I had used the already published
Likert scale for the survey. And the responses to the survey from 98 participants were collected. The survey likert scale was from
1-5 from strongly diasgree to srongle agree.
Looking at the variables the average value of one of the factors is above the 3 for all the questions. The figure below is the avg of the responses.
But while evaluating the variances the estimate, std.lv are valued seems to be negatives.
Estimate Std.Err z-value P(>|z|) Std.lv Std.all .Competence -0.188 0.105 -1.796 0.073 -0.324 -0.324
and it is giving the warning:
lavaan WARNING: some estimated lv variances are negative
Model i am using:
model <- ' Competence =~ COMP1 + COMP2 + COMP3 + COMP4 Autonomy =~ AUT1 + AUT2 + AUT3 Relatedness =~ REL1 + REL2 + REL3 + REL4 Motivation =~ Autonomy + Relatedness + Competence Vigor =~ VIGOR1 + VIGOR2 + VIGOR3 +VIGOR4 + VIGOR5 Dedication =~ DED1 +DED2 +DED3 +DED4 +DED5 Absorption =~ ABS1 +ABS2 +ABS3 +ABS4 Engagement =~ Vigor + Dedication + Absorption Motivation ~~ Engagement ' fit <- sem(model,data = Log_And_SurveyResult) summary(fit, standardized=T)
However, what these variables predict appears to be significant with other variables i.e Motivation and Engagement seems to be co-related.
Now, due to the value of negative in the estimate, I am confused about how to interpret the result?
I can add further information if need to answer the question.
Also, in the output of the LavaanPlot, the loadings are high.
I am stuck in the interpretation for many days. Any help will be appreciated.
There may be a few issues going on. The first thing that comes to me is that perhaps your estimator is incorrect. It looks like you've used the default maximum likelihood estimator, but this has some specific assumptions that may not be met with Likert scales. You may check using the WLMSV estimator instead. Also, it looks like you're doing a factor analysis on the scale, so instead of calling
sem() you might just want to use
cfa(). It shouldn't affect your results a lot, but the
cfa() function has some useful default arguments for when the goal is just a factor analysis.
Some other issues that you might want to consider is that your sample is too small and/or that there is too much collinearity in the data. I'm not terribly surprised by finding a Heywood case in this model since you're fitting a hierarchical factor analysis on just 98 people. I'd just go through some assumption checking if an alternative estimator doesn't fix the problem.
Another possible issue is just that the model is misspecified. You might consider exploratory factor analysis and see if that results in some better behaved models if nothing else works.
Answered by Billy on December 12, 2020
0 Asked on January 28, 2021 by chua-s-yang
0 Asked on January 28, 2021 by visionenthusiast
0 Asked on January 28, 2021 by zqq
1 Asked on January 28, 2021 by nlapidot
0 Asked on January 28, 2021 by tricostume
1 Asked on January 27, 2021 by mathews24
0 Asked on January 26, 2021 by thecity2
2 Asked on January 26, 2021
0 Asked on January 26, 2021 by bonesones
0 Asked on January 26, 2021 by kplauritzen
2 Asked on January 26, 2021 by user369210
0 Asked on January 26, 2021
1 Asked on January 25, 2021 by kiril-e-proykov
0 Asked on January 24, 2021 by oja-niva
0 Asked on January 24, 2021 by mephisto73
0 Asked on January 23, 2021 by valjean
1 Asked on January 23, 2021 by tjt
5 Asked on January 23, 2021 by user239903
0 Asked on January 21, 2021 by pooza
Get help from others!