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Abstract
Although implicit motives are well established, coding for implicit motives has been a time-consuming and difficult manual process. This study (i) evaluates automated motive coding schemes for Need for Achievement, Need for Affiliation, and Need for Power, (ii) assesses the relationship between these three motives and crisis initiation, and (iii) assesses the explanatory power of other psychological variables for crisis behavior. The results establish the convergent validity of the Need for Achievement and Need for Power coding schemes with hand coding and support findings of a significant relationship between Need for Power and crisis behavior. A unit increase in Need for Power doubles the probability that a leader will initiate a crisis. Even stronger models were obtained by including other previously studied psychological variables. The scope of the dataset (154 leaders across a diverse set of 93 countries) lends weight to the findings and provides many opportunities for additional research.

Although implicit motives are well established, coding for implicit motives has been a time-consuming and difficult manual process. This study (i) evaluates automated motive coding schemes for Need for Achievement, Need for Affiliation, and Need for Power, (ii) assesses the relationship between these three motives and crisis initiation, and (iii) assesses the explanatory power of other psychological variables for crisis behavior. The results establish the convergent validity of the Need for Achievement and Need for Power coding schemes with hand coding and support findings of a significant relationship between Need for Power and crisis behavior. A unit increase in Need for Power doubles the probability that a leader will initiate a crisis. Even stronger models were obtained by including other previously studied psychological variables. The scope of the dataset (154 leaders across a diverse set of 93 countries) lends weight to the findings and provides many opportunities for additional research.

Cognitive mapping has been a valuable tool in understanding how individuals view their external environment. It has been used successfully to investigate crisis decision making, juror decision making, and international negotiation, and may find further use as a support tool in negotiation and mediation. This article presents a method for enhanced cognitive mapping—WorldView, which uses the symbol-based formalism of semantic networks. WorldView provides important advantages over more traditional cognitive mapping and assists in the systematic study of belief system content and decision process. WorldView eliminates constraints on represented relationships and captures more information than previous content analysis systems for belief structures, provides aggregation over texts or subject responses, provides a synonym facility for collapsing similar concepts, incorporates structural and comparative measures for analysis, and constructs manipulable cognitive maps that provide a basis for process models of belief change and decision making.

Previous work on the measurement of leaders' foreign policy orientations by Margaret G. Hermann argues that spontaneous utterances should provide more useful indications of stable personality characteristics than prepared statements. This paper reconsiders this question for Presidents Jimmy Carter and Bill Clinton through an automated coding system. A large number of spontaneous utterances and prepared statements by these presidents were compared in an effort to determine the stability of their conceptual complexity across source types and over time. The findings support the ability of Hermann's conceptual complexity measurement to capture robust differences between individuals; however, scores derived from spontaneous comments varied significantly from those derived from prepared remarks. In addition, the trait appears to be stable for some subjects (Carter) but not for others (Clinton).

This piece is a response to the Conway, Conway, Gornick, and Houck (2014) description of an automated method for assessing integrative complexity. It builds on the authors' 15 years of experience in automating a variety of textual analysis coding schemes including that measuring conceptual complexity and is intended to address some of the issues raised in the process of automating content analysis categories.