Week 04

[sunaynagoel]

Would you be bale to open the unit overview and lab for week 04 ? please.

[sunaynagoel]

Page #12-13 of Omitted Variable PDF.
It says
" Bias is difference between Truth and Naive Slopes" shouldn't it be other way around?
Which is what it shows by calculation in page 14

"b1 =  Direct Effect + Indirect Effect 
β1 = Direct Effect 
bias = b1 – β1 = Indirect Effect "

Unless I am referring the terms intangibly.

[sunaynagoel]

LAB 04- Part I, Question 1

The code provided in the template and instructions for #1 , does not seem to be returning a table for for. It does not give an error either, it just returns a lot of texts in paragraph form.

m.full <- lm( SWB ~ ND + PSS, data=dat )
m.naive <- lm( SWB ~ ND, data=dat )

stargazer( m.naive, m.full, 
           type = "html", digits=2,
           dep.var.caption = "DV: Subjective Well-Being",
           omit.stat = c("rsq","f","ser"),
           notes.label = "Standard errors in parentheses")

Any one facing similar problem?

[lecy]

This is the one tricky thing about stargazer(). The function takes an R object as input, and returns HTML code that will create a pretty table once the file is knit. If you are working iteratively in the RMD document, however, and you want to run the chunk to see the output you need to change the type="html" argument to type="text". Otherwise you need to knit the document for the HTML code to make sense.

With small RMD files like this it works fine to just knit it when you need to view updates. If you are loading large files or doing complex functions that take time I usually change the argument to "text" so I can see the output in real-time, and change it back to "html" when I'm ready to create my final version.

It seems like a flaw in the design of stargazer(), but not sure if there is an easy fix and the package works great otherwise!

stargazer( m.naive, m.full, 
           type = "text", digits=2,
           # type = "html", digits=2,
           dep.var.caption = "DV: Subjective Well-Being",
           omit.stat = c("rsq","f","ser"),
           notes.label = "Standard errors in parentheses")

[lecy]

@sunaynagoel this confuses me every time as well. Another way to think about it is this:

b1 = total effect
B1 = direct effect
bias = indirect effect
direct effect = total effect - indirect effect
B1 = b1 - bias
b1 = B1 + bias

[lecy]

Unless you mean:

"Bias is the difference between Truth and Naive Slopes" should be, "Bias is the difference between the Naive Slope and the True Slope" ?

In that case, you are correct, that is a more precise statement from an order of operations standpoint.

I just meant conceptually it is the difference of the slope estimated from a "naive" (incomplete) model and the theoretic "full" model that includes all competing hypotheses.

[sunaynagoel]

SWB = B0 + B1 ND + B2 PSS + E1

If this is the equation for full regression, id there way to know E1 from regression table. I can still answer the lab without know it but I am just curious.

[lecy]

Nope, you have to calculate that.

[sunaynagoel]

Question #2 , part 1

Part 1:

"What happened to the slope of CSE?
What happened to the standard error of CSE?

How would our assessment of CSE change after we control for baseline self-esteem? For example, if we are a psychologist working with students should we worry if we observe a case where a person has a high need for approval from others? Will it impact their happiness? Or should we focus on other things?"

I can see from the table that slope changes but standard error does not. Does it make CSE a control variable correlated to policy variable? According to that CSE makes model accurate but imprecise. But when I make venn diagram, then according to my math its changes both slope and standard error.. I am confused.

[lecy]

CSE (the need for approval) is the policy variable in this question. RSE is the control variable.

I added a hint to the question to make it clear:

How would our assessment of CSE change after we control for baseline self-esteem? For example, if we are a psychologist working with students should we worry if we observe a case where a person has a high need for approval from others? Will it impact their happiness? Or should we focus on other things?

Hint:

Self-esteem and the need for approval are operating as competing variables. In other words, when we estimate the naive model in (1) then estimate the model with both variables in (3) we see a big difference in the results.

• What happened to the slope of CSE as we added a control in Model 3, specifically the sign representing a positive or negative relationshp between CSE and happiness?
• What happeneed to the statistical significance of b1?

Since significance guides are ability to make concrete policy recommendations, we typically include competing hypotheses because their presence can make the result disappear (like SES does with Class Size in the lecture notes). They usually results in one of two scenarios:

1. Our policy slope is significant then becomes insignificant (we lose confidence)
2. Our policy slope is insignificant then becomes signficant (we gain confidence)

This is an odd case, though, because we are “confident” about our slope on Model 1 (it’s highly statistically significant), and also confident in Model 3 (still highly significant). But what has changed about the slope? And how would that change our recommendations?

[sunaynagoel]

Thanks for all the help. I think I got it somewhat. This is the hardest LAB I have ever worked on.

[lecy]

This is the hardest lab of the semester. I'm hoping it's hard and not frustrating.

You don't go to grad school to be told what you already know!

[sunaynagoel]

It was not frustrating thanks to all the help you provided. It all came together but was slow and hardest part was combining all the concepts we have learned so far in a meaningful way. Thanks again.

[lecy]

"combining all the concepts ... in a meaningful way"
↑
↑
↑
That's the difference between learning the formulas and developing the intuition. Glad to hear it!

[jmacost5]

I am not understanding how to do part 2 in the first question. Is it asking me to put in a code from the previous part to answer the next part?

m.auxiliary <- lm(  SWB ~ ND + PSS, data=dat )

stargazer( m.auxiliary,  
           type = "html", digits=2,
           dep.var.caption = "DV: PSS",
           omit.stat = c("rsq","f","ser"),
           notes.label = "Standard errors in parentheses")

a1 <- 
B2 <- 0.32 
bias <- 
bias