Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

823
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
823
Observational Studies01:11

Observational Studies

10.6K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
10.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

VALENF-Instrument-Based Nursing Assessment and Early Occurrence of Hospital-Acquired Pressure Injuries and Falls Among Hospitalized Adults.

Nursing reports (Pavia, Italy)·2026
Same author

Corrigendum to "Subtypes of suicidal ideation among university students - An ecological momentary assessment study" [J. Affect. Disord. 391 (2025) 119865].

Journal of affective disorders·2026
Same author

Adapting rodent cued threat conditioning to planarians: Memory acquisition, consolidation, and reconsolidation.

Behavioural processes·2025
Same author

A tutorial and methodological review of linear time series models: Using R and SPSS.

Psychological methods·2025
Same author

Comparison of latent growth curves: A parameter constancy test.

Psychological methods·2025
Same author

Too Little Too Late: Perceptions of Sexual Health Education in Spain.

International journal of sexual health : official journal of the World Association for Sexual Health·2025

Related Experiment Video

Updated: Dec 26, 2025

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
14:21

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Published on: August 6, 2013

18.7K

Pooled Time Series Modeling Reveals Smoking Habit Memory Pattern.

Jesús F Rosel1, Marcel Elipe-Miravet2, Eduardo Elósegui3

  • 1Department of Evolutionary Psychology, Educative, Social Studies and Methodology, Universitat Jaume I, Castellón, Spain.

Frontiers in Psychiatry
|March 11, 2020
PubMed
Summary

Smoking behavior is strongly influenced by past smoking patterns, not gender or age. Understanding these temporal dependencies can help design more effective smoking cessation programs.

Keywords:
intensive data analysismemorymultilevel regressionpanel time seriespooled time seriestobacco

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K
Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.5K

Related Experiment Videos

Last Updated: Dec 26, 2025

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
14:21

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Published on: August 6, 2013

18.7K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K
Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.5K

Area of Science:

  • Behavioral Science
  • Addiction Research
  • Public Health

Background:

  • Nicotine addiction makes quitting smoking difficult.
  • Smoking behavior is often integrated into daily routines.
  • Understanding factors influencing daily cigarette consumption is crucial for cessation.

Purpose of the Study:

  • To investigate the influence of gender, age, day of the week, and prior smoking history on daily cigarette counts.
  • To identify temporal dependence patterns in smoking behavior.

Main Methods:

  • Panel data analysis using multilevel pooled time series modeling.
  • Collected daily records of cigarette consumption from 62 participants (36 men, 26 women) aged 18-26.
  • Analyzed smoking data over an average period of 84 days.

Main Results:

  • Daily smoking quantity was significantly predicted by the number of cigarettes smoked on multiple previous days (1, 2, 7, 14, 21, 28, 35, 42, 49, and 56 days prior).
  • The day of the week also influenced smoking patterns.
  • Neither gender nor age demonstrated a significant effect on smoking behavior patterns.
  • A single smoking model effectively described the behavior of all participants.

Conclusions:

  • Smoking behavior exhibits strong, empirically demonstrated temporal dependence.
  • These findings highlight the importance of time-based patterns in addiction.
  • The identified temporal parameters can inform the development of targeted smoking cessation interventions.