Outcome Variables that Contribute to Group Differences Between Caucasians, African Americans, and Asian Americans who are Deaf

2002 ◽  
Vol 33 (2) ◽  
pp. 8-12 ◽  
Author(s):  
Corey L. Moore
Keyword(s):  
Multivariate Analysis ◽  
Discriminant Analysis ◽  
African Americans ◽  
Analysis Of Variance ◽  
Asian Americans ◽  
Fiscal Year ◽  
Group Differences ◽  
Multivariate Analysis Of Variance ◽  
Research And Practice ◽  
Post Hoc

The purpose of this research was to identify those dimensions of outcome variables (i.e., number of VR services provided, cost of case services, income, and number of hours worked at closure) that make the greatest contribution to group differences between Caucasians, African Americans, and Asian Americans who are deaf. Multivariate analysis of variance (MANOVA) and post hoc descriptive discriminant analysis (DDA) were utilized to evaluate 1,108 case records obtained from the RSA-911 database for fiscal year 1997. DDA results indicated that African-Americans were provided with significantly more VR services and achieved significantly lower levels of income when compared to Caucasians and Asian Americans. Results are presented for the discriminating variables and the implications of findings for research and practice are discussed.

MANOVA: A Procedure Whose Time Has Passed?

2019 ◽  
Vol 64 (1) ◽  
pp. 56-60 ◽  
Author(s):  
Francis L. Huang
Keyword(s):  
Multivariate Analysis ◽  
Analysis Of Variance ◽  
Research Question ◽  
Statistical Procedure ◽  
Large Majority ◽  
Multivariate Analysis Of Variance ◽  
Dependent Variables ◽  
Research Questions ◽  
Post Hoc ◽  
Published Research

Multivariate analysis of variance (MANOVA) is a statistical procedure commonly used in fields such as education and psychology. However, MANOVA’s popularity may actually be for the wrong reasons. The large majority of published research using MANOVA focus on univariate research questions rather than on the multivariate questions that MANOVA is said to specifically address. Given the more complicated and limited nature of interpreting MANOVA effects (which researchers may not actually be interested in given the actual post hoc strategies employed) and that various flexible and well-known statistical alternatives are available, I suggest that researchers consult these better known, robust, and flexible procedures instead, given the proper match with the research question of interest. Just because a researcher has multiple dependent variables of interest does not mean that a MANOVA should be used at all.

scholarly journals Wd*-test: Robust Distance-Based Multivariate Analysis of Variance

2018 ◽  
Author(s):  
Bashir Hamidi ◽  
Kristin Wallace ◽  
Chenthamarakshan Vasu ◽  
Alexander V. Alekseyenko
Keyword(s):  
Multivariate Analysis ◽  
Analysis Of Variance ◽  
Multivariate Analysis Of Variance ◽  
General Applicability ◽  
R Language ◽  
Design Variables ◽  
Multi Level ◽  
Post Hoc ◽  
Microbiome Data ◽  
Do So

AbstractBackgroundCommunity-wide analyses provide an essential means for evaluation of the effect of interventions or design variables on the composition of the microbiome. Applications of these analyses are omnipresent in microbiome literature, yet some of their statistical properties have not been tested for robustness towards common features of microbiome data. Recently, it has been reported that PERMANOVA can yield wrong results in the presence of heteroscedasticity and unbalanced sample sizes.FindingsWe develop a method for multivariate analysis of variance, , based on Welch MANOVA that is robust to heteroscedasticity in the data. We do so by extending a previously reported method that does the same for two-level independent factor variables. Our approach can accommodate multi-level factors, stratification, and multiple post hoc testing scenarios. An R language implementation of the method is available at https://github.com/alekseyenko/WdStar.ConclusionOur method resolves potential for confounding of location and dispersion effects in multivariate analyses by explicitly accounting for the differences in multivariate dispersion in the data tested. The methods based on have general applicability in microbiome and other ‘omics data analyses.

Morphometric analysis of some populations of lymnocardiid species (Mollusca: Bivalvia) from Razelm Lake Complex (Romania)

2010 ◽  
Vol 53 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Oana Popa ◽  
Elena Iorgu ◽  
Beatrice Kelemen ◽  
Dumitru Murariu ◽  
Luis Popa
Keyword(s):  
Multivariate Analysis ◽  
Discriminant Analysis ◽  
Analysis Of Variance ◽  
Morphometric Analysis ◽  
Growth Pattern ◽  
Curve Analysis ◽  
Multivariate Analysis Of Variance ◽  
Geographical Variability ◽  
Significant Difference ◽  
Morphological Variables

Morphometric analysis of some populations of lymnocardiid species (Mollusca: Bivalvia) from Razelm Lake Complex (Romania) In this paper we report the morphometric analysis of some populations of Lymnocardiid species from the lakes Razelm and Goloviţa. We used three measurements ratios to perform a discriminant analysis and a multivariate analysis of variance (MANOVA), in order to compare the species Hypanis colorata vs. Hypanis angusticostata in the two lakes, H. colorata in Razelm vs. Goloviţa, and H. angusticostata in Razelm vs. Goloviţa. From this analysis we concluded that the multivariate means of the morphological variables used in this study were highly significantly different (p=3.2e-05) between the two species. Concerning the geographical variability, in both species, the analysis showed no significant difference between the populations in the two investigated lakes. We also determined from a fitting curve analysis, that the growth pattern of both species shows length-height isometry and width-length and width-height negative allometry.

Making Meaning out of MANOVA: The Need for Multivariate Post Hoc Testing in Gifted Education Research

2019 ◽  
Vol 64 (1) ◽  
pp. 41-55 ◽  
Author(s):  
Kendal N. Smith ◽  
Kristen N. Lamb ◽  
Robin K. Henson
Keyword(s):  
Multivariate Analysis ◽  
Education Research ◽  
Discriminant Analysis ◽  
Factorial Design ◽  
Gifted Education ◽  
Group Differences ◽  
Data Set ◽  
Making Meaning ◽  
Post Hoc Analysis ◽  
Post Hoc

Multivariate analysis of variance (MANOVA) is a statistical method used to examine group differences on multiple outcomes. This article reports results of a review of MANOVA in gifted education journals between 2011 and 2017 ( N = 56). Findings suggest a number of conceptual and procedural misunderstandings about the nature of MANOVA and its application, including pervasive use of univariate post hoc tests to interpret MANOVA results. Accordingly, this article aims to make MANOVA more accessible to gifted education scholars by clarifying its purpose and introducing descriptive discriminant analysis as a more appropriate post hoc technique. A heuristic data set is used to demonstrate the procedures for running a descriptive discriminant analysis, both in place of a one-way MANOVA and as a post hoc analysis to a factorial design. SPSS and R syntax are provided.

The Associations Between Career Decision-Making Difficulties and Negative Emotional States

2019 ◽  
pp. 089484531988411 ◽  
Author(s):  
Ella Anghel ◽  
Itamar Gati
Keyword(s):  
Decision Making ◽  
Multivariate Analysis ◽  
Analysis Of Variance ◽  
Career Decision ◽  
Emotional States ◽  
Career Decision Making ◽  
Multivariate Analysis Of Variance ◽  
Research And Practice ◽  
Preparatory Program ◽  
Academic Year

Career decision-making difficulties are the challenges encountered by individuals before, during, or after choosing one’s career. This study tested the associations between career decision-making difficulties and depression, anxiety, and stress, considering the career decision status of participants. Students in a precollege preparatory program filled out questionnaires at the beginning and near the end of the academic year ( N = 137). The correlations between career decision-making difficulties, as measured by the Career Decision-Making Difficulties, and negative emotional states measured by the Depression, Anxiety, and Stress Scales, were positive at both administrations ( r T1 = .24, r T2 = .38). Using a multivariate analysis of variance, we have found that those who became more decided had fewer career decision-making difficulties ( d = 1.26). The changes in career decision status were not associated with either depression, anxiety, or stress. However, overall negative emotions intensified among students who were still undecided at the end of the year ( d = 0.72). Implications for research and practice are discussed.

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