hr diagrams assignment data

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The Hertzsprung-Russell Diagram

Graphing or plotting data is an essential tool used by scientists. In attempting to make sense of data and see if two quantities are related we can plot them and seek trends. If we have a look at the two examples below the first shows two quantities, X and Y that an object may have. When they are plotted we can see that there is no discernable relationship between X and Y. In fact in this example there is no relationship, the data is purely random.

If we plot data for height versus mass for a small group of people, however, we see a very different pattern as shown below.

As we might expect, there does appear to be a correlation between the height of a person and their mass. In general, the taller a person is, the greater their mass but as with many other characteristics of humans there is a large variation. Some people are tall and skinny, others shorter but higher mass. There are, however, real physical limitations on both the height and mass of people. We do not expect to find a 3.5 m person with a mass of 10 kg or a 1.0 m person with a mass of 300 kg!

One of the most useful and powerful plots in astrophysics is the Hertzsprung-Russell diagram (hereafter called the H-R diagram). It originated in 1911 when the Danish astronomer, Ejnar Hertzsprung, plotted the absolute magnitude of stars against their colour (hence effective temperature). Independently in 1913 the American astronomer Henry Norris Russell used spectral class against absolute magnitude. Their resultant plots showed that the relationship between temperature and luminosity of a star was not random but instead appeared to fall into distinct groups. These are seen in the H-R diagram below. It has a few specific stars included in the plot but otherwise just shows the main regions.

The majority of stars, including our Sun, are found along a region called the Main Sequence . Main Sequence stars vary widely in effective temperature but the hotter they are, the more luminous they are, hence the main sequence tends to follow a band going from the bottom right of the diagram to the top left. These stars are fusing hydrogen to helium in their cores. Stars spend the bulk of their existence as main sequence stars. Other major groups of stars found on the H-R diagram are the giants and supergiants; luminous stars that have evolved off the main sequence, and the white dwarfs. Whilst each of these types is discussed in detail in later pages we can use their positions on the H-R diagram to infer some of their properties.

Using the H-R Diagram to Infer Stellar Properties

Let us look at the cool M-class stars as an example. If we look at the H-R diagram below we can see that in fact there are three main groups of these stars.

At the bottom-right of the diagram we can see two named stars, Proxima Centauri and Barnard's Star. These are both cool (approximately 2,500 K) and dim (absolute magnitudes of about 13, only about 1/10,000 the luminosity of our Sun). Following the broad band straight up we come across Mira, also cool but much more luminous. Travelling further up we come across Antares and Betelgeuse. Again these stars are cool but they are extremely luminous, almost 10,000× as luminous as the Sun. Why do these three groups differ so much in luminosity?

The answer to this question depends upon the Stefan-Boltzmann relationship. You may recall from equation 4.4 that the energy emitted per unit surface area per second is simply a function of the fourth power of temperature, that is:

If two stars have the same effective temperature they each have the same power output per square metre of surface area. As the H-R diagram however shows that one is much more luminous than the other it must have a greater total power output therefore must have a much greater surface area - the more luminous star is bigger. We can see this from the full expression for luminosity in equation 4.6:

The difference between the three groups of M-class stars is thus a difference in size. This is acknowledged by the names given to each of the groups. The most luminous ones are called supergiants (luminosity classes I and II), the luminous ones are called giants (luminosity class III) and the dim ones are part of the main sequence (luminosity class V) though historically the term dwarf stars was applied to this group.

If we look at the vertical band on the H-R diagram for hotter stars around type A spectral class we see a similar pattern:

In this case the supergiants Rigel and Deneb have the same effective temperature as Sirius but have extremely high luminosities. They have large radii than Sirius hence greater surface areas and higher luminosities. Sirius is a main sequence star but because it is hotter than the red main sequence Barnard's Star it is much more luminous than it. If you follow the pink band for hot stars down to the bottom of the H-R diagram you will notice that it intersects another group of stars that includes Procyon B. These are the white dwarfs. They are very hot (about 10,000 K or hotter) therefore emit a lot of energy per second for each square metre of their surface. The fact that they are so dim however, means that they must be extremely small and have a very low surface area. The terminology of white dwarf must not be confused with the old-fashioned term of dwarf stars that was applied to main sequence stars. White dwarfs are very different objects to main sequence stars as we shall see in a later page. Technically they have a luminosity class of wd . Simple calculations provide a size for white dwarfs roughly that of our Earth, less than 1/100 that of the Sun.

If we compare the dimmest stars on the H-R diagram we can also make some inferences. The following diagram shows the lower region of the H-R diagram.

Procyon B and Barnard's Star share the same low luminosity with an absolute magnitude of about +13. Procyon B however is much hotter than Barnard's Star thus emits much more energy per second per unit surface area. Given that they have the same total power output Procyon B must therefore have less surface area than Barnard's Star, that is its radius is smaller.

Axes on the H-R Diagram

This points to an interesting and sometimes confusing feature of the H-R diagram - the scales on the axes. Unlike the height/mass plot earlier in this section, the effective temperature does not increase as it goes from left to right, it actually decreases, that is the highest temperature is on the left-hand side. If colour index (B-V) rather than effective temperature is used then it goes from negative (blue) on the left to positive (red) on the right. A third alternative along the horizontal axis is to use spectral class. Of course, all three quantities are essentially showing the same thing. The diagram below shows the possible axes for an H-R diagram.

The vertical axis displays the luminosity of the stars. This is either as a ratio compared with that of the Sun or as absolute magnitude, M . One point to be careful of when using absolute magnitude is to remember that the lower or more negative the absolute magnitude, the more luminous the star. The brightest stars therefore appear at the top of the H-R diagram with the vertical axis having the most negative value of M at the top.

In some circumstances, such as when plotting stars in a specific open or globular cluster, apparent magnitude, m , or V , rather than absolute magnitude may be used. This is valid as all the stars in the cluster are effectively at the same distance away from us hence any differences in apparent magnitude are due to actual difference in luminosity or M . Diagrams where V is plotted against colour index, B-V, are also known as colour-magnitude diagrams .

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The traditional Hertzsprung-Russell (H-R) diagram activity is a plot of the nearest and brightest stars. This activity is an extension of the traditional plotting activity, and begins with an H-R diagram with enough bright and nearby stars plotted to define the shape and location of the main sequence, and the location of the giant and white dwarf branches. The activity is part of the Variable Star Astronomy (formerly Hands-On Astrophysics) curriculum developed by the American Association of Variable Star Observers (AAVSO) in Cambridge, MA. There are other versions of the activity and they will be described in the “Using the Variable Star/H-R Diagram Activity Materials in the Classroom” section below. The traditional H-R diagram plotting activity shows the different branches of the diagram and the location of giants, supergiants and white dwarfs – all part of the evolutionary track involving main sequence stars. However, it presents a rather static view of stellar evolution.

The emphasis of this activity is the plotting of four types of intrinsic pulsating variable stars – Cepheids, RR Lyraes, Miras and Semiregulars. These variable stars can be thought of as representing transition stages for some stars as they evolve from the main sequence and “move” to other branches of the H-R diagram. These variable stars occupy regions on the H-R diagram known as instability strips, and plotting their variability as they transition from one evolutionary stage to another gives a better perspective of stellar evolution as a continuously changing process. This is further emphasized by plotting these pulsating variables at both maximum and minimum brightness to show how much they vary in brightness and temperature as they transition through the instability strips.

The student handout includes all information necessary for completing the H-R diagram plotting activity. Teachers may use the background information for their own edification, or download all or part of the information for student use depending on individual classroom needs. The first 2 pages of the background information provides a description of the H-R diagram – including absolute magnitude, temperature, spectra, stellar classification, luminosity, and the major branches. The remaining 6 pages describe variable stars and light curves, cataclysmic and intrinsically pulsating variable stars, and H-R diagram instability strips. The background information includes links for additional in-depth information on stellar evolution and Type Ia and Type II supernovas on the Chandra website at http://chandra.harvard.edu/edu/formal/index.html .

The 7-page Student Variable Star H-R Diagram Activity handout provides all the information necessary for completing the activity – including the H-R diagram worksheet. The three sets of questions on page 4 are for the purpose of determining if students have a basic understanding of the H-R diagram, and the two sets of questions on page 6 are a discussion of their results. Answers will vary; however, students should have answers comparable to the following:

Before the activity:

1.) Plotting both the brightest and the nearest stars are necessary to see a normal or typical distribution of the stellar population, and the same distribution and percentage of stars on the individual branches would be seen from any position within the Milky Way Galaxy as there is no preferred view of the galaxy. This is the basis of the cosmological principle – that the universe is homogeneous and isotropic over large scale distances – the assumption that observers on Earth do not occupy a unique location within the universe.

2.) Main sequence stars have a specific relationship based on mass – the most massive stars have the brightest absolute magnitudes (luminosities) and highest surface temperatures, and the least massive stars have the dimmest absolute magnitudes and lowest surface temperatures. That relationship only holds for the main sequence – other branches have no specific relationship. As stars transition from the main sequence to other regions on the diagram, there are various tracks that they follow, and there are areas for which specific combinations of stellar luminosities and temperatures does not exist. Other stellar evolution stages and/or objects, such as supernovas, neutron stars and black holes are too extreme to be plotted on the H-R diagram.

3.) The answers will vary – from the introduction students will know that the pulsating variables occupy specific regions of instability so they will be able to reasonably predict the locations.

NOTE: A completed H-R diagram answer key with the plotted variables and a separate H-R diagram answer key with plotted variables and the branches labeled are provided.

After the activity:

1.) Various answers depending on the responses to question 3 above.

2.) The Cepheids, RR Lyaes and the Miras are grouped fairly closely together; however, the two Semiregulars are not. Students should understand that variable stars are classified from their light curves and think about why the two Semiregulars are not together. Semiregular stars are giants and supergiants so there is a large range in mass, which can lead to different evolutionary tracks along the H-R diagram.

This activity focuses on plotting pulsating variable stars. The student H-R diagram worksheet has bright and nearby stars already plotted. If you would prefer your students to plot the bright and nearby stars themselves before plotting the variable stars, a blank H-R diagram and the star data tables are available separately to download. The variable star data tables list the stars, spectral class, absolute magnitude and distance in parsecs. The distances are included because all the absolute magnitudes were calculated from parallax and apparent magnitude measurements by the HIPPARCOS mission. This allows for consistency for the absolute magnitude values. Another version of this activity on the AAVSO website lists the distance in parsecs but not the absolute magnitudes – the students have to calculate the absolute magnitudes using the parallax measurement and the distance modulus. All versions of the H-R diagram activity, including information for teachers, are part of the Variable Star Astronomy (VSA) curriculum posted on the AAVSO website: chapter 9 entitled The Life of a Star. Variable Star Astronomy (VSA) can be accessed at http://www.aavso.org/education/vsa .

The Chandra E/PO office has developed an activity with a scoring rubric that is useful as a post assessment for the Pulsating Variable Stars and the H-R Diagram activity. It is designed to be used as either a pre or a post assessment activity to determine student understanding of stellar evolution. The image set for the ac¬tivity includes images of the different stages of stellar evolution, light curves and H-R diagrams. (HTML, PDF and PowerPoint (PPT) versions) Educators can request as many classroom sets of the Stellar Life Cycle cards as necessary.

Stellar Life Cycles Page: http://chandra.harvard.edu/edu/formal/stellar_cycle/ The Stellar Life Cycles Card Sets Request Form: http://chandra.harvard.edu/edu/request_special.html Stellar Cycles Assessment Activity: http://chandra.harvard.edu/edu/formal/stellar_cycle/task_desc.html Teacher Guide and Answer Key: http://chandra.harvard.edu/edu/formal/stellar_cycle/guide.html Scoring Rubric: http://chandra.harvard.edu/edu/formal/stellar_cycle/rubric.html

Chandra is designed to observe X-rays from high-energy regions of the universe – including cataclysmic variables (supernovas, novas), and X-rays from binary systems such as the pulsating red giant Mira A and its white dwarf companion Mira B. As a result, the American Association of Variable Star Observers (AAVSO) and the Chandra X-Ray mission have collaborated with variable star observations and educational materials in their mutual quest to understand stellar processes and evolution. Two other activities and investigations from the Variable Star Astronomy materials, enhanced with extensions and flash versions, are posted on the Chandra website.

Stellar Heartbeats is an introductory activity designed to familiarize students with the magnitude scale and the Julian Day by estimating the changing magnitude of a variable star using comparison stars, plotting a light curve and determining the period. There are HTML, Flash, PDF, and PowerPoint versions. http://chandra.harvard.edu/edu/formal/variable_stars/activity1a.html

A Variable Star in Cygnus uses a set of photos of the variable star W Cyg. By using actual images of W Cyg students learn how to estimate the changing magnitudes of a variable star with actual comparison stars against a background of the real sky. Students then plot a light curve and determine the period. There are HTML, Flash, PDF, and PowerPoint versions. http://chandra.harvard.edu/edu/formal/variable_stars/activity2a.html

Chandra Chronicles Articles describing how the AAVSO amateur observers assisted the Chandra X-Ray Observatory for two observing campaigns of the variable star SS Cygni: Backyard Astronomers Trigger Multi-satellite Observing Campaign on SS Cygni Astronomers Team Up for Chandra Observations of SS Cygni

NATIONAL SCIENCE EDUCATION STANDARDS (Grades 9-12) http://www.nap.edu/openbook.php?record_id=4962&page=173

Formulate and revise scientific Explanations and Models Using Logic and Evidence: Student inquiries should culminate in formulating an explanation or model. Models should be physical, conceptual, and mathematical. In the process of answering the questions, the students should engage in discussions and arguments that result in the revision of their explanations. These discussions should be based on scientific knowledge, the use of logic, and evidence from their investigation.

Understandings about Scientific Inquiry: 5. Scientific explanations must adhere to criteria such as: a proposed explanation must be logically consistent; it must abide by the rules of evidence; it must be open to questions and possible modification; and it must be based on historical and current scientific knowledge.

The Origin and Evolution of the Universe 3. Stars produce energy from nuclear reactions, primarily the fusion of hydrogen to form helium. These and other processes in stars have led to the formation of all the other elements.

BENCHMARKS FOR SCIENCE LITERACY PROJECT 2061 (Grades 9-12) http://www.project2061.org/publications/bsl/online/index.php?home=true

1. THE NATURE OF SCIENCE

• Science is based on the assumption that the universe is a vast single system in which the basic rules are everywhere the same and that the things and events in the universe occur in consistent patterns that are comprehensible through careful, systematic study. 1A/H1*
• In science, the testing, revising, and occasional discarding of theories, new and old, never ends. This ongoing process leads to a better understanding of how things work in the world but not to absolute truth. 1A/H3bc*
• Sometimes, scientists can control conditions in order to obtain evidence. When that is not possible, practical, or ethical, they try to observe as wide a range of natural occurrences as possible to discern patterns. 1B/H3*
• Scientists often cannot bring definitive answers to matters of public debate. There may be little reliable data available, or there may not yet be adequate theories to understand the phenomena involved, or the answer may involve the comparison of values that lie outside of science. 1C/H9** (SFAA)

4. THE PHYSICAL SETTING THE UNIVERSE

• The stars differ from each other in size, temperature, and age, but they appear to be made up of the same elements found on earth and behave according to the same physical principles. 4A/H1a
• Eventually, some stars exploded, producing clouds containing heavy elements from which other stars and planets orbiting them could later condense. The process of star formation and destruction continues. 4A/H2ef
• Increasingly sophisticated technology is used to learn about the universe. Visual, radio, and X-ray telescopes collect information from across the entire spectrum of electromagnetic waves; computers handle data and complicated computations to interpret them; space probes send back data and materials from remote parts of the solar system; and accelerators give subatomic particles energies that simulate conditions in the stars and in the early history of the universe before stars formed. 4A/H3

11. COMMON THEMES B. Models

• A mathematical model uses rules and relationships to describe and predict objects and events in the real world. 11B/H1a*
• A mathematical model may give insight about how something really works or may fit observations very well without any intuitive meaning. 11B/H1b The behavior of a physical model cannot ever be expected to represent the full-scale phenomenon with complete accuracy, not even in the limited set of characteristics being studied. The inappropriateness of a model may be related to differences between the model and what is being modeled. 11B/H5** (SFAA)

C. Constancy and Change:

• Graphs and equations are useful (and often equivalent) ways for depicting and analyzing patterns of change. 11C/H4
• The present arises from the conditions of the past and, in turn, affects what is possible in the future. 11C/H6*
• It is not always easy to recognize meaningful patterns of change in a set of data. Data that appear to be completely irregular may be shown by statistical analysis to have underlying trends or cycles. On the other hand, trends or cycles that appear in data may sometimes be shown by statistical analysis to be easily explainable as being attributable only to randomness or coincidence. 11C/H9** (SFAA)

THE NATIONAL COUNCIL OF TEACHERS OF MATHEMATICS STANDARDS (Grades 9-12) http://www.nctm.org/resources/content.aspx?id=12630

STANDARD 2: Algebra Use mathematical models to represent and understand quantitative relationships.

• Draw reasonable conclusions about a situation being modeled.

STANDARD 5: Data Analysis and Probability Standard Formulate questions that can be addressed with data and collect, organize, and display relevant data to answer them:

• Understand histograms, parallel box plots, and scatterplots and use them to display data.

The engagement continues with a with a Think-Pair-Share brainstorm activity. Students will write down a name of a celebrity, or YouTube star. Students will then use the board at the front of the room and place the name on the board based on “hotness” and “star-power.” This is a great intro to teach students that this is how stars are classified as well. The teacher will help to clear any misconceptions like the brightest of stars are the biggest, and how temperature increases along the right side of the X-axis.

Estimated Class Time for the Engagement: 20-30 minutes

EXPLORATION

This student-centered station lab is set up so students can begin to explore the  H-R diagram. Four of the stations are considered input stations where students are learning new information about the H-R diagram, and four of the stations are output stations where students will be demonstrating their mastery of the input stations.  Each of the stations is differentiated to challenge students using a different learning style.  You can read more about how I set up the station labs here .

EXPLORE IT!

Students will be working in pairs to create a model of the H-R diagram using current celebrities. Students will place celebrities like Drake, Oprah, Fluffy, on a card based on “hotness” and “star-power” of each celebrity. Students will discover that the scale is a representation of how stars are actually classified. Students will then have a follow-up question to answer.

WATCH IT!

At this station, students will be watching a four-minute video about interpreting the H-R diagram. The short video explains how to read the different axes of the H-R diagram. There are 3 questions related to the H-R diagram which students will answer on their lab sheet.

RESEARCH IT!

The research station will allow students to go online and interact by categorizing stars on an H-R diagram according to brightness and temperature. Students will be allowed to check for understanding while online. Students will then be asked to answer 2 questions based on what they learned during the activities.

READ IT!

This station will provide students with a one page reading about the H-R diagram.  Students are asked four questions about the reading, including what factors determine placement on the H-R diagram, where most stars can be found, and what would a characteristic of a star have on a certain spot on the H-R diagram.

ASSESS IT!

The assess it station is where students will go to prove mastery over the concepts they learned in the lab.  The questions are set up in a standardized format with multiple choice answers.  Questions include asking about the direction of temperature along the x-axis, description of our sun compared to other stars on the H-R diagram, and which star in the brightest and hottest.

WRITE IT!

Students who can answer open-ended questions about the lab truly understand the concepts that are being taught.  At this station, the students will be answering three questions like explaining the H-R diagram, explain how to read the H-R diagram to a younger student, and how the temperature is recorded on the diagram.

ILLUSTRATE IT!

Your visual students will love this station.  Students will be completing an H-R diagram by labeling both axes, the location of star groups, and certain temperature/brightness combinations.

ORGANIZE IT!

The organize it station allows your students to use a manipulative to ensure their understanding of the H-R diagram. Students will use cards to place labels in the correct locations within and on the diagram.

  Estimated Class Time for the Exploration: 1-2, 45 minute class periods

EXPLANATION

The explanation activities will become much more engaging for the class once they have completed the exploration station lab.  During the explanation piece, the teacher will be clearing up any misconceptions about the H-R diagram with an interactive PowerPoint, anchor charts, and interactive notebook activities. The H-R diagram lesson includes a PowerPoint with activities scattered throughout to keep the students engaged.

The students will also be interacting with their journals using INB templates for the H-R diagram.  Each INB activity is designed to help students compartmentalize information for a greater understanding of the concept.  The H-R diagram INB templates will challenge the students to understand and visualize the H-R diagram.

Estimated Class Time for the Exploration: 2-3, 45 minute class periods

ELABORATION

The elaboration section of the 5E method of instruction is intended to give students choice on how they can prove mastery of the concept.  When students are given choice the ‘buy-in’ is much greater than when the teacher tells them the project they will have to create.  Each of the H-R diagram projects will allow students to explain how each star got its placement on the H-R diagram.

Estimated Class Time for the Elaboration: 2-3, 45 minute class periods (can also be used as an at-home project)

The final piece of the 5E model is to evaluate student comprehension.  Included in every 5E lesson is a homework assignment, assessment, and modified assessment.  Research has shown that homework needs to be meaningful and applicable to real-world activities in order to be effective.  When possible, I like to give open-ended assessments to truly gauge the student’s comprehension.

Estimated Class Time for the Elaboration: 1, 45 minute class period

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H-R Diagram Worksheets

Making Points

Paragraph Completion

H-R Vocabulary

Reading H-R Diagrams

Label the H-R Diagram

Studying H-R Diagrams

Surface Temperature and Luminosity

Types and Colors of Stars

4 Star Plots

Temperature and Brilliance

Naming Stars

Full Star Evaluation

Plotting Stars

Groups of Stars

Chart Fills

All about these 15 worksheets.

This collection of 15 worksheets is focused on helping students learn about the H-R Diagram or the Hertzsprung-Russell diagram, a tool used in astronomy to understand the life cycle and characteristics of stars. By completing these worksheets, students will:

• Plot stars on an H-R diagram based on their temperature and luminosity;
• Learn and define key vocabulary terms related to the H-R diagram;
• Interpret information from H-R diagrams to answer questions about star characteristics;
• Label different regions and types of stars on an H-R diagram;
• Compare and contrast the relationships between surface temperature and luminosity for different types of stars;
• Identify the different types and colors of stars on an H-R diagram;
• Plot and analyze data for four different stars on an H-R diagram;
• Identify how changes in temperature and brilliance affect a star’s position on the H-R diagram;
• Evaluate the characteristics of different types of stars, including their temperature, luminosity, and life cycle, using the H-R diagram;
• And fill in charts with information about different types of stars based on their position on the H-R diagram.

Through these worksheets, students will develop a deep understanding of the H-R diagram and the characteristics of different types of stars. They will learn how to interpret data from the diagram, make predictions about star evolution, and analyze trends in stellar populations. Additionally, studying the H-R diagram can help students develop critical thinking and problem-solving skills as they apply scientific concepts and principles to real-world phenomena. This knowledge can benefit students by providing them with a foundation for understanding astronomy and the universe around us.

What Are H-R Diagrams?

The Hertzsprung-Russell (H-R) diagram is a scatter plot of stars showing the relationship between their absolute magnitudes (luminosities) and spectral types (or surface temperatures). It is named after its creators, Ejnar Hertzsprung and Henry Norris Russell, who independently developed the diagram in the early 20th century.

H-R diagrams are important tools in astronomy because they allow us to classify stars according to their properties and evolutionary stages. By plotting the data for a large number of stars on an H-R diagram, we can see patterns and trends that provide clues about how stars form, evolve, and ultimately die.

For example, stars that are located in the upper-left corner of the H-R diagram are hot and bright, while stars in the lower-right corner are cool and dim. This tells us that there is a relationship between a star’s temperature and its luminosity. We can also use H-R diagrams to identify different types of stars, such as main-sequence stars (like the Sun), red giants, and white dwarfs.

H-R diagrams are especially useful for studying star clusters, where all the stars have similar ages and compositions. By comparing the positions of stars in a cluster on an H-R diagram with theoretical models of stellar evolution, astronomers can learn a great deal about the physical properties of stars and how they change over time.

In summary, H-R diagrams are a valuable tool in astronomy for studying the properties and evolution of stars. They allow us to classify stars, identify different types of stars, and study the physical processes that govern the behavior of stars.

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Designing an effective Human Resource Management (HRM) System is important for organizations to efficiently manage their workforce. One key aspect of designing an HRM system is creating an Entity-Relationship (ER) diagram, which visually represents the database schema .

This diagram helps in organizing and understanding the relationships between various entities such as employees , departments , attendance , payroll , and training . In this article, we’ll explore how to design ER diagrams for HRM systems with the help of key entities, attributes, and their relationships between them.

ER Diagrams for Human Resource Management (HRM) Systems

Designing an Entity-Relationship (ER) diagram for a Human Resource Management (HRM) System is an important step in developing a robust database structure. ER diagrams visually represent the data model that defines the relationships between entities in an HRM System.

This diagram helps in organizing and understanding the relationships between various entities such as employees , departments , attendance , payroll , and training .

Human Resource Management System Features

Designing an ER diagram for an HRM system involves identifying and defining the key entities and their attributes. These entities usually include employees, departments, attendance records, leave requests, payroll records and training sessions. The relationships between these entities such as an employee belonging to a department or participating in training are also important.

• This feature allows HR departments to store and manage employee data such as personal information , contact details , job history , and performance records .
• It helps in maintaining accurate employee records, tracking changes in employment status and ensuring compliance with legal requirements.
• Employee information management systems include features for onboarding new employees, managing promotions and transfers, and recording disciplinary actions .
• This feature fasts the recruitment process by allowing HR teams to post job vacancies, review applications and track candidates progress through the hiring process.
• It helps in creating job postings, managing candidate databases, scheduling interviews and making job offers .
• Applicant tracking systems also provide analytics to assess the effectiveness of recruitment strategies and improve the hiring process over time.
• This feature automates the calculation and disbursement of employee salaries, taxes and benefits .
• It helps in managing payroll schedules, tracking employee attendance and leave and ensuring compliance with tax regulations .
• Payroll and benefits administration systems also provide employees with access to their pay stubs, tax forms and benefit information.
• This feature helps organizations evaluate and improve employee performance through goal setting, performance reviews and feedback mechanisms.
• It facilitates the creation and tracking of performance goals , conducting performance appraisals and identifying areas for employee development.
• Performance management systems often include tools for 360 – degree feedback, self – assessment , and performance improvement planning.
• This feature supports the planning , delivery , and tracking of employee training and development programs.
• It helps in identifying training needs, scheduling training sessions and tracking employee participation and progress.
• Training and development systems also provide employees with access to training materials, resources and online courses.

Entities and Attributes of Human Resource Management

1. employee: represents individuals working within the organization..

• EmployeeID (Primary Key): It is a Unique identifier for each employee.
• Name: Full name of the employee.
• DepartmentID (Foreign Key): References the department to which the employee belongs.
• Position: Job position/title held by the employee.
• Hire Date: This is a date when the employee was hired.

2. Department: Represents organizational departments.

• DepartmentID (Primary Key): It is a Unique identifier for each department.
• Name: Name of the department.
• ManagerID (Foreign Key): References the employee who serves as the manager of the department.

3. Attendance: Tracks employee attendance records.

• AttendanceID (Primary Key): Unique identifier for each attendance record.
• EmployeeID (Foreign Key): References the employee associated with the attendance record.
• Date: Date of the attendance record.
• Time In: Time when the employee clocked in.
• Time Out: Time when the employee clocked out.

4. Leave: Manages employee leave requests.

• LeaveID (Primary Key): Unique identifier for each leave request.
• EmployeeID (Foreign Key): References the employee requesting the leave.
• StartDate: Start date of the leave request.
• EndDate: End date of the leave request.

5. Payroll: Handles employee salary and payment information.

• PayrollID (Primary Key): Unique identifier for each payroll record.
• Salary: Amount of salary earned by the employee.
• NetPay: Net amount received by the employee after deductions and bonuses.

6. Training: Manages employee training sessions.

• TrainingID (Primary Key): Unique identifier for each training session.
• Title: Title or name of the training session.
• Date: Date of the training session.

Relationship Between These Entities

1. employee – department relationship.

• One-to-many relationship: An employee belongs to only one department but a department can have multiple employees.
• Foreign key: DepartmentID in the Employee table references DepartmentID in the Department table.

2. Department – Employee (Manager) Relationship

• One-to-one relationship: Each department has only one manager and each manager manages only one department.
• Foreign key: ManagerID in the Department table references EmployeeID in the Employee table.

3. Employee – Attendance Relationship

• One-to-many relationship: An employee can have multiple attendance records but each attendance record belongs to only one employee.
• Foreign key: EmployeeID in the Attendance table references EmployeeID in the Employee table.

4. Employee – Leave Relationship

• One-to-many relationship: An employee can have multiple leave requests but each leave request is made by only one employee.
• Foreign key: EmployeeID in the Leave table references EmployeeID in the Employee table.

5. Employee – Payroll Relationship

• One-to-many relationship: An employee can have multiple payroll records but each payroll record belongs to only one employee.
• Foreign key: EmployeeID in the Payroll table references EmployeeID in the Employee table.

6. Employee – Training Relationship

• Many-to-many relationship: An employee can participate in multiple training sessions and each training session can have multiple employees.
• Junction table: To implement the many-to-many relationship a junction table with EmployeeID and TrainingID is used to track employee participation in training sessions.

Entity Relation Diagram of Human Resource Management System

ER Diagram of Human Resource Mangement

Tips and Tricks to Improve Database Design

• Organize your statistics to decrease redundancy and keep consistency.
• Enforce regulations to make sure statistics accuracy and integrity.
• Improve question overall performance through indexing frequently accessed columns.
• Use easy and consistent naming for higher information and preservation.
• Regularly optimize and display your database for only overall performance.
• Implement measures like encryption and get admission to controls to defend sensitive facts.
• Keep thorough documentation for less difficult knowledge and control of the database

Overall a well-designed Human Resource Management System (HRM) plays a important role in efficiently managing various aspects of an organizations personnel. From maintaining employee information to handling payroll , recruitment , performance evaluations , and training , an HRM system fast the processes, improves communication , and provides valuable insights into the organization’s human capital.

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A beginner’s guide to HR analytics

Use the contents section on the right to navigate through this article, or download our full HR analytics guide below.

Part 1. Introduction to HR analytics

The last decade has heralded a massive transformation of HR – there’s the digitization of human resources activities, the changing roles and expectations of HR professionals, and perhaps, most prominently, the phenomenal rise of data and HR analytics.

However, HR professionals were not trained to be data scientists, and it’s probably safe to say that few have any desire to be data scientists! But with the increasing emphasis on data – big data, people data, data analytics, data-driven decisions – sometimes it seems like a data scientist is just what many HR teams need (spoiler alert: it’s not).

In the past, it was enough for HR to measure attrition and celebrate when a 12% attrition rate decreased by a couple of percent. Now, we can’t hide from the fact that there’s more to the story. To tell that story we need to collect and mine more data, analyze more variables, and combine datasets to truly understand what’s driving that decrease in attrition.

For example, if all of the attrition in the business occurred within the first 12 months, or all the employees who resigned were female, then a 2% reduction doesn’t look so rosy anymore. But with data in its raw form and navigating a maze of excel spreadsheets, getting these answers is no easy feat.

In this guide, we’re going to take a deep (but accessible) dive into HR data and analytics, sharing some tips, techniques, and tools for doing best practice, data-driven HR – without the data science degree. We’ll cover:

What is HR analytics and why it’s important for HR (like you need any convincing).

The HR data cycle and best practices for collecting data

Measurement, metrics and the 4 pillars of HR analytics

Data visualization, application and avoiding bias

How to stay on top of a rapidly evolving industry so you don’t get left behind.

What is HR analytics?

In the past, measuring success was all about the balance sheet: profit and loss. Although this is typically the finance department’s remit, every business function has its own way of measuring success – legal uses time and cases resolved, marketing uses leads and engagement, and sales uses – well – sales.

So it makes sense for HR to measure success, too. Thankfully, organizations now realize that success is driven by much more than a dollar figure; that there’s value in people too, and that people, more often than not, are the reason behind that dollar figure. This is what HR analytics comes in.

HR analytics enables HR practitioners to evaluate and measure HR activities as well as human capabilities and behaviors.

Say you’ve just launched a new learning and development program for example, which has required a substantial investment both in time and hard costs. The best way to evaluate if the program is achieving what it set out to – i.e. if it’s impacted performance, engagement and turnover – is through HR analytics.

Key benefits of HR analytics:

Make data driven-decisions across recruitment, engagement, performance and L&D.

Link HR activities back to the business’s bottom line (and demonstrate the value and ROI in HR ).

Understand what’s working and what’s not, to assist with planning, budgeting and resource allocation.

Ensure your staff are engaged, performing and growing.

Terminology

People analytics, human capital analytics, workforce analytics, HR analytics. These terms are often used interchangeably, but what is the difference?

To help answer this question, we’ve delineated some broad definitions for each below, but it’s important to be aware that because these fields are still in relative infancy, they can often mean completely different things to different people.

Workforce analytics

Workforce analytics typically refers to workforce management processes and applications that surround people such as payroll, time, absenteeism, attendance.

People analytics

People analytics is a broader term than workforce analytics, encompassing payroll, time and absenteeism as well as statistics and analysis of individual and team attributes, for example psychometrics, performance, wellbeing and stress.

Human capital analytics

Human capital is the knowledge, skills and experiences of employees that influence their work and the business. Although Investopedia says that human capital is an “intangible asset or quality not listed on a company’s balance sheet”, the purpose of human capital analytics is to quantify the value of this capital and add to and improve upon it. Human capital analytics takes a more financial, economic approach, calculating things like return on investment and the value of productivity.

HR analytics

HR analytics is a broader term that encompasses all of the above. It refers to the understanding of people data from a HR perspective.

Part 2: The HR data cycle

There are four key stages to working with data: enablement, visualization, comprehension and application. These can be thought of as occurring in a cycle.

Data enablement : how does information flow through your organization? How can you collect it so that you gain access to real-time information?

Data analysis : how does your data get quantified, analyzed and augmented? Are you using the correct statistical methodology? Is there anything missing? (By choosing not to report on specific things we create bias).

Data visualization: Is your data displayed in a clear, objective and effective way? How do you interpret and make meaning out of it? Can you use it to tell stories about your people and business?

Data application : Does your data help make informed decisions and inspire action? What have you learned and are you feeding your learnings back into Stage 1 to inform the data you collect in the future?

Data enablement and the importance of real-time information

In smaller businesses, HR has full visibility and can directly see everyone and everything in real-time. If someone isn’t feeling well, they’re offered support. If another staff member is performing well, they’re recognized. Equally, issues are managed as they happen.

Beyond small teams and offices, how can HR achieve the same level of visibility? How can great performance be recognized and replicated across the business if it can’t be seen? How can managers intervene in poor performance before it’s too late? What’s the risk of decisions being made based on second-hand or incomplete info, or long after an event has occurred?

There are millions of pieces of “people” data running through every organization, every single day, as a result of existing people management practices and tools. HR has a unique opportunity to leverage this information to gain visibility over their people and enhance responsiveness, no matter how big or small the organization.

But without a clearly articulated framework for data enablement, it will be difficult to collect, prioritize and organize your data, rendering it meaningless.

Taking a design thinking approach

Harvesting the information collected from your people management practices and tools is where real-time analytics comes from. The first steps are to:

Consider your business objectives and strategic priorities, e.g. culture and values, performance management, learning and development, health and safety, and leadership.

Understand how information flows through your organization.

Identify opportunities to capture it – in real-time, e.g. are you measuring multiple aspects of your people’ performance? Do you invite contribution by asking staff to provide feedback? Do you do this in an engaging, meaningful and easy way, or do you struggle to get everyone to complete your annual engagement survey?

You’ll need to ensure your data collection processes are practical, usable and seen by staff members and their leaders as value adding – otherwise they will not be used and will become a hindrance to getting the job done.

There is also a great opportunity to harness non-compliance (e.g., non-completion of surveys) to understand where and how to improve a process or a framework to better align people with business strategy, at the same time enhancing the real-time data collection framework behind it.

Organizations that find convenient ways of embedding data capture opportunities into everyday business practices make better strategic decisions, ones that are based on real-time empirical evidence and analysis, not assumption or guesswork. ** Rob Bromage ** intelliHR CEO

Data quality

You also need to consider the quality and nature of your data. Quality refers to the data’s completeness (i.e. is there any missing data?), validity (does it measure what it sets out to?), accuracy (can we be sure the data is correct?) and representativeness (is it free from bias?).

If your data isn’t clean and high quality, your results and insights won’t be either.

When collecting data, there are a few key principles to follow to ensure you get good, clean, and most importantly, usable data:

Be transparent – what are you using the data for? What are the benefits to the respondent?

Set goals – what is the purpose of collecting the data? This will help you decide if you really need to ask a certain question.

Speak in plain language – avoid clever copy and technical jargon (or if you have to, be sure to explain terms and spell out acronyms).

Use data validation – e.g. use number-only fields if you want to capture a number (rather than a text field) or maximum values.

Be consistent – for your data to be valid and comparable over time, it needs to be collected in the same way. For example, if one week you ask “how happy are you in your role?” and then change it to “how happy are you at work” the following week, you’re technically asking a different question and thus the responses are not comparable.

Use tools to support you – the more data is handled, the more likely you are to pick up errors and inaccuracies along the way. Use data collection tools like intelliHR to do the heavy lifting.

Part 3. Measurement and metrics

To start leveraging the real-time data flowing through your organization in a structured way, you might be wondering what data to collect, or which HR metrics to measure.

Like fashion, HR analytics and metrics seem to go through cycles and trends of what’s in vogue.

“In the early 2000s it was all about costs and retention, then the focus moved to employee engagement and performance. Diversity has had a recent resurgence, as well as wellbeing which has come under the spotlight as a result of COVID and working from home,” says Glenn Donaldson, intelliHR’s President of the Americas and analytics expert.

But none of these are more or less important and they all influence each other, which is why, Glenn says, you need to take a consistent, continuous approach to measurement.

Choose the metrics that are most meaningful to your organization, ensuring that you represent each of the different functional areas of HR, and measure each metric continuously every month, not just as a flavor of the month.

The 4 Pillars of HR analytics

Here at intelliHR, we categorize HR metrics into four key areas. These include:

Culture, community and wellbeing

Performance and productivity, risk and compliance, planning and financial drivers.

Next, we’ll explain why each of these is important, the problems they can solve and dive deeper into the top 10 metrics for each pillar (which intellliHR automatically measures and analyzes for you!).

Culture is a key driver of employee engagement and wellbeing , satisfaction, retention and performance, as well as overall business performance. Although culture is tricky to define and near impossible to measure, to understand the culture of your organization, there are two things we really want to know the answer to:

Are we a great place to work?

Are our staff happy?

Although you could easily just ask staff to answer these questions, to get a more comprehensive picture of culture there are a number of other key related and predictive variables that are worth exploring:

What is the general level of employee wellbeing ? Are employees mentally healthy and well? Or are they struggling to cope?

Are staff happy in their roles ? How does happiness change over time and what factors influence it?

Are staff loyal to our company? Do they feel a sense of belonging, affinity and pride?

What is the overall sentiment of my business? Is it trending up or down?

How diverse is our organization? How is diversity impacting performance and teams?

How collaborative are we or how siloed are we? Are goals shared across the organization and teams?

What does our organization look like across key cultural impact dimensions? E.g. values, diversity, organizational networks, recognition and learning, sentiment and happiness.

Let’s check out a few of the metrics you can use to measure culture, community and wellbeing.

1. Employee wellbeing

How to measure it

Staff won’t be able to perform at their best if their wellbeing is compromised, and this can impact the culture and performance of the broader team. But unless you proactively check in on your team and ask how they’re doing, many of them won’t offer that information without being asked.

That’s why we recommend tracking employee wellbeing on an ongoing basis, so that you can identify any issues as they happen and provide appropriate support.

Use short, regular pulse surveys that ask employees how they are coping and if they need any support (see example questions below).

In the survey, provide the option for staff to give additional feedback with some open-ended questions.

Use a rating scale for wellbeing and make it meaningful. Numbers can sometimes remove the human from the data, which is why at intelliHR we use a three-point emoji scale, giving employees the option to respond with a thumbs up, thumbs down, or okay sign (the intelliHR HR software also sends notifications alerting managers to the number of employees who “aren’t coping” (thumbs down) so nothing slips through the cracks).

What to do with the data

Have a conversation with those who aren’t coping and find out how you can support them. This sounds simple but it can be daunting! If you find these conversations challenging, be sure to check out our step-by-step guide to having a mental health conversation with your staff).

Look further than individual wellbeing . Are there particular teams, locations or business units with higher or lower wellbeing than others? Which ones? Why might this be? Do junior employees have lower levels of wellbeing than more senior staff?

Look for themes by creating a word cloud with the text that will display any common issues or patterns. You can do this for free using tools like WordItOut , or intelliHR generates one automatically, allowing you to click into the individual words to get additional information like the teams and business units that the particular issue is occurring in, or drill down to see exactly who has said what.

Analyze shifts in wellbeing over time and compare this with other events and activities across your business. For example, does wellbeing drop at the end of the financial year and rise before the holidays? If so, what could you do to reduce stress at this time?

Follow up on feedback ! This helps to build trust by demonstrating that you’ve listened and you care.

intelliHR word cloud

Through intelliHR’s inbuilt automation and analytics, we’re able to quickly ascertain the ongoing mental health and wellbeing of our staff [during a COVID-19 lockdown]. Particularly relevant now more than ever, we’ve been able to use intelli to identify who in our team may need additional support, and act accordingly. In this era of remote work it’s critical that we can maintain a view of how our people are faring, especially during times of uncertainty. ** Catherine Whitehead ** Chief People Officer, Lander & Rogers

Key Takeaway

Put your wellbeing checks on autopilot by scheduling recurring employee pulse surveys.

FREE DOWNLOAD : Mental health toolkit: Understanding and improving wellbeing at work

2. Employee satisfaction

Employee satisfaction is another key indicator of employee engagement and culture, but with more of a role/job focus.

The key with employee satisfaction is to measure it regularly, so that you can build up a picture of each employee’s baseline “happiness” and identify any shifts or changes over time.

Use short, regular pulse surveys (once a month or every couple of months).

Use a rating scale for employees to rate their happiness in their role (we recommend 1-10 as this will provide a granular rating that will be more sensitive to subtle changes, versus 1-5).

Provide the option for staff to give additional feedback with open-ended questions.

Compare relative movement in satisfaction of an individual over time instead of absolute scores between staff. For example, if an employee who is typically a 10 out of 10 drops to a 7, that might mean something very different to one that usually sits at an 8, and drops to a 7.

Examine satisfaction alongside other data points . Is lower satisfaction linked with lower goal completion and wellbeing? Are there particular teams, locations or job roles where satisfaction is higher?

For those teams with higher satisfaction, how can you replicate what’s working well across the rest of the organization?

eNPS or employee net promoter score measures loyalty, which is basically a combination of how a staff member is feeling in their role and whether they feel the company is looking after them.

Ask “How likely are you to recommend [company name] as a place to work to your family and friends?”.

Ask a follow-up question to give employees the opportunity to provide additional feedback e.g. ​​“What’s one thing we could do to make you happier at work?”

Conduct eNPS pulses twice a year at a minimum so that you can be responsive to trends and issues.

What to do with the data?

Often eNPS is treated as a standalone score, but if you collect quality data across lots of different variables in your organization, then some further analysis will allow you to get a lot more out of it.

Consider scores across age demographic s, gender, department and location Look at eNPS over time – are there particular times of the year that eNPS is lower? Does this look the same every year? Are there other things occurring in the business that coincide with drops in loyalty?

Follow up on feedback. If there are common issues make sure you do something about them and if there are things you need to confirm or clarify, ask!

Check out our eNPS explained guide for more.

Happiness scores change day-to-day, whereas overall loyalty to a business stays fairly stable regardless of whether you’ve had a couple of bad days or a temporarily large workload. But if bad days or a big workload continue over a long period of time, then there would be a trend that eNPS will identify. ** Paul Trappett ** intelliHR COO

People performance directly relates to organizational performance, and being able to understand the first one will help you identify what time and resources you need to allocate to optimize both.

Here are the key variables and questions to explore.

Overall, how is the business performing along the indicators or dimensions we deem as important (e.g., safety incidents in a mine, sales outcome, budget)?

Are goals being set and met ? Do goals align with overall organizational strategy? Do they contribute to business success or objectives?

Are there people we should recognize and reward ? Are there staff or managers that might need support?

What performance issues are occurring? Are there common themes or areas that require more attention or training? What would the cost-benefit of this be?

How are leaders performing on key dimensions (e.g. leadership, team performance, team engagement, team development, team compliance?). Are there areas that need coaching, investment or improvement?

What skills gaps do we have in the business and where?

4. Business performance

Measuring performance is not only useful because it gives you granular information about how to support or develop an individual employee, but it also gives you aggregate insight into overall business performance.

Use a continuous performance management framework to conduct regular check-ins and reviews.

Ask your employees to rate themselves on key indicators such as productivity, teamwork, quality, compliance and values.

Ask supervisors to rate employees across the same variables so that you can compare the two.

Filter by business unit, supervisor, pay grade and location to identify if there are any patterns or trends in performance across entities.

Explore how performance trends over time. Are there particular times of year when it drops?

Look at performance by tenure. Are there particular milestones or times in the employee lifecycle when performance drops? How can you enhance the employee experience to protect against disengagement?

Tracking and measuring employee goals or OKRs in your organization is a key indicator of how engaged staff are, their level of productivity or performance and any issues with workload or expectations.

Have staff set goals at a regular cadence (i.e. quarterly), or cascade goals down from a team or organizational level.

Break goals into bite size components – what individual tasks need to be completed to achieve the larger goal? For a tender for example, that might include competitor research, drafting the tender, getting input from legal, review, and submission.

Define success. Is it simply completion or is there a meaningful metric or KPI that could help define the measure of success?

Use goals to understand your employee’s career aspirations and personal development goals.

Identify if any employees need training or support. Are there commonalities or trends across particular roles or teams that could be solved with a training course?

Are goals being set? If not, this might indicate that staff are too busy, overwhelmed or not invested in the long-term success of the organization.

Are goals being completed on time? If not, is there an issue with the frequency of check-ins , or workload?

Are goals aligned with organizational strategy?

FREE DOWNLOAD: Employee goal setting worksheet

6. Skills gaps

Identify which skills are critical to your organization, who has them, and where there are gaps that need to be filled through employee development, training or hiring.

Create a library of all skills and competencies in your organization with proficiency levels (i.e. beginner, intermediate, expert, want to learn).

Assign skills to staff when they’re hired, when they upskill or let them do it themselves.

Create a skills matrix to understand where your core business skills are distributed, how many people have them and at what level.

Use skills profiling to identify potential internal candidates for a job.

Use the skills matrix to identify skills gaps, learning and development opportunities and to plan your L&D budget.

Use your skills matrix and gap analysis to inform succession planning, to ensure key skills don’t leave your organization when employees do.

Measuring risk and compliance so that you can manage and minimize risk involves identifying and mapping out where in your organization there are potential risks.

Key variables and questions to ask:

Is there a risk that someone might get hurt in or by our organization?

Has someone reported an incident or something important that we should do something about?

Are people or leaders not completing important tasks such as policy sign-offs, setting goals, giving feedback, reviewing probation etc? Are their team not completing tasks?

Are we breaching any laws , acting unethically, or not being responsible?

Are we at risk of losing our top performers , key post holders or business critical staff?

What attrition trends should we investigate to reduce the risk of future regrettable leavers?

Do we have people in our business subject to disciplinary processes? How are we managing these and what are the costs to the business?

Do we have people in our business not taking annual leave regularly?

Understanding attrition in hr , the rate at which people are leaving your organization and why they’re leaving, feeds into almost every single other aspect of HR – recruitment, performance, onboarding, engagement, etc.

There are multiple formulas that HR uses for calculating attrition or employee turnover (intelliHR lets you choose between the three most common ones below).

Employee attrition formula 1: Full employee monthly count

The denominator is based on the full employee count for any given month. Employee count is determined by the count of employees at the start of the month plus the count of all new employees started in that same month.

Employee attrition formula 2: Monthly average employee count

The denominator is based on the average employee count for any given month. An employee count is made at the beginning of the month and the end of the month. This is divided by two to determine the monthly average employee count.

Employee attrition formula 3. Period start date employee count

The denominator is simply based on the employee count at the beginning of the date range selected.

This data is incredibly powerful if you layer it with other data sets and employee information, like your onboarding data, for example.

Look at average tenure and when people are leaving. Is it in the first three months? The first 12? If your early turnover rate is high, then you may need to invest more in your onboarding and training.

Explore turnover by leader, location, business unit, pay grade and more . Does a particular supervisor have a far higher retention rate than others? Why might that be?

What are the common reasons for turnover? Is it voluntary or involuntary?

Look at time trends – does turnover happen at a particular time of year (i.e. just after bonuses or around times of year with particularly high goals or targets)?

What’s your average cost of turnover? This can be quite a complex calculation once you consider all of the hard and soft costs, but we’ve created a free Employee turnover cost calculator to make it easy.

Survival analysis – how likely is someone to stay in the business and what are the deciding factors that might determine whether they leave or stay?

Be aware! Although data can help remove bias, it can also introduce bias when it implicates someone as a “problem”. For example, if there are lots of staff leaving under one leader, that doesn’t necessarily mean they are a “bad” leader. Perhaps they haven’t been trained effectively, or maybe the team dynamic is the problem.

One of the principal parts of HR is planning – people, roles, time, budget, and being able to demonstrate a return on investment of your people and activities.

What is recruitment costing per person? Where are we spending and are we getting value?

What is the true cost of recruitment by source, leader, and over time (cost/tenure)?

What are we investing into training and what is the impact? Are we getting value for our dollar spend? What budget should be set per person and for the business?

Where are our human capital resources ? Do we have headcount gaps now or coming up in the future? What is our succession plan?

How are our overall wages trending? Are they increasing per person and by how much? How does this look across roles, teams, business units and location?

How is unplanned leave impacting our organization? What are the reasons and financial cost?

How much has attrition actually cost the organization?

8. Training and development investment

When employees are engaged, challenged and growing, your organization will grow too. Measuring training investment helps to put a figure on both the monetary and time costs involved in employee development.

Quantify all of your training (even internally conducted) to assign a dollar amount by hour.

Track training completion in a tool like intelliHR (you can even link online training products like Go1 to automatically feed into intelli).

Ask staff what training they want/need.

Calculate your total training investment – how much have we invested in training in terms of indirect and direct costs?

Calculate the average training cost per employee . Do certain roles or seniority levels require more training than others?

Calculate the average number of training hours per employee . Which types of employees are receiving the most training? Is it fair and equitable?

Compare training metrics with goal achievement . What effect has the training had on goal achievement and/or performance?

Analyze training against attrition . Are employees who’ve received training more likely to stay? Were there training needs not met for exiting employees? What can you implement to address this in the future?

9. Remuneration trends

Analyzing your remuneration data can provide valuable insight to inform hiring, promotion, training and more. In addition to actual remuneration, other data points that are valuable to track/capture include employment type (e.g. contract, full time, part time) time since last remuneration change, tenure, as well as job and demographic data.

Analyze remuneration changes. Are there particular teams or business units that are increasing salaries more than others?

Forecast expected remuneration spend for the future. Does our remuneration budget reflect expected growth in the business?

Examine remuneration by recruitment source and attrition. Is one recruitment source consistently costing us more money? Do these employees typically turn over faster than others, thus delivering lower ROI?

Track time since the last remuneration change. Are there any roles or business units with longer duration between pay rises

Calculate the normalized impact of a pay rise – this tells you how a pay rise might feel to them. e.g. 10% pay rise might sound great, but if it’s the first one an employee has had in 10 years, it might only feel like a 1% increase to them. (intelliHR calculates this automatically, see how ).

10. Workforce distribution and organizational growth

Can you easily see how many staff you have and how they’re distributed? What about how your organization is changing and growing?

To analyze your workforce distribution and organizational growth doesn’t require any specific calculations, you simply need accurate, real-time data in your HR software that automatically updates when employees join, exit or change roles.

Track how many people are joining your organization and how many people are leaving.

Examine what demographics are coming in (e.g., sex, age, culture, work type). Are your processes biased toward a particular demographic? What’s the gender representation around leadership?

How diverse is our workforce (gender, gender in leadership roles, race, sexual orientation)? How diverse is your organization?

intelliHR’s workforce headcount analytics automatically reports on diversity dimensions.

Part 4: Visualization, application and avoiding bias

Data visualization and hr software.

Most HR platforms provide some level of HR reporting; however, many are still providing only operational, basic reports and dashboards. In fact, Fosway Group reports that over 85% of HR professionals indicated adopting HR analytics was a challenge because of their current solution **.**

What you really want to be looking for is HR analytics tools (like intelliHR!) that provide not only the tools for collecting quality data, but also have the capability for data augmentation – to analyze and integrate your data across the 4 pillars of analytics.

intelliHR does this, and takes it a step further, using AI and machine learning to provide predictive analytics and insights from the data to inform strategic, unbiased, data-driven decision-making.

How machine learning can help reduce biases

There are a number of unconscious biases that operate in workplaces and HR, and many of them are so deeply ingrained that they often exist outside of our conscious awareness.

Types of bias in HR and people management

Compassionate bias: when feeling pity for someone impacts our perception of other aspects of their skills and abilities.

Recency bias : evaluating situations or performance based on what’s happened most recently (a big danger with annual performance reviews!).

Proximity/distance bias : tendency to favour those closer in time and distance to us.

Similarity/ingroup bias: tendency to favour those who are more similar to us.

Confirmation bias : looking for information to confirm a pre-formed opinion and overlooking information to the contrary.

Halo effect: the impact of a single negative or positive characteristic or event on the entire evaluation.

Negativity bias : humans are more alert and impacted by negative events and information than positive.

It can be challenging to fully remove biases, and the danger of this is that leaders might not be fairly and equitably treating staff (even if they think they are). This is where machine learning and automatic insights (like those generated in intelliHR) can help.

Machine learning is where we feed data and information in the form of observations and real-world interactions to a computer and it improves its learning over time. Essentially, it gets smarter the more data it receives.

“The beauty of machine learning is that it can provide unbiased insights into your data, which is essentially the wisdom of its learning,” says Glenn.

These insights are tangible, actionable takeaways that help you to correctly understand, interpret and problem solve based on your data.

“It’s the “ah-ha” light bulb moment that helps you to turn numbers into comprehension and tools for problem-solving.”

Take a 250 headcount business, for example, with turnover that was roughly 10% of their staff (a score that HR was proud of).

First analysis of attrition data showed turnover was attributed to mostly one particular location. For that location, they were turning over roughly 60% of the team, mostly sitting under one supervisor.

When layered with offboarding survey data from both the leader of the team and employee showed high levels of negative sentiment analysis with the language being used as to why they’ve left as well as quantitative responses around the key motivator.

Comparing this against costs, revealed $150,000 cost of turnover (and when including time investment this figure increases to $300K+)>

Trends and insights showed lack of training, turnover in first 1 2 months and ‘bullying’ flagged in engagement surveys of those that turned over

You can never remove the human element in business, but adding in machine learning and data tools delivers power, rich, unbiased insights.

Inspiring action through storytelling: A case study

Part of the challenge of numbers – and analytics – is making them meaningful. So that you can convince C-suite to uptake your new initiative, allocate more budget or allow you to hire more staff.

This is where storytelling and the power of (good) data visualization is absolutely essential.

To illustrate this point, take this nation-wide corporation that used to report on lost time due to injury.

Each month they would present the numerical figure in hours in their HR report and to the board. Some months it might be 50 hours, others it could be hundreds, and it didn’t go much further than that.

But then one manager flipped the way they were reporting the metric to tell a story. Instead of lost time due to injury, they reported “we hurt X number of people at work this month”, which made everyone start listening. They made the data meaningful, and put the human at the centre of the picture.

Along similar lines, here at intelliHR we don’t just report on staff’s wellbeing at a numerical level, we translate that into the number of staff not coping ****, which, while confronting, is more likely to get people’s attention so that you can do something about it.

If you want to inspire action, don’t just report the numbers, use the data to tell a story.

Amplifying intelligence in your organization

HR analytics and data shouldn’t just be kept for HR, the benefits and learnings should be available to be leveraged throughout your organization, in particular by C-Suite.

Opens up access to attrition, team engagement, happiness, skills development, remuneration spread, training needs.

Helps allow an additional data point of context for HR when interpreting the data, directly from the leader/s.

Self-diagnosis and correction of internal biases when presented with your own data insights and patterns of behavior.

The evolution of data-driven HR and what’s next for HR analytics

Although the approach to HR and HR data has been through a major transformation, it’s not over yet.

Glenn says that the next evolution of HR data will be decentralizing it out from HR.

“In the next few years I think we’ll see the access to and visualization of people data available to all leaders in an organization – both upper management and C-suite as well as line managers.”

This will open up opportunities for leaders to analyze their own activities, self-check biases and diagnose issues.

“If a manager can see that two people in their team have consistently lower satisfaction scores, or that they’ve given everyone raises except two people, they can start to ask why.”

“Decentralized HR data will not only empower leaders to act on insights about their team, but to proactively improve their leadership capability.”

But don’t worry, HR won’t be out of a job! HR’s role will be to facilitate data collection, educate around the comprehension of the data, and most importantly, to support strategic action. With management more invested in the outcomes, it follows that they’ll be more invested in the data collection too, so all of sudden the sole responsibility of sending performance check-ins and employee feedback pulse surveys won’t lie with HR alone.

Another thing that Glenn sees changing is the way HR uses their software tools, and the unification of data across these tools to get a big picture view.

“Right now, many HR professionals see “all in one” as the ultimate solution, because, at first glance, it appears to simplify things. But two very effective tools are better than one that’s less effective and does everything.

“So I think we’re going to see HR start to choose best-of-breed tools that talk to each other and build their own HR ecosystem so that they can get a complete, rather than fragmented view of their people.

“The more we improve the tools and processes we use to collect data, the more accurate and valuable it becomes because you’ll have a larger, more powerful data set.

Good data in equals good data out. Great data in equals great data out.

Taking the next step

Now that you understand the importance of HR analytics, how HR metrics fit into not just HR strategy, but overall business strategy, as well as the questions you can answer and problems you can solve, it’s time to start using them! Although it might seem like a lot at first, the key is to start with one or two analytics and build from there (and having the right tools to support you).

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HR Diagrams

Color-magnitude diagrams.

As discussed in Stellar Spectra , the surface temperature of a star has a direct correlation to its color, so we can use our understanding from Star Colors to represent decreasing temperature as increasing redness. Luminosity is more difficult to accurately represent, however, since in order to calculate an object’s luminosity you need to first know its distance from Earth. It represents the same physical property as the absolute magnitude , which is the amount of energy being released in a given amount of time. This can be extrapolated using the object’s apparent magnitude and distance, the latter of which can often only be learned by analyzing its parallax or using other methods which are beyond the scope of this activity. This is where star clusters become very useful to astronomers, since we know that all of the stars inside a single cluster are at almost exactly the same distance from Earth, in addition to having near-uniform age and chemical composition.

There are two primary classes of clusters, open and globular. An open cluster is a relatively young group of stars (between a million and a few billion years old) which has formed from a traveling cloud of dust when it entered the galactic disk. The strong gravitational field within the galaxy can compress such clouds into groups of stars which scatter slowly over time, resulting in a loose collection of stars. Due to the nature of their formation, most open clusters are found within the arms of the Milky Way.

Globular clusters are very old—on the order of ten billion years, which approaches the age of the universe itself. They are formed from much larger clouds of matter, producing many more stars with enough total mass that they become gravitationally bound to each other. They typically appear as a bright, dense ball of stars and are found chiefly in the halo of the Milky Way, near the central bulge but outside the plane of the disk.

Because of the way they formed, we know that stars in clusters are in the same small region of space, so we approximate that each one is at the exact same distance from Earth. Since absolute magnitude is simply the apparent magnitude when viewed from a standard distance, each star’s apparent magnitude will hence be offset from its absolute magnitude by some constant which is related to its actual distance. We can thus produce a color-magnitude diagram of the stars in the cluster and it will be representative of the same object’s HR diagram, merely offset on the y-axis.

Anatomy of a Diagram

When stars first form, most of them lie on what is called the “main sequence,” the stripe of stars running diagonally from the lower-right toward the upper-left. Their exact position on the stripe is determined by their mass, with more massive stars sitting in the brighter, bluer area.

Once the most massive stars have burned through their hydrogen supply, they often evolve into red giants or supergiants, which stay at about the same luminosity but become redder. This produces the “red giant branch” as progressively less massive stars also run out of fuel; the corner or bend where the main sequence leads into the red giant branch is called the “main sequence turn-off,” or sometimes just the turn-off.

Some stars will continue from the red giant branch onto the “horizontal branch,” which reaches back toward the bluer and dimmer portion of the diagram.

Choosing an Object

The first, and very important, step is selecting a cluster with good photometric data. If you already have a favorite cluster, type it into the “name” box in Navigate and click “Resolve” to see if it’s in the SDSS database. If not, you can look at this catalog of open and globular clusters that have already been found with SDSS data and choose one that you like. When you’ve found an object, type its name into Navigate as just explained, and it will likely appear automatically. Some of the clusters aren’t listed in the SDSS index, however; if that’s the case, each object’s RA and Dec can be found by clicking on its image in the catalog. You can then type the values into Navigate by hand.

While selecting a cluster, don’t be afraid to look closely at several in order to compare your options and find the one best suited to your needs— not every cluster in the database will produce a good HR diagram. Ask yourself:

• What kinds of data do you need to build a Hertzsprung-Russell diagram?
• How many data points (stars) will be necessary to see a clear and distinct trend?
• Why do some clusters have better photometry than others? How will that affect your plot?

You may want to explore some of these questions further by gathering multiple sets of data from the same cluster, using different selection parameters, to better understand how the end result is influenced by this very first step. Additionally, it’s generally a good idea to document your work so that you can go back and repeat steps or make alterations as necessary. Keep a record of the search parameters you use and other choices you make during this activity so that you can more easily analyze the process afterward.

Collecting Data

When formulating your data request, it can be very helpful to take advantage of the tools offered by SDSS to streamline and optimize the collection process. Those who have little or no programming experience may find it easiest to dive in using the SDSS EZSearch Tool , which allows you to quickly and intuitively fill in your search parameters but allows for very little customization.

If you’re familiar with programming syntax or willing to learn the basics, the SQL Search tool below is a more powerful tool which can offer versatility and complexity in areas where the Search Form is limiting. You can find a resource to get you started with your own SQL searches using this SQL Search tool – see the instructions below, or the SkyServer SQL Tutorial .

Results will appear here

To start your query, think about exactly which data you’re looking for and what criteria you’ll use to select it. Much of the following choices are made for you in the Search Form, but if you explore the Schema Browser to better understand the options at your disposal through the SDSS database, you can come up with more creative solutions and make educated decisions about what goes into your diagram. While answering the questions below you will likely want to look through the View titled “PhotoObj,” which samples from the best photometric data available. In general, it may be a good idea to request more options than you think you’ll need, in case you change your mind later.

• Several magnitude-fitting models are available in PhotoObj: psfMag, fiberMag, petroMag, deVMag, expMag, modelMag, cModelMag, and dered. How do they differ? Which is best suited to the plot you’ll be making, where brightness and color are the two important variables?
• Of the five ugriz filters , you will need to use two in your plot. Which ones will you use? Refer to the “Color” section of Star Color if necessary.
• Should your data be limited to a certain magnitude range? What about magnitude error?
• What other criteria can you use to select more relevant and helpful data?

Once you have the structure of your search, a very important decision you still need to make is exactly which area of the sky to take data from. This can have a large impact on your final HR diagram, so it may be a good idea to pause and consider your options. You can hand-pick minimum and maximum RA and Dec values using Navigate in order to select a “rectangular” portion of the sky around your object, or you can use the SDSS fGetNearbyObjEq function to capture a circular zone around the center of the cluster. Either way, make sure to check the “Photometric objects” box in Navigate to estimate how many data points you’ll have for a given patch of sky. You can also apply the COUNT(*) function to see the exact number before making your request.

Choosing a good radius to select from is a balancing act, since if you stay close to the center there will be fewer data points—especially due to oversaturation—and it may be harder to see a trend. It’s essential to note that very few photometric data will appear in the center of any cluster, since the SDSS telescope was designed to see very dim objects, so bright bundles of stars overwhelm the CCD to make for bad data. As you expand your search, you will select more stars which are dim members of the cluster, but you will also capture some which are not part of the cluster and just happen to be in line with it from the telescope’s point of view. There is no hard-and-fast rule for this, but it can be helpful to request object counts for several sizes until you have an appropriate number of data points.

If you use the fGetNearbyObjEq, you can calculate the radius of your search by imagining a circle around the center of the object and selecting a point in Navigate on the circumference of the circle to get the RA and Dec. Make sure the point has the same RA or Dec as the center of the cluster, so that the radius of the circle will be the difference in the other coordinate. This number will be in degrees, however, and the equation takes radial inputs in arcminutes, so be sure to convert the unit first

Once you’ve acclimated yourself to using SQL and navigating the SDSS schema, those who are serious about using large amounts of data or saving their searches for later reference may want to create a free account with SciServer in order to use the CasJobs search tool.

W hen you’re satisfied with your query, output the results of your final request as a CSV file and save it to your hard driv e.

Manipulating Data

Once you’ve downloaded your data as a .CSV file, you can import it into a spreadsheet editor to begin further analysis. You can upload your .CSV file and manipulate and plot the data within Google Sheets. You can also use your favorite spreadsheet editor, such as Excel, if you feel more comfortable there. Those looking for more control can even use Python to create their diagrams, with the help of its NumPy and MatPlotLib libraries.

Recall that a color-magnitude diagram is a plot of apparent magnitude on the y-axis and color on the x-axis, so we’ll choose the magnitude through one filter to be plotted against the color index, which is calculated using two filters. If you’re not sure how color calculations work, see the “Color” section of Star Color for an explanation. The exact process for performing calculations and plotting data will differ depending on the software you use, so refer to user manuals or other guides as needed.

You need to create a column of data representing color, given the magnitudes through the two filters you selected, and then choose one of the filters to represent your apparent magnitude. By convention, this is the longer wavelength, which is the one being subtracted in the color calculation.

The image below shows three color-magnitude diagrams made from the same cluster, using different-sized searches. Pay attention to how the number of data points can affect your ability to find trends.

Reviewing Results

Our end goal is to produce a Hertzprung-Russell diagram, so make sure your axes are going in the right direction. You can check this intuitively by considering what the axes actually represent: luminosity is how much energy an object emits, so when luminosity increases, how should the apparent magnitude be affected? And since an HR diagram plots decreasing temperature on the x-axis, what does that imply about the color of light being emitted? Should it go from bluer stars to redder stars or the other way around?

A successful color-magnitude diagram can take many shapes. Your cluster may look very different from a typical HR diagram, especially since the SDSS telescope is limited in its range from about 14th to 24th magnitude. This means that you’re looking at a horizontal cross-section of the full cluster, so don’t be discouraged if all of its features aren’t visible; here are some sample clusters which may help you identify the prominent forms. Click each one to see it at original size.

Before moving on, it’s important to review the process you’ve gone through and consider where things may have gone wrong. Somebody who’s very experienced with HR diagrams might be able to tell at a glance where experimental error and irrelevant data affected the results, but building that sort of intuition takes years of practice. Read through the activity again and look at any notes you may have taken to ask yourself:

• In which steps did you need to make subjective decisions about how to collect or manipulate the data? How could those choices have changed your final diagram?
• Do any steps exist that aren’t influenced by human decisions? For example, are there procedures that everybody must perform the same way?
• If you were to repeat this activity in the future, are there any things you would do differently? Why?

If you worked in a group, it may be helpful to compare your methods and results with others in order to look for trends. Discuss which things worked well and which didn’t.

You now have a color-magnitude diagram! Depending on the properties of the cluster and your methods in selecting data, each plot should look a little bit different. Hopefully you’ll be able to identify multiple features, such as the main sequence, turn-off, and red giant branch. The exact shape depends on the unique properties of your cluster; we’ll investigate that further in the third part of this Expedition, but first we have to scale the plot to absolute magnitudes using the Distance Modulus .