Subject: EducationSpecific: Online Learning and FunType: Questionnaire survey data (csv / excel)Date: February - March 2020Content: Students' views about online learning and fun Data Source: Project OLAFValue: These data provide students' beliefs about how learning occurs and correlations with fun. Participants were 206 students from the OU

The purpose of this project is to improve the accuracy of statistical software by providing reference datasets with certified computational results that enable the objective evaluation of statistical software. Currently datasets and certified values are provided for assessing the accuracy of software for univariate statistics, linear regression, nonlinear regression, and analysis of variance. The collection includes both generated and 'real-world' data of varying levels of difficulty. Generated datasets are designed to challenge specific computations. These include the classic Wampler datasets for testing linear regression algorithms and the Simon & Lesage datasets for testing analysis of variance algorithms. Real-world data include challenging datasets such as the Longley data for linear regression, and more benign datasets such as the Daniel & Wood data for nonlinear regression. Certified values are 'best-available' solutions. The certification procedure is described in the web pages for each statistical method. Datasets are ordered by level of difficulty (lower, average, and higher). Strictly speaking the level of difficulty of a dataset depends on the algorithm. These levels are merely provided as rough guidance for the user. Producing correct results on all datasets of higher difficulty does not imply that your software will pass all datasets of average or even lower difficulty. Similarly, producing correct results for all datasets in this collection does not imply that your software will do the same for your particular dataset. It will, however, provide some degree of assurance, in the sense that your package provides correct results for datasets known to yield incorrect results for some software. The Statistical Reference Datasets is also supported by the Standard Reference Data Program.

GUI21 - Activities carried out for fun by respondents aged 25 years. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Activities carried out for fun by respondents aged 25 years...

This database includes simulated data showing the accuracy of estimated probability distributions of project durations when limited data are available for the project activities. The base project networks are taken from PSPLIB. Then, various stochastic project networks are synthesized by changing the variability and skewness of project activity durations. Number of variables: 20 Number of cases/rows: 114240 Variable List: • Experiment ID: The ID of the experiment • Experiment for network: The ID of the experiment for each of the synthesized networks • Network ID: ID of the synthesized network • #Activities: Number of activities in the network, including start and finish activities • Variability: Variance of the activities in the network (this value can be either high, low, medium or rand, where rand shows a random combination of low, high and medium variance in the network activities.) • Skewness: Skewness of the activities in the network (Skewness can be either right, left, None or rand, where rand shows a random combination of right, left, and none skewed in the network activities)
• Fitted distribution type: Distribution type used to fit on sampled data • Sample size: Number of sampled data used for the experiment resembling limited data condition • Benchmark 10th percentile: 10th percentile of project duration in the benchmark stochastic project network • Benchmark 50th percentile: 50th project duration in the benchmark stochastic project network • Benchmark 90th percentile: 90th project duration in the benchmark stochastic project network • Benchmark mean: Mean project duration in the benchmark stochastic project network • Benchmark variance: Variance project duration in the benchmark stochastic project network • Experiment 10th percentile: 10th percentile of project duration distribution for the experiment • Experiment 50th percentile: 50th percentile of project duration distribution for the experiment • Experiment 90th percentile: 90th percentile of project duration distribution for the experiment • Experiment mean: Mean of project duration distribution for the experiment • Experiment variance: Variance of project duration distribution for the experiment • K-S: Kolmogorov–Smirnov test comparing benchmark distribution and project duration • distribution of the experiment • P_value: the P-value based on the distance calculated in the K-S test

de Rigo, D. (2012). Portable pseudo-random reference sequences with Mersenne Twister using GNU Octave. Mastrave project technical report. FigShare Digital Science. doi: 10.6084/m9.figshare.94593

Portable pseudo-random reference sequences with Mersenne Twister using GNU Octave

Mastrave project technical report

Daniele de Rigo

Abstract: Computationally intensive numerical tasks such as those involving statistical resampling, evolutionary techniques or Monte Carlo based applications are known to require robust algorithms for generating large sequences of pseudo-random numbers (PRN). While several languages, libraries and computing environments offer suitable PRN generators, the underlying algorithms and parametrization widely differ. Therefore, easily replicating a certain PRN sequence generally implies forcing researchers to use a very specific language or computing environment, also paying attention to its version, possible critical dependencies or even operating system and computer architecture.

Despite the awareness of the benefits of reproducible research is rapidly growing, the definition itself of “reproducibility” for PRN based applications may lead to diverging interpretations and expectations. Where the cardinality of PRN sequences needed for data to be processed is relatively moderate, the paradigm of reproducible research is in principle suitable to be applied not only to algorithms, free software, data and metadata (classic reproducible research, CRR), but also to the involved pseudo-random sequences themselves (deep reproducible research, DRR). This would allow not only the “typical” scientific results to be reproducible “except for PRN-related statistical fluctuations”, but also the exact results published by a research team to be independently reproduced by other scientists - without of course preventing sensitivity analysis with different PRN sequences, as even classic reproducible research should easily allow.

However, finding reference sequences of pseudo random numbers suitable to enable such a deep reproducibility may be surprisingly difficult. Here, sequences eligible to be used as reference dataset of uniformly distributed pseudo-random numbers are presented. The dataset of sequences has been generated using Mersenne Twister with a period of 2^19937-1, as implemented in GNU Octave (version 3.6.1) with the Mastrave modelling library. The sequences are available in plain text format and also in the format MATLAB version 7, which is portable in both GNU Octave and MATLAB computing environments. The plain text format uses a fixed number of characters per each PRN so allowing random access to sparse PRNs to be easily done in constant time without needing a whole file to be loaded. This straightforward solution is language neutral, with the advantage of enabling wide and immediate portability for the presented reference PRN dataset, irrespective of the language, libraries, computing environment of choice for the users.

Draw prospective project areas in the Project Planning Tool application and be rewarded with a wealth of statistics to help you plan effective projects, including Wildfire Hazard and Risk averages, population statistics, parcel data and more. Try it, it’s fun and awesome! This application allows a user to draw a point, line, or area to determine the average wildfire risk index for that area. Other metrics are also offered (i.e. average wildfire hazard index, average ember load index, estimated population, and number of structures).

The statistic shows activities which families in the United States enjoyed doing together as of December 2012. During a survey it was found that 12 percent of responding parents of children aged eight or younger stated their families enjoyed playing video games together a lot.

Abstract

The Community Survey (CS) is a nationally representative, large-scale household survey which was conducted from February to March 2007. The Community Survey is designed to provide information on the trends and levels of demographic and socio-economic data, such as population size and distribution; the extent of poor households; access to facilities and services, and the levels of employment/unemployment at national, provincial and municipality level. The data can be used to assist government and the private sector in the planning, evaluation and monitoring of programmes and policies. The information collected can also be used to assess the impact of socio-economic policies and provide an indication as to how far the country has gone in its strides to eradicate poverty.

Censuses 1996 and 2001 are the only all-inclusive censuses that Statistics South Africa has thus far conducted under the new democratic dispensation. Demographic and socio-economic data were collected and the results have enabled government and all other users of this information to make informed decisions. When cabinet took a decision that Stats SA should not conduct a census in 2006, it created a gap in information or data between Census 2001 and the next Census scheduled to be carried out in 2011. A decision was therefore taken to carry out the Community Survey in 2007.

The main objectives of the survey were: · To provide estimates at lower geographical levels than existing household surveys; · To build human, management and logistical capacities for Census 2011; and · To provide inputs into the preparation of the mid-year population projections.

The wider project strategic theme is to provide relevant statistical information that meets user needs and aspirations. Some of the main topics that are covered by the survey include demography, migration, disability and social grants, educational levels, employment and economic activities.

Geographic coverage

The survey covered the whole of South Africa, including all nine provinces as well as the four settlement types - urban-formal, urban-informal, rural-formal (commercial farms) and rural-informal (tribal areas).

Analysis unit

Households

Universe

The Community Survey covered all de jure household members (usual residents) in South Africa. The survey excluded collective living quarters (institutions) and some households in EAs classified as recreational areas or institutions. However, an approximation of the out-of-scope population was made from the 2001 Census and added to the final estimates of the CS 2007 results.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design

The sampling procedure that was adopted for the CS was a two-stage stratified random sampling process. Stage one involved the selection of enumeration areas, and stage tow was the selection of dwelling units.

Since the data are required for each local municipality, each municipality was considered as an explicit stratum. The stratification is done for those municipalities classified as category B municipalities (local municipalities) and category A municipalities (metropolitan areas) as proclaimed at the time of Census 2001. However, the newly proclaimed boundaries as well as any other higher level of geography such as province or district municipality, were considered as any other domain variable based on their link to the smallest geographic unit - the enumeration area.

The Frame

The Census 2001 enumeration areas were used because they give a full geographic coverage of the country without any overlap. Although changes in settlement type, growth or movement of people have occurred, the enumeration areas assisted in getting a spatial comparison over time. Out of 80 787 enumeration areas countrywide, 79 466 were considered in the frame. A total of 1 321 enumeration areas were excluded (919 covering institutions and 402 recreational areas).

On the second level, the listing exercise yielded the dwelling frame which facilitated the selection of dwellings to be visited. The dwelling unit is a structure or part of a structure or group of structures occupied or meant to be occupied by one or more households. Some of these structures may be vacant and/or under construction, but can be lived in at the time of the survey. A dwelling unit may also be within collective living quarters where applicable (examples of each are a house, a group of huts, a flat, hostels, etc.).

The Community Survey universe at the second-level frame is dependent on whether the different structures are classified as dwelling units (DUs) or not. Structures where people stay/live were listed and classified as dwelling units. However, there are special cases of collective living quarters that were also included in the CS frame. These are religious institutions such as convents or monasteries, and guesthouses where people stay for an extended period (more than a month). Student residences - based on how long people have stayed (more than a month) - and old-age homes not similar to hospitals (where people are living in a communal set-up) were treated the same as hostels, thereby listing either the bed or room. In addition, any other family staying in separate quarters within the premises of an institution (like wardens' quarters, military family quarters, teachers' quarters and medical staff quarters) were considered as part of the CS frame. The inclusion of such group quarters in the frame is based on the living circumstances within these structures. Members are independent of each other with the exception that they sleep under one roof.

The remaining group quarters were excluded from the CS frame because they are difficult to access and have no stable composition. Excluded dwelling types were prisons, hotels, hospitals, military barracks, etc. This is in addition to the exclusion on first level of the enumeration areas (EAs) classified as institutions (military bases) or recreational areas (national parks).

The Selection of Enumeration Areas (EAs)

The EAs within each municipality were ordered by geographic type and EA type. The selection was done by using systematic random sampling. The criteria used were as follows: In municipalities with fewer than 30 EAs, all EAs were automatically selected. In municipalities with 30 or more EAs, the sample selection used a fixed proportion of 19% of all sampled EAs. However, if the selected EAs in a municipality were less than 30 EAs, the sample in the municipality was increased to 30 EAs.

The Selection of Dwelling Units

The second level of the frame required a full re-listing of dwelling units. The listing exercise was undertaken before the selection of DUs. The adopted listing methodology ensured that the listing route was determined by the lister. Thisapproach facilitated the serpentine selection of dwelling units. The listing exercise provided a complete list of dwelling units in the selected EAs. Only those structures that were classified as dwelling units were considered for selection, whether vacant or occupied. This exercise yielded a total of 2 511 314 dwelling units.

The selection of the dwelling units was also based on a fixed proportion of 10% of the total listed dwellings in an EA. A constraint was imposed on small-size EAs where, if the listed dwelling units were less than 10 dwellings, the selection was increased to 10 dwelling units. All households within the selected dwelling units were covered. There was no replacement of refusals, vacant dwellings or non-contacts owing to their impact on the probability of selection.

Mode of data collection

Face-to-face [f2f]

Research instrument

Consultation on Questionnaire Design Ten stakeholder workshops were held across the country during August and September 2004. Approximately 367 stakeholders, predominantly from national, provincial and local government departments, as well as from research and educational institutions, attended. The workshops aimed to achieve two objectives, namely to better understand the type of information stakeholders need to meet their objectives, and to consider the proposed data items to be included in future household surveys. The output from this process was a set of data items relating to a specific, defined focus area and outcomes that culminated with the data collection instrument (see Annexure B for all the data items).

Questionnaire Design The design of the CS questionnaire was household-based and intended to collect information on 10 people. It was developed in line with the household-based survey questionnaires conducted by Stats SA. The questions were based on the data items generated out of the consultation process described above. Both the design and questionnaire layout were pre-tested in October 2005 and adjustments were made for the pilot in February 2006. Further adjustments were done after the pilot results had been finalised.

Cleaning operations

Editing The automated cleaning was implemented based on an editing rules specification defined with reference to the approved questionnaire. Most of the editing rules were categorised into structural edits looking into the relationship between different record type, the minimum processability rules that removed false positive readings or noise, the logical editing that determine the inconsistency between fields of the same statistical unit, and the inferential editing that search similarities across the domain. The edit specifications document for the structural, population, mortality and housing edits was developed by a team of Stats SA subject-matter specialists, demographers, and programmers. The process was successfully

Thirty-seven percent of people in Poland found ideas for interesting leisure activities on social media in 2022.

How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

The statistic illustrates the share of individuals participating in leisure activities in the Netherlands in 2015, distributed by gender and type. It shows that women participated more in fun shopping activities than men.

Abstract

The Time Use Survey is a rich source of data on women participation in the developmental process through defining the size of participation of women in the informal work including food processing (preservation), textile work and sales, and other activities classified under informal activities in Palestine. The survey also helps in defining the size of participation of women in housework, such as cocking, cleaning, childcare and other household activities. Likewise, the Time Use Survey constitutes an important tool to assess the various activities of men and women without having to adopt the economic/non-economic definitions of the national accounts system. Generally speaking, this survey monitors the various activities of the individuals in one day consecutively.

Geographic coverage

The Data are representative at region level (West Bank, Gaza Strip), locality type (urban, rural, camp) .

Analysis unit

Individual

Universe

The target population of the Time Use Survey consists of all individuals in the age group 10 years and over who are usually resident in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The sample size for this survey consists of 8,038 individuals selected from 4,019 households. The sample of the Time Use Survey is a multistage stratified cluster random sample. The first stage involved the selection of a stratified random sample comprised of 240 enumeration areas. The second stage involved the selection of an average of 17 households in every selected enumeration area. The third stage involved the random selection of two household members, one male and one female in the age group 10 years and above. The selection of household members from the households selected in the field is done by using random (KISH) tables.

Stratification Four levels of Stratification were made: 1. Stratification by Governorates.
2. Stratification by place of residence which comprises: (a) Urban (b) Rural (c) Refugee Camps 3. Stratification by classifying localities excluding governorate capitals, into three strata based on levels of ownership of durable goods in households within selected localities. 4. Stratification by size of locality (number of households)

Sampling deviation

The sample size for this survey consists of 8,038 individuals selected from 4,019 households. The sample of the Time Use Survey is a multistage stratified cluster random sample

Mode of data collection

Face-to-face [f2f]

Research instrument

The Time Use Survey Questionnaire consists of three main sections:

1. Household Questionnaire: This questionnaire involves the identification data and the household members records. It also contains the demographic and economic background characteristics for the household members, in addition to data on household properties and levels of income.

2. Individuals (10 years and above) Questionnaire: This questionnaire is related to the randomly selected individuals to complete the time record. The individual questionnaire involves data on the educational and employment status of the individual along with information on the cultural status. This section is comprised of two main parts: A part targeting males and a part targeting females. This section aims to provide basic data on the individuals to relate them to the data derived from the time record.

3. Daily Record Questionnaire: This questionnaire involves data on the activities of the individuals and the time spent in rendering those activities. This record also shows whether the activity was performed in return for a wage or unpaid, along with information on whether the individual was accompanied by other individuals while performing the activity. Likewise, this record indicates the means of transportation used in performing the various activities in a 24 hours basis. In the adopted record of the Palestinian Time Use Survey, the day was divided into temporal intervals varying from 15 minutes during the day and 30 minutes after midnight until 6:00 morning.

Coding In order to assure accurate and consistent coding, the coding process was centrally implemented in the offices of the Fieldwork Directorate. In this stage, the activities recorded by the interviewers were coded based on a special coding manual at the third digit level. On the other hand, the occupations were coded in accordance with the International Classification of Occupations (ISCO-88) at the third - digit - level.

Cleaning operations

Three types of editing have been adopted to ensure the highest possible type of accuracy, namely: 1. Editing during data entry: All entered data have been checked for every question separately along with the basic links and consistency relations among related questions 2. Post Data Entry Sample Editing: About 10% of entered questionnaires were completely reviewed and matched with the entered values in order to immediately correct any possible errors or settle any differences among entered values. 3. Consistency: Special programs have been designed to examine logical relations between the different entered values. Upon detecting any inconsistency, then all mistakes discovered are corrected.

Response rate

Response Rates Four thousand and nineteen households were selected representing the Palestinian Territory, 2,583 households in the West Bank and 1,436 in Gaza Strip. The following table shows the status of the questionnaires at the end of fieldwork.

The response rate in the Palestinian Territory was 96%, with significant difference between the West Bank and Gaza, in the West Bank it was 94.7%, while in Gaza Strip it was 98.3%.

Sampling error estimates

Detailed information on the sampling Error is available in the Survey Report.

Data appraisal

Detailed information on the data appraisal is available in the Survey Report.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Our final map product is a geographic information system (GIS) database of vegetation structure and composition across the Crater Lake National Park terrestrial landscape, including wetlands. The database includes photos we took at all relevé, validation, and accuracy assessment plots, as well as the plots that were done in the previous wetlands inventory. We conducted an accuracy assessment of the map by evaluating 698 stratified random accuracy assessment plots throughout the project area. We intersected these field data with the vegetation map, resulting in an overall thematic accuracy of 86.2 %. The accuracy of the Cliff, Scree & Rock Vegetation map unit was difficult to assess, as only 9% of this vegetation type was available for sampling due to lack of access. In addition, fires that occurred during the 2017 accuracy assessment field season affected our sample design and may have had a small influence on the accuracy. Our geodatabase contains the locations where particular associations are found at 600 relevé plots, 698 accuracy assessment plots, and 803 validation plots.

This dataset is about book subjects. It has 1 row and is filtered where the books is Five-minute fillers : foundation-Y2 : fun and confidence boosting activities to help children memorise number facts. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Slovenia in 2009, 2013 and 2019.

The Slovenia ES 2009 was conducted between 2008 and 2009. The Slovenia ES 2013 was conducted between March 2013 and September 2013. Finally, the Slovenia ES 2019 was conducted between December 2018 and November 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must take its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

As it is standard for the ES, the Slovenia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Slovenia ES 2009, 2013, 2019 were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Slovenia 2009 ES and for Slovenia 2013 ES, and in the Sampling Note for 2019 Slovenia ES.

Three levels of stratification were used in this country: industry, establishment size, and oblast (region). The original sample designs with specific information of the industries and regions chosen are included in the attached Excel file (Sampling Report.xls.) for Slovenia 2009 ES. For Slovenia 2013 and 2019 ES, specific information of the industries and regions chosen is described in the "The Slovenia 2013 Enterprise Surveys Data Set" and "The Slovenia 2019 Enterprise Surveys Data Set" reports respectively, Appendix E.

For the Slovenia 2009 ES, industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector as defined in the sampling manual. Each industry had a target of 90 interviews. For the manufacturing industries sample sizes were inflated by about 17% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. For the other industries (residuals) sample sizes were inflated by about 12% to account for under sampling in firms in service industries.

For Slovenia 2013 ES, industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Finally, for Slovenia 2019 ES, three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Slovenia 2019 Enterprise Surveys Data Set" report, Appendix C. Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 4.0 codes 10-33), Retail (ISIC 47), and Other Services (ISIC 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For Slovenia 2009 and 2013 ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

For Slovenia 2009 ES, regional stratification was defined in 2 regions. These regions are Vzhodna Slovenija and Zahodna Slovenija. The Slovenia sample contains panel data. The wave 1 panel “Investment Climate Private Enterprise Survey implemented in Slovenia” consisted of 223 establishments interviewed in 2005. A total of 57 establishments have been re-interviewed in the 2008 Business Environment and Enterprise Performance Survey.

For Slovenia 2013 ES, regional stratification was defined in 2 regions (city and the surrounding business area) throughout Slovenia.

Finally, for Slovenia 2019 ES, regional stratification was done across two regions: Eastern Slovenia (NUTS code SI03) and Western Slovenia (SI04).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as (-8). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

For 2009 and 2013 Slovenia ES, the survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Up to 4 attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

For 2009, the number of contacted establishments per realized interview was 6.18. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The relatively low ratio of contacted establishments per realized interview (6.18) suggests that the main source of error in estimates in the Slovenia may be selection bias and not frame inaccuracy.

For 2013, the number of realized interviews per contacted establishment was 25%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 44%.

Finally, for 2019, the number of interviews per contacted establishments was 9.7%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 75.2%.

This statistic shows the most popular leisure activities among women in the United States as of September 2013. During the survey, 48 percent of the female respondents named reading as their most preferred activity during leisure time.

"Cooking / baking" and "Video gaming" are the top two answers among U.S. consumers in our survey on the subject of "Most popular hobbies & activities".The survey was conducted online among ****** respondents in the United States, in 2025.

A series of fun and engaging activities have been created to get the public outside to learn about MPAs while also enjoying them in person.

Abstract

The survey provides basic data needed for the development of national policies. The main objectives of the Time Use Survey were as follows:

1. Measurement and analysis of quality of life or general well-being.
2. Identifying demographic and socio-economic characteristics of individuals in Palestinian society.
3. Measurement and valuation of unpaid work (domestic and volunteer work) and development of household production accounts.
4. Improving estimates of paid and unpaid work.
5. Assisting planners and policy makers to develop strategies and policies that may contribute to developmental planning issues.

It is also a rich source of information about the use of time to learn about the nature and structure of individuals in Palestinian society during the year 2012/2013, in different age groups, including children, women, youth and the elderly, and to illuminate the path for decision makers and policy makers in the process of comprehensive national development in this country.

Time Use Survey is a basic tool to determine gender issues. The data enable analysis of the quality of life and an assessment of the extent of female participation in paid and unpaid work (housework and volunteer work) and women's contribution to national accounts.

Geographic coverage

1- Governorate (16 governorates in west bank and Gaza strip) 2- Locality type (urban, rural, camps)

Analysis unit

Individual

Universe

The Target population of the survey consists of all Palestinian individuals of age group 10 years and over, who are living normally with their households in Palestine in 2012/2013 .

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Design After determining the sample size, the sample type is three-stage stratified cluster sample as following:

1- First stage: selecting systematic sample of 220 clusters (enumeration areas). 2- Second stage: selection sample of 21 responded households from each EA selected in the first stage (we use the area sampling to get this number of responded households). 3- Third stage: selection two individuals male and female (10 years and more) from each household selected in second stage using random kish tables.

The population was divided to strata by:

Governorate (16 governorates in west bank and Gaza strip) Locality type (urban, rural, camps)

Sampling deviation

The sample size of the survey is 5,903 Palestinian households.

After determining the sample size, the sample type is three-stage stratified cluster sample as following:

1- First stage: selecting systematic sample of 220 clusters (enumeration areas). 2- Second stage: selection sample of 21 responded households from each EA selected in the first stage (we use the area sampling to get this number of responded households). Third stage: selection two individuals male and female (10 years and more) from each household selected in second stage using random kish tables

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaire The survey questionnaire is the main tool for data collection and was designed on the basis of international surveys specially designed for time use surveys, as well as on the basis of the recommendations of the workshop on time use surveys held in Jordan in 2010. This was organized by ESCWA in cooperation with UNSD to develop a questionnaire for a time use survey and coding manual, along with adding activities related to the Palestinian context compatible with the coding manual of the United Nations of 2006. The questionnaire meets the technical specifications for the field work phase and data processing and analysis requirements. The questionnaire included several sections:

1. Identification Data This identifies a unified means of determining data that define a household, including the divisions of sample design: the number in the enumeration area, governorate and locality, building identification number, number of household, and the name of head of household.

2. Quality Control This is the development of controls of field and office operations and the sequencing in questionnaire stages, usually beginning with data collection through to field and office auditing, data coding, data entry, checks after data entry, and ending with the storage process.

3. Household Members Background Details These include household members, relationship to the head of household, gender, date of birth and age, in addition to other demographic and economic data for the household as a whole.

4. Household Questionnaire This includes questions related to the household in terms of type of housing unit, material used as flooring in the housing unit, primary fuel type used in cooking, goods and services available, monthly household income, and other indicators.

5. Daily Record Questionnaire This part of the questionnaire comprised two time records: in the first record, one male member of the household aged 10 years and above is selected at random and in the second record, one female household member aged 10 years and above is selected at random. The day was divided into periods of time of up to 30 minutes each from midnight until six am and 10 minutes for each period during the day from six am until twelve o'clock at night. The record also contains information that shows whether the activity was performed for a fee or financial return or not. Any secondary activity is also recorded. This information identifies the respondent performing these activities, with whom and the means of transportation or venue where the individual performed the various activities throughout the day (during a 24-hour period).

Cleaning operations

Data verification: comprehensive automated rules of data verification in between questions ensured consistency and identification of answers that were out of range or irrational. This was carried out by a special program performed on a regular basis. The team reviewed error messages and modification of errors based on observations or returned the questionnaire to the field for double checking. The auditing mechanism was prepared by the project management and applied to the data entry program by a programmer where necessary. Appropriate data auditing tests proposed by the project management during the auditing procedure were inclusive and covered all questions in the questionnaire. The questionnaires were drawn from extracted lists and checked automatically, corrected and adjusted on the computer. Then a second list was extracted for the same questionnaires to ensure that the amendment was valid and that all questionnaires had been modified.

Response rate

The sample size of the survey was 5,903 households and 4,605 households were completed. Weights were adjusted to compensate for the non-response cases. The response rate in the survey in Palestine was 79.6% for households

Sampling error estimates

Survey data may be affected by statistical errors as a result of the use of a sample rather than a comprehensive survey covering all units of the study population. Thus, differences may be anticipated from the real values that emerge from a census and variations were calculated for the most important indicators.

The results indicated that there was no problem in the dissemination of data applicable to Palestine as a whole or on a regional basis (the West Bank and the Gaza Strip).

Data appraisal

The concept of data quality includes multiple aspects, starting from initial planning for the survey and ending with data dissemination and interpretation of data for optimal use. The most important components of statistical quality include accuracy, comparability, and quality control procedures. Statistical quality also includes checking and auditing data accuracy in multiple aspects of the survey, particularly statistical errors due to the use of a sample, plus non-statistical errors by staff and the use of survey tools. Response rates may also have a crucial impact on estimates

Context France Université Numérique (FUN) led at the end of 2017 a project of MOOC/SPOC survey questionnaires common to the different courses, with the main objectives of better knowing the profiles and expectations of learners (including socio-demographic issues, qualitative questions related to MOOC platforms and their functionalities, questions about online courses and the relevant course, before and after having followed it in depth), and to promote research in education sciences. Exchanges took place, for example, with the European project MOOC Knowledge, as well as in-house within the editorial guidance committee of FUN. MOOC’s start and end questionnaires were thus generalised at the beginning of 2018. In 2019, the MOOCFOLIO project was awarded a call for projects from the Future Investment Programme, which includes the dissemination of data from the project in OpenData. ### Data production The questionnaires contain mandatory fields, identical for all course-producing establishments, courses and sessions of the same course. Questionnaires may also include specific fields added by the pedagogical team. The questions have been drawn up in the light of the work of the MOOC Knowledge project, and are voluntarily harmonised in order to allow cross-analysis according to different criteria (MOOC theme, establishment typology, prerequisites,...) for the entire FUN platform and in a way compared with other platforms. The data are entered directly by respondents, registered at the MOOC concerned, on LimeSurvey questionnaires, by checkboxes or free text. These questionnaires are optional, declarative, and can be partially filled in. A review is carried out within FUN in order to delete fields that may contain directly or indirectly identifying data, according to a procedure set up with the support of the Etalab service of DINUM. ### Content of datasets The data shall include the results of the MOOC start and end questionnaires submitted to registered MOOCs accessible on the FUN platform. The files made available include all courses for the 2019-2020, 2020-2021 and 2021-2022 sessions of the MOOCFOLIO project. They are named according to the institution code and the title of the course. The files are available in CSV format. An index listing all the files is made available.