Visualized Pattern-Based Hypothesis Testing on Exhaustion, Resilience, Sleep Quality, and Sleep Hygiene in Middle-Aged Women Transitioning into Menopause or Postmenopause

Abstract

Exploratory data analysis involves observing data in graphical formats before making any assumptions. If interesting relationships or patterns among variables are identified, hypotheses are developed for further testing. This study aimed to identify significant differences in the levels of exhaustion, resilience, sleep quality, and sleep hygiene according to the personal characteristics of middle-Aged women transitioning into menopause or postmenopause through exploratory data analysis. A total of 200 women aged 44 to 55 years were recruited online in August 2023. Data were collected using valid instruments and analyzed through data visualization, pattern identification in the visualized data, and hypothesis establishment based on the visualized patterns. Hypotheses were tested through the independent-samples t test, analysis of variance, and the Kruskal-Wallis test. A total of 11 patterns and corresponding hypotheses were identified. According to the statistically supported pattern-based hypotheses, middle-Aged women who were in their perimenopausal period perceived themselves as unhealthy, had professional occupations, and had the highest level of exhaustion and the lowest levels of resilience, sleep quality, and sleep hygiene. This study demonstrated that data visualization is an efficient way to explore relationships or patterns between data. Data visualization should be considered an informatics solution that can provide insight in the field of healthcare. © Lippincott Williams & Wilkins.