library / lib9f4e736f5e944815
Predictive HR Analytics
Dr Martin Edwards
In a sentence
A hands-on guide that teaches HR and management-information professionals how to move beyond descriptive reporting to apply inferential, predictive statistical techniques to people-related data using SPSS (and R).
Predictive HR Analytics: Mastering the HR Metric is the rare book that not only explains why HR functions must adopt predictive analytics but actually walks the reader, click-by-click, through running the analyses themselves. Across detailed case studies covering diversity, employee engagement, turnover, performance, recruitment and selection, and intervention evaluation, Edwards, Edwards and Jang demonstrate how to convert messy organizational data into rigorous, statistically tested insight using techniques from chi-square and t-tests to logistic and multiple regression, survival analysis, and beyond. The book demystifies the 'magic curtain' of HR analytics, teaches readers when and how to apply each statistical test, shows how to build evidence-based business cases and predictive models, and closes with a thoughtful reflection on the ethical limitations and pitfalls of analysing data that ultimately represents living human beings. It is equally useful to HR master's students, MI practitioners and people-analytics specialists who want to build genuine quantitative capability.
The four lenses
- Science
- Statistics
- Systems
- Strategy
Tags
The model
An inferred factor model expressing how design levers and contextual conditions (HR interventions, selection/onboarding practices, job and team characteristics) influence psychological and behavioural states (engagement, perceived support, justice, job strain, satisfaction) which in turn drive HR outcome metrics (performance, turnover, diversity representation, customer loyalty/reinvestment). The book demonstrates these relationships through applied statistical case studies.
HR Intervention / Programmedesign lever
A deliberate organizational action such as training, a work-life balance programme, induction, or a values-change initiative, introduced with the intention of changing employee attitudes, perceptions, behaviours or performance outcomes.
Selection and Onboarding Practicesdesign lever
Recruitment, selection and onboarding activities and associated data including assessment-centre scores, competency ratings, aptitude/personality tests, induction day/week attendance, and assignment of an onboarding buddy that shape the quality and fit of new hires.
Job and Team Characteristicscontextual condition
Structural and contextual features of a role or team such as function/department, team size, gender mix, geographic location, job demands and job control/autonomy that form the conditions within which employees work and that influence states and outcomes.
Demographic and Diversity Compositioncontextual condition
The gender, ethnicity (underrepresented group status), age, tenure and education profile of individuals and the diversity composition of teams, used both as a predictor and as an outcome of interest in fairness and representation analyses.
Employee Engagementpsychological state
A psychological and behavioural state reflecting employees' intensity of effort, focus, absorption, pride and sense of belonging in their work and organization, treated as a latent construct measured through multiple attitude-survey items.
Perceived Organizational Support and Justicepsychological state
Employees' perceptions that the organization values their contribution and cares about their well-being (POS), together with perceptions of distributive and procedural fairness, and perceived supervisor support and organizational integrity.
Job Strain / Stresspsychological state
A psychological response to a demanding work context, including worry, lost sleep and reported stress, arising from the combination of high job demands and limited control, which can both elevate and undermine performance and well-being.
Job Satisfactionpsychological state
A pleasurable or positive emotional state resulting from appraisal of one's job or job experiences, with emotional, cognitive and behavioural components, related to other attitudes and to behavioural outcomes such as turnover.
Employee / Team Performanceoutcome metric
Indicators of how well individuals or teams perform their work, including performance appraisal ratings, sales figures, customer feedback, productivity measures such as supermarket checkout scan rates, and team-level performance composites.
Employee Turnoveroutcome metric
Voluntary departure of employees from the organization, measured at the individual level (leaver vs stayer, length of service before leaving) and at the team level as a separation rate percentage, with substantial associated replacement costs.
Customer Loyalty and Reinvestmentoutcome metric
Customer-facing performance outcomes such as customer satisfaction, expressed loyalty, intention to reinvest and amount of reinvestment, used as bottom-line performance indicators particularly in service and financial-sales contexts.
How they connect
- hr intervention − influences job strain
- hr intervention → influences employee performance
- selection onboarding practice → predicts employee performance
- selection onboarding practice − predicts employee turnover
- perceived org support justice → predicts employee engagement
- perceived org support justice → predicts employee performance
- job strain → predicts employee performance
- job strain → influences employee turnover
- employee engagement → correlates employee performance
- employee engagement − correlates employee turnover
- job team characteristics − predicts employee engagement
- job team characteristics − moderates job strain
- demographic diversity − predicts employee performance
- demographic diversity → predicts employee turnover
- selection onboarding practice → predicts demographic diversity
- perceived org support justice → predicts customer outcomes
- job satisfaction − influences employee turnover
The process
The book's operating playbook guides HR professionals and analysts to transition from producing descriptive reports to conducting predictive HR analytics. The core methodology involves a systematic, evidence-based approach to solving business problems related to people. It begins with identifying a critical business question, then gathering and meticulously preparing relevant HR and business data for analysis. The central part of the playbook is the selection and application of the appropriate statistical technique—such as regression, ANOVA, or chi-square analysis—based on the nature of the data and the question being asked. This allows the analyst to move beyond describing 'what' is happening to explaining 'why' by identifying statistically significant drivers and relationships. The final and most crucial phase of the playbook is the application of these analytical insights. The models developed are used to build robust business cases, run 'what-if' scenario models to predict the impact of potential interventions, and provide evidence-based recommendations for strategic decisions, such as recruitment and selection. By mastering this end-to-end process, the HR function transforms from a reactive administrative center into a proactive, credible, and strategic business partner that can demonstrably link people initiatives to business outcomes.
Data Preparation for Analysis
To import, clean, structure, and merge various HR and business datasets into a single, analysis-ready file within a statistical software package like SPSS.
When to use: At the beginning of any analytics project, after data has been gathered from various sources.
Step 1Set up variables in the statistical software's variable view.
Entry: A list of required variables and their characteristics has been defined.
Exit: All variables for the initial dataset are correctly defined in the software.
In: Data dictionary or list of variables · Out: Structured variable view in the software
Step 2Import or enter data into the data view.
Entry: Variables are set up and raw data is available in a digital format.
Exit: All raw data is present in the software's data view.
In: Excel files, CSV files, Manual data · Out: Populated data view
Step 3Clean and recode data as needed.
Entry: Raw data is loaded into the software.
Exit: Data is cleaned, correctly formatted, and matches the definitions in the variable view.
In: Raw data · Out: Cleaned, numeric-coded data
Step 4Merge multiple datasets using a unique key.
Entry: Two or more cleaned datasets with a common, unique key field exist.
Exit: A single, merged dataset containing all variables from the source files is created.
In: Two or more sorted, cleaned datasets · Out: A single merged dataset
Validating Survey Scales
To statistically verify that a set of survey questions (items) accurately and consistently measures the intended underlying construct (e.g., employee engagement, job satisfaction).
When to use: During survey development or before using a composite survey score in a predictive model, to ensure the measure is valid and reliable.
Step 1Perform an Exploratory Factor Analysis (EFA) to identify underlying constructs.
Entry: A dataset with individual responses to a multi-item scale is available.
Exit: The number of underlying factors (constructs) is identified.
In: Individual-level survey data · Out: Factor loading matrix, List of items that group together into factors
Step 2Interpret the factor analysis output.
Entry: Factor analysis has been run.
Exit: A clear understanding of which items measure which construct.
- Do the items group into the expected number of factors?
- Are there any items that do not load clearly onto any factor or cross-load on multiple factors?
In: Factor analysis output · Out: Validated constructs
Step 3Perform a Reliability Analysis for each identified factor.
Entry: Items have been grouped into factors.
Exit: The internal consistency of each scale is quantified.
In: Grouped survey items · Out: Cronbach's Alpha value for each scale
Step 4Interpret the reliability analysis output.
Entry: Reliability analysis has been run.
Exit: A final, reliable set of items for each construct is confirmed.
- Is the Cronbach's Alpha above the 0.70 threshold?
- Should any items be removed to improve reliability?
In: Reliability analysis output · Out: Finalized, reliable scales
Analyzing Categorical Relationships (Chi-Square Test)
To determine if a statistically significant association exists between two categorical variables by comparing observed frequencies to expected frequencies.
When to use: When asking questions like 'Is there a relationship between an employee's country and their turnover status?' or 'Is gender associated with promotion rates?'.
Step 1Formulate a hypothesis about the association between two categorical variables.
Entry: A clear research question and a dataset with two categorical variables are available.
Exit: A testable hypothesis is defined.
In: Research question, Dataset · Out: Null and research hypotheses
Step 2Run a crosstabulation analysis in the statistical software.
Entry: Data is prepared and loaded.
Exit: Crosstabs analysis is configured.
In: Two categorical variables
Step 3Select the Chi-Square statistic and cell display options.
Entry: Crosstabs analysis is being configured.
Exit: All necessary statistical tests and display options are selected.
Step 4Execute the analysis and interpret the results.
Entry: Analysis is configured.
Exit: A conclusion is drawn about the statistical significance of the association.
- Is the p-value less than 0.05?
In: Analysis output · Out: Crosstabulation table, Chi-Square test results, Conclusion on statistical significance
Step 5Conclude whether a significant association exists.
Entry: Chi-Square test results are available.
Exit: A final interpretation of the finding is articulated.
In: p-value · Out: Statement on the association between variables
Comparing Group Means (T-tests and ANOVA)
To determine if there is a statistically significant difference in the mean of a continuous variable between two or more distinct groups.
When to use: When asking questions like 'Do engagement scores differ between the Sales and HR departments?' or 'Did stress levels change after the wellness intervention?'.
Step 1Determine the appropriate test based on the number of groups and sample dependency.
Entry: A research question comparing a continuous variable across groups is defined.
Exit: The correct statistical test is chosen.
- Are there two groups or more than two?
- Are the groups independent (different subjects) or related (same subjects at different times)?
In: Research question, Number and nature of groups · Out: Selected statistical test
Step 2Run the selected test in the statistical software.
Entry: The correct test has been chosen and data is prepared.
Exit: The analysis is configured in the software.
In: Continuous variable, Categorical grouping variable
Step 3For ANOVA, configure post-hoc tests.
Entry: A One-Way ANOVA is being run.
Exit: Post-hoc tests are selected.
Step 4Interpret the output to determine significance.
Entry: Analysis has been run.
Exit: A conclusion is drawn about the difference between groups.
- Is the p-value < 0.05?
- For independent tests, is Levene's test significant (p < 0.05)?
In: Analysis output · Out: t-statistic or F-statistic, p-value, Group means
Step 5If ANOVA was significant, interpret the post-hoc tests.
Entry: The overall ANOVA F-test was significant.
Exit: Specific between-group differences are identified.
In: Post-hoc test output · Out: List of significantly different group pairs
Predicting a Continuous Outcome (Multiple Linear Regression)
To build a statistical model that explains and predicts variation in a continuous dependent variable using several independent (predictor) variables.
When to use: When asking questions like 'What are the key drivers of employee performance?' or 'Which assessment center scores best predict future sales revenue?'.
Step 1Define the model by selecting dependent and independent variables.
Entry: A clear research question about predicting a continuous outcome is defined.
Exit: A set of DV and IVs for the model is chosen.
In: Research question, Prepared dataset · Out: Model specification
Step 2Create dummy variables for categorical predictors.
Entry: The model includes categorical IVs with 3+ levels.
Exit: Dummy variables are created and ready for inclusion in the model.
In: Categorical variable · Out: Set of dummy variables
Step 3Run the linear regression analysis.
Entry: Model is fully specified with all variables prepared.
Exit: Regression analysis output is generated.
In: Dependent variable, Independent variables · Out: Regression output tables
Step 4Evaluate the overall model fit.
Entry: Regression output is available.
Exit: The model's overall predictive power and significance are understood.
- Is the overall model significant?
In: Model Summary table, ANOVA table · Out: R Square value, F-statistic and p-value
Step 5Identify significant individual predictors.
Entry: The overall model is significant.
Exit: The key drivers of the dependent variable are identified.
In: Coefficients table · Out: List of significant predictors and their Beta values
Predicting a Categorical Outcome (Logistic Regression)
To build a model that predicts the probability of a binary outcome (e.g., an employee leaving or staying) based on a set of predictor variables.
When to use: When asking questions like 'What factors predict which employees are most likely to leave?' or 'Can we predict which job applicants will be shortlisted?'.
Step 1Define the model with a binary dependent variable.
Entry: A research question about predicting a binary outcome is defined.
Exit: A set of DV and IVs for the model is chosen.
In: Research question, Prepared dataset · Out: Model specification
Step 2Run the binary logistic regression analysis.
Entry: Model is specified.
Exit: Logistic regression analysis output is generated.
In: Dependent variable, Independent variables · Out: Logistic regression output tables
Step 3Declare categorical covariates.
Entry: The model includes categorical IVs.
Exit: Categorical variables are correctly specified.
In: Categorical independent variables
Step 4Evaluate the overall model fit.
Entry: Logistic regression output is available.
Exit: The model's overall predictive power and significance are understood.
- Is the overall model significant?
In: Omnibus Tests table, Model Summary table · Out: Model Chi-square and p-value, Nagelkerke R Square
Step 5Identify significant predictors and interpret their odds ratios.
Entry: The overall model is significant.
Exit: The key drivers of the outcome and their impact on its odds are identified.
In: Variables in the Equation table · Out: List of significant predictors and their odds ratios
Analyzing Time-to-Event Data (Survival Analysis)
To analyze and predict the duration of time until a specific event occurs, such as an employee leaving the organization.
When to use: When asking questions like 'How long can we expect new graduates to stay with the company?' or 'Do men and women have different survival patterns in terms of tenure?'.
Step 1Set up the Kaplan-Meier analysis.
Entry: A dataset with a time variable (e.g., tenure), a status variable (indicating if the event occurred), and an optional factor variable (for group comparisons) is available.
Exit: Kaplan-Meier analysis is configured.
In: Time variable (e.g., 'LengthOfService'), Status variable (e.g., 'LeaverStatus'), Factor variable (e.g., 'Gender')
Step 2Define the time, status, and factor variables.
Entry: Kaplan-Meier analysis window is open.
Exit: All variables are correctly assigned.
Step 3Select comparison and plot options.
Entry: Variables have been defined.
Exit: Statistical tests and plots are selected.
Step 4Run the analysis and interpret the output.
Entry: Analysis is configured.
Exit: A conclusion is drawn about the survival patterns.
- Is the Log Rank test significant?
In: Analysis output · Out: Mean/median survival times, Log Rank test results
Step 5Analyze the survival curve plot.
Entry: Analysis has been run.
Exit: The visual pattern of survival over time is understood.
In: Survival plot · Out: Visual interpretation of survival patterns
Evaluating Interventions (Repeated Measures ANOVA)
To statistically evaluate the impact of an HR intervention by analyzing changes in a key metric over multiple time points, and to determine if this impact differs across various employee groups.
When to use: When asking 'Did our training program significantly improve performance?' or 'Did the new wellness initiative reduce stress levels differently for men and women?'.
Step 1Set up the Repeated Measures analysis in the General Linear Model (GLM) menu.
Entry: A dataset with a metric measured at multiple time points for the same subjects is available.
Exit: Repeated Measures analysis is initiated.
In: Panel dataset
Step 2Define the within-subjects and between-subjects factors.
Entry: Repeated Measures analysis is initiated.
Exit: The model structure is fully defined.
In: Time-point variables, Grouping variable (optional)
Step 3Select plots and estimated marginal means for interpretation.
Entry: Model structure is defined.
Exit: Descriptive outputs are configured.
Step 4Run the analysis and interpret the key significance tests.
Entry: Analysis is fully configured.
Exit: The significance of the intervention's impact is determined.
- Is the main effect of time significant?
- Is the interaction effect between time and group significant?
In: Analysis output · Out: F-statistics and p-values for within-subjects effects
Step 5Examine the plots and means to understand the nature of the change.
Entry: A significant effect or interaction was found.
Exit: The specific pattern of change is articulated.
In: Profile plot, Estimated Marginal Means table · Out: Narrative description of the intervention's impact
Applying Predictive Models for Scenario Planning and Decision Making
To leverage a validated predictive model to forecast outcomes under different conditions, quantify the potential impact of initiatives, and provide evidence-based input for people-related decisions.
When to use: After identifying the key drivers of an outcome, to answer 'So what?' questions like 'What is the likely ROI of our proposed training program?' or 'Which of these job candidates is predicted to be a top performer?'.
Step 1Select a finalized, significant regression model.
Entry: One or more validated regression models are available.
Exit: A specific model is chosen for application.
In: Regression model output · Out: Selected model equation
Step 2To conduct scenario modeling, use the regression equation.
Entry: A linear regression model is selected.
Exit: The model's equation is documented.
In: Coefficients table · Out: Regression equation
Step 3Model 'what-if' scenarios by changing input values.
Entry: The regression equation is available.
Exit: The quantitative impact of a hypothetical change is estimated.
In: Regression equation, Hypothetical values for predictors · Out: Predicted outcome for the scenario, Estimated impact of the change
Step 4To predict outcomes for new cases, use the 'Save' function.
Entry: A validated model and data for new cases are available.
Exit: The regression analysis is configured to save predictions.
In: Original dataset with new cases appended
Step 5Save and review the predicted values.
Entry: The regression is ready to be run.
Exit: Predicted outcomes for all cases are generated and saved.
Out: A new variable in the dataset with predicted values
Step 6Use the predicted values as an input for decision-making.
Entry: Predicted values have been generated.
Exit: An evidence-based recommendation is formulated.
In: Predicted values · Out: Data-informed decision or recommendation
A candidate measure
Predictive HR Analytics — derived measurement candidates
HR Intervention / Programme
participation flag (0/1); programme rollout date; dose/hours of programme
self-report suitability: low
Selection and Onboarding Practices
personality percentiles; competency ratings 1-5; aptitude scores; induction/buddy flags
self-report suitability: low
Job and Team Characteristics
function/department code; team size; percent male; location; job demand and control scale scores
self-report suitability: medium
Demographic and Diversity Composition
gender; UG status; age category; tenure; education; team percent UG/female
self-report suitability: high
Employee Engagement
engagement index (% favourable); multi-item engagement composite; team aggregate score
self-report suitability: high
Perceived Organizational Support and Justice
POS composite; distributive/procedural justice composites; supervisor support composite; integrity composite
self-report suitability: high
Job Strain / Stress
stress rating 1-5; strain composite; sickness absence days (downstream)
self-report suitability: high
Job Satisfaction
job satisfaction composite; facet satisfaction items
self-report suitability: high
Employee / Team Performance
appraisal rating 1-5; items scanned per minute; sales revenue; team performance composite
self-report suitability: low
Employee Turnover
leaver vs stayer flag; length of service; team separation rate %
self-report suitability: none
Customer Loyalty and Reinvestment
satisfaction rating 1-5; loyalty/reinvestment intention scale; actual investment figures
self-report suitability: high
The story
The reader An HR professional, MI/people-analytics practitioner or HR/management student who wants to master quantitative analysis and become a credible, evidence-based, persuasive contributor to people strategy.
External problem
HR functions produce endless descriptive reports but lack the capability to analyse data, identify causal drivers, and predict key people outcomes like turnover and performance.
Internal problem
The reader feels intimidated by statistics, fears being asked 'So what?' in the boardroom, and worries they lack the technical credibility to influence decisions.
Philosophical problem
It is simply wrong for HR to make important people decisions on gut instinct and copycat practices when rigorous, evidence-based methods are available.
The plan
- Understand what predictive HR analytics is and why it matters.
- Get your data into a workable form and learn the software (SPSS/R).
- Learn which statistical test to use for which data type.
- Work through applied case studies to run, interpret and interrogate analyses.
- Apply predictive models to scenario modelling, business cases and decisions.
- Reflect on ethics, limitations and the responsible use of people data.
Success
- The reader becomes a 'Master of the HR Metric', able to run and interpret sophisticated predictive models.
- HR becomes more credible and persuasive, presenting hard evidence and robust business cases.
- The organization makes sound, evidence-based people decisions that improve performance and reduce cost.
- The analyst confidently answers the 'So what?' question with actionable predictions.
At stake
- HR remains stuck producing meaningless descriptive reports that get ignored.
- Decisions continue to be made on gut instinct, bias and misdiagnosed problems.
- Costly interventions are launched without evidence and their impact is never tracked.
- The HR function loses credibility and fails to exploit the opportunities of analytics.
Chapter by chapter
ch01Understanding HR analytics
This chapter explores the emerging field of predictive HR analytics, arguing for its critical role in strategic decision-making within organizations and illustrating the methods required to effectively leverage these insights.
- Mastering predictive HR analytics is no longer optional; it is essential for organizations aiming to thrive in a data-driven business environment.
- The transition to a predictive analytics approach requires a clear business case to justify investments in technology and training.
- HR professionals must evolve from administrators to strategic partners through the active utilization of data insights.
- Breaking down data silos and fostering collaboration across departments is key to enhancing the effectiveness of HR analytics.
ch02HR information systems and data
This chapter delves into the critical role of HR information systems in managing employee data, analyzing it effectively, and leveraging big data for better decision-making within human resources.
ch03Analysis strategies
This chapter explores the vital transition from descriptive reports to predictive analytics in human resources, detailing key analysis strategies, metrics, and types of statistical tests necessary for effective data interpretation.
- Transitioning from descriptive to predictive analytics is critical to modernize HR functions.
- Ensuring data integrity is not just an option but a prerequisite for successful HR analytics.
- Understanding the differences between categorical and continuous data is fundamental for effective analysis.
- Implementing appropriate statistical tests enhances the reliability of HR metrics and their interpretations.
ch04p02Case study 1: Diversity analytics (part 2/2)
This chapter delves into the complexities of analyzing diversity data using various statistical methods, emphasizing the necessity of reliable data for optimizing organizational performance and addressing disparities.
ch05Case study 1: Diversity analytics
This chapter explores the significance of diversity and inclusion (D&I) analytics in organizations, demonstrating how statistical methods can identify and address disparities in workplace representation and engagement.
ch06Case study 2: Employee attitude surveys – engagement and workforce perceptions
This chapter investigates the critical role of employee engagement surveys, outlining how to measure engagement effectively, the importance of robust data analysis, and interpreting survey results to foster organizational success.
- Employee engagement is not a one-size-fits-all concept; organizations must define it clearly to measure it effectively.
- Validating survey measures through reliability analysis is key to ensuring accurate interpretations of employee engagement levels.
- Employing both quantitative and qualitative analyses can yield a rich understanding of employee sentiments and drive better engagement outcomes.
- Immediate communication and transparent action in response to survey results are crucial for maintaining employee trust and engagement credibility.
ch07Case study 3: Predicting employee turnover
This chapter delves into the complexities of predicting employee turnover, focusing specifically on voluntary turnover, its causes, and the methodologies employed to understand and mitigate its impact on organizations.
- Understanding employee turnover is critical for minimizing costs, with estimates suggesting turnover can reach up to 200% of an employee's salary.
- Data-backed turnover analysis can uncover misdiagnosed causes of employee departures, leading to more directed interventions.
- Predictive modeling offers insights into not just who is leaving but why they are leaving—enabling organizations to adapt.
- Gender, performance ratings, and tenure are significant predictors of turnover, highlighting areas for targeted management efforts.
ch08Case study 4: Predicting employee performance
In an era of economic uncertainty, organizations need to harness HR analytics to predict and influence employee performance, transitioning from traditional methods to more data-driven approaches.
- Transitioning from a standalone performance appraisal system to a data-rich, multi-faceted performance evaluation strategy is essential for effective HR management.
- Predicting performance based on comprehensive datasets allows organizations to invest strategically in the development of high-potential employees.
- Multiple linear regression emerges as a key tool for understanding the factors influencing performance, providing actionable insights for managerial decision-making.
- Ethical considerations in performance data analysis must be prioritized to ensure fairness and accountability in evaluation practices.
ch09Case study 5: Recruitment and selection analytics
This chapter explores the use of analytics in recruitment and selection processes to prevent bias and improve the accuracy of hiring decisions, focusing on predictive modeling and performance metrics.
ch10Case study 6: Monitoring the impact of interventions
This chapter delineates the critical role of HR analytics practitioners in assessing the effectiveness of HR interventions, emphasizing the need for valid metrics and careful analysis to prove both efficacy and return on investment.
ch11Business applications
This chapter explores how predictive HR analytics can translate into actionable business applications, effectively answering the critical question: "So what?" through concrete examples and practical applications.
ch12Business Applications
This chapter explores how predictive analytics can inform business decision-making, particularly in enhancing customer reinvestment and evaluating employee training programs.
ch14Reflection on HR analytics
This chapter critically examines the ethical, methodological, and practical challenges inherent in HR analytics, emphasizing the need for a balanced, cautious approach to harnessing predictive insights while safeguarding employee welfare.
- The effective use of HR analytics requires a disciplined, scientific approach that values data quality and research integrity.
- Analysts must recognize their dual role as organizational agents and impartial researchers to mitigate bias in outcomes.
- There is a significant risk of Metric-Oriented Behavior (MOB) distorting employee engagement, necessitating a more balanced perspective on performance metrics.
- Ethical considerations are paramount; organizations must safeguard employee privacy and dignity through transparent data practices.
Questions this book answers
- What distinguishes predictive HR analytics from the descriptive reporting most HR functions currently produce?
- Which statistical test should be used on which type of HR data, and how do you actually run it?
- What factors drive key HR outcomes such as turnover, performance, engagement and diversity?
- How can predictive models be applied to make tangible business predictions and build investment business cases?
- What ethical, methodological and practical limitations must analysts respect when modelling people data?
Glossary
- HR Intervention / Programme
- A deliberate management or HR action introduced to change employee attitudes, perceptions, behaviours or performance outcomes.
- Selection and Onboarding Practices
- The recruitment, selection and onboarding methods and the data they generate that determine new-hire quality and fit.
- Job and Team Characteristics
- Structural and contextual features of jobs and teams that condition employee experiences and outcomes.
- Demographic and Diversity Composition
- The demographic profile of individuals and the diversity composition of teams across protected and relevant characteristics.
- Employee Engagement
- A psychological-behavioural state of effort, focus, absorption, pride and belonging toward work and organization.
- Perceived Organizational Support and Justice
- Employee perceptions of organizational care/value (POS), fairness (distributive and procedural justice), supervisor support and organizational integrity.
- Job Strain / Stress
- A psychological response to demanding work conditions including stress, worry and lost sleep.
- Job Satisfaction
- A positive emotional state resulting from appraisal of one's job, with emotional, cognitive and behavioural components.
Related in the library
- People Analytics & Text Mining with Rshared: Statistics · Strategy · Systems
- 12_ The Elements of Great Managingshared: Statistics · Strategy
- First, Break All the Rules_ What the World_s Greatest Managers Do Differentlyshared: Statistics · Strategy
- One hundred years of attrition research (2017)shared: Statistics · Strategy
- Goal Setting & Team Management with OKR - Objectives and Key Results_ Skills for Effective Office Leadership, Smart Business Focus, & Growth. How to Manage Projects, People & Employees. 2nd Editionshared: Strategy
- People Analytics For Dummiesshared: Statistics · Strategy · Systems
Tools these methods power
Related in the literature
The measurement literature behind this signal — sourced, so you can defend it.
“Prescriptive analytics prescribe what action to take to remove a future problem or take advantage of a promising trend. It combines the insight from all previous analyses to ascertain the course of action to take. Whilst predictive analytics forecasts for what might happen,…”
— Predictive HR Analyticsmatch 70%
“In the table below, PETA computes risk scores for each employee and sums them into high risk groups, such as age groups, potential groups, salary groups, gender groups, tenure groups, performance groups. (1) Flight Risk Scoring [image "Flight risk scoring table"…”
— Predictive HR Analyticsmatch 69%
“Copy and paste the following codes in your RStudio left console pane, then click enter: x <- data.frame(gre=800,gpa=3.7,rank=as.factor(1)) p<- predict(logit,x) p [image file=Image00078.jpg] We see that there is 79% probability that this student will get the admit. References:…”
— People Analytics Text Mining with Rmatch 68%
Resources: Predictive HR Analytics · People Analytics Text Mining with R