improving talent management thanks to psychometrics and ... · improving talent management thanks...

Improving talent managementthanks to psychometrics and Big Data

How psychometrics and applied mathematics can help identify company culture andfactors for success in a job position

January 2016

Summary

I. Introduction 51.1. Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.2. Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.3. Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

II. Data and methodology 82.1. Data presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2. Methodology: identifying behavioral invariants . . . . . . . . . . . . . . . . . . . . . . . . . 122.3. Algorithm results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.4. Data visualization options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

III. Results 193.1. Intercultural differences analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.2. Company culture analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.3. Analysis of personality types by profession . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.4. Measuring a candidate’s fit with a specific job position . . . . . . . . . . . . . . . . . . . . 31

IV.Conclusion 34

V. Bibliography 35

VI.Appendices 366.1. Definition of personality criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366.2. Definition of the main dimensions of personality . . . . . . . . . . . . . . . . . . . . . . . . 386.3. Definition of motivation criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396.4. Definition of Predictive Professional Behaviors (P.P.B.) . . . . . . . . . . . . . . . . . . . . 40

Exclusive property of Talentoday, Inc. - 2016 © 2

List of figures

1. Personality and motivation radars after completing the mYti© questionnaire . . . . . . . . 72. Example of a forced choice question in the mYti© questionnaire . . . . . . . . . . . . . . . 83. Distribution of Talentoday’s members by nationality . . . . . . . . . . . . . . . . . . . . . . 124. Example of a correct classification (accuracy = 95%) . . . . . . . . . . . . . . . . . . . . . 135. Example of an incorrect classification (accuracy = 50%) . . . . . . . . . . . . . . . . . . . . 146. Example of a personality type record . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177. Example of favorable professional behavior: ability to take initiative . . . . . . . . . . . . . 188. Example of a similarity graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189. Graph of psychometric similarities between countries . . . . . . . . . . . . . . . . . . . . . 2010. Similar cultures by country. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2111. Ability to handle pressure by country . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2212. Ability to take initiative by country . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2213. Graph of psychometric similarities between companies . . . . . . . . . . . . . . . . . . . . . 2414. Company cultures similar to Microsoft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2515. Company cultures similar to job seekers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2616. Ability to handle pressure by company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2717. Capacity for teamwork by company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2718. Personality type for McDonald’s employees . . . . . . . . . . . . . . . . . . . . . . . . . . . 2819. Personality type for EHPAD’s employees . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2920. Consultant personality type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3021. Software Engineer personality type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3022. From personality type to the Target Profile visualization . . . . . . . . . . . . . . . . . . . 3223. Target profile of a specific team of Business Developers . . . . . . . . . . . . . . . . . . . . 3224. Candidate matches for a specific team of Business Developers . . . . . . . . . . . . . . . . 33


List of tables

1. The 8 motivation criteria in the mYti© questionnaire . . . . . . . . . . . . . . . . . . . . . 92. The 8 motivation criteria in the mYti© questionnaire . . . . . . . . . . . . . . . . . . . . . 93. Evaluation of classification algorithms applied to companies. . . . . . . . . . . . . . . . . . 154. Evaluation of classification algorithms applied to professions. . . . . . . . . . . . . . . . . . 165. Evaluation of variable selection algorithms applied to a dataset of 24 consultants . . . . . . 166. Definition of 20 personality criteria [1] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367. Definition of 20 personality criteria [2] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378. Definition of the 5 main dimensions of personality . . . . . . . . . . . . . . . . . . . . . . . 389. Definition of 8 motivation criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3910. Definition of 7 Predictive Professional Behaviors . . . . . . . . . . . . . . . . . . . . . . . 40


I. Introduction

This study was conducted by Talentoday, a company specializing in People Analytics, founded by MartinRyssen and Pierre-François Verley in 2012.

After four years of research, development and implementing recruitment tools for the general public andrecruitment professionals, over 3 million users have benefitted from Talentoday’s technology.

This study analyzes the background and lessons learned from this enterprise, combining psychometricsand applied mathematics for use in the human resources (HR) and career management industry.

1.1. Authors

Maximilien Burq is an engineer with a degree from l’Ecole Polytechnique. He is pursuing a PhD at theMassachusetts Institute of Technology on problems of placement and allocation in asymmetric markets.He is particularly interested in applying these algorithms to the job market in order to better match jobseekers with employers.

Martin Prillard is an engineer with degrees from Telecom ParisTech and l’Université de Technologie deTroyes. Specializing in Big Data, he has acquired expertise in both data science and data engineering.Martin Prillard is currently working on the creation of innovative products for Talentoday, developingpositive externalities for professional fulfillment.

Nicolas Bassan is a psychologist specializing in human resources. He has a degree from l’École des Psycho-logues Praticiens in Paris. At Talentoday, he is currently working on building tools for matching peoplewith organizations. He also participates in research programs on mindfulness meditation for improvingpeople’s wellbeing.

Martin Ryssen is Talentoday’s Chief Executive Officer (CEO). He has held various positions, from strate-gic consulting at Bearingpoint to recruitment at Robert Walters, and even teaching entrepreneurship atl’ESCP Europe. He also co-founded Terre de Talents in 2003, an international professional orientationassociation, helping over 4,000 students in 30 different countries. Martin is passionate about innovationand human resources. He holds a degree from l’ESCP Europe, a European Master of Management, aDiplom-Kaufmann and a Master from the City University of London.


Pierre-François Verley is Talentoday’s Chief Strategy Officer (CSO). Author of the book "Assess to guide,"he has 8 years of experience in operational and commercial management for major HR projects, from re-cruitment to assessment, for the human resources consulting firm Manpower. As a licensed psychologistand member of the American Psychological Association, he has found the perfect combination of his pas-sion for entrepreneurship, professional orientation and self-exploration at Talentoday. After obtaining aMaster of Psychology and Psychopathology, Pierre-François received a double Master’s degree specializingin Entrepreneurship at l’Essec and l’Ecole Centrale Paris.

1.2. Objectives

Companies wishing to attract the best talent must first understand what defines their corporate culture.This will allow them to recruit the right kind of candidates that will best fit their work environment, withdistinguishing qualities that will make their organization even more dynamic. The purpose of Talentoday’stools is to scientifically analyze this company culture. The objective of this study is to assess the validityand accuracy of these tools. The study uses a scientific approach based on data generated by the membersof Talentoday to develop a comprehensive framework for understanding company culture.

This study has three main objectives:

1. To present a new approach based on machine learning techniques for analyzing large-scale psycho-metric data.

2. To provide a methodology that allows companies to analyze and develop their corporate culture.

3. To identify differences in personality and motivation according to each position, industry and typeof company in order to help advise individuals on their career choices and adapt talent managementstrategies to the company.

Data used

Our results are based on data from the mYti© personality questionnaire created by Talentoday 1, thanks tothe latest innovations in psychometrics and data science. The questionnaire results have been synthesizedbased on 20 personality criteria and 8 motivation criteria, as shown in Figure 1.

1this questionnaire is available online at: www.talentoday.com


Figure 1: Personality and motivation radars after completing the mYti© questionnaire

1.3. Applications

Analyzing company culture

Analyzing company culture can be complicated and subject to numerous interpretations. Our systematicapproach is based on collecting psychometric data that allows us to measure the distinguishing, dominantfeatures of a sample by analyzing the behavioral invariants of the individuals in the sample. Through thisapproach, we can visualize the differences in personality and motivation from various samples as distances.We can then establish groups of countries, companies or similar professions.

Personality around the world

It is hard to separate multicultural management from geographic-related stereotypes. This arbitrary clas-sification is based on our past experiences, but does not necessarily reflect an evolving reality. Talentoday’sinternational audience presents a new approach for analyzing these variations, which scientifically addressesquestions of personality and motivation according to geographic location.

Calculating match scores between candidates and job positions

You can optimize your performance and professional fulfillment by finding a job that matches your per-sonality. Psychometric-based algorithms can offer recommendations for companies and job positions thatbetter match your personality and motivations. They also offer companies the possibility of recruiting theindividuals that will best fit their needs and corporate identity.


II. Data and methodology

2.1. Data presentation

Data collection and organization

The data analyzed in this study was collected from the mYti© questionnaire, which uses an ipsative or"forced choice" scale: users are asked to choose between two options based on their personal preferences.This questionnaire format offers the dual advantage of minimizing acceptance and social desirability bi-ases. Because there is no "right answer", the results accurately reflect the specific characteristics of eachindividual user.

Figure 2: Example of a forced choice question in the mYti© questionnaire

We cross-referenced the answers to the questionnaire’s 128 questions in order to confirm their internalconsistency. We then developed 28 personality and motivation criteria based on these results.

The 20 personality criteria were divided into 5 major categories (table 8), which we consider essentialtoa person’s professional life: Communicate, Manage, Dare, Adapt, and Excel.


Communicate Manage Dare Adapt ExcelEase in public Leading Self-confidence Stress manage-

mentDetermination

Opening up to oth-ers

Taking respons-ability

Independent mind Responsiveness Ambition

Diplomacy Organization Creativity Patience Work ethicPersuasion Vision Autonomy Respect for au-

thorityCompetitive spirit

Table 1: The 8 motivation criteria in the mYti© questionnaire

Motivations were synthesized into 8 different criteria:

Social recognition Pay Security Private/professional lifePhilanthropy Need to belong Need for relations Need for variety

Table 2: The 8 motivation criteria in the mYti© questionnaire

The stability of Talentoday’s tool is essential to obtaining reliable, consistent measurements. We havetherefore taken measures to confirm the questionnaire’s validity, accuracy and sensitivity. Internal con-sistency and stability are telling of the accuracy of the psychometric tool, while validity and predictivevalidity are telling of its quality.

The Talentoday questionnaire

Calibration:The questionnaire results were calibrated using a large sample, which allowed us to standardize and

ensure the psychometric validity of these results. 2

In addition to the existence of potential geographic biases due to cultural differences, there may alsobe differences based on the language in which the questionnaire was taken. To minimize these effects, weanalyzed the trends by taking into account the respective cultures of each country in order to standardizeand adapt their respective translations.

Internal Consistency:Internal consistency evaluates the accuracy and reliability of a test, which is measured by Cronbach’s

alpha (1951) (Cronbach's Alpha 3). This assessment tool is a calculation method for the covariance of2Thanks to the quality of our database, the most recent calibration (October 2014) was conducted with a sample of 2,350,430individuals.

3https://en.wikipedia.org/wiki/Cronbach%27s_alpha


the items and questions for each criterion, allowing us to measure their homogeneity. The higher thecovariance, the more the obtained score corresponds to the measured data.

Cronbach’s alpha is calculated using the following

α =k

k − 1(1−

∑ki=1 σ

2Yi

σ2X

)

Where k is the number of questions (items), σ2X is the variance of the question’s variance, and σ2

Yiis the

variance of the question (item), i.

The American Psychological Association recommends α ≥ 0.7. TAll the items in Talentoday’s mYti©questionnaire have a Cronbach’s alpha greater than 0.7. The average alpha for each item in the question-naire is 0.74.

Stability:The questionnaire’s stability is measured by the consistency of the variance between an individual’s

results the first time t1 and the second time t2 taking the test. This test-retest method makes it possibleto calculate a correlation coefficient r between the two assessments, t1 and t2. According to the AmericanPsychological Association’s recommendations, if this coefficient is high enough (r ≥ 0.6), that means thatthe results were stable over the course of the subsequent six-month period, as defined by the personalityand motivation measurements.

Note that after six months, certain criteria may change.

Test-retest Analysis Methodology:

• Administer a test to a sample of users.

• Re-administer the same test to the same group, six months later.

• Calculate the correlation coefficients of the collected results.

We confirmed that the 28 mYti© criteria all presented a correlation greater than 0.6 in the retest atsix months.

Predictive Validity:A test is valid if it measures what it is supposed to measure. This validity defines the meaning of the

scores: they reflect what they are supposed to measure, no more no less. Thanks to this validity, we


can draw specific conclusions about an individual based on their score - in this case their personality andmotivations.

A predictive validity study compares the results from the psychometric assessment tool and an externalassessment index. We therefore calculated the correlation between the mYti© score and a second score,which evaluates the same measurements using a different method.

Predictive Validity Analysis Methodology:

• Administer the mYti© questionnaire to a sample of subjects.

• Next, ask the managers of these subjects to assess them using a second questionnaire that evaluatesthe same measurements.

• Finally, ask each subject to conduct a self-assessment using the second questionnaire.

After analyzing and comparing the results, the relationship between the mYti© scores, the managers’assessment scores and the self-assessment scores indicates a high degree of predictive validity 4.

Representative dataset

Talentoday has a pool of over three million assessed candidates. Users are of all ages and categories:adolescents, students, employees, job seekers, and retirees. 160 nationalities are represented in this data.

The following figure illustrates the variety and diversity of the assessed individuals. They come from allover the world: America, Europe, Asia, Oceania, and Africa.The sample meets representative criteria: 142 companies with over 200 employees (Apple Inc., IBM,

Walmart, etc.) and 402 universities with over 1,000 students (University of California, Berkeley, NationalUniversity of Singapore, etc.). This diversity allows us to apply individual analysis to an extensive poolof candidates - the starting point for large-scale psychometrics.For this study, we exclusively used the results from users who are not students.

4 It is interesting to note that certain criteria are statistically better assessed by a third-party, such as opening up to others,patience and autonomy; while other more intimate criteria are better evaluated through self-assessment, such as ease inpublic, self-confidence and stress management. This observation reinforces the reliability of the mYti© in measuringwhat it is supposed to measure.


Figure 3: Distribution of Talentoday’s members by nationality

2.2. Methodology: identifying behavioral invariants

It is always difficult to scientifically grasp the psychology of a group. Behavioral invariants are the mostdeeply rooted personality and motivation traits of each individual within the group. Recent advances inmachine learning and data science combined with Talentoday’s database have enabled us to calculate theseinvariants with an unparalleled degree of accuracy.

Understanding data organization

The goal of our approach is to use the latest innovations in machine learning algorithms to better define thepsychology of a group of individuals, thanks to Talentoday’s data. Artificial intelligence algorithms allowus to understand the organization of personality data from a new perspective. This supervised approach tomachine learning, when conducted with accurate, reliable data, allows us to very precisely differentiate thepoint clouds describing people’s soft skills (human skills). When put into perspective through an expert’svision, the analysis proves to be very relevant.

Evaluating algorithm performance

Evaluating our tools is an essential part of our scientific approach. We follow a methodology that involvesself-assessing the performance of our algorithms: after having taught an algorithm to differentiate betweenindividuals based on their position or company, we test this differentiation capacity and calculate thereliability odds.


Interpreting the results

Our algorithms not only allow us to appreciate an individual’s sense of belonging to a group (companyor position), but also to analyze the group in and of itself. We can actually analyze the structure of thepsychometric data that defines a group of individuals; in other words, the psychological characteristicsthat define that group.

The graph below corresponds to an algorithm with a high degree of reliability, showing a very goodprediction score and associated area under the ROC curve (AUC, Area Under the Curve 5) rattachés.The area under the ROC curve represents the ability of our algorithm to distinguish between individualswho do or do not belong to a specific group. An area of 50 means that our algorithm will not predict anybetter than at random. On the other hand, an area of 100 means a perfect prediction: no false positives(what seems to be positive, but is in fact negative) and no false negatives (what seems to be negative, butis in fact positive). The area under the ROC curve can never be less than 50.

Figure 4: Example of a correct classification (accuracy = 95%)

Here, an accuracy of 95% means that the algorithm is capable of detecting individuals belonging to areference group (for example, a company) from new data 95% of the time.

The graph below corresponds to an algorithm with poor performance - it is difficult to identify the behav-ioral invariants. A random candidate is not greatly differentiated from a candidate belonging to a referencegroup.

5http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics


Figure 5: Example of an incorrect classification (accuracy = 50%)

2.3. Algorithm results

Reference group selection

To measure the accuracy of our algorithms, we sampled different predetermined groups within our memberbase.In order to obtain a sufficiently large, representative sample, these groups were selected based on variouscriteria: company size, nationality and industry.

The following companies were selected for this study 6:

• Spring France (recruitment firm, subsidiary of the Adecco Group)

• Total (CAC40 French group in the petroleum industry)

• CNRS ("Centre National de Recherche Scientifique", French institute for scientific research)

• EHPAD ("Établissement d'Hébergement pour Personnes Âgées Dépendantes", a French health es-tablishment for the elderly)

• McDonald’s (American fast food company)

• US Air Force (American military organization)

6Note that users provided the identifying information regarding which companies they belong to.


We also selected groups of individuals in similar positions at different companies:

• Software Engineers at Google, Facebook and IBM

• Automotive Engineers at Tesla, Renault and PSA

• Consultants at Ernst & Young, McKinsey and Total

While these individuals have certain aspects in common (similar positions), they also present significantdifferences (very different company cultures). We then selected a group of 1,500 individuals at random inorder to confirm any matches a second time around.

Selection of the best classification algorithm

The classification algorithm predicts the odds percentage of an individual working in a specific companyor position (based on their personality and motivation results from the mYti© questionnaire). In orderto select the best classification algorithm, we tested and evaluated different algorithms. This selection wasbased on three performance criteria: execution time, prediction score and the area under the ROC curve(AUC).

The tables below present the results of this comparative assessment, showing that the larger the dataset,the higher the prediction score. The best scores are shown in bold.

KNN Log reg SVM RBF Random ForestCompany Size Score AUC Score AUC Score AUC Score AUC

Spring France 208 80 87 84 91 85 92 88 94Total 158 68 74 74 82 75 83 80 88CNRS 142 68 75 75 83 77 86 79 89EHPAD 113 77 85 80 88 82 91 83 92

McDonald’s 2954 64 70 74 81 74 82 94 98US Air Force 1232 64 68 71 79 72 79 91 96

Groupe aléatoire 1500 48 51 50 50 48 49 48 47

Table 3: Evaluation of classification algorithms applied to companies.

We decided to use the Random Forest algorithm because of its good prediction scores, execution time,and ability to interpret results.


Software EngineersKNN Log reg SVM RBF Random Forest

Company Size Score AUC Score AUC Score AUC Score AUCFacebook 21 71 79 77 87 72 86 73 84Google 31 66 72 73 82 61 55 66 77IBM 21 58 64 59 66 53 50 59 69

Facebook, Google et IBM 73 70 77 75 83 77 87 80 88

Automotive EngineersKNN Log reg SVM RBF Random Forest

Company Size Score AUC Score AUC Score AUC Score AUCTesla 11 51 61 55 64 44 41 52 60

Renault 14 60 66 61 71 52 45 61 73PSA 13 59 68 68 80 50 34 60 72

Tesla, Renault et PSA 38 66 73 70 77 68 71 71 81

ConsultantKNN Log reg SVM RBF Random Forest

Company Size Score AUC Score AUC Score AUC Score AUCErnst & Young 12 53 66 57 70 45 42 55 68

McKinsey 12 55 64 60 66 45 38 55 62Total 24 62 70 67 74 60 52 65 74

Ernst & Young, McKinsey et Total 48 68 75 79 84 79 89 77 88

Table 4: Evaluation of classification algorithms applied to professions.

Algorithms Total execution time in secondsLogistic Regression 0.55Random Forest Classifier 2.1Extra Trees Classifier 2.12Lasso CV 6.89ElasticNet CV 7.28Randomized Logistic Regression 18.51Randomized Lasso 48.97

Table 5: Evaluation of variable selection algorithms applied to a dataset of 24 consultants


2.4. Data visualization options

We opted to present the study results in three different visualization formats:

Personality type

This visualization is intended to highlight the distinguishing personality and motivation criteria for aparticular group. These criteria are organized in order of importance. The higher the criterion’s value,the more distinguishing it will be. If the value is positive, that means that the individuals in the grouphave a high score for that criterion. If the value is negative, then the individuals have a low score. Allscores (high or low) are important and define a personality type without value judgments. In the graphbelow, the distinguishing personality traits with high scores are Ambition, Responsiveness and Leading,while Creativity is significant based on its low score. The average person responding to these personalitycriteria would have a score similar to this group.

Figure 6: Example of a personality type record

Predictive professional behaviors

This visualization shows a comparison of different groups (countries or companies, for example) accordingto two established psychometric axes. The area is divided into four parts, with a separation at the averagelevel. The average of the individuals in each group is calculated over these two axes.


Figure 7: Example of favorable professional behavior: ability to take initiative

Similarity graph

This graphic is used to visualize the psychometric proximity (psychological and cultural) between groups.These graphs are formed by comparing all the samples, two by two, and determining the correspondingdistance between each pair. We apply a clustering method in order to identify communities (clusters),where each color represents a different community. For the final image, we only show the connectionsabove a certain threshold in order to present an accurate, significant visualization.

Figure 8: Example of a similarity graph


III. Results

3.1. Intercultural differences analysis

Similarity graph by country

Our objective is to determine the cultural proximity between different countries based solely on the per-sonality and motivation data from our Talentoday members.If two countries, A and B, are separated by a short distance, our algorithm will not be able to correctlydiscern an individual of nationality A from an individual of nationality B. Conversely, if countries A andB are separated by a longer distance, their cultures will be far enough apart to be able to predict eachindividual’s country of origin. We can then group the different countries into clusters. All the countriesin each cluster should have relatively similar cultures.

We applied this methodology to a selection of countries represented by the Talentoday member base,and observed that the clusters in the resulting graph form familiar cultural groupings (fig 9):

• Asian countries (tan)

• Western countries (green)

• Eastern countries (red)

• Hispanic countries (pink)

The purple, blue, light green, and turquoise clusters do not seem to reflect known cultural similarities,although the results indicate that these countries are close, according to our data.

Focusing on certain countries (fig 10), we can see that their closest neighbors are often adjacent coun-tries. This is due to proximity and trade between countries, which favor cultural proximity. Nevertheless,there appear to be more subtle connections. Take the example of the USA and Ireland, Spain and LatinAmerica, or even the UK and Australia. These psychological proximities could be due to the impact oflanguage or history on these countries’ cultural development.

We can also see that France is culturally similar to New Caledonia, while the USA is similar to NewZealand. These connections may be explained by colonial history influencing the cultures of these coun-tries.


Afghanistan

Argentina

Belgium

Bulgaria

Brazil

Canada

Switzerland

Chile

China

Colombia

Costa Rica

Germany Algeria

Ecuador

Egypt

Spain

FranceUnited Kingdom

Hong Kong SAR China

Hungary

Indonesia

Ireland

India

Italy

Japan

South Korea Lithuania

Morocco

Mexico

Malaysia

New CaledoniaNetherlands

New Zealand

Panama

Peru

Philippines

Poland

Puerto Rico

Portugal

Romania

Serbia

Russia

Singapore

Thailand

Tunisia

Trinidad and Tobago

Taiwan

United States

UruguayVenezuela

Vietnam

Figure 9: Graph of psychometric similarities between countries. The colors represent the different clustersobtained from the clustering algorithm.


Figure 10: Similar cultures by country. We measure the respective distances for each country, two by two,thanks to the One VS One prediction score. We then select the closest neighbors for eachcountry based on a set threshold.

Predictive professional behaviors by country

To proceed further, we cross-referenced the countries, defining the favorable professional behaviors bycountry according to Talentoday’s model (table 10). To do so, we calculated the average of individualsfrom each country according to two specific psychometric criteria.It is important to remember that this analysis is based on a sample of individuals from each country andshould therefore only be taken for reference purposes. The samples, even if they have a large enoughnumber of individuals, are not necessarily representative of the total population of these countries.

The example below shows the ability of these countries to handle pressure (fig 11) and take initiative (fig12). We can see that individuals from developed countries state that they are less prone to handle pressurethan in developing countries. However, many of the countries that could be described as "Reactive" (aboveaverage work ethic, lower stress management) are European. In regards to the ability of these countriesto take initiative, Italy is ranked as one of the most creative countries, whereas China and the USA seemto be the most enterprising countries.


Figure 11: Ability to handle pressure by country, calculated from the average based on two psychometriccriteria defined in the mYti©

Figure 12: Ability to take initiative by country, calculated from the average based on two psychometriccriteria defined in the mYti©.


3.2. Company culture analysis

Detection of company industries

The objective of this section is to identify the similarities between different company cultures and then togroup these companies by industry based solely on psychometric data. To perform this analysis, we usea classification algorithm to distinguish among the companies. The algorithm then gives us a predictionreliability rate, which we use as a measure of the "difficulty" of distinguishing the companies based onthe personalities of their members. Consequently, the lower the score, the more similar the individuals ofthese countries are, and the more similar the company cultures will be.

As might be expected, the data on personality and motivations aren’t distinguishing enough in and ofthemselves to determine clear differences between all the companies. Nevertheless, we can still observeinteresting groupings (fig 13). There are different clusters for the armed forces, banking and finance, therestaurant business, high tech, and finally healthcare. We can see that the armed forces cluster also in-cludes subcategories based on different units and nationalities.


Citibank, N.A.

KPMG Advisory

HSBC

OCBC Bank

DBS Bank

Unilever

KPMG

Banco de Oro (BDO)

Bank Mandiri

J.P. Morgan Chase & Co.

Goldman Sachs Group

Procter & Gamble

Deloitte

Maybank

Deutsche Bank

Ernst & Young

PricewaterhouseCoopers (PwC)

Armee de Terre - Ministere de la Defense

Armee de l'Air

Marine Nationale

Police Nationale

Quick

flunch

Super U

Chronodrive

E.LeclercAuchan

McDonald's

Maison de retraite

ADMR

hopital public

Creche

Croix-Rouge Francaise

Clinique Medicale

EHPAD

United States Air Force (U.S. Air Force)

United States Army (U.S. Army)

United States Marine Corps (U.S. Marine Corps)

United States Navy (U.S. Navy)

Google

Microsoft

Apple Inc.

Figure 13: Graph of psychometric similarities between companies. The colors represent the different clus-ters obtained from the clustering algorithm.


Similar company cultures

The following graphics show companies according to similarities in their corporate cultures. As previouslynoted, the armed forces, restaurant and healthcare organizations have different cultures.

Figure 14: Company cultures similar to Microsoft. We measure the respective distances for each company,two by two, thanks to the One VS One prediction score. We then select the closest companiesbased on a set threshold.


Figure 15: Company cultures similar to job seekers. We measure the respective distances for each company,two by two, thanks to the One VS One prediction score. We then select the closest companiesbased on a set threshold.

Behavioral invariants by company

We selected companies in order to analyze their favorable professional behaviors based on the Talentodaymodel (table 10). We observed that transport companies and military organizations score high in StressManagement (fig 17). Military organizations also have a strong disposition towards independence. Wecan see that AXA, an insurance company, scores low in Stress Management and Work Ethic. Our analysisallows us to conclude that AXA’s employees seem more comfortable anticipating challenges than whenfaced with unexpected difficulties. In contrast, Shell’s employees seem to keep their calm better in theface of difficulty. In regards to teamwork, military organizations are characterized by a higher degree ofindependence, in contrast to restaurant and High Tech companies, which are more sociable. Adecco’semployees, specializing in recruitment, have high scores in Need for Relations and Opening up to Others.

Considering the company cultures of McDonald’s (fig 18) and EHPAD (fig 19), the employees of thesetwo companies can be described as Open to Others. It is nevertheless interesting to note that Ambition isa more significant personality trait for McDonald’s employees.


Figure 16: Ability to handle pressure by company, calculated from the average based on two psychometriccriteria defined in the mYti©.

Figure 17: Capacity for teamwork by company, calculated from the average based on two psychometriccriteria defined in the mYti©.


Figure 18: Personality type for McDonald’s employees

Similar to McDonald’s, EHPAD’s employees do not seem especially motivated by their Pay or SocialRecognition. However, they demonstrate a significant degree of Responsiveness and Philanthropy is a keymotivation, which seems to be a common characteristic in the healthcare field. It is also noteworthy thatCompetitive Spirit is low for this type of company.


Figure 19: Personality type for EHPAD’s employees

3.3. Analysis of personality types by profession

We selected two types of professions in order to identify their personality type: consultants and SoftwareEngineers. In order to conduct this analysis, we selected and grouped employees in these professions fromdifferent companies.

Consultants (fig 20) score high in Vision and Persuasion. They generally have important Communica-tion skills. Consultants are not necessarily motivated by Pay, but rather by Social Recognition. Wenoticed a significant Need for Variety among consultants.

On the other hand, developers (Fig. 21) are characterized by a high Work Ethic and less of an Inde-pendent Mind. Management and leadership skills are characteristic of the profession, as well as a lowNeed for Security.


Figure 20: Consultant personality type

Figure 21: Software Engineer personality type


3.4. Measuring a candidate’s fit with a specific job position

At Talentoday, our mission is to simplify and optimize the recruitment process thanks to a better selectionof candidates. One of our strategic levers is to help recruiters target candidates with the necessarypersonality and motivation characteristics for each position. Therefore, engagement, productivity andteam spirit are improved while turnover is reduced.

Talentoday has developed a professional tool for this purpose based on our matching algorithm - TargetProfile. This tool highlights the points people have in common within a group. It can therefore be usedto identify the points in common for specific employees (for example, top performers). Then it’s just aquestion of recruiting the individuals that best fit in with the selected group. As human personalities arevery complex, in order to deal with them as a whole, we cannot base our assessment on simple averagecomparisons - a matching algorithm is a necessary tool.

Talentoday’s Target Profile

We can use the Target Profile tool to identify common personality traits among the different members ofa team and calculate their profile type.

This tool works in 4 stages:

1. It defines the specific characteristics of a team based on its data.

2. It creates a profile type based on these specific characteristics.

3. A scoring algorithm calculates the matching rate for each individual on the team with the profiletype.

4. A match score is calculated for the candidate and the corresponding selected team.

VisualizationIn order to compare the candidates’ psychological profiles with a company culture or type of position,

we have visually synthesized the most distinguishing criteria. Criteria values in the profile type calculationthat fall below a certain threshold are not considered to be distinguishing. Individuals’ scores for thesecriteria are therefore not necessarily telling of their potential success in that position. The example belowalso shows the criteria of Respect for Authority, Leading, Ambition, Security, Private/Professional Life,and the Need for Variety.


Figure 22: Example of distinguishing criteria selection, from personality type to the Target Profile visual-ization

After selecting the most distinguishing criteria, they are shown in a radar with two levels of intensity:low (towards the center) and high (towards the outside):

Figure 23: Target profile of a specific team of Business Developers


Users matching this Target Profile are more likely to:

• Have a low level of opening up to others - less than 5.5 / 10

• Have a low level of creativity - less than 5.5 / 10

• Have a low level of autonomy - less than 5.5 / 10

• Have a low level of responsiveness - less than 5.5 / 10

• Have a high level of respect for authority - more than 5.5 / 10

• Have a low level of work ethic - less than 5.5 / 10

• Be more motivated by social recognition - more than 5.5 / 10

• Be more motivated by need to belong - more than 5.5 / 10

MatchingThe Target Profile scores help discern between candidates that are well-matched for a position and those

who do not have the desired personality traits or motivations. Matching scores over 50% correspond toindividuals that are a strong potential match. Team members used to define the profile type should obtaina match score of approximately 70-100%. These scores present a 2% margin of error. At least a 10% dis-tance is relevant for distinguishing between two candidates. For shorter distances, a more comprehensiveassessment may be required in order to differentiate the candidates, including an interview, for example.

Figure 24: Candidate matches for a specific team of Business Developers


IV. Conclusion

We have seen how analyzing psychometric data on a large scale can provide us with information regardingthe cultural differences among individuals. This data analysis may be conducted with increasingly finegranularity, from a country-based scale to an individual-based scale. It is clear that detecting and inter-preting the behavioral invariants of a group is a complex task that requires comprehensive expert analysis.

It is important to note, however, that this psychometric data alone provides us with enough informa-tion to extract knowledge by identifying the invariants characterizing a given population (e.g. company,team, group of individuals with the same profession, etc.). Psychometric data analysis coupled with ex-ternal data, such as performance, age and experience, allows us to substantiate a company’s culture.

Consequently, we can incorporate new development methods in our professional environment, therebyencouraging employee engagement and team cohesiveness. Today it is possible to objectively analyze acompany’s data thanks to new technologies that highlight the essence of success. With machine learning,human resources can now offer their (future) staff the most suitable positions for them to flourish andoutperform as professionals.


V. Bibliography

Dasarathy, B. (1991). Nearest Neighbor Pattern Classification Techniques, IEEE Computer Society Press,Los Alamitos, CA.

Dietterich, T. and Bakiri, G. (1995). Solving multiclass learning problems via error-correcting outputcodes, Journal of Artificial Intelligence Research.

Forgy, E. (1965). Cluster analysis of multivariate data: efficiency vs. interpretability of classifications,Biometrics.

Fu, W. (1998). Penalized regressions: the bridge vs. the lasso, Journal of Computational and Graph-ical Statistics.

Hastie, T., R. Tibshirani, and J. Friedman (2009), The Elements of Statistical Learning: Data Min-ing, Inference, and Prediction, Springer.

Cronbach L. J. Psychometrika, Coefficient alpha and the internal structure of tests.

Barrick M. R., Mount M. K. (1991) The Big Five Personality Dimensions and Job Performance: AMeta-Analysis, Personnel Psychology.

Beebe J., (1988) A New Model of Psychological Types.

Cattell R. B., (1957) Personality and motivation: Structure and measurement, New York, Harcourt,Brace & World, Journal of Personality Disorders.

De Fruyt F., McCrae R. R., Szirmák Z., & Nagy J. (2004) The Five-Factor personality inventory asa measure of the Five-Factor Model: Belgian, American, and Hungarian comparisons with the NEO-PI-R.Assessment.

Cattell R. B., (1965) The scientific analysis of personality, Harmondsworth, Penguin.

Reuchlin M., (2006) Differential Psychology (La psychologie différentielle), Paris, Presses Universitairesde France.


VI. Appendices

6.1. Definition of personality criteria

Feature name Low score High scoreEase in public A reserved individual who does not

naturally want to speak in public orin front of new people

An individual who likes to be the cen-ter of attention, speaks in public andwho spontaneously meets others

Opening up to others An individual who pays careful atten-tion and distances themselves from theideas of others

An individual who takes the time tolisten, is openminded and interested inthe ideas of others

Diplomacy An individual who expresses disagree-ment or opinion directly and is notafraid of conflict

An individual who favors a careful ap-proach and restraint, avoids situationsof conflict

Persuasion An individual who does not wish tocompel others to do, believe or wantsomething; avoids negotiations

An individual who values negotiationsand reasoning and has the ability toconvince and persuade others

Leading An individual who prefers to collabo-rate and work with others rather thanto act as a leader

An individual who adopts a position asleader within a team and likes to makedecisions

Taking responsability An individual who is careful and showsrestraint before making a contributionto a project

An individual who likes to be assignedtasks, assumes responsibility for suc-cess and failures

Organization An individual who prefers flexible or-ganizations and improvisation

An individual who plans and allocatestasks, values a structured and orga-nized timetable

Vision An individual who favors a detailedand meticulous approach to situations

An individual who is objective and ap-proaches situations as a whole

Self-confidence An individual who needs to be reas-sured about his/her potential and abil-ities

An individual who is self-assured andis aware of his/her ability to succeed

Independent mind An individual who is sensitive to criti-cism and stands by the opinion of thegroup

An individual who is distanced fromthe opinion of others, favoring his/herown ideas

Table 6: Definition of 20 personality criteria [1]


Creativity An individual who adopts a conven-tional approach to tasks, prefers to relyon an already existing approach

An individual who likes to discoveroriginal solutions for a given problemand finds it easy to be imaginative andcreative

Autonomy An individual who likes to be super-vised and have his/her work checked

An individual who is keen to act alone,works independently

Stress management An individual who gets nervous easily,may be disconcerted or even inhibitedby stressful situation

An individual who is naturally re-laxed, manages pressure, is motivatedby stressful situations

Responsiveness An individual who takes time to makedecisions, analyzes situations in depth,prefers to plan for the future

An individual who rapidly makes andimplements decisions, favors actionover reflection, reacts spontaneously

Patience An individual who wishes to obtain im-mediate results, favors the short termover looking to the future

An individual who opts for a long-termvision, accepts stages and waits for re-sults

Respect for authority An individual who favors his/her ownfreedom of action to the detriment ofapplicable rules and established au-thority

An individual who accepts authority,complies with the rules of the group,respects customs

Determination An individual who readjusts his/hergoals according to difficulties encoun-tered

An individual who is perseverant anddetermined to meet his/her goals

Ambition An individual who does not prioritizehis/her career and is not focused onprofessional success

An individual who wants to rapidlyprogress and believes that professionalsuccess is important

Work ethic An individual who prefers a moderatework pace and who is able to measurehis/her efforts

An individual who likes to get througha large amount of work, enjoys periodsof intense activity

Competitive spirit An individual who does not wish to bein competition with others, prefers co-operation to competition

An individual who enjoys being moresuccessful than others, feels the need tobe the best, and motivated by rivalry

Table 7: Definition of 20 personality criteria [2]


6.2. Definition of the main dimensions of personality

Dimension DefinitionCommunicate To be linked to others, to transmit something to someone, generally through language

(written or oral). This cluster refers to the way in which a person interacts with others,the relationships they have, and the ability to pass on information or a message toothers.

Manage To organize or direct something or someone. This cluster refers to the way in which anindividual organizes and takes responsibility for a project or a group.

Dare To have courage, the confidence to do something. This cluster refers to the ability togo beyond existing limits and what is already established. For this, a certain degree ofconfidence and independence is necessary.

Adapt To modify one’s thoughts and behavior to agree with a situation, to adjust. This clusterrefers to the attitude we adopt when faced with new situations or constraints.

Excel The ability to go beyond a limit and achieve results that are better than those previouslyproduced. This cluster focuses on the energy that a person puts into his/her work, witha need or desire to stretch the limits.

Table 8: Definition of the 5 main dimensions of personality


6.3. Definition of motivation criteria

Feature name Low score High scoreSocial recognition An individual who is not sensitive to

the reputation of a position or com-pany

An individual who thinks that thereputation of companies or the pres-tige of positions are important

Pay Pay is not a priority, money is not anobjective

An individual who is motivated bythe prospect of earning more, moneysymbolizes success

Security An individual who likes to take risksand is not happy to take the back seat

An individual who seeks stability andsecurity at work

Private/professional life An individual who prioritizes profes-sional activity

An individual who wants a balancebetween professional and non work-related activity

Philanthropy An individual who is not motivatedby the idea of mixing professionalactivity and humanitarian or socialcauses

An individual who likes to give a hu-man meaning to actions and valuesactivities with a social dimension

Need to belong An individual who is not motivatedby belonging to a group or network,appreciates independence

An individual who is motivated bybelonging to a group or network

Need for relations An individual who prefers to workalone and is able to make progress in-dependently

An individual who seeks contact withothers, prefers to work as part of ateam

Need for variety An individual who likes to focus onone main activity, prefers repetitiveassignments

An individual who thinks that diver-sity in tasks and assignments is im-portant

Table 9: Definition of 8 motivation criteria


6.4. Definition of Predictive Professional Behaviors (P.P.B.)

P.P.B. DefinitionTeamwork Teamwork is a work mode, where each individual shares his/her ideas, and listens

to others’ contributions during meetings and cross-functional projects. Teamworkcalls for team-members to consistently and constructively interact with each otherto leverage the skill synergies in the team towards achieving a common goal.

Ability to take ini-tiative

The ability to take initiative denotes the capacity to come up with ideas andspontaneously and independently take charge of the projects to ensure they arecorrectly implemented. In a professional setting, we look at how a person is engagedand how they propose solutions for fresh momentum.

Management style Management style is the way that one interacts with team members to achieveresults, lead change, and reach desired goals. Management style is the result of acombination of the manager’s personality as well as the organization’s environment.There is no ideal management style- an individual may change styles in differentsituations, but will retain a consistent management style.

Ability to handlepressure

Ability to handle pressure is the way one copes with the demands and requirementsfor a company. In all different types of jobs, one faces unexpected events that needto be managed without losing productivity, sometimes in limited amounts of time.This is how one reacts to the constraints of everyday life in order to continue beingefficient, despite the pressures of one’s working environment.

Reasoning style Reasoning style depicts how people approach situations and problems to under-stand what is at stake and the critical components. This professional behaviorlooks at how people process situations, since a preference for synthesis or for anal-ysis shapes one’s way of working.

Communicationstyle

Our communication style describes how we express ourselves and how we relate toothers. The aim is to accurately share a message between a sender and a receiver.It is key for organizations that their employees are able to relate to others openlyand efficiently, by formulating their ideas clearly and explicitly while adjusting tovarious situations.

Flexibility Flexibility is the ability to rapidly adjust to changes that oftentimes interfere withour workplace. Being flexible means understanding the stakes with the adjustmentsas well as facing the uncertainties that may come along.

Table 10: Definition of 7 Predictive Professional Behaviors


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