The social relations model (SRM) is commonly used in the analysis of interpersonal judgments and behaviors that arise in groups. The SRM was developed only for use with cross-sectional data. Here, we introduce an extension of the SRM to longitudinal data. The social relations growth model represents a person's repeated SRM judgments of another person as a function of time. We show how the model's parameters can be estimated using restricted maximum likelihood, and how the effects of covariates on interindividual and interdyad variability in growth can be computed. An example is presented to illustrate the suggested approach. We also present the results of a small simulation study showing the suitability of the social relations growth model for the analysis of longitudinal SRM data.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s11336-016-9546-5 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Implementation of an Early Referral Programme for Patients With Hand Arthralgia.
J Eval Clin Pract
February 2025
Instituto Mexicano del Seguro Social, IMSS Hospital General de Zona Número 17, Monterrey, Nuevo León, México.
Introduction: Rheumatoid arthritis (RA) is a progressive autoimmune inflammatory disease. According to the European League Against Rheumatism (EULAR), the stages of RA progression include pre-RA, preclinical RA, inflammatory arthralgia, arthralgia with positive antibodies, arthralgia suspected of progressing to RA, undifferentiated arthritis and finally established RA. According to the Community Oriented Program for Control of Rheumatic Diseases (COPCORD), the prevalence of RA in Mexico is 1.
View Article and Find Full Text PDFThe Gift of Time, How Do I Want to Spend It? Exploring Preferences for Time Allocation Among Women with and without a Breast Cancer Diagnosis.
Appl Health Econ Health Policy
December 2024
Health Systems and Health Economics, School of Public Health, Curtin University, Bentley, Perth, Australia.
Background: Women's preferences for time allocation reveal how they would like to prioritise market work, family life, and other competing activities. Whilst preferences may not always directly translate to behaviour, they are an important determinant of intention to act.
Objective: We present the first study to apply a discrete choice experiment (DCE) to investigate time allocation preferences among women diagnosed with breast cancer and women without a cancer diagnosis.
Volatile hydrocarbon exposures and immune-related illnesses among Deepwater Horizon oil spill workers.
J Expo Sci Environ Epidemiol
December 2024
Department of Epidemiology, UNC Gillings School of Public Health, Chapel Hill, NC, USA.
Background: Despite evidence from experimental studies linking some petroleum hydrocarbons to markers of immune suppression, limited epidemiologic research exists on this topic.
Objective: The aim of this cross-sectional study was to examine associations of oil spill related chemicals (benzene, toluene, ethylbenzene, xylene, and n-hexane (BTEX-H)) and total hydrocarbons (THC) with immune-related illnesses as indicators of potential immune suppression.
Methods: Subjects comprised 8601 Deepwater Horizon (DWH) oil spill clean-up and response workers who participated in a home visit (1-3 years after the DWH spill) in the Gulf Long-term Follow-up (GuLF) Study.
Measuring the impact of COVID-19 exposure on reporting of intimate partner violence in India using an intent-to-treat framework.
Sci Rep
December 2024
International Institute for Population Sciences, Mumbai, Maharashtra, 400088, India.
The COVID-19 pandemic has not only posed alarming health challenges but also exacerbated the scenarios of intimate partner violence (IPV) against women globally. While global studies indicate a conspicuous increase in IPV during COVID-19 lockdowns; Indian studies exhibit mixed evidence. This ambiguity in world's most populous country underscores a greater need to examine the nexus between exposure to COVID-19 and IPV using a large nationally representative sample of India.
View Article and Find Full Text PDFApplication of artificial intelligence based on state grid ESG platform in clean energy scheduling optimization.
Sci Rep
December 2024
School of Public Administration, Guangzhou University, Guangzhou, 510006, China.
The randomness and volatility of existing clean energy sources have increased the complexity of grid scheduling. To address this issue, this work proposes an artificial intelligence (AI) empowered method based on the Environmental, Social, and Governance (ESG) big data platform, focusing on multi-objective scheduling optimization for clean energy. This work employs a combination of Particle Swarm Optimization (PSO) and Deep Q-Network (DQN) to enhance grid scheduling efficiency and clean energy utilization.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!