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Related Concept Videos

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Factorial Design02:01

Factorial Design

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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...
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Stereotypes, Prejudice, and Discrimination02:55

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Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
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One-Way ANOVA01:18

One-Way ANOVA

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One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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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.
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Test for Homogeneity01:23

Test for Homogeneity

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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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Related Experiment Video

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Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
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Generalized Discriminant Analysis: Some Illustrations.

R L Tate

    Multivariate Behavioral Research
    |January 15, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Discriminant analysis offers versatile, parsimonious descriptions for multivariate studies beyond traditional variance analysis. Its application in multilevel and aptitude-treatment-interaction models reveals significant descriptive value, akin to external factor analysis.

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    Area of Science:

    • Behavioral Sciences
    • Multivariate Statistics

    Background:

    • Discriminant analysis is a powerful statistical technique for simplifying complex multivariate data.
    • Its application is often limited to multivariate analysis of variance (MANOVA), underutilizing its potential.
    • Current behavioral science research seeks more parsimonious descriptive methods for complex models.

    Purpose of the Study:

    • To review generalized discriminant analysis within the multivariate general linear model framework.
    • To demonstrate the descriptive utility of discriminant analysis in novel applications.
    • To highlight discriminant analysis as an 'external factor analysis' for enhanced data interpretation.

    Main Methods:

    • Review of generalized discriminant analysis principles.
    • Application of discriminant analysis to real-world data.
    • Analysis of contextual effects using multilevel models.
    • Analysis of aptitude-treatment interactions.

    Main Results:

    • Demonstrated successful application of discriminant analysis in multilevel modeling.
    • Showcased discriminant analysis utility in aptitude-treatment-interaction studies.
    • Results indicate discriminant analysis provides valuable descriptive insights beyond MANOVA.

    Conclusions:

    • Discriminant analysis is a versatile descriptive tool for multivariate behavioral science research.
    • Its application extends effectively to multilevel models and aptitude-treatment interactions.
    • The technique offers significant value when conceptualized as an 'external factor analysis'.