Hi
Based on a data set I have to answer whether the use of tele-diagnosis and tele-evaluation by health professionals in Andalusia has a positive impact in their perception of the technology usefulness.
I have the variables:
dependent variable:
useful -how useful is tele-diagnosis and tele-evaluation in the medical practice and the possible answers are \Very Useful"=4, \Useful"= 3, \A bit useful"=2, and \Useless"=1
My explanatory variable of interest is:
Use- answer to \How often do you use tele-diagnosis and tele-evaluation in the medical practice. The answers are \Very often"= 5, \Often"= 4, \Rarely"=3, \Never"= 2, and \Not available"=1
My problem is simultaneous causality. I want to estimate the causal effect of use on useful, but there is also a causal effect from useful to use.
I know I can use instrumenal variables to correct for the bias.
But I don't seem to have any IVs I can use.
Here is the rest of the explanatory variables I can put into the model:
Age
Male
The rest of the variables are statements where the respondent exress the degree of agreement with the statements... Possible answers are \Totally agree"= 4, \Agree"=3, \Disagree"=2, and \Totally disagree"=1
B1: Electronic health record systems are not suciently adapted to the professionals
needs.'
ˆ B2: `There are incompatibilities between the use of applications and the working processes'
ˆ B3: `There are infrastructure and connectivity limitations'
ˆ B4: `Lack of agreement regarding the data that must be stored'
ˆ B5: `It is dicult to integrate the applications I already use with new applications'
ˆ B6: `I am worried about the security and condentiality of data'
ˆ B7: `Lack of funding for ICT (information and communication technologies) integration'
ˆ B8: `Lack of economic incentives for the use of these applications'
ˆ B9: `Lack of knowledge and skills'
ˆ B10: `Lack of training '
ˆ B11: `Its use takes too much time during my clinical practice
Any suggestions to how I correct for the endogeneity??
Based on a data set I have to answer whether the use of tele-diagnosis and tele-evaluation by health professionals in Andalusia has a positive impact in their perception of the technology usefulness.
I have the variables:
dependent variable:
useful -how useful is tele-diagnosis and tele-evaluation in the medical practice and the possible answers are \Very Useful"=4, \Useful"= 3, \A bit useful"=2, and \Useless"=1
My explanatory variable of interest is:
Use- answer to \How often do you use tele-diagnosis and tele-evaluation in the medical practice. The answers are \Very often"= 5, \Often"= 4, \Rarely"=3, \Never"= 2, and \Not available"=1
My problem is simultaneous causality. I want to estimate the causal effect of use on useful, but there is also a causal effect from useful to use.
I know I can use instrumenal variables to correct for the bias.
But I don't seem to have any IVs I can use.
Here is the rest of the explanatory variables I can put into the model:
Age
Male
The rest of the variables are statements where the respondent exress the degree of agreement with the statements... Possible answers are \Totally agree"= 4, \Agree"=3, \Disagree"=2, and \Totally disagree"=1
B1: Electronic health record systems are not suciently adapted to the professionals
needs.'
ˆ B2: `There are incompatibilities between the use of applications and the working processes'
ˆ B3: `There are infrastructure and connectivity limitations'
ˆ B4: `Lack of agreement regarding the data that must be stored'
ˆ B5: `It is dicult to integrate the applications I already use with new applications'
ˆ B6: `I am worried about the security and condentiality of data'
ˆ B7: `Lack of funding for ICT (information and communication technologies) integration'
ˆ B8: `Lack of economic incentives for the use of these applications'
ˆ B9: `Lack of knowledge and skills'
ˆ B10: `Lack of training '
ˆ B11: `Its use takes too much time during my clinical practice
Any suggestions to how I correct for the endogeneity??
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