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The Korean Society for Journalism & Communication Studies - Vol. 65 , No. 6

[ Article ]
Korean Journal of Journalism & Communication Studies - Vol. 65, No. 6, pp. 5-46
Abbreviation: KSJCS
ISSN: 2586-7369 (Online)
Print publication date 31 Dec 2021
Received 04 Oct 2021 Accepted 01 Dec 2021 Revised 07 Dec 2021
https://doi.org/10.20879/kjjcs.2021.65.6.001

어린이 미디어 이용에 관한 부모 중재효과 연구 : 성향점수매칭을 활용한 복합설문분석
박인서** ; 백영민***
**연세대 통계데이터사이언스학과 석사과정 (rinseo@yonsei.ac.kr)
***연세대 언론홍보영상학부 부교수 (ymbaek@yonsei.ac.kr)

Evaluating the Effectiveness of Parental Mediation on Children’s Media Use : Propensity Score Matching with Complex Survey Data
Rinseo Park** ; Young Min Baek***
**Graduate Student, Department of Statistics and Data Science, Yonsei University (rinseo@yonsei.ac.kr)
***Associate Professor, Department of Communication, Yonsei University, corresponding author (ymbaek@yonsei.ac.kr)

초록

현대사회에서 어린이는 끊임없이 전자·디지털 미디어에 노출되며, 성장기 어린이의 발달과 건강을 위해 부모의 미디어 중재 역할이 더없이 강조되고 있다. 이에 따라 정성적 연구방법이 주류를 이루었던 어린이 미디어 이용 연구에 대해서도 정량적 연구방법을 통한 부모 중재효과의 체계적 측정이 요구되고 있다. 그러나 정량적 연구방법을 택하는 경우, 조사설계나 데이터의 특성이 데이터 분석단계에서 충분히 반영되지 않는 문제가 빈번하게 발생한다. 특히 ‘만 10세 미만의 어린이’를 대상으로 한 기존 미디어 이용 연구들은 대부분 통계적 대표성을 담보할 수 없는 비과학적 표집 방식 혹은 표본 설계를 고려하지 않은 분석 기법을 사용하고 있다. 본 연구에서는 그에 대한 방법론적 대안으로써 성향점수매칭 및 복합설문분석 기법을 소개하고, 제안된 방법을 이용해 한국언론진흥재단 <2020 어린이 미디어 이용조사>에 나타난 부모 중재효과를 살펴보았다. 분석 결과, 부모의 ‘제한적 중재(restrictive mediation)’ 중 (1) 비밀번호를 설정하거나 (2) 콘텐츠를 제한하는 방식은 자녀의 미디어 이용시간을 줄이는 효과가 발견되었으나, 이용일수 측면에서는 감소효과가 뚜렷하지 않았다. (3) 미디어이용관리 프로그램 및 앱은 실제로 미디어 이용량 감소효과는 통계적으로 유의미하지 않았으나, 해당 중재방식을 선택한 부모들에게서는 자녀의 미디어 이용량을 많이 감소시킬수록 중재방식의 인지된 유용성이 증가하는 것이 확인되었다. 아울러 성향점수매칭 및 복합설문분석 결과를 비교함으로써 각각 공변량 및 표본설계를 고려하는 것이 타당한 추론으로 이어짐을 실증하였다. 결론 및 제언으로 방법론적 측면에 주목하여 연구의 함의 및 확장가능성을 논의하였다.

Abstract

Nowadays children are continually exposed to electronic and digital media, calling for parental mediation to secure children’s healthy growth and development. Despite the fact that previous studies on children’s media use have mostly relied on qualitative research examining a limited number of parents, there is a growing demand for systematical estimation of the effect of parental mediation. Nevertheless, for large-scale quantitative research, research design or data structure have not been properly discussed, nor have they even been considered, especially for children younger than 10 years. As a methodological alternative, our research introduces propensity score matching and complex survey analysis to study in depth complex surveys on children’s media use, and to properly address their methodological issues or concerns. Using the Children and Media in Korea 2020 survey data, gathered and maintained by the Korea Press Foundation, we estimated the treatment effect of three restrictive mediation strategies: using (1) passwords, (2) content-filters, and (3) management programs or applications. The results and discussion are two-fold. First, upon examination of average treatment effects for the treated (ATT) on hours and days of media use, passwords and content-filters significantly reduced children’s overall media use; however, the effect of parental mediation adopting management programs or applications was statistically insignificant. Second, we examined the individual treatment effect (ITE) on perceived usefulness of mediation. Among the three mediation strategies, only management programs or applications showed a significant positive correlation between the estimated individual treatment and the perceived effectiveness of mediation. It can be inferred that parents who use management programs or applications focus on both quantitative and qualitative aspects of mediation. This research finding has important policy implications, as it suggests that parents have different motivations or purposes for choosing a specific mediation strategy to monitor their children’s media use, as well as perceptions regarding the chosen strategy’s treatment effect or perceived usefulness. In the policy making process, therefore, effective mediation strategies for specific purposes or different types of family should be taken into consideration. Additionally, the methodological implications and suggestions for future studies were discussed. This study clearly shows that researchers conducting secondary analysis of public survey data gathered via multi-stage cluster sampling should take into account both the ‘confounding effect’ and ‘design effect.’ In particular, the design effect was closely related to the validity of statistical inferences using complex survey data, consideration of which made notable changes in our estimates of average and individual treatment effects. It is strongly suggested for future studies to employ complex survey analysis techniques to the Children and Media in Korea 2020 survey data or other similar data sources.


Keywordspropensity score matching (PSM), complex survey analysis (CSA), parental mediation, children’s media use
키워드: 복합설문분석, 성향점수매칭, 부모 중재, 어린이 미디어 이용

Acknowledgments

This study is based on the paper presented at the 6th Annual Korea Press Foundation Conference Using the KPF Media Statistics Data, November 27, 2021 (이 논문은 2021년 11월 27일 개최되었던 한국언론진흥재단 주최 ‘제6회 언론 통계 자료 활용 학술대회’에서 발표한 논문을 수정 및 보완한 것입니다).


References
1. Ahn, J. I. (2008). Types of Internet mediation and its relationship with precedent variables. Korean Journal of Broadcasting and Telecommunication Studies, 22(6), 230-266.
2. Baek, Y. M., Kim, E.-M., & Rhee, J. W. (2012). Validity of self-reported Internet use and its problem of over-reporting: Why people over-report their Internet use? Does people’s over-reporting distort inferential statistics of Internet use?. Korean Society For Journalism And Communication, 56(2), 124-144.
3. Baek, Y. M. & Park, R. (2021). Propensity score analysis using R: Causal inference based on the Rubin's Causal model. Seoul: Hannarae Publishing Co.
4. Bleakley, A., Jordan, A. B., & Hennessy, M. (2013). The relationship between parents’ and children’s television viewing. Pediatrics, 132, 364-371.
5. Boulos, R., Vikre, E. K., Oppenheimer, S., Chang, H., & Kanarek, R. B. (2012). ObseiTV: How television is influencing the obesity epidemic. Physiology & Behavior, 107, 146-153.
6. Brookhart, M. A., Schneeweiss, S., Rothman, K. J., Glynn, R. J., Avorn, J., & Stürmer, T. (2006). Variable selection for propensity score models. American journal of epidemiology, 163(12), 1149-1156.
7. Buijzen, M., & Valkenburg, P. M. (2005). Parental mediation of undesired advertising effects. Journal of Broadcasting & Electronic Media, 49, 153-165.
8. Chen, L. & Shi, J. (2019). Reducing harm from media: A meta-analysis of parental mediation. Journalism & Mass Communication Quarterly, 96(1), 173–193.
9. Cho, Y.-H. & Bae, J.-A. (2010). Study on parental mediation of children’s digital media use within the home environment. Media, Gender & Culture, 13, 37-74.
10. Choi, Y. J. & Lee, J. H. (2021). The effects of parental mediation and media literacy education on youth’s use of YouTube: Focusing on the moderating role of age. Journal of Broadcasting and Telecommunications Research, 141-171.
11. Choo, H., Sim, T., Liau, A. K. F., Gentile, D. A., & Khoo, A. (2015). Parental influences on pathological symptoms of video-gaming among children and adolescents: A prospective study. Journal of Child and Family Studies, 24, 1429-1441.
12. Desmond, R. J., Hirsch, B., Singer, D., & Singer, J. (1987). Gender differences, mediation, and disciplinary styles in children's responses to television. Sex Roles, 16(7-8), 375-389.
13. De Vreese, C. H. & Neijens, P. (2016). Measuring media exposure in a changing communications environment. Communication Methods and Measures, 10(2-3), 69-80.
14. DuGoff, E. H., Schuler, M., & Stuart, E. A. (2014). Generalizing observational study results: Applying propensity score methods to complex surveys. Health services research, 49(1), 284-303.
15. Ellis, G. F., Lumley, T., Zoltak, T., & Schneider, B. (2021). srvyr (R package version 1.0.1) [Computer software]. Retrieved from https://cran.r-project.org/web/packages/srvyr
16. Gu, X. S. & Rosenbaum, P. R. (1993). Comparison of multivariate matching methods: Structures, distances, and algorithms. Journal of Computational and Graphical Statistics, 2(4), 405-420.
17. Guo, S. & Fraser, M. W. (2014). Propensity score analysis: Statistical methods and applications. Thousands Oaks, CA: Sage.
18. Harrison, K., Liechty, J. M., & The STRONG Kids Program. (2012). US preschoolers’ media exposure and dietary habits: The primacy of television and the limits of parental mediation. Journal of Children and Media, 6, 18-36.
19. He, M., Irwin, J. D., Bouck, L. M. S., Tucker, P., & Pollett, G. L. (2005). Screen-viewing behaviors among preschoolers: Parents’ perceptions. American Journal of Preventive Medicine, 29(2), 120-125.
20. Heeringa, S. G., West, B. T., & Berglund, P. A. (2017). Applied survey data analysis. Boca Raton, FL: CRC Press.
21. Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007). Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political analysis, 15(3), 199-236.
22. Ho, D. E., Imai, K., King, G., Stuart, E. A., & Whitworth, A. J. (2021). MatchIt (R package version 4.2.0) [Computer software]. Retrieved from https://cran.r-project.org/web/packages/MatchIt
23. Kang, N. J. & Baek, Y. M. (2004). Sampling bias of Internet and mobile phone survey: Correctional sample balancing through propensity score weighting and iterative proportional fitting method. Journal of Communication Research, 41(2), 43-78.
24. Kim, S.-J., Lee, S. J., & Park, M. (2020). Children and Media in Korea 2020 (Report No. 2020-4). Retrieved 07/15/21 from https://www.kpf.or.kr/front/research/consumerDetail.do?seq=591511
25. Kim, Y. & Kim, S.-Y. (2018). Maternal perception of and attitudes towards digital device use by toddlers: Difference between electronic picture book and other digital devices. Journal of Children’s Literature and Education, 19(3), 291-314.
26. Kish, L. (1995). Methods for design effects. Journal of Official Statistics, 11(1), 55-77.
27. Kumpulainen, K. & Gillen, J. (2020). Young children’s digital literacy practices in the home: Past, present, and future research directions. In Erstad, O., Flewitt, R., Kummerling-Meibauer, B., & Pereira, I. S. P. (Eds.), The Routledge Handbook of Digital Literacies in Early Childhood (pp. 95-108), New York, NY: Routledge.
28. Künzel, S. R., Sekhon, J. S., Bickel, P. J., & Yu, B. (2019). Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences, 116(10), 4156-4165.
29. Kwon, Y.-J. & Lee, S. Y. (2013). Mothers’ perceptions of guidance, difficulties, and needs for support on their toddlers’ smart device usage. Journal of Children’s Media & Education, 12(3), 73-119.
30. Lamont, A., Lyons, M. D., Jaki, T., Stuart, E., Feaster, D. J., Tharmaratnam, K., ... & Van Horn, M. L. (2018). Identification of predicted individual treatment effects in randomized clinical trials. Statistical Methods in Medical Research, 27(1), 142-157.
31. Lee, B.-K. & Lee, Y.-J. (2014). The impact of television public campaign for preventing tuberculosis: An application of propensity score matching. Korean Journal of Journalism & Communication Studies, 58(4), 157-182.
32. Lee, S. J. & Jeon, S. H. (2010). Parental mediation of children’s Internet use: Effect on Internet addiction. Korean Journal of Broadcasting and Telecommunication Studies, 24(6), 289-322.
33. Lenis, D., Nguyen, T. Q., Dong, N., & Stuart, E. A. (2019). It’s all about balance: Propensity score matching in the context of complex survey data. Biostatistics, 20(1), 147-163.
34. Lumley, T. (2011). Complex surveys: A guide to analysis using R. Hoboken, NJ: John Wiley & Sons.
35. Lumley, T. (2021). survey (R package version 4.1-1) [Computer software]. Retrieved from https://cran.r-project.org/web/packages/survey
36. Mittal, M. (2011). Television viewing and perception of parental concern among urban Indian children. Management and Labour Studies, 36, 45-59.
37. Nikken, P. & Jansz, J. (2006). Parental mediation of children's videogame playing: A comparison of the reports by parents and children. Learning, Media and Technology, 31(2), 181-202.
38. Nikken, P. & Schols, M. (2015). How and why parents guide the media use of young children. Journal of Child and Family Studies, 24, 3423-3435.
39. Prior, M. (2009). Improving media effects research through better measurement of news exposure. The Journal of Politics, 71(3), 893-908.
40. Rosenbaum, P. R. (1991). A characterization of optimal designs for observational studies. Journal of the Royal Statistical Society: Series B (Methodological), 53(3), 597-610.
41. Rosenbaum, P. R. & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
42. Sakshaug, J. W. & West, B. T. (2014). Important considerations when analyzing health survey data collected using a complex sample design. American Journal of Public Health, 104(1), 15-16.
43. Schmidt, M. E., Haines, J., O'brien, A., McDonald, J., Price, S., Sherry, B., & Taveras, E. M. (2012). Systematic review of effective strategies for reducing screen time among young children. Obesity, 20(7), 1338-1354.
44. Shah, B. R., Laupacis, A., Hux, J. E., & Austin, P. C. (2005). Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. Journal of Clinical Epidemiology, 58(6), 550-559.
45. Schutt, R. K. (2018). Investigating the social world: The process and practice of research. Thousands Oaks, CA: Sage.
46. Seo, H. & Lee, C. S. (2017). Emotion matters: What happens between young children and parents in a touchscreen world. International Journal of Communication, 11, 561-580.
47. Strasburger, V. C., Mulligan, D. A., Altmann, T. R., Brown, A., Christakis, D. A., Clarke-Pearson, K., ... & Noland, V. L. (2011). Policy statement-Children, adolescents, obesity, and the media. Pediatrics, 128(1), 201-208.
48. Stürmer, T., Joshi, M., Glynn, R. J., Avorn, J., Rothman, K. J., & Schneeweiss, S. (2006). A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. Journal of Clinical Epidemiology, 59(5), 437-e1-437-e.24.
49. Theunissen, N. M., Vogels, T., Koopman, H. M., Verrips, G. H., Zwinderman, K. A. H., Verloove- Vanhorick, S. P. & Wit, J. M. (1998). The proxy problem: Child report versus parent report in health-related quality of life research. Quality of Life Research, 7(5), 387-397.
50. Van den Bulck, J. & Van den Bergh, B. (2000). The influence of perceived parental guidance patterns on children’s media use: Gender differences and media displacement. Journal of Broadcasting & Electronic Media, 44, 329-348.
51. Vandewater, E. A., Park, S. E., Huang, X., & Wartella, E. A. (2005). “No-You can’t watch that”: Parental rules and young children’s media use. American Behavioral Scientist, 48, 608-623.
52. Ward, B. W. (2018). Peer reviewed: Analytic errors in analysis of public health survey data are avoidable. Preventing Chronic Disease, 15, E43.
53. Warren, R. (2003). Parental mediation of preschool children’s television viewing. Journal of Broadcasting & Electronic Media, 47, 394-417.
54. Warren, R., & Bluma, A. (2002). Parental mediation of children’s Internet use: The influence of established media. Communication Research Reports, 19, 8-17.
55. Waters, E., Stewart-Brown, S., & Fitzpatrick, R. (2003). Agreement between adolescent self-report and parent reports of health and well-being: Results of an epidemiological study. Child: Care, Health and Development, 29(6), 501-509.
56. Yang, K., Seo, S. H., & Ok, H. (2021). Parents’ perceptions and guidance behavior on media use of children aged 3-9 years old: Focusing on differences in parents’ media education experience. Journal of Reading Research, 59, 107-134.
57. Zhao, Y., & Phillips, B. M. (2013). Parental influence on children during educational television viewing in immigrant families. Infant and Child Development, 22, 401-421.

부록Ⅰ. 국내 참고문헌
1. 강남준·백영민 (2004). 대안적 여론조사의 표본편파(Sample Bias) 문제점과 가중치를 사용한 보정방법: 성향점수 가중과 반복비례 가중을 중심으로. <언론정보연구>, 41권 2호, 43-78.
2. 권연정·이승연 (2013). 만 2세반 영아를 둔 어머니들의 스마트기기 이용지도 양상과 어려움 및 지원요구. <어린이미디어연구>, 12권 3호, 73-119.
3. 김유미·김소영 (2018). 영아기 디지털 기기 사용에 대한 어머니의 인식 및 태도: 전자책 전용기기와 일반 디지털 기기에 대한 차이를 중심으로. <어린이문학교육연구>, 19권 3호, 291-314.
4. 김수지·이숙정·박민규 (2020). 2020 어린이 미디어 이용조사. (한국언론진흥재단 연구보고서, 조사분석 2020-4). Retrieved 07/15/21 from https://www.kpf.or.kr/front/research/consumerDetail.do?seq=591511
5. 백영민·박인서 (2021). <R기반 성향점수분석: 루빈 인과모형 기반 인과추론>. 서울: 한나래.
6. 백영민·김은미·이준웅 (2012). 자기응답방식에서 나타나는 인터넷이용시간 과도응답과 그 원인: 인터넷 연구의 가설검증시 함의를 중심으로. <한국언론학보>. 56권 2호, 124-144.
7. 안정임 (2008). 인터넷 이용 중재유형과 선행요인에 관한 연구. <한국방송학보>, 22권 6호, 230-266.
8. 양길석·서수현·옥현진 (2021). 만 3-9세 아동의 미디어 이용에 대한 부모의 인식 및 지도 행위 연구: 부모의 미디어교육 경험에 따른 차이를 중심으로. <독서연구>, 59권, 107-134.
9. 이병관·이윤재 (2014). 결핵 예방을 위한 텔레비전 공익 캠페인의 효과. <한국언론학보>, 58권 4호, 157-182.
10. 이숙정·전소현 (2010). 인터넷 중독에 대한 부모 중재 효과 연구. <한국방송학보>, 24권 6호, 289-322.
11. 조연하·배진아 (2010). 디지털 미디어 환경에서의 가정 내 미디어 이용중재 연구. <미디어, 젠더 & 문화>, 13권, 37-74.
12. 최윤정·이종혁 (2021). 부모 중재와 미디어 교육이 청소년 유튜브 이용에 미치는 영향: 학년에 따른 효과의 차이 분석. <방송통신연구>, 141-171.