scholarly journals Multivariate Analysis of Variance (MANOVA) untuk Memperkaya Hasil Penelitian Pendidikan

AKSIOMA ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 37
Author(s):  
Sutrisno Sutrisno ◽  
Dewi Wulandari
Keyword(s):  
Multivariate Analysis ◽  
Linear Model ◽  
Analysis Of Variance ◽  
General Linear Model ◽  
General Linear ◽  
Multivariate Analysis Of Variance ◽  
Wilks Lambda ◽  
Post Hoc ◽  
One Way Anova

MANOVA merupakan solusi teknik analisis data kuantitatif bagi peneliti di dunia pendidikan yang ingin mengamati hasil belajar peserta didik dalam rangka menerapkan prinsip kebulatan dalam Kurikulum 2013 (prinsip evaluasi hasil belajar meliputi aspek kognitif, afektif, dan psikomotor). MANOVA mampu mengungkapkan perbedaan yang tidak ditampilkan ANOVA secara terpisah, sehingga dapat meningkatkan kesempatan untuk menemukan perubahan sebagai akibat dari perlakuan yang berbeda dan interaksinya. Dengan demikian, temuan hasil penelitian akan semakin kaya dan sangat berguna bagi perkembangan ilmu pengetahuan. Terdapat dua model analisis variansi yaitu model overparameterized dan model rerata sel. Model rerata sel memberikan pendekatan sederhana dan tidak ambigu, yang dapat digunakan pada data seimbang atau data tidak seimbang. Model ini menggunakan kontras untuk menyatakan efek utama dan interaksi. Uji persyaratan MANOVA meliputi uji normalitas multivariat dengan uji Mardia dan uji homogenitas matriks kovariansi dengan uji Box’s M. Terdapat beberapa statistik uji MANOVA yaitu Wilks’ Lambda, Pillai, Lawley-Hotelling, dan Roy’s Largest Root. Ketika hipotesis nol MANOVA ditolak, maka dilanjutkan ANOVA pada setiap variabel terikat. Apabila hipotesis nol ANOVA ditolak dan variabel bebas memiliki lebih dari dua nilai, maka dilakukan uji post hoc dengan metode Scheffe’. Prosedur ini menjaga taraf kesalahan α. Uji komparasi rerata antar sel tidak dapat dilakukan secara langsung menggunakan General Linear Model (GLM) pada SPSS. Prosedur yang dapat dilakukan adalah memanipulasi data dengan merubah kondisi eksperimentasi menjadi nilai-nilai yang dianggap satu variabel bebas, sehingga dapat dianalisis dengan One-Way ANOVA atau GLM. Kesulitan analisis multivariat pada perhitungannya yang terlalu rumit, sudah terpecahkan dengan adanya software statistik yang semakin canggih.Kata kunci: MANOVA, analisis multivariat, memperkaya hasil, penelitian pendidikan

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.

scholarly journals Cognitive flexibility of smokers in the context of social anxiety

2020 ◽  
pp. 93-99
Keyword(s):  
Multivariate Analysis ◽  
Social Anxiety ◽  
Analysis Of Variance ◽  
Cognitive Flexibility ◽  
Univariate Analysis ◽  
Multivariate Analysis Of Variance ◽  
Nicotine Abuse ◽  
Significant Difference ◽  
Wilks Lambda ◽  
Study Population

Background: The cognitiveCognitive dysfunction may be an important factor in smoking and nicotine abuse. However, there are very few studies that have examined the effects of psychiatric conditions on the cognitive flexibility of smokers. Objectives: This research was conducted with the aim of examination theto examine cognitive flexibility (perceive theperceived controllability and cognitive alternatives) ofamong smokers in the context of with social anxiety. MaterialMaterials and methods: The research was a study withpresent causal-comparative design. The populationstudy was allconducted on 60 smoker students ofstudying at Arak University, Arak, Iran, in 2018-2019 years. For selecting the research sample the. The study population was selected using the purposive sampling was usedtechnique. At first, the participants completed the Social Phobia Inventory (SPIN) and Cognitive Flexibility Inventory (CFI).. Then, based on the cutoff point scores of SPIN (19 to above),≤), the participants were divided into two smoker groups (n=30 in each group) were selected: smoker groupsof smokers with and without social anxiety. (n=30 in each group). Finally, these groups were compared in perceive the terms of perceived controllability and cognitive alternatives by Multivariate Analysis of Variance (MANOVA).using the multivariate analysis of variance. Results: The results indicated a significant difference in the linerlinear composition of the dependent variables ofin the two groups (wilks,Wilks’ lambda= 0/.799, F50,2= 6/.726, p= P=0/.004). UnivariateThe results of the univariate analysis of variance indicated that the smoker group with social anxiety had lower perceive theperceived controllability and cognitive alternatives, compared to the smoker group without social anxiety. Conclusion: In generalAs the findings indicated, the level of cognitive flexibility in the smokers with and without social anxiety iswas different. Therefore, it is necessary to consideringconsider the evaluation and treatment of cognitive deficits in smokers based on their level of social anxiety.

scholarly journals Dynamics of a smile in different age groups

2012 ◽  
Vol 83 (1) ◽  
pp. 90-96 ◽  
Author(s):  
Patil Chetan ◽  
Pradeep Tandon ◽  
Gulshan K. Singh ◽  
Amit Nagar ◽  
Veerendra Prasad ◽  
...  
Keyword(s):  
Gender Differences ◽  
Multivariate Analysis ◽  
Analysis Of Variance ◽  
Age Groups ◽  
Dynamic Measurements ◽  
Multivariate Analysis Of Variance ◽  
Upper Lip ◽  
Males And Females ◽  
Post Hoc

Abstract Objective: To evaluate smile in different age groups and to detect gender differences in smile. Materials and Methods: Digital videographic records of 241 randomly selected subjects were obtained for smile analysis. The subjects were divided into four groups by age (15–20 years, 21–30 years, 31–40 years, and 41–50 years). Each group was further subdivided by gender. After 41 subjects were excluded, the smile dimensions of 200 subjects were analyzed by two-way multivariate analysis of variance (MANOVA) with Duncan's multiple range post hoc test. Results: All dynamic measurements (change in upper lip length, upper lip thickness, commissure height, and intercommissural width from rest to smile) decreased with age in both males and females. Changes in upper lip length and commissure height on smiling were greater in males as compared with females of the same age groups. Changes in intercommissural width on smiling were greater in females as compared with males in all age groups. Conclusion: Smile changes with increase in age, and the changes differ between males and females. Females had a wider smile as compared with males of similar age groups.

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