new_guess_cv

internal

Provides tools to adjust estimates of learning for guessing-related
bias in educational and survey research. Implements standard guessing
correction methods and latent class models that leverage informative
pre-post test transitions to account for guessing behavior.
The package helps researchers obtain more accurate estimates of actual
learning when respondents may guess on closed-ended knowledge items.
For theoretical background and empirical validation, see Cor and Sood (2016)
<https://gsood.com/research/papers/guess.pdf>.

Gaurav Sood

guess

Adjust Estimates of Learning for Guessing

Ken Cor

new_guess_cv function

<dl><dt>fold_results</dt>
<dd>data.frame of per-fold results</dd>
<dt>mean_ll</dt>
<dd>mean log-likelihood</dd>
<dt>total_ll</dt>
<dd>total log-likelihood</dd>
<dt>perplexity</dt>
<dd>perplexity</dd>
<dt>se</dt>
<dd>standard error of perplexity across folds</dd>
<dt>cv_type</dt>
<dd>"items" or "individuals"</dd>
<dt>k</dt>
<dd>number of folds</dd>
<dt>call</dt>
<dd>original function call</dd></dl>

Arguments

Create a guess_cv object — new_guess_cv

<dl>

<dt>fold_results</dt>
<dd>data.frame of per-fold results</dd>


<dt>mean_ll</dt>
<dd>mean log-likelihood</dd>


<dt>total_ll</dt>
<dd>total log-likelihood</dd>


<dt>perplexity</dt>
<dd>perplexity</dd>


<dt>se</dt>
<dd>standard error of perplexity across folds</dd>


<dt>cv_type</dt>
<dd>"items" or "individuals"</dd>


<dt>k</dt>
<dd>number of folds</dd>


<dt>call</dt>
<dd>original function call</dd>

</dl>

new_guess_cv: Create a guess_cv object

Description

Usage

Value

Arguments