 Seuraavaa, kun esitämme modelille ja ensimmäisenä, kun esitämme dataa ja esitämme dataa, meidän pitää ensimmäisenä, että modelilla on identiteettänyt. Olemme esittämään identiteettänsä, faktor- ja cfa-modelilla, uudelleen video- ja path-analysis-modelilla, uudelleen videoilla. Tämä videot voivat vain sanoa, miten strukturerikroson modelilla on identiteettänyt. The identification strategy of structural regression models, or structural regression models, is actually very simple, if you know how converter factor analysis models and path analytical models are identified. The identification is basically what we call a two-step rule. We first check that the latent variables are identified, so if the converter factor analysis can be meaningfully estimated. A latent variable is identified if it has three indicators or if it has two indicators and it is embedded in a larger system or two indicators and one of the indicators is fixed to one and single indicator latent variables are identified if we constrain the reliability of the single indicator to a known value. The known value would be taken from existing research or we could, for example, set it to zero if we think that the measurement error is very small in the data. After that, we take a look at the latent variable part of the model. Are the relationships between the latent variables identified? In this case, it's useful to know that all recursive models are identified and that covers majority of the cases. The cases that it doesn't cover, the non-recursive models, we simply apply the identification rules from simultaneous equations in econometrics and that establishes the identification of the full model. So choose the procedure, first you apply the rules for factor analysis to identify the latent variables and then you apply the econometrics rules for simultaneous equations to identify the latent variable part of the model and that establishes that the model is fully identified.