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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Published on: January 23, 2017

Examining temporal processes in diary studies.

Robert E Wickham1, C Raymond Knee

  • 1Clinical Psychology PhD Program, Palo Alto University, 1791 Arastradero Road, Palo Alto, CA 94304, USA. rwickham@paloaltou.edu

Personality & Social Psychology Bulletin
|June 27, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a temporal framework for diary data analysis, revealing how past daily events influence current feelings and behaviors. This method enhances understanding of event consequences over time.

Keywords:
diary analysislagged effectslongitudinal methodssatiationsensitization

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Area of Science:

  • Psychology
  • Behavioral Science
  • Data Analysis

Background:

  • The diary method is valuable for tracking daily variations in thoughts, feelings, and behaviors.
  • Daily events can have immediate and long-term consequences.
  • Existing analyses often overlook the temporal dynamics of diary data.

Purpose of the Study:

  • To present a conceptual and statistical framework for analyzing diary data with an emphasis on temporal effects.
  • To expand hypothesis testing by incorporating lagged and concurrent effects.
  • To provide practical guidance for dataset preparation and analysis.

Main Methods:

  • Developing a statistical framework that includes temporal effects (lagged and concurrent × lagged terms).
  • Describing procedures for preparing diary datasets for temporal analysis.
  • Conducting a secondary data analysis to illustrate the proposed methods.

Main Results:

  • The inclusion of temporal effects allows for examination of prior day's events on current outcomes.
  • This approach reveals how past events moderate responsiveness to current events.
  • The proposed framework offers a more comprehensive understanding of event-consequence relationships.

Conclusions:

  • A temporal framework significantly enhances the analysis of diary data.
  • Researchers can better understand the prospective consequences of daily events.
  • This method provides deeper insights into psychological and behavioral processes over time.