Meta reported 67.32K in Employees for its fiscal year ending in December of 2023. Data for Meta | FB - Employees Total Number including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Meta Platforms had ****** full-time employees as of December 2024, down from ****** people in 2023. As of December 2023, more than ******* employees at tech companies worldwide were laid off throughout the year across more than 1,000 companies. Facebook: how it all began In 2003, a sophomore at named Mark Zuckerberg hacked into protected areas of the university's computer network in order to find photos of other students. He then would pair two of them next to each other on a program called “Facemash” and ask users to choose the more attractive person. At the beginning of 2004, Zuckerberg launched “The Facebook,” a social network dedicated to Harvard students, which later grew to encompass Columbia, Yale and Stanford. The popularity of this new service sky-rocketed and in mid-2004, Zuckerberg interrupted his studies and moved his operation to Palo Alto, California, in the heart of Silicon Valley. By 2006, Facebook was open to the general public. In 2020, the company reported almost ** billion U.S. dollars in revenue and a net income of ***** billion US dollars. It is also the most popular social network in the world, with *** billion monthly active users as of December 2020. Facebook employee diversity criticism Like many other tech companies, Facebook has been criticized for having a diversity problem. As of June 2020, tech positions, as well as management roles in U.S. offices were overwhelmingly occupied by men. Furthermore, almost ** percent of Facebook employees in the U.S. are White and only *** percent are African-American, which has sparked concern regarding representation and equal opportunities. Around **** percent of senior level positions are occupied by White employees and only *** percent by Hispanic-Americans.

This data set was retrieved from PhantomBuster.

Described is the employee name at Meta, role within Product Management or Data Science, location, and LinkedIn profile URL.

About this dataset

Context

The dataset tabulates the Meta population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Meta. The dataset can be utilized to understand the population distribution of Meta by age. For example, using this dataset, we can identify the largest age group in Meta.

Key observations

The largest age group in Meta, MO was for the group of age 65 to 69 years years with a population of 18 (14.06%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Meta, MO was the 20 to 24 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Meta is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Meta total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta Population by Age. You can refer the same here

We include a description of the data sets in the meta-data as well as sample code and results from a simulated data set. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The R code is available on line here: https://github.com/warrenjl/SpGPCW. Format: Abstract The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publicly available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. File format: R workspace file. Metadata (including data dictionary) • y: Vector of binary responses (1: preterm birth, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate). This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Meta. The dataset can be utilized to gain insights into gender-based income distribution within the Meta population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Meta, among individuals aged 15 years and older with income, there were 66 men and 50 women in the workforce. Among them, 38 men were engaged in full-time, year-round employment, while 23 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 5.26% fell within the income range of under $24,999, while 13.04% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: none of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

https://i.neilsberg.com/ch/meta-mo-income-distribution-by-gender-and-employment-type.jpeg" alt="Meta, MO gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta median household income by gender. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Meta. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Meta, the median income for all workers aged 15 years and older, regardless of work hours, was $40,000 for males and $25,893 for females.

These income figures highlight a substantial gender-based income gap in Meta. Women, regardless of work hours, earn 65 cents for each dollar earned by men. This significant gender pay gap, approximately 35%, underscores concerning gender-based income inequality in the city of Meta.

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.08 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta median household income by race. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Meta, MO population pyramid, which represents the Meta population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Meta, MO, is 15.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Meta, MO, is 41.7.
• Total dependency ratio for Meta, MO is 57.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Meta, MO is 2.4.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Meta population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Meta for the selected age group is shown in the following column.
• Population (Female): The female population in the Meta for the selected age group is shown in the following column.
• Total Population: The total population of the Meta for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta Population by Age. You can refer the same here

Describe your research hypothesis, what your data shows, any notable findings and how the data can be interpreted. Please add sufficient description to enable others to understand what the data is, how it was gathered and how to interpret and use it.

This dataset considers some socioeconomic variables of professional individuals working in the Meta department in Colombia. They have been randomly simulated for use as a teaching resource in the learning of concepts and methodologies in the Descriptive Statistics course. The data does not contain any sensitive information and can be used for practical learning activities.

Do you need a data science project in finance domain?

Do you want to work on freshly collected dataset?

Do you need a newly created dataset to work on so that your project can help you get a job?

Do you also need an example on how to perform exploratory data analysis on time series data?

Then look no where. This is because you are at the right place and at the right time.

This dataset was collected using pandas_datareader library. Data source is stooq. This dataset consist of the stock price of Meta/Facebook from January, 2019 till April, 2023. This data can be used to perform time series analysis on any stock price such as open, close, high or low.

I have also created a notebook showing how you can perform exploratory data analysis on time series data.

TASK: 1. Determine moving average. 2. Determine weighted moving average. 3. Determine cumulative moving average. 4. Determine exponential moving average.

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

Public dataset that everyone can use Creating a dataframe from the tweets list above. E-mail supervision In order to keep a regular discussion going, it is useful to use e-mail. There are many distance discussions that take place by e-mail or fax, backed up with some visits for full-blown supervisions. If the supervisor is in another country, then e-mail contact is essential, as the face-to-face supervisory contacts will be condensed into the periods when you can both be in the same country. Make e-mail contacts lucid, short and precise, with some friendly tone to establish a personal touch. Try not to get involved in excessively chatty discussions but concentrate on asking questions, seeking information and reporting on findings for comment. E-mail is quite an insistent medium. If you make contact too frequently, the supervisor will feel harassed. If you make contact too infrequently, the supervisor will feel guilty (and so will you), wondering what you are up to. Regular brief contact with some very full discussions on work in progress at regular intervals will maintain a sense of a working relationship over time and space.

About this dataset

Context

The dataset tabulates the population of Meta by race. It includes the population of Meta across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Meta across relevant racial categories.

Key observations

The percent distribution of Meta population by race (across all racial categories recognized by the U.S. Census Bureau): 90.63% are white and 9.38% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Meta
• Population: The population of the racial category (excluding ethnicity) in the Meta is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Meta total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Meta by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Meta. The dataset can be utilized to understand the population distribution of Meta by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Meta. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Meta.

Key observations

Largest age group (population): Male # 65-69 years (14) | Female # 60-64 years (11). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Meta population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Meta is shown in the following column.
• Population (Female): The female population in the Meta is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Meta for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta Population by Gender. You can refer the same here

Scholars have conducted numerous studies on how distal antecedents influence motivational states, subsequently affecting employee proactive work behavior. However, there is still debate about the strength of the effects different motivational states have on employee proactive behavior. This paper employs meta-analysis to explore the motivational mechanism proposed in Parker's (2010) model of proactive work behavior. It analyzes the relative strength of the effects of three motivational states—"can do," "reason to," and "energized to"—on employee proactive work behavior. Through literature screening, this study conducted a meta-analysis of 94 Chinese and English studies (with a total sample size of 30,724) that adopted Parker's theoretical model. It compared the correlations between distal antecedents, different motivational perspectives, and proactive work behavior. The results show that all three motivational states positively influence proactive work behavior. "Reason to" has a stronger effect on proactive work behavior compared to the other motivational states, and "can do" is stronger than "energized to," highlighting the importance of "reason to" in promoting employee proactive work behavior. Further discussion on this topic is provided.

Facebook probably needs no introduction; nonetheless, here is a quick history of the company. The world’s biggest and most-famous social network was launched by Mark Zuckerberg while he was a...

ABSTRACT Purpose: We aimed to identify the effects of predictors of work-related well-being in the Brazilian Psychology literature. Originality/value: Researchers have focused on employee well-being to optimize working conditions and work performance in organizations. Despite a long research tradition about well-being, the predictors of employee well-being are not clear in the Brazilian literature. Design/methodology/approach: First, in the literature review, we selected five studies using the descriptor “well-being” in the Brazilian portal of Electronic Journals in Psychology (Periódicos Eletrônicos em Psicologia [PePSIC]) and applied inclusion and exclusion criteria. Next, we compiled those studies’ evidence to perform a meta-analysis using the software Jamovi 0.9.5.12 and the plugin MAJOR Meta-analysis 1.0.0 R. Findings: The prediction of employee well-being - performed by means of intra-individual variables, connections with organizations and labor (O&L), and macro variables - was clear about the positive and negative impacts of variables on well-being. However, further research studies are necessary, especially those in the interface with Administration and other areas, in order to optimize the generalization of the effects we found. In summary, this study contributes to the field of study by presenting preliminary evidence to elaborate high impact, evidence-based policies and practices on people management, including a possible interdisciplinary association between Psychology and Administration.

The attached document is the manual how to use this portal. It is only accessible for logged-in users.

All Senckenberg employees should be able to log in using their institutional credentials.

IMPORTANT: For your first login, please

1. Add your full name to your profile!

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Read further details in the manual.

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ABSTRACT Meta-analysis is an adequate statistical technique to combine results from different studies, and its use has been growing in the medical field. Thus, not only knowing how to interpret meta-analysis, but also knowing how to perform one, is fundamental today. Therefore, the objective of this article is to present the basic concepts and serve as a guide for conducting a meta-analysis using R and RStudio software. For this, the reader has access to the basic commands in the R and RStudio software, necessary for conducting a meta-analysis. The advantage of R is that it is a free software. For a better understanding of the commands, two examples were presented in a practical way, in addition to revising some basic concepts of this statistical technique. It is assumed that the data necessary for the meta-analysis has already been collected, that is, the description of methodologies for systematic review is not a discussed subject. Finally, it is worth remembering that there are many other techniques used in meta-analyses that were not addressed in this work. However, with the two examples used, the article already enables the reader to proceed with good and robust meta-analyses. Level of Evidence V, Expert Opinion.