Charles J. Brody

Evaluating an alcohol and drug treatment program for the homeless: An econometric approach

The New Orleans Homeless Substance Abusers Project (NOHSAP) was designed as a randomized field experiment to test the effectiveness of a residential alcohol and drug treatment program on the sobriety, employment, housing, and social integration of homeless substance abusers. However, program staff sabotaged randomization into treatment and control groups, and research attrition was also non-random. Non-random assignment to treatment and non-random research attrition threaten internal and external validity by biasing OLS estimates of the effects of treatment and necessitate use of econometric selection bias correction modeling techniques. Results of these corrected models are then used in subsequent estimates of treatment effects on a variety of outcome measures. After correction, positive treatment effects prove relatively modest. However, subsequent analysis suggests that NOHSAP exerted a critical indirect effect on outcomes by facilitating subjects participation in outside substance abuse groups. We conclude with some observations on the policy implications of the substantive results.

Latent variables analysis : applications for developmental research

PART ONE: THEORETICAL ISSUES: CONCEPTS IN LATENT VARIABLES ANALYSIS Causal Inference in Latent Variable Models - Michael E Sobel The Theory of Confounding and its Application in Causal Modeling with Latent Variables - Rolf Steyer and Thomas Schmitt The Specification of Equivalent Models before the Collection of Data - Scott L Hershberger PART TWO: ANALYSIS OF LATENT VARIABLES IN DEVELOPMENTAL RESEARCH: CONTINUOUS VARIABLES APPROACHES The Effect of Unmeasured Variables and their Interactions on Structural Models - Phillip K Wood Exploratory Factor Analysis with Latent Variables and the Study of Processes of Development and Change - John R Nesselroade Dynamic Latent Variable Models in Developmental Psychology - Peter C M Molenaar Latent Variables Models and Missing Data Analysis - Michael J Rovine On the Arbitrary Nature of Latent Variables - Peter C M Molenaar and Alexander von Eye PART THREE: ANALYSIS OF LATENT VARIABLES IN DEVELOPMENTAL RESEARCH: CATEGORICAL VARIABLES APPROACHES Latent Class Models for Longitudinal Assessment of Trait Acquisition - George B Macready and C Mitchell Dayton Latent Trait Models for Measuring Change - Christiane Spiel Measuring Change Using Latent Class Analysis - Anton K Formann Mixture Decomposition when the Components are of Unknown Form - Hoben Thomas Latent Variables in Log-Linear Models of Repeated Observations - Jacques A Hagenaars Latent Logit Models with Polytomous Effects Variables - Allan L McCutcheon Latent Variables Markov Models - Rolf Langeheine PART FOUR: TESTING IN THE ANALYSIS OF LATENT VARIABLES Corrections to Test Statistics and Standard Errors in Covariance Structure Analysis - Albert Satorra and Peter M Bentler Testing in Latent Class Models Using a Posterior Predictive Check Distribution - Donald B Rubin and Hal S Stern

An Evaluation of an Alcohol and Drug Treatment Program for Homeless Substance Abusers

This article evaluates a residential alcohol/drug treatment program for the homeless. The process evaluation documents numerous deviations from the program as designed and other implementation problems. Foremost among these: The project was designed as a randomized experiment, but randomization was sabotaged by the treatment staff. Nonrandom research attrition constituted another potential source of bias. The authors employ econometric modeling techniques to correct for these selection biases. Results indicate that, although treatment effects are in the expected direction, they are rarely significant. However, consistent with the drug treatment literature, evidence suggests that retention in treatment is a critical variable predicting program effects. Clients who remain in treatment for more than a few months exhibit more positive outcomes than those staying for shorter periods.

Sociological applications of fuzzy classification analysis

Abstract Research methodology and data analysis in sociology are dominated by probability theory. However, probability theory cannot always deal with the imprecisions of the real world. In this paper, the basic concepts of fuzzy sets theory and fuzzy classification are discussed. An empirical example is presented. The fuzzy classifications of respondents based on their attitude of self protection and offense are highly related to the behavior of carrying weapons. By incorporating the classifications into our analytical model, we find that the predictive power of the model is enhanced and multicollearity among independent variables is avoided.

Canonical Correlation and Chi-Square: Relationships and Interpretation

Abstract A 2 × 2 chi-square can be computed from a phi coefficient, which is the Pearson correlation between two binomial variables. Similarly, chi-square for larger contingency tables can be computed from canonical correlation coefficients. The authors address the following series of issues involving this relationship: (a) how to represent a contingency table in terms of a correlation matrix involving r - 1 row and c - 1 column dummy predictors; (b) how to compute chi-square from canonical correlations solved from this matrix; (c) how to compute loadings for the omitted row and column variables; and (d) the possible interpretive advantage of describing canonical relationships that comprise chi-square, together with some examples. The proposed procedures integrate chi-square analysis of contingency tables with general correlational theory and serve as an introduction to some recent methods of analysis more widely known by sociologists.