Four Assumptions of Multiple Regression That Researchers Should Always Test.

By J. Osbourne, Elaine Waters. 2002

1,282 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d3e638f364d9c5d485b63a97f713ad8297779e22

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In Statistics and Probability

Citations in the corpus

Community Service in the Transition: Shifts and Continuities in Participation from High School to College (2004, positive): “Low reliability may attenuate the relationship of the independent variable to the dependent variable and, at the same time, it may enhance that relationship for another independent variable in the model (Osborne & Waters, 2002).”
Multicollinearity and Regression Analysis (2017, background): “In regression analysis there are many assumptions about the model, namely, multicollinearity, nonconsistant variance (non-homogeneity), linearity, and autocorrelation [6].”
The Role of Conspiracist Ideation and Worldviews in Predicting Rejection of Science (2013, methodology): “Alternative methods of analysis, such as multiple regression based on composite scores with less than perfect reliability, yield results contaminated by measurement error which make their interpretation difficult [61].”
Curriculum design for inquiry: Preservice elementary teachers' mobilization and adaptation of science curriculum materials (2010, methodology): “This model met the requirements of linearity, independence, homoscedasticity, and normality (Osborne & Waters, 2002) and was therefore suitable for analysis.”
Comparisons of close relationships: An evaluation of relationship quality and patterns of attachment to parents, friends, and romantic partners in young adults. (2012, positive): “Non-normal distributions are considered permissible under circumstances in which existing outliers have been removed to reduce the probability of type I and type II errors (Osborne & Waters, 2002).”
Assumptions of multiple regression: Correcting two misconceptions (2013, neutral): “Osborne and Waters’ (2002) attempt to draw attention to the assumptions of multiple regression is therefore commendable, especially so in that it was published in an open-access journal (PARE).”
The Impact of Customer Satisfaction on Share-of-Wallet in a Business-to-Business Environment (2003, background): “It is important that the curvilinear aspects of the relationship be accounted for in order to best assess the relationship between variables (Osborne and Waters 2001).”