Blog Archives

R Is Not So Hard! A Tutorial, Part 3

January 11, 2013
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R Is Not So Hard! A Tutorial, Part 3

Today we learn how to obtain useful diagnostic information about a regression model and then how to draw residuals on a plot.

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Factor Analysis: A Short Introduction, Part 4–How many factors should I find?

November 16, 2012
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Factor Analysis: A Short Introduction, Part 4–How many factors should I find?

One of the hardest things to determine when conducting a factor analysis is how many factors to settle on. Statistical programs provide a number of criteria to help with the selection.

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Factor Analysis: A Short Introduction, Part 3-The Difference Between Confirmatory and Exploratory Factor Analysis

November 2, 2012
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Factor Analysis: A Short Introduction, Part 3-The Difference Between Confirmatory and Exploratory Factor Analysis

In the last five posts I wrote about factors as latent variables, rotations, and variable and factor selection. Now I would like to address a question that the consultants at The Analysis Factor are frequently asked: what is the difference between a confirmatory and an exploratory factor analysis?

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R is Not So Hard! A Tutorial, Part 2

October 30, 2012
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R is Not So Hard! A Tutorial, Part 2

In Part 1 we installed R and used it to create a variable and summarize it using a few simple commands. Today let’s re-create that variable and also create a second variable, and see what we can do with them.

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Factor Analysis: A Short Introduction, Part 2–Rotations

September 24, 2012
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Factor Analysis: A Short Introduction, Part 2–Rotations

This post will focus on how the final factors are generated. An important feature of factor analysis is that the axes of the factors can be rotated within the multidimensional variable space. What does that mean?

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Factor Analysis: A Short Introduction, Part 1

September 10, 2012
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Factor Analysis: A Short Introduction, Part 1

The key concept of factor analysis is that multiple observed variables have similar patterns of responses because of their association with an underlying latent variable, the factor, which cannot easily be measured. For example, people may respond similarly to questions about income, education, and occupation, which are all associated with the latent variable socioeconomic status.

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R is Not So Hard! A Tutorial, Part 1

June 29, 2012
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R is Not So Hard!  A Tutorial, Part 1

OK. Indeed, R has a longer learning curve than other systems, but don’t let that put you off! Once you master the syntax, you have control of an immensely powerful statistical tool. Actually, much of the syntax is not all that difficult. Don’t believe me? To prove it, let’s look at some syntax for providing summary statistics on a continuous variable.

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Stratified Sampling for Oversampling Small Sub-Populations

June 11, 2012
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Stratified Sampling for Oversampling Small Sub-Populations

How to do it In stratified sampling, the population is divided into different sub-groups or strata, and then the subjects are randomly selected from each of the strata. So, in the above example, you would divide the population into different linguistic sub-groups (one of which is Yiddish speakers). Here are two simple steps you should follow:

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