Error message in running multilevel CFA

RStudio IDE
1

Hi Guys, I am running the multilevel CFA for my thesis and I encounter the following problem.

Here is my Rsutdio syntax:

Title: Multilevel CFA

Data <- read.csv("Data_Within Person Original.csv")

twolevel <- '
level: 1
WFSSB =~ Q1+Q2+Q3+Q4+Q5+Q6+Q7+Q8+Q19+Q10+Q11+Q12+Q13+Q14
WWFBS =~ Q15+Q16+Q17+Q18
WWP =~ Q19+Q20+Q21+Q22
WBI =~ Q23+Q24+Q25+Q26+Q27+Q28+Q29+Q30
WPSEP =~ Q31+Q32+Q33+Q34+Q35+Q36+Q37
WNSEP =~ Q38+Q39+Q40+Q41+Q42+Q43
WPRS =~ Q44+Q45+Q46+Q47+Q48+Q49+Q50
WPAWB =~ Q51+Q52+Q53+Q54+Q55+Q56+Q57+Q58+Q59+Q60

level: 2
BGen =~ FSSB+WFBS+WP+BI+PSEP+NSEP+PRS+PAWB
'

results <- cfa(twolevel, data = Data, cluster = 'ID')
summary(results, fit.measures = TRUE, standardized = TRUE)

The variables in level 1 are within-person variables and I use gender as the between-person variable. When I run the syntax, it shows the following message:

Error in lav_data_full(data = data, group = group, cluster = cluster, :
lavaan ERROR
Some between-level (only) variables have non-zero variance at the within-level. Please double-check your data.

May I know how should I fix this problem?

Thank you very much!

2

Welcome!

I think we need more information, for example what package are you using in your analysis. What does your data look like? You migt find some useful hints in this FAQ.

A handy way to supply some sample data is the dput() function. In the case of a large dataset something like dput(head(mydata, 100)) should supply the data we need. Just do dput(mydata) where mydata is your data. Copy the output and paste it here.

3

Thank you for the advice. I used the lavaan package to run the multilevel CFA by following Francis Huang's suggestion (see the link here: Multilevel CFA (MLCFA) in R, part 2 | Francis L. Huang (francish.net) He suggests using the Lavaan package to automatically set up multilevel CFA in R.

Here is what my data looks like:

structure(list(ID = c(1L, 1L, 1L, 1L, 1L, 1L), Day = 1:6, Gen = c(0L,
0L, 0L, 0L, 0L, 0L), Gen_S = c(1L, 1L, 1L, 1L, 1L, 1L), Age = c(2L,
2L, 2L, 2L, 2L, 2L), Age_S = c(2L, 2L, 2L, 2L, 2L, 2L), EduLevel = c(2L,
2L, 2L, 2L, 2L, 2L), EduLevel_S = c(2L, 2L, 2L, 2L, 2L, 2L),
Child = c(1L, 1L, 1L, 1L, 1L, 1L), WorkExp = c(3L, 3L, 3L,
3L, 3L, 3L), WorkExp_S = c(3L, 3L, 3L, 3L, 3L, 3L), Tenure = c(1L,
1L, 1L, 1L, 1L, 1L), Tenure_S = c(1L, 1L, 1L, 1L, 1L, 1L),
JobLevel = c(1L, 1L, 1L, 1L, 1L, 1L), JobLevel_S = c(1L,
1L, 1L, 1L, 1L, 1L), Res = c(4.56, 4.56, 4.56, 4.56, 4.56,
4.56), SRes = c(4.52, 4.52, 4.52, 4.52, 4.52, 4.52), Q1 = c(5L,
5L, 5L, 4L, 1L, 5L), Q2 = c(5L, 5L, 4L, 5L, 1L, 5L), Q3 = c(3L,
3L, 4L, 5L, 3L, 5L), Q4 = c(5L, 3L, 5L, 2L, 1L, 5L), Q5 = c(5L,
5L, 3L, 3L, 4L, 3L), Q6 = c(3L, 3L, 5L, 3L, 1L, 4L), Q7 = c(5L,
3L, 3L, 3L, 1L, 5L), Q8 = c(5L, 5L, 2L, 4L, 4L, 5L), Q9 = c(5L,
3L, 4L, 5L, 4L, 4L), Q10 = c(5L, 4L, 3L, 2L, 1L, 4L), Q11 = c(3L,
5L, 5L, 3L, 1L, 4L), Q12 = c(3L, 4L, 5L, 5L, 2L, 5L), Q13 = c(4L,
5L, 2L, 5L, 1L, 5L), Q14 = c(4L, 5L, 3L, 4L, 1L, 5L), FSSB = c(4.29,
4.14, 3.79, 3.79, 1.86, 4.57), Q15 = c(5L, 5L, 4L, 4L, 4L,
2L), Q16 = c(3L, 4L, 5L, 4L, 1L, 2L), Q17 = c(4L, 3L, 2L,
3L, 1L, 5L), Q18 = c(5L, 4L, 4L, 2L, 2L, 5L), WFBS = c(4.25,
4, 3.75, 3.25, 2, 3.5), Q19 = c(4L, 4L, 5L, 4L, 5L, 3L),
Q20 = c(5L, 1L, 4L, 4L, 5L, 1L), Q21 = c(2L, 5L, 5L, 5L,
3L, 1L), Q22 = c(2L, 1L, 5L, 5L, 5L, 1L), WP = c(3.25, 2.75,
4.75, 4.5, 4.5, 1.5), Q23 = c(4L, 4L, 5L, 1L, 1L, 4L), Q24 = c(5L,
3L, 5L, 2L, 1L, 5L), Q25 = c(5L, 5L, 4L, 4L, 2L, 5L), Q26 = c(5L,
5L, 4L, 1L, 3L, 3L), Q27 = c(5L, 4L, 2L, 1L, 1L, 5L), Q28 = c(4L,
3L, 5L, 1L, 2L, 5L), Q29 = c(3L, 5L, 5L, 1L, 1L, 5L), Q30 = c(5L,
4L, 5L, 1L, 1L, 3L), BI = c(4.5, 4.125, 4.375, 1.5, 1.5,
4.375), Q31 = c(5L, 3L, 3L, 2L, 3L, 5L), Q32 = c(5L, 4L,
2L, 3L, 3L, 4L), Q33 = c(4L, 5L, 3L, 5L, 1L, 5L), Q34 = c(5L,
3L, 2L, 5L, 1L, 3L), Q35 = c(5L, 5L, 5L, 5L, 2L, 3L), Q36 = c(5L,
3L, 1L, 4L, 1L, 3L), Q37 = c(4L, 5L, 1L, 2L, 1L, 5L), PSEP = c(4.71,
4, 2.43, 3.71, 1.71, 4), Q38 = c(2L, 1L, 3L, 1L, 4L, 1L),
Q39 = c(1L, 1L, 3L, 3L, 5L, 2L), Q40 = c(3L, 1L, 1L, 1L,
5L, 1L), Q41 = c(1L, 1L, 3L, 2L, 5L, 1L), Q42 = c(4L, 1L,
1L, 1L, 3L, 1L), Q43 = c(3L, 1L, 1L, 1L, 4L, 1L), NSEP = c(2.33,
1, 2, 1.5, 4.33, 1.17), Q44 = c(5L, 3L, 5L, 4L, 1L, 5L),
Q45 = c(5L, 4L, 3L, 3L, 2L, 3L), Q46 = c(3L, 5L, 4L, 3L,
4L, 3L), Q47 = c(5L, 5L, 5L, 5L, 1L, 5L), Q48 = c(5L, 3L,
5L, 3L, 2L, 3L), Q49 = c(4L, 3L, 4L, 3L, 1L, 5L), Q50 = c(4L,
3L, 5L, 5L, 1L, 5L), PRS = c(4.428571429, 3.714285714, 4.428571429,
3.714285714, 1.714285714, 4.142857143), Q51 = c(4L, 4L, 5L,
5L, 1L, 5L), Q52 = c(2L, 3L, 5L, 5L, 1L, 5L), Q53 = c(1L,
5L, 3L, 5L, 1L, 3L), Q54 = c(1L, 4L, 5L, 5L, 3L, 5L), Q55 = c(1L,
3L, 5L, 5L, 1L, 5L), Q56 = c(3L, 5L, 3L, 5L, 3L, 5L), Q57 = c(2L,
5L, 4L, 4L, 1L, 5L), Q58 = c(3L, 5L, 3L, 5L, 1L, 5L), Q59 = c(2L,
5L, 5L, 5L, 1L, 5L), Q60 = c(2L, 5L, 3L, 5L, 1L, 5L), PAWB = c(2.1,
4.4, 4.1, 4.9, 1.4, 4.8)), row.names = c(NA, 6L), class = "data.frame")

This is my first time raising question here. If there is anything I am still missing, do let me know.

Thank you very much for your help!

4

Looks good to me. I know nothing about latent variable analysis so I am not likely to be of much help but i think you have given us enough that some who knows a bit about the subject should be able to help.

5

It's alright. Thank you very much for the advice!

6

I think I see a problem. Do you have the mcfa.input function that Huang writes about? His links do not seem to work, it looks like the university has made a change and I am not sure of what he is doing on GitHub Update mcfa2.R · flh3/mcfa@fa0426d · GitHub
There is a google groups for lavaan that may be of usu.

7

Thanks for the advice.

As I read his instruction in the website. mcfa.input is the function he uses to run MCFA manually, I don't think R needs that if I want to set up MCFA automatically, right?

8

I really do not know. I only gave the associated article a quick glance but I think he's partitioning the variances differently. You might want to send him a note asking a bout the new (apparently non-functioning) links.

9

Thank you. I will try to ask him then.

10

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