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

Introduction to R01:11

Introduction to R

4.1K
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
4.1K
Econometric Views (EViews)01:29

Econometric Views (EViews)

535
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
535
Statistical Package for the Social Sciences (SPSS)01:22

Statistical Package for the Social Sciences (SPSS)

1.1K
The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...
1.1K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.4K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.4K
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

1.5K
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
1.5K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

888
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:
888

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

Comparison of latent growth curves: A parameter constancy test.

Psychological methods·2025
Same author

Subtypes of suicidal ideation among university students - An ecological momentary assessment study.

Journal of affective disorders·2025
Same author

Ecological Momentary Assessment of Mental Health Problems Among University Students: Data Quality Evaluation Study.

Journal of medical Internet research·2024
Same author

Multicenter randomized controlled trial to assess the effectiveness of PASSEO-LUX DCB<sup>®</sup> drug coated balloon compared to plain balloon angioplasty of arteriovenous fistulae for hemodialysis: Two-years results.

The journal of vascular access·2024

Related Experiment Video

Updated: Jan 11, 2026

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

11.0K

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

Jesús F Rosel1, Sara Puchol, Marcel Elipe1

  • 1Faculty of Health Sciences, Universitat Jaume I.

Psychological Methods
|November 13, 2025
PubMed
Summary

This guide simplifies autoregressive (AR) linear models for psychology research, explaining their use in SPSS and R. It emphasizes practical application and interpretation of time series data to avoid common statistical errors.

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.7K
A User-friendly and Powerful R Analysis of Large-scale Datasets
10:56

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

328

Related Experiment Videos

Last Updated: Jan 11, 2026

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

11.0K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.7K
A User-friendly and Powerful R Analysis of Large-scale Datasets
10:56

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

328

Area of Science:

  • Psychology
  • Behavioral Sciences
  • Quantitative Methods

Background:

  • Autoregressive (AR) linear models are crucial for time series analysis but underutilized in behavioral sciences due to complexity.
  • Conceptual challenges in interpreting autocorrelation and seasonality hinder AR model adoption.

Purpose of the Study:

  • To simplify the implementation and interpretation of AR linear models for psychology students and researchers.
  • To present time series models as accessible linear regression cases with practical examples.
  • To enhance understanding of residual diagnostics and their impact on statistical significance.

Main Methods:

  • Step-by-step tutorial using SPSS and R software.
  • Illustrates AR estimation with real data, including lagged variables as predictors.
  • Focuses on residual diagnostics with figures and statistical tests.
  • Compares polynomial and AR models using the confounding test.
  • Provides annotated scripts and data for replication.

Main Results:

  • Demonstrates how serially correlated residuals can lead to inflated Type I errors (false positives).
  • Offers visualizations and decision rules for model building, lag selection, and seasonality detection.
  • Highlights practical advantages of AR-only models in psychological research contexts.

Conclusions:

  • AR models can be effectively implemented in psychological research with clear guidance.
  • Accurate residual diagnostics are essential for valid statistical inference in time series analysis.
  • Aligning statistical models with data's temporal structure and theoretical assumptions is paramount.