Correlation,Social Psychology N
Hi, i calculate some calculations for a specific target variable. People should rate for 3 things some independends and one dependend variable. People should not difference in importances as possible between the 1.,2.and the 3.Chose. Now i filter all people which rate the same thing (i.e. a Product/Company) and calculate the different correlations dependend from the thing is rated as 1./2./3. or all togehter! Not a surprise for me are the different values in correlation, but make the use of correlations for this doubtful. What is more a cause for this results'S ? (1) The small N's (2) The social-psychological phenomen that people want prefer something and not really indifferent when they ask for 3 rating's relative to a product/company. Result: So people who rate a product/company as 1. and others as 2. or 3. ( what is not a ranked order but it works neverthless in consciousness ) must be different analyze, because their exist different attitudes ? r N r N r N r N 1.Chose 2.Chose 3.Chose Cummulate 1. to 3.Choose 0.53 28 0.66 23 0.19 20 0.43 71 0.13 28 0.28 22 0.41 22 0.25 72 0.37 23 0.34 24 0.17 22 0.28 69 0.50 20 0.02 17 0.48 17 0.35 54 0.55 27 0.54 22 0.44 20 0.52 69 0.14 21 0.20 19 0.15 16 0.21 56 0.13 27 0.10 22 0.26 20 0.17 69 0.75 24 0.38 21 0.28 18 0.51 63 0.51 23 0.36 18 0.57 18 0.50 59 0.83 26 0.46 22 0.29 20 0.57 68 Thanks for any suggestion, alternatives and regards, Christian = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
Graphics CORRESPONDENCE ANALYSIS
Hello, ...have anybody experience or a good idea how i can display the result from the correspondence analysis in a better way like spss (i.e. excel ), because the visual performance in my humble opinion is not the best !? thanks for advance regards, christian = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
K-Means QuickClusteranalysis (OptimalSolution)
Hello, i look for a good way to find the optimal number of cluster for a special problem ! Is it right to sum the distanceValues^2 and look for the point where these sumOfEuclideanDistance have the most lost from one to the next ClusterSolution ? P.S. I hope my english is not to bad ;-( thanks for advance regards christian = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
comparing factor patterns ?
Hi everybody! Sure thing that some will find this request annoying and pointless again, but there's still a lot of confusion about it. Yes, we do want to compare factor patterns. Actually we need to evaluate whether factorial structures of 2 tests are similar enough, roughly, to assume they're parallel. For my own part I think one should use LISREL (or the like), constrain the loadings of one model to the other one, and evaluate (mis)fit. It shouldn't be too hard to set this up in SAS. Unfortunately, our client insists on keeping to SPSS (no LISREL module) and now asks for a formal comparison of two explo- ratory PCAs. He doesn't want to hear that it cannot be done. Still, I feel that I should collect some opions on the issue before giving up. Anybody out there to provide some hints ? Thank you in advance for your consideration! Hans - PD Dr. Hans C Waldmann Methodology Applied Statistics in Psychology the Health Sciences ZFRF / University of Bremen / Grazer Str 6 / 28359 Bremen / Germany [EMAIL PROTECTED] http://samson.fire.uni-bremen.de/waldmann friend of: AIX PERL POSTGRES ADABAS SAS TEX - = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
the gallup international millenium survey
Hi, Oops wrong email before, sorry Does anyone have any experience on analyzing the millenium survey 2000, by gallu international, when all the data is aggregatet to mean values for each country? In that respect I have a problem with discussing alternative interpretations of the four democracy segments presentet by GI , when the only availlible data is the above. Does it make any sense to construct a index bassed on mean values. Anyone know any links concerning the gallup international millenium survey 2000, other than the gallup.com any help is much appriciated thanks, C. Bechmann = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: reasonable probability
In article 90k3vl$[EMAIL PROTECTED], [EMAIL PROTECTED] (Herman Rubin) wrote: AFAIK there is general agreement that unbiased humans are better at identifying the difference between unpunched holes and imperfectly punched holes than current counting machines -- which after all were only designed to distinguish between unpunched and perfectly punched holes. UNBIASED humans, yes. There is considerable evidence of bias. Some of this bias is inadvertent, the type of observer bias found in many experimental situations in other fields. This is especially the case if it is not merely a piece of hanging chad, but a dimple. It also occurs if there is a question of multiple voting for an office. Both the United Kingdom and Germany use the old fashioned piece-of-paper-take-a-pencil-mark-your-candidate method, and the papers are always handcounted. I don't think there is ever any question about "voter intention" unless a voter deliberately chooses to make his ballot paper undecidable. Just because a system is oldfashioned doesn't mean it can't be better anyway. = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Summary: 120 subjects on 120 occasions
Dear List(s), Last week I have posted a request for help pertaining to the issue of how to analyse repeated measurement data with some rather unusual dimensions (N=120 subjects giving a time series with T=120 occassions each). To my pleasure, there have been as much as 12 replies which are given below. Beforehand, the original posting is given. For those of you who don't want to read through all of these, here is a short summary. Roughly, responses/recommendations can be classified into 5 categories: a) Make use of all information available and put them into a form suitable for state-space models or vector-ARIMA-analyses. Frankly: this seems to go beyond my capabilities, and I am inclined to take the penalty of reducing the data as pro- posed by other responders. b) Perform time series analyses for each subject and boil the data down to certain parameters. Read these into a secondary data set and merge it somehow with the original one in order to preserve design variables like treat/control, sex and age or the like. Finally, use these data for "standard" analyses to test for hypotheses of homogeneity of subjects within groups or differences across groups (implying some MANOVA-style model). Going to extremes, one could obtain a single parameter like a slope for each subject and perform univariate analyses with regard to higher stratum levels. Another response in this direction suggested fitting a spline model for each subject and use spline components for subsequent ANOVA-style models. I understand that this could be done using proc transreg in SAS, but I am not sure whether this procedure does in fact account for the time dependency in the individual data giving the spline. c) Reduce the repeated measurement frequency in the first place and then perform (M)ANOVA-style analyses with a time factor of (then) suitable level count. Test for time effects using standard contrast like polynomial decomposition or helmert coefficients (when interest lies with the point in time when responses cease to change any further). d) A particuarly interesting response suggested identifying "change profiles" within time series and submitting these to further analyses like permutation test. Still, I am unclear about how to aggregate data in order to make best use of all subjects' data. e) General remarks and caveats like paying regard to sample size issues, looking for cyclicity in individual data that generalze to the stratum, adjusting for cross-correlations in case of multi-variable outcome measures, and the complexity of assumptions required when analysing complex factorial designs involving a repeated measures factor. Again, thanks to all who took their time to help. I am committed to parti- cipate in this way of mutual assistance. Hans C Waldmann - Dr. Hans C Waldmann Methodology Applied Statistics in Psychology the Health Sciences ZFRF / University of Bremen / Grazer Str 6 / 28359 Bremen / Germany [EMAIL PROTECTED] / http://samson.fire.uni-bremen.de friend of: AIX PERL ADABAS SAS TEX - Following: 0) Original posting (request) 1-12) replies 0) - Original Message ----- From: "Dr. Hans-Christian Waldmann" [EMAIL PROTECTED] To: [EMAIL PROTECTED] Sent: Thursday, October 12, 2000 2:18 PM Subject: a model for time series (T=120) for N=120 persons ? Hello everybody, in one of the clinical projects we consult on data analysis, I am facing a problem I have not yet come across and that leaves me with no idea on how to proceed. The problem pertains to the dimension of the outcome data set. In a repeated measures design, let N be the number of people treated and T be the number of measurement occassions. I understand that N=1 (or _some_) more and T=120 would make up a time series, and that I am supposed to fit ARIMA-MOdels or Transfer functions. I could detect effects by structural breaks around the point of time of intervention, that is: performing intervention analyses as proposed in McDowell, McCleary, Meidinger and Hay, 1980, Interrupted time series analysis, or other books on how to analyse data from single subject designs. Allright. I understand that N=120 (or any number more) and repeated measures like 2=T="the-smaller-the-better" would make up a dataset suitable for an ANOVA approach or mixed models using special covariance structures like SAS's proc mixed. I know how to do that. Allright. I understand that for each of this variants there are some alternatives in statistical modeling (like non-parametric analyses etc.). Now,
120 subjects on 120 occassion: a model ?
Hello everybody, in one of the clinical projects we consult on data analysis, I am facing a problem I have not yet come across and that leaves me with no idea on how to proceed. The problem pertains to the dimension of the outcome data set. In a repeated measures design, let N be the number of people treated and T be the number of measurement occassions. I understand that N=1 (or _some_) more and T=120 would make up a time series, and that I am supposed to fit ARIMA-MOdels or Transfer functions. I could detect effects by structural breaks around the point of time of intervention, that is: performing intervention analyses as proposed in McDowell, McCleary, Meidinger and Hay, 1980, Interrupted time series analysis, or other books on how to analyse data from single subject designs. Allright. I understand that N=120 (or any number more) and repeated measures like 2=T="the-smaller-the-better" would make up a dataset suitable for an ANOVA approach or mixed models using special covariance structures like SAS's proc mixed. I know how to do that. Allright. I understand that for each of this variants there are some alternatives in statistical modeling (like non-parametric analyses etc.). Now, what am I supposed to do with data from a design giving a T=120 time series for _each_ of 120 subjects ? There has been a controlled study where patients in three independent groups were asked to keep a diary on some outcome variables for ca. 4 months. There are some design variables like treat/control or sex and age that are expected to contribute systematically to variation between outcome measures. But this outcome measure apparently is a time series. I don't think I should perform an ANOVA-style analysis with a 120-level time factor. Pooling data and performing ARIMA/transfer-functions on a single time series of subjects' means for each point in time doesn't make sense either, assuming that subjects differ in both measurement level and covariance structure of their individual time series. I admit that I have no idea how to evaluate, say, an effect of treatment on this kind of outcome measure. Does anybody else have an idea ? I promise to post a summary of res- ponses to the list. Thanks in advance Hans-Christian Waldmann - Dr. Hans C Waldmann Methodology Applied Statistics in Psychology the Health Sciences ZFRF / University of Bremen / Grazer Str 6 / 28359 Bremen / Germany [EMAIL PROTECTED] / http://samson.fire.uni-bremen.de friend of: AIX PERL ADABAS SAS TEX - = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Adjusting a Correlation Matrix
Does anyone know if there is a structured way to adjust a negative definite matrix such that it becomes semi-definite, while "minimizing" the induced changes to the matrix? Cheers, Christian Sent via Deja.com http://www.deja.com/ Before you buy. === 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/ ===
programming stata 6
I'm a beginner in using stata 6.0. I wrote ado-files for calculating the gini coefficient and the index of dissimilarity. However the 'by', 'if' and 'in' syntax don`t work correctly. Who can help me? === 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/ ===
