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

@article{Walters2010TaxometricsPC,
  title={Taxometrics, polytomous constructs, and the comparison curve fit index: a Monte Carlo analysis.},
  author={Glenn D. Walters and Robert E. McGrath and Raymond A Knight},
  journal={Psychological assessment},
  year={2010},
  volume={22 1},
  pages={
          149-56
        }
}
The taxometric method effectively distinguishes between dimensional (1-class) and taxonic (2-class) latent structure, but there is virtually no information on how it responds to polytomous (3-class) latent structure. A Monte Carlo analysis showed that the mean comparison curve fit index (CCFI; Ruscio, Haslam, & Ruscio, 2006) obtained with 3 taxometric procedures-mean above minus below a cut (MAMBAC), maximum covariance (MAXCOV), and latent mode factor analysis (L-Mode)-accurately identified 1… 

Figures and Tables from this paper

Taxometric analysis as a general strategy for distinguishing categorical from dimensional latent structure.
TLDR
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.
Differentiating categorical and dimensional data with taxometric analysis: are two variables better than none?
TLDR
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.
Modeling psychological attributes: Merits and drawbacks of taxometrics and latent variable mixture models
The longstanding debate whether psychological attributes are represented by dimensions or categories is considered an important issue in psychology. To resolve this debate, researchers have turned to
Using the Comparison Curve Fix Index (CCFI) in Taxometric Analyses: Averaging Curves, Standard Errors, and CCFI Profiles
TLDR
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.
Illustration of Step-Wise Latent Class Modeling With Covariates and Taxometric Analysis in Research Probing Children's Mental Models in Learning Sciences
TLDR
Two psychometric methods, latent class analysis (LCA) and taxometric analysis (TA) are illustrated using empirical data from research probing children's mental representation in science learning to answer the fundamental hypothesis about children's naïve knowledge on the matters under study.
Does nature have joints worth carving? A discussion of taxometrics, model-based clustering and latent variable mixture modeling
TLDR
This review shows why the results of a taxometric procedures, model-based clustering and latent variable mixture modeling investigation depend on the characteristics of the observed symptoms and the sample and argues that the choice of method should optimally match and make use of the existing knowledge about the data that are analyzed.
Taxometric Analysis of the Latent Structure of Pedophilic Interest
TLDR
The finding of trichotomous latent structure in pedophilic interest is both consistent and inconsistent with previous taxometric studies and has implications for research, assessment, and treatment of Pedophilic interest.
Latent Structure of Early Adolescent Bullying Perpetration: A Taxometric Analysis of Raw and Ranked Scores
TLDR
It was surmised that bullying perpetration is continuously organized and that rank normalized scores may improve the interpretability of taxometric findings derived from highly nonnormal indicators.
In search of the psychopathic sexuality taxon: indicator size does matter.
TLDR
Results suggest that psychopathy (with or without coercive and precocious sexuality) is a dimensional construct, similar to the vast majority of earlier research.
...
1
2
3
4
...

References

SHOWING 1-10 OF 34 REFERENCES
Taxometric Analysis: II. Detecting Taxonicity Using Covariance of Two Quantitative Indicators in Successive Intervals of a Third Indicator (Maxcov Procedure)
TLDR
Given three quantitative indicators of a conjectured latent taxon, a statistical function defined as the covariance of two indicators computed within successive intervals along the third indicator reveals whether the latent structure of the data is taxonic or not.
Assigning Cases to Groups Using Taxometric Results
  • J. Ruscio
  • Environmental Science
    Assessment
  • 2009
TLDR
The present study compares the performance of two classification techniques—Bayes' theorem and a base-rate technique—across a wide range of data conditions and concludes that the base- rate classification technique achieves greater classification accuracy and a more even balance between sensitivity and specificity.
Taxometric analysis. I: Detecting taxonicity with two quantitative indicators using means above and below a sliding cut (MAMBAC procedure)
TLDR
Given two quantitative indicators of a conjectured latent taxon, a statistical function defined as the difference between the observed means for cases of one indicator falling above and below a sliding cut indicates whether the latent structure is taxonic or nontaxonic.
A Nontechnical Introduction to the Taxometric Method
TLDR
A nontechnical introduction to the taxometric method for assessing latent structure and a number of refinements and extensions to taxometric methodology including a useful interpretive aid based on the parallel analysis of simulated taxonic and dimensional comparison data.
Using taxometric analysis to distinguish a small latent taxon from a latent dimension with positively skewed indicators: the case of involuntary defeat syndrome.
TLDR
The authors discuss methodological strategies for conducting and interpreting taxometric analyses under the adverse conditions commonly encountered in psychopathology research, including skewed indicators and small putative taxa.
Differentiating Categories and Dimensions: Evaluating the Robustness of Taxometric Analyses
TLDR
The impressive accuracy of the CCFI was consistent with prior findings and robust across novel manipulations of asymmetry, tail weight, and heterogeneous variances, as well as the practical implications for differentiating categories and dimensions.
Inferential Errors in Taxometric Analyses of Ordered Three-Class Constructs
  • R. McGrath
  • Psychology
    Journal of personality assessment
  • 2008
TLDR
It was demonstrated both mathematically and empirically that under various circumstances, the results of taxometric analyses can lead to incorrect conclusions about the population structure.
Taxometric Analysis of Fuzzy Categories: A Monte Carlo Study
TLDR
Fuzzy data sets tended to yield taxonic findings on plot inspection and two popular consistency tests, even when the degree of fuzziness was large, suggesting that fuzzy categories represent a source of pseudotaxonic inferences, if “taxon” is understood in the usual binary, “either-or” fashion.
Bootstraps taxometrics. Solving the classification problem in psychopathology.
  • P. Meehl
  • Psychology
    The American psychologist
  • 1995
TLDR
Two taxometric procedures, MAMBAC and MAXCOV-HITMAX, provide independent tests of the taxonic conjecture and satisfactorily accurate estimates of theTaxon base rate, the latent means, and the valid and false-positive rates achievable by various cuts.
To sum or not to sum: taxometric analysis with ordered categorical assessment items.
TLDR
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.
...
1
2
3
4
...