Re: comparing 2 slopes
mccovey@psych [EMAIL PROTECTED] wrote in message news:[EMAIL PROTECTED]... in article [EMAIL PROTECTED], Tracey Continelli at [EMAIL PROTECTED] wrote on 6/13/01 4:14 PM: Mike Tonkovich [EMAIL PROTECTED] wrote in message news:3b20f210_1@newsfeeds... Was hoping someone might be able to confirm that my approach for comparing 2 slopes was correct. I ran an analysis of covariance using PROC GLM (in SAS) with an interaction statement. My understanding was that a nonsignificant interaction term meant that the slopes were the same, and vice versa for a significant interaction term. Is this correct and is this the best way to approach this problem with SAS? Any help would certainly be apprectiated. Mike Tonkovich -- Michael J. Tonkovich, Ph.D. Wildlife Research Biologist ODNR, Division of Wildlife [EMAIL PROTECTED] The slopes need not be the same if the interaction term is non-significant, BUT, the difference between them will not be statistically significant. If the differences between the slops *are* statistically significant, this will be reflected in a statistically significant product term. I have preferred using regression analyses with interaction terms, which can be easily incorporated by simply multiplying the variables together and then running the regression equation with each independent variable plus the product term [which is simply another name for the interaction term]. The results are much more straightforward in my mind. Tracey Continelli SUNY at Albany I agree completely but there can be problems interpreting the regression Output (e.g., mistakes like talking about main effects). For advice on avoiding the common interpretation pitfalls, see Aiken West (1991). Multiple regression: Testing and interpreting interactions. Sage. Irwin McClelland (2001). In Journal of Marketing Research. Gary McClelland Univ of Colorado Quite so. Once you add the product term, the interpretation changes, and the parameter estimates are now known as simple main effects. The interpretation is pretty straightforward however. The parameter estimate, or slope, for your focal independent variable in the interaction model simply represents the effect of your independent variable upon your dependent variable when your moderator variable is equal to zero, holding constant all other independent variables in your model. The same may be said for the slope of your moderator variable - it represents the effect of that variable upon your dependent variable when your focal independent variable is equal to zero. Because in my research [the social science variety] that information isn't terribly useful [because most of the time you won't realistically see the moderator variable at zero, i.e., a zero crime rate or a zero poverty rate], what I will do is a mean centering trick. I'll subtract the mean from the moderator variable, rerun the equation with the new mean centered variable and product term, and NOW the parameter estimates of the simple main effects are meaningful for me. Now, when I look at the parameter estimates of the focal independent variable, it is telling me the effect of that independent variable upon the dependent variable when my moderator variable is at its mean. The actual product term remains identical to the original equation [of course], but now the simple main effects are realistically meaningful. I'll also apply the same technique for when the moderator variable is 2 standard deviations below the mean, 1 below the mean, all the way up to 2 standard deviations above the mean. This gives one a nice graphic sense of the way in which the slope between your focal independent variable and your dependent variable changes with successive changes in your moderator variable. Tracey Continelli Doctoral candidate SUNY at Albany = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Factor Analysis
Hi there, would someone please explain in lay person's terms the difference betwn. principal components, commom factors, and maximum likelihood estimation procedures for factor analyses? Should I expect my factors obtained through maximum likelihood estimation tobe highly correlated? Why? When should I use a Maximum likelihood estimation procedure, and when should I not use it? Thanks. Rita [EMAIL PROTECTED] Unlike the other methods, maximum likelihood allows you to estimate the entire structural model *simultaneously* [i.e., the effects of every independent variable upon every dependent variable in your model]. Most other methods only permit you to estimate the model in pieces, i.e., as a series of regressions whereby you regress every dependent variable upon every independent variable that has an arrow directly pointing to it. Moreover, maximum likelihood actually provides a statistical test of significance, unlike many other methods which only provide generally accepted cut-off points but not an actual test of statistical significance. There are very few cases in which I would use anything except a maximum likelihood approach, which you can use in either LISREL or if you use SPSS you can add on the module AMOS which will do this as well. Tracey = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: comparing 2 slopes
Mike Tonkovich [EMAIL PROTECTED] wrote in message news:3b20f210_1@newsfeeds... Was hoping someone might be able to confirm that my approach for comparing 2 slopes was correct. I ran an analysis of covariance using PROC GLM (in SAS) with an interaction statement. My understanding was that a nonsignificant interaction term meant that the slopes were the same, and vice versa for a significant interaction term. Is this correct and is this the best way to approach this problem with SAS? Any help would certainly be apprectiated. Mike Tonkovich -- Michael J. Tonkovich, Ph.D. Wildlife Research Biologist ODNR, Division of Wildlife [EMAIL PROTECTED] The slopes need not be the same if the interaction term is non-significant, BUT, the difference between them will not be statistically significant. If the differences between the slops *are* statistically significant, this will be reflected in a statistically significant product term. I have preferred using regression analyses with interaction terms, which can be easily incorporated by simply multiplying the variables together and then running the regression equation with each independent variable plus the product term [which is simply another name for the interaction term]. The results are much more straightforward in my mind. Tracey Continelli SUNY at Albany -= Posted via Newsfeeds.Com, Uncensored Usenet News =- http://www.newsfeeds.com - The #1 Newsgroup Service in the World! -== Over 80,000 Newsgroups - 16 Different Servers! =- = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: multivariate techniques for large datasets
Sidney Thomas [EMAIL PROTECTED] wrote in message news:[EMAIL PROTECTED]... srinivas wrote: Hi, I have a problem in identifying the right multivariate tools to handle datset of dimension 1,00,000*500. The problem is still complicated with lot of missing data. can anyone suggest a way out to reduce the data set and also to estimate the missing value. I need to know which clustering tool is appropriate for grouping the observations( based on 500 variables ). One of the best ways in which to handle missing data is to impute the mean for other cases with the selfsame value. If I'm doing psychological research and I am missing some values on my depression scale for certain individuals, I can look at their, say, locus of control reported and impute the mean value. Let's say [common finding] that I find a pattern - individuals with a high locus of control report low levels of depression, and I have a scale ranging from 1-100 listing locus of control. If I have a missing value for depression at level 75 for one case, I can take the mean depression level for all individuals at level 75 of locus of control and impute that for all missing cases in which 75 is the listed locus of control value. I'm not sure why you'd want to reduce the size of the data set, since for the most part the larger the N the better. Tracey Continelli = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
