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An effective technique for data analysis in the social sciences The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters governing these trajectories. The authors synthesize a vast amount of research and findings

Instrumental variable (IV) methods provide a powerful but underutilized tool to address many common problems with observational sociological data. Key to their successful use is having IVs that are uncorrelated with an equation's disturbance and that are sufficiently strongly related to the problematic endogenous covariates. This review briefly defines IVs, summarizes their origins, and describes their use in multiple regression, simultaneous equation models, factor analysis, latent variable structural equation models, and limited dependent variable models. It defines and contrasts three methods of selecting IVs: auxiliary instrumental variable, model implied instrumental variable, and randomized instrumental variable. It provides overidentification tests and weak IV diagnostics as methods to evaluate the quality of IVs. I review the use of IVs in models that assume heterogeneous causal effects. Another section summarizes the use of IVs in contemporary sociological publications. The conclusion suggests ways to improve the use of IVs and suggests that there are many areas in which IVs could be profitably used in sociological research.

The TETRAD search procedure has several limitations: It does not screen data for outliers; it relies on Wishart's test for vanishing tetrads that assumes a multinormal distribution for the random variables; and the significance tests do not take into account that multiple tetrad differences are being tested. I propose several ways to overcome these problems. First, I present several diagnostic statistics to help identify outliers and influential cases. Then I develop new, more general asymptotic tests for vanishing tetrads for variables with nonnormal distributions and derive a simultaneous test for multiple tetrad differences. Finally, the tests are extended to apply to tetrad differences of covariances as well as differences of correlations computed for random variables with "arbitrary" distributions.

Problems that surround both the definition & measurement of political democracy are addressed, focusing on the failure to develop an adequate theoretical definition of this concept, the confounding of the concept with others, & treating democracy as a binary rather than a continuous concept. Four problems of measurement are also identified: invalid indicators, subjective indicators, ordinal or dichotomous measures, & the failure to test reliability or validity. Several suggestions are offered to improve measurement, & the danger of repeating past errors is discussed. 3 Tables, 1 Appendix, 38 References. Adapted from the source document.

Assessing overall model fit is an important problem in general structural equation models. One of the most widely used fit measures is Bentler and Bonett's (1980) normed index. This article has three purposes: (1) to propose a new incremental fit measure that provides an adjustment to the normed index for sample size and degrees of freedom, (2) to explain the relation between this new fit measure and the other ones, and (3) to illustrate its properties with an empirical example and a Monte Carlo simulation. The simulation suggests that the mean of the sampling distribution of the new fit measure stays at about one for different sample sizes whereas that for the normed fit index increases with N. In addition, the standard deviation of the new measure is relatively low compared to some other measures (e.g., Tucker and Lewis's (1973) and Bentler and Bonett's (1980) nonnormed index). The empirical example suggests that the new fit measure is relatively stable for the same model in different samples. In sum, it appears that the new incremental measure is a useful complement to the existing fit measures.

A common occurrence in structural equation models are "improper solutions." Estimates of negative variances of measurement errors, negative variances of equation errors, or correlations between latent variables that are greater than one are instances of improper solutions. Recent work has begun to examine the causes and cures for these problems but the role of outliers in generating improper solutions has been overlooked. The purposes of this article are threefold: (1) to explain how outliers can lead to improper solutions, (2) to use a confirmatory factor analysis example to demonstrate this, and (3) to encourage researchers to check for this possibility.