Paul R. Lohnes

Factorial Modeling in Support of Causal Inference

This paper describes and justifies a new method for analyzing correlations in support of causal inference. Named Factorial Modeling (FaM), its motivations are (1) Social scientists have an obligation to hypothesize the probable causes of the phenomena they seek to explain, and (2) In the interests of discipline and parsimony, causes should be operationalized as uncorrelated variables. Applying a simple algebra to the correlation matrix, FaM produces a structural equation for each variate in the research, thus analyzing all the variances and covariances. The advantage of the FaM method is that the natural language of domains of measurement which are known to be relevant can be respected in the hypothesizing of causes. Since the algorithm does not attempt to maximize or minimize anything, a loose fit to the data will be obtained; but it is suggested that such loose-fitting models may travel well to other situations to which generalization is attempted. The FaM method is illustrated on a small example, for which path analysis, LISREL-type analysis, canonical correlation, and commonality analysis results are also given to provide comparisons with other methods of modeling. Predictions of the impacts of policy manipulations under a model obtained from FaM are demonstrated.

Implications of data-analysis models for careers guidance

Abstract Data-analysis models for careers guidance have been mostly of the regression type, operating to transform trait assessments into predictions of career adjustments. If understanding of self and life-space is a prerequisite to decision-making and planning, models of the correlation type – which transform trait distributions of populations into knowledge of the antecedents of variance in careers phenomena – may need to take precedence in guidance programmes. Models of the two types have some guidance implications in common, but each type of model also has some special implications, which are discussed. It is argued that studying correlation models for career development data in the context of a sequential, structured guidance curriculum can provide young people with scientific attitudes and skills which will make them ready for personal predictions, decisions, and planning.