빅데이터 분석은 데이터 저장소에 저장된 대용량 데이터 속에서 의미 있는 새로운 상관관계, 패턴, 추세를 발견하여 새로운 가치를 창출하는 과정이다. 메타분석은 유의성 검정이라 하기보다는 예측변수가 종속변수에 미치는 영향의 크기가 중요한 이슈이다. 따라서 본 연구는 2000년-2016년 사이 콜라겐 섭취가 피부에 미치는 영향에 관한 관련 주제로 출판된 연구 6개 논문 236개 샘플을 대상으로 하였다. 연구결과를 요약하면 다음과 같다. 첫째, 피지 사전사후 경로에서 가장 큰 효과크기 (r = .412)를 가지는 것으로 나타났다. 따라서 콜라겐 섭취 중재는 약 17 (%) 설명력을 가지는 것으로 나타났다. 다음으로, 수분 사전사후 경로에서는 효과크기 (r = .318)를 가지는 것으로 나타났다. 분석결과를 바탕으로 연구의 한계와 시사점을 제시하였다.
빅데이터 분석은 데이터 저장소에 저장된 대용량 데이터 속에서 의미 있는 새로운 상관관계, 패턴, 추세를 발견하여 새로운 가치를 창출하는 과정이다. 메타분석은 유의성 검정이라 하기보다는 예측변수가 종속변수에 미치는 영향의 크기가 중요한 이슈이다. 따라서 본 연구는 2000년-2016년 사이 콜라겐 섭취가 피부에 미치는 영향에 관한 관련 주제로 출판된 연구 6개 논문 236개 샘플을 대상으로 하였다. 연구결과를 요약하면 다음과 같다. 첫째, 피지 사전사후 경로에서 가장 큰 효과크기 (r = .412)를 가지는 것으로 나타났다. 따라서 콜라겐 섭취 중재는 약 17 (%) 설명력을 가지는 것으로 나타났다. 다음으로, 수분 사전사후 경로에서는 효과크기 (r = .318)를 가지는 것으로 나타났다. 분석결과를 바탕으로 연구의 한계와 시사점을 제시하였다.
Big data analysis, in the large amount of data stored as the data warehouse which it refers the process of discovering meaningful new correlations, patterns, trends and creating new values. The important issue of a meta-analysis is not the significance test, the effect size of the predictor variable...
Big data analysis, in the large amount of data stored as the data warehouse which it refers the process of discovering meaningful new correlations, patterns, trends and creating new values. The important issue of a meta-analysis is not the significance test, the effect size of the predictor variable on the criterion variable. We reviewed a total of 236 samples among 6 studies published on the topic related Collagen intake on skin between 2000 and 2016 in Korea. The results of the study are summarized as follows. First, we concluded that the path between before and after of Sebum (SB) had the largest effect size of (r = .416) Therefore, the effect of the Collagen intake intervention showed an explanatory power of 17 (%) about. Next, the path between before and after of Moisture (MS) had the higher the effect size of (r = .318). Thus, we present the theoretical and practical implications of these results.
Big data analysis, in the large amount of data stored as the data warehouse which it refers the process of discovering meaningful new correlations, patterns, trends and creating new values. The important issue of a meta-analysis is not the significance test, the effect size of the predictor variable on the criterion variable. We reviewed a total of 236 samples among 6 studies published on the topic related Collagen intake on skin between 2000 and 2016 in Korea. The results of the study are summarized as follows. First, we concluded that the path between before and after of Sebum (SB) had the largest effect size of (r = .416) Therefore, the effect of the Collagen intake intervention showed an explanatory power of 17 (%) about. Next, the path between before and after of Moisture (MS) had the higher the effect size of (r = .318). Thus, we present the theoretical and practical implications of these results.
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문제 정의
The conceptual model is shown in Figure 1. This study will report meaningful effect of Collagen intake intervention for criterion variables that affect pre and post Collagen intake studies, on the basis of the results of a meta-analysis. The papers included in this study meta-analysis were identified using the keywords “Collagen”, “Collagen intake”, specifying on RISS, DBpia, eArticle, and Kyobo Scholar in database articles of social science.
제안 방법
This study reanalyzed the research studies with the purpose of classifying the results of the previous studies which analyzed causal relationships between before and after as Wrinkles (WK), Melanin index (MI), Erythema index (EI), Viscoelastic (VC), Moisture (MS), and Sebum (SB) in the designed the conceptual model. Based on information from these literature reviews, paths presented in the conceptual model in this study are converted to values of average effect size by using the random-effects model and the fixed-effect model, as the effect size (r) shown Table 3.
The following Table 1 lists authors, terms, goal and samples used as the raw data for the meta-analysis. By using collected raw data, the calculated number of standard deviations and samples were coded that verified studies influencing before and after in factors of the conceptual model. Therefore, the mean, standard deviation, and the number of samples calculated the effect size using Equation 1.
This study reanalyzed the research studies with the purpose of classifying the results of the previous studies which analyzed causal relationships between before and after as Wrinkles (WK), Melanin index (MI), Erythema index (EI), Viscoelastic (VC), Moisture (MS), and Sebum (SB) in the designed the conceptual model. Based on information from these literature reviews, paths presented in the conceptual model in this study are converted to values of average effect size by using the random-effects model and the fixed-effect model, as the effect size (r) shown Table 3.
대상 데이터
The papers included in this study meta-analysis were identified using the keywords “Collagen”, “Collagen intake”, specifying on RISS, DBpia, eArticle, and Kyobo Scholar in database articles of social science.
성능/효과
Consequently, the effect of soybean intervention listed an explanatory power of 4, 2 (%) about. In conclusion, the result of the study is significant in that we can estimate effect sizes on the basis of path constructs.
후속연구
Finally, the study on “The Effect of Collagen and Enzyme Food Intake on 40’s~60’s Women’s Facial Skin” [9], to evaluate how collagen and enzyme food intake affect 40’s~60’s women’s facial skin aging, found that enzyme foods are more important than cosmetics for the beauty of skin of middle-aged women. Thus, To define the physiological correlation between collagen and enzyme food and skin aging, the further research is required.
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