This study compares the performance of two approaches in analysing fourpoint Likert rating scales with a factorial model: the classical factor analysis (FA) and the item factor analysis (IFA). For FA, maximum likelihood and weighted least squares estimations using Pearson correlation matrices among items are compared. For IFA, diagonally weighted least squares and unweighted least squares estimations using items polychoric correlation matrices are compared. Two hundred and ten conditions were simulated in a Monte Carlo study considering: one to three factor structures (either, independent and correlated in two levels), medium or low quality of items, three different levels of item asymmetry and five sample sizes. Results showed that IFA procedures achieve equivalent and accurate parameter estimates; in contrast, FA procedures yielded biased parameter estimates. Therefore, we do not recommend classical FA under the conditions considered. Minimum requirements for achieving accurate results using IFA procedures are discussed.

No abstract

No abstract

No abstract

The current study examines the performance of the extended unconstrained approach (EXUC) and the latent moderated structural equation modeling procedure (LMS) in situations where quadratic and interaction terms are tested simultaneously and investigates their limitations with regard to the employment of parallel and congeneric measures, relatively low indicator reliabilities, and relatively large numbers of indicators. By means of a Monte Carlo study, we found LMS to be the best option for testing multiple nonlinear effects given sufficient sample size (n ≥ 500) and normally distributed exogenous variables. Its advantages became more prominent when indicator reliabilities were heterogeneous and small. The EXUC was a viable option for estimating the model when indicators were parallel and exhibited large indicator reliabilities. An empirical example of the results is provided, and the relevance of measurement model characteristics to assess nonlinear relationships is discussed.

¿Por qué surgen los estallidos sociales? Emociones, redes interpersonales, rituales y participación en protestas POR QUE SURGEM OS SURTOS SOCIAIS? EMOÇÕES, REDES INTERPESSOAIS, RITUAIS E PARTICIPAÇÃO EM PROTESTOS RESUMOEste artigo visa explicar o caráter explosivo das fases iniciais de diferentes ciclos de protestos (incluindo o surto social acontecido no Chile em 2019). Para isso, formulamos um modelo teórico que explica os surtos sociais em função da sintonização, sincronização e amplificação da experiência emocional produzida nas interações sociais que acontecem nos protestos, as que poderiam ser entendidas como espaços rituais. Avaliamos preliminarmente este modelo em um estudo quantitativo realizado com jovens estudantes universitários. Os resultados mostram que experimentar intensamente emoções desagradáveis está associado a uma maior participação e a convidar outros para participar. A experiência de protestar está desenvolvida em redes interpessoais fortes, e a participação produz emoções agradáveis, associadas à percepção da manifestação como um sucesso. Estes resultados confirmam a importância das emoções e interações sociais como elementos que potencializam o caráter explosivo das fases iniciais dos ciclos de protestos.

No abstract

The idea that item response theory (IRT) models yield invariant parameter estimates is widely accepted among scholars interested in achieving truly scientific measurements in social and behavioral sciences. Starting from a conceptual and mathematical definition of invariance, this article presents a critical examination of the theoretical and empirical support for the property of invariance with regard to populations and samples of items and subjects by means of simulated data. The distinction between internal and external invariance is introduced to clarify the meaning and limitations of invariance in IRT models. Furthermore, the consequences of “giving in to the sirens’ call” of achieving invariant measurements in behavioral sciences are also discussed.