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Learn to test for heteroscedasticity in SPSS with data from the Early Childhood Longitudinal Study (1998).

This dataset example introduces testing for heteroscedasticity following a linear regression analysis, which rests on several assumptions: one is that the variance of the residuals from the model is constant and unrelated to the independent variable(s). Constant variance is homoscedasticity, while n...

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Bibliographic Details
Format: eBook
Language:English
Published: London : SAGE Publications, 2015.
Subjects:
Early childhood education > United States > Statistics.
Heteroscedasticity > Case studies.
Heteroscedasticity.
Research > Statistical methods > Case studies.
Research > Statistical methods.
Éducation de la première enfance > États-Unis > Statistiques.
Recherche > Méthodes statistiques > Études de cas.
Recherche > Méthodes statistiques.
Early childhood education.
United States.
Case studies.
Statistics.
Online Access:Connect to the full text of the electronic book
Description
Summary:This dataset example introduces testing for heteroscedasticity following a linear regression analysis, which rests on several assumptions: one is that the variance of the residuals from the model is constant and unrelated to the independent variable(s). Constant variance is homoscedasticity, while non-constant variance is called heteroscedasticity. In this example, a simple regression model is estimated using a subset of data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 to examine whether math performance in kindergarten predicts reading performance.
Physical Description:1 online resource : illustrations (black and white, and colour)
Audience:Specialized.
ISBN:9781473947948
1473947944