Here's a fun pet project I've been working on: udreamed.com/. It is a dream analytics app. Here is the RUclips channel where we post a new video almost three times per week: ruclips.net/channel/UCiujxblFduQz8V4xHjMzyzQ Also available on iOS: apps.apple.com/us/app/udreamed/id1054428074 And Android: play.google.com/store/apps/details?id=com.unconsciouscognitioninc.unconsciouscognition&hl=en Check it out! Thanks!
+Mary Saczawa RUclips won't let me reply directly for some reason... so, I'll reply ehre. The use of gender will really depend on your theory. If you are really interested in gender effects, then you should include it as a moderator. If you just want to control for it, then you can do that. If you want to know if a given variable's values depend on gender, you can do an ANOVA or t-test using gender.
trimming is optional. it simply isolates the significant effects and tends to strengthen them. In this particular video I also trim because I'm only interested in paths that are significant for one or the other group. However, You can get by without trimming anything. I also highly recommend using my new video for multigroup analysis. It is WAY easier.
@sgcraig84 You can get it from my wiki. I can't post the link here in the comments section (like, youtube won't post the comment if there is a url in it...), so I created a note in the video that has the url. The note pops up around 3:15.
Yes, I've seen this and some require a thorough invariance test across loadings, intercepts, errors, and variances. These are the purists and I am prone to only comply when requested by a particular reviewer to do so.
@XenovaKyo Great question. The answer is that you only remove the paths that are not significant for BOTH groups. If it is significant on one, but not the other, then you leave it in. Also, the easier way to do this is shown in my other youtube video: "Multigroup Moderation in Amos - Made Easy" I highly recommend doing it the way shown in this other video. Much easier, much faster, less room for error, and better results, plus you can do it for more than two groups.
Hi James, the new tool didn't work for me (also syntax error, although i checked everything) so I used this instead and I think you just made me graduate! Thanks for all of your tutorials, wouldnt be MSc without them :-) cheers!
Not off the top of my head, but you can probably google "chi-square difference test" and "moderation" to find something. Use scholar. google. com for best results. Also, the new method I have for this is so much less painful. I highly recommend it. See the link at the beginning of the video.
+zeal2fly, For some reason, RUclips isn't letting me reply directly... 1. The difference in degrees of freedom and in chi square can't be negative (flaw of my program). So just put the larger set on top. 2. You can only compare two at a time. Or you can compare one to the other two. 3. With 25 datasets, it would still be less exhausting to run it the easy way (I think). If you can consolidate the datasets, that would be best.
If you mean you are trying to create a covariance arrow between the interaction and the others, the only reason that wouldn't work is if the interaction term has an error term on it, or if it is endogenous (has an arrow pointing into it).
@camerinobarron It is on my wiki: statwiki. kolobkreations. com Also, there is a better way to do this kind of analysis now. Check out my new video "multigroup moderation in amos made easy".
There is an easier way to do this. I have a new video called "Multigroup moderation in Amos made easy". That video will show you a super fast way to do it all. The tool referenced in the video is available on my wiki: statwiki. kolobkreations. com
Hi, thanks for the video. Very useful!!! I used your method and found the sig. difference at the group level but when testing each path I couldn't find the sig. path when comparing with the chi-square threshold. Have you ever had that issue before? May I get some suggestions? Thank you!
This can happen when there are lots of small differences at the path level that accumulate to a significant difference at the model level. This means that there is a difference between groups when it comes to the set of relationships in the model, but there is no single path that is significantly different. It seems a bit counterintuitive, but it does happen occasionally. Bootstrapping (and then using the bootstrap sample estimates) *might* emphasize these differences a bit more.
ٍSamar Ahmed Amos does this for you in the datafiles area. I show how to do this in this video. I use the Gender variable which has a 2 for Male and a 1 for Female.
ٍSamar Ahmed Multigroup analysis can be done in SPSS if you have a simple model with no mediators. But if there are mediators, then it cannot be done. It also doesn't work if you have multiple dependent variables (except I think with general linear modeling).
James Gaskin one more question please If i have a variable with 3 or more groups and all critical ratios are significant, can i arrange the impact level of these groups by standard weights or by what?
ٍSamar Ahmed I'm not sure I understand the question. Relative impact for each group is represented by the standardized regression weight. The extent of difference can be determined by the z-statistic.
I used 0.1 because moderations are often difficult to find. So, 0.1 is much more open to potentially significant effects than 0.05. I have made an easier way to do this test that involves z-scores and p-values. The video is called "multigroup moderation in amos - made easy".
@jrm236 the p value is highly sensitive to large sample size and complex models. I would not rely on it. Just go with the chi-square difference test. And for fit, just rely on the CFI and RMSEA, and the Chi-square/DF.
Yes, assuming you already achieved metric and configural invariance in the measurement model. If not, then go ahead and let the latent-->indicator paths be constrained.
Firstly thanks SO MUCH for providing clear and concise explanations of SEM! Secondly, I have followed your recommendation to trim insignificant paths, to good effect. However I've been asked to cite published sources - what sources did you refer to for this method or is this your own development? Thanks in advance..
+Melanie Weaver I'm not sure if there is a good citation for this. The idea is to increase degrees of freedom (thereby improving model fit) by removing insubstantial paths. There are different schools of thought on this. If you have theorized about it though, you need to test it.
Just separate like I did. Also, I recommend the easier way of doing this analysis. See my newer video called "Multigroup Moderation in Amos - Made Easy"
@Gaskination Thank you for your quick response :) Yes. I have watch the easier way recommended by you. Indeed it is much error-proof and faster way of doing the same process. Just because of academic requirement, we are suggested to show the step-by-step process of constrain the model and of cause to better understand the works behind it. But I found the stat tools created by you is a really valuable tool for many situations, I have suggested it to many of my colleagues and friends here.
Hello James! your tutorials are great and very helpfull! I was wondering if you could recomend one or two papers that used multigroup moderation analysis so that I could have an idea how to present the results? Thanks in advance for your help!
Hi James - thanks for the video (and all the others on this channel): they are so helpful. I have recently tested a model similar to this (i.e., no moderation or mediation) on AMOS with three DVs. I was surprised to find that the beta coefficients were exactly the same as I found in multiple regressions for each DV separately, even after I covaried the moderately correlated DVs (r = .10 to .50) in the model. I just wondered, is this the norm, or would you suggest that I have done something wrong and need to seek guidance? Many thanks, and thanks again for the videos :)
It is somewhat unusual, but not altogether surprising. This can happen if the independent variables are not very correlated (i.e., zero multicollinearity).
Dear James, first of all many thanks for your effort and congratulations on one of many great tutorials. I followed each step of it using a dichotomous moderator, but the results are somewhat puzzling. I would like to report the path coefficients in a table for both groups in order to compare them. For some paths, I read large between-group differences but they are not significant, while for other paths differences are smaller but they still result as significant . I guess I should use the standardized coefficient values that are reported in the fully unconstrained model output - am I right? Many thanks in advance and keep up the good work!
Thanks again for the tutorials! Love them! I am working on a multigroup SEM since I collected my data in two countries and I would like to compare them. I started by analysing the factor loading, Cron. Alpha and Item-to-Total-Korrelation for each country and the dataset as a whole. My factors look good ;-) Now, I am playing around with AMOS: Should I first create one model with all my data (both countries in one) and fix GFI, AGFI and so on or should I directly start with a Multi-Group analysis?
James, I always appreciate your great videos and helpful advices. Here is a question I came across after reading two SEM papers which include a moderator, company size. One paper tested the moderation effect of size by spliting the entire sample into two groups (small group - large group). The other paper formed interaction terms of the size and all the other IV factors. Which approach would be more prefered or desirable? My guess is the second approach as the size itself is a continuous variable. Your thought??
Interaction retains more of the variance than splitting. However, due to the algorithms involved in interactions vs multigroup, you will more often find significant effects with multigroup (despite the drop in variance and the drop in sample size), and it is easier to interpret multigroup moderation.
That is not unreasonable, but it is unlikely. This just means that the two groups are no different. I highly recommend my newer video called, "Multigroup moderation in AMOS - Made Easy"
Hi James: first thank you for a simple and useful lecture . I need to ask you question, in this video you have explained the structural model test by using chi-square , but you didn't mention to the measurement model test. It is not necessary to do ?
I have other videos for explaining the invariance test. Here is one that follows the approach from this video: ruclips.net/video/6j4_ZrkCxTc/видео.html Here is a simpler approach (the link is queued to the right time position): ruclips.net/video/MCYmyzRZnIY/видео.htmlm55s
James Gaskin Hi James, if I wanted to identify whether a model works better when applied to two different groups which test would I use, the invariance test or the multigroup test?
Georgina Smith Invariance shows whether the measures have similar distributions and variance (i.e., if the two groups answered your survey the same way). Moderation is all about whether the groups differ in terms of the relationships between the constructs. Invariance is measurement level. Moderation is structural level. Most theories are structural level.
The video is awesome. It helps me a lot in understanding the multigroup comparison in SEM. If possible can I ask where I can get the Excel file demonstrated in the video to calculate the Chi Square Difference tests?
Your AMOS lessons are really helpful for me for quite a long time, so thanks a lot for uploading! But I have one (interesting?) question: Does a SEM-equivalent exist for a sequential / hierarchical regression? Where you can realise the pathway / process of certain variables within the total model while adding new variables? Best regards from Germany!
James Gaskin Thank you! But is it actually possible to use nominal scale level of measurement for the dependent variable in SEMs? Like in logistic models with e.g. two categories?
@catsfancyful Testing differences between more than two groups is always a case of testing the pairwise differences (i.e., between group 1 and 2, then 1 and 3, then 2 and 3). This excel tool will do it; you just have to do it two at a time.
Dear Dr. Gaskin, Thank you so much for all your informative videos. I have a quick question about the chi-square difference test if I am trying to figure out whether items from a new measure load better on one factor or two factors. Somewhere I read that there are two ways of performing it. The first involves reading the chi square value off of AMOS for the one-factor model and then subtracting it from the chi square value for the two-factor model. The second one involves subtracting the chi square value from a two-factor model where the path between the two factors is constrained to one from the chi square value from the two-factor model where the path between the two factors is not constrained. From what I understand, both are supposed to test for the discriminant validity. Could you please help me with understanding the difference between the two and when each is supposed to be used? Again thank you so much in advance!
Thanks for this, very interesting. I did wonder why you didn't just do a multi-group analysis though. Wouldn't that be easier? You could then also ask for Critical Ratios of Differences - That way you can check which paths differ across the groups without having to resort to laboriously checking each one. Or am I missing something?!
Hi Nisrein Shabsogh RUclips wouldn't let me reply directly, so I'll reply here. Removing non-significant paths is optional. The main benefit of doing it is to increase your degrees of freedom (and therefore improve model fit). However, the drawback is that you are no longer accounting for that effect.
Hi James. Thanks for you really helpful video. I have three questions: 1) X2 threshold sheet: unconstrained (88.138,3) and constrained (85.734, 8). The p-val it is show #NUM ! error. So I have 4 IV ON 1 DV, I constrained all the IV as W1-W4. Should I have left regression weight of one of the IV as 1? Then the constrained becomes (148.98, 9) and p-val is NO. 2) I have 3 groups, so the result is telling me that if all the 3 group differ or not. And it could be that 2 groups are same and 3rd is different. What we do in that case? 3) I saw the other video too, it is quiet easy to do that. But same question, how things will be differ between 3 groups? One of the problem is I have 3 groups and 25 data set. So doing isolated grp 1-2, 2-3 and 1-3 will be quiet exhausting. And consolidating and interpretation that data. I am just happy to report that three groups are different/not different.
Thanks for these great tutorials! One question: Could you have done a multiple group analysis here, constraining the model by equating individual paths between the groups (e.g. pathA_male=pathA_female) and see if they statistically differ from each other? Or is multiple group analysis any different from what you show here?
Hi, James, I use both chi-square method and critical ratio method and get different result. Chi-square method shows that there is difference between two groups path by path via critical ratio show no difference path by path.
Did you do a path by path analysis with Chi-square to see which path it is? It may be that with the Chi-square test at the model-level, you have enough small differences on each path to add up to being not invariant, but no single path is actually significantly different.
Respected Sir, Sorry for inconvenience. I have gone through the whole process in the video. Its just awesome. i have done an analysis for my research project. Sir, could you please inform me How can i write the interpretation of Chi square differences of the model in my report??? What will be the references for chi square differences for the process in this videos. Thanks.
Here are some useful references for how to write it up and how to cite it: scholar.google.com/scholar?hl=en&q=%22chi-square+difference%22+%22multigroup%22&btnG=&as_sdt=1%2C45&as_sdtp=
@jrm236 Can't you just do a path by path chi-square difference test as demonstrated in this video? Or do you mean that you have 300 attrition from the 700 and 900? in which case you have smaller n and serious attrition issues.
Thanks for this awesome video. I just wanted to ask a question: Could I test for group differences in a model with an interaction (moderation) term using this same method or does this only work for a model of mediation? For example: the dependent variable would be distress, the independent variable would be victimization, the moderator would be social support, and the group variable would be gender (Male, Female).
Your Stat Tools package is fantastic. Thank you very much. Do you happen to have a version which would allow for testing multigroup moderation between more than two groups? E.g. four groups?
Hi James, Thank you so much for uploading these great videos! I have learned a lot from you. I had a question when following this video. I am comparing two groups. I cannot trim the non-significant variables as the model will not perform (Error message:" Iteration limit reached" and "The model is probably unidentified...". These errors stop me from getting the unconstraint model and the full constraint model. What should I do now? Many thanks, Racheal PS: I have watched your videos about the iteration limit reached and taken the steps. Some of the items cannot be removed as the model will be unidentified...
The error about being unidentified usually means there is a parameter constraint missing. Check to make sure all latent factors have at least one indicator path constrained (usually this means there is a "1" assigned to one of the paths). If you have a second order factor, then make sure one of the paths to the first order factors is constrained.
Great videos, very helpful. I have 3 questions, hope you can help me in the right direction: (a) How do I create the interaction variable to test for moderating effects between constructs, i.e. if each construct (incl. moderator) is composed of a number of variables? (b) How do I control a model in AMOS for 2+ variables (not necessarily binary)? (c) We found high CMB in our model (during CFA). When we start SEM, should I leave the common method variable in the model to eliminate its effect?
Hi James, the video is indeed very useful to perform the moderation analysis. I just need one clarification, do we first need to run the overall model without constraint and then with constraints and only after that perform path by path analysis to examine the moderating impact for a particular path. Or is it possible that we first just run the complete model without any constraints and then constraint only the selected path to see if that path has a moderating impact by putting the chi-square values in unconstrained and constrained cells in the excel sheet. Regards, Bilal
Bilal Ahmad Khan It is best to run the full model first. Some schools of thought on this argue that if you do not observe strong enough differences at the model level, then any effects you might find at the path level could be false positives.
James Gaskin Thanks for the clarification James. I based my moderation analysis on the work of Molina-Castillo et al. (2011). 10.1016/j.indmarman.2010.12.017. Where they just constrained only the preselected paths. Could you please suggest that If I need to refer to your step by step process of testing the moderation impacts, which reference I can quote.
Bilal Ahmad Khan I can't think of one off the top of my head, but you will probably find someting if you go to google scholar and search for: "chi-square difference test" multiple group analysis moderation
Hi Dr. Gaskin. How would you recommend handling a situation in which there is a binary predictor/exogenous variable and we want to know if its effects are moderated by a continuous variable? Thanks for all these videos!
T Om In such a case, I would probably just convert the continuous moderator into a binary (low/high) variable, and then create four groups (2x2) with the predictor and the moderator, and then do an ANOVA using that new grouping variable as the factoring variable.
@sgcraig84 There are a couple work-arounds. The best method is probably to add a control variable and hope it is not significant for females... Or, add another independent variable that you expect will be significant for males, but not for females. Or, simply trim the least significant path for females (assuming it is not the only path tying the exogenous variable to the model). Feel free to email me directly if these options don't sound appealing or if they don't work. email: jeg82@case.edu
@jrm236 There are different methods to test this, and none of them are great. Additionally, if you are comparing a group of 100 to a group of 1000, the chi-square difference test may pose problems, and instead you could look at the change in CFI (typically looking for a difference of more than a tenth).
Somebody can answer me pleas (it's quite urgent). For example at 6.02 you have your khi square, df and a probability value which is 0.00. So it's higly significant and we can reject the H0. What is the H0?? I thought when we test a model adjustment (khi square representing the distance from our data) we want a non significant p-level. Thank you!
The null hypothesis is always "there is no difference" or, "there is no effect". So, in the case of a multigroup, a significant chi-square indicates that there IS a difference between the constrained and unconstrained models.
Thank you so much for your answer! Could you explain one more point? What about the p-value = .000 in the adjustment analysis by groupe (adjustment for men and the adjustment for women separately) in the unconstrained model? H0 would be "there's no difference / effect ..." what effect? I myself have done an analysis where, I analysed for the adjustment of the "default model" of men, then of women separetely (not a multi-group analysis), however using a data from an Excel output containing two sheets (men, women). So, I have there this p-value = 0. Could you see what hypothesis have I tested there`? Anyway, your answer was helpful for the biggest part of my multigroupe analysis!
When doing multigroup, the null hypothesis is that there is no difference between the groups. I don't understand what you mean by analyzing the adjustment of the default model. I have never heard of this.
Dear James It was much needed video. Please tell us how to make this excel sheet with formulas. Can you teach us to draw a graph which could show the moderation effects. Secondly,, how do we add controls in this analysis...Should we do them as IVs??
Thanks James. It was very helpful. I have another question. My paper is under review and reviewers suggested me to use weighted least squares for ordinal data instead of MLE. The issue is AMOS does not have WLS or D-WLS. I wanted to know how much difference it creates while using different estimation techniques... What option I can use in AMOS thats the best replacement of WLS. Thanks in advance
@Gaskination Much appreciated your sharing. A Quick Question: To achieve a unconstrained model, we need to trim (delete) the path that is insignificant. In your example, the paths deleted are insignificant for both groups (male & female). What if the specific path is significant for female and not significant for male? Then should we delete that path to reach unconstrained model? Thanks in advanced.
Hi James! I suppose I can also use this procedure with a full SEM model (not only with path analysis), am I wrong? If I do this, and once I have demonstrated measurement invariance, should I maintain the constraints of each factor loading of each variable or should I only constrain the paths between factors? And another question: Is the chi-square test more relliable or accurate than the critical ratios procedure for testing group differences? Thank you very much. Regards and Merry Christmas! Octavio PD: Your videos and your website (with my beloved Stats Tools Package) are incredible, congratulations!
+Octavio Luque Yes, you can do this will a latent SEM model. To test multigroup moderation in this case, just constrain the structural paths, not the measurement paths. Currently, the chi-square test is the preferred approach, although somewhat more tedious. In order to make the critical ratios approach work accurately, you have to divide the desired alpha threshold (e.g., 0.05) by the number of simultaneously tested hypotheses. So, if you are theorizing five moderated paths, you would have to reach an alpha of 0.025 in order to claim 95% confidence your findings are significant. This is called a Bonferroni correction of family-wise error.
+James Gaskin Thanks a lot, my research wouldn't be the same without your help. But I have another doubt in this regard: in order to test differences path by path, how can I calculate the minimum threshold (specially p 0.05) with non-normal data? I have the Satorra and Bentler's executable (homepages.abdn.ac.uk/j.crawford/pages/dept/sbdiff.htm) which calculate the SB scaled difference, the df difference and the chi-square probability, but I haven't a threshold to contrast each constrained path. I'm not completely sure if my question makes sense. Sorry for this mess and thanks again.
+James Gaskin Sorry, probably I’m not explaining the question well. The Stats Tool Package, provide 3 thresholds which indicate the minimum Chi-Square value that a model with only one constrained path have to reach, in order to prove variation between groups in this path. With non-normal data, I don’t know if I can simply get the SB Chi-square value of the less constrained model (I use EQS software) and obtain this threshold simply adding 3.84 (in the case CI=95%) to it or if I have to do other procedure. My model is globally invariant but I found path differences between groups and I want to prove this with a robust estimator. Thanks for your interest and your patience. Regards from Spain.
+Octavio Luque I've never used EQS before. I'm also not sure how non-normality affects the chi-square difference test. Sorry, I'm not much help on this one.
There are lots of different sample size calculations and formulae. I recommend 50+5x, where x is the number of observed variables. For multigroup, it might be okay to use 50+3x for each group.
Dear Professor Gaskin, Thank you for the video! It made my analysis easier for direct impacts. However, there are indirect impacts in my model, so would you please let me know how to do the moderating effects for indirect impacts? I look forward to hearing from you soon. Thank you for your consideration! Kind regards, Minh Phuc Nguyen
It was really helpful and valuable. Thanks I have a quick question regarding this analysis. I conducted last part of path by path analysis. but all path chi-square are the same? Does it usually happen?
Oh, the regression weights in the moderation tool are not standardized. These are unstandardized. Look back in the AMOS output to find the standardized regression weights.
SO that main the result as the follow is correct: difference in chi-square=38 and df= 60 in measurement model test difference in chi-square=45 and df=78 in structural model test
I'm not sure what you are asking. To me, those do appear to be different (i.e., not invariant). I recommend just using my easier approach: ruclips.net/video/ZMYS90AU8bs/видео.html
I would recommend going to google scholar and searching for these words: "chi-square difference test" moderation (quotes included). Any of the top results should help.
Hi James, thank you for suggesting me to watch this video and use this method. My model is a little bit more complex than the demo one, for each variable it includes 3 or more items. When I clicked "Name Parameters" and checked Regression weights, AMOs came up with 18 regression weights. After that I didn't see any difference on the number of Chi-square results. I also tried to constrain one path at a time and the number of Chi-square results didn't change either. Is this a common problem? Should I check any part of my model? Thank you!
Liang Yuli The chi-square and the degrees of freedom should change when you unconstrain one path at a time. Make sure the "all groups" box is unchecked when you make the changes to a parameter constraint.
Hi James, My hypothesis simply posits that country (A binary variable with 0=Country A and 1=Country B) negatively moderates the relationship between service quality and online satisfaction. That is, the relationship between service quality and online satisfaction will be stronger for Country A. My findings show that I can confirm this hypothesis indeed with the interaction having a significant and negative effect on online satisfaction. Nevertheless, I need to obtain the effect of IV on DV for Country A and B separately (like simple slope analysis for interactions between two continuous variables) but my moderator is categorical (Country) rather than continuous. So, could I run multigroup moderation for each country to obtain these values? (e.g. running the model for Country A participants and Country B participants separately)
Thanks! testing chi-2 is well documented for nested models (and this case applies). But Dabholkar and Bagozzi 2002, p193 states that you should compare 4 models in order to take into account differences coming from error variances, not just loading or corelations. What do you think?
Oh, then that is bad. This means you have estimation errors. Look at the standard error (S.E.) values to see if any of those are also off the charts. If so, you will have identified the problem. If you run it through an EFA first, you should be able to identify the problem. This could be due to high kurtosis values as well.
Dear James, if the regression coefficient of interaction effect is significant and negative, can we conclude that the moderator makes the principal relationship weaker? and if it is positive, the moderator strengthens the principal effect? thanks for answering
Thank you for this clear explanation of the moderation. However I don't understand how to analyse and interpret the correlation between error terms. In the model that I design, I have the my two variable (Happiness and self-esteem) who are correlated but AMOS does not allow me to insert the double arrows. Thus I drew the correlation between the errors (I don't know if it makes sense). How could I explain it statistically. How should I actually interpret the existence of errors terms. Thanks!
Thank you for your video. Is it possible to use categorial variables with more levels (i.e., not only 2 levels like gender: either male or female as you showed; but more than two levels such as"faculty", e.g., mathematics, psychology, engineering etc.)????
Dear Prof. Gaskin, greetings. I have a doubt. If the path is not significant in the structural model analysis (A to B), should we proceed for checking on the multi-group invariance for the path by moderator, or should we avoid it
If it is not significant for either group, then there is no need to check if it is different across groups because for neither group are we confident it is different from zero. However, if one is significant and the other is not, you can still check, because you may be able to include that the IV is a good predictor for one group, but not for the other.
Hi James again :) Help. In doing group analysis, I noticed that in only one group, 3 paths showed insignificant p-value. When I looked at their standarized regression weights, these three have greater the 1 estimate value (5.2, 1.2, and -6.1). What does this mean? Something wrong?? I thought these regression weights shouldn't be greater than 1 normally? Based on my theory, I expected this group, to have insignificant paths. But their large weights is my concern. Thanks a lot. You the best
Hi. It was a much needed video. Thanks. Using this technique I find one of my paths moderated by gender.Now, how to decide whether male strongly moderates the path or females. How to decide the causal relationship is stronger for male or for female? Can you pls explain.
+Payal Anand If the path is different for male vs female, then look at the regression weights in the amos output. If you look on the bottom left of the output window, you will see the two groups listed. Click on estimates, regression weights, and then click on Male (bottom left), then toggle to Female. This will show you the strength of the effect for each group.
Hi, Thank you for your lecture, again! I have data that collected from three regions and want to compare whether each region has different result among each others. When coding the data, do I need to have three different format of data set for each region? or just separate it 1,2, and 3 in SPSS as like gender you did.
Dear James, thank you for your amazing tutorials, they are really helpful! I tried to run a multigroup moderation analysis like you explained in the video. My model consists of 1 exogenous latent variable with 3 indicators and 1 endogenouse manifest variable. I want to test 3 moderators, each having 3 groups. So I build the model and created my 9 groups. But when I click on plugins and name parameters amos also constrains the regression weights of the latent variable. Could you please be so kind and tell me if that is the correct approach for my model as I'm only interested in the path between the factor and the endogenous variable?
I am not sure I follow you. I was referreing to AMOS output. Estimates > Scalars > Standarized Regression Weights. This is the table I am referring to. It does show three 'greater than 1' values for one of the groups. Is there another table?
Thank you so much for all these wonderful tutorials. I noticed that when running a latent structural model and I use the critical ratios test of differences between groups, I get z score outputs using your Stat Tools Excel program you developed. However, when I change the unity-constrained indicators (required by AMOS) from one indicator to another, and re-run the critical ratios test, I get very different results. I cant explain this and was hoping you could offer some guidance.
I'm not sure why that would happen. The results should be fairly consistent. Either way, the chi-square difference approach is considered more appropriate, so I would recommend that approach.
Thank you for the fast response - I believe the reason I get the different results is because I am using latent constructs (with indicators) in my multi-group model and the spreadsheet was built for observed variable (e.g, using scale scores) only models. Still, a very useful tool for a first analysis prior to doing the the usual constraining and unconstraining method.
Dear James, I'm wondering whether it is possible to run two multi-group analyses simultaniously within one model? I have one baseline mediation model, and I would like to test whether the model is moderated by country of residence (I have 3 countries) and gender (within country). Would you recommend to make interaction terms with country*gender and then use them as moderators in multi-group analysis, or is there a better way? Thanx in advance!
@Gaskination I believed it is to delete the path if the path is significant on either 1 group. In my case, there are only 3 paths (in the whole model, and All three are hypothesized to be moderated by the moderating variables). 1 path is insignificant on both groups, so we trim the path. The other 2 paths are insignificant only on group A, but significant on group B. Is this means that I don't have to continue to proceed to the constrained model? or there is other ways?
Thank you James once again. I have other question, the degree of freedom in measurement model test was 60 and it was 78 in the structural model test, although I have just 13 hypotheses. Is this normal or I have mistake?
It is normal to have different number of degrees of freedom. You will usually have more in the structural model because you are removing many covariance arrows.
Respected Sir, Hope you are fine. Sir i am a research student of social sciences. My research model contains serial/sequential mediation (with 2 mediators in chain and 2 IVs and 1 DVs). I learnt about CFA , SEM, simple mediation and moderation from you videos on youtube channel. Thankyou so much for making such helpful videos with clear and easy communication. Hereby i am interested to know that what is the procedure to run Sequential mediation in AMOS because i am not finding any speciofic video on the concerned topic. I just want to confirm will SEM for serial mediation will be the same as we draw for simple mediation and we ll run it through bootstrapping ? Is there any additional step i need to do ? Am right? Please confirm my query. I will be grateful Best Regards
Hi, Your videos are excellent. I am planning to use the chi square difference test. I am using the structural model with the indicators included. When I constrain the full model, it fixes the paths from the latent constructs to their respective indicators also. Should I remove them and just fix all the regression paths from the exogenous to the endogenous constructs? Please help. Thank you.
thank you very much for your video. I learn most part of amos and sem from your videos. Could you give me some advice for my master thesis. I do a 2X2 factorial expriment, and then want to use the research data to testify the model. in this case, should i use multigroup sem to test the model?
@ecjboon 1. Check out my video called, "PLS Interaction" to see an example. Do it the same way in AMOS. 2. Check out my friend's video called, "Working with controls in AMOS" 3. Good question... I would remove it. I hope it is not too high. You'll need to explain it in your paper as a limitation. Hope this helps!
To clarify, you are constraining the path of interest to be equal across groups then using the chi sq test to determine if gender moderates the strength of the relationship, correct? And all the other paths are also allowed to be different across groups?
Carol L Sorry for the delay in responding. This comment by Marissa must have slipped through the cracks. In this video she is correct. That is what I'm doing. I have seen other literature though which constrains the whole model and then only unconstrains one path at a time. My preference now though is to simply use a critical ratios test, which directly tests the amplitude of difference for each path between groups: ruclips.net/video/ZMYS90AU8bs/видео.html
Not sure if you still answer questions, however I tried to follow your instruction on Multigroup analysis however, you said that you will trim the path that is not significant in both model, well I have only one group with insignifcant path, what should I do
Hi James When I go to ChiSquare and degree of Freedom, I get the following: Iteration limit reached The results that follow are therefore incorrect. Chi-square = 4483,683 Degrees of freedom = 672 Probability level = ,000 Not sure what to do with that?? also, I do not know where to find you Excel table Thank you for your help Best Gwarlann
Mr,James very good day, I really I can't thank you enough for your real help, however my question, what if you want to test the moderating effect of Y on the relationship on X1 and X2 where this moderator (Y) have no groups such as gender or whatever? Thanks....
Here's a fun pet project I've been working on: udreamed.com/. It is a dream analytics app. Here is the RUclips channel where we post a new video almost three times per week: ruclips.net/channel/UCiujxblFduQz8V4xHjMzyzQ
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And Android: play.google.com/store/apps/details?id=com.unconsciouscognitioninc.unconsciouscognition&hl=en
Check it out! Thanks!
+Mary Saczawa RUclips won't let me reply directly for some reason... so, I'll reply ehre. The use of gender will really depend on your theory. If you are really interested in gender effects, then you should include it as a moderator. If you just want to control for it, then you can do that. If you want to know if a given variable's values depend on gender, you can do an ANOVA or t-test using gender.
trimming is optional. it simply isolates the significant effects and tends to strengthen them. In this particular video I also trim because I'm only interested in paths that are significant for one or the other group. However, You can get by without trimming anything. I also highly recommend using my new video for multigroup analysis. It is WAY easier.
@sgcraig84 You can get it from my wiki. I can't post the link here in the comments section (like, youtube won't post the comment if there is a url in it...), so I created a note in the video that has the url. The note pops up around 3:15.
Yes, I've seen this and some require a thorough invariance test across loadings, intercepts, errors, and variances. These are the purists and I am prone to only comply when requested by a particular reviewer to do so.
@XenovaKyo Great question. The answer is that you only remove the paths that are not significant for BOTH groups. If it is significant on one, but not the other, then you leave it in. Also, the easier way to do this is shown in my other youtube video: "Multigroup Moderation in Amos - Made Easy" I highly recommend doing it the way shown in this other video. Much easier, much faster, less room for error, and better results, plus you can do it for more than two groups.
I usually create three groups: one for all data, one for each group. This way I can see effects before and after moderation.
Hi James, the new tool didn't work for me (also syntax error, although i checked everything) so I used this instead and I think you just made me graduate! Thanks for all of your tutorials, wouldnt be MSc without them :-) cheers!
Glad you eventually got it worked out. Best of luck to you in your studies and career!
Not off the top of my head, but you can probably google "chi-square difference test" and "moderation" to find something. Use scholar. google. com for best results. Also, the new method I have for this is so much less painful. I highly recommend it. See the link at the beginning of the video.
+zeal2fly, For some reason, RUclips isn't letting me reply directly...
1. The difference in degrees of freedom and in chi square can't be negative (flaw of my program). So just put the larger set on top.
2. You can only compare two at a time. Or you can compare one to the other two.
3. With 25 datasets, it would still be less exhausting to run it the easy way (I think). If you can consolidate the datasets, that would be best.
Thanks James. I will do what you suggested and see where the results take me :-)
Have a great day!
P.S. May be I need check my settings for RUclips.
If you mean you are trying to create a covariance arrow between the interaction and the others, the only reason that wouldn't work is if the interaction term has an error term on it, or if it is endogenous (has an arrow pointing into it).
@jefferychang0703 You can find it on my statwiki: statwiki. kolobkreations. com
@camerinobarron It is on my wiki: statwiki. kolobkreations. com Also, there is a better way to do this kind of analysis now. Check out my new video "multigroup moderation in amos made easy".
Thank you for your response! I will see newer video!!
There is an easier way to do this. I have a new video called "Multigroup moderation in Amos made easy". That video will show you a super fast way to do it all. The tool referenced in the video is available on my wiki: statwiki. kolobkreations. com
Hi, thanks for the video. Very useful!!! I used your method and found the sig. difference at the group level but when testing each path I couldn't find the sig. path when comparing with the chi-square threshold. Have you ever had that issue before? May I get some suggestions? Thank you!
This can happen when there are lots of small differences at the path level that accumulate to a significant difference at the model level. This means that there is a difference between groups when it comes to the set of relationships in the model, but there is no single path that is significantly different. It seems a bit counterintuitive, but it does happen occasionally. Bootstrapping (and then using the bootstrap sample estimates) *might* emphasize these differences a bit more.
I have exactly the opposite. No difference in the overall model but in paths
Hi James, first thank you very much for your helpful videos..
did you divide the data into two files, one for male and one for female?
thank you
ٍSamar Ahmed Amos does this for you in the datafiles area. I show how to do this in this video. I use the Gender variable which has a 2 for Male and a 1 for Female.
James Gaskin
thank you sir
i have another question.. is there any way to make multigroup analysis by SPSS rather than AMOS
ٍSamar Ahmed Multigroup analysis can be done in SPSS if you have a simple model with no mediators. But if there are mediators, then it cannot be done. It also doesn't work if you have multiple dependent variables (except I think with general linear modeling).
James Gaskin one more question please
If i have a variable with 3 or more groups and all critical ratios are significant, can i arrange the impact level of these groups by standard weights or by what?
ٍSamar Ahmed I'm not sure I understand the question. Relative impact for each group is represented by the standardized regression weight. The extent of difference can be determined by the z-statistic.
I used 0.1 because moderations are often difficult to find. So, 0.1 is much more open to potentially significant effects than 0.05. I have made an easier way to do this test that involves z-scores and p-values. The video is called "multigroup moderation in amos - made easy".
Is there any reference for this? Thanks
@@pinaracet9273 Here are several references for moderation: statwiki.gaskination.com/index.php?title=References#Moderation_and_Multigroup
It works the same. This is just a bit faster. An even faster way is shown in my "multigroup moderation in amos made easy" video.
This is an excellent tool. Thanks a lot. Good luck with your study.
@jrm236 the p value is highly sensitive to large sample size and complex models. I would not rely on it. Just go with the chi-square difference test. And for fit, just rely on the CFI and RMSEA, and the Chi-square/DF.
Yes, assuming you already achieved metric and configural invariance in the measurement model. If not, then go ahead and let the latent-->indicator paths be constrained.
Firstly thanks SO MUCH for providing clear and concise explanations of SEM! Secondly, I have followed your recommendation to trim insignificant paths, to good effect. However I've been asked to cite published sources - what sources did you refer to for this method or is this your own development? Thanks in advance..
+Melanie Weaver I'm not sure if there is a good citation for this. The idea is to increase degrees of freedom (thereby improving model fit) by removing insubstantial paths. There are different schools of thought on this. If you have theorized about it though, you need to test it.
Just separate like I did. Also, I recommend the easier way of doing this analysis. See my newer video called "Multigroup Moderation in Amos - Made Easy"
@Gaskination Thank you for your quick response :) Yes. I have watch the easier way recommended by you. Indeed it is much error-proof and faster way of doing the same process. Just because of academic requirement, we are suggested to show the step-by-step process of constrain the model and of cause to better understand the works behind it. But I found the stat tools created by you is a really valuable tool for many situations, I have suggested it to many of my colleagues and friends here.
Hello James! your tutorials are great and very helpfull! I was wondering if you could recomend one or two papers that used multigroup moderation analysis so that I could have an idea how to present the results? Thanks in advance for your help!
Hi James - thanks for the video (and all the others on this channel): they are so helpful.
I have recently tested a model similar to this (i.e., no moderation or mediation) on AMOS with three DVs. I was surprised to find that the beta coefficients were exactly the same as I found in multiple regressions for each DV separately, even after I covaried the moderately correlated DVs (r = .10 to .50) in the model. I just wondered, is this the norm, or would you suggest that I have done something wrong and need to seek guidance? Many thanks, and thanks again for the videos :)
It is somewhat unusual, but not altogether surprising. This can happen if the independent variables are not very correlated (i.e., zero multicollinearity).
Dear James, first of all many thanks for your effort and congratulations on one of many great tutorials. I followed each step of it using a dichotomous moderator, but the results are somewhat puzzling. I would like to report the path coefficients in a table for both groups in order to compare them. For some paths, I read large between-group differences but they are not significant, while for other paths differences are smaller but they still result as significant .
I guess I should use the standardized coefficient values that are reported in the fully unconstrained model output - am I right? Many thanks in advance and keep up the good work!
Thanks so much.
Your newer video is awesome! it was so helpful!!
yes, you can do a moderated moderation model. it will work just fine.
Great. I tend to agree! Thanks a lot for all your work. You are really helping.
Thanks again for the tutorials! Love them! I am working on a multigroup SEM since I collected my data in two countries and I would like to compare them. I started by analysing the factor loading, Cron. Alpha and Item-to-Total-Korrelation for each country and the dataset as a whole. My factors look good ;-) Now, I am playing around with AMOS: Should I first create one model with all my data (both countries in one) and fix GFI, AGFI and so on or should I directly start with a Multi-Group analysis?
James, I always appreciate your great videos and helpful advices. Here is a question I came across after reading two SEM papers which include a moderator, company size. One paper tested the moderation effect of size by spliting the entire sample into two groups (small group - large group). The other paper formed interaction terms of the size and all the other IV factors. Which approach would be more prefered or desirable? My guess is the second approach as the size itself is a continuous variable. Your thought??
Interaction retains more of the variance than splitting. However, due to the algorithms involved in interactions vs multigroup, you will more often find significant effects with multigroup (despite the drop in variance and the drop in sample size), and it is easier to interpret multigroup moderation.
That is not unreasonable, but it is unlikely. This just means that the two groups are no different. I highly recommend my newer video called, "Multigroup moderation in AMOS - Made Easy"
Hi James: first thank you for a simple and useful lecture . I need to ask you question, in this video you have explained the structural model test by using chi-square , but you didn't mention to the measurement model test. It is not necessary to do ?
I have other videos for explaining the invariance test. Here is one that follows the approach from this video: ruclips.net/video/6j4_ZrkCxTc/видео.html
Here is a simpler approach (the link is queued to the right time position): ruclips.net/video/MCYmyzRZnIY/видео.htmlm55s
James Gaskin Hi James, if I wanted to identify whether a model works better when applied to two different groups which test would I use, the invariance test or the multigroup test?
Georgina Smith Invariance shows whether the measures have similar distributions and variance (i.e., if the two groups answered your survey the same way). Moderation is all about whether the groups differ in terms of the relationships between the constructs. Invariance is measurement level. Moderation is structural level. Most theories are structural level.
The video is awesome. It helps me a lot in understanding the multigroup comparison in SEM. If possible can I ask where I can get the Excel file demonstrated in the video to calculate the Chi Square Difference tests?
Your AMOS lessons are really helpful for me for quite a long time, so thanks a lot for uploading!
But I have one (interesting?) question: Does a SEM-equivalent exist for a sequential / hierarchical regression? Where you can realise the pathway / process of certain variables within the total model while adding new variables?
Best regards from Germany!
xThomas79x I don't know. I've never had the opportunity to do sequential or hierarchical regressions.
James Gaskin Thank you! But is it actually possible to use nominal scale level of measurement for the dependent variable in SEMs? Like in logistic models with e.g. two categories?
xThomas79x Only if the nominal variable is binary. If it is polynomial, then it is best to figure a way to do it in multivariate logistic regression.
James Gaskin Thank you very much :-)
@catsfancyful
Testing differences between more than two groups is always a case of testing the pairwise differences (i.e., between group 1 and 2, then 1 and 3, then 2 and 3). This excel tool will do it; you just have to do it two at a time.
Dear Dr. Gaskin, Thank you so much for all your informative videos. I have a quick question about the chi-square difference test if I am trying to figure out whether items from a new measure load better on one factor or two factors. Somewhere I read that there are two ways of performing it. The first involves reading the chi square value off of AMOS for the one-factor model and then subtracting it from the chi square value for the two-factor model. The second one involves subtracting the chi square value from a two-factor model where the path between the two factors is constrained to one from the chi square value from the two-factor model where the path between the two factors is not constrained. From what I understand, both are supposed to test for the discriminant validity. Could you please help me with understanding the difference between the two and when each is supposed to be used? Again thank you so much in advance!
Thanks for this, very interesting. I did wonder why you didn't just do a multi-group analysis though. Wouldn't that be easier? You could then also ask for Critical Ratios of Differences - That way you can check which paths differ across the groups without having to resort to laboriously checking each one. Or am I missing something?!
Hi Nisrein Shabsogh RUclips wouldn't let me reply directly, so I'll reply here. Removing non-significant paths is optional. The main benefit of doing it is to increase your degrees of freedom (and therefore improve model fit). However, the drawback is that you are no longer accounting for that effect.
The video is great. Can you tell me why specifically you are "trimming" the insignificant paths from both groups? Thanks.
Very useful, especially the excel macro, thanks!
Thanks a lot Dr.James. you are amazing.
It's very helpful tutorial.
Hi James. Thanks for you really helpful video. I have three questions:
1) X2 threshold sheet: unconstrained (88.138,3) and constrained (85.734, 8). The p-val it is show #NUM ! error. So I have 4 IV ON 1 DV, I constrained all the IV as W1-W4. Should I have left regression weight of one of the IV as 1? Then the constrained becomes (148.98, 9) and p-val is NO.
2) I have 3 groups, so the result is telling me that if all the 3 group differ or not. And it could be that 2 groups are same and 3rd is different. What we do in that case?
3) I saw the other video too, it is quiet easy to do that. But same question, how things will be differ between 3 groups? One of the problem is I have 3 groups and 25 data set. So doing isolated grp 1-2, 2-3 and 1-3 will be quiet exhausting. And consolidating and interpretation that data. I am just happy to report that three groups are different/not different.
Thanks for these great tutorials! One question: Could you have done a multiple group analysis here, constraining the model by equating individual paths between the groups (e.g. pathA_male=pathA_female) and see if they statistically differ from each other? Or is multiple group analysis any different from what you show here?
Hi, James, I use both chi-square method and critical ratio method and get different result. Chi-square method shows that there is difference between two groups path by path via critical ratio show no difference path by path.
Did you do a path by path analysis with Chi-square to see which path it is? It may be that with the Chi-square test at the model-level, you have enough small differences on each path to add up to being not invariant, but no single path is actually significantly different.
Respected Sir,
Sorry for inconvenience.
I have gone through the whole process in the video. Its just awesome. i have done an analysis for my research project.
Sir, could you please inform me How can i write the interpretation of Chi square differences of the model in my report???
What will be the references for chi square differences for the process in this videos.
Thanks.
Here are some useful references for how to write it up and how to cite it: scholar.google.com/scholar?hl=en&q=%22chi-square+difference%22+%22multigroup%22&btnG=&as_sdt=1%2C45&as_sdtp=
@jrm236 Can't you just do a path by path chi-square difference test as demonstrated in this video? Or do you mean that you have 300 attrition from the 700 and 900? in which case you have smaller n and serious attrition issues.
Thanks for this awesome video. I just wanted to ask a question: Could I test for group differences in a model with an interaction (moderation) term using this same method or does this only work for a model of mediation? For example: the dependent variable would be distress, the independent variable would be victimization, the moderator would be social support, and the group variable would be gender (Male, Female).
@sgcraig84 Yes this is a good way to try gender differences. You will still have sample size issues though.
Your Stat Tools package is fantastic. Thank you very much. Do you happen to have a version which would allow for testing multigroup moderation between more than two groups? E.g. four groups?
Hi James,
Thank you so much for uploading these great videos! I have learned a lot from you. I had a question when following this video. I am comparing two groups. I cannot trim the non-significant variables as the model will not perform (Error message:" Iteration limit reached" and "The model is probably unidentified...". These errors stop me from getting the unconstraint model and the full constraint model. What should I do now?
Many thanks,
Racheal
PS: I have watched your videos about the iteration limit reached and taken the steps. Some of the items cannot be removed as the model will be unidentified...
The error about being unidentified usually means there is a parameter constraint missing. Check to make sure all latent factors have at least one indicator path constrained (usually this means there is a "1" assigned to one of the paths). If you have a second order factor, then make sure one of the paths to the first order factors is constrained.
thanks a lot James, I have done it
Great videos, very helpful. I have 3 questions, hope you can help me in the right direction:
(a) How do I create the interaction variable to test for moderating effects between constructs, i.e. if each construct (incl. moderator) is composed of a number of variables?
(b) How do I control a model in AMOS for 2+ variables (not necessarily binary)?
(c) We found high CMB in our model (during CFA). When we start SEM, should I leave the common method variable in the model to eliminate its effect?
This video was very helpful; thank you for the post! Do you have a reference that would assist in writing up the analyses?
Hi James, the video is indeed very useful to perform the moderation analysis. I just need one clarification, do we first need to run the overall model without constraint and then with constraints and only after that perform path by path analysis to examine the moderating impact for a particular path. Or is it possible that we first just run the complete model without any constraints and then constraint only the selected path to see if that path has a moderating impact by putting the chi-square values in unconstrained and constrained cells in the excel sheet.
Regards,
Bilal
Bilal Ahmad Khan It is best to run the full model first. Some schools of thought on this argue that if you do not observe strong enough differences at the model level, then any effects you might find at the path level could be false positives.
James Gaskin Thanks for the clarification James. I based my moderation analysis on the work of Molina-Castillo et al. (2011). 10.1016/j.indmarman.2010.12.017. Where they just constrained only the preselected paths. Could you please suggest that If I need to refer to your step by step process of testing the moderation impacts, which reference I can quote.
Bilal Ahmad Khan I can't think of one off the top of my head, but you will probably find someting if you go to google scholar and search for: "chi-square difference test" multiple group analysis moderation
James Gaskin Thanks for the suggestion James
Hi Dr. Gaskin. How would you recommend handling a situation in which there is a binary predictor/exogenous variable and we want to know if its effects are moderated by a continuous variable?
Thanks for all these videos!
T Om In such a case, I would probably just convert the continuous moderator into a binary (low/high) variable, and then create four groups (2x2) with the predictor and the moderator, and then do an ANOVA using that new grouping variable as the factoring variable.
@sgcraig84 There are a couple work-arounds. The best method is probably to add a control variable and hope it is not significant for females... Or, add another independent variable that you expect will be significant for males, but not for females. Or, simply trim the least significant path for females (assuming it is not the only path tying the exogenous variable to the model). Feel free to email me directly if these options don't sound appealing or if they don't work. email: jeg82@case.edu
@jrm236 There are different methods to test this, and none of them are great. Additionally, if you are comparing a group of 100 to a group of 1000, the chi-square difference test may pose problems, and instead you could look at the change in CFI (typically looking for a difference of more than a tenth).
Somebody can answer me pleas (it's quite urgent). For example at 6.02 you have your khi square, df and a probability value which is 0.00. So it's higly significant and we can reject the H0. What is the H0??
I thought when we test a model adjustment (khi square representing the distance from our data) we want a non significant p-level.
Thank you!
The null hypothesis is always "there is no difference" or, "there is no effect". So, in the case of a multigroup, a significant chi-square indicates that there IS a difference between the constrained and unconstrained models.
Thank you so much for your answer! Could you explain one more point? What about the p-value = .000 in the adjustment analysis by groupe (adjustment for men and the adjustment for women separately) in the unconstrained model? H0 would be "there's no difference / effect ..." what effect?
I myself have done an analysis where, I analysed for the adjustment of the "default model" of men, then of women separetely (not a multi-group analysis), however using a data from an Excel output containing two sheets (men, women). So, I have there this p-value = 0. Could you see what hypothesis have I tested there`?
Anyway, your answer was helpful for the biggest part of my multigroupe analysis!
When doing multigroup, the null hypothesis is that there is no difference between the groups. I don't understand what you mean by analyzing the adjustment of the default model. I have never heard of this.
Dear James
It was much needed video. Please tell us how to make this excel sheet with formulas. Can you teach us to draw a graph which could show the moderation effects.
Secondly,, how do we add controls in this analysis...Should we do them as IVs??
1. the excel workbook is available on the homepage of the StatWiki.
2. Yes, just include them like IVs
Thanks James.
It was very helpful.
I have another question. My paper is under review and reviewers suggested me to use weighted least squares for ordinal data instead of MLE. The issue is AMOS does not have WLS or D-WLS. I wanted to know how much difference it creates while using different estimation techniques... What option I can use in AMOS thats the best replacement of WLS. Thanks in advance
Unweighted least squares is available in the analysis properties estimation tab, left column.
@Gaskination Much appreciated your sharing. A Quick Question: To achieve a unconstrained model, we need to trim (delete) the path that is insignificant. In your example, the paths deleted are insignificant for both groups (male & female). What if the specific path is significant for female and not significant for male? Then should we delete that path to reach unconstrained model?
Thanks in advanced.
Hi James!
I suppose I can also use this procedure with a full SEM model (not only with path analysis), am I wrong? If I do this, and once I have demonstrated measurement invariance, should I maintain the constraints of each factor loading of each variable or should I only constrain the paths between factors? And another question: Is the chi-square test more relliable or accurate than the critical ratios procedure for testing group differences? Thank you very much.
Regards and Merry Christmas!
Octavio
PD: Your videos and your website (with my beloved Stats Tools Package) are incredible, congratulations!
+Octavio Luque Yes, you can do this will a latent SEM model. To test multigroup moderation in this case, just constrain the structural paths, not the measurement paths. Currently, the chi-square test is the preferred approach, although somewhat more tedious. In order to make the critical ratios approach work accurately, you have to divide the desired alpha threshold (e.g., 0.05) by the number of simultaneously tested hypotheses. So, if you are theorizing five moderated paths, you would have to reach an alpha of 0.025 in order to claim 95% confidence your findings are significant. This is called a Bonferroni correction of family-wise error.
+James Gaskin Thanks a lot, my research wouldn't be the same without your help. But I have another doubt in this regard: in order to test differences path by path, how can I calculate the minimum threshold (specially p 0.05) with non-normal data? I have the Satorra and Bentler's executable (homepages.abdn.ac.uk/j.crawford/pages/dept/sbdiff.htm) which calculate the SB scaled difference, the df difference and the chi-square probability, but I haven't a threshold to contrast each constrained path. I'm not completely sure if my question makes sense. Sorry for this mess and thanks again.
+Octavio Luque I'm not sure what you mean. I've worked with the S&B executable.
+James Gaskin Sorry, probably I’m not explaining the question well. The Stats Tool Package, provide 3 thresholds which indicate the minimum Chi-Square value that a model with only one constrained path have to reach, in order to prove variation between groups in this path. With non-normal data, I don’t know if I can simply get the SB Chi-square value of the less constrained model (I use EQS software) and obtain this threshold simply adding 3.84 (in the case CI=95%) to it or if I have to do other procedure. My model is globally invariant but I found path differences between groups and I want to prove this with a robust estimator. Thanks for your interest and your patience. Regards from Spain.
+Octavio Luque I've never used EQS before. I'm also not sure how non-normality affects the chi-square difference test. Sorry, I'm not much help on this one.
Hi, thank you for your super clear explanation. What minimum sample do you recommend before conducting this analysis?
There are lots of different sample size calculations and formulae. I recommend 50+5x, where x is the number of observed variables. For multigroup, it might be okay to use 50+3x for each group.
@@Gaskination thank you 🙏
Dear Professor Gaskin,
Thank you for the video! It made my analysis easier for direct impacts.
However, there are indirect impacts in my model, so would you please let me know how to do the moderating effects for indirect impacts?
I look forward to hearing from you soon. Thank you for your consideration!
Kind regards,
Minh Phuc Nguyen
Here is a video about it: ruclips.net/video/glJhg_fxBag/видео.html
It was really helpful and valuable. Thanks
I have a quick question regarding this analysis.
I conducted last part of path by path analysis. but all path chi-square are the same? Does it usually happen?
Oh, the regression weights in the moderation tool are not standardized. These are unstandardized. Look back in the AMOS output to find the standardized regression weights.
Yes. That is exactly correct.
SO that main the result as the follow is correct:
difference in chi-square=38 and df= 60 in measurement model test
difference in chi-square=45 and df=78 in structural model test
I'm not sure what you are asking. To me, those do appear to be different (i.e., not invariant). I recommend just using my easier approach: ruclips.net/video/ZMYS90AU8bs/видео.html
I would recommend going to google scholar and searching for these words: "chi-square difference test" moderation (quotes included). Any of the top results should help.
Hi James, thank you for suggesting me to watch this video and use this method. My model is a little bit more complex than the demo one, for each variable it includes 3 or more items. When I clicked "Name Parameters" and checked Regression weights, AMOs came up with 18 regression weights. After that I didn't see any difference on the number of Chi-square results. I also tried to constrain one path at a time and the number of Chi-square results didn't change either. Is this a common problem? Should I check any part of my model? Thank you!
Liang Yuli The chi-square and the degrees of freedom should change when you unconstrain one path at a time. Make sure the "all groups" box is unchecked when you make the changes to a parameter constraint.
Yes. It changed. Thank you!
Hi James,
My hypothesis simply posits that country (A binary variable with 0=Country A and 1=Country B) negatively moderates the relationship between service quality and online satisfaction. That is, the relationship between service quality and online satisfaction will be stronger for Country A. My findings show that I can confirm this hypothesis indeed with the interaction having a significant and negative effect on online satisfaction. Nevertheless, I need to obtain the effect of IV on DV for Country A and B separately (like simple slope analysis for interactions between two continuous variables) but my moderator is categorical (Country) rather than continuous. So, could I run multigroup moderation for each country to obtain these values? (e.g. running the model for Country A participants and Country B participants separately)
Yes; that is exactly what I would recommend. Here is my latest video on this subject: ruclips.net/video/w5ikoIgTIc0/видео.html
Thanks! testing chi-2 is well documented for nested models (and this case applies). But Dabholkar and Bagozzi 2002, p193 states that you should compare 4 models in order to take into account differences coming from error variances, not just loading or corelations. What do you think?
Oh, then that is bad. This means you have estimation errors. Look at the standard error (S.E.) values to see if any of those are also off the charts. If so, you will have identified the problem. If you run it through an EFA first, you should be able to identify the problem. This could be due to high kurtosis values as well.
Dear James,
if the regression coefficient of interaction effect is significant and negative, can we conclude that the moderator makes the principal relationship weaker?
and if it is positive, the moderator strengthens the principal effect?
thanks for answering
Hi, Thank you very much for your tutorials. Would it be possible to send your excel file to use it and calculate the differences?
Thank you for this clear explanation of the moderation.
However I don't understand how to analyse and interpret the correlation between error terms. In the model that I design, I have the my two variable (Happiness and self-esteem) who are correlated but AMOS does not allow me to insert the double arrows. Thus I drew the correlation between the errors (I don't know if it makes sense). How could I explain it statistically. How should I actually interpret the existence of errors terms.
Thanks!
Thank you for your video. Is it possible to use categorial variables with more levels (i.e., not only 2 levels like gender: either male or female as you showed; but more than two levels such as"faculty", e.g., mathematics, psychology, engineering etc.)????
Yes, but then you still compare them in pairs, or one vs all other.
Dear Prof. Gaskin, greetings. I have a doubt. If the path is not significant in the structural model analysis (A to B), should we proceed for checking on the multi-group invariance for the path by moderator, or should we avoid it
If it is not significant for either group, then there is no need to check if it is different across groups because for neither group are we confident it is different from zero. However, if one is significant and the other is not, you can still check, because you may be able to include that the IV is a good predictor for one group, but not for the other.
Thank you so much Sir. It was of great help.
Hi James again :) Help. In doing group analysis, I noticed that in only one group, 3 paths showed insignificant p-value. When I looked at their standarized regression weights, these three have greater the 1 estimate value (5.2, 1.2, and -6.1). What does this mean? Something wrong?? I thought these regression weights shouldn't be greater than 1 normally?
Based on my theory, I expected this group, to have insignificant paths. But their large weights is my concern.
Thanks a lot. You the best
Hi. It was a much needed video. Thanks. Using this technique I find one of my paths moderated by gender.Now, how to decide whether male strongly moderates the path or females. How to decide the causal relationship is stronger for male or for female? Can you pls explain.
+Payal Anand If the path is different for male vs female, then look at the regression weights in the amos output. If you look on the bottom left of the output window, you will see the two groups listed. Click on estimates, regression weights, and then click on Male (bottom left), then toggle to Female. This will show you the strength of the effect for each group.
Thanks a lot. Please keep uploading videos.
Excellent. Many thanks.
Thanks for the great tutorial. I just want to ask if you have a template for reporting the important values in a multigroup moderation analysis.
Hi, Thank you for your lecture, again! I have data that collected from three regions and want to compare whether each region has different result among each others. When coding the data, do I need to have three different format of data set for each region? or just separate it 1,2, and 3 in SPSS as like gender you did.
Dear James, thank you for your amazing tutorials, they are really helpful! I tried to run a multigroup moderation analysis like you explained in the video. My model consists of 1 exogenous latent variable with 3 indicators and 1 endogenouse manifest variable. I want to test 3 moderators, each having 3 groups. So I build the model and created my 9 groups. But when I click on plugins and name parameters amos also constrains the regression weights of the latent variable. Could you please be so kind and tell me if that is the correct approach for my model as I'm only interested in the path between the factor and the endogenous variable?
This is fine. However, if you want more control, just do it manually, instead of with a plugin. It is just one path to constrain.
I am not sure I follow you. I was referreing to AMOS output. Estimates > Scalars > Standarized Regression Weights. This is the table I am referring to. It does show three 'greater than 1' values for one of the groups. Is there another table?
Thank you so much for all these wonderful tutorials. I noticed that when running a latent structural model and I use the critical ratios test of differences between groups, I get z score outputs using your Stat Tools Excel program you developed. However, when I change the unity-constrained indicators (required by AMOS) from one indicator to another, and re-run the critical ratios test, I get very different results. I cant explain this and was hoping you could offer some guidance.
I'm not sure why that would happen. The results should be fairly consistent. Either way, the chi-square difference approach is considered more appropriate, so I would recommend that approach.
Thank you for the fast response - I believe the reason I get the different results is because I am using latent constructs (with indicators) in my multi-group model and the spreadsheet was built for observed variable (e.g, using scale scores) only models. Still, a very useful tool for a first analysis prior to doing the the usual constraining and unconstraining method.
Dear James, I'm wondering whether it is possible to run two multi-group analyses simultaniously within one model? I have one baseline mediation model, and I would like to test whether the model is moderated by country of residence (I have 3 countries) and gender (within country). Would you recommend to make interaction terms with country*gender and then use them as moderators in multi-group analysis, or is there a better way? Thanx in advance!
If you have plenty of data, then just create six groups (male country A, female country A, male country B, etc.)
@Gaskination
I believed it is to delete the path if the path is significant on either 1 group.
In my case, there are only 3 paths (in the whole model, and All three are hypothesized to be moderated by the moderating variables). 1 path is insignificant on both groups, so we trim the path. The other 2 paths are insignificant only on group A, but significant on group B.
Is this means that I don't have to continue to proceed to the constrained model? or there is other ways?
Thank you James once again. I have other question, the degree of freedom in measurement model test was 60 and it was 78 in the structural model test, although I have just 13 hypotheses. Is this normal or I have mistake?
It is normal to have different number of degrees of freedom. You will usually have more in the structural model because you are removing many covariance arrows.
Respected Sir,
Hope you are fine. Sir i am a research student of social sciences. My research model contains serial/sequential mediation (with 2 mediators in chain and 2 IVs and 1 DVs). I learnt about CFA , SEM, simple mediation and moderation from you videos on youtube channel. Thankyou so much for making such helpful videos with clear and easy communication.
Hereby i am interested to know that what is the procedure to run Sequential mediation in AMOS because i am not finding any speciofic video on the concerned topic. I just want to confirm will SEM for serial mediation will be the same as we draw for simple mediation and we ll run it through bootstrapping ? Is there any additional step i need to do ? Am right?
Please confirm my query. I will be grateful
Best Regards
+hafsa noreen That is correct. Just run it with bootstrap and you'll be able to see all the indirect effects.
Hi, Your videos are excellent. I am planning to use the chi square difference test. I am using the structural model with the indicators included. When I constrain the full model, it fixes the paths from the latent constructs to their respective indicators also. Should I remove them and just fix all the regression paths from the exogenous to the endogenous constructs? Please help. Thank you.
thank you very much for your video. I learn most part of amos and sem from your videos. Could you give me some advice for my master thesis. I do a 2X2 factorial expriment, and then want to use the research data to testify the model. in this case, should i use multigroup sem to test the model?
@ecjboon
1. Check out my video called, "PLS Interaction" to see an example. Do it the same way in AMOS.
2. Check out my friend's video called, "Working with controls in AMOS"
3. Good question... I would remove it. I hope it is not too high. You'll need to explain it in your paper as a limitation.
Hope this helps!
Thank you so much, you saved me!
To clarify, you are constraining the path of interest to be equal across groups then using the chi sq test to determine if gender moderates the strength of the relationship, correct? And all the other paths are also allowed to be different across groups?
marissa1787 I have this same question. Have you figured out the answer to this?
Carol L Sorry for the delay in responding. This comment by Marissa must have slipped through the cracks. In this video she is correct. That is what I'm doing. I have seen other literature though which constrains the whole model and then only unconstrains one path at a time. My preference now though is to simply use a critical ratios test, which directly tests the amplitude of difference for each path between groups: ruclips.net/video/ZMYS90AU8bs/видео.html
Not sure if you still answer questions, however I tried to follow your instruction on Multigroup analysis however, you said that you will trim the path that is not significant in both model, well I have only one group with insignifcant path, what should I do
Hi James
When I go to ChiSquare and degree of Freedom, I get the following:
Iteration limit reached
The results that follow are therefore incorrect.
Chi-square = 4483,683
Degrees of freedom = 672
Probability level = ,000
Not sure what to do with that??
also, I do not know where to find you Excel table
Thank you for your help
Best
Gwarlann
I have a video about this. It's called "iteration limit reached in amos" or something like that.
Mr,James very good day, I really I can't thank you enough for your real help, however my question, what if you want to test the moderating effect of Y on the relationship on X1 and X2 where this moderator (Y) have no groups such as gender or whatever? Thanks....
Use interaction effects instead. Here is a video to explain: ruclips.net/video/LRdiYe387e0/видео.html