Anchors software for anchoring vignette data

All of the response variables must be in the form of consequetive non-negative numeric integers, i. By default, anchors only deletes those cases with missing values that affect the method of analysis requested. For example,. Also deletes cases with ANY missing values in any responses self or vignettes. Journal of Statistical Software. Wand, Jonathan and Gary King. For more information on customizing the embed code, read Embedding Snippets. Functions Source code Man pages In anchors: Statistical analysis of surveys with anchoring vignettes Description Usage Arguments Details Value anchors and anchors.

Related to anchors. R Package Documentation rdrr. We want your feedback! Note that we can't provide technical support on individual packages. You should contact the package authors for that. Log in with Facebook Log in with Google. Remember me on this computer.

Enter the email address you signed up with and we'll email you a reset link. Need an account? Click here to sign up. Download Free PDF. Anchors: Software for anchoring vignette data Gary King. A short summary of this paper. Download Download PDF. Translate PDF. Edu, , King Harvard. Murray, Joshua A. The software also includes results from our own work in progress. Note: you must use R 1. Use the default installation options and locations locations provided by the auto- mated installer.

This means that the GUI can look very odd and only partly painted. You will regain control once the estimation is finished—but this can take a LONG time. Use at least R 1. From the R command line. From a unix shell. Consider two questions along with response categories which [depending on the choice within the square brackets] are each asked as a self-assessment of the respondent and about each of the vignettes: 1.

A Very comfortable, B somewhat comfortable, C not comfortable at all. Each question also has a corresponding set of vignettes. The vignettes can be the same or different for each question. Here is an example set: 1. She frequently publishes her opinion in newspapers, criticizing decisions by officials and calling for change.

She sees little reason these actions could lead to government reprisal. He makes his opinion known on most issues without regard to who is listening.

He has heard of people oc- casionally being arrested for speaking out against the government, and government leaders sometimes make political speeches condemning those who criticize. He sometimes writes letters to newspapers about politics, but he is careful not to use his real name. She has a friend who was arrested for being too openly critical of governmental leaders, and so she avoids voicing her opinions in public places.

He knows several men who have been taken away by government officials for saying negative things in public. Everyone he knows who has spoken out against the government has been arrested or taken away. He never says a word about anything the government does, not even when he is at home alone with his family.

Each question above is asked of the respondent and of each vignette. Self-assessment questions can also be added that do not have corresponding vignettes. Response categories are the same for self-assessments and vignette assessments, and missing data is allowed throughout. Each set of questions self-assessment and vignettes must have the same number of choice categories coded as increasing sequential integers starting with 1.

In practice, we drop indices that are constant. Under the model, one or more of the self-assessment questions have corresponding vi- gnettes. To identify the model, two choices must be made: 1. The mean of the actual level must be set, by choosing one point. The variance of the actual level must also be set. Two common parameterizations are as follows: 1. The ordinal probit parameterization is useful for comparing chopit to this simpler model.

Another option is parameterization defined by the extreme vignettes. For example, the following commands will first load the library anchors and load an example dataset poleff. The summary. Note that it is unneces- sary to type out the name of the function in full at each invocation—you can simply type summary.

This is the default normalization if you do not specify a particular type of normalization. This person, who could be hypo- thetical, is not one of our survey respondents, and so we have no observed response on our outcome variable.

We could also use that algorithm for people we have asked a self-assessment question, but such a procedure would be inefficient, as well as more sensitive to model misspecification. And then cranks it up to a higher order e. In a number of cases this has provided a desireable speedup to final convergence, but improvements will likely be data dependent. The tradeoff, of course, is that the higher the order of GH, the longer the estimation will take, and it already takes too long.

In the future, there will be a port of the computa- tionally intensive part of the code to C instead of purely in R. Grouping continuous data If you have a number of continuous explanatory variables, it is possible to greatly improve computation speed by rounding the data such that individuals with proximate values share common coding.

This is because the chopit code actually groups data by unique cases. If you set, chopit In this example with observations, cases shared exactly the same explanatory variables. The first count.

If you keep the scale the same, eg. Inconsistencies in the ordinal ranking are grouped and treated as ties. When few categories exist with which to distinguish these categories, additional collapsing may occur.