dealing with confounders and residuals
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dealing with confounders and residuals
 
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I'm looking at the possible association between environmental contaminants (heavy metals and PCBs in human blood) and various menstrual cycle response variables in the study participants. Obscuring these possible relationships are some confounding variables shown to influence the menstrual cycle dependent variables: age, caffeine consumption, alcohol consumption, etc.
I have run a regression analysis with one of the menstrual cycle variables (dependent) and the suite of confounders, and then saved the residuals. In a regression on the residuals (dependent) with the blood contaminant variables, I get some interesting and significant regression coefficients and an overall significant regression model. I should also mention that I've used PCA on the suite of contaminant concentrations to create 6 uncorrelated predictor variables. Is this a reasonable approach, or is there a better way to block out the effects of the confounders to see the effects of contaminants on the dependent variables? best regards, Ian ____________________ Ian D. Martin, Ph.D. Data Analysis & Environmental Consulting _________________________________________________________________ This e-mail and any attachments may contain confidential information. If you are not the intended recipient, please notify the sender immediately by return e-mail, delete this e-mail and destroy any copies. Any dissemination or use of this information by a person other than the intended recipient is unauthorized and may be illegal. Ce message électronique et les fichiers qui y sont joints peuvent contenir des renseignements confidentiels. Si vous n'êtes pas le destinataire visé, veuillez en aviser immédiatement l'expéditeur en répondant à ce message; effacez ensuite le message et détruisez toute copie. La diffusion ou l'usage de ces renseignements par une personne autre que le destinataire visé n'est pas autorisé et peut constituer un acte illégal. _________________________________________________________________ ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD |
Re: dealing with confounders and residuals
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Please reply to the list and not to my personal email.
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Re: dealing with confounders and residuals
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In reply to this post by Ian Martin-2
I suggest (as a start) you look into MANCOVA (you really have a multivariate problem here and I'll bet the MCV's are correlated. Two stage analysis using PC's as predictors was archaic 20+ years ago when I was studying SEM models. Do you have theoretical hypotheses supported by previous research or is this a fishing expedition? You could do much more efficient and directed analyses if you were to apply the appropriate tools.
Please reply to the list and not to my personal email.
Those desiring my consulting or training services please feel free to email me. --- "Nolite dare sanctum canibus neque mittatis margaritas vestras ante porcos ne forte conculcent eas pedibus suis." Cum es damnatorum possederunt porcos iens ut salire off sanguinum cliff in abyssum?" |
Re: dealing with confounders and residuals
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Thanks, I think....
Yes, there are some well supported hypotheses about the role of organic contaminants and heavy metals as endocrine disruptors. Perhaps I'm not understanding your suggestion, but note that I have only one group, not a control/exposure design? regards, Ian On Oct 7, 2011, at 9:36 PM, David Marso wrote: > I suggest (as a start) you look into MANCOVA (you really have a multivariate > problem here and I'll bet the MCV's are correlated. Two stage analysis > using PC's as predictors was archaic 20+ years ago when I was studying SEM > models. Do you have theoretical hypotheses supported by previous research > or is this a fishing expedition? You could do much more efficient and > directed analyses if you were to apply the appropriate tools. > > Ian Martin-2 wrote: >> >> I'm looking at the possible association between environmental contaminants >> (heavy metals and PCBs in human blood) and various menstrual cycle >> response variables in the study participants. Obscuring these possible >> relationships are some confounding variables shown to influence the >> menstrual cycle dependent variables: age, caffeine consumption, alcohol >> consumption, etc. >> >> I have run a regression analysis with one of the menstrual cycle >> variables (dependent) and the suite of confounders, and then saved the >> residuals. In a regression on the residuals (dependent) with the blood >> contaminant variables, I get some interesting and significant regression >> coefficients and an overall significant regression model. I should also >> mention that I've used PCA on the suite of contaminant concentrations to >> create 6 uncorrelated predictor variables. >> >> Is this a reasonable approach, or is there a better way to block out the >> effects of the confounders to see the effects of contaminants on the >> dependent variables? >> >> best regards, >> Ian >> ____________________ >> Ian D. Martin, Ph.D. >> >> Data Analysis & >> Environmental Consulting >> _________________________________________________________________ >> This e-mail and any attachments may contain confidential information. If >> you are not the intended recipient, please notify the sender immediately >> by return e-mail, delete this e-mail and destroy any copies. Any >> dissemination or use of this information by a person other than the >> intended recipient is unauthorized and may be illegal. >> >> Ce message électronique et les fichiers qui y sont joints peuvent >> contenir des renseignements confidentiels. Si vous n'êtes pas le >> destinataire visé, veuillez en aviser immédiatement l'expéditeur en >> répondant à ce message; effacez ensuite le message et détruisez toute >> copie. La diffusion ou l'usage de ces renseignements par une personne >> autre que le destinataire visé n'est pas autorisé et peut constituer un >> acte illégal. >> _________________________________________________________________ >> >> ===================== >> To manage your subscription to SPSSX-L, send a message to >> LISTSERV@.UGA (not to SPSSX-L), with no body text except the >> command. To leave the list, send the command >> SIGNOFF SPSSX-L >> For a list of commands to manage subscriptions, send the command >> INFO REFCARD >> > > > -- > View this message in context: http://spssx-discussion.1045642.n5.nabble.com/dealing-with-confounders-and-residuals-tp4881181p4881812.html > Sent from the SPSSX Discussion mailing list archive at Nabble.com. > > ===================== > To manage your subscription to SPSSX-L, send a message to > [hidden email] (not to SPSSX-L), with no body text except the > command. To leave the list, send the command > SIGNOFF SPSSX-L > For a list of commands to manage subscriptions, send the command > INFO REFCARD ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD |
Re: dealing with confounders and residuals
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If your "various menstrual cycle response variables" are correlated then there are numerous advantages to using Multivariate Multiple Regressions over a series of Univariate Multiple Regressions (Power, Type II Error, understanding the dimensionality of the response hyperspace...). My referring to MANOVA was perhaps a misnomer (I think of MANOVA really as an umbrella for the GLM of which regression, anova, manova, mancova etc are special cases). SEM is sort of the granddaddy of all of this .
In SPSS you would specify: GML dependent variable list WITH independent list (See FM for other specs including saving residuals). You will see the B coefficients are identical for the Univariate regressions and the Multivariate regressions however the Univariate tests and Multivariate tests will typically vary if the dependent variables are correlated. Here is a nice presentation of Multivariate Regression. http://www.google.com/url?sa=t&source=web&cd=14&ved=0CDUQFjADOAo&url=http%3A%2F%2Fwww.psych.yorku.ca%2Flab%2Fpsy6140%2Flectures%2FMultivariateRegression2x2.pdf
Please reply to the list and not to my personal email.
Those desiring my consulting or training services please feel free to email me. --- "Nolite dare sanctum canibus neque mittatis margaritas vestras ante porcos ne forte conculcent eas pedibus suis." Cum es damnatorum possederunt porcos iens ut salire off sanguinum cliff in abyssum?" |
Re: dealing with confounders and residuals
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Adding to my previous:
Google "Testing Mediation using SEM" http://www.google.com/search?q=testing+mediation+using+SEM&ie=utf-8&oe=utf-8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a Shall keep you busy for awhile.
Please reply to the list and not to my personal email.
Those desiring my consulting or training services please feel free to email me. --- "Nolite dare sanctum canibus neque mittatis margaritas vestras ante porcos ne forte conculcent eas pedibus suis." Cum es damnatorum possederunt porcos iens ut salire off sanguinum cliff in abyssum?" |
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In reply to this post by David Marso
Hi all
I have SPSS version 19 but the bootstrap option is not available at all. Does somebody has a idea why. Thanks kelly ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD |
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