vocab: Dataframe of response to vocabulary items from the 2018...
In pleLMA: Pseudo-Likelihood Estimation of Log-Multiplicative Association Models

Description Usage Format Source Examples

These data are responses to 10 vocabulary items from the GSS collected in 2018 and retrieved July 2019 from https://gss.norc.org. These data are provided as an example of binary items and how the package can fit two parameter logistic models as either a GPCM or nominal model. Both models should give the same results. There are 10 words and responses to them were were either correct or incorrect. There are 1309 respondents in the data who gave answers to all items. The specific words used are not released due to test security reasons. The instructions given to answering these items are as follows: "We would like to know something about how people go about guessing words they do not know. On this card are listed some words–you may know some of them, and you may not know quite a few of them. On each line the first word is in capital letters – like BEAST. Then there are five other words. Tell me the number of the word that comes closest to the meaning of the word in capital letters. For example, if the word in capital letters is BEAST, you would say "4" since "animal" comes closer to BEAST than any of the other words. If you wish, I will read the words to you. These words are difficult for almost everyone– just give me your best guess if you are not sure of the answer. CIRCLE ONE CODE NUMBER FOR EACH ITEM BELOW."