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Manipulation of Intensive Longitudinal Data: A Tutorial in R With Applications on the Job Demand-Control Model.

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This tutorial provides R code for intensive longitudinal designs (ILD) data manipulation, crucial for psychological research. It simplifies complex data preparation for accurate within- and between-individual analyses.

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Area of Science:

  • Psychology
  • Quantitative Psychology
  • Methodology

Background:

  • Intensive longitudinal designs (ILD) are increasingly prevalent in applied psychology.
  • Complex analyses like cross-level interactions are trending, but data manipulation guidance is lacking.
  • Effective data handling is critical for valid ILD research outcomes.

Purpose of the Study:

  • To provide a step-by-step tutorial for intensive longitudinal design (ILD) data manipulation.
  • To offer open-source R code for essential data pre-processing and psychometric procedures.
  • To address methodological barriers in handling ILD data for researchers and practitioners.

Main Methods:

  • Tutorial based on an illustrative example of the job demand-control model.
  • Utilized data from 211 back-office workers with up to 18 surveys over three workdays.
  • Demonstrated data reading, merging, cleaning, reliability, centering, lagging, and leading procedures.

Main Results:

  • The tutorial successfully illustrates data manipulation for testing the job demand-control model at the within-individual level.
  • Findings support the strain hypothesis and partially support the buffer hypothesis.
  • The provided R code facilitates essential ILD data pre-processing and analysis steps.

Conclusions:

  • This tutorial and R code aim to demystify ILD data manipulation for applied psychology.
  • Standardized data handling procedures enhance the quality and validity of ILD research.
  • Accessible methodological guidance empowers researchers to conduct robust ILD studies.