Psihologija individualnih razlika – Ambulatorno procenjivanje kao pristup u merenju

Neko će se zapitati, zašto je potrebno da imamo još jednu knjigu o metodama i tehnikama koje se koriste u psihologiji (individualnih razlika). Do sada, u našoj sredini je izdato nekoliko veoma vrednih i kvalitetnih knjiga i udžbenika koji su u svom fokusu imali metodologiju i tehnike koje se koriste u psihološkim istraživanjima (npr. Todorović, 2008; Popadić et al., 2019). Najveći deo dosadašnje karijere posvetila sam izučavanju problema merenja u psihologiji individualnih razlika, odnosno merenju bazičnih crta ličnosti. Ipak, isprva nisam razmišljala o tome da je psiholozima i drugim radoznalim stručnjacima potrebna još jedna “metodološka” knjiga. Međutim, pregledom postojećih knjiga i udžbenika uvidela sam da ipak postoji značajan prostor koji do sada u našem regionu nije bio “pokriven”. Psihologija individualnih razlika je oblast u okviru koje se dešava verovatno najviše inovacija u metodologiji istraživanja. Iako razvoj metoda i tehnika u ovoj oblasti ima već zavidno dugačku istoriju, iznenađujuće je koliko se novih procedura i metoda osmišljava kako bismo mogli da razumemo sve aspekte veoma komplikovane ljudske prirode koja se oslikava u ponašanju. Među najsavremenije i najinovativnije procedure koje se u ovoj oblasti psihologije koriste spadaju, metode i tehnike ambulatornog procenjivanja. Ambulatorno procenjivanje obuhvata veliki skup metoda i tehnika u kojima je akcenat na tome da se prikupljanje podataka odvija dok se ljudi nalaze u realnom, svakodnevnom okruženju tokom njihovih svakodnevnih aktivnosti. Knjiga pred čitaocima se sastoji iz dva dela. Prvi uvodni deo je posvećen glavnim izvorima prikupljanja podataka u psihologiji individualnih razlika kao što su mere samoizveštaja, procene od strane drugih, bihejvioralne procene, objektivne mere, i tako dalje. Iako je o ovoj temi već diskutovano u domaćoj naučnoj literaturi, do sada ova tema nije bila sistematizovano prikazana, a neophodna je za jednostavno praćenje drugog dela knjige. U drugom delu knjige, fokus je na tehnikama ambulatornog procenjivanja. Ovaj deo knjige je takođe organizovan prema izvoru iz kojeg prikupljamo podatke. Imajući u vidu da je izvođenje istraživanja koje podrazumeva primenu neke od tehnika ambulatornog procenjivanja prilično zahtevno, pokušala sam da olakšam zainteresovanim koleginicama i kolegama planiranje i izvođenje istraživanja kroz različite praktične istraživačke smernice. Takođe, kvalitetna savremena nauka postavlja vrlo visoke stan10 Psihologija individualnih razlika darde kada je reč o ophođenju prema ispitanicima. Primena ambulatornog procenjivanja u istraživanjima nužno eliminiše mogućnost anonimnog učešća ispitanika zbog prirode podataka koji se prikupljaju. Imajući to u vidu, pokušala sam da olakšam istraživačima dajući pregled koraka koji će omogućiti da nesmetano izvode ambulatorna istraživanje koja ispunjavaju sve etičke standarde. Na kraju, poslednje poglavlje daje osvrt na poziciju ambulatornog procenjivanja u multimetodskoj proceni, koja se smatra najboljim, savremenim, standardom za validaciju konstrukata. Kako je područje metodologije istraživanja izuzetno kompleksno, poglavlja u knjizi su napisana tako da se međusobno dopunjuju i zajedno grade jednu celinu. Međutim, poglavlja su pisana i tako da se mogu i pojedinačno čitati, te stručnjak koji je zainteresovan za neki specifični aspekt ambulatornog procenjivanja može samo tome da se posveti. Knjiga je namenjena pre svega istraživačima zainteresovanim za procenu individualnih razlika u ličnosti i intelektualnim sposobnostima. Knjiga može biti od koristi i studentima psihologije svih nivoa studija. Iako je primarno namenjena psiholozima koji se dominantno bave psihologijom individualnih razlika, biće korisno sredstvo u planiranju i izvođenju istraživanja i drugima koji se bave i socijalnom, kliničkom, razvojnom, i psihologijom rada i organizacije. Imajući u vidu da je procena individualnih razlika važna za različite kontekste, kao što su radno okruženje i selekcija, psihodijagnostička procena, obrazovni kontekst, knjiga takođe može biti i od koristi psiholozima praktičarima koji rade u radnim organizacijama, obrazovnom sistemu ili na klinici

Relationship between electrocardiogram‐based features and personality traits: Machine learning approach

Background: Based on the known relationship between the human emotion and standard surface electrocardiogram (ECG), we explored the relationship between features extracted from standard ECG recorded during relaxation and seven personality traits (Honesty/humility, Emotionality, eXtraversion, Agreeableness, Conscientiousness, Openness, and Disintegration) by using the machine learning (ML) approach which learns from the ECG-based features and predicts the appropriate personality trait by adopting an automated software algorithm. Methods: A total of 71 healthy university students participated in the study. For quantification of 62 ECG-based parameters (heart rate variability, as well as temporal and amplitude-based parameters) for each ECG record, we used computation procedures together with publicly available data and code. Among 62 parameters, 34 were segregated into separate features according to their diagnostic relevance in clinical practice. To examine the feature influence on personality trait classification and to perform classification, we used random forest ML algorithm. Results: Classification accuracy when clinically relevant ECG features were employed was high for Disintegration (81.3%) and Honesty/humility (75.0%) and moderate to high for Openness (73.3%) and Conscientiousness (70%), while it was low for Agreeableness (56.3%), eXtraversion (47.1%), and Emotionality (43.8%). When all calculated features were used, the classification accuracies were the same or lower, except for the eXtraversion (52.9%). Correlation analysis for selected features is presented. Conclusions: Results indicate that clinically relevant features might be applicable for personality traits prediction, although no remarkable differences were found among selected groups of parameters. Physiological associations of established relationships should be further explored.Ministry of Education, Science, and Technological Development, Republic of Serbia, Grant/Award Number: 179018 and TR33020; Abbott Laboratorie

Many Labs 5: Testing Pre-Data-Collection Peer Review as an Intervention to Increase Replicability

Replication studies in psychological science sometimes fail to reproduce prior findings. If these studies use methods that are unfaithful to the original study or ineffective in eliciting the phenomenon of interest, then a failure to replicate may be a failure of the protocol rather than a challenge to the original finding. Formal pre-data-collection peer review by experts may address shortcomings and increase replicability rates. We selected 10 replication studies from the Reproducibility Project: Psychology (RP:P; Open Science Collaboration, 2015) for which the original authors had expressed concerns about the replication designs before data collection; only one of these studies had yielded a statistically significant effect (p < .05). Commenters suggested that lack of adherence to expert review and low-powered tests were the reasons that most of these RP:P studies failed to replicate the original effects. We revised the replication protocols and received formal peer review prior to conducting new replication studies. We administered the RP:P and revised protocols in multiple laboratories (median number of laboratories per original study = 6.5, range = 3–9; median total sample = 1,279.5, range = 276–3,512) for high-powered tests of each original finding with both protocols. Overall, following the preregistered analysis plan, we found that the revised protocols produced effect sizes similar to those of the RP:P protocols (Δr = .002 or .014, depending on analytic approach). The median effect size for the revised protocols (r = .05) was similar to that of the RP:P protocols (r = .04) and the original RP:P replications (r = .11), and smaller than that of the original studies (r = .37). Analysis of the cumulative evidence across the original studies and the corresponding three replication attempts provided very precise estimates of the 10 tested effects and indicated that their effect sizes (median r = .07, range = .00–.15) were 78% smaller, on average, than the original effect sizes (median r = .37, range = .19–.50).Additional co-authors: Ivan Ropovik, Balazs Aczel, Lena F. Aeschbach, Luca Andrighetto, Jack D. Arnal, Holly Arrow, Peter Babincak, Bence E. Bakos, Gabriel Baník, Ernest Baskin, Radomir Belopavlovic, Michael H. Bernstein, Michał Białek, Nicholas G. Bloxsom, Bojana Bodroža, Diane B. V. Bonfiglio, Leanne Boucher, Florian Brühlmann, Claudia C. Brumbaugh, Erica Casini, Yiling Chen, Carlo Chiorri, William J. Chopik, Oliver Christ, Antonia M. Ciunci, Heather M. Claypool, Sean Coary, Marija V. Cˇolic, W. Matthew Collins, Paul G. Curran, Chris R. Day, Anna Dreber, John E. Edlund, Filipe Falcão, Anna Fedor, Lily Feinberg, Ian R. Ferguson, Máire Ford, Michael C. Frank, Emily Fryberger, Alexander Garinther, Katarzyna Gawryluk, Kayla Ashbaugh, Mauro Giacomantonio, Steffen R. Giessner, Jon E. Grahe, Rosanna E. Guadagno, Ewa Hałasa, Rias A. Hilliard, Joachim Hüffmeier, Sean Hughes, Katarzyna Idzikowska, Michael Inzlicht, Alan Jern, William Jiménez-Leal, Magnus Johannesson, Jennifer A. Joy-Gaba, Mathias Kauff, Danielle J. Kellier, Grecia Kessinger, Mallory C. Kidwell, Amanda M. Kimbrough, Josiah P. J. King, Vanessa S. Kolb, Sabina Kołodziej, Marton Kovacs, Karolina Krasuska, Sue Kraus, Lacy E. Krueger, Katarzyna Kuchno, Caio Ambrosio Lage, Eleanor V. Langford, Carmel A. Levitan, Tiago Jessé Souza de Lima, Hause Lin, Samuel Lins, Jia E. Loy, Dylan Manfredi, Łukasz Markiewicz, Madhavi Menon, Brett Mercier, Mitchell Metzger, Venus Meyet, Jeremy K. Miller, Andres Montealegre, Don A. Moore, Rafał Muda, Gideon Nave, Austin Lee Nichols, Sarah A. Novak, Christian Nunnally, Ana Orlic, Anna Palinkas, Angelo Panno, Kimberly P. Parks, Ivana Pedovic, Emilian Pekala, Matthew R. Penner, Sebastiaan Pessers, Boban Petrovic, Thomas Pfeiffer, Damian Pienkosz, Emanuele Preti, Danka Puric, Tiago Ramos, Jonathan Ravid, Timothy S. Razza, Katrin Rentzsch, Juliette Richetin, Sean C. Rife, Anna Dalla Rosa, Kaylis Hase Rudy, Janos Salamon, Blair Saunders, Przemysław Sawicki, Kathleen Schmidt, Kurt Schuepfer, Thomas Schultze, Stefan Schulz-Hardt, Astrid Schütz, Ani N. Shabazian, Rachel L. Shubella, Adam Siegel, Rúben Silva, Barbara Sioma, Lauren Skorb, Luana Elayne Cunha de Souza, Sara Steegen, L. A. R. Stein, R. Weylin Sternglanz, Darko Stojilovic, Daniel Storage, Gavin Brent Sullivan, Barnabas Szaszi, Peter Szecsi, Orsolya Szöke, Attila Szuts, Manuela Thomae, Natasha D. Tidwell, Carly Tocco, Ann-Kathrin Torka, Francis Tuerlinckx, Wolf Vanpaemel, Leigh Ann Vaughn, Michelangelo Vianello, Domenico Viganola, Maria Vlachou, Ryan J. Walker, Sophia C. Weissgerber, Aaron L. Wichman, Bradford J. Wiggins, Daniel Wolf, Michael J. Wood, David Zealley, Iris Žeželj, Mark Zrubka, and Brian A. Nose

Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer fiveoriginal research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams renderedstatistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.</div

The psychological science accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges

The Psychological Science Accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

A Serbian version of the ANPS and its link to the five-factor model of personality

This short communication presents a Serbian version of the Affective Neuroscience Personality Scales (ANPS). The ANPS is a self-report measure assessing individual differences in primary emotional systems as derived from Jaak Panksepp’s Affective Neuroscience Theory. As a recent work by Montag & Panksepp (2017a) confirmed the original demonstration of strong associations between primary emotions and the Five-Factor Model of Personality (Davis et al., 2003) across different cultures (USA, Germany, China), we replicated these findings in a Serbian sample. Moreover, following the idea of a recent commentary of Di Domencio & Ryan (2017) on Montag & Panksepp’s (2017a), we present for the first time detailed associations between Five-Factor Model facets as assessed with the NEO-PI-R and primary emotions