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The whole is greater than the sum of its parts: In meta-study format, LEADING EMPLOYERS analyses an extensive number of sources, feedbacks and topics. By combining all data, a significantly higher validity than in a stand-alone study can be achieved.
The study’s
process in detail:
In a multi-stage process, the TOP 1% of all employers are identified.
01. Discovering and evaluating sources02. Classifying and standardising sources03. Data gathering04. Data cleaning and enrichment05. Scoring and ranking06. Quality assurance07. Enhanced reporting and updates
The discovery of new data sources is an ongoing process.
Potential sources are identified through corporate career websites, HR blog entries, keyword-based Google research, cross-country comparisons, and suggestions from the public or our Advisory Board.
To maintain quality, new sources are assessed quarterly. The evaluation process considers their integrity and quality across multiple dimensions, such as the type of source (audit reports, polls, review portals), the nature of the organisation (non-profit vs. for-profit), and the research methodology (self-enrolment vs. independent studies). Additional considerations include validation mechanisms, manipulation barriers, complaint tools, and whether the source is supervised by academic institutions or government bodies.
To maintain quality, new sources are assessed quarterly. The evaluation process considers their integrity and quality across multiple dimensions, such as the type of source (audit reports, polls, review portals), the nature of the organisation (non-profit vs. for-profit), and the research methodology (self-enrolment vs. independent studies). Additional considerations include validation mechanisms, manipulation barriers, complaint tools, and whether the source is supervised by academic institutions or government bodies.
Sources are classified based on their relevance and quality to ensure consistency throughout the assessment process. This classification examines various aspects of the sources:
- Type of Source: Whether it is a structured audit, a poll, or a public review portal.
- Type of Organisation: Differentiating between non-profit and for-profit entities to account for potential biases.
- Research Approach: Evaluating whether the data stems from independent research or self-enrolment mechanisms.
- Validation and Oversight: Ensuring the source has strong validation processes, robust barriers to manipulation, accessiblecomplaint resolution mechanisms, and, where possible, academic or governmental endorsement.
Data is collected from sources that differ in their update frequency
Frequently updated sources, such as employee review portals andsocial media, are managed with custom-designed scrapers that automatically retrieve data on a scheduled basis. Less frequentlyupdated sources, like annual awards, are accessed using generic tools that allow scraping through customisation.
Our approach ensures the collection of maximum information from publicly accessible sources, including historical archives.
Our approach ensures the collection of maximum information from publicly accessible sources, including historical archives.
To guarantee accuracy and relevance, the data undergoes a meticulous cleaning process.
Proprietary natural language processing (NLP) algorithms match company names to existing records in our database or identify new entries. Manual verification complements thisprocess to ensure precision in key data points.
Additional details, such as company addresses, industries, and employee counts, are manually gathered. Once consolidated, inclusionand exclusion criteria are applied to remove companies that are too small, inactive, or have suffered significant reputational issues.
Additional details, such as company addresses, industries, and employee counts, are manually gathered. Once consolidated, inclusionand exclusion criteria are applied to remove companies that are too small, inactive, or have suffered significant reputational issues.
The scoring process is a multistep evaluation designed to ensure fairness and accuracy.
The process begins with assessing the relevance of data points to nine key performance categories. Once the data is organised, ananomaly detection algorithm is applied to identify potential errors or inconsistencies, such as mistakes during data collection orinstances where employee review scores appear to have been manipulated.
Employee review scores are then adjusted for relevance. These scores are included in the final report, but their weight is proportional tothe percentage of employees who have participated in the reviews. This ensures that smaller or less representative datasets do notunduly influence the results.
Next, overall scores and category-specific scores are calculated. These scores form the basis of company rankings. The TOP 1% ofcompanies in the target population are recognised for their outstanding performance.
Employee review scores are then adjusted for relevance. These scores are included in the final report, but their weight is proportional tothe percentage of employees who have participated in the reviews. This ensures that smaller or less representative datasets do notunduly influence the results.
Next, overall scores and category-specific scores are calculated. These scores form the basis of company rankings. The TOP 1% ofcompanies in the target population are recognised for their outstanding performance.
To ensure accuracy, we employ robust quality assurance measures.
Our data science team is led by a Data Scientist with a master's degree in Advanced Mathematics and pursuing a doctoral degree, with additional short-term degrees from Università degli Studi diPerugia, Ecole Normale Superieur of Bucharest and research experience at Cambridge University. Our research team contains members of American Mathematical Society, Royal Statistical Society and the International Society for Bayesian Analysis.
For clients opting for detailed reports, we provide enhanced data collection and updated scores every three months.
This ensures stakeholders have access to the most current and comprehensive insights. Through this rigorous and structured methodology, we deliver transparent, data-driven evaluations of corporate performance, combining advanced technologies with meticulous human oversight to ensure quality and reliability at every stage.
Detailed methodology PDF
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