group_adj

Adjusts observed 1s based on propensity to guess (based on observed 0s) and item level \(\gamma\). 
You can also put in your best estimate of hidden knowledge behind don't know responses.

Provides tools to adjust estimates of learning for guessing-related
bias in educational and survey research. Implements standard guessing
correction methods and a sophisticated latent class model that leverages
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 (2018)
<https://gsood.com/research/papers/guess.pdf>.

Gaurav Sood

guess

Adjust Estimates of Learning for Guessing

Ken Cor

group_adj function

<dl><dt>pre</dt>
<dd>pre data frame. Required. Each vector within the data frame should only take values 0, 1, and 'd'.</dd>
<dt>pst</dt>
<dd>pst data frame. Required. Each vector within the data frame should only take values 0, 1, and 'd'.</dd>
<dt>gamma</dt>
<dd>probability of getting the right answer without knowledge</dd>
<dt>dk</dt>
<dd>Numeric. Between 0 and 1. Hidden knowledge behind don't know responses. Default is .03.</dd></dl>

Arguments

Group Level Adjustment That Accounts for Propensity to Guess — group_adj

<dl>

<dt>pre</dt>
<dd>pre data frame. Required. Each vector within the data frame should only take values 0, 1, and 'd'.</dd>


<dt>pst</dt>
<dd>pst data frame. Required. Each vector within the data frame should only take values 0, 1, and 'd'.</dd>


<dt>gamma</dt>
<dd>probability of getting the right answer without knowledge</dd>


<dt>dk</dt>
<dd>Numeric. Between 0 and 1. Hidden knowledge behind don't know responses. Default is .03.</dd>

</dl>

group_adj: Group Level Adjustment That Accounts for Propensity to Guess

Description

Usage

Value

Arguments

Examples