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41 Cards in this Set
 Front
 Back
Symmetric

a word to describe correlation of DV and IV if something is symmetric, it doesn't make a distinction between the IVs and DV


What is to be considered a “strong” relationship and a “weak” relationship? When is this useful?

Strong relationship is when the regression has substantive and statistical significance. A weak relationship is when it doesn't. This is useful when calculating the correlation of a DV and IV.


Regression

A procedure that derives statistics to evaluate the relationship between two interval variables.


Leastsquares line

the single best fitting lines that explains the relationship of the variables (where the R squared is the highest)


Slope

Measured by coefficients, the angle at which the line rises or falls, the effect of the IV on the DV and it can tell us if the relationship is positive or negative (highly dependent on the units of mesurement)


Pearsons's R

Measures how closely points are around the regression line (fit)


Rsquared

The percent in the variance of the DV explained by the IV (closer to 1 is best)


Ftest

How many standard deviations the entire regression is away from the mean (same as Tstat = slope/standard error)


Describe the principle behind regressions

The key idea is that there is a line that best describes the relationship between the IV and DV


Explain the function of Pearson’s r

It tells us the strength of association and is a measure of how good a predictor one variable is of another


List ways to calculate substantive significance

Coefficients (slope) for each IV
R squared (fit) for overall regression 

List ways to calculate statistical significance

 PValue for each IV
 Tstat for each IV  Fstat for overall regression  Sig. F for overall regression 

Coefficients for regression essay

Found not in brackets, 1 unit increase in x correlates with a ________ increase in Y


R squared for regression essay

Found at bottom of graph, % variance in DV explained by IV's, above .3 or closer to 1 for substantive


Pvalue for regression essay

Found at bottom indicated by stars, less than .05 to determine relationship


Tstat for regression essay

Found by dividing coeffient/standard error (found in brakets), ought to be more than absolute value of 2


Fstat test for regression essay

Found by dividing coeffient/standard error (found in brakets) for whole regression, more than 2


Significant F for regression essay

Found by stars, less than .05 to determine relationship of whole regression


Three standard deviation numbers

68, 95, 99.7


Method problems for regression essay

Multicollinearity, Amount of Cases, Endogeneity, Spurious Relationship, AutoCorrelation


Spurious

A relationship where the independent variable really does not affect the dependent.


Intervening Variable

When the independent variable goes in between the other two variables =b where a=b=c


Beta Weights

Also called standardized regression coefficients, they are standardized results of multiple regressen analyses. They show effect of each indpenednt variable on the dependent variable, controlling for all other independent variables, and they use the standard deviation of the variables to remove the effects of the particular units in which the variables are measured.


Reciprocal

In causation, for X to influence Y while Y influences X (causal relationships should only be one direction). Ex. ideology and partiy indentification.


What is the most important principle to remember when dealing with control variables?

not finished


What are the criteria for inferring causality? Briefly explain each.

not finished


How should you interpret regression coefficients, or b's? Compare this to the interpretation of beta, or beta weights.

not finished


Difference between independent causation, spurious correlation, intervening variable, and complete causation.

look at pictures on page 180 of monroe


Causal Mechanisms

The intervening connection between indepedent and dependent variables.


Process Tracing

Using time and sequence to test the causal story. (tracing sequence or history, and chart the events that happen between X and Y.)


Causal Effect

The change in the expecetd value of the outcome variable brought about by a specified change in the vaule of an independent variable.


Why can a causal mechanism not be directly observed?

"They are a component of the of a causal process that intervenes between agetns with causal capacities and outcomes." Or in laymen terms it is because they are like the plus sign between two numbers, without the numbers the plus sign cannot be observed to do anything.


Which is more important: the causal mechanism or the causal effect? Why?

They are equally necessary components of explaining causal theories. This is because they illustrate different but important connections between the independent and dependent variable.


What are the advantages of process tracing in theory development and testing?

Process tracing is the empircal tracing of the causal mechanisms. Theory development and testing: identify paths to an outcome, point out excluded variables, check for spuriousness, and permit causal inference on the basis of a few or even a single case.


Goodluck on the test

I'm sure you'll do awesome...this is kind of a cheesy thing to do, so sue me. Nathan


Knock Knock!
Who's there? Wendy. Wendy who? 
Wendy wind blows de cradle will rock (www.knockknockjoke.com 2006).


Crosscutting cleveages

Ask Allison about it...


quantitative research

uses numbers and statistical methods, based on numerical measurements of specific aspects of phenomena, seeks general discriptions, seeks measurements and analyses that are easily replicable by other researchers


qualitative research

focuses on one or a small number of cases, use intensive interviews or in depth analysis of history, discursive method, concerned with rounded or comprehensive account of some event.


falsifiable

capable of generating as many observable implications as possible, allows more tests of the theory with more data and a greater variety of data.


parsimony

a simple assumption about the nature of the world
