PsychArchives (ZPID Leibniz-Zentrum für Psychologische Information und Dokumentation)
Not a member yet
20791 research outputs found
Sort by
Killed the Cat, But Also Saved It: Curiosity's Dual Impact on Active and Passive Risk Behaviors
No full textDecision-making often involves uncertainty and risk, which can stem from active behaviors (e.g., gambling) or passive ones (e.g., avoiding medical tests). Passive risks, those arising from omission or neglect, pose unique challenges for prevention and intervention. Although instrumental information is crucial for risk assessment, individuals frequently avoid it. The current research proposes a model linking epistemic curiosity—the drive to seek knowledge—to tendencies toward passive and active risk-taking.
In Study 1 (N = 213, MTurk), epistemic curiosity was negatively associated with passive risk-taking and positively associated with active risk-taking. Study 2 (N = 403, MTurk) further explored this pattern, showing that the inverse relationship between curiosity and passive risk-taking is mediated by information-seeking, while the positive relationship with active risk-taking is mediated by sensation-seeking. These findings reveal the dual role of curiosity in shaping risk behavior and offer insights for risk management strategies that leverage curiosity while addressing its potential downsides.This research was supported by the Israel Science Foundation (#2373/22)notReviewedothe
KImAge - AI-supported systematization of views on aging in central domains of everyday life
No full textBackground: Negative views on aging are linked to discrimination (e.g. in working life or healthcare) and reduced health outcomes. Despite well-documented effects, there is a lack of effective methods to assess views on aging in daily experience.
Objectives and Research questions: A photography-based method (Klusmann, 2023; Klusmann & Schüz, 2024) will be used to capture views of aging in everyday life. An AI model will be trained offline to enable a large-scale systematization of photographs based on a category system developed by an expert panel. This will help to estimate the occurrence and relative prevalence of views on aging in daily life of different age groups, in rural and urban areas as well as in settings in which age discrimination is potentially harmful (working life, healthcare).
Participants: n = 1,200 German-speaking participants (aged 18+ years) are planned to be enrolled for the study.
Study method: Participants will upload their own photographs on an online database hosted by Hetzner Online GmbH. The aim is to generate, analyze and systematize these views on aging captured by photographs. A mixed-methods approach will combine expert-led qualitative coding (development of a category system with coding scheme) with AI-based image classification (offline AI-model being trained based on the coding scheme and then being applied to the full data set). A quantitative content analysis will be used to determine descriptive frequencies and possible differences in age groups, in rural and urban areas as well as in settings in which age discrimination is potentially harmful (working life, healthcare).unknownothe
Dataset for CFA of the RPFC-2
No full textThe Resilience Protective Factors Checklist (RPFC) is a strengths-based oriented clinical questionnaire that assists in the identification of protective factors that have been empirically linked to resilience and positive outcomes (Powell et al., 2021). This study utilized a Confirmatory Factor Analysis to assist in the identification of additional protective factors linked to resilience, which informed the creation of the Resilience Protective Factors Checklist – Second Edition (RPFC-2). Participants were undergraduate college students (n = 652). The results revealed a total of 34 protective factors that represent three interrelated areas of protection—Individual, Family, and Community strengths and resources. The results are further broken down into eleven areas of protection that can be targeted in human services. An important goal of the present study was to identify and strengthen the factors that assist people in leading resilient, well-adjusted lives.unknow
Memory and false memory for information that is either expected or unexpected based on age stereotypes
No full textAge is a major social categorization information because it is one of the first attributes that is perceived about an individual. The present study used the misinformation paradigm to investigate memory and false memory for information that is either expected or unexpected based on age stereotypes. Young adults were presented with a passage depicting a crime. The passage also contained information about the physical performance and social behavior of the main character that was either expected (expected information condition) or unexpected (unexpected information condition) for his age. The main character was a young adult in the expected information condition and an older adult in the unexpected information condition. Next, misinformation was provided about a detail related to the crime. After a non-verbal filler task, participants recalled the exact sentences from the passage, and then they completed a forced-choice recognition test for them. Measures of attitudes toward older adults did not differ across the groups. The results revealed worse recognition memory for the sentences and higher false recognition of the misinformation in the expected information condition than in the unexpected information condition. The recall test revealed higher commission errors in the expected information condition than in the unexpected information condition. Commission errors were in general consistent with the information in the passage. The results imply that stereotypically expected information is automatically processed, making it more vulnerable to memory errors. The study contributes to the understanding of the memory processes underlying stereotyping that can lead to prejudice and discrimination.peerReviewedpublishedVersio
Self-Efficacy Expectancy as a Function of Gender Identity?! Effects of Manifestations of Gender Stereotypes on General Self-Efficacy Expectation and Specific Self-Efficacy Expectation in Relation to Different Complex Activities
No full textOur study is part of a bachelor’s thesis in the field of social psychology as part of the Bachelor of Science in Psychology at Friedrich Schiller University Jena. It will be a quantitative one-time online questionnaire study. The work deals with the topic of “gender stereotypes and self-efficacy expectations” and will examine the possible influence of gender-stereotypical self-attribution on different forms of self-efficacy expectations, both in various complex tasks and in general. For exploratory research, several group comparisons (education, gender category, type of stimulus) regarding reference to specific self-efficacy expectations will be conducted.
Our idea is based on a study of Jordan at al. (2022) with the title: “Trivially informative semantic context inflates people’s confidence they can perform a highly complex skill” and will use a variety of different tasks and self-assasment scales. The calculated number of test subjects is above 240 participants, based on
a three-factor design and including the exploratory group-comparisons. For analysis, we are going to do multiple linear regressions and linear mixed models, as well as ANOVAs and t-tests for exploratory group comparisons.unknownothe
Development of Crossmodal Sound–Shape Correspondence: The Role of Intuitive Audio-Visual Physical Knowledge in the Bouba–Kiki Effect
No full textSuccessfully navigating the world involves integrating sensory inputs and selecting appropriate motor actions. Yet, what information is perceived as belonging together? In addition to spatial and temporal factors, correspondences between sensory features are important. The Bouba-Kiki (BK) effect is a well-documented example of a sound-shape crossmodal correspondence, where people tend to associate pseudowords, such as “baba” and “kiki”, with round and spiky abstract shapes, respectively. Previous research suggests that the strength of BK associations varies with development: associations are weak early in development, up to 3 years of age, and strengthen with age. These findings suggest that sound symbolic associations could be experience-dependent and, to some extent, learned through statistical co-occurrence in the environment. Here, we investigate one mechanism that could explain changes in the strength of BK associations as children develop, namely changes in the strength of intuitive audiovisual knowledge of physical events (IP), an understanding of what sounds will be produced as objects move in the world. This study aims to investigate the relationship between intuitive audiovisual physical knowledge (IP) and Bouba-Kiki (BK) associations, Specifically, we examine whether individuals with stronger intuitive physics abilities also demonstrate stronger Bouba-Kiki associations and how this relationship develops across different age groups and also how participants’ performance in the BK and the IP task changes over the course of the experiment. Importantly, differences in intuitive audio-visual physical knowledge (IP) could underlie the individual differences and variability found in the strength of sound-shape crossmodal correspondences (BK) associations early in development. A total of 180 healthy English-speaking participants will be studied, with 60 participants in each of three age groups: 6–8, 9–11, and 18–35 years old. Participants will be recruited via email advertisements, as well as through the Connecticut Science Center (Hartford, CT) and the Acton Discovery Museum (Acton, MA). The study will be conducted online via Zoom or in person at our laboratory on campus or at various local science and children’s museums. Within a given age group, participants will be randomly assigned such that half will complete the BK task first and half will complete the IP task first. The basic task is a two-alternative forced-choice task: two images are displayed on a screen, one on the left and one on the right, and then a sound is played via headphones. Participants must judge which shape best matches the sound by pressing one of two keys on a buttonbox. In the BK task, the images are abstract round and spiky shapes, and the sounds are pseudowords (see Chow & Ciaramitaro, 2019). In the IP task, the images are pictures of real-world round or spiked balls, and the corresponding real-world sound produced when they roll (see Fort & Schwartz, 2022). Each participant will complete 16 trials of each task.unknownothe
Interactive Technologies in Education
No full textThe research investigates the impact of interactive learning technologies on development of moral reasoning in higher education students.unknow
Executive Networks in Language Prediction (EXNAT_4)
No full textThis study aims to investigate the functional roles of the Angular Gyrus and DLPFC in language prediction and comprehension using transcranial magnetic stimulation (TMS) during naturalistic reading and dual-task paradigms.unknownothe
Primärdaten der drei Erhebungszeitpunkte der ISPO-Studie 2023/2024
No full textStage 1 Registered Report: http://dx.doi.org/10.23668/psycharchives.14372.
The rapid expansion of online sports betting has raised concerns about its potential impact on individual health and public health. In order to further develop etiological models for gambling disorder (GD) in sports betting, it is essential to unravel the underlying causal processes. Recent studies have identified risky online gambling behavior as an early indicator of GD. This study focuses on impulsivity as a well-documented risk factor for GD and investigated whether increased impulsivity leads to risky online gambling behavior and subsequently contributes to GD. Impulsivity, risky gambling behavior, and GD symptoms were assessed three times at three-month intervals using a longitudinal cross-lagged panel design. We recruited a final sample of n = 427 regular sports bettors from the online gambling provider Tipico. Impulsivity and GD were assessed using a combination of online experimental tasks and questionnaires. As a measure of risky gambling behavior, Tipico provided player tracking data for all participants. Random intercept cross‐lagged panel models were used to test the evidence for our hypotheses. Results showed partial support for the role of impulsivity in the development of GD, particularly through facets such as impulsive choice and certain impulsive personality traits. However, the findings suggest that impulsivity does not directly predict risky betting behavior, nor does such behavior mediate the relationship between impulsivity and GD severity. These results emphasize the complexity of pathways to GD, highlighting the need to explore multifactorial models incorporating emotional, cognitive, and environmental mediators.Stage 1 Registered Report: http://dx.doi.org/10.23668/psycharchives.14372.
Die rasche Ausbreitung von Online-Sportwetten hat Bedenken hinsichtlich ihrer potenziellen Auswirkungen auf die Gesundheit des Einzelnen sowie die öffentliche Gesundheit aufkommen lassen. Um ätiologische Modelle für Glücksspielsucht (GSS) bei Sportwetten weiterzuentwickeln, müssen die zugrunde liegenden kausalen Prozesse entschlüsselt werden. Jüngste Studien haben risikoreiches Online-Glücksspielverhalten als Frühindikator für Spielsucht identifiziert. Diese Studie konzentriert sich auf Impulsivität als gut dokumentierten Risikofaktor für Spielsucht und untersucht, ob erhöhte Impulsivität zu riskantem Online-Glücksspielverhalten führt und anschließend zu Spielsucht beiträgt. Impulsivität, riskantes Glücksspielverhalten und GSS-Symptome wurden dreimal in dreimonatigen Abständen in einem längsschnittlichen, verzögerten Paneldesign untersucht. Wir rekrutierten eine endgültige Stichprobe von n = 427 regelmäßigen Sportwettern des Online-Glücksspielanbieters Tipico. Impulsivität und GSS wurden mit einer Kombination aus experimentellen Online-Aufgaben und Fragebögen untersucht. Als Maß für riskantes Spielverhalten stellte Tipico für alle Teilnehmer Spieler-Tracking-Daten zur Verfügung. Um die Evidenz für unsere Hypothesen zu testen, wurden kreuzverzögerte Panelmodelle mit zufälligem Intercept verwendet. Die Ergebnisse zeigten, dass die Rolle der Impulsivität bei der Entwicklung von GSS teilweise unterstützt wird, insbesondere durch Facetten wie impulsive Entscheidungen und bestimmte impulsive Persönlichkeitseigenschaften. Die Ergebnisse deuten jedoch darauf hin, dass Impulsivität weder direkt riskantes Wettverhalten vorhersagt, noch die Beziehung zwischen Impulsivität und dem Schweregrad von GSS vermittelt. Diese Ergebnisse unterstreichen die Komplexität der Entstehungswege von GSS und verdeutlichen die Notwendigkeit, multifaktorielle Modelle zu erforschen, die emotionale, kognitive und umweltbedingte Mediatoren einbeziehen.readme ispo.txt: readme file for data flow of the ISPO study;
jbac24pr29_readme.txt: Description of the files;
2024-11-07_ispo_long.do: Stata script file for creating the merged longitudinal dataset;
2025-05-06_ispo_stata2mplus.do: Stata script file for creating the data file for testing the hypotheses in MPlus;
2024-11-07_raw_transaction_data_tipico.do: Stata script file for converting the raw transaction data file into a Stata data file;
2024-11-07_t1 transaction data outcomes.do: Stata script file for analysing the transaction data at measurement point 1;
2024-11-07_t2_transaction data outcomes.do: Stata script file for analysing the transaction data at measurement point 2;
2024-11-07_t3_transaction data outcomes.do: Stata script file for analysing the transaction data at measurement point 3;
all_data_22042024.dta: Original Stata dataset for the primary data set jbac24pr29_pd3.csv ;
all_data_24012024.dta: Original Stata dataset for the primary data set jbac24pr29_pd2.csv;
all_data_24112023.dta: Original Stata dataset for the primary data set jbac24pr29_pd1.csv;
drop_out_22042024.do: Stata script file for dropout analysis for measurement point 3;
drop_out_24012024.do: Stata script file for dropout analysis for measurement point 2;
drop_out_24112023.do: Stata script file for dropout analysis for measurement point 1;
dsm_analysis_22042024.do: Stata script file for analysing the DSM for measurement point 3;
dsm_analysis_24012024.do: Stata script file for analysing the DSM for measurement point 2;
dsm_analysis_24112023.do: Stata script file for analysing the DSM for measurement point 1;
dsm_analysis_24112023.do: Stata script file for analysing the DSM for measurement point 1;
extract_gonogo_22042024.do: Stata script file for preparing the Go/NoGo task analysis for measurement point 3;
extract_gonogo_24012024.do: Stata script file for preparing the Go/NoGo task analysis for measurement point 2;
extract_gonogo_24112023.do: Stata script file for preparing the Go/NoGo task analysis for measurement point 1;
extract_mcq_22042024.do: Stata script file for the extraction of MCQ data for measurement point 3;
extract_mcq_24012024.do: Stata script file for the extraction of MCQ data for measurement point 2;
extract_mcq_24112023.do: Stata script file for the extraction of MCQ data for measurement point 1;
extract_quest_22042024.do: Stata script file for extracting questionnaire data for later analysis for measurement point 3;
extract_quest_24012024.do: Stata script file for extracting questionnaire data for later analysis for measurement point 2;
extract_quest_24112023.do: Stata script file for extracting questionnaire data for later analysis for measurement point 1;
gonogo_analysis_22042024.do: Stata script file for analysing the GoNoGo task for measurement point 3;
gonogo_analysis_24012024.do: Stata script file for analysing the GoNoGo task for measurement point 2;
gonogo_analysis_24112023.do: Stata script file for analysing the GoNoGo task for measurement point 1;
k-value_18122023.do: Stata script file for determining the k-value of the MCQ-27 for measurement point 1;
k-value_19062024.do: Stata script file for determining the k-value of the MCQ-27 for measurement point 3;
k-value_24012024.do: Stata script file for determining the k-value of the MCQ-27 for measurement point 2;
sessions_22042024.do: Stata script file for merging the individual sessions at measurement point 3;
sessions_24012024.do: Stata script file for merging the individual sessions at measurement point 2;
sessions_24112023.do: Stata script file for merging the individual sessions at measurement point 1;
soziodem_22042024.do: Stata script file for processing the socio-demographic data for measurement point 3;
soziodem_24012024.do: Stata script file for processing the socio-demographic data for measurement point 2;
soziodem_24112023.do: Stata script file for processing the socio-demographic data for measurement point 1;
suppsp_analysis_22042024.do: Stata script file for creating the subscales of the SUPPS-P for measurement point 3;
suppsp_analysis_24012024.do: Stata script file for creating the subscales of the SUPPS-P for measurement point 2;
suppsp_analysis_24112023.do: Stata script file for creating the subscales of the SUPPS-P for measurement point 1;
trials_22042024.do: Stata script file for merging the trials at measurement point 3;
trials_24012024.do: Stata script file for merging the trials at measurement point 2;
trials_24112023.do: Stata script file for merging the trials at measurement point 1;
stata2mplus.ado: Stata program that prepares data for analysis in Mplus;
import factor scores.do: Stata script file for importing the factor scores;
ispo_long_toMplus.do: Stata script file for preparing the longitudinal data set for analysis with Mplus;
efa_t1.inp: Mplus input file for the exploratory analysis of the tracking data at measurement point 1;
efa_t2.inp: Mplus input file for the exploratory analysis of the tracking data at measurement point 2;
efa_t3.inp: Mplus input file for the exploratory analysis of the tracking data at measurement point 3;
t1_esem_factor scores.inp: Mplus input file for the exploratory analysis of the tracking data in the form of an exploratory structural equation model at measurement point 1;
t2_esem_factor scores.inp: Mplus input file for the exploratory analysis of the tracking data in the form of an exploratory structural equation model at measurement point 2;
t3_esem_factor scores.inp: Mplus input file for the exploratory analysis of the tracking data in the form of an exploratory structural equation model at measurement point 3;
hypothesis 1a.inp: Mplus input file for testing hypothesis 1a;
hypothesis 1a_without56.inp: Mplus input file for testing hypothesis 1a without participants who are older than or equal to 56;
hypothesis 1b.inp: Mplus input file for testing hypothesis 1b;
hypothesis 1b_without56.inp: Mplus input file for testing hypothesis 1b without participants who are older than or equal to 56;
hypothesis 1c_loc.inp: Mplus input file for testing hypothesis 1c_loc;
hypothesis 1c_loc_without56.inp: Mplus input file for testing hypothesis 1c_loc without participants who are older than or equal to 56;
hypothesis 1c_ss.inp: Mplus input file for testing hypothesis 1c_ss;
hypothesis 1c_ss_without56.inp: Mplus input file for testing hypothesis 1c_ss without participants who are older than or equal to 56;
hypothesis 1c_urg.inp: Mplus input file for testing hypothesis 1c_urg;
hypothesis 1c_urg_without56.inp: Mplus input file for testing hypothesis 1c_urg without participants who are older than or equal to 56;
hypothesis_2a_1.inp: Mplus input file for testing hypothesis 2a_1;
hypothesis_2a_2.inp: Mplus input file for testing hypothesis 2a_2;
hypothesis_2a_3.inp: Mplus input file for testing hypothesis 2a_3;
hypothesis_2b_1.inp: Mplus input file for testing hypothesis 2b_1;
hypothesis_2b_2.inp: Mplus input file for testing hypothesis 2b_2;
hypothesis_2b_3.inp: Mplus input file for testing hypothesis 2b_3;
hypothesis_2c_1.inp: Mplus input file for testing hypothesis 2c_1;
hypothesis_2c_2.inp: Mplus input file for testing hypothesis 2c_2;
hypothesis_2c_3.inp: Mplus input file for testing hypothesis 2c_3;
hypothesis_2d_1.inp: Mplus input file for testing hypothesis 2d_1;
hypothesis_2d_2.inp: Mplus input file for testing hypothesis 2d_2;
hypothesis_2d_3.inp: Mplus input file for testing hypothesis 2d_3;
hypothesis_2e_1.inp: Mplus input file for testing hypothesis 2e_1;
hypothesis_2e_2.inp: Mplus input file for testing hypothesis 2e_2;
hypothesis_2e_3.inp: Mplus input file for testing hypothesis 2e_3;
hypothesis_3a_1.inp: Mplus input file for testing hypothesis 3a_1;
hypothesis_3a_1_med.inp: Mplus input file for testing hypothesis 3a_1_med;
hypothesis_3a_2.inp: Mplus input file for testing hypothesis 3a_2;
hypothesis_3a_2_med.inp: Mplus input file for testing hypothesis 3a_2_med;
hypothesis_3a_3.inp: Mplus input file for testing hypothesis 3a_3;
hypothesis_3a_3_med.inp: Mplus input file for testing hypothesis 3a_3_med;
hypothesis_3b_1.inp: Mplus input file for testing hypothesis 3b_1;
hypothesis_3b_1_med.inp: Mplus input file for testing hypothesis 3b_1_med;
hypothesis_3b_2.inp: Mplus input file for testing hypothesis 3b_2;
hypothesis_3b_2_med.inp: Mplus input file for testing hypothesis 3b_2_med;
hypothesis_3b_3.inp: Mplus input file for testing hypothesis 3b_3;
hypothesis_3b_3_med.inp: Mplus input file for testing hypothesis 3b_3_med;
hypothesis_3c_1.inp: Mplus input file for testing hypothesis 3c_1;
hypothesis_3c_1_med.inp: Mplus input file for testing hypothesis 3c_1_med;
hypothesis_3c_2.inp: Mplus input file for testing hypothesis 3c_1;
hypothesis_3c_2_med.inp: Mplus input file for testing hypothesis 3c_2_med;
hypothesis_3c_3.inp: Mplus input file for testing hypothesis 3c_3;
hypothesis_3c_3_med.inp: Mplus input file for testing hypothesis 3c_3_med;
hypothesis_3d_1.inp: Mplus input file for testing the hypothesis 3d_1;
hypothesis_3d_1_med.inp: Mplus input file for testing the hypothesis 3d_1_med;
hypothesis_3d_2.inp: Mplus input file for testing the hypothesis 3d_2;
hypothesis_3d_2_med.inp: Mplus input file for testing the hypothesis 3d_2_med;
hypothesis_3d_3.inp: Mplus input file for testing the hypothesis 3d_3;
hypothesis_3d_3_med.inp: Mplus input file for testing the hypothesis 3d_3_med;
hypothesis_3e_1.inp: Mplus input file for testing hypothesis 3e_1;
hypothesis_3e_1_med.inp: Mplus input file for testing hypothesis 3e_1_med;
hypothesis_3e_2.inp: Mplus input file for testing hypothesis 3e_2;
hypothesis_3e_2_med.inp: Mplus input file for testing hypothesis 3e_2_med;
hypothesis_3e_3.inp: Mplus input file for testing hypothesis 3e_3;
hypothesis_3e_3_med.inp: Mplus input file for testing hypothesis 3e_3_med;
2025-05-06_Flowchart data analysis ISPO study.jpg: Flowchart to illustrate the data analysis in the ISPO study;
jbac24pr29_pd1.csv: Primary data set for measurement point 1;
jbac24pr29_pd2.csv: Primary data set for measurement point 2;
jbac24pr29_pd3.csv: Primary data set for measurement point 3;
jbac24pr29_pd4.csv: Primary data set for the tracking data;
jbac24pr29_kb1.txt: Codebook for primary data set for measurement point 1 (jbac24pr29_pd1.csv);
jbac24pr29_kb2.txt: Codebook for primary data set for measurement point 2 (jbac24pr29_pd2.csv);
jbac24pr29_kb3.txt: Codebook for primary data set for measurement point 3 (jbac24pr29_pd3.csv);
jbac24pr29_kb4.txt: Codebook for primary data set for the tracking data (jbac24pr29_pd4.csv);
jbac24pr29_ad1.csv: Data set with derived variables for measurement point 1;
jbac24pr29_ad2.csv: Data set with derived variables for measurement point 2;
jbac24pr29_ad3.csv: Data set with derived variables for measurement point 3;
jbac24pr29_ad4.csv: Longitudinal data set merged over all three measurement points with derived variables;
jbac24pr29_aa1.txt: Codebook for the data set with derived variables for measurement point 1 (jbac24pr29_ad1.csv);
jbac24pr29_aa2.txt: Codebook for the data set with derived variables for measurement point 2 (jbac24pr29_ad2.csv);
jbac24pr29_aa3.txt: Codebook for the data set with derived variables for measurement point 3 (jbac24pr29_ad3.csv);
jbac24pr29_aa4.txt: Codebook for the longitudinal data set merged over all three measurement points with derived variables (jbac24pr29_ad4.csv)unknow
Collapsing or not? A practical guide to handling sparse responses for polytomous items
No full textIn ordinal data analysis, category collapse is the process of combining adjacent response options to create fewer response categories than were originally measured. When collapsing response categories, researchers need to be aware of inducing data-model misfit and of obtaining biased parameter estimates. Through mathematical derivation we show that category collapse induces data-model misfit when using Generalized Partial Credit IRT model (GPCM) generated data. This data-model misfit is not present when using Graded Response IRT model (GRM) generated data. Using simulation studies, we found that category collapse can indicate better data-model fit in GRM- and GPCM-generated data. In the case of GPCM data, this result is spurious and can lead practitioners to draw conclusions from models that do not fit the data well. Recovered GPCM IRT item parameters were also significantly biased. Recommendations for practitioners who wish to collapse categories are provided.peerReviewedpublishedVersio