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About 10,000 papers for “Longitudinal sample”

Sample Data for Accelerated Longitudinal Design

Xi‐Nian Zuo · Figshare · 2019

These are sample data from three subjects who are from a primary, middle and high school, respectively. The data only contains neuroimaging samples including T1/T2/REST images.

Cited by 0Open access

Turkey Couples Longitudinal Datasets | Sample 2

Gul Gunaydin, Emre Selçuk · UNC Dataverse · 2026

<b>Research Program Overview</b><br> This dataset is Sample 2 of five interrelated datasets collected as part of the Turkey Couples Longitudinal Datasets, a research program that includes both fledgling and newlywed couples and spans multiple designs (e.g., daily diaries, weekly surveys, laboratory interactions, and long-term follow-ups). Across the five samples, the program includes overlapping constructs and measures, enabling replication, comparison, and cumulative analyses across relationship stages and study designs. Please see the attached research program overview (Gunaydin_Selcuk_fiveSamples_overview.pdf) for information on the other datasets.<br> <br> <b>Participants:</b> Sample 2 consists of 152 romantic couples in the early stages of their relationships (“fledgling couples”). At study entry, couples had been romantically involved for an average of 3.6 months. Participants were young adults residing in Turkey. Data were collected from both partners.<br> <br> <b>Relationship type:</b> Romantic couples (fledgling relationships)<br> <br> <b>Data structure:</b> Dyadic, repeated-measures<br> <br> <b>Unit of analysis:</b> Individual responses nested within couples and across time<br> <br> <b>Procedure:</b><br> Participants completed a longitudinal protocol consisting of:<br> 1) 8 weekly diaries<br> 2) 6-month follow-up <br><br> <b>Methods:</b><br> <b>Design:</b> Longitudinal, diary<br> <br> <b>Measures:</b> The dataset includes core constructs that overlap with the other four samples in the research program, enabling cross-sample synthesis and cumulative analyses.<br> <br> <b>Weekly diaries:</b> Perceived partner responsiveness, Daily hassles (whether each involves partner?), Positive & negative affect, Attachment orientations (general & partner-specific), Relationship satisfaction, Relationship commitment, Demographics (only @ Week 1)<br><br> <b>6-month follow-up:</b> Breakup status (yes/no), (if yes) Breakup initiator (self/partner) and whether the participant is currently in another relationship. <br><br> <b>Institutional Affiliation and Ethics</b> Data collection was conducted by the research team during the investigators’ institutional appointments in Turkey. The PI was employed at Bilkent University at the time of Sample 2 data collection. All procedures were reviewed and approved by the relevant university ethics review board(s) and informed consent was obtained from all participants prior to participation. Sample 2 procedures were approved by the Ethics Board at Bilkent University, protocol number: GG-2017-12-25.

Cited by 0Open access

Turkey Couples Longitudinal Datasets | Sample 1

Gul Gunaydin, Emre Selçuk · UNC Dataverse · 2026

<b>Research Program Overview</b><br> This dataset is Sample 1 of five interrelated datasets collected as part of the Turkey Couples Longitudinal Datasets, a research program that includes both fledgling couples and newlywed couples and spans multiple designs (e.g., daily diaries, weekly surveys, laboratory interactions, and long-term follow-ups). Across the five samples, the program includes overlapping constructs and measures, enabling replication, comparison, and cumulative analyses across relationship stages and study designs. Please see the attached research program overview (Gunaydin_Selcuk_fiveSamples_overview.pdf) for information on the other datasets.<br> <br> <b>Participants:</b> Sample 1 consists of 151 romantic couples in the early stages of their relationships (“fledgling couples”). At study entry, couples had been romantically involved for an average of 2.1 months. Participants were young adults residing in Turkey. Data were collected from both partners.<br> <br> <b>Relationship type:</b> Romantic couples (fledgling relationships)<br> <br> <b>Data structure:</b> Dyadic, repeated-measures<br> <br> <b>Unit of analysis:</b> Individual responses nested within couples and across time<br> <br> <b>Procedure:</b> <br> Participants completed a multi-phase longitudinal protocol consisting of:<br> 1) Pre-diary assessment (lab), capturing baseline individual and relationship characteristics.<br> 2) A 21-day daily diary phase (online), during which both partners completed brief daily surveys assessing relationship experiences, affective states, and interpersonal processes.<br> 3) Immediate post-diary assessment (lab)<br> 4) Four quarterly follow-ups (online)<br> This allows for the examination of both short-term daily dynamics and longer-term relationship trajectories. <br><br> <b>Methods:</b><br> <b>Design:</b> Longitudinal, intensive repeated-measures design <br> <b>Time scale:</b> pre- and post-diary assessments, daily diary assessments (21 consecutive days) followed by quarterly follow-ups<br> <br> <b>Measures:</b> The dataset includes core constructs that overlap with the other four samples in the research program, enabling cross-sample synthesis and cumulative analyses.<br> <br> <b>Pre-diary measures: </b> Attachment orientations (partner-specific), Life satisfaction, Psychological well-being, Fear of being single, Relationship quality, Demographics <br><br> <b>Diary measures (21-day):</b> Perceived partner responsiveness, Daily hassles & uplifts (whether each involves partner?), Positive & negative affect, Self-disclosure <br><br> <b>Immediate post-diary measures:</b> Same as pre-diary, (minus) demographics <br><br> <b>Follow-ups (4x quarterly):</b> Same as pre-diary, (plus) Perceived partner responsiveness, (if broken up) initiator status & new relationship status <br><br> <b>Institutional Affiliation and Ethics</b> Data collection was conducted by the research team during the investigators’ institutional appointments in Turkey. The PI was employed at Bilkent University at the time of Sample 1 data collection. All procedures were reviewed and approved by the relevant university ethics review board(s) and informed consent was obtained from all participants prior to participation. Sample 1 procedures were approved by the Ethics Board at Bilkent University, protocol number: GG-2015-04-30.

Cited by 0Open access

Tracking sample members in longitudinal studies

Lisa Calderwood · Survey Practice · 2012

Tracking sample members in longitudinal studiesMinimizing sample attrition through failure to locate sample members who move is a major concern for all longitudinal studies.Most major longitudinal surveys employ a wide range of tracking procedures and have relatively low rates of sample attrition due to failure to locate.As Couper and Ofstedal (2009) discuss, the likelihood of locating sample members who move is influenced primarily by survey design factors such as tracking procedures and the interval between waves and by structural or societal factors, such as the availability of population registers and mail forwarding rules.In this article, I report the main themes emerging from a two-day workshop on tracking sample members in longitudinal studies which took place in London in July 2010.The workshop brought together around 50 participants from child cohort and household panel studies around the world to share experiences and current practices.There were presentations from longitudinal studies based in Europe, United States, and Australia.The broad range of countries and types of studies represented at the workshop highlighted the importance of the institutional and legal contexts in different countries and the similarities and differences in approaches taken by studies with different designs, in particular between household panel and child cohort studies and between local area and national studies.

Cited by 9Open access

Sample Data for Accelerated Longitudinal Design

Xi-Nian Zuo · Figshare · 2019

These are sample data from three subjects who are from a primary, middle and high school, respectively. The data only contains neuroimaging samples including T1/T2/REST images.

Cited by 0Open access

longpower: Sample Size Calculations for Longitudinal Data

Michael Donohue · 2010

Compute power and sample size for linear models of longitudinal data. Supported models include mixed-effects models and models fit by generalized least squares and generalized estimating equations. The package is described in Iddi and Donohue (2022) &lt;<a href="https://doi.org/10.32614%2FRJ-2022-022" target="_top">doi:10.32614/RJ-2022-022</a>&gt;. Relevant formulas are derived by Liu and Liang (1997) &lt;<a href="https://doi.org/10.2307%2F2533554" target="_top">doi:10.2307/2533554</a>&gt;, Diggle et al (2002) &lt;ISBN:9780199676750&gt;, and Lu, Luo, and Chen (2008) &lt;<a href="https://doi.org/10.2202%2F1557-4679.1098" target="_top">doi:10.2202/1557-4679.1098</a>&gt;.

Cited by 6Open access

Bayesian Predictive Inference for Longitudinal Sample Surveys

Balgobin Nandram, J. Sedransk · Biometrics · 1993

We describe and apply methodology for Bayesian predictive inference that is appropriate for many multistage, longitudinal sample surveys. The Pattern of Care Studies, two-stage cluster samples of cancer patients conducted on two occasions, are emphasized. For the application we fit a super-population model to the survey data, and provide posterior inference for the desired finite population parameters.

Cited by 7

Sample size determination for constrained longitudinal data analysis

Kaifeng Lu, Devan V. Mehrotra, Guanghan Liu · Statistics in Medicine · 2008

The longitudinal data analysis model proposed by Liang and Zeger (Sankhyā: Indian J. Stat. Ser. B 2000; 62:134-148) uses the baseline as well as postbaseline values as the dependent variables, and the baseline mean responses are constrained to be the same across treatment groups due to randomization. Compared with the conventional longitudinal analysis of covariance, this approach can correctly estimate the variance of within-group mean changes and achieve the specified coverage probabilities. General results on the sample size and power calculations for this model in the presence of missing data are obtained. The sample size relationship between the constrained and unconstrained longitudinal data analysis is established. Simple expressions for sample size calculation are obtained for the compound symmetry and first-order autoregressive correlation structures. The sensitivity of the sample size requirement to the configuration of correlation structure and retention pattern is assessed. The performance of several ad hoc approximations for longitudinal data analysis sample size calculation is evaluated. Simulation studies are conducted to assess the validity of the proposed sample size formulas with deviation from normality. The sample size formulas are also illustrated in detail using real clinical trial data.

Cited by 30

Officer Career Development: Longitudinal Sample--Fiscal Year 1982

Regina L. Burch, Reginald A. Bruce, Gerald L. Russell · 1991

Abstract : An extensive research project was initiated in 1981 which investigated the career development and decision-making processes of naval officers. An officer career questionnaire was sent to a sub-sample (N = 20,999) of the larger population (N = 23,769). These questionnaires were administered during 1982 and then readministered during 1986-1987. As a result of this study, two databases were constructed: (1) a cross-sectional database that includes data for all individuals who completed a survey in FY86-FY87 (N = 12,319), and (2) a longitudinal database that includes data for all individuals who completed a questionnaire in FY82 (N = 9,109) and in FY86-FY87 (N = 5,633). The documentation for the longitudinal database is provided in two separate volumes. The present documentation is the first of this two-part series and provides the data layout for all items included in the FY82 survey.

Cited by 0