The paper deals with powerlessness as one of the possible meanings of alienation, and presents Neal’s Powerlessness Scale as a means for measuring this concept. The aim of this research is to find out if it is possible to adopt the Neal’s research technique, developed in the context of the American culture in the late 1950s, to empirical sociological research in the Czech Republic. This issue is important because there is at present no standard attitudinal instrument for measuring a persons’ perception of their power to exert influence over socio-political events. An initial test of the reliability and internal and external validity of Neal’s scale is undertaken using a non-representative sample of the Czech population. Results of this quantitative analysis suggest that a subset of nine items from the original twelve item scale is the most reliable and valid measure of a person’s sense of control over the socio-political events within the Czech cultural milieu. Importantly, the process of data collection reveals several problematic features of Neal’s powerlessness scale, and it is recommended that certain alterations before its further use in the Czech context.
This article examines the reliability of statistical models that use visualization of word distances using computer-assisted text analysis. This study looks at the choice of parameters in the COOA - software for word co-occurrence analysis. The word co-occurrence analysis enables visualization of text structure through the exploration of the number of co-occurrences of words. The data visualization provided by a multi-dimensional scaling (MDS) procedure is susceptible to a particular form of error. The nonlinear relationship between words with significantly different frequencies lies at the root of this problem where words with higher frequencies are placed in the middle of a two-dimensional MDS map visualization. Words with lower frequency, on the other hand, are forced by the MDS estimator to the edge of the two-dimensional map and their estimated spatial positions are unstable. These two processes are potentially a major source of error in making inferences. One solution for reducing this source of error is to (a) reduce the number of words in a model or (b) increase of the number of model dimensions. This article, however, suggests that a detailed investigation of the word structure and a thorough analysis of the error sources and their meaningful interpretation may be a better solution.