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How to Use Excel for Scientific Research and Analysis

Excel is a powerful tool that can be used for scientific research and analysis. With its vast array of functions, tools, and features, it has become a go-to software for many researchers and analysts. In this blog, we will explore how to use Excel for scientific research and analysis.

Data Management

The first step in any scientific research and analysis is to manage data effectively. Excel allows users to store and organize data in spreadsheets, making it easy to work with. To manage data effectively, users can create tables and charts, add formulas, and use conditional formatting to highlight important information.

Data Analysis

Once data is organized, it’s time to analyze it. Excel provides numerous functions for data analysis, including regression analysis, hypothesis testing, and correlation analysis. These functions enable researchers to identify trends, patterns, and relationships in their data, making it easier to draw conclusions and make recommendations.

Data Visualization

Data visualization is a critical part of scientific research and analysis. Excel provides several options for visualizing data, including charts, graphs, and histograms. These tools can help researchers understand the data better and communicate their findings to others effectively.

Statistical Analysis

Statistical analysis is an essential part of scientific research and analysis. Excel has a wide range of statistical functions that enable researchers to analyze data and draw meaningful conclusions. These functions include t-tests, ANOVA, and chi-square tests.

Graphical Analysis

Graphical analysis is another critical component of scientific research and analysis. Excel provides a range of tools for creating graphs and charts, including scatter plots, line charts, and bar charts. These graphical tools enable researchers to visualize data and identify trends quickly.

Data Manipulation

In scientific research and analysis, it’s often necessary to manipulate data. Excel provides a range of tools for manipulating data, including sorting, filtering, and conditional formatting. These tools can help researchers identify outliers and anomalies, making it easier to draw accurate conclusions.

Collaboration and Sharing

Excel allows researchers to collaborate and share data with others easily. It enables users to share spreadsheets via email, OneDrive, or SharePoint, making it easy to work with others remotely. Excel also enables users to protect data and restrict access, ensuring data security and integrity.

Quality Control

Excel can also be used for quality control purposes in scientific research and analysis. By using functions like conditional formatting and data validation, researchers can identify errors and inconsistencies in their data. This can help ensure that the data used for analysis is accurate and reliable.

Macros and Custom Functions

Excel allows users to create custom macros and functions, which can help automate repetitive tasks and streamline workflows. This can be particularly useful in scientific research and analysis, where data sets can be large and complex. By automating certain tasks, researchers can save time and reduce the risk of errors.

Importing and Exporting Data

Excel also provides options for importing and exporting data from other software and applications. For example, researchers can import data from a laboratory instrument or export data to a statistical software package. This flexibility enables researchers to work with data from a wide range of sources and ensures compatibility with other tools and software.

Pivot Tables

Pivot tables are a powerful tool in Excel that can help researchers analyze and summarize large data sets quickly. Pivot tables enable users to rearrange and summarize data, making it easier to identify trends and patterns. This can be particularly useful when working with large and complex data sets.

Data Mining

Excel also provides options for data mining, which involves using statistical algorithms to identify patterns and relationships in data. For example, researchers can use Excel’s data mining tools to identify trends in sales data or identify commonalities between different samples. This can help researchers identify new research questions or opportunities.

Conclusion: Excel is an incredibly powerful tool for scientific research and analysis. With its vast array of functions, tools, and features, it provides researchers with the ability to manage, analyze, and visualize data effectively. By utilizing Excel, researchers can draw meaningful conclusions and make informed recommendations, advancing their scientific research and analysis.

If you’re looking to enhance your expertise in Excel , LearnTube has got you covered with an array of online courses tailored to your needs. With the help of our specialized learning app and WhatsApp bot, you can enjoy a seamless learning experience. Our platform offers an extensive range of courses that cater to both novices and seasoned learners. For valuable insights, explore our diverse selection of courses on our website .

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Using Microsoft Excel for Social Research

• Charlotte Brookfield - Cardiff University, Cardiff, UK
• Description

Providing step-by-step instructions for how to use Microsoft Excel for doing statistics, Charlotte Brookfield discusses different stages of the research process, from first planning to writing and presenting your research. With a focus on conducting robust data analysis, the book is reassuring, clear and straightforward, helping you to:  

·       Learn important data skills, such as preparing, cleaning and managing data

·       Reduce anxiety about approaching statistics and quantitative data

·       Boost your employability, showing you how to develop transferable skills, such as effective time management.

Whether you’re learning data skills for the first time or translating your statistics knowledge from other software, this book will help you successfully carry out social research in any setting with confidence, via an engaging pedagogy that includes: colour-coded chapters by difficulty, activities, 'Remember' boxes, further reading and skills checklists.

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

For assistance with your order: Please email us at [email protected] or connect with your SAGE representative.

SAGE 2455 Teller Road Thousand Oaks, CA 91320 www.sagepub.com

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This is an invaluable resource for people learning statistics. Brookfield provides a clear, accessible and engaging introduction to using Excel to explore, analyze and report quantitative data. 

It makes perfect sense to train our students in Microsoft Excel: not only does the programme have increased functionality for social research, but it is also a staple in many graduate workplaces. This text is distinct from other Excel help guides in that it is explicitly designed around the social research process and with undergraduate students in mind. Using real-life datasets and tools such as ‘reminder boxes’, it is a highly comprehensive, engaging and accessible resource for introductory quantitative research methods modules. 

This is the book I have been waiting for. We have learned that employers value Excel and that many small workplaces cannot afford SPSS licences. This covers everything we would do in SPSS (possibly excepting recoding variables). Sold.

Preview this book

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• Afr J Emerg Med
• v.10(Suppl 2); 2020

Research skills and the data spreadsheet: A research primer for low- and middle-income countries

David mcd. taylor.

a Department of Medicine, University of Melbourne, Parkville, Victoria, Australia

b Emergency Department, Austin Health, Heidelberg, Victoria, Australia

Peter W. Hodkinson

c Division of Emergency Medicine, University of Cape Town, Groote Schuur Hospital, Cape Town, South Africa

Abdus Salam Khan

d Shifa International Hospital, Islamabad, Pakistan

Erin L. Simon

e Cleveland Clinic Akron General, Department of Emergency Medicine, Akron, OH, United States of America

f Northeast Ohio Medical University, Rootstown, OH, United States of America

The specialty of Emergency Medicine continues to expand and mature worldwide. As a relatively new specialty, the body of research that underpins patient management in the emergency department (ED) setting needs to be expanded for optimum patient care. Research in the ED, however, is complicated by a number of issues including limited time and resources, urgency for some therapeutic investigations and interventions, and difficulties in obtaining truly informed patient consent. Notwithstanding these issues, many of the fundamental principles of medical research apply equally to ED research. In all medical disciplines, data needs to be collected, collated and stored for analysis and a data spreadsheet is employed for this purpose. Like other aspects of clinical research, the use of the data spreadsheet needs to be exacting and appropriate.

This research primer explores the choice of available spreadsheets and a range of principles for their best-practice use. It is deliberately, not an exhaustive review of the subject. However, we aim to explore basic principles and some of the most accessible and widely used data spreadsheets.

African relevance

• • Clinical research should be most relevant to the population where it is undertaken.
• • Research capacity should move forward as a country develops.
• • Generation and management of a spreadsheet is a fundamental research skill.

The International Federation for Emergency Medicine global health research primer

This paper forms part 10 of a series of how to papers, commissioned by the International Federation for Emergency Medicine. This research primer explores the choice of available spreadsheets and a range of principles for their best-practice use. It explores basic principles and some of the most accessible and widely used data spreadsheets.

As a relatively new specialty, research in emergency medicine is still developing. Only in the last three decades has substantial research been undertaken specifically in the emergency department (ED) setting. The importance of this lies in that ED patients differ in many ways from those in other settings. They are, by definition, undifferentiated and often affected by anxiety, pain, fear, and vulnerability. Hence, ED research must be undertaken in the ED setting and inferences from other settings are unacceptable.

One fundamental research skill is the generation and management of a data spreadsheet. Essentially, this is an electronic document where data on each enrolled patient or entity under investigation is stored in a systematic way. Spreadsheets can be used for organization, analysis and storage of data in tabular form. They also allow the manipulation of data and the generation of graphics and summary or simple statistics. Where complicated statistical analysis is required, that cannot be done using spreadsheet software, datasets can usually be imported into statistical packages and analyzed further.

Spreadsheets can also serve as data storage facilities. Subsequent access to the data may be required well after its original analysis and publication of the project's findings e.g. secondary data analysis, merger with data from similar projects and the sharing of data with other researchers (an increasing trend) [ 1 , 2 ].

Organizations ensure high quality, ethical research by utilizing research governance [ 3 ]. Governance is involved at all stages of research especially at project submission, during patient enrolment and at closure. Requirements also include data storage for many years and it may be audited to ensure good research practice [ 2 ].

Which spreadsheet to use

A wide variety of spreadsheets are now available including Microsoft Excel® [ 4 ], Libre Office® [ 5 ], Open Office® [ 6 ] and Google Sheets® [ 7 ]. In addition, there are database applications with considerable functionality behind the spreadsheet interface e.g. Access® [ 4 ] and RedCap® [ 8 ]. As each has its own characteristics and functionality, the researcher needs to consider the ways in which the spreadsheet will be used and this should be an informed decision. It is recommended, however, that the researcher becomes thoroughly familiar with one variety of spreadsheet.

For the novice researcher, Microsoft Excel® [ 4 ], Libre Office® [ 5 ] or Open Office® [ 6 ] will likely more than suffice. Microsoft Excel® [ 4 ] is relatively simple, inexpensive, widely accessible and has an increasing range of functionality. A free comprehensive instruction manual is also available on-line [ 9 ]. Given this, Microsoft Excel will be used throughout this primer where examples are provided.

Setting up the spreadsheet

Time and care in setting up the spreadsheet prior to data entry is important and should be done in concert with the data collection document. This will facilitate data entry, help to avoid confusion and mistakes and provide the most appropriate formatting for data analysis either within the spreadsheet or when exported to a statistical program. If a biostatistician will be undertaking the data analysis, it is highly recommended that he/she assists in setting up the spreadsheet. This may save considerable time and effort later on. A data collection and management practice instruction document is also recommended to assist in the efficient and accurate data procurement and process [ 10 ].

The most commonly used spreadsheet format for studies involving individual patients is to assign each patient to a single row of data cells ( Fig. 1 ). Row 1 is usually reserved for the column headings and patient data in the rows below. Next, the data from patient study identification (ID) number 1 is placed in row 2, data from patient number 2 in row 3 and so on.

Excel spreadsheet of sample data from a laceration repair study.

Each column is reserved for data on specific characteristics (variables) of the patients ( Fig. 1 ). Commonly, column A is reserved for the patient study ID number and all other data in the columns to the right. It is advisable to group the columns according to the type of data they contain e.g. columns B, C, D and E may contain patient demographics (e.g. age, sex weight, co-morbidities). Other data groups may include:

• • other patient characteristics (e.g. ethnicity, religion, employment status)
• • study details (e.g. group allocation, date of enrolment)
• • potential confounding and bias variables (e.g. triage score, pain scores, language fluency)
• • outcome variables (e.g. patient satisfaction, procedural outcome, adverse events)

Within each column, the data may be numerical (e.g. blood pressure), ordinal (e.g. age group) or categorical (e.g. ethnic group). For ease of data analysis, assign ordinal and categorical subgroup variables with a number and use these numbers to populate the cells. For example, in ‘laceration depth’ column, insert 1 if epidermal, 2 if dermal and so on ( Fig. 1 ). In anticipation of merging data with that of others, unambiguous (‘Pat. ID’ not ‘PTID’) or standard variable nomenclature (e.g. 1 = male, 2 = female) should be used.

Determining the number of variable subgroups will depend upon the nature of the study and the relevance of each variable. In general, approximately 5-6 subgroups are appropriate. Where possible, adjust the subgroups to ensure that each contains similar numbers of patients. For example, if the age subgroup of 80–100 years has few patients, that group could be adjusted to 70–100 years. Another approach may be to use actual data in the first data spreadsheet and then form the sub-groups. It is advisable to add parameters for the expected data item range in drop down boxes. This will prevent inaccurate data entry e.g. age of 201 entered instead of 101 years.

Once the spreadsheet has been set up, it should be trialed before definitive data entry is commenced. This involves entering data for a sample of patients (e.g. 50). At this stage, it is not uncommon to find errors in the way the spreadsheet has been set up or in the way data is to be entered. For example, country subgroups may comprise European, Asian, North American, South American, Australasian and ‘other’. However, if the ‘other’ subgroup is, unexpectedly, found to be large then one or more new subgroups could be added or a new column. Sometimes, there may be two items to be entered into the one cell. For example, pain management subgroups might be oral simple analgesia, parenteral simple analgesia, oral opioid, parenteral opioid etc. However, a patient might have received more than one of these medications. Entering ‘3,4’ in a cell to reflect the nature of the analgesia received, will make data analysis difficult. It is advisable to add additional columns – in this example, the first column will contain ‘3’ and the next will contain ‘4’.

Navigation around the spreadsheet

Data entry, cleaning and analysis involves moving up and down and across the spreadsheet. While this can become rather confusing, there are tricks to mitigate this. As discussed above, there may be a number of subgroups for each study variable. It is important to devise a way of finding out which subgroup classification a certain cell number represents. This could be on a separate electronic or hard copy form that states ‘column F, country of birth: 1 = European, 2 = Asian and so on’. However, this is cumbersome. It is recommended that the column headings have a ‘comment’ inserted. To do this, left click on the column heading cell (row 1), then right click on the same cell and choose ‘insert comment’ from the dropdown list. A small dialogue box will appear. Within this box, type the variable subgroups (e.g. 1 = European, 2 = Asian) with each on a separate line. Once done, left click on any other cell. You should then see a small red ‘flag’ appear on the top right of the cell you have just added the comment to ( Fig. 1 ). If you then move your cursor over that cell (do not click), the dialogue box will appear with all the subgroup details within. You can edit the comment by right clicking on the cell and then choosing ‘edit comment’.

Color coding of the column headings (or even the entire column) will help to identify particular data groups. For example, the column headings (in row 1) for the demographic, study detail and outcome variables could be colored yellow, pink and blue, respectively ( Fig. 1 ). This makes finding the column much easier e.g. simply cross to the columns of the appropriate color and search through those ones.

When an Excel® spreadsheet is opened for the first time, moving to the columns on the right may push column A out of view. Similarly, moving down the spreadsheet may push row 1 out of view. When this happens, it is easy to become disorientated if you cannot view the column heading or the patient ID numbers. To avoid this, it is advisable to use the ‘freeze panes’ function to freeze row 1 and column A in place so they are always visible. To do this, left click in cell B2, then the ‘view’ tab and then ‘freeze panes’ in the drop down list. This can be undone at any time and various numbers of columns and rows can be frozen depending upon which cell you first click on.

The transfer of data from its source to the study spreadsheet is commonly associated with mistakes. When done manually, the mistakes usually involve misinterpretation of a data item to be transferred (e.g. entering 456 instead of 654) or typographical errors (e.g. entering 653 instead of 654).

Ideally, data would be transferred electronically e.g. vitals signs transferred directly from ED computers into the spreadsheet [ 11 ]. However, this requires sophisticated computer systems and is usually not possible. A reasonable option is to enter data manually, from its source directly into the spreadsheet. In this option, data are extracted from the medical record (or other source) and typed directly into the spread sheet, often via portable devices such as computer tablets or laptops. The least favorable option is manual transfer of data from the source onto hard copy data collection forms and subsequently into the spreadsheet. The more transfer required, the greater the risk of mistakes.

To mitigate the risk of mistakes, data ‘double entry’ can be employed, where two persons enter the data separately. Upon completion, the two datasets are compared, inconsistencies between the data sets identified, those data are double-checked and reconciled, and the spreadsheet corrected if necessary. However, ‘double entry’ is resource intensive and this may preclude its use.

An alternative is to scan the data into tables using optical mark recognition (OMR) and optical character recognition (OCR) software. Machine readable forms can be created using this special software. When scanned or faxed, the handwritten information on these forms is read into the database. The advantage is that keyboard entry is eliminated. The disadvantage is that they are more difficult and costly to set up.

In all studies where a single person manually enters the data, a data quality assurance exercise should be undertaken. This involves a second person extracting at least 10% of all data as well (e.g. data from 10% of patients). Like ‘double entry’, the data from both extractors is compared. Differences (if any) are then reconciled. It may sometimes be necessary to check the entire dataset if more than the very occasional mistake is identified. It should be noted that many journals now require a description of the data quality assurance exercise to be included in the Methods section of the research paper [ 12 ]. Failure to undertake and report this exercise will likely result in rejection of the paper.

Data cleaning

Once all data entry is complete and the quality assurance exercise has been undertaken, the data needs to be ‘cleaned’. Data cleaning refers to identifying incomplete, inaccurate or irrelevant data and then replacing coarse data with clean entries in a methodical way [ 13 ]. In most cases, this involves identifying missing or incorrect data in the spreadsheet. Even if 10% of the data has been checked by a second person, there may be mistakes in the remaining 90% that should be sought.

Rather than scanning every spreadsheet cell to identify inconsistencies, ‘range checking’ can be undertaken. In Excel®, this technique involves highlighting an entire column of data and clicking on ‘sort smallest to largest’ (for numerical data) or ‘sort A to Z’ (for text data) on the ‘Sort & Filter’ dropdown box on the Home tab. Incorrect data items will be found at either the top or the bottom of the sorted data column. For example, a range check of sex (where 1 = male and 2 = female) may identify an errant ‘3’ ( Fig. 1 ). This value is ‘out of range’ for the study and needs to be corrected.

Once the range check of a column has been done, ‘undo’ the sorting before correcting any errors or moving on to the next column. To find the error once the sorting has been undone, highlight the column, click ‘Find’ from the ‘Find and Select’ dropdown box on the Home tab. Enter the incorrect value in the ‘find what’ box (e.g. 13 or 134 in the age example above) and click ‘Find Next’. The incorrect cell will be highlighted, the patient's study number determined and the data can be corrected.

If data are collected by several investigators or across different sites, then means and medians should be compared across investigators and sites. If there are substantial differences this can indicate systematic differences in measurement or data collection.

Version control

It is very easy to make mistakes or lose track of progress, especially during the data cleaning process and formatting for data analysis. In this regard, spreadsheet ‘version control’ is vital with the saving of all versions of the spreadsheet, appropriately named and dated. The importance of this lies in the possibility of mistakes being made in the cleaning or analysis (e.g. forgetting to ‘undo’ sorted columns, accidental deletion of data). If these mistakes cannot be corrected, then at least it is possible to go back to the immediately preceding version and start again.

It is recommended that the first version of the spreadsheet is saved as its own file before every major data manipulation. For example, once all data has been entered, that file could be named and saved as ‘raw data’. To progress with the data, the ‘raw data’ file should be opened and saved as the next version e.g. ‘raw data–cleaned’. Once cleaned and saved, the file is opened and saved as the next version e.g. ‘raw data–cleaned–formatted for the statistical software’.

It is recommended that version numbers and dates are also added to the file names e.g. ‘v2–raw data–cleaned–11062019’. Having the version number first has the advantage of having all the files stored in the correct version order. This could be lost if the file name comes first, or if it is changed for some reason in a subsequent version. The date is an additional means of tracking the versions if files names are written incorrectly.

Finally, like any electronic documentation, all files need to be backed up in the event of computer failure, theft, fire or other catastrophe. Many facilities have dedicated institutional hard drives on which files can be backed up. An alternative is to use an external computer hard drive. These are now relatively inexpensive, with some having the function of automatically backing up files on a regular basis (e.g. daily). Spreadsheets can also be stored on CD discs or ‘in the cloud’.

One consideration for important data spreadsheets (and other files), is to plan for the worst case scenario. Consider the consequences of a fire in your office that destroys your computer, your external hard drive and your storage discs. If these amounted to 3 years of PhD research then it may all be lost. Given such possibilities, it is recommended that important files be kept in at least one remote facility (e.g. your home computer, in the ‘cloud’).

Confidentiality

Personal data on research subjects must always be treated confidentially. Presently, Institutional Review Boards and Ethics Committees require a description of how the data will be stored confidentially and securely.

In regard to confidentiality, one sound principle is never to have patient identifying information (e.g. name, date of birth) on data collection forms or spreadsheets that also contain their personal data. We recommend setting up a unique identification number (“patient study ID”) for each study participant. Using a unique subject identifier that has no meaning external to the study database simplifies the process of “de-linking” study data from personal identifiers for purposes of maintaining subject confidentiality. A separate document called a ‘Master List’ should be developed. This document will contain and link the ID numbers with the patients' identities. This may be important if the original patient data source needs to be accessed again to check on data items. The Master List and all other files should be stored separately and should not be shared by all investigators. In the event that either the Master List or the spreadsheet is accessed by an unauthorized person, they will not be able to link patient identity with their data.

In regard to security, all hardcopy data collection sheets should be stored in a locked cabinet within an office that is locked when unattended. Similarly, electronic files, including the study spreadsheets, should be password protected and stored on password protected computers. Only authorized study investigators should be able to access these files.

Tips on this topic

• • Prior to data entry, perfect the design, trial and revise the spreadsheet. Invite your statistician to assist at this stage. There are many tutorials on line to assist with spreadsheet set up [ 14 , 15 ]
• • Train yourself on the type of spreadsheet that you plan to use. Make mock files and test interactivity, graphs and statistics functions.
• • Clean data thoroughly before analyses – this will save time and effort
• • Undertake a data quality assurance exercise prior to data analysis. This will ensure high quality data and is required by many journals.

Pitfalls to avoid

• • Do not forget the importance of version control and backup of your spreadsheet
• • Avoid the possibility of breaches of confidentiality and security of the data
• • Avoid multiple persons entering data into the spreadsheet. Also, minimize the number of times data needs to be transferred manually, wherever possible.

Authors' contribution

Authors contributed as follow to the conception or design of the work; the acquisition, analysis, or interpretation of data for the work; and drafting the work or revising it critically for important intellectual content: DT contributed 70%; and PH, ASK and ES 10% each. All authors approved the version to be published and agreed to be accountable for all aspects of the work.

Annotated bibliography

Harvey G. (1) Excel 2016 All-in-one for Dummies. Available at: http://www.allitebooks.in/excel-2016-all-in-one-for-dummies/ (accessed December 11, 2019).

This book is free to download from the Internet. It is a comprehensive guide to the use of Excel. As such it can be somewhat heavy going. It is certainly a reference source when learning new skills but is not for a casual read. A 2019 edition is available.

Declaration of competing interest

The authors declared no conflicts of interest.

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Using Microsoft Excel for Social Research

• Charlotte Brookfield - Cardiff University, Cardiff, UK
• Description

Providing step-by-step instructions for how to use Microsoft Excel for doing statistics, Charlotte Brookfield discusses different stages of the research process, from first planning to writing and presenting your research. With a focus on conducting robust data analysis, the book is reassuring, clear and straightforward, helping you to:

·       Learn important data skills, such as preparing, cleaning and managing data

·       Reduce anxiety about approaching statistics and quantitative data

·       Boost your employability, showing you how to develop transferable skills, such as effective time management

Whether you’re learning data skills for the first time or translating your statistics knowledge from other software, this book will help you successfully carry out social research in any setting with confidence, via an engaging pedagogy that includes: colour-coded chapters by difficulty, activities, 'Remember' boxes, further reading and skills checklists.

Supplements

This is an invaluable resource for people learning statistics. Brookfield provides a clear, accessible and engaging introduction to using Excel to explore, analyze and report quantitative data. 

It makes perfect sense to train our students in Microsoft Excel: not only does the programme have increased functionality for social research, but it is also a staple in many graduate workplaces. This text is distinct from other Excel help guides in that it is explicitly designed around the social research process and with undergraduate students in mind. Using real-life datasets and tools such as ‘reminder boxes’, it is a highly comprehensive, engaging and accessible resource for introductory quantitative research methods modules. 

This is the book I have been waiting for. We have learned that employers value Excel and that many small workplaces cannot afford SPSS licences. This covers everything we would do in SPSS (possibly excepting recoding variables). Sold.

Preview this book

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What Is Excel? Formulas, Functions, and More

Microsoft Excel is a software that you can use to organize data for your work and everyday life. Learn about formulas, functions, and more that you can apply when using Excel.

Microsoft Excel can be an incredibly powerful tool to learn for your career, with benefits for everyone from data analysts , to social media marketers . It has capabilities for the everyday user to create charts, graphs, and more to organize and visualize data. 

In this article, you’ll learn what Excel is and does, formulas and functions to know, and some resources to help you get started.

What is Excel?

Excel is part of Microsoft’s 365 suite of software, alongside Word, PowerPoint, Teams, Outlook, and more. Microsoft Excel is a spreadsheet program that allows users to organize, format, and calculate data in a spreadsheet. Excel users can create pivot tables and graphs to help them compute and visualize complex data sets. 

Excel vs. Google Sheets: What’s the difference?

Excel and Google Sheets offer similar capabilities and features. The main difference is that Google Sheets offers a free version where several users can edit the doc at the same time, which makes it convenient for real-time collaboration. When you share your Google Sheets link with others, they can then edit the file.

Documents you can create in Excel

There’s no shortage of things you can do with an Excel spreadsheet. Here are just a few common documents you can create:

Balance sheet

Data report

Income statement

Mailing list

Planning document

All of these documents can be applied to your business or personal life. Excel is a versatile tool that can help you stay organized and calculate important information. 

How to use Excel

When using Excel, you’ll want to be sure to know the basics of a spreadsheet program. Once you’re familiar with its interface and features, you can add data to the cells or create a document by formatting the cells to your liking. Then, you can learn formulas and functions to calculate sums of money, for example, or the number of products needed for a launch.

Basics of Excel

When you’re starting out with Excel, here are a few commands you’ll want to know.

How to create a new spreadsheet

How to format column or row text and titles

How to add , subtract, multiply, and divide numbers in two or more cells

How to add or delete columns, rows, and pages (within the same spreadsheet)

How to sort your data

Once you’ve got the basics down, you can start to learn the different Excel formulas to help you compute data.

Excel formulas

There are many formulas available in Excel that you can use to work with data. Each formula in Excel begins with an equal sign . Before you create a formula, you’ll need to write an equal sign (=) in the cell where you want the formula’s result to appear.

These are some of the basic formulas to keep in mind.

Add: To add the values of two or more cells, use the plus (+) sign.

Example: =A4+D5

Subtract: To subtract the values of two or more cells, use the minus (-) sign.

Example: =A4-D5

Multiply: To multiply the values of two or more cells, use the asterisk (*). 

Example: =A4*D5

Divide: To divide the values of two or more cells, use the forward slash (/). 

Example: =A4/D5

You can use parentheses to create a large formula that combines these actions. Example: =((A4+C4)/(D5-C5)*3).

Excel functions

On Excel, you can use “functions” to automate tasks you normally use in a formula. Instead of using the plus sign to add a range of cells, you can use the SUM function. Let’s go through a few popular functions:

SUM: The SUM function adds up a range of cells. To input the function, use parentheses to indicate the range of cells. If you are summing up the numbers in cell A1 through A17, your formula would be: =SUM(A1:A17).

AVERAGE: Similar to the SUM function, the AVERAGE function calculates the mean of the values of a range of cells. For example: =AVERAGE (A1:A17).

IF: With the IF function, you can ask Excel to return values based on a logical test. The syntax looks like: IF(logical_test, value_if_true, [value_if_false]). For example: =IF(A1>B1,”Over Budget”,”OK”).  

VLOOKUP: The VLOOKUP function allows you to search for anything in your spreadsheet’s columns or rows. The syntax looks like: VLOOKUP(lookup value, table array, column number, Approximate match (TRUE) or Exact match (FALSE)). For example: =VLOOKUP([@Engineer],tbl_Engineers,7,TRUE).  

COUNTIF: The COUNTIF function is another useful one that returns the number of cells that meet certain criteria. The syntax looks like: COUNTIF(range, criteria). For example: =COUNTIF(A1:A17,”San Francisco”).

Tutorials to get started

We’ve put together some tutorials for Google Sheets, all of which are applicable to Microsoft Excel. Here’s a list so you can build your skills in the most common actions:

How to Alphabetize in Google Sheets: Your Guide to Sorting

How to Merge Cells in Google Sheets

How to Highlight Duplicates in Google Sheets

How to Use Conditional Formatting in Google Sheets

How to Add and Subtract a Column in Google Sheets

How to Concatenate in Google Sheets (Combine Cells without Losing Data)

How to Use VLOOKUP in Google Sheets

How to Use SUMIF in Google Sheets

Using IMPORTRANGE to Reference Another Google Sheet

How to Fix Formula Parse Error: Google Sheets

Excel guided projects

In your journey to using Excel, you may want to learn specific skills that will help you on the job while working with data or creating a budget. Check out these short, one or two-hour guided projects to get started.

Introduction to Microsoft Excel

Using Basic Formulas and Functions in Microsoft Excel

Introduction to Data Analysis using Microsoft Excel

Create Charts and Dashboards Using Microsoft Excel

Creating a Budget with Microsoft Excel

How to Use Lookup Reference Math and Text Functions in Excel  

Who needs to know Excel?

Excel is applicable to nearly every industry, from finance to project management to marketing, and beyond. Food service managers can use it to track invoices. Social media associates can use it to consolidate multiple MailChimp mailing lists. 

It’s most pertinent to those in careers that work daily with data, such as data analysts, marketing managers, accountants, and business owners. But teachers, non-profit professionals, and social workers may also find Excel useful for their everyday work to organize information and create charts or graphs. 

Excel for data and business

If you’re embarking on a career in data or business analytics, you may be interested in IBM’s Excel Basics for Data Analysis and Johns Hopkins University’s Business Analytics with Excel: Elementary to Advanced . Both require less than 24 hours to complete.

Learn Excel today

Coursera offers several options for learning how to use Excel. If you’re learning Microsoft Excel for a job or even for personal use, consider taking Microsoft’s course Work Smarter with Microsoft Excel , which is part of their Microsoft 365 Fundamentals Specialization .

If you’re specifically interested in learning Excel for business, enroll in Macquarie University’s highly-rated course Excel Skills for Business Specialization . With both courses, you can learn at your own pace and build your skills on your computer—anytime, anywhere.

Coursera Staff

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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Scott Kuban

How to enter research data into excel.

As a professor that often leads the data and analysis of scholarly research projects, and collaborates across many universities, I have encountered good, bad, and downright terrible data being passed around in excel files. This is usually the result of Ph.D. students still moving up the learning curve but can be the result of other issues. So I created this document to help students not be terrible at hand-collecting, recording, transferring, and (if we absolutely must) manipulating data in excel. Further, research professors may find a tip that allows them to further optimize their already no-doubt finely-honed data skills. This -much like the vaunted U.S. Constitution- will be a living document that evolves and grows with time. Constitution constructionist trolling aside, this simply means that I will be updating this document as I think of new tips or students find new ways to anger me with the data sent to me for a project. Tips are numbered merely for easy reference not to confer some order or importance upon them. Importance and/or recency of data anger will be conferred with exclamation points and/or italics. That said, the number 1 rule is pretty important.

Why should I care? First, there is the classic computer science mantra: garbage in, garbage out. Heaven forbid, but badly input data could, if not corrected along the way by a professor overseeing the work, bias a project’s results that will be submitted to a conference or journal. Second (and more likely with skilled researchers guiding the project), poorly collected data negatively impacts a research project through additional time wasted getting the data to merge properly, cleaning the messy data, and analyzing models with poorly thought out variables. The latter usually results in additional data collecting (if still possible) to replace the poorly thought out variables with better ones. All this wasted time and effort also creates frustration. If the same person is doing all of these tasks, you are only hurting yourself by ignoring these tips. If a student is collecting data poorly for a professor this can lead to… unpleasantries. Avoid future pain by following these rules.

Note: While using excel is a reasonable choice for data that must be hand-collected, it should be an absolute last resort for otherwise interacting with the data. And no, already knowing how to do something in excel rather than learning how to do it in SAS, Stata, or R is not a valid reason. Don’t be lazy.

• A column in excel represents a single variable that someone will do statistics on later! Treat it as such!!! This means a) figuring out what the variable will be later in the model to determine how the data should be entered and b) only entering that type of data. For instance, faced with collecting if an executive is a CEO a) figuring out that a binary variable is a wise choice for this b) entering a 1 or 0 only!!! Rather than the terrible text strings that often become an incoherent combination of YES, y, no, YeS, 1, Y, N, NO, Co-CEO, Interm CEO, [blank], Interm CEO that was later approved to be full CEO, NA, N/A, I couldn’t find this one, Not applicable, President Only, etc.
• Use dummy variables whenever possible. Many, many things can be converted into one or more dummies if you consider it first. So instead of ‘executive position’ with a bunch of free form text (similar to the example text in #1) create a dummy for each position: CEO, COO, CFO, etc.
• Expand your dummy variables as needed rather than breaking the #1 rule! For instance, after setting up dummies in #2 if one encounters a Co-CEO instead of abandoning your principals and writing “Co-CEO” in the column for CEO, create a new Co-CEO column that can still be coded with a 1 or 0. Make a note (see #4) as to whether Co-CEOs are still coded as a CEO (i.e., is the Co-CEO variable additive to the CEO variable or replacing it).
• Put any notes in a separate column or columns. Depending on the amount and type of data being collected in excel a single ‘notes’ column may suffice or it may be better to create a notes field for each variable that needs it. Name it something obvious like executive_scandal_notes for notes on the executive_scandal variable.
• Enter dates numerically (no text months) and consistently!!! Generally, this should be either: 2020/3/25 or 3/25/2020. Yes, doing the placeholder zeros (e.g., 2020/03/25) is a little cleaner but most programs can handle importing correctly without them and people tend to struggle with including them. I often see a huge mix of date styles in a single column: 3/25/1999, March 25th, 1999, 3-25-1999, 3.25.1999, Mar 25, Mar 25 99, 25th Mar 1999, etc. Which neither imports well or is easy to clean with a script as it is inconsistent (e.g., a script splitting on the /, or grabbing the final 4 characters for the year).
• Never change an ID in excel. These are often used to merge the excel data with other data so if you change the ID the merge will fail. Instead, create a new column such as ID_updated with the change you want to make. This allows for merging on the newly updated ID without throwing away the old ID through your reckless action.
• Unless you explicitly know otherwise, assume every variable is an ID variable (or otherwise similarly important). Disk space is practically free while time is incredibly valuable , so instead of changing data in an existing variable (column) make a new variable (column) to make your changes. For instance, to get the stock market reaction to an event the date often will need to not the event_date but the first day the stock market was open following the event_date. The programs that retrieve stock market data also want the data formatted in a particular way. This, therefore, presents two temptations to change the event_date while retrieving this additional data – don’t do it. Make a new stock_reaction_date variable and leave the event_date unmolested so that one can easily merge the new data into the existing dataset.
• Really it is best to avoid changing any variables data if at all possible. In addition to breaking merges if the variable is an ID, the variable in question may undergo cleaning and or other manipulation in code after import that your changes might break thus also wasting others’ precious time. This is extremely likely when adding additional data (usually at the request of reviewers) later in a project. Again, disk space is practically free while time is incredibly valuable so make another column and use another 0.0000001% of your disk space rather than potentially waste hours of time.
• Be careful of text IDs with preceding zeros (e.g., 0023817). Excel likes to throw these zeros away. One must make the column a ‘text’ format (rather than general, number, etc) to keep it from doing this. Compustat’s unique ID named gvkey is such a variable.
• Don’t hand make a variable that can be created in a program. For instance, perhaps one’s eventual model will consider if CEOs are fired the same day a scandal is announced by the firm. Collect the two dates (see #5) in two variables scandal_announcement_date and CEO_fired_date, but do not create a human entered dummy (it should be a dummy see #2) CEO_fired_on_announce_day as one can easily and without mistakes create this dummy in Stata/SAS/R by comparing the two dates.
• Any time one wants to enter N/A in a variable consider if a) the way you are creating this variable is a good one or if b) one merely doesn’t understand what N/A actually means. A) Could you use a different coding and/or additional variables (see #3) to capture the data without a useless N/A that both breaks #1 and doesn’t capture whatever is driving you to put N/A in the first place? B) If you are entering N/A into a mutually exclusive dummy you are using it wrong. E.g., CEO_has_former_CEO_experience has to be a 1 or a 0 (or a missing if one doesn’t know) the newly hired CEO either was a CEO before or not, therefore N/A makes no sense. And senseless data makes everyone unhappy.
• Watch out for spaces before and after data being entered. Macs particularly like to “help you” by adding a space after any and every word ever because using the space bar is not elegant or something. Depending on the data an extra space can break things on import or later in analysis and are a pain to locate as visually the data looks right.
• If you are asked to fill in missing data in excel, don’t change a bunch of other things without checking with your professor first. You may be changing IDs needed to merge your collected missing data back into the dataset or otherwise messing up the dataset (see #6, #7, and #8 ).
• Do not use multiple tabs in excel. If you are using multiple tabs in excel this means that you are likely doing some combination of the following: creating unnecessary duplicates, reusing variable names that can only be told apart by referencing the tab names, using variable names that are generally unclear without the tab names, and/or collecting data in a format different than the rest of the data that will require additional work to merge.
• The variable name (top excel row) of the data should a short but meaningful  name. Preferably in lowercase with underscores for spaces unless the variable exists somewhere else already then it should match that other naming exactly. For instance, firm_age is obvious in the data it contains, but even more complicated variables such as firm_FEC_total_amt_cycle reasonably conveys that is the firm’s total amount of FEC (federal election commission tracked campaign contribution) dollars in an election cycle (rather than in a year). Stata’s limit of 32 characters only allows so much description in the name but it should be helpful without referencing the longer variable label.
• The top excel row is for the variable names of the data below it and nothing else. This is not a place for notes to self (see #4) or even explanations of the data below. If you need a note to explain the data collected below, the variable is either ill-conceived (see #1, #2, and #3) or poorly named (see #15), or both.

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Using Microsoft Excel for Social Research 1st Edition

Purchase options and add-ons.

Providing step-by-step instructions for how to use Microsoft Excel for doing statistics, Charlotte Brookfield discusses different stages of the research process, from first planning to writing and presenting your research. With a focus on conducting robust data analysis, the book is reassuring, clear and straightforward, helping you to:  

·       Learn important data skills, such as preparing, cleaning and managing data

·       Reduce anxiety about approaching statistics and quantitative data

·       Boost your employability, showing you how to develop transferable skills, such as effective time management.

Whether you’re learning data skills for the first time or translating your statistics knowledge from other software, this book will help you successfully carry out social research in any setting with confidence, via an engaging pedagogy that includes: colour-coded chapters by difficulty, activities, ′Remember′ boxes, further reading and skills checklists.

• ISBN-10 1526468344
• ISBN-13 978-1526468345
• Edition 1st
• Publisher SAGE Publications Ltd
• Publication date May 25, 2021
• Language English
• Dimensions 6.69 x 0.5 x 9.61 inches
• Print length 208 pages
• See all details

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Editorial Reviews

About the author.

Charlotte Brookfield is a senior lecturer of social sciences at Cardiff University. Charlotte is based in the Cardiff Q-Step Centre of Excellence in Quantitative Methods Teaching and Learning. The Centre is one of eighteen across the UK which aim to enhance the quantitative research methods training experience for social science students. The pedagogic activities of the Centre have influenced Charlotte’s research interests and in particular, she is interested in exploring the extent to which British sociology engages with quantitative approaches and the possible factors that may contribute toward sociology students’ resistance to study and use quantitative techniques.

Charlotte teaches on a range of research methods and substantive modules at both undergraduate and postgraduate level. Specifically, she convenes the Real World Research Placement module, where students are afforded the opportunity to put into practice the quantitative skills they have acquired in lectures in a local work organisation. Organisations involved in this module include the Welsh Government, the Welsh Blood Service and the Welsh Wheelchair Basketball Association. It was through leading this module that Charlotte came to realise the necessity for social science students and graduates to have a greater familiarity with Microsoft Excel.

In her spare time, Charlotte enjoys baking and crafting.

Product details

• Publisher ‏ : ‎ SAGE Publications Ltd; 1st edition (May 25, 2021)
• Language ‏ : ‎ English
• Hardcover ‏ : ‎ 208 pages
• ISBN-10 ‏ : ‎ 1526468344
• ISBN-13 ‏ : ‎ 978-1526468345
• Item Weight ‏ : ‎ 1.15 pounds
• Dimensions ‏ : ‎ 6.69 x 0.5 x 9.61 inches
• #1,638 in Microsoft Excel Guides
• #4,235 in Social Sciences Methodology
• #7,722 in Social Sciences Research

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• Microsoft Excel

New to Excel? Here's Super Easy Tricks to Get You Started

Last Updated: October 29, 2022 Fact Checked

This article was co-authored by wikiHow staff writer, Nicole Levine, MFA . Nicole Levine is a Technology Writer and Editor for wikiHow. She has more than 20 years of experience creating technical documentation and leading support teams at major web hosting and software companies. Nicole also holds an MFA in Creative Writing from Portland State University and teaches composition, fiction-writing, and zine-making at various institutions. There are 8 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 773,671 times. Learn more...

Are you new to Microsoft Excel and need to work on a spreadsheet? Excel is so overrun with useful and complicated features that it might seem impossible for a beginner to learn. But don't worry—once you learn a few basic tricks, you'll be entering, manipulating, calculating, and graphing data in no time! This wikiHow tutorial will introduce you to the most important features and functions you'll need to know when starting out with Excel, from entering and sorting basic data to writing your first formulas.

Things You Should Know

• Use Quick Analysis in Excel to perform quick calculations and create helpful graphs without any prior Excel knowledge.
• Adding your data to a table makes it easy to sort and filter data by your preferred criteria.
• Even if you're not a math person, you can use basic Excel math functions to add, subtract, find averages and more in seconds.

Understanding Workbooks and Worksheets

• To start from scratch, click Blank workbook . Otherwise, you can open an existing workbook or create a new one from one of Excel's helpful templates, such as those designed for budgeting .

• Columns are vertical and labeled with letters, which appear above each column.
• Rows are horizontal and are labeled by numbers, which you'll see running along the left side of the worksheet.
• Every cell has an address which contains its column letter and row number. For example, the top-left cell in your worksheet's address is A1 because it's in column A, row 1.
• A workbook can have multiple worksheets, all containing different sets of data. Each worksheet in your workbook has a name—you can rename a worksheet by right-clicking its tab and selecting Rename.
• To add another worksheet, just click the + next to the worksheet tab(s).

• Click the File menu and select Save As .
• Choose a location to save the file, such as on your computer or in OneDrive.
• Type a name for your workbook. All workbooks will automatically inherit the the .XLSX file extension.
• Click Save .

Entering and Formatting Data

• When you type something into a cell, the input text is called a value . Entering data into Excel is as simple as typing values into each cell.
• For example, if you're adding a list of dates in column A, you might click cell A1 and type Date into the cell as the column header.

• When you start practicing more advanced Excel features like creating formulas, this bar will come in handy.
• You can also copy and paste text from other applications into your worksheet, tables from PDFs and the web.

• In a blank column, type 1 into the first cell, 2 into the second cell, and then 3 into the third cell.
• Hover your mouse cursor over the bottom-right corner of the last cell in your series—it will turn to a crosshair.
• Click and drag the crosshair down the column, then release the mouse button once you've gone down as far as you like. By default, this will fill the remaining cells with the value of the selected cell—at this point, you'll probably have something like 1, 2, 3, 3, 3, 3, 3, 3.
• Click the small icon at the bottom-right corner of the filled data to open AutoFill options, and select Fill Series to automatically detect the series or pattern. Now you'll have a list of consecutive numbers. Try this cool feature out with different patterns!
• Once you get the hang of AutoFill, you'll have to try flash fill , which you can use to join two columns of data into a single merged column.

• To expand the contents of column B, hover the cursor over the dividing line between the B and C at the top of the worksheet—once your cursor is right on the line, it will turn to two arrows pointing in either direction. [2] X Trustworthy Source Microsoft Support Technical support and product information from Microsoft. Go to source
• Click and drag the separator until the column is wide enough to accommodate your data, or just double-click the separator to instantly snap the column to the size of the widest value.

• To delete the contents of a cell, click the cell once and press delete on your keyboard.

• Select a cell, column, row, or multiple cells at once.
• On the Home tab, click Cell Styles if you'd like to quickly apply quick color styles.
• If you'd rather use more custom options, right-click the selected cell(s) and select Format Cells . Then, use the colors on the Fill tab to customize the cell's background, or the colors on the Font tab for value colors.

• Select the cell you want to format. If you're working with an entire column or row, you can just click the column letter or row number to select the whole thing.
• On the Home tab, click the drop-down menu at the top-center—it'll say General by default, unless you selected cells that Excel recognizes as a different type of number like Currency or Time .
• Choose one of the formatting options in the list, such as Short Date or Percentage , or click More Number Formats at the bottom to expand all options (we recommend this!).
• If you selected More Number Formats , the Format Cells dialog will expand to the Number tab, where you'll see several categories for number types.
• Select a category, such as Currency if working with money, or Date if working with dates. Then, choose your preferences, such as a currency symbol and/or decimal places.
• Click OK to apply your formatting.

Creating, Sorting, and Filtering Tables

• Tables traditionally apply different or alternating colors to every other row for easy viewing. Many table options also add borders between cells and/or columns and rows.

• Once you get the hang of tables, you can return here to customize your table further by selecting New Table Style .

• If you chose a numerical column, select Number Filters , then choose an option like Greater Than… or Does Not Equal to be extra specific about which values to hide.
• For text columns, you can choose Text Filters , where you can specify things like Begins with or Contains .
• You can also filter by cell color .

• To unfilter your data, click the funnel icon, click Clear filter from (column name) , and then click OK .
• You can also filter columns that aren't in tables. Just select a column and click Filter on the Data tab to add a drop-down to that column.

• If you're working with numbers, click Smallest to Largest to sort in ascending order, or Largest to Smallest for descending order. [6] X Trustworthy Source Microsoft Support Technical support and product information from Microsoft. Go to source
• If you're working with text values, Sort A to Z will sort in ascending order, while Sort Z to A will sort in reverse.
• When it comes to sorting dates and times, Sort Oldest to Newest will sort with the earliest date at the top and the oldest date at the bottom, and Newest to Oldest displays the dates in descending order.
• When you sort a column, all other columns in the table adjust based on the sort.

Doing Quick Analysis

• For math calculations, click the Totals tab, where you can select Sum , Average , Count , %Total , or Running Total . You'll be able to choose whether to display the results at the bottom of each column or to the right.
• To create a chart, click the Charts tab, then select a chart to visualize your data. Before you settle on a chart, just hover the cursor over each option to see a preview.
• To add quick chart data to individual cells, click the Sparklines tab and choose a format. Again, you can hover the cursor over each option to see a preview.
• To instantly apply conditional formatting (which is usually a little more complex in Excel) based on your data, use the Formatting tab. Here you can choose an option like Color or Data Bars , which apply colors to your data based on trends.

Performing Basic Calculations

• Click the cell below the numbers you want to add (if a column) or to the right (if a row). [8] X Trustworthy Source Microsoft Support Technical support and product information from Microsoft. Go to source
• On the Home tab, click AutoSum toward the upper-right corner of the app. A formula beginning with =SUM(cell+cell) will appear in the field, and a dotted line will surround the numbers you're adding.
• Press Enter or Return . You should now see the total of the numbers in the selected field. This is here because you created your first formula—which you didn't have to write by hand!
• If you change any numbers in your data after using AutoSum, the AutoSum value will update automatically.

• If you want to add all of the numbers in a whole column (or in a section of a column), type =SUM(cell:cell) (e.g., =SUM(A1:A12) ) into the cell you want to use to display the result.
• Subtract: Type =SUM(cell-cell) (e.g., =SUM(A3-B3) ) to subtract one cell value from another cell's value.
• Divide: Type =SUM(cell/cell) (e.g., =SUM(A6/C5) ) to divide one cell's value by another cell's value.
• Multiply: Type =SUM(cell*cell) (e.g., =SUM(A2*A7) ) to multiply two cell values together.

Creating Advanced Formulas

• For example, to select the formula for finding the tangent of an angle, you would scroll down and click the TAN option.

• For example, if you select the TAN function, you'll type in the number for which you want to find the tangent, or select the cell that contains that number.
• Depending on your selected function, you may need to click through a couple of on-screen prompts.

Building Charts & Graphs

• Typically speaking, the left column is used for the horizontal axis and the column immediately to the right of it represents the vertical axis.

Community Q&A

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• ↑ https://support.microsoft.com/en-us/office/save-your-workbook-92e4aae0-452d-497f-a470-570610ff720a
• ↑ https://support.microsoft.com/en-us/office/adjust-the-column-size-to-see-everything-4b72b631-ee0a-4539-b1be-499fedc14fe2
• ↑ https://support.microsoft.com/en-us/office/available-number-formats-in-excel-0afe8f52-97db-41f1-b972-4b46e9f1e8d2
• ↑ https://support.microsoft.com/en-us/office/overview-of-excel-tables-7ab0bb7d-3a9e-4b56-a3c9-6c94334e492c
• ↑ https://support.microsoft.com/en-us/office/create-and-format-tables-e81aa349-b006-4f8a-9806-5af9df0ac664
• ↑ https://support.microsoft.com/en-us/office/sort-data-in-a-range-or-table-62d0b95d-2a90-4610-a6ae-2e545c4a4654
• ↑ https://support.microsoft.com/en-us/office/instant-charts-using-quick-analysis-9e382e73-7f5e-495a-a8dc-be8225b1bb78
• ↑ https://support.microsoft.com/en-us/office/use-autosum-to-sum-numbers-543941e7-e783-44ef-8317-7d1bb85fe706

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Microsoft New Future of Work Report 2022

• Jaime Teevan ,
• Nancy Baym ,
• Jenna Butler ,
• Brent Hecht ,
• Sonia Jaffe ,
• Kate Nowak ,
• Abigail Sellen ,
• Longqi Yang ,
• Marcus Ash ,
• Kagonya Awori ,
• Mia Bruch ,
• Piali Choudhury ,
• Adam Coleman ,
• Scott Counts ,
• Shiraz Cupala ,
• Mary Czerwinski ,
• Elizabeth Fetterolf ,
• Mar Gonzalez Franco ,
• Kunal Gupta ,
• Aaron L Halfaker ,
• Constance Hadley ,
• Brian Houck ,
• Kori Inkpen ,
• Shamsi Iqbal ,
• Eric Knudsen ,
• Stacey Levine ,
• Siân Lindley ,
• Jennifer Neville ,
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• Neha Parikh Shah ,
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• Adam D. Troy ,
• Mengting Wan

MSR-TR-2022-3 | May 2022

Published by Microsoft

Due to the “Great Remote Work Experiment” that began in March 2020 when workplaces around the world rapidly shut down, work is changing faster than it has in a generation. As many people now return to the workplace and begin to experiment with hybrid work, a range of different outcomes is possible. Thankfully, researchers at Microsoft and from around the world have been investigating evolving hybrid work practices and developing technologies that will address the biggest new challenges while taking advantage of the biggest new opportunities.​

This Microsoft New Future of Work Report 2022 summarizes important recent research developments related to hybrid work. It highlights themes that have emerged in the findings of the past year and brings to the fore older research that has become newly relevant. Our hope is that the report will facilitate knowledge sharing across the research community and among those who track research related to work and productivity. This research area is unfolding as rapidly as work is changing, and the purpose of this report is to help the community build on what has been learned this past year.​

Never before has there been such an opportunity to actively shape the future of work. With research and careful study, we can create a new future of work that is meaningful, productive, and equitable.

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The Future of Work Report summarizes the most important research and developments that can help us understand and improve the future of work. This report is organized by changes in work practices at four different “scales”: individual, team, organization, and societal. Throughout, researchers at Microsoft are using methods like the latest advances in AI, causal inference, surveys and interviews, and prototype-building to uncover challenges and opportunities facing workers. But this report does not only feature research done at Microsoft: there is amazing research creating the future of work done at universities around the world. The report also includes this work as well, with pointers about how to find out more. About this video: People who feel valued at work are less likely to suffer burnout…

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The New Future of Work Report summarizes important research and developments that can help us understand and improve the future of work. This report is organized by changes in work practices at four different “scales”: individuals, teams, organizations, and society. Throughout, researchers at Microsoft are using methods such as the latest advances in AI, causal inference, surveys and interviews, and prototype-building to uncover challenges and opportunities facing workers. But this report does not only feature research done at Microsoft: there is amazing research shaping the future of work done by researchers around the world. The report includes this work as well and forecasts from some of these experts. About this video: AI-curated online communities can break down workplace siloes and improve knowledge sharing—leading to happier…

Microsoft New Future of Work Report 2022: Hybrid workspaces

The New Future of Work Report summarizes important research and developments that can help us understand and improve the future of work. This report is organized by changes in work practices at four different “scales”: individuals, teams, organizations, and society. Throughout, researchers at Microsoft are using methods such as the latest advances in AI, causal inference, surveys and interviews, and prototype-building to uncover challenges and opportunities facing workers. But this report does not only feature research done at Microsoft: there is amazing research shaping the future of work done by researchers around the world. The report includes this work as well and forecasts from some of these experts. About this video: By one rule of thumb, workspace expenses equal 10% of wage costs, which means…

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