Robust regression
I know that robust regression can downweight outliers. Should someone apply robust regression when the data have skewed distributions but do not have outliers? Regression assumptions require normality of residuals, but not the normality of raw scores. So does it help at all to use robust regression in this situation. Any help will be appreciated. = Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =
Re: When does correlation imply causation?
Whether we can get causal inferences out of correlation and equations has been a dispute between two camps: For causation: Clark Glymour (Philosopher), Pearl (Computer scientist), James Woodward (Philosopher) Against: Nancy Cartwright (Economist and philosopher), David Freedman (Mathematician) One comment fromm this list is about that causal inferences cannot be drawn from non-experimental design. Clark Glymour asserts that using Causal Markov condition and faithfulness assumption, we can make causal interpretation to non-experimental data. Chong-ho (Alex) Yu, Ph.D., MCSE, CNE, CCNA Psychometrician and Data Analyst Assessment, Research and Evaulation Cisco Systems, Inc. Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: likert scale items
The following is extracted from one of my webpage. Hope it can help: -- The issue regarding the appropriateness of ordinal-scaled data in parametric tests was unsettled even in the eyes of Stevens (1951), the inventor of the four levels of measurement: "As a matter of fact, most of the scales used widely and effectively by psychologists are ordinal scales ¡K there can be involved a kind of pragmatic sanction: in numerous instances it leads to fruitful results." (p.26) Based on the central limit theorem and Monte Carlo simulations, Baker, Hardyck, and Petrinovich (1966) and Borgatta and Bohrnstedt (1980) argued that for typical data, worrying about whether scales are ordinal or interval doesn't matter. Another argument against not using interval-based statistical techniques for ordinal data was suggested by Tukey (1986). In Tukey's view, this was a historically unfounded overreaction. In physics before precise measurements were introduced, many physical measurements were only approximately interval scales. For example, temperature measurement was based on liquid-in-glass thermometers. But it is unreasonable not to use a t-test to compare two groups of such temperatures. Tukey argued that researchers painted themselves into a corner on such matters because we were too obsessed with "sanctification" by precision and certainty. If our p-values or confidence intervals are to be sacred, they must be exact. In the practical world, when data values are transformed (e.g. transforming y to sqrt(y), or logy), the p values resulted from different expressions of data would change. Thus, ordinal-scaled data should not be banned from entering the realm of parametric tests. For a review of the debate concerning ordinal- and interval- scaled data, please consult Velleman and Wilkinson (1993). from: http://seamonkey.ed.asu.edu/~alex/teaching/WBI/parametric_test.html ******** Chong-ho (Alex) Yu, Ph.D., MCSE, CNE Academic Research Professional/Manager Educational Data Communication, Assessment, Research and Evaluation Farmer 418 Arizona State University Tempe AZ 85287-0611 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
EdStat: Probabilistic inference in resampling?
John Tukey differentiates "data analysis" and "statistics." The former may or may not employ probability while the latter is based upon probability. Resampling techniques use "empirical probability." In the Fisherian sense, probability is based upon infinite hypothetical distributions. But for Rechenbach and von Mises, probability is empirically based on limited cases that generate relative frequency. It seems to me that resampling is qualified as a probabilistic model in Rechenbach and von Mises' view, but not in the Fisherian tradition. My question is: Should resampling be counted as a probabilistic model? What is the nature of inference resulted from bootstrapping? Is it a probabilistic inference? As I recall, Philip Good said that permutuation tests are still subject to the Behrens-Fisher problem (unknown population variance). If resampling is based on empirical probability within the reference set, then why do we care about the population variance? Any help will be greatly appreciated. **** Chong-ho (Alex) Yu, Ph.D., MCSE, CNE Academic Research Professional/Manager Educational Data Communication, Assessment, Research and Evaluation Farmer 418 Arizona State University Tempe AZ 85287-0611 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
EdStat: Triangular coordinates
I am trying to understand Triangular coordinates--a kind of graph which combines four dimensions into 2D by joining three axes to form a triangle while the Y axis "stands up." The Y axis can be hidden if the plot is depicted as a contour plot or a moasic plot rather than a surface plot. I have a hard time to follow how a point is determine with the three axes as a triangle. Is there any website/paper than can explain this? Thanks. Chong-ho (Alex) Yu, Ph.D., MCSE, CNE Academic Research Professional/Manager Educational Data Communication, Assessment, Research and Evaluation Farmer 418 Arizona State University Tempe AZ 85287-0611 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: On-line survey software
I recommend FileMaker Pro for online survey. For more info, please visit: http://seamonkey.ed.asu.edu/~alex/computer/FMP/FMP.html Chong-ho (Alex) Yu, Ph.D., MCSE, CNE Academic Research Professional/Manager Educational Data Communication, Assessment, Research and Evaluation Farmer 418 Arizona State University Tempe AZ 85287-0611 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: probability definition
For a quick walk through of various prob. theories, you may consult "The Cambridge Dictionary of Philosophy." pp.649-651. Basically, propensity theory is to deal with the problem that frequentist prob. cannot be applied to a single case. Propensity theory defines prob. as the disposition of a given kind of physical situation to yield an outcome of a given type. The following is extracted from one of my papers. It brielfy talks about the history of classical theory, Reichenbach's frequentism and Fisherian school: Fisherian hypothesis testing is based upon relative frequency in long run. Since a version of the frequentist view of probability was developed by positivists Reichenbach (1938) and von Mises (1964), the two schools of thoughts seem to share a common thread. However, it is not necessarily true. Both Fisherian and positivist's frequency theory were proposed as an opposition to the classical Laplacean theory of probability. In the Laplacean perspective, probability is deductive, theoretical, and subjective. To be specific, this probability is subjectively deduced from theoretical principles and assumptions in the absence of objective verification with empirical data. Assume that every member of a set has equal probability to occur (the principle of indifference), probability is treated as a ratio between the desired event and all possible events. This probability, derived from the fairness assumption, is made before any events occur. Positivists such as Reichenbach and von Mises maintained that a very large number of empirical outcomes should be observed to form a reference class. Probability is the ratio between the frequency of desired outcome and the reference class. Indeed, the empirical probability hardly concurs with the theoretical probability. For example, when a dice is thrown, in theory the probability of the occurrence of number "one" should be 1/6. But even in a million simulations, the actual probability of the occurrence of "one" is not exactly one out of six times. It appears that positivist's frequency theory is more valid than the classical one. However, the usefulness of this actual, finite, relative frequency theory is limited for it is difficult to tell how large the reference class is considered large enough. Fisher (1930) criticized that Laplace's theory is subjective and incompatible with the inductive nature of science. However, unlike the positivists' empirical based theory, Fisher's is a hypothetical infinite relative frequency theory. In the Fisherian school, various theoretical sampling distributions are constructed as references for comparing the observed. Since Fisher did not mention Reichenbach or von Mises, it is reasonable to believe that Fisher developed his frequency theory independently. Backed by a thorough historical research, Hacking (1990) asserted that "to identify frequency theories with the rise of positivism (and thereby badmouth frequencies, since "positivism" has become distasteful) is to forget why frequentism arose when it did, namely when there are a lot of known frequencies." (p.452) In a similar vein, Jones (1999) maintained that "while a positivist may have to be a frequentist, a frequentist does not have to be a positivist." ******** Chong-ho (Alex) Yu, Ph.D., MCSE, CNE Academic Research Professional/Manager Educational Data Communication, Assessment, Research and Evaluation Farmer 418 Arizona State University Tempe AZ 85287-0611 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: probability definition
Probability can be defined in at least five different ways: 1. Classical Laplacean theory of probability: The prob.is derived from the fairness assumption e.g. a fair coin. It is also called equiproability. 2. Frequentist theory: It is developed by von Mises and Reichenbach. Prob. is the relative frequency in the long run by limiting observations. 3. Propensity: It is based upon the physical or the objective property of the events. 4. Logical: developed by Carnap. Prob. is defined like Y logically entails X. 5. Subjective or Bayesian: degree of belief There is no easy answer to your question. It depends on which point of view you chose. Chong-ho (Alex) Yu, Ph.D., MCSE, CNE Academic Research Professional/Manager Educational Data Communication, Assessment, Research and Evaluation Farmer 418 Arizona State University Tempe AZ 85287-0611 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Effect statistics for non-normality
> You see, you are using a qualitative estimate of non-normality (box plot)! I want a rule based on a quantitative estimate. I may disagree to the above notion. Yes, data visualization such as using boxplots, histograms, and Q-Q plots involves subjective judgment and does not have a strict cut-off rule. Still, there are rules (e.g. how to divide quantite, what bandwidth and smoothing algorithms to use) to make graphs. On the other hand, so-called rule based methods also involve subjective decisions (why .05 as the alpha? why .80 as the power level?) The line between "qualitative" and "quantitative" are a bit blurred. **** Chong-ho (Alex) Yu, Ph.D., MCSE, CNE Academic Research Professional/Manager Educational Data Communication, Assessment, Research and Evaluation Farmer 418 Arizona State University Tempe AZ 85287-0611 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
EdStat: Factoring tetrachoric matrix in SAS
I ran a SAS macro and output a tetrachoric correlation matrix of 236 variables successfully. However, when I ran a factor analsyis using the matrix as the infile, it fails. Although I have specified 'corr' for _type_, SAS said that: "Data set WORK.MATRIX2 has _TYPE_ and _NAME_ variables but is not TYPE=ACE, CORR, COV, EST, FACTOR, SSCP, UCORR, or UCOV. ERROR: CORR matrix incomplete in data set WORK.MATRIX. The following is the SAS program. I would appreciate it if any SAS expert out there can give me a hand: data matrix (type=corr); infile "plcorr2.txt"; _type_='corr'; input _name_ $ as1-as24 bs1-bs24 cs1-cs25 ds1-ds25 es1-es25 fs1-fs25 gs1-gs25 hs1-hs60;*/ data matrix2; set matrix; proc factor data=matrix method = prinit scree; run; **** Chong-ho (Alex) Yu, Ph.D., MCSE, CNE Academic Research Professional/Manager Educational Data Communication, Assessment, Research and Evaluation Farmer 418 Arizona State University Tempe AZ 85287-0611 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Logistic regression
Hi, I have a dependent variable as a binary variable, and independent variables as interval-scaled variables and dummy variables (1, 0), which are converted from grouping variables. When dummy variables are included in the independent variables, is it legitimate to do a logistic regression? Thanks. Chong-ho (Alex) Yu, Ph.D., MCSE, CNE Academic Research Professional/Manager Educational Data Communication, Assessment, Research and Evaluation Farmer 418 Arizona State University Tempe AZ 85287-0611 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: sample size program for regression
PASS released by NCSS can calculate sample size for OLS regression and logistic regression. Chong-ho (Alex) Yu, Ph.D., CNE, MCSE Instruction and Research Support Information Technology Arizona State University Tempe AZ 85287-0101 Voice: (602)965-7402 Fax: (602)965-6317 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ === 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/ ===
ANOVA, Robustness, and Power
ANOVA is said to robust against assumption violations when the sample size is large. However, when the sample size is huge, it tends to overpower the test and thus the null may be falsly rejected. Which is a lesser evil? Your input will be greatly appreciated. Chong-ho (Alex) Yu, Ph.D., CNE, MCSE Instruction and Research Support Information Technology Arizona State University Tempe AZ 85287-0101 Voice: (602)965-7402 Fax: (602)965-6317 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ === 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/ ===
Re: Shapiro-Wilks
Three notes: 1. Shapiro test is for testing a sample size under 2000. For a large sample size which is over 2000, Kolmogorov test should be used instead. 2. Test statistic alone may not be sufficient. To test normality, it is recommended to use normality probability plot, too. 3. Shapiro test is for testing univariate normality. For multivariate normality, use Q-Q plots and some other tests. Hope it helps. Chong-ho (Alex) Yu, Ph.D., CNE, MCSE Instruction and Research Support Information Technology Arizona State University Tempe AZ 85287-0101 Voice: (602)965-7402 Fax: (602)965-6317 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ === 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/ ===
Re: Number of factors to be extracted
There are several rules. The most popular two are: 1. Kasier criterion: retain the factor when eigenvalue is larger than 1 2. Scree plot: Basically, it is eyeballing. Plot the number of factors and the eigenvalue and see where the sharp turn is. Hope it helps. Chong-ho (Alex) Yu, Ph.D., CNE, MCSE Instruction and Research Support Information Technology Arizona State University Tempe AZ 85287-0101 Voice: (602)965-7402 Fax: (602)965-6317 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ On Tue, 2 May 2000 [EMAIL PROTECTED] wrote: > Would any of you know a rule of thumb for selecting the proper (of > optimal) number of factors to be extracted from a factor analysis. > Also, how many variables can there be in such factor (is two variable > in one factor not enough?). > > Sorry for my english... > > > 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/ > === > === 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/ ===
Re: Exploratory data analysis
> and here's one that will give you a headache > > http://seamonkey.ed.asu.edu/~behrens/asu/reports/Peirce/Logic_of_EDA.html Actually the link above is an older version. Try: http://seamonkey.ed.asu.edu/~alex/pub/Peirce/Logic_of_EDA.html The citation is: Yu, C. H. (1994, April). Induction? Deduction? Abduction? Is there a logic of EDA? Paper presented at the Annual Meeting of American Educational Researcher Association, New Orleans, Louisiana. (ERIC Document Reproduction Service No. ED 376 173) === 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/ ===
Re: Exploratory data analysis
The following website summarize EDA and data visualization, as well as citing several useful references: http://seamonkey.ed.asu.edu/~alex/teaching/WBI/EDA.html http://seamonkey.ed.asu.edu/~alex/pub/multi-vis/multi-vis.html Hope it helps. Chong-ho (Alex) Yu, Ph.D., CNE, MCSE Instruction and Research Support Information Technology Arizona State University Tempe AZ 85287-0101 Voice: (602)965-7402 Fax: (602)965-6317 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ === 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/ ===
Maple V and regression function
I am trying to plot a regression function with three-way interaction such as: y = a + x1b1 + x2b2 + x3b3 + x1x2b4 + x1x3b5 + x2x3b6 + x1x2x3b7 In Maple V I used the following syntax and Maple created an animated 3D plot: animate3d(2.345+ x1*0.98 + x2*0.76 + x3*1.23 + x1*x2*0.076 + x1*x3*0.087 + x3*x2*1.0765 + x1*x2*x3*1.456,x1=1..7,x2=1..7,x3=1..7); However, there is nowhere for me to specify the highest and lowest values of Y. I looked through the manual but yielded no result. Any help will be greatly appreciated. Chong-ho (Alex) Yu, Ph.D., CNE, MCSE Instruction and Research Support Information Technology Arizona State University Tempe AZ 85287-0101 Voice: (602)965-7402 Fax: (602)965-6317 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ === 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/ ===
Re: Weighted Kappa
As I recall, Kappa is a measurement of agreement. It is best used for dichotomous outcomes such as judgment by raters in terms of "mastery/non-mastery" "pass/fail". I am not sure if it is proper for your data. If the data are continuous-scaled and more than two raters involved, a repeated measures approach can be used to check the reliability: Horst, P. (1949). A Generalized expression for the reliability of measures. Psychometrika, 14, 21-31. ******** Chong-ho (Alex) Yu, Ph.D., MCSE, CNE Instruction and Research Support Information Technology Arizona State University Tempe AZ 85287-0101 Voice: (602)965-7402 Fax: (602)965-6317 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ === 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/ ===
Re: ANOVA causal direction
A statistical procedure alone cannot determine casual relationships. Rather it involves the design and measurement issues. The following is extracted from my handout: One of the objectives of conducting experiments is to make causal inferences. At least three criteria need to be fulfilled to validate a causal inference (Hoyle, 1995): Directionality: The independent variable affects the dependent variable. Isolation: Extraneous noise and measurement errors must be isolated from the study so that the observed relationship cannot be explained by something other than the proposed theory. Association: The independent variable and the dependent variable are mathematically correlated. To establish the direction of variables, the researcher can apply logic (e.g. physical height cannot cause test performance), theory (e.g. collaboration affects group performance), and most powerfully, research design (e.g. other competing explanations are ruled out from the experiment). To meet the criterion of isolation, careful measurement should be implemented to establish validity and reliability, and to reduce measurement errors. In addition, extraneous variance, also known as threats against validity of experiment, must be controlled in the design of experiment. Last, statistical methods are used to calculate the mathematical association among variables. However, in spite of a strong mathematical association, the causal inference may not make sense at all if directionality and isolation are not established. In summary, statistics analysis is only a small part of the entire research process. Hoyle (1995) explicitly warned that researchers should not regard statistical procedures as the only way to establish a causal and effect interpretation. Hoyle, R. H.. (1995). The structural equation modeling approach: Basic concepts and fundamental issues. In R. H. Hoyle (Eds.), Structural equation modeling: Concepts, issues, and applications (pp. 1-15). Thousand Oaks: Sage Publications. Chong-ho (Alex) Yu, Ph.D., CNE, MCSE Instruction and Research Support Information Technology Arizona State University Tempe AZ 85287-0101 Voice: (602)965-7402 Fax: (602)965-6317 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/ === This list is open to everyone. Occasionally, people lacking respect for other members of the list send messages that are inappropriate or unrelated to the list's discussion topics. Please just delete the offensive email. For information concerning the list, please see the following web page: http://jse.stat.ncsu.edu/ ===
Re: search engines
I use meta-search-engine such as savvysearch and Web Ferret. Chong-ho (Alex) Yu, Ph.D., CNE, MCSE Instruction and Research Support Information Technology Arizona State University Tempe AZ 85287-0101 Voice: (602)965-7402 Fax: (602)965-6317 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/
Re: GLM vs. ANOVA
In SAS, ANOVA is for design of one-way and balanced multi-way classifications. The main point here is "balanced." ANOVA may be used for unbalanced data if the factors do not interact, otherwise, GLM is a better procedure. Chong-ho (Alex) Yu, Ph.D., CNE, MCSE Instruction and Research Support Information Technology Arizona State University Tempe AZ 85287-0101 Voice: (602)965-7402 Fax: (602)965-6317 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/
Re: Disadvantage of Non-parametric vs. Parametric Test
Disadvantages of non-parametric tests: Losing precision: Edgington (1995) asserted that when more precise measurements are available, it is unwise to degrade the precision by transforming the measurements into ranked data. Low power: Generally speaking, the statistical power of non-parametric tests are lower than that of their parametric counterpart except on a few occasions (Hodges & Lehmann, 1956; Tanizaki, 1997). Inaccuracy in multiple violations: Non-parametric tests tend to produce biased results when multiple assumptions are violated (Glass, 1996; Zimmerman, 1998). Testing distributions only: Further, non-parametric tests are criticized for being incapable of answering the focused question. For example, the WMW procedure tests whether the two distributions are different in some way but does not show how they differ in mean, variance, or shape. Based on this limitation, Johnson (1995) preferred robust procedures and data transformation to non-parametric tests. Hope it helps. Chong-ho (Alex) Yu, Ph.D., CNE, MCSE Instruction and Research Support Information Technology Arizona State University Tempe AZ 85287-0101 Voice: (602)965-7402 Fax: (602)965-6317 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/
Correction: Factor analysis instructional materials
Two days ago I posted a URL for downloading a tutorial of factor analysis. However, when I uploaded the program, I forgot to include the associated QuickTime movies. The corrected zip file has been re-uploaded to: http://seamonkey.ed.asu.edu/~alex/alex/multimedia/factor.zip I made the same correction to another tutorial of collinearity: http://seamonkey.ed.asu.edu/~alex/alex/multimedia/collinear.zip Sorry for wasting your bandwidth and downloading time. Chong-ho (Alex) Yu, Ph.D., CNE, MCSE Instruction and Research Support Information Technology Arizona State University Tempe AZ 85287-0101 Voice: (602)965-7402 Fax: (602)965-6317 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/
Re: factor analysis instructional materials
Hi, I have several things. The following is a website explaining concepts of factor, vector, eigenavector, eigenvalue, variable space, subject space...etc: http://seamonkey.ed.asu.edu/~alex/computer/sas/biplot.html I also have a multimedia program: http://seamonkey.ed.asu.edu/~alex/multimedia/factor.zip The program is a self-contained movie made by Macromedia Director. The file size is 8 meg. You need Winzip to decompress it. Hope it helps. Chong-ho (Alex) Yu, Ph.D., CNE, MCSE Instruction and Research Support Information Technology Arizona State University Tempe AZ 85287-0101 Voice: (602)965-7402 Fax: (602)965-6317 Email: [EMAIL PROTECTED] URL:http://seamonkey.ed.asu.edu/~alex/
