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

Archival Research01:40

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
Data Collection by Experiments01:13

Data Collection by Experiments

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public clinical trial...
Review and Preview01:13

Review and Preview

Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Observational Studies01:11

Observational Studies

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 example of...
Data Validation01:15

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:

You might also read

Related Articles

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

Sort by
Same author

Determinants of 30-day readmissions in adult patients with heart failure in Pakistan: a prospective cohort study protocol.

BMJ open·2025
Same author

Effect of a structured educational intervention delivered through a mobile application on glycated haemoglobin and self-efficacy in adolescents with type 1 diabetes mellitus: a systematic review and meta-analysis.

BMJ open·2025
Same author

Focused ethnography of nurses' interactions and behaviours in the therapeutic positioning of patients with traumatic brain injury in Pakistan: study protocol.

BMJ open·2025
Same author

Effectiveness of patient education on adherence to treatment regimen and quality of life in hemodialysis patients: a systematic review and meta-analysis.

Minerva urology and nephrology·2025
Same author

Factors Predicting Dependence on Waterpipe Smoking Among Waterpipe Exclusive Smokers.

Journal of addictions nursing·2023
Same author

A Survey on Cardiovascular Nursing Occupational Standard: Meeting the Needs of Employers.

Policy, politics & nursing practice·2020

Related Experiment Video

Updated: Jun 20, 2026

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
05:02

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

Using an existing data set to answer new research questions: a methodological review.

Daniel M Doolan1, Erika S Froelicher

  • 1University of San Francisco, California, USA. doolan@sonic.net

Research and Theory for Nursing Practice
|September 23, 2009
PubMed
Summary

Secondary data analysis offers a faster, cheaper, and safer research method when existing datasets are suitable. Researchers must carefully evaluate data quality and statistical power for new research questions.

More Related Videos

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Related Experiment Videos

Last Updated: Jun 20, 2026

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
05:02

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Area of Science:

  • Research Methodology
  • Data Science

Background:

  • Traditional research design assumes study creation after question formulation.
  • Secondary data analysis deviates from this by utilizing pre-existing datasets.
  • Researchers require distinct skills for secondary analysis beyond new study design.

Purpose of the Study:

  • To provide guidance for researchers conducting secondary data analysis.
  • To highlight specific challenges and techniques relevant to using existing data.
  • To inform decisions about the appropriateness of secondary data analysis.

Main Methods:

  • Discussion of techniques for evaluating secondary data suitability.
  • Recommendations for data identification, acquisition, and assessment.
  • Guidance on refining research questions, data management, power calculation, and reporting.

Main Results:

  • Secondary analysis is advantageous when data quality and power are adequate.
  • It offers reduced time, cost, and subject risk compared to primary studies.
  • Careful consideration of dataset limitations is crucial for valid results.

Conclusions:

  • Secondary data analysis is a viable and often preferable research approach.
  • Successful secondary analysis hinges on rigorous evaluation of existing data.
  • Researchers must balance the benefits of secondary analysis with potential data limitations.