MGMT 442 - HRIS and People analytics
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Attend anywhere
Participation grade
Reading / video before almost every class
Weekly announcements posted every week
Weekly open note quiz
Office hours 12- 1 pm tuesday + thursday
Have assign readings
Extra credit opportunity participating in university research
Late assignment policy 24 grace period with data exercises - email night of due each day
late after is ten percent reduction
Four things due by sunday
Data exercises take 30 min to an hour usually
HRIS
○ A system used to acquire store analyze and or distribute pertinent information
about an organizations human resources
Goals of hris
○ Provide accurate and timely data
Hris importance
○ Comprehensive info into integrated databases
○ Improve hr operations + mgmt operations
○ Facilitate user decision making
○ Shift focus from transaction to transformative action ( atomization plays a big part
)
Hr analytics
○ Process of collecting analyzing and reporting people related dat for the purpose
of improved decision making and achieving strategic direction
Hr analytics project life cycle
○ Question formulation - data acquisition - data management - dat analysis - data
interpretation and storytelling - deployment and implementation
Evolution of hris and hr analytics
Limits of tech
○ Acquiring and using low quality data ( bias, stale, inaccurate) data will lead to
problems
Need for ethical decision making
Competencies needed for hr analytics
○ Hr expertise / data literacy
1/6/2022
●
●
Tutorials + help in canva
Tip watch material before taking checki-in quiz
●
Four assignments due by sunday at 11:59 pm
● Notes
● Question formulation : process of posing strategy inspired research questions
● Good questions = better data
● Hypothesis - statement about expected nature of a phenomenon of interest
● Install R - watch canvas chapter 9
This weeks links
1/11/2022
●
●
●
●
●
●
●
●
●
●
●
●
Data exercise 2 and week 2 knowledge check in due by sunday
Read before class from now on and take notes
Data acquisition - collecting, retrieving, gathering and sourcing info
○ Done through
■ Employee surveys
■ Rating forms
■ Surveillance monitoring
■ Database queries
■ Scraping
Employee surveyed - good for observing perceptions, attitudes, job satisfaction,
○ Disadvantages - more subjective in nature than some other tool, influenced by
social desirability
Rating forms - similar to employee surveys, tend to be more focused on measuring
performance
○ Disadvantages - achieving high reliability can be challenging across raters
Surveillance and monitoring
○ Nonintrusive and operate behind the scenes
○ Tech improving rapidly with capabilities to measure geolocation, tone of voice,
interactions, heart rate, sleep quantity/quality etc.
■ Disadvantages
● Invasive, privacy rights,
Database queries - way to gather already present data and manage and leverage it
○ Disadvantages - quality of data, lack there of data, important characteristics
unless clearly defined may be hard to find
Scraping - new tools make it easier than ever, new insights into previously
difficult-to-reach data ( could do it with twitter data - r has packages to do it)
○ Accuracy, invasion of privacy - erosion of privacy
Guiding principle with data acquisition acquire data with a purpose
HRIS Analysis
Systems development life cycle
○ Planning - analysis - design - implementation - maintenance
Needs analysis - gathering, prioritizing, and documenting and orgs requirements
●
●
●
●
●
●
●
●
●
●
●
Five stages of need analysis
○ Planning - looking at current system and what we need
○ Observation ○ Exploration - build on analysis from observation stage, gather additional data
○ Evaluation - review and asses data collected
○ Reporting - prepare and report summarized findings.
1/13/2022
Design
Two step process logical ->physical design
Logical design - translate business requirements into improved processes, irrespective
of tech implementation
○ For example applicant racking process
Process models
○ Include things like data flow diagram
■ Which is a graphical representation of key business activities and
processes in the hr system
■ Context level diagram - describes full system, boundaries, and the entities
the system interacts with
■ Level 0 diagram - greater level of detail then context level
■ Additional level diagram
Data flow diagram symbols
○ Rectangle represents an external agent or entity
○ Arrow represents data flow
○ Rounded rectangles represent process or business activity
○ Open ended rectangle represents data store, data at rest
For charting
○ https://www.lucidchart.com/pages/
Physical design
○ Determine the most effective means of translating the business process into a
physical system that includes hardware and software
Physical design three choices
○ Do nothing
○ Make changes to only the hr business processes without new tech
○ Implement process change with new or upgraded tech
Benefits and drawbacks of building hr system internally versus outsourcing
○ You can make it specified to your needs
○ More flexible in terms of changing
○ The ability to integrate when making your own system
○ Huge upfront cost
○ Need access to skills and ability to do it
Working with vendors
○ Org develops request for proposal
○ Venderos review rfp and provide feedback
●
○ Org evaluates vendors reviews
○ Org selects vendor
Assessing system feasibility
○ Tech feasibility
○ Operational feasibility
○ Legal and political feasibility
○ Economic feasibility
1/18/2022
READ BEFORE CLASS!!!!
●
●
●
●
●
Change management
○ Systematic process of applying knowledge, tools, and resources to transform
organizations from one state of affairs to another
Addressing resistance to change
○ Perceived need
○ Upper management buy-in
○ Timely change conversion
○ Clearly communication of change
○ Provide proper training
Assessing Individual change
○ There are different theoretical models that can help assess preparedness for
change
■ Readiness for change model ( transtheoretical model )
● Precontemplation \/
● Contemplation \/
● Preparation \/
● Action \/
● Maintenance
■ Intention to enact behavior ( theory of planned behavior )
● attitude , norms, control \/
● Intention \/
● Use HRIS
Thursday we have a case study for the first part of class
Logical operators
○ < less than
○ > greater than
○ <= less than or equal to
○ >= greater than or equal to
○ == equal to
○ != not equal to
○ | or
○
○
& and
! not
1/27/2022
●
Rewatch lecture from tuesday + take notes
●
Examples of HRIS applications
Does our company meet industry averages in terms of equity and inclusions for such protected
classes as disability status, race, sexual orientation etc?
2/3/2022
●
Watch lecture from tuesday 2/1/2022
●
Measurement Scales Practice
○ Length ( ordinal )
■ O-1 years
■ 2-4 years
■ 5-10 years
■ 10+
Types of Descriptive Statistics
Categorical - variables can be described using counts/frequencies
Continuous
○ Indices of central tendency
■ Mean
■ Median
■ Mode
○ Indices of dispersion/variability
■ Range ( highest score minus lowest score )
■ Variance ( collective distance of value difference )
■ Standard deviation
Interquartile range
○ Lower quartile ¼ middle 50% upper quartile 75%
●
●
●
●
2/10/2022
●
●
Writing Survey Items
Things to Do
○ When writing employee survey items, do the following :
■ Use clear, simple language
■ Keep the survey as short as possible
■ Consider the expectations you might create with your items
●
●
●
●
●
●
●
●
●
●
Things to Avoid
○ Too cute or too clever
○ Unanswerable by employees
○ Too heavily reliant upon open-ended questions
○ Double-barreled
○ Loaded or leading
○ Non-specific or too broad
○ Negatively worded
○ Yea saying or nay saying
Things to consider
○ Put careful thought into writing items that are;
■ Required or force a response
Developing a measure for a construct
○ Measure are used to gather observations (data) related to a construct
○ But we never perfectly measure a construct ( or a person’s true level of
something )
○ In general, it is a best practice to use multiple items to measure a single
psychological construct - and to avoid single - item measures when possible
Developing a Measure for a construct
○ Steps to follow for developing construct measures
■ Carefully define the construct
■ Discuss the meaning of the definition and construct
■ Write many items that are in terms of content and redundancy
■ Pilot a large number of items than you plan to eventually use
■ Use a factor analysis to reduce the number of items
■ Determine the internal consistency reliability
Reliability
○ Extent to which a measure consistently or dependably measures something
Validity - accuracy
Conventionally, a measure demonstrates an acceptable level of reliability when a
reliability estimate is .7 or higher, where .00 indicates very low reliability and 1.00
indicates very high reliability
Strive for reliability to be as close as one
Different types of reliability
○ Test-retest reliability
○ Inter-rater reliability
○ Internal consistence reliability
Internal Consistency Reliability
○ A reliability estimate based on the intercorrelation ( homogeneity) among items
on a test. With alpha being a prime example.
■ For example, if a measure consists of four items/questions and is
administered to a group of applicants cronbachs alpha can be computed
for the scores on those four items and serve as an estimate of reliability
○ Cronbachs alpha
■
■
■
■
■
■
●
●
●
●
●
●
.95-1.0 is excellent
.9-.94 is great
.8-.89 good
.7-.79 acceptable
.6-.69 questionable
.0-.59 unacceptable
Survey administration
○ Survey administration goals
■ Pilot survey - small but representative group
■ Obtain a representative sample of respondents from the employee
population - ( how to reduce sampling error )
■ Maintain employee trust
■ Analyze data in timely manner to avoid “stale” data
■ Be ready to act on survey findings even if the findings look unfavorable to
the organization
■ Train managers on what to do with the findings based on their team result
■ Consider how frequently to survey - once a year vs “continuous listening”
Survey administration
○ Respondent faking and social desirability
○ Anonymity vs confidentiality vs identified
○ Continuum of creepiness
Faking and social desirability
○ When responding to survey items, respondents might distort or fake their
responses to conform to what they think is socially desirable or to what they think
decision makers would like to see
■ How to address it
● Tell them to answer honestly
● Tell them faking can be detected
● Use lie-detection items ( “ I always practice what I preach” )
Anonymous vs confidential vs identified
○ Anonymous
○ Confidential
○ Identified
A continuum of creepiness?
○ How creepy is the going to be for our employees?
Advanced Survey Techniques
○ Surveys aren't the only way to gather information
■ Focus groups & interviews ( ie., exit interviews )
■ Look to the literature ( Research )
○ Employee sensing ( data mining, ie emails )
■ Obvious ethical concerns
■ Requires more advanced analyses like natural language processing
○
○
Use AI and machine learning
■ To automate analysis, reporting, and individualized recommendations
Emotive analytics
■ Measuring attitudes via touch, image, or speech
2/15/2022
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Experimental design
○ A study in which an intervention is deliberately introduced to observes its effects
Effect
○ Observed difference between what did happen when employees were exposed
to intervention and what would happen if those employees simultaneously had
not been exposed to the intervention
Our goal with experimental design is to approximate a counterfactual using experimental
design
True experiment design
○ Employees are randomly assigned to a treatment or control condition, with key
characteristics being random assignment and control group
Quasi-Experiment
○ similar to true experiment but lacks random assignment to conditions
Pre-experiment
○ Lacks random assignment and control group - but includes an intervention
Nonexperiment design (observational, correlational)
○ Lacks random assignment, control group, and deliberate intervention. Typically
just involves observing association between variables
Confidence in casual inference decreases from true experiment to nonexperiment design
Experimental design is a type of research design
Common training evaluation research designs
○ Post-test only without control group
○ Pre-test, post test without control group
○ Post test only with control group
○ Pre-test,post test with control group
Statistical and practical significance
○ Statistical significance
■ Addresses whether a hypothesized association or difference exists in
underlying population Practical significance
■ Addressees the size of an association or a difference
Null hypothesis significance testing
Inferential statistics
○ Are valuable
■ Because results are based on a single sample size
■ Date are not often based on entire population scores
■ There is a measurement error and sampling error
Inferences about population
●
●
●
●
●
○ Must infer whether the statistics accurately represents the population
Two types of hypothesis
○ Research Hypothesis
■ association between the two variables
○ Null hypothesis
■ No association between the two variables
Statistical significance: p-value
○ P-value used to reject or fail to reject the null hypothesis
■ Example - when testing null hypothesis that two means are equally and
we find a p-value of.03
■ Typically alpha level represents the cutoff point for statistical significance
● Conventionally alpha is .05 or 5% probability
● For our purpose we’ll use a two tailed test
○ False positive (
■ We reject the null hypothesis based on the p-value, but the null
hypothesis is actually true
○ False negative
■ We fail to reject the null hypothesis, but the null hypothesis is actually
false
Confidence intervals
○ Provides a range of values that likely contains an unknown population parameter;
represents the margin of error around a point estimate.
Practical significance - only focus on if significant
○ Effect size can be used as indicators of practical significance
■ Examples of effect size
● Cohen’s d
● Cohen’s f
● r
● R2
● Odds ratios
Watch rest of video for r-studio
2/17/2022
●
●
Criteria for causation
○ For x to be interpreted as a cause of y
■ Y must not occur until after x ( temporal precedence )
■ X and y are associated with one another ( cooccur )
■ Other explanations of the association between x and y can be eliminated
○ Rewatch r for todays lecture
For this class, when the p-value is less than .05, we reject the null hypothesis and thus
conclude that an effect or relationship is statistically significant.
2/22/2022
●
Review of correlation
●
●
●
●
●
●
●
●
●
●
●
●
Correlation
○ When wish to estimate a pearson product-moment correlation, both variables
should have an interval or ratio measurement scale
A correlation coefficient can range from -1 to 1
A correlation coefficient equal to zero equals no linear association between two variables
If the p-value associated with a correlation coefficient is less that .05 then the association
is significantly significant
Importance of validity in selection
High reliability is a necessary but not sufficient condition for high validity
Validity
○ Is the degree to which available evidence supports inferences made from scores
on selection measures
Validation study
○ Research process used to investigate how well a selection tool measures a
particular construct and whether scores on the selection tool are job related
■ Types
● Criterion related validity
○ Concurrent
○ Predictive
● Content validity
● Construct validity
○ Convergent
○ Divergent
Criterion-related validity
○ Degree to which scores are related to scores on a criterion
■ Criterion could include
● Manager ratings of job performance
● Customer satisfaction ratings
● Absenteeism
● Theft
● Accidents
In selection we can use correlation and regression to assess the criterion-related validity
of a selection
Concurrent validation study
○ Selection tool given to people on the job and correlate it with the same criterion at
the same time
Predictive validation study
○ New applicants given the selecting tool, but dont use to hire, and several months
later, correlate it with the criterion scores of those who were hired
●
2/24/2022
●
●
●
●
●
●
●
●
●
Simple linear regression models include one predictor variable and one outcome
variable
The regression coefficient associated with a predictor variable represents the linear
association between the predictor and outcome variables
The model intercept value represents the value on the outcome variable when the
predictor variable is equal to zero
The r2 value represents the model’s fit to the data or the proportion of variance
explained by the predictor variable in the outcome variable
Yhat = b0 +b1x1
○ B0 is the model intercept
○ Yhat is the predicted score on the outcome variable
○ B1 represents the regression coefficient
○ X1 represents the predictor variable
Predict-ish analytics
○ Predict what will happen in the future based off a snap shot from the pass without
verification
○ Short coming - tendency to overfit model, making it unlikely to work well with new
(future) data
Predict-ish analytics process
○ Past data
■ Data can b e acquired from a concurrent validation study
○ Build model
■ We can estimate a simple or multiple linear regression model
○ Results
■ The regression model coefficients can be used to construct a predictive
equation, for which applicant scores on the selection tool could be
plugged in
Predictive analytics
○ Predict-ish analytics with verification
Predictive analytics process
○ Phase one
■ Past data
■ Build model
■ Results
○ Phase 2
■ New data
■ Apply model
■ Predictions
3/1/2022
●
●
Can remove one task from team project
Multiple linear regression
○
●
●
●
●
●
●
●
Regression coefficient is the linear association between the predictor and
outcome variables when statistically control for the effects of other predictor
variables
○ R2 value represents the effect size or the collective variance explained by the
predictor variables
Multiple linear regression equation
○ Yhat = b0 + b1x1 + b2x2
○ B0 = y-intercept
○ Yhat = predicted outcome value
○ B1 b2 represent the regression coefficients predictor variables
○ Watch video finish this part
Incremental validity
○ Evidence of incremental validity can signify that a selection tool explains unique
variance in the criterion when accounting for the effects of other selection tools
Multi tool selection systems
○ Any instance when more that one selection tool is used to select candidates
Different multi tool selection systems
○ Multiple hurdle approach
○ Single hurdle
■ Compensatory
■ Non compensatory
Multiple hurdle - multiple stages where people have to make it through different stages
Compensatory approach
○ Adds weighted scores to different predictor variables with the application of
multiple linear regression ( with the regression coefficients )
Noncompensatory
○ Bench mark scores need to be met to move through the hurdles ( not able to
compensate for deficiencies from selection tool to selection tool )
●
3/3/2022
●
●
●
●
●
Model & algorithms
Model is a parsimonious representation of phenomenon
An algorithm can be described as a set of rules often laid out in some process
We must keep in mind that a model is inherently subjective as somewhere during the
decision humans made decisions regarding
○ What datas used
○ What features are included in the model
○ What algorithm is used
○ How to interpret the output of the model
Ai and selection tools
○ Increasingly, vendors like Hirevue are offering ai-based assessment products that
can be used for recruitment/selection
●
●
In 2022 the society for industrial and organizational psych published this set of
guidelines for evaluating ai-based assessment tools
Compensatory approach to selection decisions
○ Uses applicant selection scores from multiple tools - helps compensate for weak
areas potentially
3/08/2022
●
●
●
●
●
●
●
●
●
●
●
Data Privacy and Security
Ethics
○ Standards and practices that tell us how human beings ought to act in many
situations in which they find themselves
○ Frame work for ethical decisions
■ Id ethical issue
■ Gather facts
■ Consider and eval alternatives
■ Select decision alternative
Data privacy
○ Individuals ( perceived ) control over the collection, storage, access, and
reporting of their personal data
According to a study by eddy et all people perceived the use of hris as invasive when
○ Supervisors are able to access the information in the employee records
○ Employees do not have the ability to check the accuracy before the decisions
were made
Scaping data
○ Technique used to extract data from websites and other text documents ( what i
could do for crypto )
○ Can be rich info but is also a point of contention
Data Security
○ As of 2015 some sources claim that over half billion sensitive organizational
records have been breached since 2005
3/10/2022
Storytelling with data
Strategic storytelling
○ “ in my work at nasa i coach leaders on how to tell stories that accelerate positive
change.”
Understand the context to narrow audience
○ Who
○ What
○ How
Choose an appropriate visual display
Data visualization
○ Is a set of processes via which data are graphically displayed and interpreted
with a particular goal in mind to ultimately derive meaning
●
●
●
●
●
Minimize cognitive load
Focus attention where you want it
Attention focusing consideration
○ Size denotes relative importance
○ Use color sparingly
○ Use color consistently
○ Color evokes emotion
○ Zigzag of taking in info
Think like a designer
○ Four A’s of design
■ Affordances
● Aspects inherent to design that make it obvious how a product is
to be used
■ Accessibility
● Designs should be usable by people of diverse abilities
■ Aesthetics - make it pretty
● People perceive more aesthetic designs as easier to use than less
aesthetic designs
■ Acceptance - make sure people like it
● For a design to be effective, it must be accepted by intended
audience
Good storytelling
○ Find subject you care about
○ Don't ramble about subject
○ Keep it simple
○ Have guts to cut it
○ Keep your tone
○ Say what you mean to say
○ Pity the audience