Forum about the more advanced form of simple linear regression and multiple linear regression. http://forums.stat.ucla.edu/list.php?8 Mon, 23 Nov 2009 22:46:31 -0800 Phorum 5.2.10 http://forums.stat.ucla.edu/read.php?8,162,162#msg-162 http://forums.stat.ucla.edu/read.php?8,162,162#msg-162 kdelrahim Hierarchical Linear (Multilevel) Models Wed, 18 Nov 2009 15:48:16 -0800 http://forums.stat.ucla.edu/read.php?8,150,150#msg-150 http://forums.stat.ucla.edu/read.php?8,150,150#msg-150
I would like to compare the effect of different covariates in a multi level regression. Especially I am interested in comparing the effects of individual level variables with the effect of their respective group mean or with the effect of other (non aggregated) level 2 characteristics.

I read about the effect size measure suggested by Tymms (e.g. in Schagen & Elliot, 2004). As far as I understand the chapter this measure only quantifies the effect of a L2 variable. So I wonder how I would have to calculate a respective measure for the individual level variable.

In a multiple regression I could at least compare the standardized coefficients (leaving aside the discussion about the comparability of betas ...). Analogously I could take the standardized coefficients that are offered for example in the TWOLEVEL analysis by Mplus. But is this also a suitable practice for cross level comparisons in multi level analysis?

Are there any ideas or references that explain how I can quantify the effects of variables of different levels and how I can assure comparability of these measures?
Thank you for any further hints
Katrin]]> Zerpy Hierarchical Linear (Multilevel) Models Fri, 13 Nov 2009 02:23:59 -0800 http://forums.stat.ucla.edu/read.php?8,141,141#msg-141 http://forums.stat.ucla.edu/read.php?8,141,141#msg-141
I try to specify a multilevel model and face the fowllowing questions:

1) Centering
I am interested in testing whether the level 1 coefficients vary over my clusters. From Raudenbush & Bryk (2002, Ch. 5) I know that I should use group mean centering for this purpose. Now I ask myself whether I should also add the group mean of the respective variables on Level 2. In Ch. 5.2 Kreft and de Leeuw (2004) show that the use of the group mean may substantially changes the results - whereas this seems to apply more to the fixed level 1 coefficients. Are there any (statistical) guidelines when to add the respective group mean? Or is this just a matter of theory resp. of the state of research on the topic?

2) Reliability of random level-1 coefficients
In the HLM output I saw that HLM calculates reliability estimates of the random level-1 coefficients. I have two questions regarding these estimates:

a) Raudenbush & Bryk (2002, p. 125) say that "whenever the reliability of a random level-1 coefficient drops below 0.05, that coefficent is a candidate for treatment either as fixed or nonrandomly varying". So I conclude that this is a second kind of significance test for the variance components. But which criterion is the more important one? In my case the chi-square test and the reliability estimate would partly lead to different conclusions.

b) All reliability estimates in my model are very low - especially those of the random slopes (.20 or less). So I asked myself whether I have to deal with these estimates like with ordinary reliabilities of a scale for example - especially after reading about the above mentioned (very low) criterion value. Could you give me any advice how to inetrpret reliabilities like these?

I would really appreciate any comments and ideas!

Katrin]]> Zerpy Hierarchical Linear (Multilevel) Models Sun, 25 Oct 2009 00:57:36 -0700 http://forums.stat.ucla.edu/read.php?8,75,75#msg-75 http://forums.stat.ucla.edu/read.php?8,75,75#msg-75
my question is how to model a certain multilevel data structure.

Specifically, I have survey data with persons (i) nested in regions (j), and these surveys have been collecetd over several time points (k).

So for persons, it is a repeated cross-section, not a panel study. Person-level predictors for the dependent variable aside, in my model
- a first predictor (Z1) varies only across time points (k).
- another predictor (W1) refers to the regions (j). W1, however, takes on different values for each region j at each year k.

This means that W1 varies across both time points (k) and regions (j).

Initially, I thought it would be adequate to modelt his data structure as a cross-classified model. This was motivated by the ideat that persons living in any of the j regions could have been interviewed at a certain time point k, while respondents interviewed at a certain time point k could also have lived in any of the j regions.

So I used cells (= cross-classifications) of regions j (= rows) and time points k (= columns) at the highest level of analysis.

But there seems to be a problem involved. Because W1 takes on different values across both time points and regions, doesn’t this mean that, conceptually, W1 neither represents a unique row respectively column variable? Or is it possible that a predictor variable in a cross-classified model can account simultaneously for row AND column variance???

Many thanks for your comments!]]> student09 Hierarchical Linear (Multilevel) Models Wed, 29 Jul 2009 00:56:55 -0700 http://forums.stat.ucla.edu/read.php?8,62,62#msg-62 http://forums.stat.ucla.edu/read.php?8,62,62#msg-62 I am trying to resolve two - for me - incongruent informations: I always thought that in cluster samples standard errors are underestimated due to a smaller variance of the cluster sample compared to the variance of a random sample of equal size. After having learned more about the design effect I know that this assumption must be wrong.
Now I ask myself: a) How does it happen that the variance of a cluster sample is larger than the variance of a random sample of equal size - I had assumed that the higher similarity of the cluster members not only leads to smaller intra-group variances but also to a smaller total variance. b) Why are the standard errors underestimated - if not due to a smaller variance of the cluster sample?
Do you have a recommendation for a reference that gives an explanation? - I know that, concerning sampling questions as these, I should probably consult classics like Kish or Cochran. But unfortunateley my statistical education is not so profound that I could understand them. So I would be very glad to begin with some rather introductory references.
Thank you in advance for any comments! Katrin]]> Zerpy Hierarchical Linear (Multilevel) Models Sat, 27 Jun 2009 12:49:35 -0700 http://forums.stat.ucla.edu/read.php?8,50,50#msg-50 http://forums.stat.ucla.edu/read.php?8,50,50#msg-50
currently I am working on multilevel data (students within classes). I would like to specify a 2-level-model and I assume that certain class characteristics (e.g. class size etc.) will influence the betas of level 1. But I don't want to explicitly specify this within the model. I would rather like to find out whether there are "natural" groups of classes and explore afterwards by which class characteristics they differ.

Unfortunately I am a real beginner in multilevel modeling. So, I would be glad for some feedback: Is this - for whatever reasons - a stupid idea? And if not: Could please someone give me advices where to read more about this and how to do this in Mplus?

Thanks in advance for any comments and advices!

Katrin]]> Zerpy Hierarchical Linear (Multilevel) Models Sat, 13 Jun 2009 04:23:13 -0700 http://forums.stat.ucla.edu/read.php?8,45,45#msg-45 http://forums.stat.ucla.edu/read.php?8,45,45#msg-45
For my master thesis i wanted to investigate the effect of different reward schemes on cooperation and knowledge sharing and whether these effects were moderated by individual characteristics such as: risk aversion, overconfidence and social value orienation. I investigated my research topic by means of a factorial survey (vignette study). In order to analyse my results i want to run a multilevel mixed effects analysis in SPSS via the mixed-model procedure.

However I am not sure whether to use the random or repeated function, and whether i need to check for random effects on the level 2 (individual characteristics).

Could someone please help me out?

Thanks alot in advance!

Nicole]]> nicolestofberg Hierarchical Linear (Multilevel) Models Wed, 22 Apr 2009 08:58:17 -0700 http://forums.stat.ucla.edu/read.php?8,19,19#msg-19 http://forums.stat.ucla.edu/read.php?8,19,19#msg-19 Jose Hales-Garcia Hierarchical Linear (Multilevel) Models Tue, 24 Mar 2009 08:35:39 -0700