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

Variability: Analysis01:11

Variability: Analysis

165
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
165
Multiple Regression01:25

Multiple Regression

3.1K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.1K
Experimental Designs01:16

Experimental Designs

11.6K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
11.6K
Biostatistics: Overview01:20

Biostatistics: Overview

305
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
305
Randomized Experiments01:13

Randomized Experiments

7.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.1K
Factorial Design02:01

Factorial Design

13.1K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.1K

You might also read

Related Articles

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

Sort by
Same author

Establishing usable innovations.

Frontiers in health services·2026
Same author

Monitoring the emergence of resistance with sotrovimab in immunocompromised patients with COVID-19: LUNAR study.

The Journal of infection·2025
Same author

Fidelity, not adaptation, is essential for implementation.

Frontiers in health services·2025
Same author

Is implementation science a science? Not yet.

Frontiers in public health·2024
Same author

Digital transformation in schools of two southern regions of Sweden through implementation-informed approach: A mixed-methods study protocol.

PloS one·2023
Same author

The association of varying treatment thresholds of mepolizumab on asthma exacerbations in adults.

The Journal of asthma : official journal of the Association for the Care of Asthma·2023

Related Experiment Video

Updated: Aug 6, 2025

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
06:05

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

1.3K

Repeated measures of implementation variables.

Dean L Fixsen1, Melissa K Van Dyke1, Karen A Blase1

  • 1Active Implementation Research Network, Inc., Chapel Hill, NC, United States.

Frontiers in Health Services
|March 17, 2023
PubMed
Summary

Repeated measures are essential for understanding long-term implementation processes. Developing relevant, sensitive, and practical measures is crucial for advancing the science of implementation.

Keywords:
implementationmeasurementreplicationscalingvalidity

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.0K
Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design
07:40

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design

Published on: May 31, 2021

3.4K

Related Experiment Videos

Last Updated: Aug 6, 2025

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
06:05

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

1.3K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.0K
Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design
07:40

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design

Published on: May 31, 2021

3.4K

Area of Science:

  • Implementation Science
  • Health Services Research
  • Program Evaluation

Background:

  • Implementation is a complex, long-term process requiring years to achieve.
  • Understanding implementation trajectories necessitates repeated measurements of key variables.
  • Existing measures must be relevant, sensitive, consequential, and practical for real-world settings.

Purpose of the Study:

  • To review existing literature on repeated measurement of implementation variables.
  • To identify the range of implementation variables assessed over time.
  • To inform the development of robust measures for implementation science.

Main Methods:

  • Exploratory literature review.
  • Searched for articles using repeated measures of implementation variables.
  • Included studies focused on achieving outcomes.

Main Results:

  • 32 articles met the criteria for repeated measurement of implementation variables.
  • 23 distinct implementation variables were measured repeatedly.
  • Variables included innovation fidelity, sustainability, organizational change, scaling, training, and implementation teams.

Conclusions:

  • Repeated measurements are vital for a comprehensive understanding of implementation.
  • Longitudinal studies with appropriate measures are needed to address implementation complexities.
  • Developing and utilizing practical, sensitive, and consequential measures will advance implementation science.