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

Factorial Design02:01

Factorial Design

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...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Experimental Designs01:16

Experimental Designs

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...
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

You might also read

Related Articles

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

Sort by
Same author

Correction: Tracing the threads: a system dynamics and network diffusion model of kangaroo care implementation in neonatal care.

Implementation science communications·2026
Same author

Midwife-led care versus obstetrician-led care for childbearing women in early labor: A systematic review.

European journal of midwifery·2026
Same author

The Future of Implementation Science for Public Health and Healthcare: Insights From the Swiss Implementation Science Network (IMPACT) Conference 2024.

International journal of public health·2026
Same author

Tracing the threads: a system dynamics and network diffusion model of kangaroo care implementation in neonatal care.

Implementation science communications·2026
Same author

From plan to practice: a structured report on implementation strategies for preventing non-ventilator hospital-acquired pneumonia (nvHAP).

Infection control and hospital epidemiology·2026
Same author

Organizational readiness for implementing infection control in European hospitals: insights from Coincidence Analysis.

Implementation science communications·2026

Related Experiment Video

Updated: Jun 21, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Factor selection in configurational comparative analysis - guidance for implementation researchers.

Laura Caci1, Kathrin Blum2, Lauren Clack2,3

  • 1Institute for Implementation Science in Health Care, Medical Faculty, University of Zurich, Zurich, Switzerland. laura.caci@uzh.ch.

Implementation Science Communications
|June 19, 2026
PubMed
Summary

This study offers a four-stage methodology for selecting factors in configurational comparative methods (CCMs) before data collection, emphasizing intentional data gathering and stakeholder involvement for implementation science.

Keywords:
Coincidence analysisConfigurational analysisConfigurational comparative methodsImplementation scienceInfection control

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

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

Related Experiment Videos

Last Updated: Jun 21, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

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

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

Area of Science:

  • Implementation Science
  • Configurational Comparative Methods (CCMs)
  • Methodology Development

Background:

  • Configurational Comparative Methods (CCMs) are valuable for identifying necessary and sufficient conditions in complex implementation studies.
  • Factor selection, a crucial stage in CCMs, lacks pre-data collection guidance for implementation researchers.
  • Existing CCM guidance often focuses on factor selection during data analysis, not before data collection.

Purpose of the Study:

  • To provide a structured methodology for pre-data collection factor selection in Configurational Comparative Methods (CCMs).
  • To emphasize intentional data collection and broad stakeholder engagement throughout the factor selection process.
  • To enhance the accessibility and application of CCMs in implementation science research.

Main Methods:

  • A four-stage approach: factor identification, prioritization, determination, and operationalization.
  • Systematic review for initial factor identification.
  • Nominal Group Technique and project workshops involving stakeholders for prioritization and final selection.
  • Operationalization of factors using mixed qualitative and quantitative methods.

Main Results:

  • Successful illustration of the four-stage factor selection methodology using a multinational implementation-effectiveness trial.
  • Demonstrated effective stakeholder engagement in prioritizing and selecting relevant factors.
  • Factors were successfully operationalized and assessed using mixed methods.

Conclusions:

  • Presents a practical, four-stage methodology for pre-data collection factor selection in CCMs, balancing rigor and pragmatism.
  • Highlights the importance of meaningful stakeholder engagement in the factor selection process.
  • Addresses a critical gap in CCM guidance, promoting wider adoption in implementation science.