생태순간평가를 이용한 성장모형개발: 노년 활력 지수를 활용하여
Development of Growth Model Using Ecological Momentary Assessment: Based on Senior Vitality Quotient
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초록
본 연구는 EMA 연구방법을 소개하고 연구에 적용하는 방법을 보여주는 것을 목적으로 한다. 노년 활력 증강을 측정하는 지속개발을 위한 사전연구로, 노인의 종단자료를 이용하여 성장모형을 성립하고 분석방법으로써 다층모형의 적합성을 설명하였다. 절편만 존재하는 모형을 통해 종속변수의 총분산을 확인한 결과, 전체분산 중 약 47%는 개인 차이에 의해 발생하였고 53%는 측정 시점 차이에 의해 발생하였다. 두 번째는 일차항을 고정효과로 추가한 모형으로, 시간을 집단평균 중심화하여 노년 활력 지수에 미치는 영향을 개인마다 다르게 확인하였다. 그 결과, 시간에 대한 효과는 유의하지 않았다. 이는 본 연구가 처치를 투입하지 않은 사전연구이기 때문이며, EMA 자료 수집 과정에서 외부개입 없이 변화가 크지 않은 자료를 보임을 의미한다. 세 번째는 성별을 독립변수로 추가한 모형으로, 변화는 시간과 성별 모두에서 유의하지 않은 결과를 보였다. 마지막으로 각 모형에 대한 PRD를 비교해본 결과, 성별변수를 투입하지 않는 모형이 더 효과적으로 자료에 적합함을 보였다. 이는 EMA 연구에서 응답자의 시간과 맥락을 고려하여 개인의 특성을 측정할 수 있는 다층모형을 이용해야 함을 시사한다.
Abstract
This study was to introduce ecological momentary assessment and show how to apply it to real-world research. As preliminary study for sustainable development, the result explained growth model using senior’s longitudinal data and suitability of multi-level model in EMA data with regression analysis. The total variance of dependent variable was determined through a base model with only intercept and approximately 47% of total variance was caused by individual differences and 53% by time point differences. Second model was used to verified that each individual has a different effect on the senior vitality and effect on time was not significant. This is because it is the result of a preliminary stage where treatment is not involved and there is no significant change in process of collecting EMA data without external intervention. Third model that add gender as an independent variable showed significant change in both time and gender. Finally compared the PRD for each model and found models that without gender variables fit the data more effectively. This suggests that studies dealing with longitudinal data such as EMA data should adopt multi-level model that can measure individual characteristics, taking into account respondents’ time and context.
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