Modeling by the categorical regression analysis of the factors affecting the participation rate

Modeling By the Categorical Regression Analysis of The Factors Affecting The Participation Rate Aim: Independent variable saffecting job involvement; Migration, gender, age, household size, salary, education, working status, sector studied, inflation and labor force indice. To see the predicted variance of the predicted variance on the dependent variable with the optimal scaling of the specified variables, to see the partial contributions of the variables and to test their statistical significance. Material and Method: Analyzes, TUIK (Turkish Statistical Institute) Categorical regression (CATREG) was applied to 2463 households data over the last six months of 2016 data of household labor force data. The analysis was done in the IBM SPSS 20 program. Findings: When the data were analyzed by scaling appropriately, theR^2 value was found to be low even though the model was significant. It was found that when the variables were scale done by one within the certain constraint by the optimal scaling, the model was significant and the R^2value increased significantly. Conclusion: With optimal scaling, changes in dependent variable variance of independent variables were observed. R^2value after the optimal model obtained is lowandmeaningfulpredictedvariablesaremeaninglessbecausetheconsistency of the analyzed data cannot be obtained. Keywords: Labor Force Participation, Categorical Regression, Household Size, Unemployment Rate and Inflation.