This paper illustrates the transformation of a raw data matrix into a matrix of associations, and then into a factor matrix. Factor analysis attempts to distill the most important relationships among a set of variables, thereby permitting some theoretical simplification. In this heuristic data, a correlation matrix was derived to display associations among the variables. Multiple regression, both with and without Z-score transformation, was used to generate structure coefficients and the factor matrix. This matrix shows the correlation of each variable with the factors extracted from the association pattern. Two factors were extracted in this case. For each, the scores constitute a new variable, a weighted combination of the scores on each of the original variables. Each factor is treated as a hypothetical construct, to be interpreted from the pattern of factor scores. Communality is the proportion of variance of each variable that is reproduced in the extracted factors. If multiple regression is the factoring method, then the sum of communalities is equivalent to the multiple correlation coefficient. (LPG)