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Symptoms Measurement, Community Sampling and the Zero-Problem: A Case for Two-Part Modeling

The difference between mental health and mental ability measurement hinges on a single concept—zero. Dysfunctional mental health is manifested by symptoms defined as self-reported feelings of unpleasantness due to pathological causes. Symptoms can be meaningfully reported as present or absent whereas mental abilities are generally considered to be ever present in some positive amount. Absence of symptoms creates a population zero class with unknown membership and proportion. Inadvertent mixture of zero- and non-zero classes, as often occurs in community samples, biases symptom estimates of means, variances, and covariance for the non-zero class, resulting in what is herein referred to as the zero-problem.

Two-part modeling is proposed as a means of circumventing the zero-problem. In Part I, zero-class sample members are identified and deleted. Part II provides users a symptoms research paradigm based on a multiplicative measurement model. Data are logarithmically transformed, and the log-normal distribution assumed. The hypothesis that symptom statements are unidimensional is tested by confirmatory factor analysis (CFA). If accepted, statements are combined into a weighted pathology score. Pathology scores can be correlated, corrected for attenuation, and used as input to multivariate statistical applications. Computer routines are provided as a user service.


Donald W Drewes*, Chia-Lin Ho and William E Schlenger

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