POL242Y1 Lecture Notes - Lecture 6: Principal Component Analysis, Latent Variable, Recode

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21 Nov 2012
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Week 6-Lecture
Pearson’s R and Spearman’s p
Spearman’s P
As the dots spread out, correlation decreases
The idea of spearman’s row is the same idea as correlation. One of the things we are looking for
is: Logan regression (nonlinear relation)
Assignment #2 Write up
Tablesassessment (explain whether there are relations.) (tell why you are using the statistic
you have chosen) is it a strong relationship?
Graphics assessment of graphics (2-3 sentences) What is the graph telling you?
o Basic idea of what it is demonstrating
R help
Anything that is at the end of arrow <- is the new name
For example: tom <- read.spss)”/users
Bill <- read.spss
Step 1: Attach (Tom)
Step 2: Names (Tom)
Step 3: Is.factor(e033) asking if an object is a factor or not (in this asking if tom is a factor)
Step 4: Lftrght <- as.numeric(e033) changing data into numeric
False= good thing b/c it means it is simpler to work with.
Step 5: Is.factor(lftrght)
Step 6: Lftrght <- lftrght -5
Lftrght
Creating table
LR tab<- table(lftrght)
LRtab
LRfreq <- c(1210, 707, 1394 etc…)
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Creating a Mosaic Plot
LR3cat <= lftrght (recoding left right into LR3cat)
LR3cat[lftrght>0 & lftrght<4] <-0
LR3cat[lftrght>3 & lftrght<8] <-1
LR3cat[lftrght>7 & lftrght<11] <-2
Quantitative Measurement: How indicators fit together
Indexes
Recall that in first week, create measures that could be constructed using more than one
indicator of a given concept.
When we use multiple indicators in order to measure a concept, we call the resulting concept an
“index”
4 major steps in creating index:
1) Find a set of indicators that are closely related to underlying latent variable that you are
interested in measuring.
2) Recode indicators so that they have same direction and range.
3) Test indicators that you have recoded to see how well they fit together.
4) Once you have identified a set of indicators that are a good fit for the underlying latent concept,
combine them into a single index.
Step one: finding indicators
Generally the most difficult.
Combing through data set that you are interested in using in order to find suitable indicators of
the key concepts you are looking for.
Step two: recoding indicators
Direction and range are concerns (Range has to be 0 and 1)
Directionality of indicators
We want to make sure that each of our indicators (higher means morally traditional and lower
means less morally traditional)
What does same direction and range mean?
Same directionality conceive of your latent variable in terms of one end of the spectrum of
views that you intend to consider.
o E.g. moral traditionalism
o What’s the opposite of moral traditionalism?
Morally liberal? Morally progressive? Morally corrupt?
Then recode each of your variables so that the higher values of you indicators represent this end
of the spectrum of views that you are intending to measure.
o Do you think abortion is murder? This is on moral traditionalism scale.
Strongly agree-> high value
o DO you think women have the right to abortion?
Strongly disagreelow value
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