We undertake a comprehensive investigation into several common critiques of career politicians. Career politicians are said to be self-serving. Active and assertive when it suits their career interests, and much more interested in attaining higher offices than in serving as constituency-oriented MPs. Yet, empirical investigations of their alleged behaviours are few, and the results are patchy and mixed. Focusing on the UK case and using a multi-dimensional conceptualisation that accords with academic and popular understandings of career politicians, our paper draws on uniquely rich attitudinal and longitudinal behavioural data covering the first large generational wave of career politicians to be elected to parliament in the early 1970s. It reports findings consistent with contemporary critiques, suggesting that such dispositions are inherent in the role of career politician. The strongest career politicians among this first wave concentrated strategically on career-serving activities, voted strategically to safeguard their careers, attained and retained successfully ministerial offices, and prioritized their personal goals over their party obligations. The paper further demonstrates that different measures used by researchers can produce contradictory results, and that future comparative research should seek to range beyond unidimensional indicators.

This paper examines the concept career politician. It seeks to clarify, systematize, and measure this ambiguous multidimensional concept in order to facilitate testing theories and hypotheses associated with it. We argue that career politicians are full-time politicians who lack significant experience in the wider world and have other distinguishing attributes for which they are both appreciated and criticized. From claims and critiques put forward by political scientists, journalists, publics, and politicians, we extract four principal dimensions. Strong Commitment, Narrow Occupational Background, Narrow Life Experience, and Strong Ambition. These dimensions and their indicators fit Wittgenstein’s family-resemblance conceptual structure, which is how we analyze, measure and validate them with data from a longitudinal study of British MPs spanning 1971–2016.

Ideological scales derived from policy position items are prevalent in political psychology and behavioral research. However, the underlying spatial assumptions of these scales are rarely scrutinized. This study investigates how assumptions about the dimensionality of the latent ideological space can significantly impact empirical estimates, even when using the same data from the same respondents. Through a comprehensive literature review and statistical simulations using data from the ANES, we demonstrate that the optimal number of latent ideological dimensions increases without bound as researchers include additional items for analysis. At the same time, nearly all latent ideological factors found within same attitude set are sizably and positively correlated with one another. In light of these findings, we propose an alternative modeling framework that seeks to reconcile unidimensional and multi-dimensional aspects of mass ideology. Our Bayesian hierarchical latent variable approach simultaneously estimates mass ideology as a higher-level, uni-dimensional expression of correlated, lower-level, multi-dimensional building blocks. This approach enables researchers to assess whether particular socio-demographic or psychological predictors, such as income, gender, or egalitarianism, are consistently related to specific sub-dimensions (e.g., economic, socio-cultural, racial ideology) or instead a generalized, uni-dimensional representation thereof. Our results underscore the potential value of this approach, offering insights into the unique characteristics of different ideological factors and their overarching parent dimension.

Public opinion research has made tremendous progress in identifying the conditions under which individual- and group-level factors induce citizens to form coherent political attitudes, yet comparatively little attention has been given to the role of national political context for belief system coherence. By modeling political beliefs as dedicated statistical networks based on nationally representative surveys covering 38 European countries between 2002 and 2020, the present article shows that national-level belief systems vary substantially and systematically in attitude constraint. In simultaneously relying on a novel, network-derived measure of attitude constraint and node-level centrality metrics, the analysis reveals that belief system coherence can, in part, be explained by programmatic linkages between citizens and ideologically differentiable parties. Approximately one quarter of this belief structuring effect is mediated by the relative centrality of symbolic ideological attachments (i.e. abstract left-right positions) within belief networks. Abstract ideological summary positions are not central to all belief systems, but where they are, mass beliefs tend to be more coherent overall.

Smart societies promise to enhance social systems by leveraging the constantly increasing stream of information around us. However, the possibility of optimizing various social indicators implicitly hinges on our ability to reliably reduce complex and abstract constructs (such as inter-personal trust) into numeric measures. While many scientists are aware of the issue of measurement error, there is widespread hope that access to more data will eventually render this issue irrelevant. This paper delves into the nature of measurement error under quasi-ideal conditions. We show mathematically and by employing simulations that single measurements fail to converge even when we have access to progressively more information. Then, by using data from the Social Capital Benchmark Surveys, we demonstrate how the addition of new information increases the dimensionality to the measured construct which further contributes to measurement divergence. In our conclusion, we stress that alternative evaluations to indexes and scores need to be evaluated for a fair society.

Political scientists often use survey instruments to indirectly infer latent constructs such as social trust, political efficacy, and ideology. However, traditional techniques used to reconstruct latent quantities from survey data, such as principal components, factor analysis, and item response theory models, have limitations as they assume linearity among the underlying co-dependencies among a given set of survey items. This assumption does not hold for complex nonlinear expressions of latent constructs, such as when survey respondents from opposite ends of the ideological spectrum share a stance on a particular issue but for completely different reasons. For example, a left-leaning respondent may see cash benefits for child care as an important instrument for gender equality while conservatives welcome incentives for women to stay home and give birth to more children. To liberals, GMO’s might appear as environmental hazards while conservatives reject them as they could tamper with God’s eternal creation. To address such non-linearities, we propose a new method that models survey item-responses as force-directed statistical networks. By treating item responses as physical particles in finite space, a force-directed algorithm can detect complex, non-linear relationships within latent constructs. Generalized additive models and LOESS-fit models applied to the spatial equilibrium of item response particles can subsequently be used to calculate sparse, (non-)linear latent factors that optimally characterize this space. This method is flexible, allows for multiple scoring options, and outperforms traditional latent variable models in the presence of non-linear expressions of the underlying latent construct.