POL 242Introduction to Research Methods

 

Assignment Five TutorialIndexes

July 12, 2011  

Anthony Sealey anthony.sealey@utoronto.ca

http://individual.utoronto.ca/sealey

Agenda

(1) Introduction • (2) Example

(3) Exercise

Introduction

Please be sure to sign the attendance form.

Research QuestionsExample

Research Questions“Old” and “New”

Left-Right Cleavages

Smiling Stephen

Smiling Jack Smiling Liz

Research QuestionsExample: ‘Old Politics’ Index

Go to: http://individual.utoronto.ca/sealey

Go to: Teaching/POL 242/

Assignment Five Tutorials/Code

Research Questions… and of course, go to Webstats!

Recall that in the tutorial for assignment one, we considered attitudes towards citizens’ attitudes about the competence of the two most successful political leaders in the most recent election: Harper and Layton.

Here, we are going to learn how to make an index, out of these 2 variables (plus 3 others).

Let’s begin using the point and click method, which gets us set to build on the syntax.

First, begin by selecting the CES 2004 data set and choosing the ‘Frequencies’ type of analysis.

Continuing with the ‘point and click’ method, select the ‘where would you rate Stephen Harper’ variable and ‘all’ statistics.

This is exactly the same process used to run frequency distributions.

After running the output we can then start building our index using syntax by selecting ‘here’

After selecting ‘here’ in the output page we are taken to the syntax page where we can edit and add code.

Here, insert the code I’ve prepared that eliminates missing values and recodes variables so that they are oriented in the proper direction. In order to ensure that all measures contribute equal weight to the index, we recode so that each ranges from 0 to 10 as the first variables (leader competency scores) did.

You can download the code here:http://individual.utoronto.ca/sealey/Site/POL_242_files/POL242-IndexesStatsCoding.rtf

As we go through the syntax process, you will notice that I’ve made notes to denote each of the measures used, as well as the final steps in the analysis. For example, for the ‘job creation’ variable, notice that it’s has ‘/* ** --- ** */’ surrounding it. This helps us to organize the analysis and remember what we’re doing at each stage. As long as we use asterisks (***) , Webstats will ignore the note when running the commands.

After entering the syntax that explains to Webstats how to declare missing values and recode each of the variables, here we find the syntax used to run the reliability test.

Remember, the reliability test (or Cronbach’s Alpha) is a test we use to see how related each of our individual measures are to each other.

The actual syntax will always follows the same basic format; all you have to do is copy the syntax and insert the variables you are using (instead of ‘shcomp’, ‘jlcomp’, ‘conbb’, ‘conun’ and ‘jcpriv’).

When eventually running your commands, review what the Cronbach’s Alpha result tells you. Do your variables seem to fit together? If not, have you coded them properly, so they are all measuring the same direction? Is there anything else we might think is going on?

Finally, we can create our new index variable (here called “OldLRI”). We can also tell Webstats to run our new index variable, along with the descriptive statistics we think useful in describing the index.

Here we focus on the reliability (or Cronbach’s Alpha) output.

We want to focus on three things. First, the overall Alpha score. If the score is around 0.50, we are pretty confident that our variables are inter-connected and measuring some broader concept. Here, the score is a little bit lower, indicating that we have reason to be concerned about whether appropriately questions measure the same underlying concept.

Second, if our Alpha score is low, we want to check if we can increase our score by deleting a variable. We do so, by looking at the “Alpha if Item Deleted” column. Here, we could improve our measure by deleting the ‘Jack Layton competency dimension’.

Third, to make sure our variables are all going in the right direction (ex. all measuring high levels of knowledge, we can look at the “Corrected Item-Total Correlation”. If any of the values are negative, we need to go back and check our coding.

Finally, we come to the frequency distribution of our index.

Once we create an index variable (or conceptual variable) we now describe it in broad terms (such as old politics left-right), rather than the individual parts (such levels of confidence in big business or unions). Notice that the index ranges from 0 to (about) 50. Those with higher scores (say 40 to 48) can be described as being very ‘old politics right’ and those with very low scores (say 0 to 10) can be described as being very ‘old politics left’.

Research QuestionsExercise

Option A: Using the same data set, create a better ‘old politics’ index.

Option B: Using the same data set, create a ‘new politics’ index.

Option C: Using any data set you like, create an index for any concept that you like.