Development and test of a model linking safety-specific transformational leadership and occupational safety

The authors developed, tested, and replicated a model in which safety-specific transformational leadership predicted occupational injuries in 2 separate studies. Data from 174 restaurant workers (M age = 26.75 years, range = 15-64) were analyzed using structural equation modeling (LISREL 8; K. G. J?reskog & D. S?rbom, 1993) and provided strong support for a model whereby safety-specific transformational leadership predicted occupational injuries through the effects of perceived safety climate, safety consciousness, and safety-related events. Study 2 replicated and extended this model with data from 164 young workers from diverse jobs (M age = 19.54 years, range = 14-24). Safety-specific transformational leadership and role overload were related to occupational injuries through the effects of perceived safety climate, safety consciousness, and safety-related events.     

A multi-analyses approach of inductive/deductive asymmetry in the affective priming paradigm

Rapidly evaluating our environment's beneficial and detrimental features is critical for our successful functioning. A classic paradigm used to investigate such fast and automatic evaluations is the affective priming (AP) paradigm, where participants classify valenced target stimuli (e.g., words) as good or bad while ignoring the valenced primes (e.g., words). We investigate the differential impact that verbs and adjectives used as primes and targets have on the AP paradigm. Based on earlier work on the Linguistic Category Model, we expect AP effect to be modulated by non-evaluative properties of the word stimuli, such as the linguistic category (e.g., if the prime is an adjective and the target is a verb versus the reverse). A reduction in the magnitude of the priming effect was predicted for adjective–verb prime-target pairs compared to verb–adjective prime-target pairs. Moreover, we implemented a modified crowdsourcing of statistical analyses implementing independently three different statistical approaches. Deriving our conclusions on the converging/diverging evidence provided by the different approaches, we show a clear deductive/inductive asymmetry in AP paradigm (exp. 1), that this asymmetry does not require a focus on the evaluative dimension to emerge (exp. 2) and that the semantic-based asymmetry weakly extends to valence (exp. 3).     

What do We Know about Family Interventions for Psychosis at the Process Level? A Systematic Review

The evidence regarding effectiveness of family interventions for psychosis (FIP) is strong and consistent. However, there is a gap in the research on the process of these interventions, and little is known about their active ingredients. This review aims to identify the active ingredients of FIP. We conducted a systematic literature review, focusing on qualitative research, and analyzed 22 papers in total. We found a single study comprehensively exploring the process of FIP. All other studies focused on particular aspects of process‐related variables. The key elements of FIP seem to be the so‐called “common therapeutic factors”, followed by education about the illness and coping skills training. This review supports the value of a stepped model of intervention according to the needs of the families. However, the evidence reviewed also reveals a gap in the research findings based on the limited research available. FIP are complex, psychosocial interventions with multiple components, and more intensive, qualitative research is needed to establish linkages between process and outcome.     

Children's learning of number words in an indigenous farming‐foraging group

We show that children in the Tsimane', a farming‐foraging group in the Bolivian rain‐forest, learn number words along a similar developmental trajectory to children from industrialized countries. Tsimane' children successively acquire the first three or four number words before fully learning how counting works. However, their learning is substantially delayed relative to children from the United States, Russia, and Japan. The presence of a similar developmental trajectory likely indicates that the incremental stages of numerical knowledge – but not their timing — reflect a fundamental property of number concept acquisition which is relatively independent of language, culture, age, and early education.     

Why computational models are better than verbal theories: the case of nonword repetition

Tests of nonword repetition (NWR) have often been used to examine children's phonological knowledge and word learning abilities. However, theories of NWR primarily explain performance either in terms of phonological working memory or long‐term knowledge, with little consideration of how these processes interact. One theoretical account that focuses specifically on the interaction between short‐term and long‐term memory is the chunking hypothesis. Chunking occurs because of repeated exposure to meaningful stimulus items, resulting in the items becoming grouped (or chunked); once chunked, the items can be represented in short‐term memory using one chunk rather than one chunk per item. We tested several predictions of the chunking hypothesis by presenting 5–6‐year‐old children with three tests of NWR that were either high, medium, or low in wordlikeness. The results did not show strong support for the chunking hypothesis, suggesting that chunking fails to fully explain children's NWR behavior. However, simulations using a computational implementation of chunking (namely CLASSIC, or Chunking Lexical And Sub‐lexical Sequences In Children) show that, when the linguistic input to 5–6‐year‐old children is estimated in a reasonable way, the children's data are matched across all three NWR tests. These results have three implications for the field: (a) a chunking account can explain key NWR phenomena in 5–6‐year‐old children; (b) tests of chunking accounts require a detailed specification both of the chunking mechanism itself and of the input on which the chunking mechanism operates; and (c) verbal theories emphasizing the role of long‐term knowledge (such as chunking) are not precise enough to make detailed predictions about experimental data, but computational implementations of the theories can bridge the gap.     

Algorithmic complexity for short binary strings applied to psychology: a primer

As human randomness production has come to be more closely studied and used to assess executive functions (especially inhibition), many normative measures for assessing the degree to which a sequence is randomlike have been suggested. However, each of these measures focuses on one feature of randomness, leading researchers to have to use multiple measures. Although algorithmic complexity has been suggested as a means for overcoming this inconvenience, it has never been used, because standard Kolmogorov complexity is inapplicable to short strings (e.g., of length l?≤?50), due to both computational and theoretical limitations. Here, we describe a novel technique (the coding theorem method) based on the calculation of a universal distribution, which yields an objective and universal measure of algorithmic complexity for short strings that approximates Kolmogorov–Chaitin complexity.     

A latent class distance association model for cross‐classified data with a categorical response variable

In this paper we propose a latent class distance association model for clustering in the predictor space of large contingency tables with a categorical response variable. The rows of such a table are characterized as profiles of a set of explanatory variables, while the columns represent a single outcome variable. In many cases such tables are sparse, with many zero entries, which makes traditional models problematic. By clustering the row profiles into a few specific classes and representing these together with the categories of the response variable in a low‐dimensional Euclidean space using a distance association model, a parsimonious prediction model can be obtained. A generalized EM algorithm is proposed to estimate the model parameters and the adjusted Bayesian information criterion statistic is employed to test the number of mixture components and the dimensionality of the representation. An empirical example highlighting the advantages of the new approach and comparing it with traditional approaches is presented.     

The double-edged effects of emotional intelligence on the adaptive selling–salesperson-owned loyalty relationship

Emotional intelligence can positively impact sales outcomes. However, research is needed to enhance our understanding about how salesperson's emotional intelligence affects adaptive selling and customer outcomes. This study uses dyadic data from 150 customers matched with 25 salespeople to investigate relationships among emotional intelligence, adaptive selling (AS) and customer loyalty perceptions. Research findings show that regulation of emotions negatively affects salesperson-owned loyalty (SOL), while it positively moderates the AS-SOL relationship. These findings are critical as they challenge the pervasive conception of the positive impact of regulation of emotions in sales. Results also provide further evidence that SOL is important to the firm as it positively affects loyalty to the service provider and through mediating processes also impacts word of mouth.