# Assigning Cases to Groups Using Taxometric Results

@article{Ruscio2009AssigningCT, title={Assigning Cases to Groups Using Taxometric Results}, author={John Ruscio}, journal={Assessment}, year={2009}, volume={16}, pages={55 - 70} }

Determining whether individuals belong to different latent classes (taxa) or vary along one or more latent factors (dimensions) has implications for assessment. For example, no instrument can simultaneously maximize the efficiency of categorical and continuous measurement. Methods such as taxometric analysis can test the relative fit of taxonic and dimensional models, but it is not clear how best to assign individuals to groups using taxometric results. The present study compares the…

## 48 Citations

Taxometric analysis as a general strategy for distinguishing categorical from dimensional latent structure.

- Computer SciencePsychological methods
- 2012

It is concluded that the taxometric method may be an effective approach to distinguishing between dimensional and categorical structure but that other latent modeling procedures may be more effective for specifying the model.

Taxometrics, polytomous constructs, and the comparison curve fit index: a Monte Carlo analysis.

- BiologyPsychological assessment
- 2010

It is concluded that using the simulated data curve approach and averaging across procedures is an effective way of distinguishing between dimensional (1-class) and categorical (2 or more classes) latent structure.

Differentiating categorical and dimensional data with taxometric analysis: are two variables better than none?

- PsychologyPsychological assessment
- 2011

As long as high-quality data are available, it appears that one can have confidence in the results of taxometric analyses performed with only 2 variables, and the potential utility of 2- variables is illustrated using data on proactive and reactive childhood aggression.

Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches

- PsychologyStructural equation modeling : a multidisciplinary journal
- 2010

FMM generally outperformed MAXEIG in terms of class detection and class assignment, and Substantially different class sizes negatively impacted the performance of both approaches, whereas low class separation was much more problematic for MAXEig than for the FMM.

To sum or not to sum: taxometric analysis with ordered categorical assessment items.

- PsychologyPsychological assessment
- 2009

A Monte Carlo study compared the accuracy of taxomet analyses implemented in the traditional way (without summing items) and taxometric analyses implemented with the summed-input method, which substantially reduced discriminating power for 2 of the 3 procedures studied.

Comparing the relative fit of categorical and dimensional latent variable models using consistency tests.

- PsychologyPsychological assessment
- 2010

An approach to consistency testing is presented that builds on prior work demonstrating that parallel analyses of categorical and dimensional comparison data provide an accurate index of the relative fit of competing structural models.

Using comparison data to differentiate categorical and dimensional data by examining factor score distributions: resolving the mode problem.

- PsychologyPsychological assessment
- 2009

Performing parallel analyses of categorical and dimensional comparison data and calculating an index of the relative fit of these competing structural models achieves much greater accuracy, improved base rate estimation, and afforded consistency checks with other taxometric procedures.

Group membership prediction when known groups consist of unknown subgroups: a Monte Carlo comparison of methods

- PsychologyFront. Psychol.
- 2014

Results of the study indicated that CART and mixture discriminant analysis were the most effective tools for situations in which known groups were not homogeneous, whereas LDA, LR, and GAM had the highest rates of misclassification.

RTaxometrics : An R Package for Taxometric Analysis

- Computer Science
- 2017

The RTaxometrics package provides more user-friendly output and incorporates a recent development known as a CCFI profile that can rigorously test the structure of the data and, if it appears to be categorical, provide an estimate of the taxon base rate.

Using the Comparison Curve Fix Index (CCFI) in Taxometric Analyses: Averaging Curves, Standard Errors, and CCFI Profiles

- PsychologyPsychological assessment
- 2018

A series of simulation studies examine the use of the CCFI to flesh out some empirically supported guidelines and find that constructing a CCFI profile can help to differentiate categorical and dimensional data and provide a less biased and more precise estimate of the taxon base rate than conventional methods.

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