According to a survey conducted in 2022, 46 percent of teenagers in the United States said that social media had a positive effect on them personally due to being able to connect and socialize on such services. Overall, six percent of respondents reported feeling that social media provided benefits to their well-being and mental health.

This statistic presents the perceived positive influence of classroom usage of education technology according to educators in the United States. During the 2016 survey, 79 percent of teachers stated that they found that technology made a big difference in making learning more interesting for students.

Introduction

Social Media Mental Health Statistics: Social media has many uses, but it often causes the most harm to younger users. Teens face significant mental health issues due to social media, and the COVID-19 pandemic made things worse by increasing screen time and social media use. This created more opportunities for teens to encounter online problems, worsening the situation.

Teens also use social media to find communities and interest groups, watch live streams, and support good causes. It’s important to US teens that they feel welcome and safe online. Despite some problems, social media offers many chances for connection and entertainment. We shall shed more light on the Social Media Mental Health Statistics through this article.

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

Financial overview and grant giving statistics of Positive People Making Decisions

Financial overview and grant giving statistics of Negative To Positive

We wish to answer this question: If you observe a 'significant' p-value after doing a single unbiased experiment, what is the probability that your result is a false positive? The weak evidence provided by p-values between 0.01 and 0.05 is explored by exact calculations of false positive risks. When you observe p = 0.05, the odds in favour of there being a real effect (given by the likelihood ratio) are about 3 : 1. This is far weaker evidence than the odds of 19 to 1 that might, wrongly, be inferred from the p-value. And if you want to limit the false positive risk to 5%, you would have to assume that you were 87% sure that there was a real effect before the experiment was done. If you observe p = 0.001 in a well-powered experiment, it gives a likelihood ratio of almost 100 : 1 odds on there being a real effect. That would usually be regarded as conclusive. But the false positive risk would still be 8% if the prior probability of a real effect were only 0.1. And, in this case, if you wanted to achieve a false positive risk of 5% you would need to observe p = 0.00045. It is recommended that the terms 'significant' and 'non-significant' should never be used. Rather, p-values should be supplemented by specifying the prior probability that would be needed to produce a specified (e.g. 5%) false positive risk. It may also be helpful to specify the minimum false positive risk associated with the observed p-value. Despite decades of warnings, many areas of science still insist on labelling a result of p < 0.05 as 'statistically significant'. This practice must contribute to the lack of reproducibility in some areas of science. This is before you get to the many other well-known problems, like multiple comparisons, lack of randomization and p-hacking. Precise inductive inference is impossible and replication is the only way to be sure. Science is endangered by statistical misunderstanding, and by senior people who impose perverse incentives on scientists.

This package includes information gathered during research into Positive Money, conducted between 2019 and 2024. Information includes i) statistics on funding received by Positive Money between 2010 and 2023, including grant amounts, donation amounts, and topics or activity areas funded - categorised as 'climate' or 'other'. Please note that 'other' also includes projects which contain elements of focus on climate change. This information points to a decline in grassroots donations and a significant increase in service-dependent grants, with a growing majority of these grants funding projects which focus primarily on climate issues. ii) an inexhaustive list of recorded policy proposals advocated by Positive Money between 2012 and 2023. This information points to the expansion of Positive Money's scope and its drift from monetary reform. ii) statistics on the number of Positive Money supporters and grassroots local groups between 2009 and 2023. This information points to a decline in support for Positive Money in line with its drift away from monetary reform. iv) the results of supporter surveys conducted by Positive Money, and published on its website, in 2017, 2019, and 2022. This information points to a lack of endorsement from Positive Money supporters for its drift away from monetary reform. v) the anonymised list of interviewees who participated in this research project. Overally, this information is used to understand the mission drift, mission displacement, and mission creep of Positive Money, a formerly a money creation reform campaign, which adopted a new mission for social justice and green finance after 2016.

According to a survey carried out in 2019, 74 percent of respondents in the United Kingdom said that the enablement of flexible working was an advance in technology which had a positive effect of employee well-being. 52 percent of respondents also said that technology allowed more effective communication which was positive for well-being in the work place.

Map shows the percentage change in number of occupied and unoccupied private dwellings between the 2018 and 2023 Censuses.Download lookup file from Stats NZ ArcGIS Online or Stats NZ geographic data service.FootnotesGeographical boundariesStatistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018. Caution using time series Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data). About the 2023 Census dataset For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings. Data quality The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.Quality rating of a variable The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable. Dwelling occupancy status quality rating Dwelling occupancy status is rated as high quality. Dwelling occupancy status – 2023 Census: Information by concept has more information, for example, definitions and data quality.Dwelling type quality rating Dwelling type is rated as moderate quality. Dwelling type – 2023 Census: Information by concept has more information, for example, definitions and data quality.Using data for good Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.Confidentiality The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.Symbol-998 Not applicable-999 Confidential

About this dataset

Context

The dataset tabulates the population of Good Thunder by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Good Thunder across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 56.97% of total population being male. 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.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Good Thunder is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Good Thunder 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 Good Thunder Population by Race & Ethnicity. You can refer the same here

The Positive Psychology Coaching market is rapidly evolving, reflecting a growing shift towards mental well-being and personal development within various industries. As organizations increasingly recognize the value of employee well-being, Positive Psychology Coaching has emerged as a transformative approach that ha

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"Ensure healthy lives and promote well-being for all at all ages : Some progress has been made against key mortality measures. Maternal mortality ratios have already fallen below the 2030 target in three-quarters of Pacific countries and territories, and one-half have achieved the under-five mortality rate target of fewer than 25 deaths per 100,000; The increasing burden of non-communicable diseases, both with respect to the risk of premature mortality and health care costs, is the dominant health issue in the Pacific region. A mixed pattern is found in the two lifestyle risk factors of alcohol and smoking, with three Pacific countries featuring among the top ten world countries in prevalence of current tobacco use among persons aged 15 years and older; Health worker density remains below WHO guidelines in most countries in the region; Malaria is still present in three countries (PNG, Solomon Islands and Vanuatu), although the incidence is decreasing due to awareness and preventative measures.

Find more Pacific data on PDH.stat.

Financial overview and grant giving statistics of Positive Images of Self-Expression

Financial overview and grant giving statistics of Positive Expressions

Characteristics of people testing positive for coronavirus (COVID-19) taken from the Coronavirus (COVID-19) Infection Survey.