護理追蹤性研究之統計分析模式發展

Modern Statistical Models for the Longitudinal Study in Nursing Research

10.6530/YYN/2015.5.13

黃惠娟(Hui-Chuan Huang)；林寬佳(Kuan-Chia Lin)

追蹤資料 ； 護理研究 ； 時間變異 ； 長期資料分析模式 ； longitudinal study ； nursing research ； time-varying ； longitudinal data analysis model

源遠護理

9卷4期（2015 / 12 / 01）

12 - 23

繁體中文

追蹤（長期）資料統計分析在護理研究中扮演十分重要的角色，研究的設計中若重覆觀察的時間點愈多，則研究變項（例如：介入措施、或某項暴露因素）與時間之交互關係就更為重要，尤其當各研究變項具有時間變異的特質時，則需要進一步的考量。本文提出了現代長期資料分析二大特色，第一特色是可有三種分析視角，第二特色則是具有四種“個案—時間＂效應，將有助於護理研究人員於面臨不同研究設計時加以應用之。就單純以三種分析視角而言（基線點長期分析角度、時間相依長期評估角度、長期累積評估角度），不同之替代模式可回應不同之研究問題與可能之因果關係，再以四種個案—時間效應而言，應該使用哪一項評估法端視研究者之主要研究問題意識與需求，本文中作者透過兩階段綜整方法呈現各長期資料分析模式之選擇與運用時機藍圖，以作為護理長期追蹤資料分析之參考。

This article offers two perspectives of modern longitudinal data analysis, their strengths and key issues that nurse researchers should know before choosing this method. Longitudinal study data analysis plays an important role in nursing study. This research design provides multiple time points, interactions between follow-ups and study variables (e.g. intervention or exposure factor) when time varying explanatory variable characteristics requires additional considerations. The first point is to introduce three analytic angles: the baseline tracking model, time-dependent model and cumulative model that provides different analytical outcomes for causal-effect research. The second point is to integrate four ＂subject-time＂ effects which introduce different alternative longitudinal approaches to incorporate time-varying variables and their cumulative effects. The author recommends nurse researchers to incorporate the two-stage alternative methods into the longitudinal data analyses when the study involves explanatory variables with time variations or to measure cumulative effects on specific variables.

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