Relations between Tucker's three-mode multidimensional scaling and Carroll and Chang's INDSCAL are discussed. A technique to transform a three-mode solution to the general form of an INDSCAL solution along with applications to two sets of data from the literature are presented. (Author/JKS)

The role of conditionality in the INDSCAL and ALSCAL multidimensional scaling procedures is explained. The effects of conditionality on subject weights produced by these procedures is illustrated via a single set of simulated data. Results emphasize the need for caution in interpreting subject weights provided by these techniques. (Author/JKS)

A Monte Carlo study was carried out to investigate the ability of the ALSCAL multidimensional scaling program to recover true structure inherent in simulated proximity data. The results under varying conditions were mixed. Practical implications and suggestions for further research are discussed. (Author/JKS)

A Monte Carlo study investigated the ability of the ALSCAL multidimensional scaling program to recover true structure inherent in simulated proximity measures when data were missing. The program worked well with up to 60 percent missing data as long as sample size was large and random error was low. (Author/JKS)

Concerned with consequences of employing the INDSCAL model when one of its assumptions are known to be violated. Under study is the notion that all individuals perceive the object space dimensions to be independent. (RC)