Relevant bibliographies by topics / Longitudinal data analysi

Academic literature on the topic 'Longitudinal data analysi'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Longitudinal data analysi"

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Vakulenko, Elena S. "Comparative Analysis of Interregional and Intersectoral Mobility in Russia." Economy of Region 16, no. 4 (December 2020): 1193–120. http://dx.doi.org/10.17059/ekon.reg.2020-4-13.

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One of the most important characteristics of the labour market is labour mobility that allows assessing the economic efficienc y o f labour . A comparativ e analysi s i s necessar y fo r determinin g th e degre e o f mobility . I n term s o f spatia l and sectoral characteristics, the paper assesses the degree and dynamics of mobility in the Russian labour market based on previously published studies, as well as the authors’ findings. To determine the degree of mobility, the research uses various approaches, applying both direct (mobility costs, transition matrices) and indirect indicators (structural unemployment, wage differentiation, unemployment rate, gross regional product (GRP)). The analysis uses the data of the Russia Longitudinal Monitoring Survey — Higher School of Economics (RLMS-HSE) and Federal State Statistic Service (Rosstat) for 2000– 2016. The obtained results demonstrate a relatively low intersectoral and interregional mobility in Russia compared to Organisation for Economic Co-operation and Development (OECD) countries. Low intersectoral mobility may indicate weak exchangeability of the sectors and high mobility costs. The largest number of transitions is observed in trade, where employees do not need any specific knowledge. Generally, other transitions are made between related sectors that require similar knowledge from employees. The lowest intersectoral mobility is characteristic for the education and health sectors. According to the Shorrocks index, in Russia, interregional mobility is lower than intersectoral mobility. Low spatial mobility is explained by high migration costs, including those associated with “poverty traps”, the peculiarity of statistical accounting of migrants and the size of Russian regions. The obtained results are correct for the examined period and the applied criteria. The changes in labour mobility in Russia caused by global digitalisation of the economy and the transition to remote working require a separate study.
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Vakulenko, Elena S. "Comparative Analysis of Interregional and Intersectoral Mobility in Russia." Economy of Region 16, no. 4 (December 2020): 1193–120. http://dx.doi.org/10.17059/ekon.reg.2020-4-13.

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One of the most important characteristics of the labour market is labour mobility that allows assessing the economic efficienc y o f labour . A comparativ e analysi s i s necessar y fo r determinin g th e degre e o f mobility . I n term s o f spatia l and sectoral characteristics, the paper assesses the degree and dynamics of mobility in the Russian labour market based on previously published studies, as well as the authors’ findings. To determine the degree of mobility, the research uses various approaches, applying both direct (mobility costs, transition matrices) and indirect indicators (structural unemployment, wage differentiation, unemployment rate, gross regional product (GRP)). The analysis uses the data of the Russia Longitudinal Monitoring Survey — Higher School of Economics (RLMS-HSE) and Federal State Statistic Service (Rosstat) for 2000– 2016. The obtained results demonstrate a relatively low intersectoral and interregional mobility in Russia compared to Organisation for Economic Co-operation and Development (OECD) countries. Low intersectoral mobility may indicate weak exchangeability of the sectors and high mobility costs. The largest number of transitions is observed in trade, where employees do not need any specific knowledge. Generally, other transitions are made between related sectors that require similar knowledge from employees. The lowest intersectoral mobility is characteristic for the education and health sectors. According to the Shorrocks index, in Russia, interregional mobility is lower than intersectoral mobility. Low spatial mobility is explained by high migration costs, including those associated with “poverty traps”, the peculiarity of statistical accounting of migrants and the size of Russian regions. The obtained results are correct for the examined period and the applied criteria. The changes in labour mobility in Russia caused by global digitalisation of the economy and the transition to remote working require a separate study.
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Czernitzki, Anna-Franziska, Christina Pospisil, Martin Musalek, Rebekka Mumm, and Christiane Scheffler. "Analysis of longitudinal data of height z-scores in kindergarten children – A pilot study." Anthropologischer Anzeiger 74, no. 2 (July 1, 2017): 109–12. http://dx.doi.org/10.1127/anthranz/2017/0708.

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Baltagi, Badi H. "Longitudinal Data Analysis." Journal of the Royal Statistical Society: Series A (Statistics in Society) 172, no. 4 (October 2009): 939–40. http://dx.doi.org/10.1111/j.1467-985x.2009.00614_9.x.

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Carey, Vincent J. "Longitudinal Data Analysis." Journal of the American Statistical Association 102, no. 479 (September 2007): 1075. http://dx.doi.org/10.1198/jasa.2007.s202.

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Oppliger, Robert A., Steven W. Marshall, and Ian D. Shrier. "Longitudinal Data Analysis." Medicine & Science in Sports & Exercise 33, no. 5 (May 2001): S85. http://dx.doi.org/10.1097/00005768-200105001-00489.

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Ployhart, Robert E., Brian C. Holtz, and Paul D. Bliese. "Longitudinal data analysis." Leadership Quarterly 13, no. 4 (August 2002): 455–86. http://dx.doi.org/10.1016/s1048-9843(02)00122-4.

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Ugarte, M. Dolores. "Longitudinal data analysis." Journal of Applied Statistics 36, no. 10 (September 24, 2009): 1175–76. http://dx.doi.org/10.1080/02664760902811563.

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BEN GHOUL, Marwa, Berna YAZICI, and Ahmet SEZER. "Semiparametric Mixed Models for Longitudinal Data: Wavelets Analysis as Smoothing Approach." Turkiye Klinikleri Journal of Biostatistics 11, no. 1 (2019): 24–35. http://dx.doi.org/10.5336/biostatic.2019-64979.

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Ziegel, Eric R., P. Diggle, K. Liang, and S. Zeger. "Analysis of Longitudinal Data." Technometrics 37, no. 3 (August 1995): 356. http://dx.doi.org/10.2307/1269941.

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Dissertations / Theses on the topic "Longitudinal data analysi"

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LIU, XIAOQIU. "Managing Cardiovascular Risk in Hypertension: Methodological Issues in Blood Pressure Data Analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2017. http://hdl.handle.net/10281/154475.

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Hypertension remains in 2017 a leading cause of mortality and disability worldwide. A number of issues related to the determinants of cardiovascular risk in hypertensive patients and to the strategies for better hypertension control are still pending. In such a context, aims of my research program were: 1. To investigate the contribution of blood pressure variability to the risk of cardiovascular mortality in hypertensive patients. In this setting, different methods for assessing blood pressure variability and different models exploring the link between blood pressure variability and outcome were investigated. 2. To assess the possibility that a hypertension management strategy based on hemodynamic assessment of patients through impedance cardiography might lead to a better hypertension control over 24 hours than a conventional approach only based on blood pressure measurement during clinic visits. To these aims, this thesis summarizes data obtained by performing a). An in-depth analysis of a study conducted in the Dublin hypertensive population, including 11492 subjects, and b). The analysis of longitudinal data collected in the frame of BEAUTY (BEtter control of blood pressure in hypertensive pAtients monitored Using the hoTman® sYstem) study. In Dublin study, the proportional hazard Cox model and accelerated failure time models have been used to estimate the additional effect of blood pressure variability on cardiovascular mortality over and above the effect of increased mean BP levels, with an attempt to identify the best threshold values for risk stratification. On the other hand, in BEAUTY study, mixed model and generalized estimation equation are used for the longitudinal data analysis.
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Petzold, Max. "Evaluation of information in longitudinal data." Göteborg : Statistical Research Unit, Göteborg University, 2003. http://catalog.hathitrust.org/api/volumes/oclc/52551306.html.

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Yao, Fang. "Functional data analysis for longitudinal data /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2003. http://uclibs.org/PID/11984.

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Zhu, Liang. "Semiparametric analysis of multivariate longitudinal data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/6044.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 3, 2009) Vita. Includes bibliographical references.
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Mills, Joanna E. "The analysis longitudinal binary data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ57350.pdf.

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Fullwood, Catherine Lee. "Longitudinal analysis of anticoagulent data." Thesis, Lancaster University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431469.

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Bai, Yang, and 柏楊. "Statistical analysis for longitudinal data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42841756.

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Bai, Yang. "Statistical analysis for longitudinal data." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42841756.

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Kim, Yangjin. "Statistical analysis of longitudinal data /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p3100054.

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Modur, Sharada P. "Missing Data Methods for Clustered Longitudinal Data." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1274876785.

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Books on the topic "Longitudinal data analysi"

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Donald, Hedeker, and Gibbons Robert D. Longitudinal Data Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/0470036486.

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Funatogawa, Ikuko, and Takashi Funatogawa. Longitudinal Data Analysis. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-0077-5.

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1962-, Fitzmaurice Garrett M., ed. Longitudinal data analysis. Boca Raton, FL: Chapman & Hall/CRC, 2008.

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Robert, Crouchley, ed. Longitudinal data analysis. Aldershot, Hants, England: Avebury, 1987.

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1955-, Gibbons Robert D., ed. Longitudinal data analysis. Hoboken, NJ: J. Wiley, 2006.

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Hedeker, Donald R. Longitudinal data analysis. Hoboken, NJ: Wiley-Interscience, 2006.

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Sutradhar, Brajendra C. Longitudinal Categorical Data Analysis. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2137-9.

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Hand, David, and Martin Crowder. Practical Longitudinal Data Analysis. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-3033-0.

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Kung-Yee, Liang, and Zeger Scott L, eds. Analysis of longitudinal data. Oxford: Clarendon Press, 1994.

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Peter, Diggle, and Diggle Peter, eds. Analysis of longitudinal data. 2nd ed. Oxford: Oxford University Press, 2002.

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Book chapters on the topic "Longitudinal data analysi"

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Dugard, pat, John Todman, and Harry Staines. "Longitudinal data." In Approaching Multivariate Analysis, 359–76. 2nd ed. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003343097-15.

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Funatogawa, Ikuko, and Takashi Funatogawa. "Longitudinal Data and Linear Mixed Effects Models." In Longitudinal Data Analysis, 1–26. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-0077-5_1.

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Funatogawa, Ikuko, and Takashi Funatogawa. "Autoregressive Linear Mixed Effects Models." In Longitudinal Data Analysis, 27–58. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-0077-5_2.

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Funatogawa, Ikuko, and Takashi Funatogawa. "Case Studies of Autoregressive Linear Mixed Effects Models: Missing Data and Time-Dependent Covariates." In Longitudinal Data Analysis, 59–75. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-0077-5_3.

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Funatogawa, Ikuko, and Takashi Funatogawa. "Multivariate Autoregressive Linear Mixed Effects Models." In Longitudinal Data Analysis, 77–98. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-0077-5_4.

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Funatogawa, Ikuko, and Takashi Funatogawa. "Nonlinear Mixed Effects Models, Growth Curves, and Autoregressive Linear Mixed Effects Models." In Longitudinal Data Analysis, 99–117. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-0077-5_5.

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Funatogawa, Ikuko, and Takashi Funatogawa. "State Space Representations of Autoregressive Linear Mixed Effects Models." In Longitudinal Data Analysis, 119–38. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-0077-5_6.

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Cummings, Peter. "Longitudinal Data." In Analysis of Incidence Rates, 311–26. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429055713-19.

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Hox, Joop J., Mirjam Moerbeek, and Rens van de Schoot. "Analyzing Longitudinal Data." In Multilevel Analysis, 71–102. Third edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315650982-5.

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Das, Abhik. "Longitudinal Data Analysis." In Encyclopedia of Quality of Life and Well-Being Research, 3689–91. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_1698.

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Conference papers on the topic "Longitudinal data analysi"

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Yao, Zhenjie, Yixin Chen, Jinwei Wang, Shouling Wu, Yanhui Tu, Minghui Zhao, and Luxia Zhang. "Trend Analysis Neural Networks for Interpretable Analysis of Longitudinal Data." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671590.

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Boni, Mohammad Al, Seth Green, Megan Stiles, Katherine Harton, and Donald E. Brown. "Longitudinal Analysis of Linguistic flexibility of Value-motivated Groups." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622319.

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Li, Yimei, Hongtu Zhu, Yasheng Chen, Hongyu An, John Gilmore, Weili Lin, and Dinggang Shen. "LSTGEE: longitudinal analysis of neuroimaging data." In SPIE Medical Imaging, edited by Josien P. W. Pluim and Benoit M. Dawant. SPIE, 2009. http://dx.doi.org/10.1117/12.812432.

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Jeon, Minji, Jacob Koressel, Anne Ottenbreit-Leftwich, Alan Peterfreund, Sarah Dunton, Jeffrey Xavier, Carol Fletcher, et al. "Document Analysis of ECEP Longitudinal Data." In SIGCSE '21: The 52nd ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3408877.3439655.

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Chun-hong, Li, Xu Li-qing, and Qin Chao-yong. "A new application of variable selection in longitudinal data." In 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA). IEEE, 2017. http://dx.doi.org/10.1109/icbda.2017.8078759.

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Boni, Mohammad Al, and Trishala Neeraj. "Longitudinal Analysis of Cyber-Related Articles." In 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC). IEEE, 2020. http://dx.doi.org/10.1109/dsc50466.2020.00038.

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Tamhane, Ashay, Shajith Ikbal, Bikram Sengupta, Mayuri Duggirala, and James Appleton. "Predicting student risks through longitudinal analysis." In KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2623330.2623355.

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Gui, Bingxiu, Yun Liu, Xu Bai, and Jiaojiao Zhang. "Longitudinal Patent Analysis for Big Data Technology." In 2017 Portland International Conference on Management of Engineering and Technology (PICMET). IEEE, 2017. http://dx.doi.org/10.23919/picmet.2017.8125461.

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Sharifian-Attar, Vida, Suparna De, Sanaz Jabbari, Jenny Li, Harry Moss, and Jon Johnson. "Analysing Longitudinal Social Science Questionnaires: Topic modelling with BERT-based Embeddings." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020678.

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Senore, C., R. Fasoli, A. Arrigoni, C. Hassan, E. Riggi, and N. Segnan. "LONGITUDINAL FIT ADHERENCE ANALYSES USING ITALIAN DATA." In ESGE Days. © Georg Thieme Verlag KG, 2020. http://dx.doi.org/10.1055/s-0040-1704369.

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Reports on the topic "Longitudinal data analysi"

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Chen, Maximillian Gene, Kristin Marie Divis, James D. Morrow, and Laura A. McNamara. Visualizing Clustering and Uncertainty Analysis with Multivariate Longitudinal Data. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1472228.

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Foged, Mette, and Giovanni Peri. Immigrants' and Native Workers: New Analysis on Longitudinal Data. Cambridge, MA: National Bureau of Economic Research, August 2013. http://dx.doi.org/10.3386/w19315.

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Aassve, Arnstein, Francesco C. Billari, Stefano Mazzuco, and Fausta Ongaro. Leaving Home Ain't Easy. A comparative longitudinal analysis of ECHP data. Rostock: Max Planck Institute for Demographic Research, December 2001. http://dx.doi.org/10.4054/mpidr-wp-2001-038.

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Servais, Marita. Overview of HRS Public Data Files for Cross-sectional and Longitudinal Analysis. Institute for Social Research, University of Michigan, 2010. http://dx.doi.org/10.7826/isr-um.06.585031.001.05.0023.2010.

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Rogers, Jessa, Kate E. Williams, Kristin R. Laurens, Donna Berthelsen, Emma Carpendale, Laura Bentley, and Elizabeth Briant. Footprints in Time: Longitudinal Study of Indigenous Children. Queensland University of Technology, October 2022. http://dx.doi.org/10.5204/rep.eprints.235509.

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The Longitudinal Study of Indigenous Children (LSIC; also called Footprints in Time) is the only longitudinal study of developmental outcomes for Aboriginal and Torres Strait Islander children globally. Footprints in Time follows the development of Australian Aboriginal and Torres Strait Islander children to understand what Indigenous children need to grow up strong. LSIC involves annual waves of data collection (commenced in 2008) and follows approximately 1,700 Aboriginal and Torres Strait Islander children living in urban, regional, and remote locations. This LSIC Primary School report has been produced following the release of the twelfth wave of data collection, with the majority of LSIC children having completed primary school (Preparatory [aged ~5 years] to Year 6 [aged ~12 years]). Primary schools play a central role in supporting student learning, wellbeing, and connectedness, and the Footprints in Time study provides a platform for centring Indigenous voices, connecting stories, and exploring emerging themes related to the experience of Indigenous children and families in the Australian education system. This report uses a mixed-methods approach, analysing both quantitative and qualitative data shared by LSIC participants, to explore primary school experiences from the perspective of children, parents and teachers. Analyses are framed using a strengths-based approach and are underpinned by the understanding that all aspects of life are related. The report documents a range of topics including teacher cultural competence, racism, school-based Aboriginal and Torres Strait Islander education activities, parental involvement, engagement, attendance, and academic achievement.
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Abowd, John, and Francis Kramarz. Internal and External Labor Markets: An Analysis of Matched Longitudinal Employer-Employee Data. Cambridge, MA: National Bureau of Economic Research, July 1997. http://dx.doi.org/10.3386/w6109.

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Kaffenberger, Michelle, Danielle Sobol, and Deborah Spindelman. The Role of Low Learning in Driving Dropout: A Longitudinal Mixed Methods Study in Four Countries. Research on Improving Systems of Education (RISE), April 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/070.

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Using unique longitudinal quantitative and qualitative data, we examine the role that low learning plays in driving dropout in Ethiopia, India, Peru, and Vietnam. Regression analysis using IRT-linked test scores and data on schooling attainment and dropout shows a strong, significant association with one standard deviation higher test scores associated with 50 percent lower odds of dropping out between the ages of 8 and 12, and a similar association between the ages of 12 and 15. Qualitative analysis indicates a direct relationship between low learning and dropout, with children and parents choosing to discontinue school when they realize how little is being learned. Qualitative findings also show that low learning interacts with and exacerbates more proximate causes of dropout, with low learning often contributing to choices of early marriage (for girls) and of leaving school to work (for both genders), with families making practical decisions about which options will best provide for children in the long run. Finally, learning, work, and poverty often interact, as the need to work to help provide for the household reduces the opportunities to learn, and low learning tilts the opportunity cost of time in favor of working. These findings suggest that low learning may play a larger role in dropout decisions, by underlying and interacting with other causes, than has been typically recognized.
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Marshall-Mies, Joanne C., Tanya B. Jupton, Christina M. Hirose, Michael A. White, Jacqueline A. Mottern, and Naina C. Eshwar. First Watch on the First Term of Enlistment: Cross-Sectional and Longitudinal Analysis of Data from the First Year of the Study. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada463174.

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Ma, Yunxing, Julia Brettschneider, and Joanna Collingwood. A systematic review and meta-analysis of cerebrospinal fluid amyloid and tau levels in patients progressing from Mild Cognitive Impairment to Alzheimer’s Disease. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2022. http://dx.doi.org/10.37766/inplasy2022.7.0020.

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Review question / Objective: Reported levels of amyloid-beta and tau in human cerebrospinal fluid (CSF) are evaluated to discover if these biochemical markers can predict the transition from Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD). A systematic review and quantitative meta-analyses are performed to test relationships between three potential biomarkers in CSF (Aβ(1-42), T-tau, and P-tau181) and the evolution of AD in longitudinal evaluations of levels relative to baseline, using prior-published experimental data. The primary focus of the analysis is on the period describing the transition of a patient from MCI to AD, where it is critical to discover the main biomarker characteristics that differentiate patient outcomes for those who have a stable form of MCI, and those who progress to a confirmed diagnosis of AD. A secondary purpose of the review was to examine the status of iron in CSF as a function of disease status.
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Lester, Paul B., P. D. Harms, Mitchel N. Herian, Dina V. Krasikova, and Sarah J. Beal. The Comprehensive Soldier Fitness Program Evaluation. Report 3: Longitudinal Analysis of the Impact of Master Resilience Training on Self-Reported Resilience and Psychological Health Data. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada553635.

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