MEASURING SOCIAL DESIRABILITY IN COLLECTIVIST COUNTRIES: A PSYCHOMETRIC STUDY IN A REPRESENTATIVE SAMPLE FROM KAZAKHSTAN

Social desirability bias (SDB) is a pervasive measurement challenge in the social sciences and survey research. More clarity is needed to understand the performance of social desirability scales in diverse groups, contexts, and cultures. The present study aims to contribute to the international literature on social desirability measurement by examining the psychometric performance of a short version of the Marlowe-Crowne Social Desirability Scale (MCSDS) in a nationally representative sample of teachers in Kazakhstan. A total of 2,461 Kazakhstani teachers completed the MCSDS – Form C in their language of choice (i.e., Russian or Kazakh). The results failed to support the theoretical unidimensionality of the original scale. Instead, the results of Random Intercept Item Factor Analysis model suggest that the scale answers depend more on the method factor rather than the substantial factor that represents SDB. In addition, an alternative explanation indicates that the scale seems better suited to measuring two SDB correlated factors: attribution and denial. Internal consistency coefficients demonstrated unsatisfactory reliability scores for the two factors. The Kazakhstani version of the MCSDS – Form C was invariant across geographic location (i.e., urban vs. rural), language (i.e., Kazakh vs. Russian), and partially across age groups. However, no measurement invariance was demonstrated for gender. Despite these limitations, the analysis of the Kazakhstani version of the MCSDS – Form C presented in this study constitutes a first step in facilitating further research and measurement of SDB in post-Soviet Kazakhstan and other collectivist countries

KazNewsDataset: Single Country Overall Digital Mass Media Publication Corpus

Mass media is one of the most important elements influencing the information environment of society. The mass media is not only a source of information about what is happening but is often the authority that shapes the information agenda, the boundaries, and forms of discussion on socially relevant topics. A multifaceted and, where possible, quantitative assessment of mass media performance is crucial for understanding their objectivity, tone, thematic focus and, quality. The paper presents a corpus of Kazakhstan media, which contains over 4 million publications from 36 primary sources (which has at least 500 publications). The corpus also includes more than 2 million texts of Russian media for comparative analysis of publication activity of the countries, also about 4000 sections of state policy documents. The paper briefly describes the natural language processing and multiple-criteria decision-making methods, which are the algorithmic basis of the text and mass media evaluation method, and describes the results of several research cases, such as identification of propaganda, assessment of the tone of publications, calculation of the level of socially relevant negativity, comparative analysis of publication activity in the field of renewable energy. Experiments confirm the general possibility of evaluating the socially significant news, identifying texts with propagandistic content, evaluating the sentiment of publications using the topic model of the text corpus since the area under receiver operating characteristics curve (ROC AUC) values of 0.81, 0.73 and 0.93 were achieved on abovementioned tasks. The described cases do not exhaust the possibilities of thematic, tonal, dynamic, etc., analysis of the considered corpus of texts. The corpus will be interesting to researchers considering both multiple publications and mass media analysis, including comparative analysis and identification of common patterns inherent in the media of different countries