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Introduction to Developmental Psychology01:27

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Developmental psychology explores the changes and continuities in human abilities throughout life, encompassing physical, cognitive, linguistic, and social dimensions. Human development is not restricted to growth, but includes aspects of decline, particularly in physical abilities as individuals age. Developmental psychologists seek to understand how people change as they age and how their mental and social skills evolve.Developmental MilestonesA key concept in developmental psychology is...
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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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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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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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Answering Developmental Questions Using Secondary Data.

Pamela E Davis-Kean1, Justin Jager2, Julie Maslowsky3

  • 1University of Michigan.

Child Development Perspectives
|January 29, 2016
PubMed
Summary
This summary is machine-generated.

Secondary data analysis of large datasets enhances scientific integrity and generalizability for developmental research across the lifespan. This method strengthens research rigor and theory testing for broader population insights.

Keywords:
population studiesquantitative methodssecondary data

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Area of Science:

  • Social Sciences
  • Developmental Psychology
  • Behavioral Research

Background:

  • Secondary data analysis is a standard method in social sciences.
  • Large longitudinal and national datasets are frequently utilized.
  • Complex behavioral questions are often addressed using these data.

Purpose of the Study:

  • To detail the advantages of using secondary data for lifespan developmental research.
  • To provide an overview of how secondary data enhances scientific integrity.
  • To discuss data acquisition and methodological considerations for population data sets.

Main Methods:

  • Utilizing secondary data analysis.
  • Leveraging large longitudinal and national datasets.
  • Examining developmental questions across the lifespan.

Main Results:

  • Secondary data analysis increases scientific integrity.
  • Data sets can be obtained to answer specific research questions.
  • Methodological issues related to longitudinal, population data are discussed.

Conclusions:

  • Secondary data analysis enhances research rigor and generalizability.
  • These datasets are crucial for testing theories in behavioral science.
  • Secondary data analysis is an important method for lifespan developmental studies.