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Hello,

Two questions:

- A dependent variable with four indicators, after applying the measurement model I have been left with only one indicator, what could this be due to?

- I have a model with likert scale variables and a continuous variable, can I run the model or do I have to transform it to a categorical variable?

Thank you

Francisco wrote: ↑Sat Oct 28, 2023 10:59 am Hello,

Two questions:

- A dependent variable with four indicators, after applying the measurement model I have been left with only one indicator, what could this be due to?
geometry dash subzero
- I have a model with likert scale variables and a continuous variable, can I run the model or do I have to transform it to a categorical variable?

Thank you

If you have a dependent variable with four indicators and after applying the measurement model, you are left with only one indicator, it could be due to a variety of reasons. One possibility is that the indicators are highly correlated, which can lead to multicollinearity issues. Another possibility is that the indicators are not measuring the same construct, which can lead to construct under-representation. You may want to consider examining the factor loadings of each indicator to see if they are measuring the same construct. Additionally, you may want to consider examining the correlations between the indicators to see if they are highly correlated.

You can run a model with likert scale variables and a continuous variable without transforming it into a categorical variable. However, it is important to note that likert scale variables are ordinal in nature and do not have equal intervals between categories. Therefore, treating them as continuous variables can lead to issues with interpretation and statistical analysis. One way to address this issue is to use ordinal regression models, such as proportional odds models or partial proportional odds models. These models take into account the ordinal nature of the data and can provide more accurate results.