Multinomial Logistic Regression (MLR) Analysis: Predictive Model of Risk Factors for the Incidence of Period Pain (Primary Dysmenorrhea) in Adolescent Girls: Multinomial Logistic Regression (MLR) Analysis: Predictive Model of Risk Factors for the Incidence of Period Pain (Primary Dysmenorrhea) in Adolescent Girls - Yulis Indriyani; Ardiana Priharwanti; Irine Dwitasari Wulandari; Febi Dwirahmadi (Universitas Nahdlatul Ulama Surabaya, 2026)
World epidemiological data states that primary dysmenorrhea occurs most frequently in women aged 17-24 years. Multinomial Logistic Regression (MLR) modeling is suitable to produce predictive models with the dependent variable (menstrual pain) consisting of four categories. The study aimed to analyze the predictive model of risk factors for menstrual pain among adolescent girls in Pekalongan City. The research design was an analytic survey with a cross sectional design. Samples totaled 100 with multistage random sampling at four school sites. The questionnaires used included NRS, PSS-10 and anthropometric measurements. Of the fourteen variables studied, three variables, namely female relatives who have a history of menstrual pain, the amount of sleep time and exercise habits, proved to significantly affect the incidence of menstrual pain in adolescent girls (p value <0.05). The Multinomial Logistic Regression model produced three logit equations. The Nagelkerke model showed that all risk factors studied (14 variables) simultaneously influenced the incidence of menstrual pain by 61.2% while the other 39.8% was influenced by variables not studied. The accuracy of the classification table with a prediction truth rate of 80.9% explained that female relatives who have a history of menstrual pain are 98.319 times more likely to have moderate menstrual pain compared to not having female relatives who have a history of menstrual pain. The predictive modeling has good accuracy.