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Related Concept Videos

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...
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...
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...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Two-Way ANOVA01:17

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...
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...

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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
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Investigating Ceiling Effects in Longitudinal Data Analysis.

Lijuan Wang1, Zhiyong Zhang, John J McArdle

  • 1University of Notre Dame.

Multivariate Behavioral Research
|November 20, 2009
PubMed
Summary
This summary is machine-generated.

Ceiling effects in data analysis can distort results. The Tobit growth curve model effectively addresses these limitations in longitudinal studies, providing more accurate parameter estimates compared to standard models.

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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06:52

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Published on: September 17, 2019

Area of Science:

  • Statistics
  • Psychometrics
  • Longitudinal Data Analysis

Background:

  • Score limitation at the top of a scale, known as ceiling effect, can cause significant artifacts in statistical parameter estimates.
  • These effects are particularly problematic in longitudinal data analysis, potentially leading to biased results.
  • Understanding and mitigating ceiling effects is crucial for accurate interpretation of data over time.

Purpose of the Study:

  • To examine the consequences of ceiling effects in longitudinal data analysis.
  • To investigate methods for addressing ceiling effects using simulations and empirical data.
  • To compare the performance of standard growth curve models with the Tobit growth curve model in the presence of ceiling effects.

Main Methods:

  • Monte Carlo simulations were employed with a latent growth curve model (T=5 occasions).
  • The proportion of ceiling data (10%-40%) was manipulated by varying thresholds.
  • Estimated parameters were analyzed across R=500 replications, and the Tobit growth curve model was applied to empirical data.

Main Results:

  • Standard growth curve models showed incorrect model selection and biased parameter estimation (curve shape, change magnitude) when ceiling effects were present.
  • The Tobit growth curve model demonstrated robust performance in handling ceiling effects in longitudinal data.
  • Application to an empirical cognitive aging study validated the effectiveness of the Tobit model.

Conclusions:

  • Ceiling effects significantly distort parameter estimates in standard longitudinal growth curve models.
  • The Tobit growth curve model offers a superior approach for analyzing longitudinal data affected by ceiling effects.
  • Accurate modeling is essential for reliable conclusions in fields like cognitive aging research.