Hi List.
What will be a good statistical reference book for a Statistician who has just joined a Statistical Research consulting firm with specialization in Research Design and Statistical Analysis. Regards. |
Of course, a lot depends on what kinds of clients you have.
From a broad perspective see Raynald Levesque book on data handling, etc. and Norusis help about SPSS http://www.spss.com/PDFs/SB13INS-0405.pdf The older editions of the following have been very useful to me in consulting of something like over 1000 Congressional evaluations/investigations, and 200 doctoral dissertations in social, behavioral, and health studies. clips from Library of Congress catalog for design this is the latest version of the classic Cook and Campbell books Personal Name: Shadish, William R. <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Author&SEQ=20061129155139&PID=24540&SA=Shadish,+William+R.> Main Title: Experimental and quasi-experimental designs for generalized causal inference / William R. Shedish, Thomas D. Cook, Donald T. Campbell. Published/Created: Boston : Houghton Mifflin, [2001?] Related Names: Cook, Thomas D. <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Author&SEQ=20061129155139&PID=24540&SA=Cook,+Thomas+D.> Campbell, Donald Thomas, 1916- <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Author&SEQ=20061129155139&PID=24540&SA=Campbell,+Donald+Thomas,+1916-> Description: xxi, 623 p. : ill. ; 23 cm. ISBN: 0395615569 Notes: Includes bibliographical references (p. 514-591) and indexes. Subjects: Causation--Experiments. <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Subject&SEQ=20061129155139&PID=24540&SA=Causation+Experiments.> LC Classification: BD591 .S48 2001 Dewey Class No.: 122/.07/2 21 For solid understanding of a broad array of statistical methods Main Title: Applied multiple regression/correlation analysis for the behavioral sciences / Jacob Cohen ... [et al.]. Edition Information: 3rd ed. Published/Created: Mahwah, N.J. : L. Erlbaum Associates, 2003. Related Names: Cohen, Jacob, 1923- <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Author&SEQ=20061129160147&PID=3738&SA=Cohen,+Jacob,+1923-> Cohen, Jacob, 1923- Applied multiple regression/correlation analysis for the behavioral sciences. <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Author&SEQ=20061129160147&PID=3738&SA=Cohen,+Jacob,+1923-> Description: xxviii, 703 p. : ill. ; 26 cm. + 1 CD-ROM (4 3/4 in.) ISBN: 0805822232 (hard cover : alk. paper) Notes: Rev. ed. of: Applied multiple regression/correlation analysis for the behavioral sciences / Jacob Cohen, Patricia Cohen. 2nd ed. 1983. The CD-ROM contains the data for almost all examples as well as the command codes for each of the major statistical packages for the tabular and other findings in the book. Includes bibliographical references (p. 655-669) and indexes. Subjects: Regression analysis. <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Subject&SEQ=20061129160147&PID=3738&SA=Regression+analysis.> Correlation (Statistics) <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Subject&SEQ=20061129160147&PID=3738&SA=Correlation+%28Statistics%29> Social sciences--Statistical methods. <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Subject&SEQ=20061129160147&PID=3738&SA=Social+sciences+Statistical+methods.> LC Classification: HA31.3 .A67 2003 Dewey Class No.: 519.5/36 21 Art Kendall Social Research Consultants Edward Boadi wrote: >Hi List. >What will be a good statistical reference book for a Statistician who has just joined a >Statistical Research consulting firm with specialization in Research Design and Statistical Analysis. >Regards. > > > >
Art Kendall
Social Research Consultants |
In reply to this post by Edward Boadi
Thanks Art , I am very grateful.
-----Original Message----- From: Art Kendall [mailto:[hidden email]] Sent: Wednesday, November 29, 2006 4:38 PM To: Edward Boadi Cc: [hidden email] Subject: Re: Statistics Book Of course, a lot depends on what kinds of clients you have. From a broad perspective see Raynald Levesque book on data handling, etc. and Norusis help about SPSS http://www.spss.com/PDFs/SB13INS-0405.pdf The older editions of the following have been very useful to me in consulting of something like over 1000 Congressional evaluations/investigations, and 200 doctoral dissertations in social, behavioral, and health studies. clips from Library of Congress catalog for design this is the latest version of the classic Cook and Campbell books Personal Name: Shadish, <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Author&SEQ=20061129155139&PID=24540&SA=Shadish,+William+R.> William R. Main Title: Experimental and quasi-experimental designs for generalized causal inference / William R. Shedish, Thomas D. Cook, Donald T. Campbell. Published/Created: Boston : Houghton Mifflin, [2001?] Related Names: Cook, <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Author&SEQ=20061129155139&PID=24540&SA=Cook,+Thomas+D.> Thomas D. Campbell, <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Author&SEQ=20061129155139&PID=24540&SA=Campbell,+Donald+Thomas,+1916-> Donald Thomas, 1916- Description: xxi, 623 p. : ill. ; 23 cm. ISBN: 0395615569 Notes: Includes bibliographical references (p. 514-591) and indexes. Subjects: Causation--Experiments. <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Subject&SEQ=20061129155139&PID=24540&SA=Causation+Experiments.> LC Classification: BD591 .S48 2001 Dewey Class No.: 122/.07/2 21 For solid understanding of a broad array of statistical methods Main Title: Applied multiple regression/correlation analysis for the behavioral sciences / Jacob Cohen ... [et al.]. Edition Information: 3rd ed. Published/Created: Mahwah, N.J. : L. Erlbaum Associates, 2003. Related Names: Cohen, <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Author&SEQ=20061129160147&PID=3738&SA=Cohen,+Jacob,+1923-> Jacob, 1923- Cohen, <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Author&SEQ=20061129160147&PID=3738&SA=Cohen,+Jacob,+1923-> Jacob, 1923- Applied multiple regression/correlation analysis for the behavioral sciences. Description: xxviii, 703 p. : ill. ; 26 cm. + 1 CD-ROM (4 3/4 in.) ISBN: 0805822232 (hard cover : alk. paper) Notes: Rev. ed. of: Applied multiple regression/correlation analysis for the behavioral sciences / Jacob Cohen, Patricia Cohen. 2nd ed. 1983. The CD-ROM contains the data for almost all examples as well as the command codes for each of the major statistical packages for the tabular and other findings in the book. Includes bibliographical references (p. 655-669) and indexes. Subjects: Regression <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Subject&SEQ=20061129160147&PID=3738&SA=Regression+analysis.> analysis. Correlation <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Subject&SEQ=20061129160147&PID=3738&SA=Correlation+%28Statistics%29> (Statistics) Social <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Subject&SEQ=20061129160147&PID=3738&SA=Social+sciences+Statistical+methods.> sciences--Statistical methods. LC Classification: HA31.3 .A67 2003 Dewey Class No.: 519.5/36 21 Art Kendall Social Research Consultants Edward Boadi wrote: Hi List. What will be a good statistical reference book for a Statistician who has just joined a Statistical Research consulting firm with specialization in Research Design and Statistical Analysis. Regards. |
In reply to this post by Edward Boadi
Two that I like are:
Gerald van Belle's "Statistical Rules of Thumb" published by Wiley - Some good material on research design and consulting in there. Richard Berk's "Regression Analysis: A Constructive Critique" published by Sage -- Not a reference book on regression, but instead a good read to get you thinking about the logic of analysis and the use of regression and related methods on observational data. -----Original Message----- From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Edward Boadi Sent: Wednesday, November 29, 2006 1:55 PM To: [hidden email] Subject: Statistics Book Hi List. What will be a good statistical reference book for a Statistician who has just joined a Statistical Research consulting firm with specialization in Research Design and Statistical Analysis. Regards. |
In reply to this post by Edward Boadi
To get a handle on why we do statistics in the first place.
Personal Name: Abelson, Robert P. <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Author&SEQ=20061130105250&PID=1830&SA=Abelson,+Robert+P.> Main Title: Statistics as principled argument / Robert P. Abelson. Published/Created: Hillsdale, N.J. : L. Erlbaum Associates, 1995. Description: xv, 221 p. ; 24 cm. ISBN: 0805805273 (acid-free) 0805805281 (pbk. : acid-free) Notes: Includes bibliographical references (p. 199-211) and inde xes. Subjects: Statistics. <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=Subject&SEQ=20061130105250&PID=1830&SA=Statistics.> LC Classification: QA276 .A22 1995 Dewey Class No.: 001.4/22 20 ------------------------------------------------------------------------ CALL NUMBER: QA276 .A22 1995 <http://catalog.loc.gov/cgi-bin/Pwebrecon.cgi?SC=CallNumber&SEQ=20061130105250&PID=1830&SA=QA276> Art Kendall Social Research Consultants Edward Boadi wrote: >Hi List. >What will be a good statistical reference book for a Statistician who has just joined a >Statistical Research consulting firm with specialization in Research Design and Statistical Analysis. >Regards. > > > > |
In reply to this post by Anthony Babinec
I have always liked the Cohen and Cohen book 'Applied Multple
Regression/Correlation Analysis for the Behavioral Sciences'. I have an older copy, don't know if it's even in print anymore. I also like Pedhazur's 'Multiple Regression in Behavioral Research. For multivariate, Steven's 'Applied Multivariate Statistics for the Social Sciences'. I have always been a big fan of the Norusis books and am VERY glad to see that she is writing again. *************************************************************************************************************************************************************** Mark A. Davenport Ph.D. Senior Research Analyst Office of Institutional Research The University of North Carolina at Greensboro 336.256.0395 [hidden email] 'An approximate answer to the right question is worth a good deal more than an exact answer to an approximate question.' --a paraphrase of J. W. Tukey (1962) Anthony Babinec <[hidden email]> Sent by: "SPSSX(r) Discussion" <[hidden email]> 11/30/2006 10:12 AM Please respond to Anthony Babinec <[hidden email]> To [hidden email] cc Subject Re: Statistics Book Two that I like are: Gerald van Belle's "Statistical Rules of Thumb" published by Wiley - Some good material on research design and consulting in there. Richard Berk's "Regression Analysis: A Constructive Critique" published by Sage -- Not a reference book on regression, but instead a good read to get you thinking about the logic of analysis and the use of regression and related methods on observational data. -----Original Message----- From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Edward Boadi Sent: Wednesday, November 29, 2006 1:55 PM To: [hidden email] Subject: Statistics Book Hi List. What will be a good statistical reference book for a Statistician who has just joined a Statistical Research consulting firm with specialization in Research Design and Statistical Analysis. Regards. |
In reply to this post by Art Kendall
All,
I have some questions about cluster analysis, which I have never done or used before. I am looking for several things. First, an assessment of whether cluster analysis would be useful given my data and interests. If clustering seems feasible, then a recommendation on procedure selection (the discussion of the past few days on clustering has been helpful) and recommendations of references. I have attendance data (yes/no) for 350 persons attending a 14 session program. I have previously analyzed this data by just counting the number of sessions attended. I am interested to see if the data can be clustered by the 14 session attendance variables to yield a different and more interesting view of the dataset. I don't know that it matters but total attendance ranges from 0 to 14 with about 14% attending 0 sessions, 10% attending 14 sessions and between about 3% and 10% attend each number of sessions from 1 to 13. Given all this, is clustering even feasible? If so, what method would be recommended? And, references? Thanks, Gene Maguin |
--On 05 December 2006 11:07 -0500 Gene Maguin <[hidden email]>
asked about cluster analysis for attendance data. I think that there is one important aspect of your data which may be difficult to include in a cluster analysis. You are recording attendance at 14 sessions of a program, so that probably means that your 14 variables are ORDERED (assuming that the sessions are a program over a period of time rather than a "pick and mix" selection of topics. I can think of some theories that might produce clusters, if people fall into particular patterns of behaviour. You might think of those people who start, and then drop out, those that persist to the end, and those with "irregular" attendance, but it is unlikely that any cluster analysis program would identify these in any particularly useful way. You might find that a cluster program finds you several groups of people, and that then by looking at the data you can puzzle out what characterises each of the groups. You have to face the fact that some kinds of data cluster nice and neatly and stably, while others produce a arbitrary set of solutions which depend critically on the clustering method used. Suppose that you have one continuous variable, and when you plot your data you see two well separated heaps from the normal distribution. Almost any clustering program will separate these well. On the other hand, suppose that you see a single normal distribution. How would you (or the computer) cut this into two groups. In the middle? At one of the tails? Which tail? Somewhere else? If the data values are continuous and form a single "clump" then any attempt to cluster will produce an arbitrary result. Would you expect your people to fall into discrete groups, or are they just an amorphous mass? Are you considering cluster analysis because you can't think of anything else to do, and it might be worth trying? The most important omission in your question is a statement of the research question. Do you have any hypotheses? What questions are you asking of the data? What do you want to use the results of your analysis for? I'm sorry to face you with these difficult questions, but if (and only if) you can answer them, your choice of a method of analysis may be clearer. David Hitchin |
At 08:46 AM 12/5/2006, David Hitchin wrote:
>--On 05 December 2006 11:07 -0500 Gene Maguin <[hidden email]> >asked about cluster analysis for attendance data. > >I think that there is one important aspect of your data which may be >difficult to include in a cluster analysis. You are recording attendance at >14 sessions of a program, so that probably means that your 14 variables are >ORDERED (assuming that the sessions are a program over a period of time >rather than a "pick and mix" selection of topics. . . . This is a very important theoretical point, but I have a slightly different take on the consequences. Normally, your 14 variables are supposed to be *independent*. But if they are ordered, as noted above, then which session you see first may well influence your choice of which session you see next. Because of this linkage, you may want to consider some kind of path analysis. I am not at all familiar with the kinds of path analysis programs SPSS has, so that other than making the suggestion and pointing the way, I can't be of much use to you. But you might find a path analysis to yield some very interesting results in your situation. Bob Schacht Robert M. Schacht, Ph.D. <[hidden email]> Pacific Basin Rehabilitation Research & Training Center 1268 Young Street, Suite #204 Research Center, University of Hawaii Honolulu, HI 96814 |
In reply to this post by David Hitchin
David,
Thank you for your quick and comprehensive reply. I'll reply to your comments in a somewhat different order. >>Are you considering cluster analysis because you can't think of anything else to do, and it might be worth trying? >>The most important omission in your question is a statement of the research question. Do you have any hypotheses? What questions are you asking of the data? What do you want to use the results of your analysis for? I've already done one analysis where I divided the total number of sessions into several groups and looked at predictors of group membership. Although I haven't done this yet, I am planning on looking at attendance as a time to nonattendance (survival) analysis. When I was talking to someone else about this, it occurred to me that if I could figure out how to do it, I could include persons who attended, dropped out, and then started back via a repeated events survival analysis. Lastly, I had thought about calculating conditional probabilities. The stimulus for the clustering was an article that mentioned using clustering. However, silly me, I thought the person had used a cluster algorithm, instead they did a 'hand-clustering' procedure. Basically, I'm looking for ways to characterize people's patterns of attendance. The typical way, I think, what I have already done. I'm wondering about new ways. >>I think that there is one important aspect of your data which may be difficult to include in a cluster analysis. You are recording attendance at 14 sessions of a program, so that probably means that your 14 variables are ORDERED (assuming that the sessions are a program over a period of time rather than a "pick and mix" selection of topics. Yes, they are ordered. However, how does that enter into the underlying math theory of clustering? >>I can think of some theories that might produce clusters, if people fall into particular patterns of behaviour. You might think of those people who start, and then drop out, those that persist to the end, and those with "irregular" attendance, but it is unlikely that any cluster analysis program would identify these in any particularly useful way. >>You might find that a cluster program finds you several groups of people, and that then by looking at the data you can puzzle out what characterises each of the groups. Yes, that is my basic plan. To identify more-or-less homogenous groups of people with respect to attendance and then look for demo and substantive differences. It sounds like you hold little hope of that working because of difficulty of forming clusters >>Would you expect your people to fall into discrete groups, or are they just an amorphous mass? I don't know what to expect. I don't know if people are inclined to skip certain sessions. I had thought of looking at the frequencies of an A14 pattern variable to get an idea of patterns. When I look at the individual session attendance percentages, I see that session 1 attendance was the highest and then the percentage dropped steadily until session 4 where it more or less stabilized with a slight downward slope until rising at session 14. If you were to TRY a cluster procedure, which would you try and why? Thanks, Gene Maguin |
In reply to this post by Bob Schacht-3
Bob,
Thanks for your reply. >>This is a very important theoretical point, but I have a slightly different take on the consequences. Normally, your 14 variables are supposed to be *independent*. But if they are ordered, as noted above, then which session you see first may well influence your choice of which session you see next. After seeing your use of the word independent and the following sentence, I find myself wondering if I have a misunderstanding of how clustering works. I had thought that the procedure computes a 'distance' from one case to all others based on the values of the variables for the cases. A mahalanobis distance measure for instance. Cases near to each other are clumped together to make clusters. Have I got this all wrong? Thanks, Gene Maguin |
In reply to this post by Maguin, Eugene
TWOSTEP
Art Kendall Social Research Consultants Gene Maguin wrote: > >If you were to TRY a cluster procedure, which would you try and why? > >Thanks, Gene Maguin > > > > |
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