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Using multivariate statistics 5th ed
Obesity threatened, and we've had to consider putting the book on a diet. We've added only'one chapter this time around, Multilevel Linear Modeling (Chapter 15). and some spiffy new techniques for dealing with missing data (in Chapter 4). Otherwise, we've mostly streamlined and said goodbye to some old friends. We've forsaken the Time-Series Analysis chapter in the text, but you'll be able to download it from the publisher's web site at www.ablongman.com/tabachnick5e. Another sadly forsaken old friend is SYSTAT. We still love the program, however, for its right-to-the-point analyses and terrific graphics, and are pleased that most of the graphics have been incorporated into SPSS. Although absent from demonstrations, features of SYSTAT, and any other programs we've cut, still appear in the last sections of Chapters 5 through 16, and in online Chapter 18, where programs are compared. We've changed the order of some chapters: canonical correlation seemed rather difficult to appear as early as it did, and survival analysis seemed to want to snuggle up to logistic regression. .Act~lally, the order doesn't seem to matter much: perusal of syllabi on the Web convinces us that professors feel free to present chapters in any order they choose-and that's fine with us. Multilevel linear modeling (MLM) seems to have taken the world by storm; how did we ever live without it? Real life is hierarchical-students come to us within classrooms, teachers work within different schools, patients share wards and nursing staff, and audiences attend different performances. We hardly ever get to break these groups apart for research purposes, so we have to deal with intact groups and all their shared experiences. MLM lets us do this without violating all of the statistical assumptions we learned to know and hate. Now that SAS and SPSS can deal with these models, we're ready to tackle the real world. Hence, a new chapter. SAS and SPSS also now offer reasonable ways to impute illissing data through multipleimputation techniques and fii_llly assess miscing data patterns, respectively. We expanded Chapter 4 to detnonstrate these enhancements. SPSS and SAS keep adding goodies, which we'll try to show off. As before, we adapt our syntax from Windows menus whenever possible, and all of our data sets are available on the book's web page. We've also paid more attention to effect sizes and, especially, confidence intervals around effect sizes. Michael Simpson of [he Austraiian Nationai University has kindiy given us permission to include some nifty SPSS and SAS syntax and data files in our web page downloads. Jim Steiger and Rachel Fouladi have graciously given us permission to include their DOS program that finds confidence intervals around R? One thing we'll never change is our practical bent, focusing on the benefits and lirriitations of applications of a technique to a data set-when, why, and how to do it. The math is wonderful, and we suggest (but don't insist) that students follow along through section four of each chapter using readily available software for matrix manipulations or spreadsheets. But we still feel that understanding the math is not enough to insure appropriate analysis of data. And our readers assure us that they really are able to apply the techniques without a great deal of attention to the math of section four. Our small-sample examples remain silly; alas, our belly dancing days are over. As for our most recent reviewers, kindly provided by our publisher, we had the three bears checking out beds: too hard, too soft, and just right. So we've not changed the tone or level of difficulty. Some extremely helpful advice wax offered by Ste~e Osterlincl of the Univerxity of Missouri-Columbia and Jeremy Jewel of Southern Illinois University-Edwal-dsville. We also heartily thank Lisa Harlow of the Un~versity of Rhodr: I.land. who wrote an extenhibe. ini?htful review of the entire fourth edition of 011s book in Sti.in 7002. LVr asain thank the reviewers of earlier editions of our book, but fears of breaking the backs of current students dissuade us from listing them all once more. You know who you are; we still care. Our thanks to the reviewers of this edition: Joseph Benz, University of Nebraska-Kearney; Stanley Cohen, West Virginia University; Michael Granaas, University of South Dakota; Marie Hammond. Tennessee State University at ~ashville; Josephine Korchmaros, Southern Illinois University; and Scott Roesch, San Diego State University. As always, the improvements are largely due to reviewers and those colleagues who have taken the time to email us with suggestions and corrections. Any remaining errors and lack of clarity are due to us alone. As always, we hope the book provides a few smiles as well as help in analyzing data.
Ketersediaan
| Call Number | Location | Available |
|---|---|---|
| Tan 519. 535 Tab u | PSB lt.dasar - Pascasarjana | 3 |
| Penerbit | New York Pearson., 2007 |
|---|---|
| Edisi | - |
| Subjek | Data analysis Multivariate |
| ISBN/ISSN | - |
| Klasifikasi | - |
| Deskripsi Fisik | - |
| Info Detail Spesifik | - |
| Other Version/Related | Tidak tersedia versi lain |
| Lampiran Berkas | Tidak Ada Data |