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Related Concept Videos

Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Statistical Significance01:37

Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Review and Preview01:10

Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
What is an ANOVA?01:16

What is an ANOVA?

The Analysis of Variance or ANOVA is a statistical test developed by Ronald Fisher in 1918. It is performed on three or more samples to check for equality between their means.
Before performing ANOVA, one must ensure that the samples used for this analysis have three crucial characteristics or statistical assumptions. The first assumption states that the samples should be drawn from normally distributed samples, while the second requires that all the drawn samples should be randomly and...

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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Published on: September 27, 2024

Analyzing written communication in AAC contexts: a statistical perspective.

Lorenzo Bernardi1, Arjuna Tuzzi

  • 1Department of Sociology, University of Padua, Padova, Italy.

Augmentative and Alternative Communication (Baltimore, Md. : 1985)
|October 20, 2011
PubMed
Summary

Statistical text analysis offers valuable insights into augmentative and alternative communication (AAC). Lexicon-based quantitative measures reveal text characteristics for individuals with autism, aiding in understanding their communication patterns.

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Area of Science:

  • Computational linguistics
  • Autism spectrum disorder research
  • Augmentative and Alternative Communication (AAC)

Background:

  • Textual data analysis presents opportunities for understanding communication patterns.
  • Augmentative and Alternative Communication (AAC) requires robust methods for analysis.
  • Facilitated communication (FC) is a method used by individuals with autism.

Purpose of the Study:

  • To explore the utility of lexicon-based quantitative measures in analyzing texts from facilitated communication (FC) sessions.
  • To identify characteristics of texts and writers within the context of autism and AAC.
  • To discuss the strengths, weaknesses, opportunities, and threats of statistical text analysis in this domain.

Main Methods:

  • Statistical analysis of textual data.
  • Application of lexicon-based quantitative measures.
  • Corpus: 12 essays from individuals with autism and a control group during facilitated communication.

Main Results:

  • The study demonstrates the potential of statistical text analysis to differentiate texts based on writer characteristics.
  • Quantitative measures can reveal patterns not easily discernible through qualitative analysis alone.
  • The approach facilitates comparative analysis between different text subcorpora.

Conclusions:

  • Statistical analysis of textual data, particularly lexicon-based measures, offers significant opportunities in augmentative and alternative communication (AAC) research.
  • This approach can complement qualitative methods, providing a more comprehensive understanding of communication.
  • Further research can refine these methods for broader application in understanding diverse communication needs.