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Partial Least Squares - Science topic
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Questions related to Partial Least Squares
I have tried to estimate with PLS-SEM (with the R package 'seminr') a model that, in its measurement part (or outer part), includes a bifactor structure (i.e., two specific dimensions and a common general dimension). The general factor is the one that is "causally" related to other latent variables (and all constructs are reflective). However, R does not estimate factor loadings for the specific factors, but only for the general factor. Is it possible to estimate the full bifactor structure with PLS-SEM? Is there a way to do so in 'seminr'?
(I assume that I cannot add causal relationships between the specific factors because the bifactor models assume orthogonality.)
When introducing new constructs or variables into a model and examining their effects in accordance with specific established theory, especially in a context where the theory has not been tested before. Is this theory development or theory testing and confirmation? Given that I need to justify my use of PLS, which is generally recommended for models where the emphasis may be more on theory development.
How can I decide between Partial Least Squares (PLS-SEM) and Covariance-Based SEM (CB-SEM) for testing a conceptual model in management studies?
I have data from population based observation (not questionnaires but yearly observation from secondary database) and I already have a common model for each populations (6 groups, each has the same latent variables, observed variables, and the structural models are also the same shape). As the study is some kind of longitudinal basis (not independent to each other), am I still be able too use MGA (Multi Group Analysis)? My result does not pass the MICOM procedure, is doing a MICOM procedure an obligatory prior MGA in my specific case?
Dear Community,
I would like a question regarding the use of Partial Least Square Regression Analysis. Basically ,I am confused in units. For example, I have Year 1, Year 2, Year 3 land cover and Water Balance Components. The units of Water Balance Components are "mm", while the units of each landcover type for year 1, 2 and 3 are in square Km. I am confused how the different units will perform the PLSR test.
Either, I have to use the % difference in each Year or % of particular landcover type to the total area of the basin and similarly convert the water balance variables from "mm" to percentage.
Looking for a guidance. Please teach me.
Regards
What is partial least squared structural equation modelling?
I am currently working with a study titled "Knowledge, Competency, Adoptability and Sustainability of Artificial Intelligence (AI) Technology Among Physician Entrepreneurs in GCC Countries". I am in dilemma that which SEM model AMOS/SmartPLS will suit for the study. The total responses received so far is 220 out of the population size of 400. The main objective is to find out knowledge , competency, adoptability and sustainability of Artificial intelligence among Physician entrepreneurs.
I will be much thankful to you, if you can extend your kind feedback on this.
Regards
Dr.Sharfras Navas
Hi all,
May I know if you have any idea how to address issue related to limitation of non probability (convenience sampling) sampling in using PLS-SEM (partial least squares structural equation modeling)?
Thanks in advance.
I know that some literature said that the minimum estimate for the path coefficient should be around 0.2, but is there any discretion or other opinion regarding this matter? Thank you for the attention.
Dear All,
I want to build a higher order construct, based on literature, that has 5 components.
One of them has really high HTMT of 0.93.
If I want to perform the the two-stage approach, according to the literature, I have to check the lower order constructs, HTMT implicitly.
Is it fine to delete a dimension?
Thank you so much!
Can anybody share opensource Partial Least Squares (PLS) tools that is easy to handle, reliable and easy to learn analysis and interpretation?
I am working on urban social sustainability and completed pilot study on 109 respondents. I want to know from the respected reasearcher that what will be next procedure for propose a model?
I am a new learner to process the analysis in SMART PLS-SEM. there are many options to assess model fit. Could you please help me to provide a complete report of Model Fit as well as references?
Goodness of Fit Formula
GoF = squre root (squre R *AVE)
best regards
I have extended the UTAUT2 model and I have already developed my model hypotheses. I read about various methods/techniques used such as (Factor Analysis, Partial Least Square (PLS), Structure Equation Model (SEM), Regression Analysis) and tools such as (SPSS, Smart-PLS, Mplus, R, PLS-graph, AMOS). But I am not sure which one to use.
Please I would like some advice about recommended techniques and tools. I am really looking to find out what you consider to be the most efficient method and your rationale as cost and time are limited factors.
Many thanks
If we have 4 variables and 3 -4 statements for each variable. How many global items we are suppose to use? What is min: and max: limit for global item ? What will be the effect if we ignore global items in the moderator and mediating variable?
thanks in advance!
Dear All,
I'd like to use Smart PLS3 to run a PLS-MGA model, in which there are three variables of two values for grouping. I've come up with two questions;
1. First, do I have to report the results of model fit measures for each grouping variable separately or are the results for the complete model the one to go with?
2. Second, what if none of the fit measures is not satisfactory? What could be the reasons? What could be done to tackle the issue? I'd really appreciate any helpful reference in this regard.
An overview of the variables is as follows:
Five exogenous variables, with two of them being single-item, two three-item variables and one four-item variable.
One endogenous variable with 6 items.
Three grouping variables of two values.
The sample size is 160.
Any assistance would be welcomed.
Best Regards,
Saeed
Kind colleagues,
I am running a PLS-SEM on SmartPls 3.0. I specified the model as with two higher orderd formative components (HOCs). The lower order construcst instead have reflective causality (it's a reflective-formative HCM). To estimate it I tried with the repeated indicators approach and calculate the internal, convergent and discriminant validity statistics for HOCs by hand. Everything works fine except the overall model fit (which I know to be a very debated topic) that gives me SRMR above 0.11 and an infinite Chi-square (which I interpret as too many degrees of freedom and too tiny sample - 260). By using the disjoint two-stage approach instead, my model works perfectly. Not only the path coefficients are all significant, but also the overall model fit is amazing (SRMR below 0.06). My question is: does it make sense to consider these overall fit measure in the two stages approach? I mean, is it even comperable to a specification estimated with the repeated indicators approach? How should I choose the best model?
I need the help of the community!
Thanks everyone
The whole data was collected on Likert scale. I am using Prof. Gaston's plspm package in R for SEM modeling. I think different age groups must have differences. But, I can't test differences of more than than two groups at structural level. My question is, if I divide the data into various age based subgroups and prepare SEM models separately for each subgroup. Is it meaningful? How to justify the models are significantly different? I was not able to perform ANOVA test to check the difference among models. What should I use? Please guide me Thanks
I am using Camo Unscrambler X for building an partial least square (PLS) regression model.
I am confused which parameter should I consider for choosing optimal number of factors. Is it based on the lowest standard error of calibration (lowest SEC) and highest R2 ? Or lowest root mean square error of calibration (lowest RMSEC) and highest R2 ?
I want to conduct partial least squares structural equation modeling for my research in SmartPls software
. So, is there anyone who provides me the step by step guide of conducting SmartPls software? It may be video or written material. Thanks for your response.
'Partial least squares path modeling: Time for some serious second thoughts'.
I would like to invite experts of the PLS-SEM to comment on this paper or please indicate any paper that has already made comment with respect to this paper.
Hi,
so i have a reflective-formative type models (as seen on the picture attached) and use the repeated indicator approach, and what i did on assessing the model based on what i understand was:
1. calculate the PLS algorithm and see the cronbach alpha, composite reliability and AVE criteria.
2. Since few of my variables on the LOC has low value of AVE composite reliability and cronbachs alpha, i had to eliminate some of the indicators so that i can meets the criteria. Ive read on some journals that if you eliminate an indicator from the LOC, u also have to remove the one in the HOC and vice versa.
The problem is, after i eliminate some of the indiators all of my LOC has already meet the AVE minimum value, but somehow my HOC still has a very low AVE value (0.284, as seen on the second pic attached).
So my question is do i have to keep eliminating the indicators until my HOC's AVE value acceptable (but then id lost quite a lot of indicators on LOC too)? Or am i doing it wrong? Is there any specific guides on this cause im kinda lost right now
Thank you


How to analyze the questionnaire's results based on UTAUT model ( Venkatesh et al., 2003) via Partial Least Square (PLS)? How to measure satisfaction?
To clarify, I tend to invite a sample of users to use a web application and analyze their satisfaction by asking them to participate in the survey based on UTAUT model questionnaire. However, I am uncertain about the evaluation procedure.
Dear Colleagues:
I am focusing a ratio/quotient construct/variables which is the product of the ratio of two (latent) constructs/variables and is a dependent variable and a mediator as well.
For this purpose, I measured the two variables at the seven-points Likert scale and then calculated the mean scores of each of the two variables. Finally, I calculated the ratio/quotient variables dividing the mean values of two variables.
After this computation, I tried to run my research model in SmartPLS, however, the results show very poor regression.
I have following questions in this context:
1. Is it right way to calculate the quotient/ratio construct? I followed the methodology of the following paper for the ratio calculation.
2. Can SmartPLS/Partial least squares be applied for dealing with such a quotient/ratio variable? The variable is my case is not following the assumption of ordinal data points/equal data intervals.
3. What software/data technique could be best for analyzing such a ration variable?
I highly appreciate your kind feedback in advance, thanks so much,
Best regards,
Muhammad Shujahat
(The example in R uses vectors to illustrate Y, not matrix.)
I understand with the R exemple using the yarn database, that when using plsr :
regpls <- plsr(Y ~X, ncomp = 5, data = mydata)
typeof(Y) = double and typeof(X) = double
typeof(mydata) = list...
BUT, as Y is a matrix in my case, I don't manage to turn it into a type recognised as "double", so far R recognise it as a list of doubles.
Could anybody help me with practical example, I guess my problem is more about how to qualify my data than on the use of plsr, at the moment. I haven't find any proper example so far neither in book or on internet, I hope the answer is pretty simple, and of course I'll put it there if I find it before any help comes :)
Poring over the literature of Fault Diagnosis, I found out several conventional approaches, including Principal Component Analysis (PCA), Fisher discriminant analysis (FDA), Partial Least Squares(PLS), and Canonical variate analysis (CVA). Having said that, I am not assured of lastest or improved categories.
I am running a PLS-PM to evaluate Satisfaction "S".
I have defined a latent variable LV "D" and I concern how to define the manifest variables(MV).
I have:
- 3 variables regarding satisfaction with "D", expressed on 7-likert scale
- 1 variable regarding how many x you consume
- 1 variable regarding how much you spend to buy x
My doubt:
I expect the satisfaction MVs to be positive correlated with LV and the last two MVs to be negative correlated. I have seen someone trasforming MVs into negativeMVs to have unidimensionality condition respected, but I would like to avoid this solution becouse it conceptually does not make much sense.
- can I consider my MVs to be reflective with LV since I have a MV about consuption and a MV about expences?
- in alternative, can I state my LV "D" to be explained by 3 reflective MVs and 2 formative MVs?
- suggestions to define my LV?
Thanks a lot for the attenction, any help will be highly appreciated.
Hi everyone,
I want to analyse the correlation of phenolic compounds and bioactive activities (quantitative variables) of my samples, but I also want to include my sample information (qualitative variables) for the correlation analysis.
It seems that the common Pearson Correlation method can only used for quantitative variables. I have found that the Partial Least Squares Correlation method can be used for both quantitative and qualitative variables, but this method is not commonly used.
So my question is what is the difference between the Partial Least Squares Correlation and Pearson Correlation, which one is better ?
Thank you very much
Hi everyone!
I am working with small samples of national teams. Such small samples make it impossible to estimate the quality of the linear regression model, so I decided to apply partial least squares structural equation modeling. Though it seems to suitable for my purposes, I still have several questions regarding my models.
I developed three models. One includes both adolescents (M = 16) and adults (M ~ 23) (54 athletes), second - only adolescents (35 athletes, male and female), and third - only adults (19 athletes, female only). As can be expected, the first model is the best in terms of the significance, reliability of latent variables measures. However, I am not sure that it is the correct way to choose the model. It seems logical that the bigger sample provides "better" results. But is it ok to mix two different age groups?
I got some data for my research. I run them on SPSS SEM but the R-square value is quite low. I heard that Smart PLS can offer higher R-square than SEM. Is that reliable?
Urgent>> I am a master student doing a study about privacy behavior on Facebook.. my research model consists of two independent variables (cost and benefit), each independent variables consists of six items.. in addition to two dependent variables each of three items.. all measurement models are reflective.. the number of samples is 120.. all answers are within the range (1 to 5) or (strongly disagree to strongly agree).. I use the pls algorithm in smartpls software to analyse my model.. the problem is most of the outer loadings are below (o.7).. one variable has Cronbach's alpha below (0.7) while three varibles have AVE below (0.5). I try to delete the items with the least loadings but there is no recognized effect on the Cronbach's alpha and the AVE.. kindly I need to know how to fix this problem ??
Currently, variables are related to each other. Instead of performing a t-test for each variable between two groups, I was suggested to apply partial least square to calculate the p-value.
I read some publications about PLS and PLS-SEM. I still don't know how to use PLS to calculate group differences.
Hi,
Can anyone answer me the question about the steps to do in structural model in smart PLS?
I read in the book "Hair 2016 - A Primer on Partial Least Squares Structural Equation Modeling" they mentioned there are 6 steps to do in Structural Model Assessment procedure.
Step 1,2,3, are fine. But I wonder step 4,5,6 are always necessary to report? Or when we should report these steps?
Step 4: Assess f2 effect size
Step 5: Assess predictive relevance Q2
Step 6: Assess q2 effect size
Thank you
How partial least squares is related to Projection to Latent Structures method developed by H. Wold?
Is there any difference between partial least squares and Projection to Latent Structures method?
Hi. I'm using WEKA to build a QSAR model using PLS method with a training set included 102 instances and 206 attributes (chemical descriprtors). I want to select attributes that can:
- Build a model with correlation coefficient r2>0.5 and root mean square error RMSE<0.5.
- Cross validation (leave-one-out) r2>0.5, RMSE<0.5.
- Evaluate on external set: r2>0.5.
I'm totally newbie and have no idea to do that. I tried select attributes with CfsSubsetEval + BestFirst but the result RMSE is still >0.5. I continued to select some of attributes that could reduce the RMSE on both training set and cross validation but it's overfitting and the r2 on external set is only 0.2. Can you help me please? Thank you very much.
What is the difference between formative and reflective partial least square structural equation models? And what tests need to be conducted for the aforementioned models?
In testing moderating effect of a moderating variable on relationship between IVs and DV (all the latent variables are with reflective indicators; and sample size over 200) using SmartPLS 3, firstly it obtained a significant moderating effect by using the product indicator approach. However, the test again using the two stage approach found no moderating effect.
As referred by Henseler & Chin (2010), the two-stage approach has the higher statistic power, i.e. it is the most likely approach to detect a significant interaction. But the results here showed the opposite!!
I would appreciate any suggestions regarding this.
I would like to do analysis from SmartPLS.
The objective of my research is to establish relationship "X" to "Y" by the use of SmartPLS
X have have five domain and every domain have 12 items that mean 12x5=60 items
And "Y" have one domain with 6 items
Can I establish this relation from smartPLS? if yes than what type of test i have to with ?
kindly attached my analysis and tell me that i am going on right way or not ?

One Week Workshop on Advanced Research Analysis Tools and Techniques Being Organised by Department of Commerce (SAP, DRS-III), University of Jammu, Jammu (J&K)
Research provides new domain of knowledge, new theories and laws, solution to a problem and open new areas of thought. Further, it has the potential to question and analyse the existing theories in accordance to the present circumstances. Research involves answering unanswered questions or exploring something which currently does not exist (Goddard & Melville, 2004). Hence, it is important for educational/ professional academicians and scholars to be acquainted higher order research techniques. In the recent past, many new dimensions of research have emerged catering to the vast changes taking place in the present day social situation. The quality of research depends on the understanding of the research problem, the attendant research methodology and the appropriate tools used for the analysis. This one week workshop will focus on qualitative research techniques, experimental research design and partial least squares structural equation modelling (PLS-SEM).
Hi everyone,
I am using smart PLS for one of my study. I have first order and second order constructs. In Smart PLS i got the AVE for first order constructs above 0.5, whereas the AVE for second order construct is less (0.30). is it acceptable ?if not do i need to report AVE for second order construct? support me with literature ( My Model is Reflective- Reflective model.
the other validity for both first order and second order are satisfying the cut off level ( say convergent, internal composite reliability etc.,)
What is the free software that you have used for partial least square structural equation modeling?
Thanks in advance
Is there any impact factor journal for PLS-SEM studies only? or any current special issue?
I would like to know on how to calibrate for real-time time monitoring using FTIR probe, the reaction is a multi-components reaction for which Partial Least Square will be used.
Am conducting a multgroup analysis in PLS-SEM using SmartPLS 3 and I came across an interesting situation.
There is a moderator variable which is having an effect on one group (lets say X1) and not having an effect on another (say X2), this is evident by the t-values and probability obtained. But the inter-group difference of t-value isnt significant. How to report this? Assume that the moderator has only two categorical levels.
What I inferred was that since there were no inter-group differences in terms of p-value, even though X1 has a significant p-value, it might be considered a sampling error and for the general population there is no moderating effect of X on the relationship. Thus, no heterogeneity was observed.
Is this correct?
Our plan is to use the SLAM-framework which includes a questionnaire with 65 questions. This framework is previously tested and validated. But for our case, we have 110 respondents witch is too little for the statistical analyses, such as factor analysis where you need 5 respondents per question. Do anyone have any suggestions on how to tackle this problem?
We want to use these questions, but do we have to reduce the number of questions before we send the questionnaire out?
Data set size = 283
IVs = 4
DV = 1
Factor = 1 (out of 4, one was selected)
Results
Press = 0.91
X explained 58%, Y explained 18%
Most researchers have faced the problem disability of differentiate between A reflective analysis and conjunctive analysis in Structural Equation Modelling SEM. Kindly please I need any one assets in getting the explanation for this question.
Does Stata have the ability to perform a partial least squares analysis or another procedure which might help specify a model with low co-linearity among numerous predictors? This would be apart from a trial and error process and examining VIF for each try. Thank you in advance.
I am analysing soil microbial community data for classification and discrimination. PLS-DA seems more efficiency in seperating microbial groups. what is the difference between PCA and PLS-DA? When to use PLS-DA rather than PCA?
i want to perform a test on biophysical parameters with different SAR polarizations
I have three independent and one dependent variable in our model. I am using partial least square structural equation modeling to test the relationships. When tested separately, each independent variable has a significant relationship with the dependent variable. But when put together in the model, one of the independent variable becomes insignificant.
What does it mean? What could be the interpretation?
How can I detect common method bias in smartpls? Can you suggest some guidelines?
Does anyone here know how to create three-way interaction effect using SmartPLS?
Currently, we are working on a moderated mediation model, and we want to test this model using smart-pls is it appropriate?
I want to calculate GoF = sqrt((average AVE) * (average R2))
My AVE generated by the SmartPLS are
0.7399
0.8225
0.7582
0.6398
0.6735
Is Average AVE means to take the average of these values
like (.7399+.8225+.7582+.6398+.6735 / 5)?
Furthermore, after running PLS algorithm, the value given in the output variable is Average R2?
Dear PLS structural equation modeling professors and experts,
If I have 10 experts to judge about the indicators of a project performance (As a first order construct) and I have 20 sample of completely independent projects whose their indicators(observed variables) are to be judged by the10 experts, Could I say I have 20*10 means 200 samples and use it for parameters estimation in PLS(Partial least square), or I must get an average among 20 project and claim that I have only 10 sample or I may get an average of the expert judgments and claim that I have 20 samples?
Is it a problem of Pseudo-replication? If so will it lead to unreliable parameter estimation?
Sincerely Yours
Thanks in Advance
Hello,
I thought PLS was only interesting when the sample is not very important compared to the number of items, but I saw papers with roughly 200 responses and 20 items using PLS. Maybe it's a problem of formative vs. reflective variables? Thank you for your help.
Hello everyone,
i have two columns of data (x,y), which i want to fit with an explicit function:
y=p1*(x-p2)^p3,
using non-linear least square algorithm. So as to calculate the Jacobian maxtix, i need to know the partial derivatives of the function with respect to three parameters. For p1 and p2, it is quite obvious that:
dp1=(x-p2)^p3;
dp2=-1*p1*p3*(x-p2)^(p3-1)
how about the third one ? dp3=?
Normally, for the function 7^p3, dp3=7^p3*log(7). However, it is not my case, since (x-p2) can be negative. Is this function non-differentiable w.r.t. p3?
Thanks in advance for your answer.
Liang
Please share any excel file if any body have
I am more familiar with SEM tools than PLS. On the other hand, one student I am supervising wanted to use PLS on account that it has significantly smaller sample size requirements than SEM. However, I have my own misgiving with the lack of goodness of Fit statistics in PLS (to my knowledge). Is there any goodness of fit statistics (or its equivalent) in PLS?
I would like opinion from both PLS and SEM users on this matter.
My results from PLS-SEM analysis (in WarpPLS) shows weak effect size (f-squared). The path coefficient is 0.15 and p-value is significant. I am analyzing indirect relationship (mediation). How do I interpret such results? Also the VAF value shows full mediation. I am confused.
So my colleague and I tried to run PLSPM from a huge dataset which measures binary, ordinal, and numerical data.
Is there a way to do this in R using the plspm package? Tried using XLSTAT PLSPM package and it won't run the non-numerical data during the bootstrap.
We also like to know if there are ways to see whether the latent variable are meaningful to the model. If anyone can help us it'll be awesome! Thanks!!
Principal component analysis (PCA) to obtain the principal components (PCs) that best fits the shape of the face in order to relate the SNP data with that PCs by using the partial least squares regression (PLSR) method as it is explained in Claes et al. 2014?
Don't understand very well the methodology there explained.
Thanks in advance.
When choosing a WAPLS model, apart from relying on performance (e.g. r2, r, max bias, RMSEP, scatter-plot etc..), the recommendation is to select the successive component if the model performance increases by >5% measured by the cross validated RMSEP (e.g. Braak & Juggins, 1993; Birks, 1998; Brooks & Birks, 2000; Barlow et al., 2013).
Does this rule apply also to PLS technique?
I found this table (as appears in the attached image)
in this book
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
I need to understand how to use this table


Hello,
I need to fit PLS regression model on my data. However, I could not find proper references on how to explain the results on SPSS. How to choose latent number and how to extract the model from the results for prediction.
I really appreciate it if anyone can give me a reference or a tutorial regarding PLS on SPSS.
Thanks
hi,
I am a new user of SmartPLS. I would like to perform FIMIX-PLS, but I could not find any guideline to run the analysis in SmartPLS 3.
I have collected 84 responses through survey. However, I am not sure what analytical tools will be better to use. Any suggestions (step by step, if possible) for Quantitative data analysis would be of help.
Thanks.
My request is primarily directed towards the optics to be a good user and not towards an expertise of the mathematical dimensions
To overcome multicollinearity problem in multiple regression one uses either ridge regression, liu estimator, Partial least squares regression and etc.
I have a set of likert items all of which are constructed with scales of 7 categories, except for one likert item, which is constructed with a scale of 11 categories.
These likert items are to be used in a Structural Equation Model and analysed using Partial Least Squares.
My question is now:
- How, if at all, will this discrepancy influence the analysis?
multicoillinearity and outliers do exist in multivariate regression analysis. There are methods like ridge regression, principal component regression and etc.
Thaumastocoris peregrinus is a major pest of eucalyptus in Brazil an we need to develop efficient methodologies to monitor this insect.
I would like to ask is there any rule to determine the optimal number of PLS components i.e. above which it will overfit and below which it underfits. Is it like one third of the number of total samples?
I got a bifactor model with 4 indicator each one and two groups. I understood that samples below 100 can´t provide stable results but otherwise I can´t add more cases. Is there a way to run measurement invariance with 40 cases each group or another similar procedure (perhaps partial least squares) or alternative (montecarlo simulation to detect sample size)?