HR Analytics: New approaches, higher returns on human capital investmentShanmukha S. Potti
As global economic and political conditions continue to concern business leaders, their attention turns to the various levers that can foster success in uncertain times by looking for competitive insights to the massive data they can now capture. But to date, HR departments have lagged behind the efforts of Marketing, IT, CRM and other functions. The purpose of this paper is to show how business function leaders can start mining data to measure and improve HR's contributions to business performance.
Andreas Kyprianou: People Analytics in startups - an impossible task?- Andrea...Edunomica
Andreas Kyprianou: People Analytics in startups - an impossible task?
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
Max Blumberg: How can #PeopleAnalytics prevent incidents like the Twitter fir...Edunomica
Max Blumberg: How can #PeopleAnalytics prevent incidents like the Twitter firings?
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
HR Analytics: New approaches, higher returns on human capital investmentShanmukha S. Potti
As global economic and political conditions continue to concern business leaders, their attention turns to the various levers that can foster success in uncertain times by looking for competitive insights to the massive data they can now capture. But to date, HR departments have lagged behind the efforts of Marketing, IT, CRM and other functions. The purpose of this paper is to show how business function leaders can start mining data to measure and improve HR's contributions to business performance.
Andreas Kyprianou: People Analytics in startups - an impossible task?- Andrea...Edunomica
Andreas Kyprianou: People Analytics in startups - an impossible task?
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
Max Blumberg: How can #PeopleAnalytics prevent incidents like the Twitter fir...Edunomica
Max Blumberg: How can #PeopleAnalytics prevent incidents like the Twitter firings?
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
Marcus Baker: People Analytics at Scale
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
Jeff Higgins: Using Talent Market Data to Create Workforce IntelligenceEdunomica
Jeff Higgins: Using Talent Market Data to Create Workforce Intelligence
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
Employee Attrition Analysis
A leading organization would like to know why its best and most experienced employees are leaving early. Based on the previous data, classification was done to predict the employees who could leave early.
Cole Napper: Orgnostic's people analytics and employee listening strategy to ...Edunomica
Cole Napper: Orgnostic's people analytics and employee listening strategy to make business impact during times of economic uncertainty
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
Statistics For Data Science | Statistics Using R Programming Language | Hypot...Edureka!
( ** Data Science Certification Using R: https://www.edureka.co/data-science ** )
This Edureka tutorial on "Statistics for Data Science" talks about the basic concepts of Statistics, which is primarily an applied branch of mathematics, that attempts to make sense of observations in the real world. Statistics is generally regarded as one of the most crucial aspects of data science.
Introduction to statistics
Basic Terminology
Categories in Statistics
Descriptive Statistics
Reasons for moving to R
Descriptive Statistics in R Studio
Inferential Statistics
Inferential Statistics using R Studio
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
Contingent Workforce Management Benchmark ReportCXC Global A/NZ
The Contingent Workforce Management Benchmark Report was originally presented by Jenni Nelson, Principal Consultant at HCMS for the ATC's Flexible Workforce Conference in Sydney, Australia.
The world is fueled by data, and HR professionals everywhere are wondering how to leverage tons of people data for better insights to enhance individual and organizational performance.
HR analytics entails the use of tools (say, big data, predictive analytics) by HR in their recruiting, compensation, performance measurement, and retention efforts.
Through this presentation, you will get an introduction to HR analytics and how you can make the most of it to drive sweeping strategic success. This presentation will address the following areas of the employer branding:
- Purposeful Analytics
- Basics of Data Analysis
- Understanding the Fundamentals of Analytics Capability
Building
- Establishing an Analytical Unit and the Right Culture
- Levels & Types of HR Metrics
- Linking Metrics to Analytics
- Workforce Analytics Model
For more info:
www.hackerearth.com/recruit
* How high is your annual employee turnover?
* How much of your employee turnover consists of regretted loss?
* Do you know which employees will be the most likely to leave your company within a year?
Find the answer from HR Analytics because Human Resource analytics (HR analytics) is about analyzing an organizations’ people problems.
HR / Talent Analytics orientation given as a guest lecture at Management Institute for Leadership and Excellence (MILE), Pune. This presentation covers aspects like:
1. Core concepts, terminologies & buzzwords
- Business Intelligence, Analytics
- Big Data, Cloud, SaaS
2. Analytics
- Types, Domains, Tools…
3. HR Analytics
- Why? What is measured?
- How? Predictive possibilities…
4. Case studies
5. HR Analytics org structure & delivery model
This presentation highlights the required steps for HR Departments to transition themselves into a formidable HR Analytics Team. It will show how to apply HR Analytics to a departmental case as well as the required skill sets for your HR Team to acquire in order to become savvy analytics professionals. #hranalytics #humanresources
Running head DATA ANALYSIS1DATA ANALYSIS 7Dat.docxhealdkathaleen
Running head: DATA ANALYSIS 1
DATA ANALYSIS 7
Data Analysis
Tammie Witcher
Columbia Southern University
Data Analysis: Descriptive Statistics and Assumption Testing
Details of how data is collected and analyzed is presented here. The research that led to the achievement of Sun Coast objectives was done using quantitative research methods since they offer detailed insights pertaining to the study. Research design is the specific type of study that one would conduct and is usually consistent with one’s philosophical worldview and the methodological approach the researcher chooses
Correlation: Descriptive Statistics and Assumption Testing
Frequency distribution table
Histogram.
Descriptive statistics table.
Measurement scale. Causal-comparative research methods which was sometimes combined with the descriptive statistics one (Creswell & Creswell, 2018). The former was used to find the relationship between dependent and independent variables after the occurrence of any action in Sun Coast.
Measure of central tendency. The measure of central tendency majored on the mode even though both mean and median were employed for the frequency table to justify various aspects tested in the research.
Evaluation. Sun Coast’s leadership and other business objectives could render descriptive statistics significant since the researchers could use the past figures to analyze the current ones and make a sound forecast of future organizational performance.
Simple Regression: Descriptive Statistics and Assumption Testing
Frequency distribution table.
Histogram.
Descriptive statistics table.
Measurement scale. Regression analysis procedure would be appropriate for RQ3 since the variable, DB levels of work would be predicted before placing employees on-site for future contracts. There is no independent sample among those provided by this RQ.
Measure of central tendency. The measure of central tendency majored on the mode even though both mean and median were employed for the frequency table to justify various aspects tested in the research.
Evaluation. DB levels of work would be predicted before placing employees on-site for future contracts. There is no independent sample among those provided by this RQ.
Multiple Regression: Descriptive Statistics and Assumption Testing
Frequency distribution table.
Histogram.
Descriptive statistics table.
Measurement scale. The measurement for this case applied the regression procedure to use to test different hypotheses since the interest is whether a relationship exists between an independent variable (IV) and dependent variable (DV). Correlation will indicate if there is a relationship between PM size (IV) and the employee health (DV) and the magnitude of that impact if at all there is one
Measure of central tendency. The measure of central tendency majored on the mode even though both mean and median were also used.
Evaluation. The outcomeinvolved dividing populations in Sun Coa ...
Marcus Baker: People Analytics at Scale
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
Jeff Higgins: Using Talent Market Data to Create Workforce IntelligenceEdunomica
Jeff Higgins: Using Talent Market Data to Create Workforce Intelligence
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
Employee Attrition Analysis
A leading organization would like to know why its best and most experienced employees are leaving early. Based on the previous data, classification was done to predict the employees who could leave early.
Cole Napper: Orgnostic's people analytics and employee listening strategy to ...Edunomica
Cole Napper: Orgnostic's people analytics and employee listening strategy to make business impact during times of economic uncertainty
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
Statistics For Data Science | Statistics Using R Programming Language | Hypot...Edureka!
( ** Data Science Certification Using R: https://www.edureka.co/data-science ** )
This Edureka tutorial on "Statistics for Data Science" talks about the basic concepts of Statistics, which is primarily an applied branch of mathematics, that attempts to make sense of observations in the real world. Statistics is generally regarded as one of the most crucial aspects of data science.
Introduction to statistics
Basic Terminology
Categories in Statistics
Descriptive Statistics
Reasons for moving to R
Descriptive Statistics in R Studio
Inferential Statistics
Inferential Statistics using R Studio
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
Contingent Workforce Management Benchmark ReportCXC Global A/NZ
The Contingent Workforce Management Benchmark Report was originally presented by Jenni Nelson, Principal Consultant at HCMS for the ATC's Flexible Workforce Conference in Sydney, Australia.
The world is fueled by data, and HR professionals everywhere are wondering how to leverage tons of people data for better insights to enhance individual and organizational performance.
HR analytics entails the use of tools (say, big data, predictive analytics) by HR in their recruiting, compensation, performance measurement, and retention efforts.
Through this presentation, you will get an introduction to HR analytics and how you can make the most of it to drive sweeping strategic success. This presentation will address the following areas of the employer branding:
- Purposeful Analytics
- Basics of Data Analysis
- Understanding the Fundamentals of Analytics Capability
Building
- Establishing an Analytical Unit and the Right Culture
- Levels & Types of HR Metrics
- Linking Metrics to Analytics
- Workforce Analytics Model
For more info:
www.hackerearth.com/recruit
* How high is your annual employee turnover?
* How much of your employee turnover consists of regretted loss?
* Do you know which employees will be the most likely to leave your company within a year?
Find the answer from HR Analytics because Human Resource analytics (HR analytics) is about analyzing an organizations’ people problems.
HR / Talent Analytics orientation given as a guest lecture at Management Institute for Leadership and Excellence (MILE), Pune. This presentation covers aspects like:
1. Core concepts, terminologies & buzzwords
- Business Intelligence, Analytics
- Big Data, Cloud, SaaS
2. Analytics
- Types, Domains, Tools…
3. HR Analytics
- Why? What is measured?
- How? Predictive possibilities…
4. Case studies
5. HR Analytics org structure & delivery model
This presentation highlights the required steps for HR Departments to transition themselves into a formidable HR Analytics Team. It will show how to apply HR Analytics to a departmental case as well as the required skill sets for your HR Team to acquire in order to become savvy analytics professionals. #hranalytics #humanresources
Running head DATA ANALYSIS1DATA ANALYSIS 7Dat.docxhealdkathaleen
Running head: DATA ANALYSIS 1
DATA ANALYSIS 7
Data Analysis
Tammie Witcher
Columbia Southern University
Data Analysis: Descriptive Statistics and Assumption Testing
Details of how data is collected and analyzed is presented here. The research that led to the achievement of Sun Coast objectives was done using quantitative research methods since they offer detailed insights pertaining to the study. Research design is the specific type of study that one would conduct and is usually consistent with one’s philosophical worldview and the methodological approach the researcher chooses
Correlation: Descriptive Statistics and Assumption Testing
Frequency distribution table
Histogram.
Descriptive statistics table.
Measurement scale. Causal-comparative research methods which was sometimes combined with the descriptive statistics one (Creswell & Creswell, 2018). The former was used to find the relationship between dependent and independent variables after the occurrence of any action in Sun Coast.
Measure of central tendency. The measure of central tendency majored on the mode even though both mean and median were employed for the frequency table to justify various aspects tested in the research.
Evaluation. Sun Coast’s leadership and other business objectives could render descriptive statistics significant since the researchers could use the past figures to analyze the current ones and make a sound forecast of future organizational performance.
Simple Regression: Descriptive Statistics and Assumption Testing
Frequency distribution table.
Histogram.
Descriptive statistics table.
Measurement scale. Regression analysis procedure would be appropriate for RQ3 since the variable, DB levels of work would be predicted before placing employees on-site for future contracts. There is no independent sample among those provided by this RQ.
Measure of central tendency. The measure of central tendency majored on the mode even though both mean and median were employed for the frequency table to justify various aspects tested in the research.
Evaluation. DB levels of work would be predicted before placing employees on-site for future contracts. There is no independent sample among those provided by this RQ.
Multiple Regression: Descriptive Statistics and Assumption Testing
Frequency distribution table.
Histogram.
Descriptive statistics table.
Measurement scale. The measurement for this case applied the regression procedure to use to test different hypotheses since the interest is whether a relationship exists between an independent variable (IV) and dependent variable (DV). Correlation will indicate if there is a relationship between PM size (IV) and the employee health (DV) and the magnitude of that impact if at all there is one
Measure of central tendency. The measure of central tendency majored on the mode even though both mean and median were also used.
Evaluation. The outcomeinvolved dividing populations in Sun Coa ...
5th International Disaster and Risk Conference IDRC 2014 Integrative Risk Management - The role of science, technology & practice 24-28 August 2014 in Davos, Switzerland
Date: September 6th, 2017
Speaker: Jesse Chandler, PhD, is a survey researcher at Mathematica Policy Research and an Adjunct Faculty Associate at the Institute for Social Research at the University of Michigan.
Overview: Crowdsourcing has had a dramatic impact on the speed and scale at which scientific research can be conducted. Clinical scientists have particularly benefited from readily available research study participants and streamlined recruiting and payment systems afforded by Amazon Mechanical Turk (MTurk), a popular labor market for crowdsourcing workers. MTurk has been used in this capacity for more than five years. The popularity and novelty of the platform have spurred numerous methodological investigations, making it the most studied nonprobability sample available to researchers. This article summarizes what is known about MTurk sample composition and data quality with an emphasis on findings relevant to clinical psychological research. It then addresses methodological issues with using MTurk--many of which are common to other nonprobability samples but unfamiliar to clinical science researchers--and suggests concrete steps to avoid these issues or minimize their impact.
Contemporary Approaches to Survival Data Analysis by Dr. Idokoko A. B.Abraham Idokoko
An advanced statistics masterclass delivered in the Department of Community Health and Primary Care, Lagos University Teaching Hospital & the College of Medicine, University of Lagos, Nigeria on Wednesday, 12th April 2017
The effectiveness of various analytical formulas for
estimating R2 Shrinkage in multiple regression analysis was
investigated. Two categories of formulas were identified estimators
of the squared population multiple correlation coefficient (
2
)
and those of the squared population cross-validity coefficient
(
2 c
). The authors compeered the effectiveness of the analytical
formulas for determining R2 shrinkage, with squared population
multiple correlation coefficient and number of predictors after
finding all combination among variables, maximum correlation
was selected to computed all two categories of formulas. The
results indicated that Among the 6 analytical formulas designed to
estimate the population
2
, the performance of the (Olkin & part
formula-1 for six variable then followed by Burket formula &
Lord formula-2 among the 9 analytical formulas were found to be
most stable and satisfactory.
Sequence-to-Sequence Modeling for Time SeriesArun Kejariwal
In this talk we overview Sequence-2-Sequence (S2S) and explore its early use cases. We walk the audience through how to leverage S2S modeling for several use cases, particularly with regard to real-time anomaly detection and forecasting.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Survival Analysis for Predicting Employee Turnover
1. Survival Analysis and the
Proportional Hazards Model for
Predicting Employee Turnover
Primary source:
Hom, P. W., & Griffeth, R. W. (1995). Employee turnover.
Cincinnati, OH: Southwestern College Publishing.
Tom Briggs
tbriggs@gmu.edu
November 2014
3. “Our new Constitution is now
established, and has an appearance
that promises permanency; but in
this world nothing can be said to be
certain, except death and taxes.”
--Benjamin Franklin (1789)
TBRIGGS@GMU.EDU [ 3 ] NOVEMBER 2014
4. “In this world nothing can be said to
be certain, except death, taxes, and
employee turnover.”
--George Mason Student (2014)
TBRIGGS@GMU.EDU [ 4 ] NOVEMBER 2014
7. FIRST PIONEERS
Peters,
L.
H.,
&
Sheridan,
J.
E.
(1988).
Turnover
research
methodology:
A
criCque
of
tradiConal
designs
and
a
suggested
survival
model
alternaCve.
Research
in
personnel
and
human
resources
management,
6,
231-‐262.
Morita,
J.
G.,
Lee,
T.
W.,
&
Mowday,
R.
T.
(1989).
Introducing
survival
analysis
to
organizaConal
researchers:
A
selected
applicaCon
to
turnover
research.
Journal
of
Applied
Psychology,
74(2),
280–292.
Singer,
J.
D.,
&
Wille/,
J.
B.
(1991).
Modeling
the
days
of
our
lives:
using
survival
analysis
when
designing
and
analyzing
longitudinal
studies
of
duraCon
and
the
Cming
of
events.
Psychological
Bulle/n,
110(2),
268.
TBRIGGS@GMU.EDU [ 7 ] NOVEMBER 2014
8. WHO IS THIS MAN?
TBRIGGS@GMU.EDU [ 8 ] NOVEMBER 2014
9. SIR DAVID COX
#9 on the George Mason Department of Statistics list of
“Great Statisticians” – just below Tukey and William Sealy Gosset.
Known for the Cox proportional hazards model, an application of
survival analysis.
And yes…he rocks this look pretty much all the time.
TBRIGGS@GMU.EDU [ 9 ] NOVEMBER 2014
10. BY ANY OTHER NAME
StaCsCcs
• Survival
analysis
• Reliability
theory
Engineering
• Reliability
analysis
• DuraCon
analysis
Economics
• DuraCon
modeling
Sociology
• Event
history
analysis
TBRIGGS@GMU.EDU [ 10 ] NOVEMBER 2014
12. WHAT SIZE IS THE HERD?
TBRIGGS@GMU.EDU [ 12 ] NOVEMBER 2014
13. WHAT SIZE IS THE HERD?
A. 39 SHEEP
TBRIGGS@GMU.EDU [ 13 ] NOVEMBER 2014
14. WHAT SIZE IS THE HERD?
B. 40 SHEEP
TBRIGGS@GMU.EDU [ 14 ] NOVEMBER 2014
15. WHAT SIZE IS THE HERD?
C. DON’T KNOW
TBRIGGS@GMU.EDU [ 15 ] NOVEMBER 2014
16. WHAT SIZE IS THE HERD?
A. 39 SHEEP
B. 40 SHEEP
C. DON’T KNOW
TBRIGGS@GMU.EDU [ 16 ] NOVEMBER 2014
17. WHAT SIZE IS THE HERD?
C. DON’T KNOW - CORRECT!
TBRIGGS@GMU.EDU [ 17 ] NOVEMBER 2014
18. VOCABULARY: CENSORING
CENSORING is a missing data problem
common to survival analysis
(and cross-sectional studies…)
In the herd example, our cross-sectional
“view” was censored in two
respects: what came before and what
is yet to come!
TBRIGGS@GMU.EDU [ 18 ] NOVEMBER 2014
19. HOM & GRIFFETH ON WHY
• Cross-sectional study start and end dates
are usually arbitrary
• Short measurement periods weaken
correlations – fewer employees leave –
smaller numbers of “quitters” shrink
turnover variance
• Cross-sectional approach distorts results by
arbitrarily dictating which participant is a
stayer and which is a leaver
• Cross-sectional approach neglects tenure –
10 days or 10 years treated the same
TBRIGGS@GMU.EDU [ 19 ] NOVEMBER 2014
20. NOT WHETHER, BUT WHEN
Death, taxes, and employee turnover:
All employees will ultimately turn over, so the
question is not whether, but when?
And a related question: what effects do
potential predictor variables have on
turnover probability?
TBRIGGS@GMU.EDU [ 20 ] NOVEMBER 2014
21. VISUAL: CENSORING
leZ
stayed
Right-censoring most common in turnover research;
an employee could quit the day after the study ends!
TBRIGGS@GMU.EDU [ 21 ] NOVEMBER 2014
23. SURVIVAL ANALYSIS RESULTS
• Generates conditional probabilities – the
“hazard rate” – that employees will quit
during a given time interval.
• Generates graphs of the survival function –
the cumulative probability of staying.
• Allows for subgroup comparison based on
predictor variables.
TBRIGGS@GMU.EDU [ 23 ] NOVEMBER 2014
25. SURVIVAL PREDICTORS
1.05
1.00
0.95
0.90
0.85
0.80
Survival Rates for New Staff Accountants as Functions of
RJPs and Job Tenure
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Cumulative Survival Rate
Tenure (in months)
Traditional Job Preview Realistic Job Preview
TBRIGGS@GMU.EDU [ 25 ] NOVEMBER 2014
26. PROPORTIONAL HAZARD
• Profile comparisons “ill-suited for estimating
the temporal effects of continuous predictors
and of several predictors simultaneously.”
• Uses regression-like models – the dependent
variable is the (log of) entire hazard function
• Assumes a predictor shifts hazard profile up
(RJP = 0) or down (RJP = 1) depending on
predictor scores and that each subject’s
hazard function is some constant multiple of
the baseline hazard function
TBRIGGS@GMU.EDU [ 26 ] NOVEMBER 2014
27. PROPORTIONAL HAZARD
BENEFITS
• Can examine multiple predictors (continuous
or categorical) and estimate unique
contribution of each while statistically
controlling other predictors
• Estimated βs interpreted as regression
weights, or transformed into probability
metrics by antilogging
• RJP example: RJP subjects have 0.61 times
the risk of quitting than control subjects (or
hazard decreased by 39 percent)
TBRIGGS@GMU.EDU [ 27 ] NOVEMBER 2014
28. HAZARDS OF
PROPORTIONAL HAZARD
• Assumes different predictors all have same
log-hazard shape – Singer and Willett (1991)
found many examples of violations
• Assumes different predictors are constant
over time (parallel hazard profiles)
Investigators should test assumptions of shape
and parallelism (see Singer and Willett, 1991)
TBRIGGS@GMU.EDU [ 28 ] NOVEMBER 2014
29. CONCLUSION
Survival analysis and the proportional
hazard model can offer a compelling
alternative to cross-sectional
methodology for investigating
dynamic relations between turnover
and antecedents.
TBRIGGS@GMU.EDU [ 29 ] NOVEMBER 2014