Does discrimination beat association in the IAT? The discrimination-association model reconceived.

The discrimination-association model (DAM; Stefanutti et al. 2013) disentangles two components underlying the responses to the implicit association test (IAT), which pertain to stimuli discrimination (the strength of the association of the stimuli with their own category) and automatic association (the strength of the association between targets and attributes). The assumption of the DAM that these two components sum into a single process generates critical drawbacks. The present work provides a new formulation of the model, called DAM-4C, in which stimuli discrimination and automatic association are separate, independent, and competing processes. Results of theoretical and simulation studies suggest that the DAM-4C outperforms the DAM. The IAT effect is found to vary with the association rates of the DAM-4C and not with those of the DAM. The parameters of the DAM-4C fitted on data from a Coca-Pepsi IAT are found to account for variance in brand attractiveness, taste preference, and cola choice that is not accounted for by the D score and the diffusion model. In addition, the association rates estimated on data from a Black-White IAT are in line with expectations.

Data from a three-wave complete longitudinal design survey on career calling and related constructs (N = 6368).

This dataset provides de-identified raw responses to a non-anonymous three-wave online survey with a 12-month time lag. Data collection was part of a larger project on the development of career calling in Italian college students. The first wave was collected during the fall of 2014. Participants were bachelor's or master's students enrolled in 24 different study domains and 4 different universities. Sample sizes for each wave are NT 1 = 5,886, NT 2 = 1,700 and NT 3 = 881, 434 participants provided valid responses at all the three waves. Consent form was electronic. Dataset and codebook can be found here: https://osf.io/v56du/. The sample is mainly composed of women (63.8%, at Time 1). Participants' mean age at Time 1 was 23.37 years (SD = 5.39). The survey was in Italian and included multiple-item measures of career calling, intrinsic and extrinsic motivation, social support, engaged learning, clarity of professional identity, and quality of mentorship. Socio-demographic information and academic performance indicators are provided. The dataset is necessary to reproduce previously published results (Vianello et al., 2018) and can be useful to 1) investigate cross-cultural differences between college students from Italy and other countries; 2) investigate person-level variability in predictors and consequences of change in the variables collected over time; 3) develop and/or validate new statistical models for longitudinal data; and 4) develop and/or test original theoretical hypotheses.

Validity and measurement invariance of the Unified Multidimensional Calling Scale (UMCS): A three-wave survey study.

The accumulation of scientific knowledge on calling is limited by the absence of a common theoretical and measurement framework. Many different models of calling have been proposed, and we do not know how much research results that refer to a specific model are generalizable to different theoretical accounts of calling. In this article, we investigate whether two leading models of calling tackle the same construct. The two models were merged into a comprehensive framework that measures calling across seven facets: Passion, Purposefulness, Sacrifice, Pervasiveness, Prosocial Orientation, Transcendent Summons, and Identity. We then developed the Unified Multidimensional Calling Scale (UMCS) drawing from previous published items. Across two surveys involving college students (N = 5886) and adult employees (N = 205) the UMCS was proved to be valid and reliable. We also observed that the UMCS is invariant across time and calling domains. Finally, we found that facets of calling have very different relationships with outcomes and concurrent measures, suggesting that results obtained with a smaller set of facets are not generalizable to the higher-order construct of calling or to a different model that does not share the same facets.