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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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...
Factorial Design02:01

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
Longitudinal Studies01:26

Longitudinal Studies

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...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Cross-Sectional Research01:50

Cross-Sectional Research

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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Factorial Invariance within Longitudinal Structural Equation Models: Measuring the Same Construct across Time.

Keith F Widaman1, Emilio Ferrer, Rand D Conger

  • 1Department of Psychology, University of California at Davis.

Child Development Perspectives
|April 7, 2010
PubMed
Summary
This summary is machine-generated.

This study explores factorial invariance in longitudinal research. Using multiple indicators strengthens developmental research by ensuring measures consistently reflect constructs over time.

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Area of Science:

  • Developmental Psychology
  • Quantitative Psychology
  • Behavioral Research Methods

Background:

  • Developmental research aims to chart behavioral changes with age and understand longitudinal construct relations.
  • Traditional methods often use single measures per construct, assuming measurement consistency across time points.
  • This assumption can be tested using multiple indicators and factorial invariance analysis.

Purpose of the Study:

  • To discuss factorial invariance in the context of longitudinal developmental research.
  • To contrast different analytical approaches for assessing measurement invariance.
  • To highlight the advantages of using multiple indicators for modeling developmental processes.

Main Methods:

  • Longitudinal study design.
  • Factorial invariance testing.
  • Multiple-indicator modeling of latent constructs.

Main Results:

  • Factorial invariance allows for confident comparisons of latent variable scores across time.
  • Satisfied factorial invariance ensures measures maintain the same metric over the study's duration.
  • The multiple-indicator approach provides a robust framework for developmental research.

Conclusions:

  • Factorial invariance is crucial for valid longitudinal developmental research.
  • Multiple indicators enhance the reliability and validity of developmental measurements.
  • This methodology supports stronger conclusions about developmental changes and relationships.