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Partial Correlation: pcorr tax cons party stinc

Kendall’s tau: ktau tax cons (can add more variables)

Spearman rank correlation: spearman tax cons (can add more variables)

Gamma: see “cross tabulation and measures of association” above or tabulate

tax cons, gamma    or tab tax cons, gam

Note: you can only use two variables at a time and you may need to recode before obtaining a gamma statistic – you can have 5 categories per variable but I don’t know how many more categories are allowed

if you use the procedure listed at the beginning of “Cross Tabulation and Measures of Association” you can avoid recodes.

Cronbach’s Alpha: Cronbach's Alpha examines reliability by determining the

internal consistency of a test or the average correlation of items (variables) within the test. In Stata, the alpha command conducts the reliability test.  For example, suppose you wish to test the internal reliability of ten variables, v1 through v10. You could run the following:

alpha v1-v10, item  In this example, the item option displays the effects of removing an item from the scale.  If you want to see if a group of items can reasonably be thought to form an index/scale you could also use Cronbach’s alpha.   For example: alpha a3e a3g a3j a3o, c i   The “alpha” score in the “Test scale” row (“alpha” is in the far right column and “Test scale” is a row) should be about .80 (maximum is 1.0) to show a high degree of reliability of the components.  However, William Jacoby said the .80 threshold is very high.  He would’ve gone lower to .70 (but would never use a scale with a reliability below .5 because you’d have more error variance than substantive variance).  If the variables are measured on different scales you may want to standardize them.  If so then add “s” to the above command (i.e.,  alpha a3e a3g a3j a3o, c i s).  Since the score for a variable in the “Test scale” column is what the “Test scale” number would be if that variable were deleted, you can maximize the score in the “Test scale” row by deleting any variables whose score in the “alpha” column is greater than the alpha in the “Test scale” row.  You can make the scale into a variable by typing: alpha a3e a3g a3j a3o, c gen(anscale)  Note: “anscale” is arbitrary (you can pick any name you want – this will now appear as a variable).  If you want to exclude those respondents that had a particular score on a variable (e.g., using scores 1 and 2 on variable “petition” but excluding  3) then do the following: alpha a3e a3g a3j a3o if partition==1&2, c i s)  For a better understanding see “Intermediate Social Statistics: Lecture 6. Scale Construction” by Thomas A.B. Snijders – saved as adobe file: StataMokkenCronbach.

Factor Analysis: You could factor analyze a group of variables (principle

components method by typing: factor a3e a3g a3j a3o, pcf

Look for eigenvalues greater than 1.0 (signifying that the variables in the factor explain more of the variance than individual variables).  The entries in the “factor” column are the correlations of that particular variable with the underlying factor. The number in the “cumulative” column tells

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