Robert Hogenraad

Force and Influence in Content Analysis: The Production of New Social Knowledge

We examine the two traditions of content analysis: the first in which one substitutes words of a text with categories, and the second in which one looks for clusters of words that may refer to a theme. In the first tradition, preexisting dictionary categories give meaning to the words; in the second, meaning comes after the fact. Preexisting dictionary categories (the substitution model) are calibrated instruments applied within experimental designs that leave no space for doubt; meanwhile, the ability of the correlational model to conjure up complex themes from fragments of a text yields no unique solution. These differences have bearings on the production of new social knowledge. We expound on the epistemological foundations of the two traditions of interpretation and draw from them decision rules upon which one may rely for choosing among appropriate content-analytic tactics. Two reasons make this essay timely and critical: (1) the increasing variety of new content-analyticsoftware for particular purposes and (2) the almost exclusive focusing on software and technology at the expense of adjusting the choice of the software to the nature of the text. Two studies, one in historiometry, the other in autobiography, illustrate the liabilities and benefits of the two models of content analysis.

The enemy within: Autocorrelation bias in content analysis of narratives

Many content analysis studies involving temporal data are biased by some unknown dose of autocorrelation. The effect of autocorrelation is to inflate or deflate the significant differences that may exist among the different parts of texts being compared. The solution consists in removing effects due to autocorrelation, even if the latter is not statistically significant. Procedures such as Crosbies (1993) ITSACORR remove the effect of at least first-order autocorrelations and can be used with small samples. The AREG procedure of SPSS (1994) and the AUTOREG procedure of SAS (1993) can be employed to detect and remove first-order autocorrelations, and higher-order ones too in the case of AUTOREG, while several methods specifically intended for small samples (Huitema and McKean, 1991, 1994) have been developed. Four examples of content analysis studies with and without autocorrelation are discussed.

Replicating text: The cumulation of knowledge in social science

Obtaining a statistically significant result does not necessarily tell us whether we would obtain significant results in other, similar studies, particularly if the original sample sizes were small. This is why we are supposed to replicate experiments. The present study concerns social science events that cannot be repeated by virtue of their being historically situated. Among social science events, many textual data are datable and, by definition, unrepeatable. One solution to this quandary lies in bootstrap replications, which are based on the original data. A case in point is that of founding political speeches such as those that buoy the European construction. We analyze and compare 82 speeches made by President Delors over the period 1988–1994, and 28 by President Santer over the period 1995–1997. We have all these speeches (N = 110) concorded as to which words are used, how often, where, and when, with the help of a computer-aided content analysis package. We then test various hypotheses using replication bootstrap estimates, that is, by replicating the original sample a large number of times and recreating several thousand samples from the population so created.

Imagery, Regressive Thinking, and Verbal Performance in Internal Monologue

In order to analyze a free-associative discourse–internal monologues obtained by the method of thinking aloud–, two analyses are offered, descriptive and deductive. In the descriptive analysis, an Imagery Dictionary and a Regressive Imagery Dictionary, both in French, were used to evaluate the importance and unfolding of images and regressive thinking in three-hour long internal monologues obtained from eighteen French-speaking volunteers. The deductive analysis relies on Osgoods Naturalness Principle: that congruent sentences conjoined by and evidence faster processing times than incongruent sentences conjoined by but. A positive relationship was hypothesized and confirmed between images and regressive thinking (considered as natural cognizings) on the one hand, and the use of markers of congruence on the other hand.

Words of swords in the Caucasus : About a leading indicator of conflicts

A modest and rational way of getting communication intelligence is to analyze speeches of political leaders. This studys interest is in the communications that precede conflicts—in this case, the conflict between Georgia and the Russian Federation (January-August 2008) over the separatist regions of Abkhazia and Southern Ossetia. This study analyzes statements by President Saakashvili of Georgia, by President Medvedev of the Russian Federation, and by Georgias allies over this period. The study analyzes these statements using a computer-aided procedure of quantitative content analysis equipped with semantic filters tailored to forecast the risk of conflict based on the gap between power words (increasing) and affiliation words (decreasing) in texts, as McClelland (1975) showed. The larger the gap, the greater the risk. Because possible conflicts increase uncertainty, how much vagueness there was in the statements was also quantified. The statements by President Medvedev display a growing power motivation about Georgia and a style characterized by increasing precision. Despite the need to defend the territorial integrity of his country, President Saakashvili showed a decreasing risk of war while also using vaguer words, betraying uncertainty. Georgias allies also show an increasing risk of war, but restraint prevailed. The Russian national revival movement of Eurasianism offers insight in helping to grasp the meaning of the events of August 2008 while also confirming the efficacy of McClellands (1975) indicator of risk of conflict.

On the Thread of Discourse: Homogeneity, Trends, and Rhythms in Texts:

The rich variety of literary material can usefully be described by quantitative content analysis. Usually, such a description proceeds by segmenting the text into large aggregates that take no account of original word order. A more fine-grained analysis can be obtained by taking word order into account. The latter analysis is closer to the linear nature of the text as narrative and also closer to the true nature of language itself. PROTAN, a computer-aided content analysis system, takes care of all the operations that result in the tagging of text words into an appropriate category (here called a dictionary); meanwhile, the original sequential order of the tagged words is kept unchanged within the text. Trend analyses and time-series analyses can then be performed on the condition that pertinent categories can be shown to be non-randomly distributed throughout the text (non-homogeneity). The corpus reported upon in this article is made up of two sets of texts. The first set consists of seven reference texts—mostly short stories—to serve as foil for a second set of eighteen texts written and distributed by a Belgian terrorist group during 1984 and 1985. The results point first to the psychological significance of whether texts are homogeneous or not on a given dictionary. They point secondly to the pertinence of content rhythms for describing texts. Compared to the reference texts, the eighteen target texts are more orderly in more ways. This is in opposition to the opinion generally held by public authorities.

Structures Affective et Descriptive du Management: Etude Interculturelle

Abstract Managerial thinking is studied along a semantic differential methodology in two different cultural settings having the same language. Questions are raised concerning 1) the role of the universal affective EPA meaning structure in the meaning structure of management concepts; 2) other possible culture-specific or species-specific dimensions (factors) along which management concepts are judged; 3) the influence of the universal affective meaning components on specific components of the meaning of management concepts. The instrument is composed of 22 concepts and 30 bipolar adjectival scales, nine of which are pan-cultural, the remaining 21 resulting from a genuine elicitation procedure from the field of management. The instrument was applied to 48 Belgian French-speaking Ss and 50 French Canadian Ss, both samples consisting of male Ss, managers and non-managers, working in two production enterprises located in Belgium and in Quebec (Canada). One management-specific factor, common to both samples, w...

La Personnalité Comme Construction Sémantique Universelle et Culturelle

Abstract Personality was approached by tracing back common usage terms, easily available to the layman, to their latent semantic organization - traits - which is supposedly revealed by their inter-correlations and subsequent factorization, that is, the usual semantic differential methodology. The measuring instrument is composed of 40 concepts and 40 scales, nine of which are pan-cultural and 31 are resulting from a genuine elicitation, and was applied to a sample of 100 male and female subjects. Strong similarities are found, as expected, between personality structure and the more fundamental affective EPA meaning structure. But even when the affective component of the meaning structure, which is nevertheless the main feature of personality ratings, is mathematically neutralized by partializing the nine pan-cultural scales out of the 31 genuine personality scales, the remaining personality structure is still universal enough, beyond its fundamental affective component. It is suggested that those unique a...

Textual fingerprints of risk of war

We compute the rate of textual signals of risk of war recognizable in series of consecutive political speeches about a disputed issue serious enough to entail an international conflict. The speeches concern Irans nuclear program. We trace textual signals forewarning of risks of war that reactions to this affair lead to. The thrust of the textual analysis rests on the interplay of affiliation and power words in continuous texts, following D. C. McClellands model for anticipating wars. The speeches are those of Iranian President Mahmoud Ahmadinejad, US Secretary of State Hillary R. Clinton, Iranian Grand Ayatollah Ali Khamenei, and Israeli Prime Minister Benjamin Netanyahu. Prefiguring a military confrontation before it occurs involves structuring information from unstructured data. Despite such imperfect knowledge, by the end of January 2012, our results show a receding risk of war on the Iranian side, but an increasing risk on the American one, while remaining ambiguous on the Israeli one.

Deaf sentences1 over Ukraine: Mysticism versus ethics

We look to unlock the verbal code of President Putin and NATO Secretary-General Rasmussen during the Ukrainian crisis. On 18 March 2014, referring to the medieval history of Russia, Putin expressed a vision that invites to explore the role of ancestral mental images as an instance of people with a direct knowledge of spiritual truth. Looking for spiritual truth is often at the edge of words, forcing the speaker to resort to poetic language to convey an inspired message beyond common understanding. Using a fragment of Martindale’s creativity model, we look for indicators of insight (regressive thought, metaphors, dual-coding of images and emotions) in Putin’s and NATO’s speeches over a short take of recent history, December 2013–September 2014. Among obvious results, the annexing of Crimea to the Russian Federation (18 March 2014) sparked new words in both Putin’s and NATO’s speeches. The May 9 ‘Great Victory Day’ also affected Putin’s speeches. After May 9, a visible discontinuity marks the speeches of both Putin and NATO, but in opposite directions. Higher scores of metaphoric thought in Putin contrasts with lower scores in NATO. Finally, the threat index increases moderately in both cases, yet bears no strong relation to the ups or downs of visionary metaphoric thought in either Putin or NATO. The two corpuses contain the words ‘value’ and ‘border’ in plenty, pushing linguistic detection scholars to explore new frontiers.