The social consequences of poverty: an empirical test on longitudinal data by Mood, C, Jonsson, J

“…We apply panel data methods on longitudinal data from the Swedish Level-of-Living Survey 2000 and 2010 (n = 3089) to study whether poverty affects four social outcomes—close social relations (social support), other social relations (friends and relatives), political participation, and activity in organizations. …”

Estimating stochastic elasticities directly from longitudinal data. by Haridas, C, Tuljapurkar, S, Coulson, T

The Social Consequences of Poverty: An Empirical Test on Longitudinal Data by Mood, C, Jonsson, J

“…We apply panel data methods on longitudinal data from the Swedish Level-of-Living Survey 2000 and 2010 (n = 3089) to study whether poverty affects four social outcomes— close social relations (social support), other social relations (friends and relatives), political participation, and activity in organizations. …”

suddengains: an R package to identify sudden gains in longitudinal data by Wiedemann, M, Thew, GR, Stott, R, Ehlers, A

“…Researching these occurrences could help understand individual change processes in longitudinal data. Tang and DeRubeis (1999) suggested three criteria to define sudden gains in psychological interventions. …”

Understanding teenage fertility in Peru: An analysis using longitudinal data by Favara, M, Sanchez, A, Lavado, P

“…The use of longitudinal data allows us to reduce the methodological concerns common to this type of analysis. …”

Persistent stunting in middle childhood : the case of Andhra Pradesh using longitudinal data by Himaz, R

“…<p>This article looks at what observable characteristics influence a child being persistently stunted, moving from being stunted or moving into being stunted in middle childhood, between 7 and 12, using longitudinal data for Andhra Pradesh. It finds the key factors that help a child move out of being stunted are mother’s education and coming from the more prosperous region of Coastal Andhra. …”

Persistent stunting in middle childhood: the case of Andhra Pradesh using longitudinal Data by Himaz, F

The effect of diabetes complications on health-related quality of life: The importance of longitudinal data to address patient heterogeneity by Alva, M, Gray, A, Mihaylova, B, Clarke, P

The effect of diabetes complications on health-related quality of life: the importance of longitudinal data to address patient heterogeneity. by Alva, M, Gray, A, Mihaylova, B, Clarke, P

Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data by Johnston, I, Hoffmann, T, Greenbury, S, Cominetti, O, Jallow, M, Kwiatkowski, D, Barahona, M, Jones, N, Casals-Pascual, C

The association between crowding within households and behavioural problems in children: Longitudinal data from the Southampton Women’s Survey by Marsh, R, Salika, T, Crozier, S, Robinson, S, Cooper, C, Godfrey, K, Inskip, H, Baird, J

The relationship between wasting and stunting: a retrospective cohort analysis of longitudinal data in Gambian children from 1976 to 2016. by Schoenbuchner, SM, Dolan, C, Mwangome, M, Hall, A, Richard, SA, Wells, JC, Khara, T, Sonko, B, Prentice, AM, Moore, SE

“…Background The etiologic relationship between wasting and stunting is poorly understood, largely because of a lack of high-quality longitudinal data from children at risk of undernutrition. …”

Can two tongues live in harmony: Analysis of the National Education Longitudinal Study of 1988 (NELS88) longitudinal data on the maintenance of home language by Yeung, A, Marsh, H, Suliman, R

Statistical methodology for constructing gestational age‐related charts using cross‐sectional and longitudinal data: The INTERGROWTH‐21st project as a case study by Ohuma, E, Altman, D, for the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH‐21st Project)

“…For illustration, we used data from the INTERGROWTH‐21st Project, ie, newborn weight (cross‐sectional) and fetal head circumference (longitudinal) data as examples.</p>…”

“When life gives you lemons”: using cross‐sectional surveys to identify chronic poverty in the absence of panel data by Bolch, K, Lopez‐Calva, LF, Ortiz‐Juarez, E

“…Unfortunately, traditional approaches to identifying chronic poverty require longitudinal data that is rarely available. In its absence, this paper proposes an alternative approach that only requires 1 year of cross-sectional data on monetary and non-monetary poverty. …”

Academic self-concept, interest, grades, and standardized test scores: reciprocal effects models of causal ordering. by Marsh, H, Trautwein, U, Lüdtke, O, Köller, O, Baumert, J

“…Reciprocal effects models of longitudinal data show that academic self-concept is both a cause and an effect of achievement. …”

Can we identify adolescents at high risk for nephropathy before the development of microalbuminuria? by Dunger, D, Schwarze, C, Cooper, J, Widmer, B, Neil, H, Shield, J, Edge, J, Jones, T, Daneman, D, Dalton, R

“…An albumin excretion phenotype was derived from longitudinal data, for each individual, defining deviation from the mean of regression models, including covariates gender, age, duration of diabetes and age at assessment. …”

Developmental maturation of inhibitory control circuitry in a high-risk sample: a longitudinal fMRI study by Cope, LM, Hardee, JE, Martz, ME, Zucker, RA, Nichols, TE, Heitzeg, MM

“…<p><strong>Background</strong></p> <p>The goal of this work was to characterize the maturation of inhibitory control brain function from childhood to early adulthood using longitudinal data collected in two cohorts.</p> <p><strong>Methods</strong></p> <p>Functional MRI during a go/no-go task was conducted in 290 participants, with 88 % undergoing repeated scanning at 1- to 2-year intervals. …”

Family play, reading, and other stimulation and early childhood development in five low-and-middle-income countries by Cuartas, J, McCoy, D, Sánchez, J, Behrman, J, Cappa, C, Donati, G, Heymann, J, Lu, C, Raikes, A, Rao, N, Richter, L, Stein, A, Yoshikawa, H

“…<p>This paper used longitudinal data from five studies conducted in Bangladesh, Bhutan, Cambodia, Ethiopia, and Rwanda to examine the links between family stimulation and early childhood development outcomes (<i>N</i> = 4904; <i>M</i>_age = 51.5; 49% girls). …”

Measurement of whole-brain and gray matter atrophy in multiple sclerosis: assessment with MR imaging by Storelli, L, Rocca, M, Pagani, E, Van Hecke, W, Horsfield, M, De Stefano, N, Rovira, A, Sastre-Garriga, J, Palace, J, Sima, D, Smeets, D, Filippi, M, Magnims Study Group

“…This retrospective study, performed between March 2015 and March 2017, collected data from (a) eight simulated MR images and longitudinal data (2 weeks) from 10 healthy control subjects to assess the cross-sectional and longitudinal accuracy of atrophy measures, (b) test-retest MR images in 29 patients with MS acquired within the same day at different imaging unit field strengths/manufacturers to evaluate precision, and (c) longitudinal data (1 year) in 24 patients with MS for the agreement between methods. …”