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Cross-Sectional Research01:50

Cross-Sectional Research

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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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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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Observational Studies01:11

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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
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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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Archival Research01:40

Archival Research

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Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
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Related Experiment Video

Updated: Jan 4, 2026

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
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PROSPECTIVE VERSUS RETROSPECTIVE APPROACHES TO THE STUDY OF INTERGENERATIONAL SOCIAL MOBILITY.

X I Song1, Robert D Mare1

  • 1University of California, Los Angeles.

Sociological Methods & Research
|November 7, 2019
PubMed
Summary

This study introduces a method to correct retrospective data for social mobility research, improving its accuracy for parental generation analysis. The technique effectively reduces bias in intergenerational mobility studies.

Keywords:
demographic processesmultigenerational studyprospective dataretrospective datasocial mobility

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Area of Science:

  • Sociology
  • Demography
  • Quantitative Social Science

Background:

  • Intergenerational social mobility studies often rely on retrospective data, which can be unrepresentative of the parental generation.
  • Prospective data offer advantages for multi-generational analysis and joint modeling of mobility and demographic processes but are scarce.

Purpose of the Study:

  • To propose and validate a method for correcting retrospective social mobility data.
  • To enhance the utility of retrospective data for demographic and social mobility modeling.

Main Methods:

  • Developed a statistical method to adjust retrospective intergenerational mobility data.
  • Applied the correction method to simulated datasets and real-world data from the Panel Study of Income Dynamics.

Main Results:

  • The proposed method significantly corrects for the unrepresentativeness of the parental generation in retrospective data.
  • Demonstrated over 95% bias reduction in retrospective mobility data using simulated and empirical examples.

Conclusions:

  • This method provides a valuable tool for researchers, enabling more accurate analysis of social mobility using widely available retrospective data.
  • Facilitates robust modeling of social mobility and demographic processes, overcoming limitations of scarce prospective data.