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

Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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...
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...

You might also read

Related Articles

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

Sort by
Same journal

Exploring potential strategies to enhance memory and cognition in aging mice.

F1000Research·2026
Same journal

Construction an Implicit Block Multi-Steps Approach for Solving Sixth-Order Fractional Differential Equations.

F1000Research·2026
Same journal

Kansei Engineering in the Evolving Service Sector: A Decade of Insights.

F1000Research·2026
Same journal

A Safety-First Mindset:  Role of Patient Safety Culture in Enhancing Healthcare Workers' Emotional Intelligence.

F1000Research·2026
Same journal

Decoding Decisions: Personality-Interest Motivational Sequences as Predictors of Career Paths.

F1000Research·2026
Same journal

Beyond the Transparent Barrier: A Domain Visualization and Integrative Review of Contemporary Research on Gender-Based Professional Stasis.

F1000Research·2026

Related Experiment Video

Updated: Jun 30, 2026

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone (ITZ)
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone (ITZ)

Published on: December 16, 2019

Application of K-Means Clustering for Job Applicant Analysis in Construction Firms Using R.

Daniel Jesayanto Jaya1,2, Wahyu Muhammad Ramdhani3, Endang Wati3

  • 1Technology and Vocational Education and Training, Universitas Negeri Yogyakarta, Yogyakarta, Special Region of Yogyakarta, 55282, Indonesia.

F1000Research
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

K-Means clustering segmented construction job applicants into three competency groups based on test data. This data-driven approach enhances screening, interview prioritization, and targeted upskilling for construction hiring.

Keywords:
K-Means Clustering; data-driven recruitment; workforce selection; cluster visualization; construction competencies

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Related Experiment Videos

Last Updated: Jun 30, 2026

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone (ITZ)
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone (ITZ)

Published on: December 16, 2019

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Data Science
  • Human Resources Management
  • Construction Management

Background:

  • Effective screening of construction job applicants is crucial for project success.
  • Traditional recruitment methods may lack objectivity and consistency.
  • Data-driven approaches can optimize candidate evaluation.

Purpose of the Study:

  • To apply K-Means clustering to segment construction job applicants based on test data.
  • To support data-driven decision-making in the early stages of recruitment.
  • To enhance transparency and consistency in applicant evaluation.

Main Methods:

  • K-Means clustering algorithm applied to applicant test data.
  • Utilized R software for data analysis and visualization.
  • Analyzed three key variables: AutoCAD skills, report-writing, and adaptability.

Main Results:

  • Successfully partitioned 30 pre-screened candidates into three distinct competency groups.
  • Identified profiles with generally lower, moderate/mixed, and consistently higher scores.
  • Clustering revealed modest group separation, suitable for exploratory analysis.

Conclusions:

  • Unsupervised clustering of recruitment test data offers structured interpretation of applicant diversity.
  • Findings inform practical recruitment actions like interview prioritization and identifying skill gaps.
  • This method enhances early-stage applicant evaluation in the construction sector.