階層線性模式於追蹤研究之應用－－以子宮切除婦女之術後初期症狀困擾為例

Applying the Hierarchical Linear Model in Longitudinal Studies: An Example of Symptom Distress in Women Who Had Undergone a Hysterectomy

階層線性模式為目前處理多層次資料時最佳統計方法之一，而追蹤研究資料則屬多層次資料結構，欲瞭解同一群受試者在不同時間點重複測量或追蹤某項介入方案對其改善成效的影響，則可以採用此一統計分析模式。本研究欲介紹階層線性模式於追蹤研究，並輔以子宮切除婦女在術後期間之症狀困擾實例來說明此方法之應用。本研究採類實驗設計探討子宮切除婦女於術後初期之生理及心理症狀困擾間關係，觀察測量受試婦女三次的症狀困擾數字量表問卷，並透過階層線性模式的成長模型，進行追蹤資料的統計分析。結果發現，階層線性模式可以捕捉受試婦女在追蹤研究裡的變化軌跡，且可以驗證受試婦女的基本特徵對其變化軌跡的影響；此外，實驗組在生理或心理疾病困擾上有顯著差異，且生理與心理疾病困擾在追蹤資料上互有影響。本研究透過子宮切除婦女術後初期症狀困擾之追蹤分析來說明階層線性模式的方法與運用技巧，期望藉由此篇文章來推廣階層線性模式在追蹤研究上的應用。

The purpose of this study was to introduce the Hierarchical Linear Model (HLM) and apply it to the topic of symptom distress in women who had undergone a hysterectomy. HLM was developed to analyze multilevel data and included a longitudinal study, which focused in particular on unbalanced design. A quasi-experimental design was conducted. Data on symptom distress in women who had undergone a hysterectomy (experimental group) and those who had not (control group) were collected over a six-week period and analyzed using HLM. Findings indicated that the experimental group had a quadratic trajectory in physical distress changes and a negative linear trend in psychological distress, both of which differed significantly from the control group. Additionally, physical and psychological distress influenced one another in three measurement variables, and physical distress in the experimental group actually improved over the six week period. Using HLM was able to estimate the different trajectories for each subject in the experimental group. This study shows that HLM can be applied effectively in longitudinal studies.