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Journal articles on the topic 'Longitudinal data analysi'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Vakulenko, Elena S. "Comparative Analysis of Interregional and Intersectoral Mobility in Russia." Economy of Region 16, no. 4 (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 (st

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2

Vakulenko, Elena S. "Comparative Analysis of Interregional and Intersectoral Mobility in Russia." Economy of Region 16, no. 4 (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 (st

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3

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 (2017): 109–12. http://dx.doi.org/10.1127/anthranz/2017/0708.

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4

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

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5

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

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6

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

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7

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

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8

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

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9

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

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10

Ziegel, Eric R., D. Hand, and M. Crowder. "Practical Longitudinal Data Analysis." Technometrics 39, no. 1 (1997): 112. http://dx.doi.org/10.2307/1270801.

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11

Have, Thomas R. Ten, Peter J. Diggle, Kung-Yee Liang, and Scott L. Zeger. "Analysis of Longitudinal Data." Journal of the American Statistical Association 90, no. 431 (1995): 1123. http://dx.doi.org/10.2307/2291352.

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12

Diggle, P. J., K. Y. Liang, and S. L. Zeger. "Analysis of Longitudinal Data." Biometrics 53, no. 2 (1997): 782. http://dx.doi.org/10.2307/2533983.

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13

Rigby, Alan S. "Analysis of Longitudinal Data." Journal of the Royal Statistical Society: Series D (The Statistician) 52, no. 2 (2003): 239–40. http://dx.doi.org/10.1111/1467-9884.t01-21-00356.

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14

Ziecel, Eric R. "Analysis of Longitudinal Data." Technometrics 37, no. 3 (1995): 356. http://dx.doi.org/10.1080/00401706.1995.10484363.

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15

Walters, Stephen. "Analysis of Longitudinal Data,." Journal of the Royal Statistical Society: Series D (The Statistician) 52, no. 4 (2003): 692–94. http://dx.doi.org/10.1046/j.1467-9884.2003.t01-4-00383_6.x.

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16

Goldstein, Harvey, P. J. Diggle, K. Y. Liang, and S. L. Zeger. "Analysis of Longitudinal Data." Journal of the Royal Statistical Society. Series A (Statistics in Society) 158, no. 2 (1995): 345. http://dx.doi.org/10.2307/2983303.

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17

Everitt, B. S. "Analysis of longitudinal data." British Journal of Psychiatry 172, no. 1 (1998): 7–10. http://dx.doi.org/10.1192/bjp.172.1.7.

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BackgroundLongitudinal data arise frequently in psychiatric investigations, and are most often analysed by multivariate analysis of variance (MANOVA) procedures. However, as routinely applied, the method is not satisfactory, particularly when the data are affected by subjects dropping-out of the study. More suitable methods are now available.MethodProblems with the MANOVA approach are discussed and the advantages of alternative procedures stressed.ResultsUsing MANOVA on complete cases to analyse unbalanced longitudinal data can be seriously misleading. More recently developed methods are far m

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18

PALMGREN, JUNI. "ANALYSIS OF LONGITUDINAL DATA." Statistics in Medicine 15, no. 11 (1996): 1231–32. http://dx.doi.org/10.1002/(sici)1097-0258(19960615)15:11<1231::aid-sim282>3.0.co;2-z.

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19

Park, So Young, and Ana-Maria Staicu. "Longitudinal functional data analysis." Stat 4, no. 1 (2015): 212–26. http://dx.doi.org/10.1002/sta4.89.

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20

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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21

Duncan, Susan C., Terry E. Duncan, and Hyman Hops. "Analysis of longitudinal data within accelerated longitudinal designs." Psychological Methods 1, no. 3 (1996): 236–48. http://dx.doi.org/10.1037/1082-989x.1.3.236.

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22

Yao, Fang, Hans-Georg Müller, and Jane-Ling Wang. "Functional Data Analysis for Sparse Longitudinal Data." Journal of the American Statistical Association 100, no. 470 (2005): 577–90. http://dx.doi.org/10.1198/016214504000001745.

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23

FANG, Jie, and Zhonglin WEN. "Moderation analysis for longitudinal data." Advances in Psychological Science 30, no. 11 (2022): 2461. http://dx.doi.org/10.3724/sp.j.1042.2022.02461.

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24

Suh, Young Ju, Taesung Park, and Soo Yeon Cheong. "Linkage analysis of longitudinal data." BMC Genetics 4, Suppl 1 (2003): S27. http://dx.doi.org/10.1186/1471-2156-4-s1-s27.

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25

Purdon, Patrick, Victor Solo, and Emery Brown. "Spatio-temporal longitudinal data analysis." NeuroImage 11, no. 5 (2000): S654. http://dx.doi.org/10.1016/s1053-8119(00)91584-2.

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26

Quintana, Fernando A., Wesley O. Johnson, L. Elaine Waetjen, and Ellen B. Gold. "Bayesian Nonparametric Longitudinal Data Analysis." Journal of the American Statistical Association 111, no. 515 (2016): 1168–81. http://dx.doi.org/10.1080/01621459.2015.1076725.

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27

Jansen, J. J., H. C. J. Hoefsloot, H. F. M. Boelens, J. van der Greef, and A. K. Smilde. "Analysis of longitudinal metabolomics data." Bioinformatics 20, no. 15 (2004): 2438–46. http://dx.doi.org/10.1093/bioinformatics/bth268.

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28

Vazquez Arreola, Elsa, Jeffrey R. Wilson, and Ding-Geng Chen. "Analysis of correlated data with feedback for time-dependent covariates in psychiatry research." General Psychiatry 33, no. 5 (2020): e100263. http://dx.doi.org/10.1136/gpsych-2020-100263.

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In studies on psychiatry and neurodegenerative diseases, it is common to have data that are correlated due to the hierarchical structure in data collection or to repeated measures on the subject longitudinally. However, the feedback effect created due to time-dependent covariates in these studies is often overlooked and seldom modelled. This article reviews the methodological development of feedback effects with marginal models for longitudinal data and discusses their implementation.

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29

Tseng, Chi-hong, Robert Elashoff, Ning Li, and Gang Li. "Longitudinal data analysis with non-ignorable missing data." Statistical Methods in Medical Research 25, no. 1 (2012): 205–20. http://dx.doi.org/10.1177/0962280212448721.

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30

Yu, Tingting, Lang Wu, and Peter B. Gilbert. "A joint model for mixed and truncated longitudinal data and survival data, with application to HIV vaccine studies." Biostatistics 19, no. 3 (2017): 374–90. http://dx.doi.org/10.1093/biostatistics/kxx047.

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SUMMARY In HIV vaccine studies, a major research objective is to identify immune response biomarkers measured longitudinally that may be associated with risk of HIV infection. This objective can be assessed via joint modeling of longitudinal and survival data. Joint models for HIV vaccine data are complicated by the following issues: (i) left truncations of some longitudinal data due to lower limits of quantification; (ii) mixed types of longitudinal variables; (iii) measurement errors and missing values in longitudinal measurements; (iv) computational challenges associated with likelihood inf

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31

Barnett, Ian, John Torous, Patrick Staples, Matcheri Keshavan, and Jukka-Pekka Onnela. "Beyond smartphones and sensors: choosing appropriate statistical methods for the analysis of longitudinal data." Journal of the American Medical Informatics Association 25, no. 12 (2018): 1669–74. http://dx.doi.org/10.1093/jamia/ocy121.

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AbstractObjectivesAs smartphones and sensors become more prominently used in mobile health, the methods used to analyze the resulting data must also be carefully considered. The advantages of smartphone-based studies, including large quantities of temporally dense longitudinally captured data, must be matched with the appropriate statistical methods in order draw valid conclusions. In this paper, we review and provide recommendations in 3 critical domains of analysis for these types of temporally dense longitudinal data and highlight how misleading results can arise from improper use of these

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32

Scheffler, Aaron, Donatello Telesca, Qian Li, et al. "Hybrid principal components analysis for region-referenced longitudinal functional EEG data." Biostatistics 21, no. 1 (2018): 139–57. http://dx.doi.org/10.1093/biostatistics/kxy034.

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Summary Electroencephalography (EEG) data possess a complex structure that includes regional, functional, and longitudinal dimensions. Our motivating example is a word segmentation paradigm in which typically developing (TD) children, and children with autism spectrum disorder (ASD) were exposed to a continuous speech stream. For each subject, continuous EEG signals recorded at each electrode were divided into one-second segments and projected into the frequency domain via fast Fourier transform. Following a spectral principal components analysis, the resulting data consist of region-reference

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33

Choi, Ahyoung, and Hangsik Shin. "Longitudinal Healthcare Data Management Platform of Healthcare IoT Devices for Personalized Services." JUCS - Journal of Universal Computer Science 24, no. (9) (2018): 1153–69. https://doi.org/10.3217/jucs-024-09-1153.

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Recently, many studies have been conducted on how to manage and analyze various types of health data such as clinical data, genomic data, and wirelessly collected multiple sensory data. In this paper, we propose a web-based healthcare data integration and management platform that collects heterogeneous types of health-related medical record as well as real-time lifelogging data. This platform provides flexible architecture to different types of data exchanges. The platform manages real-time data such as heart rate, blood pressure, and activity information extracted from various healthcare devi

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34

Geiser, Christian. "Longitudinal data analysis: A complex endeavor." New Directions for Child and Adolescent Development 2021, no. 175 (2021): 7–9. http://dx.doi.org/10.1002/cad.20407.

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35

Nadarajah, Tharshanna, Asokan Mulayath Variyath, and J. Concepción Loredo-Osti. "Empirical Likelihood Based Longitudinal Data Analysis." Open Journal of Statistics 10, no. 04 (2020): 611–39. http://dx.doi.org/10.4236/ojs.2020.104037.

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36

Sorensen, Aage B., James J. Heckman, and Burton Singer. "Longitudinal Analysis of Labor Market Data." Industrial and Labor Relations Review 40, no. 4 (1987): 634. http://dx.doi.org/10.2307/2524087.

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37

Torabi, Mahmoud, and Alexander R. de Leon. "Conditional Dependence in Longitudinal Data Analysis." Journal of the Iranian Statistical Society 20, no. 1 (2021): 347–70. http://dx.doi.org/10.52547/jirss.20.1.347.

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38

Lancaster, Tony, James Heckman, and Burton Singer. "Longitudinal Analysis of Labor Market Data." Economica 55, no. 218 (1988): 289. http://dx.doi.org/10.2307/2554483.

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39

Origasa, Hideki. "Longitudinal Data Analysis Using Linear Models." Japanese Journal of Biometrics 9, no. 1 (1988): 1–10. http://dx.doi.org/10.5691/jjb.9.1.

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40

Rice, John, and Hans-Georg Muller. "Nonparametric Regression Analysis of Longitudinal Data." Journal of the American Statistical Association 85, no. 409 (1990): 261. http://dx.doi.org/10.2307/2289566.

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41

Velicer, Wayne F., Rosemarie A. Martin, and Linda M. Collins. "Latent transition analysis for longitudinal data." Addiction 91, no. 12 (1996): 197–210. http://dx.doi.org/10.1080/09652149638926.

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42

Rosel, Jesús, and Ian Plewis. "Longitudinal Data Analysis with Structural Equations." Methodology 4, no. 1 (2008): 37–50. http://dx.doi.org/10.1027/1614-2241.4.1.37.

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Abstract. In this paper we review different structural equation models for the analysis of longitudinal data: (a) univariate models of observable variables, (b) multivariate models of observable variables, (c) models with latent variables, (d) models that are unconditioned or conditioned to other variables (depending on the variability of the independent variables: time-varying or time-invariant, and depending on the type of independent variables: of latent variables or of observable variables), (e) models with interaction of variables, (f) models with nonlinear variables, (g) models with a co

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43

Gibbons, Robert D., Donald Hedeker, and Stephen DuToit. "Advances in Analysis of Longitudinal Data." Annual Review of Clinical Psychology 6, no. 1 (2010): 79–107. http://dx.doi.org/10.1146/annurev.clinpsy.032408.153550.

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44

VELICER, WAYNE F., ROSEMARIE A. MARTIN, and LINDA M. COLLINS. "Latent transition analysis for longitudinal data." Addiction 91, s12 (1996): S197—S209. http://dx.doi.org/10.1111/j.1360-0443.1996.tb02339.x.

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45

Velicer, Wayne F., Rosemarie A. Martin, and Linda M. Collins. "Latent transition analysis for longitudinal data." Addiction 91, no. 12s1 (1996): 197–210. http://dx.doi.org/10.1046/j.1360-0443.91.12s1.10.x.

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46

Fitzmaurice, Garrett M., and Caitlin Ravichandran. "A Primer in Longitudinal Data Analysis." Circulation 118, no. 19 (2008): 2005–10. http://dx.doi.org/10.1161/circulationaha.107.714618.

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47

Berkey, C. S., N. M. Laird, J. Gardner, and I. Valadian. "Longitudinal analysis of incomplete adolescent data." Annals of Human Biology 18, no. 4 (1991): 311–26. http://dx.doi.org/10.1080/03014469100001632.

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48

Haiqun Lin and Theodore R. Holford. "Longitudinal analysis methods for multivariate data." Statistical Methods in Medical Research 16, no. 5 (2007): 383–85. http://dx.doi.org/10.1177/0962280206075312.

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49

Harring, Jeffrey R., and Tessa L. Johnson. "Digital Module 16: Longitudinal Data Analysis." Educational Measurement: Issues and Practice 39, no. 3 (2020): 137–38. http://dx.doi.org/10.1111/emip.12386.

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50

Edmonston, Barry. "The Statistical Analysis of Longitudinal Data." Journal of Interdisciplinary History 47, no. 1 (2016): 85–92. http://dx.doi.org/10.1162/jinh_a_00942.

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The volume, Lives in Transition, edited by Peter Baskerville and Kris Inwood, provides a valuable sampling of empirical research by historians using longitudinal data. It improves upon the current understanding of a variety of historical issues by focusing on different aspects and stages of individual life courses and identifying questions for further study. It demonstrates the important empirical challenges and presents a variety of substantive topics for longitudinal examination. Overall, the book’s chapters show that analysis of longitudinal data illuminates historical studies in new ways,

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