During a July 2024 survey among marketers worldwide, 56 percent of respondents included connected TV (CTV) and streaming among the most important consumer trends they were watching for the second half of that year. Generative artificial intelligence (GenAI) followed closely, mentioned by 55 percent, while TikTok and social video rounded up the top three with a share of 47 percent. Generative AI in marketing Next to effective use cases of AI, such as aligning web content with search intent and improving the consumer experience on websites, AI tools in marketing are used for creative production. For example, influencers worldwide stated they were using tools such as Canva and DALL-E to generate images for their social media accounts. Moreover, entire ad campaigns exist that have been produced by prompting generative AI for creative purposes. TikTok for marketing The short-video format of TikTok has taken the scene by storm. In 2023, the Chinese platform generated solid engagement rates for all the various influencer tiers – from nano to mega. As of April 2023, TikTok was the leading global unicorn – a start-up company with a value of over one billion U.S. dollars –followed by Musk’s SpaceX. However, multiple worldwide ban discussions revolve around the social media due to its highly engaging, or as some may deem addictive, character.

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

China Personal Care and Daily Use Good: Taobao Online Sales: Product Average Price data was reported at 45.452 RMB in Mar 2025. This records an increase from the previous number of 44.420 RMB for Feb 2025. China Personal Care and Daily Use Good: Taobao Online Sales: Product Average Price data is updated monthly, averaging 48.591 RMB from Jan 2019 (Median) to Mar 2025, with 75 observations. The data reached an all-time high of 75.905 RMB in Apr 2022 and a record low of 28.566 RMB in May 2019. China Personal Care and Daily Use Good: Taobao Online Sales: Product Average Price data remains active status in CEIC and is reported by CEIC Data. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HTB: Taobao and Tmall Online Sales: By Category.

This dataset contains data collected within limestone cedar glades at Stones River National Battlefield (STRI) near Murfreesboro, Tennessee. This dataset contains information on soil microbial metabolic response for soil samples obtained from certain quadrat locations (points) within 12 selected cedar glades. This information derives from substrate utilization profiles based on Biolog EcoPlates (Biolog, Inc., Hayward, CA, USA) which were inoculated with soil slurries containing the entire microbial community present in each soil sample. EcoPlates contain 31 sole-carbon substrates (present in triplicate on each plate) and one blank (control) well. Once the microbial community from a soil sample is inoculated onto the plates, the plates are incubated and absorbance readings are taken at intervals.For each quadrat location (point), one soil sample was obtained under sterile conditions, using a trowel wiped with methanol and rinsed with distilled water, and was placed into an autoclaved jar with a tight-fitting lid and placed on ice. Soil samples were transported to lab facilities on ice and immediately refrigerated. Within 24 hours after being removed from the field, soil samples were processed for community level physiological profiling (CLPP) using Biolog EcoPlates. First, for each soil sample three measurements were taken of gravimetric soil water content using a Mettler Toledo HB43 halogen moisture analyzer (Mettler Toledo, Columbus, OH, USA) and the mean of these three SWC measurements was used to calculate the 10-gram dry weight equivalent (DWE) for each soil sample. For each soil sample, a 10-gram DWE of fresh soil was added to 90 milliliters of sterile buffer solution in a 125-milliliter plastic bottle to make the first dilution. Bottles were agitated on a wrist-action shaker for 20 minutes, and a 10-milliliter aliquot was taken from each sample using sterilized pipette tips and added to 90 milliliters of sterile buffer solution to make the second dilution. The bottle containing the second dilution for each sample was agitated for 10 seconds by hand, poured into a sterile tray, and the second dilution was inoculated directly onto Biolog EcoPlates using a sterilized pipette set to deliver 150 microliters into each well. Each plate was immediately covered, placed in a covered box and incubated in the dark at 25 degrees Celcius. Catabolism of each carbon substrate produced a proportional color change response (from the color of the inoculant to dark purple) due to the activity of the redox dye tetrazolium violot (present in all wells including blanks). Plates were read at intervals of 24 hours, 48 hours, 72 hours, 96 hours and 120 hours after inoculation using a Biolog MicroStation plate reader (Biolog, Inc., Hayward, CA, USA) reading absorbance at 590 nanometers.For each soil sample and at each incubation time point, average well color development (AWCD) was calculated according to the equation:AWCD = [Σ (C – R)] / n where C represents the absorbance value of control wells (mean of 3 controls), R is the mean absorbance of the response wells (3 wells per carbon substrate), and n is the number of carbon substrates (31 for EcoPlates). For each soil sample, an incubation curve was constructed using AWCD values from 48 hours to 120 hours, and the area under this incubation curve was calculated. The numeric values contained in the fields of this dataset represent areas under these AWCD incubation curves from 48 hours to 120 hours. Detailed descriptions of experimental design, field data collection procedures, laboratory procedures, and data analysis are presented in Cartwright (2014).References:Cartwright, J. (2014). Soil ecology of a rock outcrop ecosystem: abiotic stresses, soil respiration, and microbial community profiles in limestone cedar glades. Ph.D. dissertation, Tennessee State University.Cofer, M., Walck, J., and Hidayati, S. (2008). Species richness and exotic species invasion in Middle Tennessee cedar glades in relation to abiotic and biotic factors. The Journal of the Torrey Botanical Society, 135(4), 540–553.Garland, J., & Mills, A. (1991). Classification and characterization of heterotrophic microbial communities on the basis of patterns of community-level sole-carbon-source utilization. Applied and environmental microbiology, 57(8), 2351–2359.Garland, J. (1997). Analysis and interpretation of community‐level physiological profiles in microbial ecology. FEMS Microbiology Ecology, 24, 289–300.Hackett, C. A., & Griffiths, B. S. (1997). Statistical analysis of the time-course of Biolog substrate utilization. Journal of Microbiological Methods, 30(1), 63–69.Insam, H. (1997). A new set of substrates proposed for community characterization in environmental samples. In H. Insam & A. Rangger (Eds.), Microbial Communities: Functional versus Structural Approaches(pp. 259–260). New York: Springer.Preston-Mafham, J., Boddy, L., & Randerson, P. F. (2002). Analysis of microbial community functional diversity using sole-carbon-source utilisation profiles - a critique. FEMS microbiology ecology, 42(1), 1–14. doi:10.1111/j.1574-6941.2002.tb00990.x

This is a monthly report on publicly funded community services for people of all ages using data from the Community Services Data Set (CSDS) reported in England for May 2024. It has been developed to help achieve better outcomes and provide data that will be used to commission services in a way that improves health, reduces inequalities, and supports service improvement and clinical quality. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. More information about experimental statistics can be found on the UK Statistics Authority website (linked at the bottom of this page). A provisional data file for June 2024 is now included in this publication. Please note this is intended as an early view until providers submit a refresh of their data, which will be published next month.

China Personal Care and Daily Use Good: Jingdong Online Sales: Product Average Price data was reported at 50.293 RMB in Mar 2025. This records an increase from the previous number of 49.967 RMB for Feb 2025. China Personal Care and Daily Use Good: Jingdong Online Sales: Product Average Price data is updated monthly, averaging 65.147 RMB from Jan 2019 (Median) to Mar 2025, with 75 observations. The data reached an all-time high of 78.590 RMB in Nov 2022 and a record low of 47.495 RMB in Jan 2025. China Personal Care and Daily Use Good: Jingdong Online Sales: Product Average Price data remains active status in CEIC and is reported by CEIC Data. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HTC: Jingdong Online Sales: By Category.

China Home Daily Use Good: Tmall Online Sales: Product Average Price data was reported at 46.710 RMB in Aug 2020. This records an increase from the previous number of 45.410 RMB for Jul 2020. China Home Daily Use Good: Tmall Online Sales: Product Average Price data is updated monthly, averaging 46.710 RMB from Jun 2019 (Median) to Aug 2020, with 15 observations. The data reached an all-time high of 51.040 RMB in Mar 2020 and a record low of 31.170 RMB in Jun 2020. China Home Daily Use Good: Tmall Online Sales: Product Average Price data remains active status in CEIC and is reported by Moojing Market Intelligence. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HTB: Taobao and Tmall Online Sales: Others.

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

About this dataset

Context

The dataset tabulates the population of Good Thunder by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Good Thunder. The dataset can be utilized to understand the population distribution of Good Thunder by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Good Thunder. 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 Good Thunder.

Key observations

Largest age group (population): Male # 55-59 years (61) | Female # 55-59 years (25). 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 Good Thunder population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Good Thunder is shown in the following column.
• Population (Female): The female population in the Good Thunder 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 Good Thunder 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 Good Thunder Population by Gender. You can refer the same here

China Home Daily Use Good: Tmall Online Sales: YoY: Product Average Price data was reported at -7.340 % in Aug 2020. This records a decrease from the previous number of -5.510 % for Jul 2020. China Home Daily Use Good: Tmall Online Sales: YoY: Product Average Price data is updated monthly, averaging 1.280 % from Jun 2019 (Median) to Aug 2020, with 15 observations. The data reached an all-time high of 47.920 % in Jun 2019 and a record low of -34.980 % in Jun 2020. China Home Daily Use Good: Tmall Online Sales: YoY: Product Average Price data remains active status in CEIC and is reported by Moojing Market Intelligence. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HTB: Taobao and Tmall Online Sales: YoY: Others.

Graph and download economic data for Average Duration (in Quarters) from Business Application to Formation Within Four Quarters: Total for All NAICS in Oklahoma (BFDUR4QTOTALNSAOK) from Jul 2004 to Dec 2021 about duration, business applications, OK, average, business, and USA.

Responding to a 2024 survey, data center owners and operators reported an average annual power usage effectiveness (PUE) ratio of 1.56 at their largest data center. PUE is calculated by dividing the total power supplied to a facility by the power used to run IT equipment within the facility. A lower figure therefore indicates greater efficiency, as a smaller share of total power is being used to run secondary functions such as cooling.

This dataset contains data collected within limestone cedar glades at Stones River National Battlefield (STRI) near Murfreesboro, Tennessee. This dataset contains information on soil microbial metabolic response for soil samples obtained from certain quadrat locations (points) within 12 selected cedar glades. This information derives from substrate utilization profiles based on Biolog EcoPlates (Biolog, Inc., Hayward, CA, USA) which were inoculated with soil slurries containing the entire microbial community present in each soil sample. EcoPlates contain 31 sole-carbon substrates (present in triplicate on each plate) and one blank (control) well. Once the microbial community from a soil sample is inoculated onto the plates, the plates are incubated and absorbance readings are taken at intervals.For each quadrat location (point), one soil sample was obtained under sterile conditions, using a trowel wiped with methanol and rinsed with distilled water, and was placed into an autoclaved jar with a tight-fitting lid and placed on ice. Soil samples were transported to lab facilities on ice and immediately refrigerated. Within 24 hours after being removed from the field, soil samples were processed for community level physiological profiling (CLPP) using Biolog EcoPlates. First, for each soil sample three measurements were taken of gravimetric soil water content using a Mettler Toledo HB43 halogen moisture analyzer (Mettler Toledo, Columbus, OH, USA) and the mean of these three SWC measurements was used to calculate the 10-gram dry weight equivalent (DWE) for each soil sample. For each soil sample, a 10-gram DWE of fresh soil was added to 90 milliliters of sterile buffer solution in a 125-milliliter plastic bottle to make the first dilution. Bottles were agitated on a wrist-action shaker for 20 minutes, and a 10-milliliter aliquot was taken from each sample using sterilized pipette tips and added to 90 milliliters of sterile buffer solution to make the second dilution. The bottle containing the second dilution for each sample was agitated for 10 seconds by hand, poured into a sterile tray, and the second dilution was inoculated directly onto Biolog EcoPlates using a sterilized pipette set to deliver 150 microliters into each well. Each plate was immediately covered, placed in a covered box and incubated in the dark at 25 degrees Celcius. Catabolism of each carbon substrate produced a proportional color change response (from the color of the inoculant to dark purple) due to the activity of the redox dye tetrazolium violot (present in all wells including blanks). Plates were read at intervals of 24 hours, 48 hours, 72 hours, 96 hours and 120 hours after inoculation using a Biolog MicroStation plate reader (Biolog, Inc., Hayward, CA, USA) reading absorbance at 590 nanometers.For each soil sample and at each incubation time point, average well color development (AWCD) was calculated according to the equation:AWCD = [Σ (C – R)] / n where C represents the absorbance value of control wells (mean of 3 controls), R is the mean absorbance of the response wells (3 wells per carbon substrate), and n is the number of carbon substrates (31 for EcoPlates). For each soil sample, an incubation curve was constructed using AWCD values from 48 hours to 120 hours, and the area under this incubation curve was calculated. The numeric values contained in the fields of this dataset represent areas under these AWCD incubation curves from 48 hours to 120 hours. Detailed descriptions of experimental design, field data collection procedures, laboratory procedures, and data analysis are presented in Cartwright (2014).References:Cartwright, J. (2014). Soil ecology of a rock outcrop ecosystem: abiotic stresses, soil respiration, and microbial community profiles in limestone cedar glades. Ph.D. dissertation, Tennessee State University.Cofer, M., Walck, J., and Hidayati, S. (2008). Species richness and exotic species invasion in Middle Tennessee cedar glades in relation to abiotic and biotic factors. The Journal of the Torrey Botanical Society, 135(4), 540–553.Garland, J., & Mills, A. (1991). Classification and characterization of heterotrophic microbial communities on the basis of patterns of community-level sole-carbon-source utilization. Applied and environmental microbiology, 57(8), 2351–2359.Garland, J. (1997). Analysis and interpretation of community‐level physiological profiles in microbial ecology. FEMS Microbiology Ecology, 24, 289–300.Hackett, C. A., & Griffiths, B. S. (1997). Statistical analysis of the time-course of Biolog substrate utilization. Journal of Microbiological Methods, 30(1), 63–69.Insam, H. (1997). A new set of substrates proposed for community characterization in environmental samples. In H. Insam & A. Rangger (Eds.), Microbial Communities: Functional versus Structural Approaches(pp. 259–260). New York: Sp... Visit https://dataone.org/datasets/c4d0ffe6-2286-41f8-8fd1-90572abcaceb for complete metadata about this dataset.

Objective: The objective of this study was to leverage a state health department's operational data to allocate in-kind resources (children's car seats) to counties, with the proposition that need-based allocation could ultimately improve public health outcomes. Methods: This study used a retrospective analysis of administrative data on car seats distributed to counties statewide by the Georgia Department of Public Health and development of a need-based allocation tool (presented as interactive supplemental digital content, adaptable to other types of in-kind public health resources) that relies on current county-level injury and sociodemographic data. Results: Car seat allocation using public health data and a need-based formula resulted in substantially different recommended allocations to individual counties compared to historic distribution. Conclusions: Results indicate that making an in-kind public health resource like car seats universally available results in a less equitable distribution of that resource compared to deliberate allocation according to public health need. Public health agencies can use local data to allocate in-kind resources consistent with health objectives; that is, in a manner offering the greatest potential health impact. Future analysis can determine whether the change to a more equitable allocation of resources is also more efficient, resulting in measurably improved public health outcomes.

China Home Daily Use Good: Taobao and Tmall Online Sales: YoY: Product Average Price data was reported at 35.720 % in Aug 2020. This records a decrease from the previous number of 38.870 % for Jul 2020. China Home Daily Use Good: Taobao and Tmall Online Sales: YoY: Product Average Price data is updated monthly, averaging 38.870 % from Jun 2019 (Median) to Aug 2020, with 15 observations. The data reached an all-time high of 120.000 % in Feb 2020 and a record low of 3.040 % in Jun 2020. China Home Daily Use Good: Taobao and Tmall Online Sales: YoY: Product Average Price data remains active status in CEIC and is reported by Moojing Market Intelligence. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HTB: Taobao and Tmall Online Sales: YoY: Others.

Average (mean) rating for 'Life Satisfaction' by County and Unitary Authority in the First ONS Annual Experimental Subjective Wellbeing survey, April 2011 - March 2012.

The Office for National Statistics has included the four subjective well-being questions below on the Annual Population Survey (APS), the largest of their household surveys.

• Overall, how satisfied are you with your life nowadays?
• Overall, to what extent do you feel the things you do in your life are worthwhile?
• Overall, how happy did you feel yesterday?
• Overall, how anxious did you feel yesterday?

This dataset presents results from the first of these questions, "Overall, how satisfied are you with your life nowadays?" Respondents answer these questions on an 11 point scale from 0 to 10 where 0 is ‘not at all’ and 10 is ‘completely’. The well-being questions were asked of adults aged 16 and older.

Well-being estimates for each unitary authority or county are derived using data from those respondents who live in that place. Responses are weighted to the estimated population of adults (aged 16 and older) as at end of September 2011.

This dataset contains the mean responses: the average reported value for respondents resident in each area. It also contains the standard error, the sample size and lower and upper confidence limits at the 95% level.

The data cabinet also makes available the proportion of people in each county and unitary authority that answer with ‘low wellbeing’ values. For the ‘life satisfaction’ question answers in the range 0-6 are taken to be low wellbeing.

The ONS survey covers the whole of the UK, but this dataset only includes results for counties and unitary authorities in England, for consistency with other statistics available at this website.

At this stage the estimates are considered ‘experimental statistics’, published at an early stage to involve users in their development and to allow feedback. Feedback can be provided to the ONS via this email address.

The APS is a continuous household survey administered by the Office for National Statistics. It covers the UK, with the chief aim of providing between-census estimates of key social and labour market variables at a local area level. Apart from employment and unemployment, the topics covered in the survey include housing, ethnicity, religion, health and education. When a household is surveyed all adults (aged 16+) are asked the four subjective well-being questions.

The 12 month Subjective Well-being APS dataset is a sub-set of the general APS as the well-being questions are only asked of persons aged 16 and above, who gave a personal interview and proxy answers are not accepted. This reduces the size of the achieved sample to approximately 120,000 adult respondents in England.

The original data is available from the ONS website.

Detailed information on the APS and the Subjective Wellbeing dataset is available here.

As well as collecting data on well-being, the Office for National Statistics has published widely on the topic of wellbeing. Papers and further information can be found here.

About this dataset

Context

The dataset tabulates the population of Good Hope by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Good Hope. The dataset can be utilized to understand the population distribution of Good Hope by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Good Hope. 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 Good Hope.

Key observations

Largest age group (population): Male # 20-24 years (23) | Female # 25-29 years (25). 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 Good Hope population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Good Hope is shown in the following column.
• Population (Female): The female population in the Good Hope 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 Good Hope 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 Good Hope Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Good Hope by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Good Hope. The dataset can be utilized to understand the population distribution of Good Hope by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Good Hope. 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 Good Hope.

Key observations

Largest age group (population): Male # 65-69 years (25) | Female # 30-34 years (38). 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 Good Hope population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Good Hope is shown in the following column.
• Population (Female): The female population in the Good Hope 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 Good Hope 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 Good Hope Population by Gender. You can refer the same here