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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system. 
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Changing Employment and Work Schedule Patterns over the 30 Working Years-A Sequential Cluster Analysis.

Wen-Jui Han1, Julia Shu-Huah Wang2

  • 1Silver School of Social Work, New York University, New York, NY 10003, USA.

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Workers with disadvantaged social positions are more likely to experience unstable work schedules. This precarious employment threatens economic security, highlighting disparities in labor market dynamics over working lives.

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Area of Science:

  • Sociology of work
  • Labor economics
  • Social stratification

Background:

  • Increasing labor market volatility since the 1980s impacts worker economic security.
  • Understanding long-term employment patterns and work schedule changes is limited.
  • Social position's influence on work schedule trajectories requires further investigation.

Purpose of the Study:

  • To analyze longitudinal work schedule patterns over adult working lives.
  • To identify distinct work schedule trajectories using sequence analysis.
  • To examine how social position shapes these work schedule patterns.

Main Methods:

  • Utilized the National Longitudinal Survey of Youth-1979 (NLSY79) data.
  • Conducted sequence analysis on work schedule data from ages 22-53 (n=7987).
  • Employed multinomial logit regression to assess factors influencing work schedule patterns, focusing on social position.

Main Results:

  • Identified five distinct work schedule patterns over 31 years.
  • Prevalence of patterns: standard hours (25%), mixed standard/nonstandard (38%), transitioning (14%, 13%), mostly not working (10%).
  • Non-Hispanic Black individuals, those with lower education, and those with prior poverty/welfare experience were more likely to have nonstandard schedules.

Conclusions:

  • Employment patterns are dynamic, with significant group-based differences in work schedule volatility.
  • Disadvantaged social positions correlate with higher likelihoods of nonstandard work schedules.
  • This exacerbates vulnerability and hinders economic security for certain worker groups.