- Thread starter buntington
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in AMOS, there is one suitable option: use of Bayesian estimation (which is more precise when there are lowscale-level variables that can vary only 0/1 or 0/1/2),

this estimation should deal with your ditotomous variable.

M.

So you just select Bayesian estimation instead of using something like maximum likelihood? Would it affect the interpretation of the model--would I still get the same fit indices and everything? We had a very brief intro to Bayesian estimation in my SEM class and all I can remember is that it was confusing.

I just ran some tests and NONE of my variables are normally distributed, so I guess I can't use MLE anyway unless I run transformations on all of them? It sounds like it would be easier just to use Bayesian estimation?

Okay, more questions that I hope someone might be able to answer. Your replies so far have helped a lot!

1) I need to parcel the variance of the indicators for four of my latent variables. I read through the literature I could find on parceling and I am a bit stumped about parceling methods. Is there a good algorithm to help with this? Four of the measures should be very unidimensional; one is probably not (it taps both depression and anxiety but they are not explicitly divided into separate subscales). Is there an optimal way to parcel these items? I found some scripts for parceling algorithms but they are all in SAS or mplus...I am using SPSS and AMOS for my analyses. Additionally, the indicators for some of the measures might be tricky to parcel. From what I have read, you should basically always create three parcels because of over and underidentification. But some of my scales would be hard to divide into 3 (two scales have 8 items each, one has 25, and one has only 4). What is the best way to parcel measures like this? Any advice you can offer would be very appreciated.

2) When trying to set up my model in AMOS, I came across a problem. I will include a picture of the model of my latent variables to help explain. AMOS will let me draw one-way arrows across the model, but it won't let me draw two-way arrows (covariance) between some of the latent predictor variables. Am I doing something wrong? Is there a way to add these? I wanted to include them because they are in the theoretical model that guided the development of my model.

http://tinypic.com/view.php?pic=33yqjro&s=8

3. Dummy coding...just want to make sure I'm understanding it correctly. People with no prior experience would be coded as 0 and people with prior experience would be coded as 1, right? Do I need to do anything else? I should only have one variable for the dummy code since I only have two groups, right?

1) I need to parcel the variance of the indicators for four of my latent variables. I read through the literature I could find on parceling and I am a bit stumped about parceling methods. Is there a good algorithm to help with this? Four of the measures should be very unidimensional; one is probably not (it taps both depression and anxiety but they are not explicitly divided into separate subscales). Is there an optimal way to parcel these items? I found some scripts for parceling algorithms but they are all in SAS or mplus...I am using SPSS and AMOS for my analyses. Additionally, the indicators for some of the measures might be tricky to parcel. From what I have read, you should basically always create three parcels because of over and underidentification. But some of my scales would be hard to divide into 3 (two scales have 8 items each, one has 25, and one has only 4). What is the best way to parcel measures like this? Any advice you can offer would be very appreciated.

2) When trying to set up my model in AMOS, I came across a problem. I will include a picture of the model of my latent variables to help explain. AMOS will let me draw one-way arrows across the model, but it won't let me draw two-way arrows (covariance) between some of the latent predictor variables. Am I doing something wrong? Is there a way to add these? I wanted to include them because they are in the theoretical model that guided the development of my model.

http://tinypic.com/view.php?pic=33yqjro&s=8

3. Dummy coding...just want to make sure I'm understanding it correctly. People with no prior experience would be coded as 0 and people with prior experience would be coded as 1, right? Do I need to do anything else? I should only have one variable for the dummy code since I only have two groups, right?

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Okay, messed with my data some more...here's what I came up with.

I did EFAs on all of the measures I need to parcel.

Measure 1 is a scale of symptom severity. I came out with 2 factors: depression (16 items) and anxiety (9 items). A couple of items are really crossloaded (e.g. .423 on depression and .338 on anxiety). Should I go with these factors? Should I try to create domain-representative parcels -e.g. 3 parcels with each sampling a bit of the two factors? Should I try to create multiple parcels for each of the two factors?

All of the other measures are unidimensional and have one-factor solutions.

Measure 2 has 4 items, so I created 2 parcels that should each represent the construct equally.

Measure 3 has 6 items, so I created 3 parcels that should represent the construct equally.

Measures 4 and 5 have 8 items each, so for each scale, I created 2 parcels with 4 items each that should represent the construct equally.

To construct these parcels, I just rank-ordered the items based on how highly they loaded and then separated them so that the parcels would have a roughly equivalent loading on the factor. There wasn't any specific algorithm or formula i used to do this.

Finally, one of my latent variables is measured using a scale that has 7 subscales. There are 53 items total. This is probably going to sound stupid, but it's been 3 years since I took my SEM class and I keep getting CFA and SEM muddled in my head. When I set up the SEM in AMOS, should I be adding the total score on each of these subscales as the 7 indicators, or should I create a little box for each individual item and have them feed into another box for each of the 7 subscales? Same thing with the parcels--would I actually load any of the individual items into the SEM, or would I create a variable representing the total or average score for each parcel and load those in as the indicators? I hope that makes sense...

Thanks in advance.

I did EFAs on all of the measures I need to parcel.

Measure 1 is a scale of symptom severity. I came out with 2 factors: depression (16 items) and anxiety (9 items). A couple of items are really crossloaded (e.g. .423 on depression and .338 on anxiety). Should I go with these factors? Should I try to create domain-representative parcels -e.g. 3 parcels with each sampling a bit of the two factors? Should I try to create multiple parcels for each of the two factors?

All of the other measures are unidimensional and have one-factor solutions.

Measure 2 has 4 items, so I created 2 parcels that should each represent the construct equally.

Measure 3 has 6 items, so I created 3 parcels that should represent the construct equally.

Measures 4 and 5 have 8 items each, so for each scale, I created 2 parcels with 4 items each that should represent the construct equally.

To construct these parcels, I just rank-ordered the items based on how highly they loaded and then separated them so that the parcels would have a roughly equivalent loading on the factor. There wasn't any specific algorithm or formula i used to do this.

Finally, one of my latent variables is measured using a scale that has 7 subscales. There are 53 items total. This is probably going to sound stupid, but it's been 3 years since I took my SEM class and I keep getting CFA and SEM muddled in my head. When I set up the SEM in AMOS, should I be adding the total score on each of these subscales as the 7 indicators, or should I create a little box for each individual item and have them feed into another box for each of the 7 subscales? Same thing with the parcels--would I actually load any of the individual items into the SEM, or would I create a variable representing the total or average score for each parcel and load those in as the indicators? I hope that makes sense...

Thanks in advance.

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In terms of how to use AMOS I have never used it so I can not give technical advice but clearly you want to maintain latent variables in your SEM and not use total scores/average scores/manifest scores or regression parameters will (mot likely) be attenuated.

If I don't use parcels and I'm not doing exploratory SEM, is there some other method I could use as an alternative to parceling to address the number of items I have? Or should I just try to use the items as is?

Now I have another problem, though. One of the questions I had earlier was about my inability to add covariances in my more complicated model. Here is a picture of the structural model (http://tinypic.com/view.php?pic=33yqjro&s=8). I wanted to add covariances between attitudes, norms, and perceived behavioral control per the theory that the model is based on, but AMOS would not let me do so. Now, the second model I tested was a nested model, so it looks exactly the same but without the masculinity variable. When I entered that model into AMOS, I was able to add the covariances between those latent variables. The simpler model has much higher fit indices even though the regression weights indicate that the masculinity variable is a significant predictor of attitudes and norms. Also, the modification indices for the more complicated model (the one in the picture) actually indicate that I should add paths between attitudes, norms, and control anyway, so I think the lack of relationships between those variables is what's creating the poor fit for the more complex model. Any ideas why I can't add these covariances in AMOS, or any way that I could? Should I try drawing one-way paths amongst the three variables (but that implies a causal relationship and I only want to specify covariance)?

I found this thread that seems to be about the same topic, but it just confuses me more and it looks like the original poster never replied to Lazar to get any resolution for the problem. Does it mean that my model would be unidentified if I added the covariances, and that's why I can't? If so, why is my other model identified with the covariances?

http://www.talkstats.com/showthread.php/17255-AMOS-SEM-model-drawing-rules

It really makes it sound like the problem is conceptual/theoretical and the problem is that they are endogenous variables. But I don't understand how else you could show a relationship between them without adding the covariance.

Here's what I did--I changed up my model by moving masculinity to directly predict intentions. It makes theoretical sense because it's not totally clear whether masculinity predicts norms/attitudes or if they are just correlated. That allowed me to add in the covariances between the other variables in the model. Then when I ran my SEM the model fit was absolutely beautiful...but the model still fits better without the masculinity variable (the RMSEA is even below 5, which has never ever happened for me when doing any kind of psych stats before). Blegh! Basically it fits better with the simplest model that doesn't include any of the variables I added, which was the whole point of my dissertation. How disappointing. I don't suppose there is some kind of SEM penalty for complex models that I'm not understanding, is there?

Anyhow, thanks for your help!