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Related Concept Videos

Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
Statistical Package for the Social Sciences (SPSS)01:22

Statistical Package for the Social Sciences (SPSS)

The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
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Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
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.'
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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...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

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Updated: Jul 3, 2026

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

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

Published on: September 17, 2019

Analysis of twin data using SAS.

Rui Feng1, Gongfu Zhou, Meizhuo Zhang

  • 1Department of Biostatistics, the University of Alabama at Birmingham, Birmingham, Alabama 35294, USA.

Biometrics
|July 24, 2008
PubMed
Summary
This summary is machine-generated.

Statistical Analysis Software (SAS) offers a user-friendly alternative for analyzing twin study data, simplifying disease inheritance research. This approach yields results comparable to specialized programs, making complex genetic analyses more accessible.

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Twin studies are crucial for understanding genetic and environmental influences on disease inheritance.
  • Traditional analysis of twin data relies on specialized computational programs, posing a learning curve for many researchers.
  • Statistical Analysis Software (SAS) is widely adopted for general statistical analysis.

Purpose of the Study:

  • To evaluate the feasibility and efficacy of using SAS procedures for twin data analysis.
  • To demonstrate that SAS can produce results comparable to specialized twin analysis software.
  • To provide a more accessible alternative for researchers conducting twin studies.

Main Methods:

  • Extensive simulations were performed to compare results from SAS procedures with those from specialized twin analysis programs.
  • Numerical validation was conducted to assess SAS's capability in handling twin study designs and hypothesis testing.

Main Results:

  • SAS procedures can effectively analyze twin study data.
  • Results obtained using SAS were found to be similar to those generated by specialized computational programs.
  • SAS demonstrated its ability to handle complex study designs and statistical hypotheses relevant to twin research.

Conclusions:

  • SAS procedures serve as a convenient and accessible alternative for twin data analysis.
  • The use of SAS can lower the barrier to entry for researchers new to twin studies.
  • SAS provides a validated and practical option for disease inheritance research using twin data.