I have a dataset having 56 variables, in which 4 dependent and 52 independent variables. In those independent variables 45 variables are categorical and 3 dependent variables are categorical remains are continuous. Each variables having 1500 observations. Independent variables are nominal, and dependent Categorical variables are ordinal. I want to check, there is any effect of independent variables on each dependent variables .
This is pretty vague - there are countless ways to analyze a dataset that large. What is your goal? What do you want to test using this data? You need to have a testable hypothesis - that will drive the type of analysis you decide to do.
Actually, I want to check, there is any effect of independent variables on each dependent variables...
Hi Shikhar Tyagi
Stating that you want to check the effect of independent variables on dependent is still not very clear given the structure of your question. Since 3 of the dependent are categorical, it should be made known whether they are nominal or ordinal. Also, you would want to clarify the levels of each categorical variable as it will influence the kind of test when using the continuous variables as independent.
independent variables are nominal, and dependent Categorical variables are ordinal.
Dear Shikhar,
You should be very clear about your objective or research hypothesis before applying any arbitrary analysis to your dataset.
Second thing is that, categorical data can be nomimal variable or ordinal variable, so don't confuse between the two.
Whether your dependent variables are independent to each other?
If there are 52 independent variable, you should check for the correlation structure of the variables and try to reduce the dimension of your dataset with PCA.
Until you give the description of your independent and dependent variables, it will be very difficult to give analysis tool for your dataset.
Take a look at the attached notes. Often the properties and number of the DVs give you a start on answering this question. Best wishes, David Booth
Though your question is general, you can reduce the no. of independent variables using factor analysis.
Reduce independent variables then use chi-square,and logistic regression.
In my opinion, you can use cumulative logit models for ordinal responses and commonly used methods of variables selection based on AIC or BIC.
See Section 6.2 in An Introduction in Categorical Data Analysis by Alan Agresti and https://stats.idre.ucla.edu/r/dae/multinomial-logistic-regression/
for hints how to do this in R.
If you are interested in the general properties of your data set, like any multivariate data set with non-trivial signals encoded in a matrix, its strength to infer groups, you should give network-based exploratory data analysis a try.
You'll find a bunch of related posts with ideas and links to literature on the Genealogical World of Phylogenetic Networks (GWoN)
https://phylonetworks.blogspot.com/search/label/EDA
Especially the neighbour-net is a most versatile tool for multivariate analysis, however, largely unknown and massively underused. Its applications in phylogenetics or linguistics are multi-fold but it can be very revealing for entirely different data (you didn't mention whether you are working with biological or other data), a few of the examples we explored on GWoN:
A network of breat sizes (to test a claim) – https://phylonetworks.blogspot.com/2018/01/false-reports-of-us-womens-breast-sizes.html
A network of gun legislation in the U.S. (to illustrate diversity) – https://phylonetworks.blogspot.com/2018/03/visualizing-us-gun-laws.html
A network of moons (for the purpose of classification) – https://phylonetworks.blogspot.com/2018/06/to-boldy-go-where-no-one-has-gone.html
A network of party programmes (to illustrate overall similarity and dissimilarity, also over time) – https://phylonetworks.blogspot.com/2018/10/jumping-political-parties-in-germanys.html
A network of playmates' change over time – https://phylonetworks.blogspot.com/2016/10/changes-in-playboys-women-through-60.html
Visualisation of the data behind rankings, e.g.
bikability and public transport in U.S. cities – https://phylonetworks.blogspot.com/2019/03/which-us-cities-are-best-for-walking.html
world happiness – https://phylonetworks.blogspot.com/2018/07/a-network-of-world-happiness.html
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