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Related Concept Videos

Case Studies01:22

Case Studies

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There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it.
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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 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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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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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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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Related Experiment Video

Updated: Nov 29, 2025

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
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A data driven methodology for social science research with left-behind children as a case study.

Chao Wu1, Guolong Wang1, Simon Hu2

  • 1School of Public Affairs, Zhejiang University, Hangzhou, Zhejiang, China.

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|November 20, 2020
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Summary

This study introduces a machine learning workflow for social science research, offering a data-driven approach to uncover complex social issue mechanisms and predict outcomes, outperforming traditional methods.

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Area of Science:

  • Social Sciences
  • Computer Science
  • Data Science

Background:

  • Traditional correlation and regression models have long been utilized in social science research.
  • Advancements in machine learning offer new possibilities for analyzing social issues, potentially surpassing standard regression techniques.
  • Existing methods may not fully capture the complex, non-linear dynamics inherent in social phenomena.

Purpose of the Study:

  • To propose a versatile methodological workflow for social science research using machine learning techniques.
  • To enable a data-driven approach for uncovering underlying mechanisms of social issues, from feature selection to model building.
  • To provide accurate predictions for social issues and identify key influencing factors.

Main Methods:

  • Development of a data-driven methodological workflow for machine learning application in social science.
  • Feature selection leveraging machine learning for reduced reliance on pre-existing social science theory.
  • Model building using machine learning to identify non-linear and complex relationships within social data.
  • Application of the workflow to the specific social issue of left-behind children.

Main Results:

  • The proposed workflow successfully identified important factors related to the target social issue.
  • Machine learning models provided appropriate predictions, demonstrating potential over traditional methods.
  • The data-driven approach facilitated analysis without extensive prior theoretical constraints.
  • The case study on left-behind children illustrated the workflow's practical application and comprehensive results.

Conclusions:

  • Machine learning offers a powerful, data-driven alternative for social science research, capable of uncovering complex relationships.
  • The proposed workflow provides a structured approach to applying machine learning to social issues, enhancing predictive accuracy.
  • While machine learning aids discovery, social science theory and knowledge remain crucial for interpreting results and understanding mechanisms.