Descriptive statistics of the factor and the ordinal variables in the dataset.

Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.

NEW VERSION - Descriptive data on group (and subgroup) level for the article mentioned in the title.

Descriptive statistics for trustworthiness ratings of the 25% and 50% angry, neutral and happy faces for each group.

Hypomanic Personality Scale (HPS); Altman Self Rating Mania Index (ASRM); Responses to Positive Affect Scale (RPA); Positive Urgency Measure (PUM); Inspiration Scale (IS); External and Internal Scale of Inspiration (EISI).

Descriptive statistics for Cancel Culture project.

Note. Within rows, emotions sharing a subscript do not differ significantly. All correlations were significant at p

Note: Pearson r correlations were used for Study 1. Emotional n-back performance is measured using accuracy scores. NA = Negative Affect; PA = Positive Affect. Significant correlations are indicated in bold (p

The purpose of this dataset is to share the positive psychology intervention of university students. The results obtained from this dataset, the descriptive analysis and the statistical analyses performed on this data were developed using SPSS and give rise to a scientific article (currently under review).

This study is aimed at exploring the effects of dating anxiety on self-esteem and subjective well-being, in males and females aged between 19 to 30 years, Bangalore, Karnataka, India. The sample was determined by Convenience sampling method. “Dating Anxiety Scale”, “Rosenberg Self Esteem Scale”, “PANAS”, and “Satisfaction with Life Scale” were used for the collection of the data. In addition to descriptive statistics, correlational analysis techniques were used to analyze the data. As per the result of research; it was determined that dating anxiety has significant weak positive correlation with negative affect, and has significant weak negative correlation with positive affect, life satisfaction and self-esteem.

ABSTRACT Cognitive aging is dynamic and heterogeneous in elderly, thus adequate tools such as paper-based tests are relevant to describe the cognitive profile of this population. Objective: To describe different paper-based cognitive assessments tests in elderly people stratified by age and education. Methods: A cross-sectional study of 667 elderly (≥60 years) living in the community was conducted. Sociodemographic information was collected. Global cognition was assessed by the Addenbrooke's Cognitive Examination-Revised (ACE-R), Mini Addenbrooke’s Cognitive Examination (M-ACE) and Mini-Mental State Examination (MMSE). The data were analyzed using descriptive statistics, the t-test and Pearson’s Correlation Coefficient. Results: The findings showed a predominance of women (53.8%), mean age of 71.3 (±7.7) years and 3.6 (±3.5) years of education. The best global cognitive performance and cognitive domain assessment scores were found in the group with higher formal educational level. Each year of education was associated with an increase of up to 10% in scores on the M-ACE and MMSE and up to 11% in ACE-R scores. The mean values of the scores varied according to age, where the 60-69 years group had better scores than other age groups. The correlation matrix between the cognitive tests showed that near perfect correlations (r=1) were frequent in the subgroup with higher education. Conclusion: Younger elderly and those with higher educational level had greater global and domain scores. This study describes the scores of elderly for different strata of education and age. In practice, it is important to choose the most suitable screening instrument, considering the characteristics of the elderly.

Values are percentage (number of participants) unless otherwise stated. Data were collected between May and November 2009 in Cambridge, UK.adifferences between males and females assessed using one-way ANOVA for continuously distributed variables and Mann-Whitney U test for non-parametric data.bn

The file consists of data from 244 respondents who completed the 24-item Self-Control Scale and 30-item Herth Hope Scale. The data were analysed using composite and average scores. Statistical analysis used included descriptive analysis (frequency, mean and standard deviation) and correlation.

This study is a cross-sectional, hospital-based observational study conducted from July 2019 to March 2020 at Kasturba Hospital of Kasturba Medical College, Manipal, a tertiary care centre in Udupi district of Karnataka state in India. The sample consisted of medical practitioners working in various clinical branches of medicine of Kasturba Hospital, Manipal, with a minimum of one year of experience. Convenience sampling was used. Written informed consent was obtained from all participants. The sample size was calculated using the formula for statistically significant correlation coefficient and it was calculated as185. Participants’ age, gender and work experience details were documented in a proforma. Psychological flexibility was measured using Acceptance and action questionnaire-II (AAQ-II; Bond et al, 2011). Professional Quality of Life Scale Version 5 (ProQol 5; Stamm, 2010) was used to measure compassion satisfaction (CS), burnout (BO) and secondary traumatic stress (STS) among participants. The collected data was analyzed using IBM Statistical Package for Social Sciences (SPSS) statistics for Windows, Version 25 (IBM Corp, Armonk, NY, USA). Descriptive statistics were used to summarize the data. Mean and standard deviation (SD) were used for continuous data. Group differences across gender for continuous variables were examined using an independent t-test and P values less than 0.05 were considered significant. To establish the relationship between the variables Pearson’s’ correlation test was used.

The research hypothesis stated was that there would be no relationship between psychological flexibility and 1) compassion satisfaction, 2) burnout and 3) secondary traumatic stress among medical practitioners.

Out of the 185 that could complete the study, it included 70 females and 115 males. Mean age of the sample was 37.31 years. In terms of years of work experience, 149 doctors had less than 20 years of experience and 36 had more than 20 years of experience.

Mean scores of acceptance and action questionnaire-II and professional quality of life scale version 5 were analysed. Compassion Satisfaction had a mean score of 35.89, Burnout has a mean score of 24.97, Secondary Traumatic Stress had a mean score of 20.43 and Psychological Inflexibility had a mean score of 15.69.

The result of Pearson’s correlation showed the relationship between compassion satisfaction and psychological inflexibility was not significantly correlated. The relationship between burnout and psychological inflexibility is significantly and strongly positively correlated. The relationship between secondary traumatic stress and psychological inflexibility is significantly and strongly positively correlated. Using a t-test, it was shown that compassion satisfaction was relatively higher for females and burnout was relatively higher in males.

🧠 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.

We collect direct text data from the narrative of the people who faced psychological problem. Then, we make this dataset from the text. In this dataset there are 6 columns those are Age, Gender, Problem description, problem summary, problem category and problem psychological category.

The dataset is drawn from an online experiment conducted with 628 Australians, who were randomly assigned to one of three conditions. Two of these conditions aimed at increasing sustainable palm oil-related purchases, while one condition served as an attentional control. Follow-up data after two weeks (n=403) are also included. The SPSS data file includes all the data. SPSS output files specify the various analyses that were run, which include descriptive statistics, multiple analysis of variance and chi-square analysis (Descriptives & Preliminary Analysis; Outcome Measures), mediation analysis, and subsequent analyses after the latent profile analysis. MPlus input and output files for the latent profile analysis are included for a range of two to six groups.

ABSTRACT This research aims at describing how the Psychology area produces and understands the Methods unit in empirical academic articles. As a theoretical basis, we relied on Swales (1990) for the concepts involving academic genres and its Create a Research Space methodology. Hyland’s (2000) research was used as basis for the study of disciplinary cultures. Thus, our study, classified as an exploratory and descriptive research, has a corpus consisting of 30 copies of academic articles from 10 journals in the Psychology area, indexed in the WEBQUALIS platform from the Capes database. In our study we verified that the Methods sections had a very detailed section, providing information related to the sample size and profile, descriptions of materials or instruments used, research procedures, data related to the approval by research ethics committees and description of data analysis.

Descriptive Statistics: Demographics and Personality.

Research investigating whether depression is an adaptation or a disorder has been hindered by the lack of an experimental paradigm that can test causal relationships. Moreover, studies attempting to induce the syndrome often fail to capture the suite of feelings, thoughts, and behaviours that characterize depression. An experimental paradigm for triggering depressive symptoms can improve our etiological understanding of the syndrome. The present study attempts to induce core symptoms of depression, particularly those related to rumination, in a healthy, non-clinical sample through a controlled social experiment. These symptoms are sad or depressed mood, anhedonia, feelings of worthlessness or guilt, and difficulty concentrating. 134 undergraduate students were randomly assigned to either an Exclusion (EX) or Inclusion (IN) group. Participants in the Exclusion group were exposed to a modified Cyberball paradigm, designed to make them feel socially excluded, followed by a dual-interferenc..., Our datasets were collected through LimeSurvey, an open source on-line statistical survey web app. We first exported our data as excel files which contain participants' self-reported data. We also added the scores for each participants' writing task provided by our blind-raters. We have presented the data from our primary study and our pilot study in two seperate files., , # Data from: An experimental paradigm for triggering a depressive syndrome

https://doi.org/10.5061/dryad.v6wwpzh2v

Description of the data and file structure

# Title of Dataset: PilotExperiment

This file is our overall dataset for the pilot study detailed in our manuscript and supplementary materials.

## Description of the Data and file structure

The following is a description of each of the variables found in our data set, organized by column:Â

Note: any ‘.’ within the dataset refers to missing data.Â

**Date: **The date the participant completed the study.

**ID: **The randomized ID given to each participant.

**Cond: **The condition that the participant was assigned to (1=Exclusion; 0=Inclusion).

**Mood Variables: **Scores for the following mood variables at time 1 are found in their respective columns. These same mood variables, at time 2, have the same names but have a ‘2’ afterwards. Variables over 7 characters were...