Response to other student on MANOVA
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response_to_another_student_on_manova.docxDIRECTIONS:
Jessica Coutain
Jessica Coutain-The Logic of MANOVA
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Unit 7, Discussion 1
The multivariate analysis of variance (MANOVA) is a generalization of the analysis of variance (ANOVA) which can compare the means of several continuous dependent variables across a number of independent groups (Laerd Statistics, n.d.). The logic behind MANOVA is to create a general linear model, an equation of the linear type which would consist of a sum of matrices of covariance (Warner, 2013).
A researcher might choose to use a number of separate ANOVAs (or t-tests when there are two groups) when it is needed to compare the means of several dependent variables across a number of groups. This will allow for easier interpretation of the results (George & Mallery, 2016). However, a number of disadvantages will also be present; for instance, running several analyses increases the probability that the results are generated due to chance (George & Mallery, 2016). Also, several ANOVAs will not permit for accuracy if there exists a correlation between the dependent variables, which is often the case (George & Mallery, 2016).
On the other hand, even though MANOVA may be more difficult to interpret, it permits for reducing the probability of Type I errors (in comparison to several ANOVAs) (Field, 2013). In addition, MANOVA takes into account the relationships between the dependent variables, and can detect whether the groups differ across various combinations of variables; in this respect, MANOVA is more powerful than a series of ANOVAs (Field, 2013).
In addition, it should be noted that carrying out several ANOVAs may be more time-consuming and cumbersome, which is also a reason why MANOVA is usually the test of choice for comparing several dependent variables across independent groups.
It should also be stressed that MANOVA only detects whether there is a difference between any of the two groups involved in analysis, but does not specifically indicate which groups differed; to more precisely assess these differences, it is needed to carry additional procedures, such as post-hoc tests (Laerd Statistics, n.d.).
References
Field, A. (2013). Discovering statistics using IBM SPSS Statistics (4th ed.). Thousand Oaks, CA: SAGE Publications.
George, D., & Mallery, P. (2016). IBM SPSS Statistics 23 step by step: A simple guide and reference (14th ed.). New York, NY: Routledge.
Laerd Statistics. (n.d.). One-way MANOVA in SPSS Statistics. Retrieved from https://statistics.laerd.com/spss-tutorials/one-way-manova-using-spss-statistics.php
Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: SAGE Publications.
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