Statistical methods for analyses of incomplete clinical service records: concurrent use of longitudinal and cross-sectional data.
Statistical procedures that have become routine in other social sciences were used to analyze data from clinical service records. Despite the absence of control groups, nonrandom assignment of clients to treatment conditions, and incomplete records, effective analyses of psychotherapeutic processes were possible. Multivariate regression models, with variables that were transformed to significantly improve skewness and regression linearity, were controlled for heteroskedasticity and for end-point censoring of dependent variables. They were also used to measure the effects of a categorical variable (gender) and a scalable variable (intake distress) on a reactive outcome measure (of acute distress) and on an unreactive one (of long-term life satisfaction). Graphical methods for summarizing large data sets helped identify intake variables that could control for attrition-related sampling biases. These longitudinal covariates and corrections to adjust degrees of freedom for cases with repeated measures were then used to construct statistical models that were equivalents of pure cross-sectional designs.