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Stress in Children and Adolescents: Development and Validation of a new Questionnaire to Assess Stress Alannah Hahn, Alexander Winkler, Christiane Hermann Department of Clinical Psychology and Psychotherapy Justus-Liebig-University, Giessen, Germany Open Data and Open Materials (Codebook) Article available online: https://doi.org/10.1177/15345084251387951 Alannah Hahn: https://orcid.org/0000-0003-0549-7813 Alexander Winkler: https://orcid.org/0000-0002-4625-6314 Christiane Hermann (contact author): Justus-Liebig-University Giessen Department of Clinical Psychology and Psychotherapy Otto-Behaghel-Str. 10F 35394, Giessen, Germany E-Mail: [email protected] https://orcid.org/0000-0001-5969-2898
Hahn et al.: Open Data and Open Materials (Codebook) 2 1 Open Data The objective of this codebook is to provide an overview of all files included in the open data package. The materials consist of SPSS data and syntax files as well as an R script, which accompany our research article Stress in Children and Adolescents: Development and Validation of a New Questionnaire to Assess Stress, published in Assessment for Effective Intervention (https://doi.org/10.1177/15345084251387951). All files were created using SPSS 29 (IBM, Armonk, NY/USA) and R (version 4.5.1). The dataset contains all subjective ratings collected for the study and is stored in the file Hahn_et_al_AEI_SPSS_Data.sav. To reproduce the statistical analyses, we provide the SPSS syntax file Hahn_et_al_AEI_SPSS_Syntax.sps. In addition, the R script Hahn_et_al_AEI_R-script.R’ can be used to calculate fit indices for the factor analyses. This script requires the dataset Hahn_et_al_AEI_R_Data.sav. 2 SPSS data file ‘Hahn_et_al_AEI_SPSS_Data.sav’ 2.1 Sample Characteristics In Section 1 of the SPSS data file, we provide demographic information, including age (in years), gender (female, male, or diverse), type of school (rows 6–8), as well as an indication given by participants at the end of the questionnaire regarding whether a caregiver was present during completion (‘caregiver_presence’, row 9). Rows 3–5 contain group variables that were used for specific analyses: sample group (row 3: validation sample vs. additional sample), presence of COVID-related restrictions at the time of data collection (COVID_group, row 4: no restrictions vs. COVID restrictions), and age group (row 5: children aged 6–11 years vs. adolescents aged 12–17 years). Results for the demographic characteristics can be found in ►Table 1 of the manuscript.
Hahn et al.: Open Data and Open Materials (Codebook) 3 2.2 Subjective Ratings In Section 2 of the SPSS data file, we provide all subjective ratings: - Subjective global stress level (rated on a NRS 0–10, row 11), COVID-related stress (rated on a NRS 0–10, row 12), and ratings on acceptance and usability: perceived difficulty (rated on a NRS 0–10, row 13), comprehensibility of the items (rated on a NRS 0–10, row 14), and subjective age-appropriateness of the items (rated on a NRS 0–10, row 15). - Rows 16–32 contain subjective ratings for the 17 items of the Stress Questionnaire for Children (SQC). - Rows 33–59 include questionnaire scores from the Multidimensional Anxiety Inventory for Children and Adolescents (MAI-KJ; Hock et al., 2020). Items marked with “_r” have already been recoded according to the manual. - Rows 60–75 contain questionnaire scores from The Children’s Quality of Life Questionnaire (KINDL; Ravens-Sieberer & Bullinger, 1998). Items marked with “_r” have already been recoded according to the manual. - Rows 76–93 include questionnaire scores from the Stress and Stress Coping Questionnaire for Children and Adolescents (SSKJ 3–8; Lohaus et al., 2006). 2.3 Computed scale scores In Section 3 of the SPSS file, we provide the total scores and scale scores for each questionnaire. Rows 95–99 contain the total score and subscales of the SQC. The total score was calculated as the sum of all SQC items. Based on the results of the exploratory factor analysis, the School Stress scale was computed as the sum of items SQC_1, SQC_2, SQC_3, SQC_4, SQC_5, SQC_6, SQC_7, and SQC_8. The Time Stress scale is the sum of items SQC_13, SQC_19, SQC_20, SQC_21, SQC_22, and SQC_23. The Social Stress scale is the sum of items SQC_10, SQC_12, and SQC_17. Rows 99–101 contain the total scores, physical, and psychological symptom scores of the SSKJ; rows 102–104 include total scores and the anxiety and depression subscales of the MAI-KJ; and rows 105–109 contain total scores and the subscales self-esteem-, family-, friends-, and school-
Hahn et al.: Open Data and Open Materials (Codebook) 4 related quality of life of the KINDL. All scores of these established questionnaires were calculated according to the respective manuals. 3 R-script for EFA model fit indices 3.1 Data file The SPSS file ‘Hahn_et_al_AEI_R_Data.sav’ belongs to the R script and should be used exclusively for calculating model fit indices, as it contains only the SQC variables. The file needs to be downloaded and saved locally. The file path is required for running the R script. 3.1 R-Script for RStudio The R script ‘Hahn_et_al_AEI_R-script.R’ was created using R version 4.5.1. Execution of the script also requires RStudio (version 2025.09.0+387 or higher). Before running the script, the working directory must be set in the Source Editor on line 2: ► setwd("insert_your_path_here") Replace "insert_your_path_here" with the path to your local folder where the SQC dataset is saved, using forward slashes (/). ► Example: setwd("C:/Users/Hahn/Desktop/R-Script_SQC") On line 18, the dataset must be loaded with the correct file path: ► SQC_data <- read_sav("insert_your_path_here") Replace "insert_your_path_here" with the same folder path as above, followed by a forward slash and the filename of the SQC dataset. ► Example: SQC_data <- read_sav("C:/Users/Hahn/Desktop/R-Script_SQC/Hahn_et_al_AEI_R_Data.sav") After executing the script, the model fit results will be displayed in the console.
