Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements.

Methods To investigate the data-related practices of psychology researchers, we adapted a survey developed as part of our previous study of neuroimaging researchers. The survey was distributed via Qualtrics (http://www.qualtrics.com) from January 25 to March 25, 2019. Before beginning the survey, participants were required to verify that they were at least 18 years old and gave their informed consent to participate. Participants who did not meet these inclusion criteria or who did not complete at least the first section of the survey were not included in the final data analysis. After filtering, 274 psychology researchers from 31 countries participated in our survey.

All code for data collection and visualization is included in the Jupyter notebooks included here.

Software tools used to collect and analyze data. Parentheses for analysis software indicate the tools participants were taught to use as part of their education in research methods and statistics. “Other” responses for data collection software were largely comprised of survey tools (e.g. Survey Monkey, LimeSurvey) and tools for building and running behavioral experiments (e.g. Gorilla, JsPsych). “Other” responses for data analysis software largely consisted of neuroimaging-related tools (e.g. SPM, AFNI).

Systematic exposure to social media causes social comparisons, especially among women who compare their image to others; they are particularly vulnerable to mood decrease, self-objectification, body concerns, and lower perception of themselves. This study first investigates the possible links between life satisfaction, self-esteem, anxiety, depression, and the intensity of Instagram use with a social comparison model. In the study, 974 women age 18–49 who were Instagram users voluntarily participated, completing a questionnaire. The results suggest associations between the analyzed psychological data and social comparison types. Then, artificial neural networks models were implemented to predict the type of such comparison (positive, negative, equal) based on the aforementioned psychological traits. The models were able to properly predict between 71% and 82% of cases. As human behavior analysis has been a subject of study in various fields of science, this paper contributes towards understanding the role of artificial intelligence methods for analyzing behavioral data in psychology.

This dataset contains responses from an online survey of 2187 participants primarily located in the UK. All participants stated that they had used the UK National Health Service (NHS) at some time in their lives. The data were collected between December 2018 and August 2019. Participants' views on data sharing - this dataset contains information about people's willingness to share mental and physical health data for research purposes. It also includes information on willingness to share other types of data, such as financial information. The dataset includes participants' responses to questions relating to mental health data sharing, including the trustworthiness of organisations which use such data, how much the presence of different governance measures (such as deidentification, opt-out, etc.) would alter their views, and whether they would be less likely to access NHS mental health services if they knew their data might be shared with researchers. Participants' satisfaction and interaction with UK mental and physical health services - the dataset includes information regarding participants' views on and interaction with NHS services. This includes ratings of satisfaction at first contact and in the previous 12 months, frequency of use, and type of treatment received. Information about participants - the dataset includes information about participants' mental and physical health, including whether or not they have experience with specific mental health conditions, and how they would rate their mental and physical health at the time of the survey. There is also basic demographic information about the participants (e.g. age, gender, location etc.). ## This item has been replaced by the one which can be found at https://hdl.handle.net/10283/4467 ##

Data and script for analysing word-types for each type of sound and level of the taxonomy

Though the majority (86.13%) indicated that they collect primarily quantitative data, the participants in our sample indicated that they collect data in a wide variety of forms and formats that must also be accompanied by a diverse array of additional materials. “Other” data types included motion capture data, data from wearable devices, and data from administrative, institutional, and government records. “Other” forms of additional material included case summaries, payment information, and information about how questionnaires were modified.

This dataset consists of 3000 rows, representing daily data for 100 participants over a period of 30 days. The data captures various psychological, behavioral, and physiological attributes for each participant. Below is a breakdown of the columns:

participant_id: - Unique identifier for each participant. - Data type: Integer - Range: 1 to 100 (as there are 100 participants)

day: - The day of observation for each participant. - Data type: Integer - Range: 1 to 30 (each participant is observed over 30 days)

PSS_score:

• Perceived Stress Scale score, measuring stress levels.
• Data type: Integer
• Range: 10 to 40

Openness:

• Measure of openness to experience, a personality trait.
• Data type: Float
• Range: 1.0 to 5.0

Conscientiousness:

• Measure of conscientiousness, a personality trait.
• Data type: Float
• Range: 1.0 to 5.0

Extraversion:

• Measure of extraversion, a personality trait.
• Data type: Float
• Range: 1.0 to 5.0

Agreeableness:

Measure of agreeableness, a personality trait. Data type: Float Range: 1.0 to 5.0

Neuroticism:

Measure of neuroticism, a personality trait. Data type: Float Range: 1.0 to 5.0

sleep_time:

The time (in hours) the participant went to sleep. Data type: Float Range: 5.0 to 9.0 hours

wake_time:

The time (in hours) the participant woke up. Data type: Float Range: 5.0 to 9.0 hours

sleep_duration:

The duration (in hours) the participant slept. Data type: Float Range: 6.0 to 9.0 hours

PSQI_score:

Pittsburgh Sleep Quality Index (PSQI) score, measuring sleep quality. Data type: Integer Range: 1 to 5

call_duration:

Total duration of phone calls for the day (in minutes). Data type: Float Range: 0 to 60 minutes

num_calls:

Number of phone calls made during the day. Data type: Integer Range: 0 to 20 calls

num_sms:

Number of SMS messages sent during the day. Data type: Integer Range: 0 to 50 messages

screen_on_time:

Total screen-on time for the day (in hours). Data type: Float Range: 1.0 to 12.0 hours

skin_conductance:

Measure of skin conductance, indicating arousal or stress response. Data type: Float Range: 0.5 to 5.0 µS (microsiemens)

accelerometer:

Accelerometer data representing physical movement. Data type: Float Range: 0.1 to 2.5 g (g-force)

mobility_radius:

The radius of mobility for the participant (in kilometers). Data type: Float Range: 0.1 to 1.5 km

mobility_distance:

Total distance moved during the day (in kilometers). Data type: Float Range: 0.5 to 5.0 km

As part of the development of an information base for subsequent policy initiatives, the National Institute of Justice sponsored a nationwide survey of police psychologists to learn more about the characteristics of officers who abuse force, the types of measures police psychologists recommend to control police violence and the role of police psychologists in preventing and identifying individual police officers at risk for use of excessive force. Police personnel divisions in 50 large cities were contacted for names and addresses of the police psychologists who provided services to their departments. Data were collected using a telephone interview protocol that included 61 questions. In this study, excessive force was defined as a violation of a police department's use-of-force policy by an incumbent officer that was serious enough to warrant a referral to the police psychologist. Background information collected on respondents included years with the department, years as a police psychologist, if the position was salaried or consultant, and how often the psychologist met with the police chief. A battery of questions pertaining to screening was asked, including whether the psychologist performed pre-employment psychological screening and what methods were used to identify job candidates with a propensity to use excessive force. Questions regarding monitoring procedures asked if and how police officer behavior was monitored and if incumbent officers were tested for propensity to use excessive force. Items concerning police training included which officers the psychologist trained, what types of training covering excessive force were conducted, and what modules should be included in training to reduce excessive force. Information about mental health services was elicited, with questions on whether the psychologist counseled officers charged with excessive force, what models were used, how the psychologist knew if the intervention had been successful, what factors limited the effectiveness of counseling police officers, characteristics of officers prone to use excessive force, how these officers are best identified, and who or what has the most influence on these officers. General opinion questions asked about factors that increase excessive force behavior and what services could be utilized to reduce excessive force.

This dataset was collected through an online survey distributed between February 17 and March 29, 2023, among Polish adults. It provides quantitative and qualitative data relevant to the study of mystical-type experiences in both psychedelic and non-psychedelic contexts.

The dataset includes:

Demographics: Age, gender, education, and spiritual worldview. Meditation/Mindfulness Practice: Information on participants’ meditation and mindfulness activities. Psychedelic Use: Types and frequencies of psychedelic substances reported. Mystical-Type Experiences: Self-reported experiences assessed through structured questions and questionnaires. Revised Mystical Experience Questionnaire (MEQ30): Standardized measure of mystical-type experience intensity. Perth Empathy Scale (PES): Affective empathy subscales. Satisfaction with Life Scale (SWLS): Measure of global life satisfaction. Death Attitude Profile – Revised, Polish version (DAP-R-PL): Fear of Death subscale. Subjectively Perceived Influence: Participants’ reflections on how mystical-type experiences affected psychological metrics. Open-Ended Narratives: Participants’ written descriptions of their mystical-type experiences (in Polish).

DesignA cross-sectional, web-based survey design was employed, consisting of validated self-report measures designed to capture demographic information, insulin use, diabetes-related distress, disordered eating, and body shape perception.Inclusion/Exclusion criteria. Participants were eligible to participate if they self-described as being aged 18 or over, with a diagnosis of Type 1 diabetes and on a prescribed insulin regimen. They were required to be at least one-year post-diagnosis, as people who have been prescribed insulin for less than one year may not have settled into a routine with insulin management and may mismanage their insulin unintentionally. Additionally, participants were required to reside within the UK, as this removed a potential confound of cost or resources as a barrier to accessing insulin. People with a diagnosis of type 2 diabetes were excluded from the study, as the pathophysiology and treatment of the two illnesses are quite different. For example, as those with type 2 diabetes still produce some degree of insulin naturally, non-adherence to an insulin regimen is likely to have less of an immediate impact than for those with type 1 diabetes, who produce no insulin naturally (Peyrot et al., 2010). Potential participants were provided with a link to the study which provided detailed information about the study, details of informed consent and their right to withdraw. When the survey was completed, or participants chose to exit, a debrief page was presented with signposts towards various supports and resources. Participants were offered the opportunity to receive a brief summary of findings from the study and given the chance to win a £25 Amazon gift voucher, both of which required an email address to be supplied through separate surveys, so as to protect the confidentiality of responses. Ethical approval for this study was granted by the chair of the relevant Ethics Committee.Statistical AnalysisPrior to beginning the study, an estimate of the minimum number of participants required was calculated using statistical power tables (Clark-Carter, 2010) and G*Power version 3.1. Based on previous research (Ames, 2017), a medium effect size (.5) was used to calculate sample sizes with a power of .8 (Clark-Carter, 2010), which generated a necessary sample size of 208. All analyses were adequately powered.Data were analysed using IBM SPSS Statistics for Mac version 25. MeasuresDemographic Information. This section collected basic demographic information, including age; gender; country of residence; and current or historical diagnosis of an eating disorder. The data were screened to ensure participants met the inclusion criteria.Insulin Measure. A 16-item questionnaire has been designed to assess rates and reasons for insulin non-adherence (Ames, 2017). Eating Disorder Psychopathology. The Eating Disorder Examination-Questionnaire (EDE-Q) assesses eating disorder psychopathology, and data from this measure was key to informing the primary research questions. It was designed as a self-report version of the interview-based Eating Disorders Examination (EDE; 32), which is considered to be the gold standard measure (Fairburn, Wilson, & Schleimer, 1993). The EDE-Q assesses four subscales: Restraint, Eating Concern, Shape Concern, and Weight Concern. It was found to be an adequate alternative to the EDE (Fairburn & Beglin, 1994). Body Shape Questionnaire (BSQ). The Body Shape Questionnaire is a 34-item self-report measure, designed to assess concerns regarding body shape and the phenomenological experience of “feeling fat” (Cooper, Taylor, Cooper, & Fairbum, 1987). The BSQ targets body image as a central feature of both AN and BN and thus is a useful supplementary measure of eating disorder psychopathology. Diabetes Distress. The Diabetes Distress Scale (Polonsky et al., 2005) is a 17-item scale designed to measure diabetes-related emotional distress via four domains: emotional burden, physician distress, interpersonal distress and regimenn distress. This measure was included on the basis of results from Ames (Ames, 2017), which identified diabetes-related emotional distress as a key reason for insulin non-adherence in type 1 diabetes. Inclusion in this study allowed for further investigation of its role.

Please give me an upvote if you find it useful!! Dataset Description: Personality Traits and Social Behavior This dataset contains behavioral and psychological data aimed at classifying individuals as Introverts or Extroverts. It captures how social preferences and habits correlate with personality types, making it ideal for machine learning models, psychological research, and social behavior studies.

🔍 Key Features: Time_spent_Alone (Numeric): Average time an individual spends alone (in hours).

Stage_fear (Categorical: Yes/No): Indicates if the individual experiences stage fright.

Social_event_attendance (Numeric): Number of social events attended recently.

Going_outside (Numeric): Frequency of going outside for non-essential reasons.

Drained_after_socializing (Categorical: Yes/No): Shows whether the person feels mentally exhausted after social interaction.

Friends_circle_size (Numeric): Count of close friends in the individual’s social circle.

Post_frequency (Numeric): Frequency of social media posting.

Personality (Target Label: Introvert/Extrovert): Personality classification based on observed traits.

🎯 Potential Use Cases: Predictive modeling for personality classification.

Feature analysis to understand behavioral differences between introverts and extroverts.

Building recommendation systems or personalized experiences based on social behavior.

Educational tools for self-assessment or career guidance.

Large language models (LLMs) are being used to classify texts into categories informed by psychological theory (“psychological text classification”). However, the use of LLMs in psychological text classification requires validation, and it remains unclear exactly how psychologists should prompt and validate LLMs for this purpose. To address this gap, we examined the potential of using LLMs for psychological text classification, focusing on ways to ensure validity. We employed OpenAI's GPT-4o to classify (1) reported speech in online diaries, (2) other-initiations of conversational repair in Reddit dialogues, and (3) harm reported in healthcare complaints submitted to NHS hospitals and trusts. Employing a two-stage methodology, we developed and tested the validity of the prompts used to instruct GPT-4o using manually labeled data (N = 1,500 for each task). First, we iteratively developed three types of prompts using one-third of each manually coded dataset, examining their semantic validity, exploratory predictive validity, and content validity. Second, we performed a confirmatory predictive validity test on the final prompts using the remaining two-thirds of each dataset. Our findings contribute to the literature by demonstrating that LLMs can serve as valid coders of psychological phenomena in text, on the condition that researchers work with the LLM to secure semantic, predictive, and content validity. They also demonstrate the potential of using LLMs in rapid and cost-effective iterations over big qualitative datasets, enabling psychologists to explore and iteratively refine their concepts and operationalizations during manual coding and classifier development. Accordingly, as a secondary contribution, we demonstrate that LLMs enable an intellectual partnership with the researcher, defined by a synergistic and recursive text classification process where the LLM's generative nature facilitates validity checks. We argue that using LLMs for psychological text classification may signify a paradigm shift toward a novel, iterative approach that may improve the validity of psychological concepts and operationalizations.

LifeSnaps Dataset Documentation Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction. The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication. Data Import: Reading CSV For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command. Data Import: Setting up a MongoDB (Recommended) To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database. To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here. For the Fitbit data, run the following: mongorestore --host localhost:27017 -d rais_anonymized -c fitbit

Sport psychology practitioners (SPPs) are often educated/encouraged to set specific goals when working with athletes. Nevertheless, researchers have indicated that athletes use nonspecific goals in applied settings and that nonspecific goals can enhance performance and various positive psychological outcomes. However, researchers have yet to explore SPPs’ experiences with, and perceptions of, using nonspecific goals when working with athletes. To address this knowledge gap, we conducted semi-structured interviews with 12 SPPs who had provided sport psychology support to athletes for an average of 13.42 years (SD = 11.30). Findings from our content analysis showed that the SPPs perceived open goals (e.g., “to see what you can do”) and range goals (e.g., “to run between 40-60 minutes”) to be more beneficial than do-your-best and as-well-as-possible goals. Despite differences in perceptions across various types of nonspecific goals, the SPPs reported that all nonspecific goals were used in several situations (e.g., situations of adversity) for multiple reasons (e.g., performance/situation uncertainty). The SPPs perceived that all nonspecific goals could reduce maladaptive psychological responses, increase positive psychological responses, facilitate personal growth, and enable greater flexibility/freedom. However, SPPs perceived that all nonspecific goals could lack clarity and imply a lack of confidence in athletes. Our findings capture key differences across various types of nonspecific goals while highlighting the situations/reasons that SPPs used nonspecific goals to facilitate positive athlete outcomes. Given the perceived benefits of nonspecific goals, we suggest that different types of nonspecific goals could be considered as additional/alternate goal-setting interventions in sport. Sport psychology practitioners perceived that nonspecific goal types were useful in many situations and can lead to several psychological benefits as –well as disadvantages. They highlighted preferences for open goals (e.g., “see how well you can do”) and range goals (e.g., “to run between 40-60 minutes”) over do-your-best goals and as-well-as-possible goals. Sport psychology practitioners should consider using nonspecific goals as an additional goal type to specific goals.Not all types of nonspecific goals should be used in an overarching or uniform manner. Indeed, open goals and range goals were perceived to be more beneficial and useful in a wider range of contexts/situations.Sport psychology practitioners should consider athlete preferences, contextual factors, and their own personal beliefs/philosophies when deciding to use nonspecific goals when working with athletes. Sport psychology practitioners should consider using nonspecific goals as an additional goal type to specific goals. Not all types of nonspecific goals should be used in an overarching or uniform manner. Indeed, open goals and range goals were perceived to be more beneficial and useful in a wider range of contexts/situations. Sport psychology practitioners should consider athlete preferences, contextual factors, and their own personal beliefs/philosophies when deciding to use nonspecific goals when working with athletes.

Questions, answers, and metadata collected from 145,828 Holland Code (RIASEC) Tests. The data was hosted on OpenPsychometrics.org a nonprofit effort to educate the public about psychology and to collect data for psychological research. Their notes on the data collected in the codebook.txt

From Wikipedia:

The Holland Codes or the Holland Occupational Themes (RIASEC) refers to a theory of careers and vocational choice (based upon personality types) that was initially developed by American psychologist John L. Holland.

The US Department of Labor's Employment and Training Administration has been using an updated and expanded version of the RIASEC model in the "Interests" section of its free online database O*NET (Occupational Information Network) since its inception during the late 1990s.

1) Data Introduction • The Extrovert vs. Introvert Behavior Data is a tabular 2,900 psychological and behavioral dataset of individual social behaviors (time alone, frequency of going out, number of friends, social media activities, etc.) and personality types (outward/inward).

2) Data Utilization (1) Extrovert vs. Introvert Behavior Data has characteristics that: • Each row includes time spent alone in a day, stage fright, frequency of social gatherings, frequency of going out, post-socialization fatigue, number of friends, frequency of social media posts, and target variable, personality type (Extrovert/Introvert). • Data has some missing values, but the outward and introverted classes are distributed in a balanced way, making them suitable for personality prediction and behavioral analysis. (2) Extrovert vs. Introvert Behavior Data can be used to: • Personality Type Predictive Model Development: Using social behavioral characteristics and personality labels, we can build an outward/introverted personality predictive model based on machine learning. • Social Behavior Patterns and Psychological Analysis: It can be used for research in various fields such as psychology, sociology, and marketing by analyzing the correlation between various variables such as time alone, the number of friends, and social media activities.

🧠 Psy-Data-Books: Synthetic Medical & Psychology Conversation Dataset

Psy-Data-Books is one of the largest synthetic datasets of psychology and medical conversations, generated from verified medical and psychology literature. It is designed for building and training powerful conversational AI systems for healthcare, therapy, and mental health applications.

  📊 Dataset Summary

Domain: Psychology, Psychiatry, Mental Health, General Medicine Data Type: Synthetic… See the full description on the dataset page: https://huggingface.co/datasets/Compumacy/Psych_data.

The experiment aimed to study different types of sociality during anonymous encounters and the impact of these types of sociality on walking patterns. During the experiment different conditions were modified as e.g. communicational settings.

Data from a lab-based experiment (N = 185) in which we tested the effect of anticipatory stress on moral condemnation. The data covers severity ratings for vignettes of two content types: vignettes with an inherent disgust-eliciting element (e.g., eating human flesh) and without (e.g., lying on a resume). Participants in the anticipatory stress condition rated the vignettes as more morally wrong, and disgust-eliciting vignettes were rated as more morally wrong. No moderation by disgust content was found. Private Body Consciousness (PBC) was positively associated with condemnation for disgust-eliciting vignettes (but not with non-disgust-eliciting vignettes). The data can be used, for example, in research on incidental vs. inherent emotions, to identify the strength of induced emotions on judgments, and to identify moderators (e.g., PBC).

The risk of incidents involving mass decontamination in response to a chemical, biological, radiological, or nuclear release has increased in recent years, due to technological advances, and the willingness of terrorists to use unconventional weapons. Planning for such incidents has focused on the technical issues involved, rather than on psychosocial concerns. This paper presents a novel experimental study, examining the effect of three different responder communication strategies on public experiences and behaviour during a mass decontamination field experiment. Specifically, the research examined the impact of social identity processes on the relationship between effective responder communication, and relevant outcome variables (e.g. public compliance, public anxiety, and co-operative public behaviour). All participants (n = 111) were asked to visualise that they had been involved in an incident involving mass decontamination, before undergoing the decontamination process, and receiving one of three different communication strategies: 1) ‘Theory-based communication’: Health-focused explanations about decontamination, and sufficient practical information; 2) ‘Standard practice communication’: No health-focused explanations about decontamination, sufficient practical information; 3) ‘Brief communication’: No health-focused explanations about decontamination, insufficient practical information. Four types of data were collected: timings of the decontamination process; observational data; and quantitative and qualitative self-report data. The communication strategy which resulted in the most efficient progression of participants through the decontamination process, as well as the fewest observations of non-compliance and confusion, was that which included both health-focused explanations about decontamination and sufficient practical information. Further, this strategy resulted in increased perceptions of responder legitimacy and increased identification with responders, which in turn resulted in higher levels of expected compliance during a real incident, and increased willingness to help other members of the public. This study shows that an understanding of the social identity approach facilitates the development of effective responder communication strategies for incidents involving mass decontamination.