Case report: Chlorpromazine and deep venous thrombosis.

Purpose: Since the development of antipsychotic drugs in the 1950s, a variety of studies and case reports have been published that suggest an association between exposure to typical antipsychotics and venous thromboembolisms (VTE). Therefore, when starting treatment with antipsychotics, especially low-potency typical antipsychotics and clozapine, health-care providers must account for the patient's existing VTE risk factors.

Design/methodology/approach: In this case report, the authors describe the development of a pulmonary embolism associated with use of chlorpromazine in the treatment of an acute manic episode in a 51-year-old female patient with bipolar disorder type 1.

Testing multivariate mean collinearity via latent variable modelling.

A procedure for testing mean collinearity in multidimensional spaces is outlined, which is applicable in settings with missing data and can be used when examining group mean differences. The approach is based on non-linear parameter restrictions and is developed within the framework of latent variable modelling. The method provides useful information about the constellation of multiple response centroids in the populations studied, and is illustrated with an example.

A method of bias correction for maximal reliability with dichotomous measures.

This paper is concerned with the reliability of weighted combinations of a given set of dichotomous measures. Maximal reliability for such measures has been discussed in the past, but the pertinent estimator exhibits a considerable bias and mean squared error for moderate sample sizes. We examine this bias, propose a procedure for bias correction, and develop a more accurate asymptotic confidence interval for the resulting estimator.

Estimation of maximal reliability for multiple-component instruments in multilevel designs.

A procedure for point and interval estimation of maximal reliability of multiple-component measuring instruments in multi-level settings is outlined. The approach is applicable to hierarchical designs in which individuals are nested within higher-order units and exhibit possibly related performance on components of a given homogeneous scale. The method is developed within the framework of multi-level factor analysis.

On the Relationship Between Maximal Reliability and Maximal Validity of Linear Composites.

A linear combination of a set of measures is often sought as an overall score summarizing subject performance. The weights in this composite can be selected to maximize its reliability or to maximize its validity, and the optimal choice of weights is in general not the same for these two optimality criteria. We explore several relationships between the resulting reliability and validity estimates in different situations.

Maximal reliability and power in covariance structure models.

In covariance structure modelling, the non-centrality parameter of the asymptotic chi-squared distribution is typically used as an indicator of asymptotic power for hypothesis tests. When a latent linear regression is of interest, the contribution to power by the maximal reliability coefficient, which is associated with used latent variable indicators, is examined and this relationship is further explicated in the case of congeneric measures. It is also shown that item parcelling may reduce power of tests of latent regression parameters.

A Direct Method for Obtaining Approximate Standard Error and Confidence Interval of Maximal Reliability for Composites With Congeneric Measures.

Unlike a substantial part of reliability literature in the past, this article is concerned with weighted combinations of a given set of congeneric measures with uncorrelated errors. The relationship between maximal coefficient alpha and maximal reliability for such composites is initially dealt with, and it is shown that the former is a lower bound of the latter. A direct method for obtaining approximate standard error and confidence interval for maximal reliability is then outlined.

Estimation of reliability for multiple-component measuring instruments in test-retest designs.

A covariance structure modelling method for the estimation of reliability for composites of congeneric measures in test-retest designs is outlined. The approach also allows an approximate standard error and confidence interval for scale reliability in such settings to be obtained. The procedure further permits measurement error components due to possible transient condition influences to be accounted for and evaluated, and is illustrated with a pair of examples.

On Structural Equation Model Equivalence.

A necessary and sufficient condition for equivalence of structural equation models is presented. Compared to existing rules for equivalent model generation (Stelzl, 1986; Lee & Hershberger, 1990; Hershberger, 1994), it is applicable to a more general class including models with parameter restrictions and models that may or may not fulfil assumptions of the rules, to show that two models are nonequivalent, or to nonidentified models. The validity of the replacement rule by Lee and Hershberger, Stelzl's rules, and Hershberger's inverse indicator rule is implied from the present method.