"High" r squared (= .28), although no significant predictor (VIFs below 5)
Posted: Tue Jan 26, 2021 12:50 pm
Dear all,
in my model, I want to predict different facets of competence development (e. g. development of methodological competencies; defined as four different latent variables) through different aspects of working conditions (autonomy, colleages, ...; in total 11 different constructs specified as latent variables).
I specified four different models (for the different facets of competence development) and in case of two models, the analysis shows at least one significant predictor. But in the other two cases, none of the independen variables conducts an significant influence on the dependent variable/latent construct.
That´s of course also a possible result, but I´m wondering about the relatively high R-squared in these models (.28 in each model; r squared adjusted: .21). I checked for multicollinearity, but the VIFs are all below the proposed threshold of 5 (although two independent variables are above 3.5). Correlation analysis of the different latent variables used in the PLS-analysis show significant correlations between almost all of the variables; some with a coefficient of .7.
Questions:
- What might be an explanation for the "high" r squared? (are the high correlations the reason, although the VIFs are below 5?)
- Are you aware of any sources dealing with that problem? (of course, I found a lot about multicollinearity, but as I said, these values seem to be okay)
- What are possible solutions? I might delete some constructs in these two models, but the variables included address different aspects of working conditions, therefore that would be critical from a content-wise perspective).
I´m happy about any hints.
Thank you!
Alex
in my model, I want to predict different facets of competence development (e. g. development of methodological competencies; defined as four different latent variables) through different aspects of working conditions (autonomy, colleages, ...; in total 11 different constructs specified as latent variables).
I specified four different models (for the different facets of competence development) and in case of two models, the analysis shows at least one significant predictor. But in the other two cases, none of the independen variables conducts an significant influence on the dependent variable/latent construct.
That´s of course also a possible result, but I´m wondering about the relatively high R-squared in these models (.28 in each model; r squared adjusted: .21). I checked for multicollinearity, but the VIFs are all below the proposed threshold of 5 (although two independent variables are above 3.5). Correlation analysis of the different latent variables used in the PLS-analysis show significant correlations between almost all of the variables; some with a coefficient of .7.
Questions:
- What might be an explanation for the "high" r squared? (are the high correlations the reason, although the VIFs are below 5?)
- Are you aware of any sources dealing with that problem? (of course, I found a lot about multicollinearity, but as I said, these values seem to be okay)
- What are possible solutions? I might delete some constructs in these two models, but the variables included address different aspects of working conditions, therefore that would be critical from a content-wise perspective).
I´m happy about any hints.
Thank you!
Alex