在資通訊快速轉換的社會趨勢下，資料量不斷加倍成長，龐大的資料帶來了更多值得研究的隱藏資訊。次級資料分析便是從與主題相關的文獻或過往的資料中整理蒐集完成後進行的分析。次級資料分析是透過既存的大型研究資料庫 (例如：中央研究院社會變遷調查) 或政府統計資料 (例如：工商普查) 來對該研究進行深化實證探究的方法。而資料視覺化是透過圖像化工具 (例如：各種統計圖表、立體模型等) 從複雜浩大的資料庫中篩選出合適且可用的數據資料，經由轉化或介接分析後，進而成為簡易閱讀、容易理解的可靠訊息，資料視覺化可以快速提供即時的方式理解資料。此外，政府開放資料為近年研究的熱門議題，我國政府也將其資料列為政策一大重點，並要求其所屬機關開放資料給予社會公眾使用。以教育部體育署而言，目前有將近 20 個資料庫，因為委辦或承辦單位不同， 目前較缺少整合或介接分析。因此，本研究目的是從非反應式研究角度，探究不同資料庫整合後，視覺化分析呈現的樣態。本研究以體育運動資訊相關之運動現況調查、運動消費支出、體適能等資料庫為例，探究不同資料庫進行視覺化分析的樣態。本研究發現任何的資料分析及視覺化之前，首先應將資料再整理與計算， 有乾淨、完整和有意義的資料，才能呈現真實的現況。其次，根據研究目的與設計，將資料庫進行介接或合併，例如：歷年規律運動人口與縣市對應之趨勢；各縣市運動現況與消費支出之關聯。最後，從資料可呈現性的角度，以多元圖像方式呈現其資料視覺化分析，亦可展現出更有效果的資料訊息傳之遞能力，但是， 從學術研究角度仍須強化資料的可檢驗性與價值性，並進一步進行量化的技術分析。

Due to the trend of information conversion in nowadays society, data volume unceasingly grows and multiplies. In the meanwhile, mass data brings more hidden information that is valuable to investigate. Secondary data analysis refers to the method of collecting major-related literature and tracing pass data to conduct in-depth analysis, by utilizing existing extensive research database (e.g., Academia Sinica Taiwan Social Change Survey) or official statistics performed by governmental agencies (e.g., Industry, Commerce and Service Census), to deepen the empirical research. Data Visualization uses image tools (e.g., Statistic chart, Three-dimensional model, etc.) to filter proper and useful data from massive complex databases, which offers a fast and immediate way of understanding the data by converting, interfacing, analyzing these data into easily comprehensible, reliable information. Since open government data becomes a popular subject in recent years, the Taiwan government places it as one of its significant policies, also demands agencies to allow its data to be used by the public. According to Sports Administration, Ministry of Education, there are around twenty databases. Due to the difference in commission units and organizations, there's a lack of data integration and data interface. Therefore, our research probe into the integration of different databases and the style of data visualization analysis via the perspective of non-reactive research.
This research uses related sports information databases of the current investigation, Sports consuming expenditure, Physical fitness, to present various styles of data visualization by probing into different databases. First, we need to refresh the data by calculation, to provide clean, complete, meaningful data to display the actual current state before presenting any data analysis and visualization. Second, we integrate and combine databases, according to research goal and design. (e.g., the trend of the regular exercising population relative to each cosmopolitan; the relation of Sports consuming expenditure to each cosmopolitan.) Finally, we use various image tools to present data visualize analytics by possible presentable perspectives, which also performs more effective information delivery. However, we still need to strengthen the liability, inspection, and value to conduct further quantitative technical analysis.

江詠宸、魏正、陳秀惠、徐振德 (2017)。我國運動健身教練證照現況分析。運動管理，38，47-64。
吳作樂、吳秉翰 (2018)。圖解統計與大數據 (第2版)。臺北市：五南。
施登堯 (2017)。建構優質學校體育學習環境>談提升體育教師專業養成與持續發展的政策介入。學校體育，160，4-6。
倪瑛蓮、陳龍弘、張仁和 (2017)。臺灣地區青少年身體質量指數、肥胖盛行率與社經地位的關聯：2007-2012體適能資料庫追蹤。體育學報，50(s)，47-60.
許懷中、黃致豪 (2017)。以大數據分析球員技術面表現、對戰組合與中華職棒歷年票房之相關性。體育學報，50(s)，79-90.
陳麒文 (2011)。智慧型手機之Android作業系統之混搭應用 (未出版碩士論文)。中原大學，桃園市。
蔡明春、鄭青展，(2006)。銀髮族生活品質之探討－以新竹市為例。中華管理學報，7(1)，111-123。
蔡鋒樺、楊燦、莊德豐、李昭憲(2011)。銀髮族規律運動習慣對睡眠品質影響之研究。亞洲高齡全人健康及產業發展期刊，1，28-37。
鄭志富、洪聰敏、張少熙 (2016)。別剝奪學生上體育課！台師大研究：四肢發達的人，頭腦更不簡單。取自https://www.thenewslens.com/article/39872
鄭志富、洪聰敏、張少熙 (2016)。我國中學生體適能與學業成就之關係研究。臺灣師大邁向頂尖大學計畫研究團隊發表學術成果，國立臺灣師範大學行政大樓會議室。
謝邦昌 (2017)。大數據和雲計算成為橋梁從此人腦接計算機的時代來臨。中國統計，9，16-17。
謝邦昌、斯介生 (2016)。大數據分析中軌跡數據挖掘的現狀與挑戰。中國統計，8，13-15。
簡全亮、康正男 (2016)。開放水域戲水和游泳活動風險因子與其因應方式。學校體育，157，34-43。
羅晉、楊東謀、王慧茹、項靖 (2014)。政府開放資料的策略與挑戰—使用者觀點的分析。電子商務研究，12(3)，283-300。
關秉寅 (2008)。非反應式研究和次級資料分析。取自www3.nccu.edu.tw/~soci1005/nonreactive%20research.ppt.
Allen, C. (1992). Restructuring an authoritarian state: ‘democratic renewal’ in Benin. Review of African Political Economy, 19(54), 42-58.
Bennett, G. G., Mcneill, L. H., Wolin, K. Y., Duncan, D. T., Puleo, E., & Emmons, K. M. (2007). Safe To Walk? Neighborhood Safety and Physical Activity Among Public Housing Residents. PLoS Medicine, 4(10), 1599-1607.
Bertot, J. C., Jaeger, P. T., & Grimes, J. M. (2010). Using ICTs to create a culture of transparency: E-government and social media as openness and anti-corruption tools for societies. Government Information Quarterly, 27(3), 264-271.
Borzacchiello, M. T., & Craglia, M. (2012). The impact on innovation of open access to spatial environmental information: A research strategy. International Journal of Technology Management, 60(1/2), 114.
Borzacchiello, M. T., & Craglia, M. (2013). Estimating benefits of Spatial Data Infrastructures: A case study on e-Cadastres. Computers, Environment and Urban Systems, 41, 276-288.
Brodersen, N. H., Steptoe, A., Williamson, S., & Wardle, J. (2005). Sociodemographic, developmental, environmental, and psychological correlates of physical activity and sedentary behavior at age 11 to 12. Annals of Behavioral Medicine, 29(1), 2-11.
Butterworth, M. L. (2014). Nate Silver and Campaign 2012: Sport, the Statistical Frame, and the Rhetoric of Electoral Forecasting. Journal of Communication, 64(5), 895-914.
Church, R. M. (2002). The Effective Use of Secondary Data. Learning and Motivation, 33(1), 32-45.
Cunningham, G. O., & Michael, Y. L. (2004). Concepts Guiding the Study of the Impact of the Built Environment on Physical Activity for Older Adults: A Review of the Literature. American Journal of Health Promotion, 18(6), 435-443.
Conway, D. (2010). The Data Science Venn Diagram. Retrieved from http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
Giles-Corti, B., & Donovan, R. J. (2002). The relative influence of individual, social and physical environment determinants of physical activity. Social Science & Medicine, 54(12), 1793-1812.
Jaeger, P. T., & Bertot, J. C. (2010). Designing, Implementing, and Evaluating User-centered and Citizen-centered E-government. International Journal of Electronic Government Research, 6(2), 1-17.
Jetzek, T., Avital, M., & Andersen, N. B. (2013). The Strategic Value of Open Government Data. Reykjavík, Iceland: Nordic Academy of Management.
Jetzek, T., Avital, M., & Bjorn-Andersen, N. (2014). Data-Driven Innovation through Open Government Data. Journal of Theoretical and Applied Electronic Commerce Research, 9(2), 15-16.
Johnston, M. P. (2017). Secondary Data Analysis: A Method of which the Time Has Come. Qualitative And Quantitative Methods In Libraries, 3(3), 619-626.
Lankow, J., Ritchie, J., & Crooks, R. (2012). Infographics: The power of visual storytelling. Hoboken, NJ: Wiley.
Loucaides, C. A. (2004). Differences in physical activity levels between urban and rural school children in Cyprus. Health Education Research, 19(2), 138-147.
Macfarlane, D. J., & Thomas, G. N. (2009). Exercise and diet in weight management: Updating what works. British Journal of Sports Medicine, 44(16), 1197-1201.
Markowetz, A., Błaszkiewicz, K., Montag, C., Switala, C., & Schlaepfer, T. E. (2014). Psycho-Informatics: Big Data shaping modern psychometrics. Medical Hypotheses, 82(4), 405-411.
Mota, J., Almeida, M., Santos, P., & Ribeiro, J. C. (2005). Perceived Neighborhood Environments and physical activity in adolescents. Preventive Medicine, 41(5-6), 834-836.
O'Donoghue, P. (2009). Research Methods for Sports Performance Analysis. Abingdon-on-Thames, England: Routledge.
O’Halloran, K. L., Tan, S., Pham, D., Bateman, J., & Moere, A. V. (2016). A Digital Mixed Methods Research Design: Integrating Multimodal Analysis With Data Mining and Information Visualization for Big Data Analytics. Journal of Mixed Methods Research, 12(1), 11-30.
Plunkett Research, Ltd. (2016). Complete Guide to the Consulting Industry From Plunkett Research 2016. Retrieved from https://www.plunkettresearch.com/complete-guide-to-the-consulting-industry-from-plunkett-research-2016/
Sallis, J. F., Johnson, M. F., Calfas, K. J., Caparosa, S., & Nichols, J. F. (1997). Assessing Perceived Physical Environmental Variables that May Influence Physical Activity. Research Quarterly for Exercise and Sport, 68(4), 345-351.
Sallis, J. F., Linton, L., & Kraft, M. K. (2005). The first Active Living Research Conference. American Journal of Preventive Medicine, 28(2), 93-95.
Silver, N. (2015). The signal and the noise: Why so many predictions fail-- but some don't. New York, NY: Penguin Books.
Thompson, A. M., Rehman, L. A., & Humbert, M. L. (2005). Factors Influencing the Physically Active Leisure of Children and Youth: A Qualitative Study. Leisure Sciences, 27(5), 421-438.
Timperio, A. (2004). Perceptions about the local neighborhood and walking and cycling among children. Preventive Medicine, 38(1), 39-47.
Troped, P. J., Saunders, R. P., Pate, R. R., Reininger, B., Ureda, J. R., & Thompson, S. J. (2001). Associations between Self-Reported and Objective Physical Environmental Factors and Use of a Community Rail-Trail. Preventive Medicine, 32(2), 191-200.
Wen, C. P., Wai, J. P., Tsai, M. K., Yang, Y. C., Cheng, T. Y., Lee, M., . . . Wu, X. (2011). Minimum amount of physical activity for reduced mortality and extended life expectancy: A prospective cohort study. The Lancet, 378(9798), 1244-1253.
Wilbur, J. (2003). Correlates of physical activity in urban Midwestern Latinas. American Journal of Preventive Medicine, 25(3), 69-76.
Wilcox, S. (2000). Determinants of leisure time physical activity in rural compared with urban older and ethnically diverse women in the United States. Journal of Epidemiology & Community Health, 54(9), 667-672.