And if they do, are those differences big enough to be useful for career counseling and personal development?
Looking for tips from those who answered "Yes" to the question in the title of this post 🙂
... at least in my projects 😉
How to align observations in organizational data with the results of one research on the relationship between tenure and employee satisfaction?
An interesting research paper by Wang, Luan & Ma (2024) in Nature explores the causal relationship between work motivation and job performance using longitudinal data from 11 independent studies and meta-analytic structural equation modeling.
If interested, here are a few quick thoughts of mine along with some light data exploration on the topic.
It is quite common to encounter the opinion that responses in exit surveys or interviews are more honest than those in regular engagement surveys, despite their anonymity and/or confidentiality, especially when it comes to more politically sensitive topics such as satisfaction with one's direct manager. It is usually argued that people no longer fear repercussions or negative consequences for speaking openly about their real experiences and opinions because they leave the organization and have less incentive to conceal critical feedback. Let's see if we can find any evidence of it in the data...
How surprising was Sha’Carri Richardson’s victory in the women’s 100m race at the World Athletics Championships in Budapest 2023? Let's check it out with some data.
A brief reflection on one of the predictors of voluntary employee attrition.
Results from the exploration of data on team effectiveness and efficiency across ~40 C-suite teams.
A brief demonstration of an awesome tool for exploring multidimensional data, using personality and work-life satisfaction data.
If you're curious about what your LinkedIn connections are up to, keep reading...
Probably each of us has experienced, or at least heard of, the practice of using job insecurity as a motivational tool at some point in our careers. A well-known example of this is stack-ranking performance reviews, where bonuses are given to top performers and those at the bottom are let go. While we may not personally like this method of motivation, it would be beneficial to have some data on its effectiveness.
A showcase on how to use network analysis to display the co-occurrence of topics in employee comments.
The answer might be "yes", at least in the case of team collective intelligence, defined as a general ability of a team to work together across a wide array of tasks.
Why not?
Is it satisfaction or dissatisfaction that drives comments? Or perhaps it’s the extremes on both ends of the satisfaction spectrum?
An example of how to get a better understanding of various complex phenomena through simulation in NetLogo, a free programmable multi-agent modelling environment.
...depending on where it directs your locus of attention.
Do nice guys really finish last?
During my career, I have been part of several teams that have organized their work using some of the existing agile methodologies. For the most part, agile principles made good sense to me and subjectively seemed to work, so I had no reason to question their supposed benefits.
Who you got your money on? 😉
Sharing one learning from the awesome book Probably Overthinking It by Allen B. Downey.
Every tool used for employee selection introduces its own potential biases into the process. This holds true also for video interviews, which have become increasingly popular among recruiters in recent years.
Description of a simple hack to simulate the work of a qualitative researcher classifying comments from respondents using GenAI.
Can a simple intervention such as an email campaign help with fostering psychological safety in the workplace?
A brief summary of the main results of a meta-analysis comparing psychometric characteristics of forced-choice and single-stimulus personality assessments in relation to faking.
A brief overview of the results of a meta-analysis on the relationship between team design and team creativity and innovation.
Does a power-law distribution of performance, as opposed to a normal distribution, support the concept of strength-based development?
Is there enough evidence to bet on this technique and give it a try?
A short reflection on one of the impacts of using GenAI on my work habits.
A new construct on the block for employee selection and development?
Check how your personality supports your earnings.
Let's create your own consultant to help you apply the principles of Evidence-Based Management.
Using LLM and text embeddings to assist in implementing new constructs into the existing employee survey.
Interesting results from a pre-registered meta-analysis of 44 years of field experiments on gender gaps in hiring decisions.
I just came across an interesting and surprising result from a Bayesian meta-analysis on the effect of the interaction between job demands and job control on worker well-being.
A demonstration of one method useful for sharing insights from fitted ML models.
An attempt to validate a zero-shot sentiment classification.
How to make onboarding experience a little bit smoother with the help of NLP and LLM.
As I was going through my calendar recently to check who I had already met during my onboarding at Sanofi, I realized that one way to look at the calendar is through the lens of the Exploration vs. Exploitation trade-off. What lessons can we take from this?
A demonstration of how the outputs of Bayesian analysis can be used to simulate business processes while preserving inherent uncertainties.
An illustration of one of the lessons I took away from studying the use of meta-learners for causal inference.
In my new job, we currently rely a lot on Power BI when presenting people-related insights to our stakeholders. Since I know Power BI quite superficially and we also want to share insights from more complex analyses with our stakeholders, I spent part of the weekend studying how to incorporate ML models created in R or Python into Power BI dashboards. I put my learnings in this blog post. It's definitely not rocket science, but it may still shorten the learning path for some of you who are in a similar situation.
Quite surprising (at least to me) findings on the validity of vocational interests for predicting a range of important work outcomes.
A post about useful complement to common ONA centrality measures.
I'm sharing a by-product of my learning about vector database search that may be useful to some of you who want to learn something new about People Analytics.
When you report turnover rates by team, do you take into account the size of individual teams, or do you take the turnover rate numbers as they are?
Sharing a by-product of my search for a new full-time job.
Reading the latest release of Deloitte Global HC Trends made me wonder what common themes this regular series has been covering throughout its 12 years long history.
Team processes seem to beat team composition and structure when it comes to innovation at work.
How listening to a podcast about conspiracies and disinformation inspired me to try out a "new" statistical tool popular among sociologists.
Is it possibile to improve the objectivity of decision making through mindfulness meditation?
Bayesian networks seem to have some interesting properties that could make them useful for various people analytics use cases, but for some reason this is not the case.
Let's take a slopegraph perspective to assess changes in estimates of the validity of selection procedures.
A demonstration of how psychometric network analysis can be used to gain insights into employee survey data.
A nice illustration of the regression to the mean phenomenon in the space of people analytics.
How my own experience of exploring new job opportunities gave me the idea of how the company's career site could be easily improved using OpenAI's tools.
How did GPT-4 perform in the knowledge test of evidence-based HRM practices? Let's check it out.
How to use GPT and embeddings from OpenAI for identifying topics and related sentiments in employee feedback.
Comparison of Glassdoor ratings from current and former employees.
A post about a great R package to reach for when you need to calculate correlations on nested data.
Some interesting insights from a meta-analytic review of the consequences of time management behaviors in the workplace.
There's a new kid on the block in the R ecosystem that can help analysts understand the behavior of their ML models.
Can Generative AI like GPT meaningfully interpret personality profiles?
What evidence do we have for the effectiveness of interventions for increasing employee engagement? Let's check it out.
In which areas are managers and leaders prone to overconfidence, and how can this overconfidence potentially impact team functioning? Let's check some data to address this question.
What is the benefit of using the difference-in-differences method in combination with piloting a new business app, and how can this help estimate the app's effectiveness on key outcomes like time spent with prospects or closed deals?
One of our clients was struggling with meeting overload and wanted to know if the people who attend too many meetings are the kind of "yes-men" who just can't say no to meeting invites. You know the type - always saying "yes" and never protecting their precious time. What did they find?
While there is evidence supporting the connection between employee satisfaction and a company's bottom line, it's essential to determine whether higher satisfaction directly causes better performance. Is there some evidence for that? Let's check it out.
Using combination of Excel and Python for semi-automatic Word document generation.
What employee outcomes are predicted by what employees' job attitudes? Let's check it out.
Quite satisfying news from a meta-analysis on the efficacy of micro-breaks for increasing well-being and performance in the workplace.
Just a few data-backed thoughts on why many of us may often feel more distracted when working in an office.
What's the potential use of tools like ChatGPT in analyzing open-ended feedback from employee engagement and satisfaction surveys? Let's take a look at the result of my little experiment in this area.
Let's briefly discuss the potential benefits of focusing on optimizing large recurring meetings to save time in the workplace.
What's the link between the HRM value chain and structural equation modeling? Let’s check it out.
How to effectively combine information about the formal organizational structure of a company and the actual collaborative activities of its employees?
How can collaboration data be used to determine the "optimal" scope of control?
Slack and other instant messaging platforms can be both a blessing and a curse. Can we attach numbers to some of the recommendations on how to use them effectively? Let's take a look.
Let's take a look at two concepts from computer science that can be used in the workplace to improve people's focus and productivity, and expose two methods for measuring their related behaviors when collaborating on instant messaging platforms.
What tools and/or signals can we use to identify employees at increased risk of overload? Let's take a look at some of the options we have in this regard.
Have we made any progress in knowledge of evidence-based HRM practices in the last 20 years? Apparently not. But let's look at the details.
What suggestions do people have for improving the effectiveness of meetings? Let's check it out.
If you are a regular organiser or attendee of meetings, you may be interested in what people think about the reasons why the meetings they attend are in/effective, as this can give you a better chance of contributing to making your meetings more effective and meaningful for you and others.
Do you think it's possible to find signals in collaboration (meta)data that someone is a good manager? Let's give it some thought.
Interested in testing your knowledge of evidence-based HRM practices? If so, click and get started.
With the Christmas holidays approaching, the following question is more relevant than ever, with the exception of the summer vacations: Can we really disconnect from work during the vacations? And what can collaboration data tell us about this?
Breaking down one weekend association.
Let's take a look at some tips and tricks to make dasboards more useful for their users.
Meet the Eisenhower matrix for meetings ;)
Probably due to the current situation in the talent market, where many companies are laying people off and at the same time are worried about losing their key employees, a few people have contacted me in recent weeks asking for some tips on evidence-based approaches to dealing with retention and downsizing.
Pay inequality between men and women is not only an ethical and legal issue for companies, but also a marketing issue - it can have a negative impact on their "employer brand" and attractiveness as an employer. This means that if companies want to attract and retain talented employees, they must be able to ensure that they treat men and women equally in this respect. Let's look at what the existing evidence tells us about what might help us with this.
And it definitely applies to the shape of the distribution of many HR metrics. Let's look at this in a little more detail.
Teacher: "Bayesian belief updating involves combining existing or prior beliefs with an assessment of the strength of new evidence." Student: "And could I please see this in action?"
A "new" real-world dataset useful for training in people analytics.
Finding the breakpoint when people start to score significantly higher/lower on a given criterion - the use case for the Conditional Inference Tree algorithm.
Checking with real-world collaboration data whether timeboxing has a protective function in terms of time available for focused work.
Don't chase (statistical) ghosts and use multilevel models instead!
Personality is not fate, at least when it comes to the level of engagement in corporate communication and collaboration.
One of the most useful insights that can be gleaned from collaboration data is where hot spots of potential collaboration overload and/or collaboration bottlenecks may exist in a company. Such insight can be especially valuable these days, when many companies are trying to fight the upcoming economic downturn by achieving more with less.
A short post about the practice of back-to-back meetings and how to determine when it's for bad and when it's rather for good.
Many people analytics professionals think that after the COVID pandemic, organizations are more willing to listen to their insights and recommendations. Can we find any empirical support for their hunch? Let's check it out with data provided by Google Trends and segmented regression analysis of interrupted time series.
A brief summary of my participation in Orgnostic's People Analytics Challenge.
Visual statistical inference represents a valid alternative to standard statistical inference, and as a by-product it also helps with building intuition about the difference between signal and noise. Give it a try.
When modeling a phenomenon, one usually can't get by with just raw data but must use one's domain knowledge to select and transform the most relevant variables from raw data to be able to successfully grasp regularities in the domain of one's interest. Let's look at one simple example of such feature engineering from the domain of collaboration analytics.
One of the most effective ways to fight meeting overload is to better plan meetings in terms of the time we spend in them. Let's look at how data can tell us how much room for improvement we have in this area.
Have you ever wondered exactly how much chance of success people give a project when they say they believe in it? If so, then you may find this post useful, as it attempts to answer that question at least in part with data.
Cultural diversity brings both positive effects and some challenges. To deal with the latter, it is useful to have some kind of map to help people better navigate the cultural specificities of people from different societies. Hofstede's theory of cultural dimensions is useful for such a purpose. Let's check how dis/similar countries are on these cultural dimensions with a simple app that could help us better understand, manage and appreciate cultural differences a little better.
Many of us have probably already heard of Paul Graham's two types of schedules - one that meets the needs of makers and one that meets the needs of managers. But can these two types of schedules be found in any real collaborative data? Let's find out.
An introduction of a simple R Shiny application for analysing LinkedIn connections.
An introduction of a simple R Shiny application to facilitate extraction and digestion of information from meta-analysis of predictors of voluntary employee turnover.
Platová nerovnost mezi muži a ženami není pro firmy jen záležitostí etickou a právní, ale také marketingovou - může mít totiž negativní dopad na jejich "employer brand" a atraktivitu coby zaměstnavatele. To znamená, že pokud firmy chtějí přilákat a také si udržet talentované zaměstnance, musí být schopny zajistit, že se u nich s muži a ženami bude v tomto ohledu zacházet stejně. Prvním krokem k tomu je zjistit, jak velký je rozdíl mezi platy mužů a žen ve firmě a do jaké míry ho lze vysvětlit jinými faktory než je samotné pohlaví zaměstnance. V tomto článku demonstruji, jak takovou analýzu provést s pomocí analytického nástroje R a dat, která má většina firem běžně k dispozici. Stručně se zmiňuji rovněž o tom, jaké mohou být případné další kroky a doporučení vyplývající z výsledků provedné analýzy.
Illustration of Bayesian segmented regression analysis of interrupted time series data with a testing hypothesis about the impact of the COVID-19 pandemic on increase in people's search interest in work-life balance and well-being.
Které faktory přispívají k odchodovosti zaměstnanců a u kterých konkrétních zaměstnanců je zvýšené riziko, že firmu během několika příštích měsíců opustí? Na tyto otázky se čím dál tím více firem snaží odpovědět pomocí analýzy dat o svých vlastních zaměstnancích. V tomto článku se prostřednictvím analytického nástroje R a vizualizačního nástroje Shiny podíváme, jak může být tento druh HR analytického projektu pro firmy užitečný.
Přes popularitu tématu HR analytiky mezi HR profesionály je stále relativně málo společností, které HR analytiku reálně a systematicky využívají. Jednou z možných příčin je to, že tradiční HR mnohdy postrádá analytický mindset a některé z kompetencí, které jsou klíčové pro úspěšnou realizaci HR analytických projektů. V takové situaci může být užitečné podívat se ve větším detailu na celkovou logiku i na konkrétní analytické kroky nějakého úspěšného příkladu využití HR analytiky k optimalizaci některého z HR procesů s pozitivním dopadem na obchodní výsledky společnosti. V tomto článku se tímto způsobem podíváme na známý příběh oaklandského baseballového týmu "Áček", jehož management poměrně radikálně - a podle všeho i úspěšně - přehodnotil svůj dosavadní přístup k výběru nových hráčů na základě výstupů statistické analýzy sabermetrických dat o herním chování hráčů. Využijeme při tom volně dostupný statistický software R a veřejně dostupnou databázi historických údajů o výsledcích v americké baseballové lize.