The document discusses human resource information sources and analysis software options for human resource analytics. It covers topics like what an HRIS is and its functions, different types of data for analysis including categorical and continuous variables, ensuring data integrity, and strategies for analysis from descriptive reporting to predictive analytics. Statistical software packages like SPSS, Minitab, Stata, SAS and R are compared in terms of their interfaces, capabilities and suitability for different types of analyses.

1. Human Resource
Analytics (HRA)
Lec-2
Human Resource Information
Sources

2. Human Resources Information Sources
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• HRIS stands for Human Resources Information System.
• The HRIS is a system that is used to collect and store data on an
organization’s employees.
• In most cases, an HRIS encompasses the basic functionalities needed
for end-to-end Human Resources Management (HRM).
• It is a system for recruitment, performance management, learning &
development, and more.
• An HRIS is also known as HRIS software. The HRIS is, in essence, an HR
software package.
• The HRIS can either run on the company’s own technical
infrastructure, or, more common nowadays, be cloud-based.

3. Information sources
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4. Information sources
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5. Information sources
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6. Information sources
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7. Information sources
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8. Analysis software options
SPSS (Statistical Package for the Social Sciences)
• A very user-friendly statistical package with a graphical user interface
and a point-and-click menu for running procedures.
• Leader of all the statistical software packages as it runs many
complex procedures.
• SPSS is the best package for being able to transfer output to other
formats for reports.
• SPSS is good for extracting subsets of smaller data for analysis and
the process to do this is relatively simple to learn.
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9. Analysis software options
Minitab
• Minitab also has a user-friendly menu-based user interface for
running procedures and has been noted as the simplest package to
learn.
• Like SPSS, Minitab enables users to do most analysis procedures
without having to understand the coding syntax.
• It covers what is required by 95 per cent of users.
• In terms of manipulating data, once the data is in Minitab, it can
sometimes be difficult to change (Kane, 2012).
• It can also be difficult to transfer output to other formats for inclusion
in reports.
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10. Analysis software options
Stata (Statistical software for data science)
• Although Stata does have some menus, it is primarily command-line
driven and hence takes a bit longer to master.
• Stata does offer a more comprehensive range of statistical procedures
and is often used by economists.
• It is quite good at being able to manipulate data once it is in the
system; however, the user will need to learn the command-line
interface systems in the first instance.
• On the negative side, it can be difficult to transfer data and output to
other formats for reports (Kane, 2012).
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11. Analysis software options
SAS (Statistical Analysis System)
• SAS is primarily a command-line-driven package with relatively few menu-
driven procedures.
• Time taking to master.
• Very powerful package and offers a more comprehensive range of
statistical procedures.
• It is also the leader in being able to handle large amounts of data, which
could be pertinent depending on the type of organization, and particularly
relevant considering the section on ‘big data’ later in this chapter.
• It is also possible to extract small subsets of large data quickly.
• It can also be difficult in SAS to transfer output to other formats for reports
(Kane, 2012).
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12. Analysis software options
R
• "R" name is derived from the first letter of the names of its two
developers, Ross Ihaka and Robert Gentleman,
• R is free.
• R is command-line syntax driven, requires a fair amount of learning
time.
• it is far more difficult to learn than SPSS or Minitab.
• R is one of the leading packages when it comes to the range of
statistical procedures offered.
• Finally, it requires command-line edits in order to manipulate the data
once it has been imported.
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Using SPSS

14. Analysis strategies
1. From descriptive reports to predictive analytics
• Descriptive HR reports usually refer to the illustration of a set of data, a
‘snapshot’ of what is occurring at that time, presenting the current state of play.
• It’s taking historical data and summarizing it into something that is
understandable.
• For example, a headcount report of all employees within the organization is a
form of descriptive analytics.
• Even taking it a step further to break it down by demographics would still be in
the same category.
• More sophisticated metrics like turnover rates or time-to-fill would be descriptive
as well. They rely on the past and aim to explain why something already
happened.
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15. Analysis strategies
• Predictive analytics
• Where descriptive analytics look backward, predictive analytics work to
look ahead.
• Statistical models and forecasts are used to answer the question of what
could happen.
• Models are built on patterns that were found within the descriptive
analytics with the goal is to proactively find the needs of the organization.
• Predictive analytics can help determine if someone will be a good cultural
fit for the organization before they’re hired.
• It could even provide estimations on how long the person will stay with the
company.
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16. Analysis strategies
2. Statistical significance
Statistical significance is a term used to describe how certain we are
that a difference or relationship between two variables exists and isn’t
due to chance.
• Research / alternate hypothesis: attendance at a customer-service
training course by sales employees will increase customer satisfaction
survey scores.
• Null hypothesis: attendance at a customer-service training course by
sales employees will have no impact on customer satisfaction survey
scores.
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17. Analysis strategies
Type I or Type II errors.
• Type I error
• A Type I error occurs when the null hypothesis is rejected when it
should have been retained (i.e., a false positive).
• This means that the results are identified as significant when they
actually occurred by chance.
• Because they occurred by chance, it is unlikely to happen in the real
world and so should have been identified as non-significant.
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18. Analysis strategies
• Type II Error
• A Type II error occurs when the null hypothesis is retained when it
should have been rejected (i.e., a false negative).
• This means that the results are identified as non-significant when
they actually did not occur by chance.
• Not occurring by chance suggests that it is likely to happen in the real
world, and so should have been identified as significant.
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19. Analysis strategies
• This is referred to as the alpha value, and represents the probability you
are going to make a Type I error (i.e., reject the null hypothesis when it is
true).
• Alpha values are typically set at .05 (5%), meaning that we are 95%
confident that we won’t make a Type I error.
• Alpha is not to be confused with the p value, which is the specifically
calculated probability of the obtained result occurring by chance.
• For statistical significance, alpha is used as the threshold value and the p
value is compared to it.
• If the p value is above the alpha value (p> .05), our result is not statistically
significant. If it is below our alpha (p< .05), then it is statistically significant.
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20. Analysis strategies
3. Data Integrity
• Data integrity is a process or
a set of practices that
ensures the security,
accuracy, and overall quality
of data.
1. Cybersecurity,
2. Physical safety,
3. Database Management.
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21. Types of data
• The data collected can be grouped into certain types based on what it
is measuring and how it behaves.
• Variables are things that can vary, or change.
• Each data field, whether it is salary, name, gender, or anything else in
the data set, is termed a variable.
• Variables can be classed into two Major Types.
1. Categorical
2. Continuous
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22. Types of data
1. Categorical variable types (Qualitative Variables)
• A categorical variable is one that is made up of categories or
Categorical variables represent groupings of some kind.
• They are sometimes recorded as numbers, but the numbers
represent categories rather than actual amounts of things.
• There are three types of categorical variables:
1. Binary Variables
2. Nominal Variables
3. Ordinal Variables.
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24. Types of data
2. Continuous Variables (Quantitative Variables)
• When you collect quantitative data, the numbers you record
represent real amounts that can be added, subtracted, divided, etc.
• There are two types of quantitative Varaibles.
• Discrete Variables : A discrete variable is a variable whose value is
obtained by counting.
• Continuous Variables : A continuous variable is a variable whose
value is obtained by measuring.
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