Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Systematic Error: Methodological and Sampling Errors

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

Reliability and Validity

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.

Study Designs in Epidemiology

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...

Experimental Designs

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...

Friedman Two-way Analysis of Variance by Ranks

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...

You might also read

Related Articles

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

Sort by

Same author

Review of Protection Against Bots and Fraudulent Survey Submissions in Nursing Research.

Nursing research·2026

Same author

Forecasting the Burden of Cardiovascular Disease and Stroke in Women in the United States Through 2050: A Scientific Statement From the American Heart Association.

Circulation·2026

Same author

Natural Language Processing Identifies Symptoms Predicting Complete Coronary Artery Occlusion in Patients with NSTEMI.

European journal of cardiovascular nursing·2026

Same author

Message Humanness as a Predictor of AI's Perception as Human: Secondary Data Analysis of the HeartBot Study.

JMIR AI·2026

Same author

Assessment of Discriminant Validity for Measures of Hope and Hopelessness in Adults With an Acute Cardiac Event.

Research in nursing & health·2026

Same author

Myths and Misconceptions in the Assessment and Diagnosis of Type 1 Myocardial Infarction.

AACN advanced critical care·2025

Search research articles

Related Experiment Video

Updated: Mar 7, 2026

Enactive Phenomenological Approach to the Trier Social Stress Test: A Mixed Methods Point of View

Enactive Phenomenological Approach to the Trier Social Stress Test: A Mixed Methods Point of View

Published on: January 7, 2019

The MIXED framework: A novel approach to evaluating mixed-methods rigor.

Ann L Eckhardt¹, Holli A DeVon²

¹School of Nursing, Illinois Wesleyan University, Bloomington, IL, USA.

Nursing Inquiry

|February 10, 2017

Summary

This summary is machine-generated.

Evaluating mixed-methods research rigor is challenging. The new MIXED framework offers a practical, eight-item scale for assessing published mixed-methods studies, benefiting researchers and consumers.

Keywords:

evaluation methods mixed methods nursing research qualitative methods quantitative methods research methodology research rigor

More Related Videos

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

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

Published on: September 27, 2019

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

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

Published on: February 19, 2021

Related Experiment Videos

Last Updated: Mar 7, 2026

Enactive Phenomenological Approach to the Trier Social Stress Test: A Mixed Methods Point of View

Enactive Phenomenological Approach to the Trier Social Stress Test: A Mixed Methods Point of View

Published on: January 7, 2019

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

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

Published on: September 27, 2019

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

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

Published on: February 19, 2021

Area of Science:

Scientific methodology
Research evaluation

Background:

Mixed-methods (MM) research presents unique rigor evaluation challenges.
Existing evaluation methods lack practical utility for research consumers.
MM research is increasingly common in nursing and healthcare.

Purpose of the Study:

To introduce the MIXED framework for assessing MM research rigor.
To provide a practical tool for evaluating published MM studies.
To bridge the gap between academic rigor assessment and practical application.

Main Methods:

Development of the MIXED framework (Methods, Inference, Expertise, Evaluation, Design).
Creation of an experimental eight-item scale for integrated MM rigor assessment.
Framework designed for practical use by research consumers.

Main Results:

The MIXED framework provides a comprehensive approach to MM rigor.
The eight-item scale allows for integrated assessment of published MM manuscripts.
The framework is intended to be a useful tool for research evaluation.

Conclusions:

The MIXED framework offers a practical solution for evaluating MM research rigor.
This framework aids both researchers and consumers in assessing the quality of MM studies.
Addressing MM rigor is crucial given its increasing prevalence in healthcare research.