F Farshbaf Manei Sefat , A Abolghasemi, U Barahmand, N Hajloo,
Volume 13, Issue 3 (Vol.13, No.3, Atumn 2017)
Abstract
Background and Objectives: Menarche is an important issue for teenagers and dysmenorrhea is a common problem in adolescence. This study aimed to determine the menstruation pattern and prevalence of dysmenorrhea in second high school students in the city of Ardabil.
Methods: This research was a cross-sectional study. The study population included all second high school girls in districts one and two of Ardabil in 1394. A sample of 1,600 girls was selected by a two-stage cluster sampling method from 5 schools in district one and 7 schools in district two. A researcher- made questionnaire and a visual analogue scale were used for data collection.
Results: According to the results, the mean age at the first menstruation was 12.88 years. The prevalence of dysmenorrhea was 91.9% (95% CI: %90 - %92). Dysmenorrhea was severe in 25% of the girls. The pattern of menstrual characteristics in students showed that 61.2%, 88%, and 93.2% had regular menstrual cycles, normal menstrual bleeding days, and normal duration of the menstrual cycle, respectively. In this research, 84.8% of the girls stated that their activities were affected by menstruation and 29.7% of the girls were absent due to menstrual pain.
Conclusion: The prevalence of dysmenorrhea is high. It is a common problem in adolescent girls which affects their activities and school attendance. Therefore, it is necessary to educate students regarding menstrual hygiene, and menarche pain control methods.
Parisa Amjadi Zin Hajloo, Mohammad Heidari,
Volume 21, Issue 4 (Vol.21, No.4, Winter 2026)
Abstract
Missing data is a common and unavoidable challenge in medical and epidemiological research, often leading to biased estimates, reduced statistical power, and misleading interpretations when not properly addressed. Despite its importance, accessible and practical educational resources on this topic remain limited in Persian. This educational article provides a clear and structured overview of the fundamental concepts of missing data, including definitions, common patterns (univariate and multivariate), and the three major mechanisms of missingness: MCAR, MAR, and MNAR. A range of widely used approaches for handling missing data is summarized, from basic methods such as case deletion and simple imputation to more advanced techniques including multiple imputation and likelihood-based procedures (EM and MLE). Practical examples and visual illustrations are incorporated to facilitate conceptual understanding. The ultimate goal of this article is to provide a practical framework for researchers and students, enabling them to select the appropriate approach for dealing with missing data in the design and analysis of their research and to prevent analytical errors.
