What is the easy way to run Endogeneity for survey data/primary research?

Endogeneity refers to a situation in which a variable in a statistical model is correlated with the error term or disturbance term. This correlation can lead to biased and inconsistent parameter estimates, making it challenging to establish causal relationships between variables. Endogeneity often arises in observational or cross-sectional research designs, where the researcher cannot control for all possible confounding factors.

To address endogeneity in your analysis using survey data in SPSS/AMOS, you can consider implementing the two-stage least squares (2SLS) method. This method is commonly used in econometrics to address endogeneity by using instrumental variables. Here is a step-by-step procedure:

Identify potential endogenous variables: Identify the variables in your model that you suspect may be endogenous. These are the variables that are potentially correlated with the error term.

Find suitable instrumental variables: Instrumental variables are variables that are correlated with the endogenous variable but not directly correlated with the error term. Look for variables that satisfy this condition and can be used as instruments.

Run the first-stage regression: In SPSS, run a regression model where you predict the endogenous variable(s) using the instrumental variables. Obtain the predicted values (fitted values) from this regression.

Run the second-stage regression: Use the predicted values obtained from the first-stage regression as a new independent variable in your main regression model. This is done to address the endogeneity issue. Run your main regression model using the predicted values as a control variable.

Interpret the results: Examine the coefficient estimates and their significance in the second-stage regression. These estimates represent the effects of the exogenous variables on the dependent variable, taking into account the endogeneity issue.

In AMOS, which is a software primarily used for structural equation modeling (SEM), you can also address endogeneity using a similar two-stage approach. You would specify a measurement model for your latent variables and a structural model for the relationships between the latent variables. You can include instrumental variables as exogenous variables in your structural model to account for endogeneity.

It's important to note that implementing the 2SLS method or addressing endogeneity, in general, requires careful consideration of the specific research context, availability of instrumental variables, and the assumptions underlying the method. It is recommended to consult with a statistician or econometrician who can guide you through the process and ensure its appropriate application to your specific research design.

Muhammad Mumtaz Khan

There are two types of endogeneity. Observed and unobserved. Control variables may explain the observed one while FIMIX is the response to the unobserved.

Chuck A Arize

To run endogeneity for survey data/primary research, you can use instrumental variables (IV) regression. IV regression is a statistical technique that can be used to address endogeneity by using an instrument variable that is correlated with the endogenous variable but not correlated with the error term. The instrument variable is used to estimate the effect of the endogenous variable on the outcome variable.

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