Manipulation of Intensive Longitudinal Data: A Tutorial in R With Applications on the Job Demand‐Control Model.
Saved in:
| Title: | Manipulation of Intensive Longitudinal Data: A Tutorial in R With Applications on the Job Demand‐Control Model. |
|---|---|
| Authors: | Menghini, Luca (AUTHOR), Perinelli, Enrico (AUTHOR), Balducci, Cristian (AUTHOR) |
| Source: | International Journal of Psychology. Apr2025, Vol. 60 Issue 2, p1-9. 9p. |
| Subjects: | Job applications, Research questions, Applied psychology, Multilevel models, Psychological research, Job stress |
| Abstract: | Intensive longitudinal designs (ILD) are increasingly used in applied psychology to investigate research questions and deliver interventions at both within‐ and between‐individual levels. However, while relatively complex analyses such as cross‐level interaction models are trending in the field, little guidance has been provided on ILD data manipulation, including all procedures to be applied to the raw data points for getting the final dataset to be analysed. Here, we provide an introductory step‐by‐step tutorial and open‐source R code on required and recommended data pre‐processing (e.g., data reading, merging and cleaning), psychometric (e.g., level‐specific reliability), and other ILD data manipulation procedures (e.g., data centering, lagging and leading). We built our tutorial on an illustrative example aimed at testing the job demand‐control model at the within‐individual level based on data from 211 back‐office workers who received up to 18 surveys over three workdays, supporting both the strain and (partially) the buffer hypotheses. Being the common starting point of many types of analyses, data manipulation is crucial to determine the quality and validity of the resulting study outcomes. Hence, this tutorial and the attached code aim to contribute to removing methodological barriers among applied psychology researchers and practitioners in the handling of ILD data. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Intensive longitudinal designs (ILD) are increasingly used in applied psychology to investigate research questions and deliver interventions at both within‐ and between‐individual levels. However, while relatively complex analyses such as cross‐level interaction models are trending in the field, little guidance has been provided on ILD data manipulation, including all procedures to be applied to the raw data points for getting the final dataset to be analysed. Here, we provide an introductory step‐by‐step tutorial and open‐source R code on required and recommended data pre‐processing (e.g., data reading, merging and cleaning), psychometric (e.g., level‐specific reliability), and other ILD data manipulation procedures (e.g., data centering, lagging and leading). We built our tutorial on an illustrative example aimed at testing the job demand‐control model at the within‐individual level based on data from 211 back‐office workers who received up to 18 surveys over three workdays, supporting both the strain and (partially) the buffer hypotheses. Being the common starting point of many types of analyses, data manipulation is crucial to determine the quality and validity of the resulting study outcomes. Hence, this tutorial and the attached code aim to contribute to removing methodological barriers among applied psychology researchers and practitioners in the handling of ILD data. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 00207594 |
| DOI: | 10.1002/ijop.70040 |
