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Journal articles on the topic 'Longitudinal data analysi'

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Published: 9 March 2023
Last updated: 10 March 2023

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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 (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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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 (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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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

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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10

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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11

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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12

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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13

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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14

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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15

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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16

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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17

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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18

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 more suitable, but only if the missing values are non-informative.ConclusionsRoutine use of MANOVA for the analysis of longitudinal data, particularly when there is a substantial proportion of drop-outs, is ill advised. Statisticians have considerably enriched the available methodologies during the past decade, and psychiatric researchers dealing with such data should be aware of the advantages of the newer methods.
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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

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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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

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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29

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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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 inference. In this article, we propose a joint model of complex longitudinal and survival data and a computationally efficient method for approximate likelihood inference to address the foregoing issues simultaneously. In particular, our model does not make unverifiable distributional assumptions for truncated values, which is different from methods commonly used in the literature. The parameters are estimated based on the h-likelihood method, which is computationally efficient and offers approximate likelihood inference. Moreover, we propose a new approach to estimate the standard errors of the h-likelihood based parameter estimates by using an adaptive Gauss–Hermite method. Simulation studies show that our methods perform well and are computationally efficient. A comprehensive data analysis is also presented.
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31

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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32

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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33

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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34

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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35

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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36

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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37

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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38

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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39

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 constant, (h) with single level and multilevel measurement, and (i) other advances in SEM of longitudinal data (latent growth curve model, latent difference score, etc.). We pay more attention to the interaction of variables and to nonlinear transformations of variables because they are not frequently used in empirical investigation. They do, however, offer interesting possibilities to researchers who wish to verify relations between the variables they obtain. Potential applications are described, with their advantages and disadvantages.
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40

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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41

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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42

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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43

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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44

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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45

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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46

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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47

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, providing insights about the factors affecting changes in individual lives.
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48

Shao, Xin. "What is quantitative longitudinal data analysis?" Educational Research and Evaluation 24, no. 1-2 (2018): 88–90. http://dx.doi.org/10.1080/13803611.2018.1515749.

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49

Chan, David. "Data Analysis and Modeling Longitudinal Processes." Group & Organization Management 28, no. 3 (2003): 341–65. http://dx.doi.org/10.1177/1059601102250814.

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50

Posey Garrison, Chlotia, and Matoteng Ncube. "A longitudinal analysis of data breaches." Information Management & Computer Security 19, no. 4 (2011): 216–30. http://dx.doi.org/10.1108/09685221111173049.

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