Survey on Equipment and Use of Information and Communication Technologies in Households: Stated advantages and disadvantages of teleworking by autonomous city and community. Autonomous City and Community.

The Index of Household Advantage and Disadvantage (IHAD) provides a summary measure of relative socio-economic advantage and disadvantage for households, based on the characteristics of dwellings and the people living within them, using 2021 Census data.

All in-scope households are ordered from lowest to highest score. A low score indicates relatively greater disadvantage and a lack of advantage in general. A high score indicates a relative lack of disadvantage and greater advantage in general.

This dataset presents IHAD data in quartiles. The lowest 25% of households are given a quartile number of 1, the next lowest 25% of households are given a quartile number of 2 and so on, up to the highest 25% of households which are given a quartile number of 4. This means that households are divided into four equal sized groups, depending on their score. In practice these groups won’t each be exactly 25% of households as it depends on the distribution of the IHAD scores. The data is grouped by Statistical Area Level 2 (SA2 2021). SA2s are defined by the Australian Statistical Geography Standard (ASGS) Edition 3.

Key Attributes:

      Field alias
      Field name
      Description


      Statistical Areas Level 2 2021 code
      SA2_CODE_2021
      2021 Statistical Areas Level 2 (SA2) codes from the Australian Statistical Geography Standard (ASGS), Edition 3. SA2s are medium-sized general purpose areas built to represent communities that interact together socially and economically. 


        Statistical Areas Level 2 2021 name
      SA2_NAME_2021
      2021 Statistical Areas Level 2 (SA2) names from the Australian Statistical Geography Standard (ASGS), Edition 3. SA2s are medium-sized general purpose areas built to represent communities that interact together socially and economically. 


      Area in square kilometres
      AREA_ALBERS_SQKM
      The area of a region in square kilometres, based on the Albers equal area conic projection.


      Uniform Resource Identifier
      ASGS_LOCI_URI_2021
      A uniform resource identifier can be used in web linked applications for data integration. 


      IHAD quartile 1
      IHAD_QUARTILE1
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 1, indicating relatively greater disadvantage and a lack of advantage in general.


      IHAD quartile 2
      IHAD_QUARTILE2
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 2.


      IHAD quartile 3
      IHAD_QUARTILE3
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 3.


      IHAD quartile 4
      IHAD_QUARTILE4
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 4, indicating a relative lack of disadvantage and greater advantage in general.


      Occupied private dwellings
      OPD_2021
      Dwellings in-scope of the IHAD i.e. classifiable occupied private dwellings.


      SEIFA IRSAD quartile
      IRSAD_QUARTILE
      Index of Relative Socio-economic Advantage and Disadvantage quartile. All SA2s are ordered from lowest to highest score, the lowest 25% of SA2s are given a quartile number of 1, the next lowest 25% of SA2s are given a quartile number of 2 and so on, up to the highest 25% of SA2s which are given a quartile number of 4. This means that SA2s are divided into four equal sized groups, depending on their score. In practice these groups won’t each be exactly 25% of SA2s as it depends on the distribution of SEIFA scores.


      Usual resident population
      URP_2021
      Population counts in this column are based on place of usual residence as reported on Census Night. These include persons out of scope of the IHAD.


      Dwellings
      DWELLING
      Total dwellings at Census time, including dwellings out of scope of the IHAD e.g. unoccupied private dwellings.

Please note: Proportional totals may equal more than 100% due to rounding and random adjustments made to the data. When calculating proportions, percentages, or ratios from cross-classified or small area tables, the random error introduced can be ignored except when very small cells are involved, in which case the impact on percentages and ratios can be significant. Refer to the Introduced random error / perturbation Census page on the ABS website for more information.

Data and geography references

Source data publication: Index of Household Advantage and Disadvantage Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: Index of Household Advantage and Disadvantage methodology, 2021 Source: Australian Bureau of Statistics (ABS)

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This dataset presents information from 2016 at the household level; the percentage of households within each Index of Household Advantage and Disadvantage (IHAD) quartile for Statistical Area Level 2 (SA2) 2016 boundaries. The IHAD is an experimental analytical index developed by the Australian Bureau of Statistics (ABS) that provides a summary measure of relative socio-economic advantage and disadvantage for households. It utilises information from the 2016 Census of Population and Housing. IHAD quartiles: All households are ordered from lowest to highest disadvantage, the lowest 25% of households are given a quartile number of 1, the next lowest 25% of households are given a quartile number of 2 and so on, up to the highest 25% of households which are given a quartile number of 4. This means that households are divided up into four groups, depending on their score. This data is ABS data (catalogue number: 4198.0) used with permission from the Australian Bureau of Statistics. For more information please visit the Australian Bureau of Statistics. Please note: AURIN has generated this dataset through aggregating the original SA1 level data (with calculated number of households/quartile) to SA2 level.

Survey on Equipment and Use of Information and Communication Technologies in Households: Stated advantages and disadvantages of teleworking, by demographic characteristics. National.

This spread sheet shows ABS geographic standards from 2006 across Australia and the % of the 15-64 year old population within each Socio-Economic Indexes for Individuals (SEIFI) IRSAD group. The data used to create this information was the same as used in the research paper “Socio-Economic Indexes for Areas: Getting a handle on individual diversity within areas” by Phillip Wise and Rosalynn Mathews. It is advised that this paper is read to further develop an understanding of the concepts and caveats associated with the analytical output contained in the spreadsheet.] Roughly, the most disadvantaged 10% of the 15–64 year old population falls into group 1, whilst group 10 contains the most advantaged 10%. The smallest group in terms of 15–64 year old population proportion is group 6 with 7.78%, compared to group 7 with the largest percentage at 12% due to clustering at this point in the distribution of scores. Group 1 – Approx. 9.6% of the 15-64 year old population Group 2 – Approx. 10.0% of the 15-64 year old population Group 3 – Approx. 11.5% of the 15-64 year old population Group 4 – Approx. 8.6% of the 15-64 year old population Group 5 – Approx. 11.4% of the 15-64 year old population Group 6 – Approx. 7.8% of the 15-64 year old population Group 7 – Approx. 12.0% of the 15-64 year old population Group 8 – Approx. 9.1% of the 15-64 year old population Group 9 – Approx. 9.5% of the 15-64 year old population Group 10 – Approx. 10.5% of the 15-64 year old population

Replication data for a manuscript in the International Journal of Science Education. This data set is the export from qualitative software Dedoose of the raw excerpts coded throughout the project and can be transformed to produce chord diagrams.

This study was designed to explore whether civil protection orders were effective in providing safer environments for victims of domestic violence and enhancing their opportunities for escaping violent relationships. The researchers looked at the factors that might influence civil protection orders, such as accessibility to the court process, linkages to public and private services and sources of support, and the criminal record of the victim's abuser, and then examined how courts in three jurisdictions processed civil protection orders. Wilmington, Delaware, Denver, Colorado, and the District of Columbia were chosen as sites because of structural differences among them that were believed to be linked to the effectiveness of civil protection orders. Since these jurisdictions each had different court processes and service models, the researchers expected that these models would produce various results and that these variations might hold implications for improving practices in other jurisdictions. Data were collected through initial and follow-up interviews with women who had filed civil protection orders. The effectiveness of the civil protection orders was measured by the amount of improvement in the quality of the women's lives after the order was in place, versus the extent of problems created by the protection orders. Variables from the survey of women include police involvement at the incident leading to the protection order, the relationship of the petitioner and respondent to the petition prior to the order, history of abuse, the provisions asked for and granted in the order, if a permanent order was not filed for by the petitioner, the reasons why, the court experience, protective measures the petitioner undertook after the order, and how the petitioner's life changed after the order. Case file data were gathered on when the order was filed and issued, contempt motions and hearings, stipulations of the order, and social service referrals. Data on the arrest and conviction history of the petition respondent were also collected.

This data is SA2 based SEIFA data on The Index of Relative Socio-economic Advantage and Disadvantage, 2016. Data is based upon 2016 ASGS boundaries. Socio-Economic Indexes for Areas (SEIFA) is an ABS product that ranks areas in Australia according to relative socio-economic advantage and disadvantage. The indexes are based on information from the five-yearly Census of Population and Housing. SEIFA 2016 has been created from Census 2016 data and consists of four indexes: The Index of Relative Socio-economic Disadvantage (IRSD); The Index of Relative Socio-economic Advantage and Disadvantage (IRSAD); The Index of Education and Occupation (IEO); The Index of Economic Resources (IER). Each index is a summary of a different subset of Census variables and focuses on a different aspect of socio-economic advantage and disadvantage. This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics. For more information on this data please visit the Australian Bureau of Statistics.

This dataset presents information from 2016 at the household level; the percentage of households within each Index of Household Advantage and Disadvantage (IHAD) quartile for Local Government Area (LGA) 2017 boundaries. The IHAD is an experimental analytical index developed by the Australian Bureau of Statistics (ABS) that provides a summary measure of relative socio-economic advantage and disadvantage for households. It utilises information from the 2016 Census of Population and Housing. IHAD quartiles: All households are ordered from lowest to highest disadvantage, the lowest 25% of households are given a quartile number of 1, the next lowest 25% of households are given a quartile number of 2 and so on, up to the highest 25% of households which are given a quartile number of 4. This means that households are divided up into four groups, depending on their score. This data is ABS data (catalogue number: 4198.0) used with permission from the Australian Bureau of Statistics. For more information please visit the Australian Bureau of Statistics. Please note: AURIN has generated this dataset through aggregating the original SA1 level data (with calculated number of households/quartile) to LGA level.

This dataset presents the percentage of the 15-64 year old population within each Socio-Economic Indexes for Individuals (SEIFI) Index of Relative Socio-economic Disadvantage (IRSD) group. The data has been aggregated to the 2006 Census Collection Districts (CD). This datasets presents the IRSD groups to 10 categories, where group 1 is the 10% most disadvantaged 15-64 year old population and group 10 presents the most advantaged 10% of 15-64 year old population. For more information please visit the ACT Government Data Portal. Please note: AURIN has spatially enabled the original data.

The Socio-Economic Indexes for Areas (SEIFA) rank areas according to their relative socio-economic advantage and disadvantage using 2021 Census data. This dataset consists of the standardised variable proportions of the Index of Relative Socio-economic Disadvantage (IRSD) represented as quintiles. IRSD is one of the four SEIFA indexes. IRSD is a general socio-economic index that summarises a range of information about the economic and social conditions of people and households within an area. IRSD only includes measures of relative disadvantage.For detailed information on how to use the SEIFA data, please refer to the SEIFA Technical Paper.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It will bring together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas.

Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: Data downloads (Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, Data downloads Source: Australian Bureau of Statistics (ABS)

Socio-Economic Indexes for Areas (SEIFA) is a product developed by the ABS that ranks areas in Australia according to relative socio-economic advantage and disadvantage. The indexes are based on information from the five-yearly Census. SEIFA 2011 is the latest version of this product and consists of four indexes. The Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD) summarises information about the economic and social conditions of people and households within an area, including both relative advantage and disadvantage measures. Data last updated: 28th March 2013. Users of this data are advised to carefully read the accompanying information on the SEIFA web page and in the Technical Paper. SEIFA Homepage SEIFA Technical Paper For further information about these and related statistics, contact the National Information and Referral Services on 1300 135 070. Periodicity: 5-Yearly.

I argue that the national political environment can meaningfully affect variation in aggregate demand for partisan media. I focus on the relationship between the political context—namely, political advantage and disadvantage derived from elections—and media demand in the form of partisan newspaper circulations. Using a dataset that characterizes the partisan slant of local newspapers and their circulation levels between 1932 and 2004, I find that when parties are electorally advantaged in presidential contests, demand for their affiliated newspapers decreases relative to demand for papers affiliated with disadvantaged parties. I uncover evidence of similar patterns in a case study of Florida newspapers, and I also compare the power of presidential versus congressional outcomes in shaping feelings of advantage and disadvantage. Taken together, these results provide evidence of a negative link between political advantage derived from presidential elections and the relative demand for partisan news.

This data is Local Government Areas (LGA) based Socio-Economic Indexes for Areas (SEIFA) Index of Advantage/Disadvantage (IRSAD) - Is a continuum of advantage to disadvantage. Low values indicate areas of disadvantage; and high values indicate areas of advantage. This data is based on the 2006 census and follows the 2006 Australian Standard Geographical Classification (ASGC) boundaries. The Australian Bureau of Statistics (ABS) has developed indexes to allow ranking of regions/areas, providing a method of determining the level of social and economic wellbeing in that region. There are four indexes included in the SEIFA 2006 product. They relate to socio-economic aspects of geographic areas. Each index summarises a different aspect of the socio-economic conditions in an area. The indexes have been obtained by a technique called principal components analysis. This technique summarises the information from a variety of social and economic variables, calculating weights that will give the best summary for the underlying variables. For the SEIFA indexes, each index uses a different set of underlying variables. All the indexes (including the Index of Relative Socio-Economic Disadvantage) have been constructed so that relatively disadvantaged areas (e.g. areas with many low income earners) have low index values. This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics.

The Politbarometer has been conducted since 1977 on an almost monthly basis by the Research Group for Elections (Forschungsgruppe Wahlen) for the Second German Television (ZDF). Since 1990, this database has also been available for the new German states. The survey focuses on the opinions and attitudes of the voting population in the Federal Republic on current political topics, parties, politicians, and voting behavior. From 1990 to 1995 and from 1999 onward, the Politbarometer surveys were conducted separately in the eastern and western federal states (Politbarometer East and Politbarometer West). The separate monthly surveys of a year are integrated into a cumulative data set that includes all surveys of a year and all variables of the respective year. The Politbarometer short surveys, collected with varying frequency throughout the year, are integrated into the annual cumulation starting from 2003.

Purpose and summary description of the Census The Census 2021 is a photograph as of 1 January 2021 of the population residing in Belgium. It provides a wide range of figures on housing and the demographic, socio-economic and educational characteristics of citizens. The purpose of the Census is twofold: respond to the European regulation[1] and produce statistics for specific national needs (administrations, international organisations, researchers, businesses and individuals). Once based on a comprehensive survey of all citizens, since 2011 the Census has relied exclusively on the use of administrative databases. Definitions The different statistical units The population The population taken into account for the 2021 Census corresponds to the resident population, as entered in the National Register of Natural Persons (RNPP), on 1 January 2021. The Belgian population includes Belgians and non-Belgians admitted or authorised to settle or stay on the territory but does not include non-Belgians staying for less than three months on the territory, asylum seekers and non-Belgians in an irregular situation[2]. Private households This group includes people living alone in a dwelling and groups of several people living in the same dwelling and providing together for the basic needs of life. Family nuclei A family nucleus consists of two or more persons who live in the same household and whose ties are those of husband and wife, partners in registered partnerships, partners living in consensual unions, or parents and children. Residential Premises This group includes all premises used as the usual residence of one or more persons. Classic dwellings Classic dwellings are separate (surrounded by walls and covered with a roof) and independent (with a direct entrance to the street or a staircase, a corridor), which are designed to serve as permanent dwellings. Occupied conventional dwellings These are conventional dwellings used as usual residences for one or more private households. The variables and their description Gender This variable distinguishes men from women. Age Age is indicated in years past January 1, 2021. Place of habitual residence The place of residence is that registered in the National Register on 1 January 2021. It is therefore the place of legal residence. Belgian municipalities changed between 2011 and 2021. In the comparisons presented on this site, the figures for 2011 are broken down by municipality in 2021. Locality The locality is a distinct population agglomeration, i.e. an area defined by a population group living in adjacent or contiguous buildings. This area is a group of buildings, none of which are more than 200 metres away from the nearest building. Belgian municipalities changed between 2011 and 2021. In the comparisons presented on this site, the figures for 2011 are broken down by municipality in 2021. Education level Education level refers to the highest level of education successfully completed. Field of study The field of study of the diploma is classified according to the ISCED-F 2013 nomenclature (https://statbel.fgov.be/en/open-data/code-isced-f-2013-4-digits) Note Comparison with the previous Census requires some caution: This publication is based on the Belgian population and the 2011 Census has as reference the European definition of population. Information on the difference between the Belgian population and that of the 2011 Census Comparisons with the results of surveys such as the LFS (Labour Force Survey) also deserve some caution. The results presented in the Census reflect the highest educational attainment according to available administrative data, while the LFS figures are based on a survey that measures the highest educational attainment by (a sample of) respondents. Both sources have their own characteristics and have advantages and disadvantages. LFS has less to deal with, for example, the problem of missing values at the level of education, such as diplomas obtained abroad, but measurement errors and other forms of distortion characteristic of this type of survey may occur. On the other hand, the great advantage of the current administrative data is that the figures are available at a very detailed level and can therefore be linked to other administrative sources. More detailed information on the specific differences between the two sources is available upon request from Statbel. Metadata Metadata for the topic ‘Education’ Education level comparison in LFS and Census Education level comparison in LFS and Census - Summary [1] COMMISSION IMPLEMENTING REGULATION (EU) 2017/543 of 22 March 2017 laying down rules for the application of Regulation (EC) No 763/2008 of the European Parliament and of the Council on population and housing censuses as regards the technical specifications of the topics and their subdivisions. [2]More information on how this population is determined

This survey of crime victims was undertaken to determine whether state constitutional amendments and other legal measures designed to protect crime victims' rights had been effective. It was designed to test the hypothesis that the strength of legal protection for victims' rights has a measurable impact on how victims are treated by the criminal justice system and on their perceptions of the system. A related hypothesis was that victims from states with strong legal protection would have more favorable experiences and greater satisfaction with the system than those from states where legal protection is weak. The Victim Survey (Parts 1, 4-7) collected information on when and where the crime occurred, characteristics of the perpetrators, use of force, police response, victim services, type of information given to the victim by the criminal justice system, the victim's level of participation in the criminal justice system, how the case ended, sentencing and restitution, the victim's satisfaction with the criminal justice system, and the effects of the crime on the victim. Demographic variables in the file include age, race, sex, education, employment, and income. In addition to the victim survey, criminal justice and victim assistance professionals at the state and local levels were surveyed because these professionals affect crime victims' ability to recover from and cope with the aftermath of the offense and the stress of participation in the criminal justice system. The Survey of State Officials (Parts 2 and 8) collected data on officials' opinions of the criminal justice system, level of funding for the agency, types of victims' rights provided by the state, how victims' rights provisions had changed the criminal justice system, advantages and disadvantages of such legislation, and recommendations for future legislation. The Survey of Local Officials (Parts 3 and 9) collected data on officials' opinions of the criminal justice system, level of funding, victims' rights to information about and participation in the criminal justice process, victim impact statements, and restitution.

The Socio-Economic Indexes for Areas (SEIFA) rank areas according to their relative socio-economic advantage and disadvantage using 2021 Census data. This layer presents data by Statistical Area Level 1 (SA1), 2021. SEIFA 2021 consists of four indexes: The Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) The Index of Relative Socio-economic Disadvantage (IRSD) The Index of Education and Occupation (IEO) The Index of Economic Resources (IER) Each index summarises different subsets of 2021 Census variables and focuses on a different aspect of socio-economic advantage and disadvantage.For detailed information on how to use the SEIFA data, please refer to the SEIFA 2021 Technical Paper.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It will bring together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas.

Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, Data downloads Source: Australian Bureau of Statistics (ABS)

Political scientists routinely face the challenge of assessing the quality (validity and reliability) of measures in order to use them in substantive research. While stand-alone assessment tools exist, researchers rarely combine them comprehensively. Further, while a large literature informs data producers, data consumers lack guidance on how to assess existing measures for use in substantive research. We delineate a three-component practical approach to data quality assessment that integrates complementary multi-method tools to assess: 1) content validity; 2) the validity and reliability of the data generation process; and 3) convergent validity. We apply our quality assessment approach to the corruption measures from the Varieties of Democracy (V-Dem) project, both illustrating our rubric and unearthing several quality advantages and disadvantages of the V-Dem measures, compared to other existing measures of corruption.