Two-Stage least squares (2SLS) regression analysis is a statistical technique that is used in the analysis of structural equations. This technique is the extension of the OLS method. It is used when the dependent variable’s error terms are correlated with the independent variables. Additionally, it is useful when there are feedback loops in the model. In structural equations modeling, we use the maximum likelihood method to estimate the path coefficient. This technique is an alternative in SEM modeling to estimate the path coefficient. This technique can also be applied in quasi-experimental studies.
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Key concepts and terms:
Problematic causal variable: The dependent or endogenous variable whose error term is correlated with the other dependent variable error term. A problematic causal variable is replaced with the substitute variable in the first stage of the analysis.
Instruments: An instrument variable is used to create a new variable by replacing the problematic variable.
Stages: In ordinary least square method, there is a basic assumption that the value of the error terms is independent of predictor variables. When this assumption is broken, this technique helps us to solve this problem. This analysis assumes that there is a secondary predictor that is correlated to the problematic predictor but not with the error term. Given the existence of the instrument variable, the following two methods are used:
All statistical software does not perform this regression method. In SPSS, to perform this analysis, the following steps are involved:
Quantitative Results Section (Descriptive Statistics, Bivariate and Multivariate Analyses, Structural Equation Modeling, Path analysis, HLM, Cluster Analysis)
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