL PROTECTED] > (Paul Gardner) wrote: > > > George. M. Diekhoff, Basic Statistics for the Social and Behavioral > > Sciences, Prentice Hall, 1996, has an excellent chapter at the end which > > presents a decision tree. This summarises the various statistical > > procedures in the text and helps learners to determine which statistics > > are appropriate under various conditions. > > ================= > - Pardon; I haven't seen Diekoff, but 'decision tree' sounds too > cheap. There is certainly a place for a mechanical framework of tests > and procedures; but I read the original question as less particular > than that, and more general ("how to design studies"); and the first > answer, that way, too. > > An enormous decision tree may give the right technical answer to 100% > of the narrow questions, but -- since it takes knowledge to frame the > right question -- that will be a misleading answer, I would guess, for > 1/3 of the naive questioners, at least. People just can't tell you > what they never thought to ask, concerning > 'reliability' (of various kinds); > 'dependence' (ditto); > 'shape of the distribution'; > 'outliers'; and > 'What numbers are meaningful when we use this measurement?' or, > 'What transformations might be useful?' > > (I am still answeriing the big question, Why can't a computer give us > all the stats advice that we need? So far, no one has programmed a > computer with 10,000 well-classified examples....) > > If they have not learned the whole statistical vocabulary, they won't > be able to argue persuasively that their own answers are correct. And > you can't thoroughly learn the vocabulary until you are expert enough > to know something about all the available techniques. > > In addition to the statistics, there are particular problems in each > area about their own sorts of statistical designs. To learn what to > do in various situations, I think you have to *read*, you have to be > exposed to a large number of various situations. You have to read > some good examples, and you have to read criticisms which include > examples that were not-so-good. > > -- > Rich Ulrich, [EMAIL PROTECTED] > http://www.pitt.edu/~wpilib/index.html > > =========================================================================== > This list is open to everyone. Occasionally, less thoughtful > people send inappropriate messages. Please DO NOT COMPLAIN TO > THE POSTMASTER about these messages because the postmaster has no > way of controlling them, and excessive complaints will result in > termination of the list. > > For information about this list, including information about the > problem of inappropriate messages and information about how to > unsubscribe, please see the web page at > http://jse.stat.ncsu.edu/ > ===========================================================================
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