Multivariate Data Analysis by Josef hair.

Please take help from the following:

1. Any book on Multivariate analysis

2. Any book on Advance Research Methodology

3. SPSS Package

4. Any other customized statistical package, etc.

Hi Here are some references

Multivariate Data Analysis by Josef hair.

Dear Ali,

Maybe you will find this chapter:

http://www.uk.sagepub.com/burns/website%20material/Chapter%2025%20-%20Discriminant%20Analysis.pdf

useful?

I would also recommend this book:

http://www.crcpress.com/product/isbn/9781439816806

Hope this helps.

Best,

tom

Hi Ali,

The book by Hair et al "Multivariate analysis" is definitely the best starting point. I see you have already read the 1992 edition, however I suggest you go to the last one, there are some additional info and examples that will be helpful.

This document can be helpful as well: http://spu.fem.uniag.sk/cvicenia/ksov/sojkova/Viacrozmerna%20sttatistika/Discriminant%20Analysis.pdf

hope it helps. Regards.

Dear All,

Thanks for your great suggestions, Hair's book (all editions) was like a Bible for me and still. The problem is HOW to analyze the data more than understanding the method itself.

I tried youtube videos but did not find that much there. Any suggestions would be much appriciated

Again thank you so much for your suggestions and sorry for misleading you !!

You may follow this link https://lirias.kuleuven.be/bitstream/123456789/122365/1/0575.pdf  and related ones.

Thanks

Dear Ali

the following reference can help you

Tabachnick, B.G., & Fidell, L.S. (2007), Using Multivariate Statistics, 5th edn, Boston, Pearson education Inc.

Hair, J.F., Black, W.C., Babin, B.J.,  Anderson,R.E. and Tatham, R.L. (2006), Multivariate Data Analysis, 6th edn, Prentice Hall international, Upper Saddle River, New Jersey, USA.

Cohen, J. & Cohen, P. (1983), Applied Multiple Regression/Correlation Analysis for the Behavioural Sciences, 2nd edn, New Jersey, Lawrence Erlbaum Associates. 

Malhotra, N.K. (2004), Marketing Research: an applied orientation, 4th edn, New Jersey, Pearson Prentice Hall. Upper Saddle River, USA.

Pallant, J. (2005), SPSS Survival Manual: a Step by Step Guide to Data Analysis Using SPSS Version 12, 2nd edn, Open university Press, McGraw-Hill Education, NY.

Dear Ali

Thank you so much for your suggestions. Its much appreicated

Thank you Ali for bringing out this question because this group of colleagues are superior in their generosity to provide information.

"Multivariate analysis" by Hair et al  and "Marketing Research" by N K Malhotra are good books.

I am attaching my notes. Tell me if is it helpful

Regards

Dear Karuthan,

This is exactly what I am looking for !! Thank you so much :)

If you need the data set, I can send it to you

I will let you know... Many thanks again

Maybe this is interesting to you: https://www.researchgate.net/publication/225718403_Linear_Discrimination_Ordination_and_the_Visualization_of_Selection_Gradients_in_Modern_Morphometrics

Article Linear Discrimination, Ordination, and the Visualization of ...

Thank you so much philipp. In deed it helps.

Hi Karuthan, is it possible that you share your notes with me too?

Thanks

Hi Hussin I am attaching the notes

Thanks Karuthan for making my life much easier ;)

Initially,  you can use notes of SPSS package. That gives explanation of the concept and  how to use "multiple-linear discriminant analysis" .

Thanks Subhash, much appreciated. We are doing that already.

You may be interested in this

http://math.arizona.edu/~hzhang/math574m/Read/LogitOrLDA.pdf

which considers the circumstances under which logit modeling is preferred to discriminant analysis

and this is a short summary of the arguments

http://www.kmentor.com/socio-tech-info/2003/12/what-is-the-difference-between.html

You can use multinomial logistic model to analyze more than 2 outcomes.

Thanks Kelvyn,

I will for sure consider this alternative.

To be honest - I have never used discriminant analysis and yet routinely use logit models. Indeed when I was taught the latter (in the mid 1970's !!)  discriminant analysis was seen even then as rather old-fashion and superseded by the whole family of what became to be called generalized linear models [ Nelder, John; Wedderburn, Robert (1972). "Generalized Linear Models". Journal of the Royal Statistical Society. Series A (General) (Blackwell Publishing) 135 (3): 370384.doi:10.2307/2344614] 

But I really did not know why one would be preferred to the other. Your question prompted me to dig a bit and I now understand that the logit models offer flexibility in analyzing non-normally distributed predictor variables and the area in which I work often has categorical predictors. 

This simulation study is clear

Comparison of Logistic Regression and Linear Discriminant Analysis: A Simulation Study Maja Pohar , Mateja Blas , and Sandra Turk

http://mrvar.fdv.uni-lj.si/pub/mz/mz1.1/pohar.pdf

They conclude

"we can say that LDA is a more appropriate method when the explanatory variables are normally distributed. In the case of categorised variables, LDA remains preferable and fails only when the number of categories is really small (2 or 3). The results of LR, however, are in all these cases constantly close and a little worse than those of LDA. But whenever the assumptions of LDA are not met, the usage of LDA is not justified, while LR gives good results regardless of the distribution. As the estimates for LR are obtained by the maximum likelihood method, they have a number of nice asymptotic properties as well."

So I shall be sticking to my logit models for the analysis I undertake. So thanks for the question - I am better informed.

Thanks Kelvyn for enrishing our knowledge.

To be honest, i never used either of the two methods to anlyze my data before. I am more into using Structural Equation Modeling. But, SEM is not appropriate for situations. I attached the research-in-progress paper that we are currently extending for your reference. As you will notice we only have one dependent variable (categorical type) which make it impossible for us to use SEM. we are about to finish the data collection stage. so, based on your recommendation, LDA is more preferable when we have normal distribution then  discriminant analysis . I will definitely take that into consideration before making up my mind.