You can have a look at Bayesian inference based Classifiers, Naive Bayes approach might be an useful initial approach.

Since the dependent variable is binary, the appropriate method is binary logistic regression, rather than linear regression. Linear regression assumes that the dependent variable is continuous and normally distributed, whereas a binary variable does not meet these assumptions, leading to biased results. In contrast, logistic regression models the probability values of the dependent variable using the logit transformation, ensuring that the predicted values remain within the 0 to 1 range. Although Likert-scale independent variables are ordinal, they are often treated as interval scales in regression analysis, but they can also be categorized using dummy coding or analyzed with ordinal logistic regression if the outcome variable has multiple categories. Therefore, to properly analyze the data, binary logistic regression is the recommended approach.

I have uploaded my questionnaire. please, which econometric model is suitable for regression analysis Jesse W. Campbell

@ Bikram Karki ,

I seethe following two ways:

(1) Prepare one 2x2 contingency table fromthe data and apply Chi-square test of association of attributes.

(2) Apply the Analysis of Variance Technique for testing the effects of the factors to be known.

In the questionnaire, intention to migrate is the main variable examined. It can be:

Binary variable (yes/no, i.e. whether or not you want to go abroad) → Logistic regression

Ranked scale (i.e. how likely is it to go abroad) → Ordinal regression

Continuous variable (e.g. intensity of intention to migrate on a 1-10 scale) → Linear regression

OR:

Multinomial regression:

If your dependent variable consists of several categories, for example, "Which country would you choose as a migration destination?" (Japan, Korea, Arab countries, etc.), then multinomial logit regression is appropriate because it models several categories of decisions at once.