Two studies that compare the use of analysis of patterns in time (APT) (formerly non-metric temporal path analysis) and the linear models approach (LMA) are reported. In the first study, scores on the College Entrance Examination Board French Achievement Test (CEEBFAT), number of years of study of high school French (YHSF), and course achievement points (CAPs) of 87 undergraduate students at Indiana University (Bloomington) were analyzed. The 87 subjects were placed in one of three different levels of French courses. A linear regression analysis was conducted with placement test score (PTS) and YHSF as predictor variables and CAPs as the dependent variable. APT was subsequently used in data analyses. The LMA-predicted probabilities were compared to APT probabilities using the chi-square goodness-of-fit test. In the second study, a hypothetical data set was constructed in which CAP was not correlated with YHSF and PTSs; thus, the data set lacked linearity. Both the actual and hypothetical data sets had the same number of subjects in each corresponding PTS category/YHSF block. LMA and APT analyses were conducted on this hypothetical data set as in the first study. The results of these two studies show that APT is a more powerful tool than LMA in analyzing data with no inherent linear or deterministic relations. APT probabilities retain more specific information in the data by not averaging data across levels of various variables, as in LMA. APT is thus a better tool for analyzing stochastic educational processes. However, the results fail to illustrate a very important component of APT--temporal sequencing. Nine data tables are included. (RLC)