chap 1 (targeted analytics)

bad news

statistical skills needed to do technically sophisticated analysis tend not to be located in HR, and when they are located in HR, tend to be concentrated in HR analytics centers of excellence

good news

that the limited availability of advanced statistical skills does not always restrict HR professionals’ ability to do meaningful analytics

1. Capability-Opportunity-Motivation Model

2. Labor Markets Model

3. Organization Design Model

3 proven framework can be applied to make better-on-the spot decisions, even in situations where there is little time for extensive data collection

Capability-Opportunity-Motivation Model

for diagnosing work-related behavior and productivity, for job design

Labor Markets Model

which can be used to analyze the cost-benefit of job design, staffing and talent management decisions

organization design model

for diagnosing structural barriers to enterprise-wide collaboration and performance

human capital analytics

most powerful when they help tell and validate a story that illustrates
the driving forces behind individuals’ and groups’ behaviors and performance

Bourdreau and Ramstad

— point out that analytics need to be:

1. Embedded within a logic framework (LF) that is linked to the business, LF ensures that the analytcs are focused on the right issues and are set up to maximize the discovery of data and analysis results that are actionable.

2. Change process (CP) is needed so they are used in a way that ensures maximum impact. CP for using the results of the analytics ensures the data is turned into action.

   Challenge: choosing from the wide array of statistical and analytic techniques that are available

• mean

• median

• minimum and maximum; range

• percentiles

basic data analysis examples

basic data analysis

level of statistical expertise required:

• beginning course in basic statistics

• minimal on the job experience

• highschool/ undergraduate level education

• correlation

• statistically significant differences

• standard deviation

intermediate data analysis examples

intermediate data analysis

level of statistical expertise required:

• one to two courses in basic statistics

• 3-6 months of on-the-job experience applying the techniques

• highschool/ undergraduate education

• ANOVA/ ANCOVA

• Regression

• Factor analysis

basic multivariate models examples

basic multivariate models

level of statistical expertise required:

• course in advanced statistics

• 1-2 years on the job experience

• Undergraduate/ MBA education

• structural equations models

• hierarchical linear models

• bivariate/ multivariate choice models

• cross-level models, including adjustments for grouped and non-normal errors

advanced multivariate models examples

advanced multivariate models

level of statistical expertise required:

• degree or concentration in statistical methods

• substantial experience applying the techniques on-the-job experience (multiple years)

• graduate degree (Masters or Ph.D.)

• identify data for analytics

• prepare/ clean the data for analysis (transform, identify outliers, etc)

data preparation other analytic competencies

data preparation

level of statistical expertise required:

• one to two courses in basic statistics

• 3-6 months on the job experience

• highschool/ undergraduate education

(other analytics)

• identify causal paths

• six sigma analysis

root cause analysis other analytic competencies

root cause analysis

level of statistical expertise required:

• one to two courses in basic statistics

• 6-12 months on the job experience

• highschool/ undergraduate education

(other analytics)

• treatment vs control groups

• experimental design vs natural experiments

research design other analytic competencies

research design

level of statistical expertise required:

• course in advanced statistics

• 1-2 years on the job experience applying the techniques

• undergraduate/ MBA education

(other analytics)

1. survey design

2. qualitative data collection and analysis

level of statistical expertise required:

• course in advanced statistics

• 1-2 years on the job experience

• undergraduate/ MBA education

(other analytics)

• interview techniques

• interview coding

• content analysis

qualitative data collection and analysis other analytic competencies

turn-over reports

are commonly used as a type of “temperature gauge for what is happening with employees

Average to Below Average People

- both voluntary turnover and productivity will be low

Job Demands are Raised

- both turnover and productivity should increase

Time Productivity is short

- OJT is needed for new employee to become fully productive