DOI: 10.57760/sciencedb.psych.00998
title: The psychological mechanisms underlying stress-induced gaming craving in individuals with Internet Gaming Disorder (IGD)_data
title: 压力诱发网游成瘾者游戏渴求的心理机制_data

description: This dataset was derived from a laboratory-based psychological and behavioral study employing an acute stress induction paradigm, aiming to investigate the mechanisms through which acute psychosocial stress influences gaming craving in individuals with Internet Gaming Disorder (IGD). The study adopted the Trier Social Stress Test (TSST) as a standardized acute stress induction task, which was administered in a controlled laboratory environment. After completing baseline assessments, participants sequentially underwent the stress induction phase and the recovery phase. Subjective stress, negative affect, and gaming craving were repeatedly assessed at multiple time points. All experimental procedures were conducted by uniformly trained research staff following standardized protocols to ensure the consistency and reliability of data collection.Data were collected within a single experimental session, covering five time points (T1–T5) corresponding to the pre-stress baseline, stress task, and post-stress recovery phases. Time intervals between measurements were on the order of minutes, allowing the dataset to capture the dynamic trajectories of psychological responses under acute stress. Spatially, all data were collected in the same laboratory setting, with experimental conditions (e.g., lighting, noise level, equipment placement) kept constant across participants; therefore, no spatial resolution differences are applicable. The dataset does not involve geographic coordinates or spatial stratification information.The dataset is stored in tabular format using a long-format structure. Each row represents a single observation for one participant at one time point. The primary variables include participant identifier (ID), group membership (IGD group vs. control group), time point (T1–T5), subjective stress score, negative affect score, and gaming craving score. Subjective stress and negative affect were measured using standardized self-report scales, with values recorded as raw scale scores. Gaming craving was assessed using Likert-type ratings, with higher scores indicating stronger craving intensity. The full dataset includes repeated measurements from 58 participants across five time points, yielding a theoretical maximum of 290 observations; the exact number of valid records is provided in the data file.During data preprocessing, the raw data underwent systematic screening and cleaning procedures, including outlier detection, variable name harmonization, and necessary centering operations (e.g., grand-mean centering or within-person centering), to meet the analytical requirements of subsequent linear mixed-effects models and two-level Bayesian dynamic structural equation modeling (DSEM). A small proportion of data points were missing at certain time points, primarily due to participants discontinuing the experiment or failing to complete specific questionnaire items. The proportion of missing data was low, and Bayesian estimation methods or full information maximum likelihood approaches were applied in the statistical analyses to reduce potential bias associated with missingness.Overall, measurement error in the dataset primarily arises from the subjective nature of self-report instruments and individual variability in acute stress responses. However, the use of standardized experimental procedures and repeated measurements across multiple time points helped mitigate random error. No evidence of systematic measurement bias or abnormal data fluctuations was observed, and the data quality is adequate for longitudinal and multilevel modeling analyses.The dataset is stored in Excel (.xlsx) format, with clear variable labels and numerical coding descriptions provided within the file. The data are compatible with commonly used statistical and modeling software packages, including SPSS, R, and Mplus. In particular, Mplus version 8.3 was used for subsequent dynamic structural equation modeling analyses. The software can be obtained from the official Mplus website (https://www.statmodel.com). The file structure is well organized, facilitating reproducibility and secondary analyses.
description: This dataset was derived from a laboratory-based psychological and behavioral study employing an acute stress induction paradigm, aiming to investigate the mechanisms through which acute psychosocial stress influences gaming craving in individuals with Internet Gaming Disorder (IGD). The study adopted the Trier Social Stress Test (TSST) as a standardized acute stress induction task, which was administered in a controlled laboratory environment. After completing baseline assessments, participants sequentially underwent the stress induction phase and the recovery phase. Subjective stress, negative affect, and gaming craving were repeatedly assessed at multiple time points. All experimental procedures were conducted by uniformly trained research staff following standardized protocols to ensure the consistency and reliability of data collection.Data were collected within a single experimental session, covering five time points (T1–T5) corresponding to the pre-stress baseline, stress task, and post-stress recovery phases. Time intervals between measurements were on the order of minutes, allowing the dataset to capture the dynamic trajectories of psychological responses under acute stress. Spatially, all data were collected in the same laboratory setting, with experimental conditions (e.g., lighting, noise level, equipment placement) kept constant across participants; therefore, no spatial resolution differences are applicable. The dataset does not involve geographic coordinates or spatial stratification information.The dataset is stored in tabular format using a long-format structure. Each row represents a single observation for one participant at one time point. The primary variables include participant identifier (ID), group membership (IGD group vs. control group), time point (T1–T5), subjective stress score, negative affect score, and gaming craving score. Subjective stress and negative affect were measured using standardized self-report scales, with values recorded as raw scale scores. Gaming craving was assessed using Likert-type ratings, with higher scores indicating stronger craving intensity. The full dataset includes repeated measurements from 58 participants across five time points, yielding a theoretical maximum of 290 observations; the exact number of valid records is provided in the data file.During data preprocessing, the raw data underwent systematic screening and cleaning procedures, including outlier detection, variable name harmonization, and necessary centering operations (e.g., grand-mean centering or within-person centering), to meet the analytical requirements of subsequent linear mixed-effects models and two-level Bayesian dynamic structural equation modeling (DSEM). A small proportion of data points were missing at certain time points, primarily due to participants discontinuing the experiment or failing to complete specific questionnaire items. The proportion of missing data was low, and Bayesian estimation methods or full information maximum likelihood approaches were applied in the statistical analyses to reduce potential bias associated with missingness.Overall, measurement error in the dataset primarily arises from the subjective nature of self-report instruments and individual variability in acute stress responses. However, the use of standardized experimental procedures and repeated measurements across multiple time points helped mitigate random error. No evidence of systematic measurement bias or abnormal data fluctuations was observed, and the data quality is adequate for longitudinal and multilevel modeling analyses.The dataset is stored in Excel (.xlsx) format, with clear variable labels and numerical coding descriptions provided within the file. The data are compatible with commonly used statistical and modeling software packages, including SPSS, R, and Mplus. In particular, Mplus version 8.3 was used for subsequent dynamic structural equation modeling analyses. The software can be obtained from the official Mplus website (https://www.statmodel.com). The file structure is well organized, facilitating reproducibility and secondary analyses.