SMARTPLS 4 lec 4 Structural model 1: Multicollinearity, Bootstrapping, significance testing, effect

link for the data set used in this video: https://shorturl.at/hsMP3 data set less than 100 for student version: https://shorturl.at/tCW69 In this lecture, we will discuss the following topics related to structural modeling in SmartPLS 4: Multicollinearity: Multicollinearity is a problem that occurs when two or more independent variables in a model are highly correlated. This can lead to unstable estimates of the path coefficients in the model, and can make it difficult to determine which variables are actually having an effect on the dependent variable. Bootstrapping: Bootstrapping is a statistical technique that can be used to estimate the standard errors of path coefficients and to conduct significance testing. Bootstrapping involves resampling the data many times to create a distribution of estimates for each path coefficient. The standard error of a path coefficient is then estimated as the standard deviation of this distribution. Bootstrapping can also be used to conduct significance testing by comparing the observed path coefficient to the distribution of bootstrapped estimates. Significance testing: Significance testing is used to determine whether the path coefficients in a structural model are statistically significant. This is done by comparing the observed path coefficient to a critical value that is determined by the significance level of the test. If the observed path coefficient is greater than the critical value, then it is said to be statistically significant. We will also use a real-world data set to illustrate these concepts. The target audience for this lecture is researchers who are new to structural modeling in SmartPLS 4. This lecture will provide a basic understanding of the concepts of multicollinearity, bootstrapping, and significance testing, and how to use them in SmartPLS 4. Here are some additional details about SmartPLS: SmartPLS is a software program that is used for structural equation modeling (SEM). SEM is a statistical technique that can be used to test causal relationships between variables. SmartPLS is a popular choice for SEM because it is relatively easy to use and it can be used with small sample sizes. SmartPLS 4 is the latest version of SmartPLS. It includes a number of new features, such as the ability to conduct bootstrapping and significance testing.