Fluency

This dataset contains the scores of the Fruits Verbal Fluency Test. The \(N = 1241\) test participants were instructed to generate as many words as possible that belong to the category `fruits' (e.g., apple, orange, banana, etc.) within \(60\) seconds. These are simulated data based on the results described in Rivera et al. (2019).

dataset

Normative data are often used to estimate the relative position of a raw test score in the population. This package allows for deriving regression-based normative data. It includes functions that enable the fitting of regression models for the mean and residual (or variance) structures, test the model assumptions, derive the normative data in the form of normative tables or automatic scoring sheets, and estimate confidence intervals for the norms. This package accompanies the book Van der Elst, W. (2024). Regression-based normative data for psychological assessment. A hands-on approach using R. Springer Nature.

Wim der Elst

NormData

Derivation of Regression-Based Normative Data

Wim Van der Elst

Fluency function

A <code>data.frame</code> with \(1241\) observations on \(3\) variables.<dl>
 <dt><code>Id</code></dt>
<dd>The Id number of the test participant.</dd> <dt><code>Country</code></dt>
<dd>The country where the test participant lives, coded as a factor.</dd> <dt><code>Fruits</code></dt>
<dd>The number of correctly generated fruit names. Higher score is better.</dd>
</dl>

Format

Verbal fluency data — Fluency

A <code>data.frame</code> with \(1241\) observations on \(3\) variables.<dl>
 <dt><code>Id</code></dt>
<dd>The Id number of the test participant.</dd>

 <dt><code>Country</code></dt>
<dd>The country where the test participant lives, coded as a factor.</dd>

 <dt><code>Fruits</code></dt>
<dd>The number of correctly generated fruit names. Higher score is better.</dd>


</dl>

Fluency: Verbal fluency data

Description

Usage

Arguments

Format

References