How do you create a wealth index using PCA?

How do you create a wealth index using PCA?

1. Select variables.
2. Explore variables.
3. Recode into binary variables.
4. Principal components analysis (PCA)
5. Create wealth index quintiles.
6. Graph the index.
7. Select the final result and report the variables.

Are PCA scores standardized?

The factor weights are used in conjunction with the original variable values to calculate each observation’s score. The factor scores are standardized to reflect a z-score. Principal components analysis (PCA): PCA seeks a linear combination of variables such that the maximum variance is extracted from the variables.

What is the wealth index?

The wealth index is a composite measure of a household’s cumulative living standard. The wealth index is calculated using easy-to-collect data on a household’s ownership of selected assets, such as televisions and bicycles; materials used for housing construction; and types of water access and sanitation facilities.

How is wealth quintile calculated?

The resulting combined wealth index has a mean of zero and a standard deviation of one, and once it is obtained, national-level wealth quintiles are obtained by assigning the household score to each de jure household member, ranking each person in the population by their score and then dividing the ranking into five …

What does principal component analysis ( PCA ) do?

Principal Component Analysis (PCA) is a handy statistical tool to always have available in your data analysis tool belt. It’s a data reduction technique, which means it’s a way of capturing the variance in many variables in a smaller, easier-to-work-with set of variables.

How can be build an index by using PCA?

Using NIPALS algorithm you can extract 1 or 2 factor and express your index like the explained variance of both factors related to the total explained variance (or Eigenvalues). So, your index will be related directly to the phenomenon variance explained by PCA. Can you help by adding an answer?

How to create an index using principal component analysis?

The index corresponds to the component scores that can be exported to other statistical units by simple linear regression on the training set (Y = component score, X= measures of performances). You’re right, but you need to avoid any dummy variables or variables with limited values.

Why is standardization the first step in PCA?

Step 1: Standardization The aim of this step is to standardize the range of the continuous initial variables so that each one of them contributes equally to the analysis. More specifically, the reason why it is critical to perform standardization prior to PCA, is that the latter is quite sensitive regarding the variances of the initial variables.