100+ datasets found
  1. Data from: PISA Data Analysis Manual: SPSS, Second Edition

    • catalog.data.gov
    • s.cnmilf.com
    Updated Mar 30, 2021
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    U.S. Department of State (2021). PISA Data Analysis Manual: SPSS, Second Edition [Dataset]. https://catalog.data.gov/dataset/pisa-data-analysis-manual-spss-second-edition
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    Dataset updated
    Mar 30, 2021
    Dataset provided by
    United States Department of Statehttp://state.gov/
    Description

    The OECD Programme for International Student Assessment (PISA) surveys collected data on students’ performances in reading, mathematics and science, as well as contextual information on students’ background, home characteristics and school factors which could influence performance. This publication includes detailed information on how to analyse the PISA data, enabling researchers to both reproduce the initial results and to undertake further analyses. In addition to the inclusion of the necessary techniques, the manual also includes a detailed account of the PISA 2006 database and worked examples providing full syntax in SPSS.

  2. A dataset from a survey investigating disciplinary differences in data...

    • zenodo.org
    • data.niaid.nih.gov
    • +1more
    bin, csv, pdf, txt
    Updated Jul 12, 2024
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    Anton Boudreau Ninkov; Anton Boudreau Ninkov; Chantal Ripp; Chantal Ripp; Kathleen Gregory; Kathleen Gregory; Isabella Peters; Isabella Peters; Stefanie Haustein; Stefanie Haustein (2024). A dataset from a survey investigating disciplinary differences in data citation [Dataset]. http://doi.org/10.5281/zenodo.7555363
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    csv, txt, pdf, binAvailable download formats
    Dataset updated
    Jul 12, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Anton Boudreau Ninkov; Anton Boudreau Ninkov; Chantal Ripp; Chantal Ripp; Kathleen Gregory; Kathleen Gregory; Isabella Peters; Isabella Peters; Stefanie Haustein; Stefanie Haustein
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    GENERAL INFORMATION

    Title of Dataset: A dataset from a survey investigating disciplinary differences in data citation

    Date of data collection: January to March 2022

    Collection instrument: SurveyMonkey

    Funding: Alfred P. Sloan Foundation


    SHARING/ACCESS INFORMATION

    Licenses/restrictions placed on the data: These data are available under a CC BY 4.0 license

    Links to publications that cite or use the data:

    Gregory, K., Ninkov, A., Ripp, C., Peters, I., & Haustein, S. (2022). Surveying practices of data citation and reuse across disciplines. Proceedings of the 26th International Conference on Science and Technology Indicators. International Conference on Science and Technology Indicators, Granada, Spain. https://doi.org/10.5281/ZENODO.6951437

    Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). Tracing data:
    A survey investigating disciplinary differences in data citation.
    Zenodo. https://doi.org/10.5281/zenodo.7555266


    DATA & FILE OVERVIEW

    File List

    • Filename: MDCDatacitationReuse2021Codebook.pdf
      Codebook
    • Filename: MDCDataCitationReuse2021surveydata.csv
      Dataset format in csv
    • Filename: MDCDataCitationReuse2021surveydata.sav
      Dataset format in SPSS
    • Filename: MDCDataCitationReuseSurvey2021QNR.pdf
      Questionnaire

    Additional related data collected that was not included in the current data package: Open ended questions asked to respondents


    METHODOLOGICAL INFORMATION

    Description of methods used for collection/generation of data:

    The development of the questionnaire (Gregory et al., 2022) was centered around the creation of two main branches of questions for the primary groups of interest in our study: researchers that reuse data (33 questions in total) and researchers that do not reuse data (16 questions in total). The population of interest for this survey consists of researchers from all disciplines and countries, sampled from the corresponding authors of papers indexed in the Web of Science (WoS) between 2016 and 2020.

    Received 3,632 responses, 2,509 of which were completed, representing a completion rate of 68.6%. Incomplete responses were excluded from the dataset. The final total contains 2,492 complete responses and an uncorrected response rate of 1.57%. Controlling for invalid emails, bounced emails and opt-outs (n=5,201) produced a response rate of 1.62%, similar to surveys using comparable recruitment methods (Gregory et al., 2020).

    Methods for processing the data:

    Results were downloaded from SurveyMonkey in CSV format and were prepared for analysis using Excel and SPSS by recoding ordinal and multiple choice questions and by removing missing values.

    Instrument- or software-specific information needed to interpret the data:

    The dataset is provided in SPSS format, which requires IBM SPSS Statistics. The dataset is also available in a coded format in CSV. The Codebook is required to interpret to values.


    DATA-SPECIFIC INFORMATION FOR: MDCDataCitationReuse2021surveydata

    Number of variables: 94

    Number of cases/rows: 2,492

    Missing data codes: 999 Not asked

    Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.

  3. PISA 2003 Data Analysis Manual SPSS

    • catalog.data.gov
    • gimi9.com
    • +1more
    Updated Mar 30, 2021
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    U.S. Department of State (2021). PISA 2003 Data Analysis Manual SPSS [Dataset]. https://catalog.data.gov/dataset/pisa-2003-data-analysis-manual-spss
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    Dataset updated
    Mar 30, 2021
    Dataset provided by
    United States Department of Statehttp://state.gov/
    Description

    This publication provides all the information required to understand the PISA 2003 educational performance database and perform analyses in accordance with the complex methodologies used to collect and process the data. It enables researchers to both reproduce the initial results and to undertake further analyses. The publication includes introductory chapters explaining the statistical theories and concepts required to analyse the PISA data, including full chapters on how to apply replicate weights and undertake analyses using plausible values; worked examples providing full syntax in SPSS®; and a comprehensive description of the OECD PISA 2003 international database. The PISA 2003 database includes micro-level data on student educational performance for 41 countries collected in 2003, together with students’ responses to the PISA 2003 questionnaires and the test questions. A similar manual is available for SAS users.

  4. Raw data in SPSS Software

    • zenodo.org
    Updated Jul 16, 2023
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    Esubalew Tesfahun; Esubalew Tesfahun (2023). Raw data in SPSS Software [Dataset]. http://doi.org/10.5281/zenodo.8151987
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    Dataset updated
    Jul 16, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Esubalew Tesfahun; Esubalew Tesfahun
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Raw data used for analysis

  5. g

    Statistical Computing: SPSS

    • search.gesis.org
    • dataverse.unc.edu
    • +1more
    Updated Oct 29, 2021
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    Zimmer, Catherine (2021). Statistical Computing: SPSS [Dataset]. https://search.gesis.org/research_data/datasearch-httpsdataverse-unc-eduoai--hdl1902-2911631
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    Dataset updated
    Oct 29, 2021
    Dataset provided by
    GESIS search
    UNC Dataverse
    Authors
    Zimmer, Catherine
    License

    https://search.gesis.org/research_data/datasearch-httpsdataverse-unc-eduoai--hdl1902-2911631https://search.gesis.org/research_data/datasearch-httpsdataverse-unc-eduoai--hdl1902-2911631

    Description

    Part 1 of the course will offer an introduction to SPSS and teach how to work with data saved in SPSS format. Part 2 will demonstrate how to work with SPSS syntax, how to create your own SPSS data files, and how to convert data in other formats to SPSS. Part 3 will teach how to append and merge SPSS files, demonstrate basic analytical procedures, and show how to work with SPSS graphics.

  6. f

    This is the SPSS data exported to excel.

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    Updated Mar 31, 2025
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    Abdu, Seid Mohammed; Geneti, Soressa Abebe; Muhaba, Endris Seid; Melka, Dereje (2025). This is the SPSS data exported to excel. [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0002080988
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    Dataset updated
    Mar 31, 2025
    Authors
    Abdu, Seid Mohammed; Geneti, Soressa Abebe; Muhaba, Endris Seid; Melka, Dereje
    Description

    Analysis was conducted from this data. It contains the values from which the means, standard deviations and other measures are taken and the values used to build graphs and figures. (XLSX)

  7. 2008 National Survey of Drinking and Driving Attitudes and Behaviors - SPSS...

    • catalog.data.gov
    Updated May 1, 2024
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    National Highway Traffic Safety Administration (2024). 2008 National Survey of Drinking and Driving Attitudes and Behaviors - SPSS Database File [Dataset]. https://catalog.data.gov/dataset/2008-national-survey-of-drinking-and-driving-attitudes-and-behaviors-spss-database-file
    Explore at:
    Dataset updated
    May 1, 2024
    Description

    The 2008 National Survey of Drinking and Driving Attitudes and Behaviors was composed of a single questionnaire administered to a sample of randomly selected individuals 16 and older, with ages 16 through 24 over-sampled. The respondents were asked about their drinking behavior, their drinking and driving behavior, use of designated drivers, their hosting events in which drinking occurred, risks they perceive associated with drinking and driving, experience with anti-DWI enforcement activity, and their attitudes concerning major intervention strategies.The survey was administered from September 10, 2008 to December 22, 2008. A total of 6,999 respondents completed the survey, including 5,392 landline interviews and 1,607 cell phone interviews. The total number of completed interviews for each of the four Census regions (Northeast, Midwest, South, and West) was 1,409, 1,654, 2,390, and 1,546, respectively.

  8. f

    SPSS data.

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    • +1more
    Updated Sep 8, 2023
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    Mekonen, Alemayehu Gonie; Mitikie, Esubalew Guday; Abayneh, Abrham Demis; Haile, Mitiku Tefera; Seid, AbdulWahhab; Ayele, Abebe Nigussie (2023). SPSS data. [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001003472
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    Dataset updated
    Sep 8, 2023
    Authors
    Mekonen, Alemayehu Gonie; Mitikie, Esubalew Guday; Abayneh, Abrham Demis; Haile, Mitiku Tefera; Seid, AbdulWahhab; Ayele, Abebe Nigussie
    Description

    BackgroundObesity causes a serious diet-related chronic disease, including type-2 diabetes, cardiovascular disease, hypertension, osteoarthritis, and certain forms of cancer. In Sub- Saharan Africa including Ethiopia, most nutritional interventions mainly focused on a child undernutrition and ignored the impacts of obesity among children. In Ethiopia, the magnitude and associated factors of obesity among school-age children were not clearly described. Therefore this study assesses the predictors of obesity among school- age children in Debre Berhan City, Ethiopia, 2022.MethodsA cross-sectional study design was conducted from June to July, 2022. Participants were selected by using multistage sampling method. Data were collected using pre-tested and structured questions. Data were coded and entered in Epi-data version 4.6 and exported and analyzed using SPSS version 25.ResultA total of 600 children were participating in the study. The prevalence of obesity was 10.7% (95% CI: 8.3, 13.2). In this study, attending at private school (AOR = 4.24, 95% CI: 1.58, 11.32), children aged between 10-12years (AOR = 2.67, 95% CI: 1.30, 5.48), soft drink available in home (AOR = 2.27, 95% CI: 1.25,18.13), Loneliness (AOR = 1.67 95% CI: 1.12, 3.15) and mothers with occupational status of daily labour (AOR = 8.54 95% CI: 1.12, 65.39) were significantly associated with childhood obesity.ConclusionIn this study, the overall magnitude of childhood obesity was (10.7%) which means one in eleven children and relatively high as compare to the EDHS survey. Therefore, more attention should be given to strengthening physical activities, providing nutritional education, and creating community awareness about healthy diets as well as other preventive measures.

  9. f

    SPSS Statistics Data file.

    • plos.figshare.com
    bin
    Updated May 31, 2023
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    Filip Ventorp; Anna Gustafsson; Lil Träskman-Bendz; Åsa Westrin; Lennart Ljunggren (2023). SPSS Statistics Data file. [Dataset]. http://doi.org/10.1371/journal.pone.0140052.s001
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    binAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Filip Ventorp; Anna Gustafsson; Lil Träskman-Bendz; Åsa Westrin; Lennart Ljunggren
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    A SPSS file with data used in the statistical analysis. Covariates were excluded in the file due to restrictions of the ethical permission. However a complete file is provided for researchers after request at publication@ventorp.com. (SAV)

  10. SPSS Data Set S1 Logistic Regression Model Data

    • figshare.com
    bin
    Updated Jan 19, 2016
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    Michelle Klailova; Phyllis Lee (2016). SPSS Data Set S1 Logistic Regression Model Data [Dataset]. http://doi.org/10.6084/m9.figshare.1051748.v2
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    binAvailable download formats
    Dataset updated
    Jan 19, 2016
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Michelle Klailova; Phyllis Lee
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Data set from PLOS ONE Article Published Entitled: Western Lowland Gorillas Signal Selectively Using Odor

  11. RAW Data Excel and SPSS

    • figshare.com
    xlsx
    Updated Sep 25, 2024
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    Jamil Ahmed (2024). RAW Data Excel and SPSS [Dataset]. http://doi.org/10.6084/m9.figshare.27101149.v1
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    xlsxAvailable download formats
    Dataset updated
    Sep 25, 2024
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Jamil Ahmed
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This cross-sectional study aimed to determine the prevalence of obesity and perceived barriers to weight loss in 1453 Bahraini adults who had used any intervention to lose weight in the past year. We found a high prevalence (78.2%) of overweight and obesity. Females were more likely to have obesity compared to males (81.4% vs. 66.7%). Older individuals aged 36-45 were 3.37 times, and 45 or older were 3.56 times more likely to have obesity. Married participants had higher odds of obesity compared to single participants (OR=1.79). Participants with obesity were more likely to be unemployed compared to students (OR=1.49). The most common contributing factors to weight gain were lack of physical activity (29.5%) and unhealthy diet (29.2%). Participants with obesity were more likely to have relied on dieting (OR=2.53) or exercise (OR=1.47) for weight loss and used medication (OR=5.23). This study highlights the complex relationship between sociodemographic factors, lifestyle behaviors, and obesity and sustaining weight loss.

  12. H

    Current Population Survey (CPS)

    • dataverse.harvard.edu
    • search.dataone.org
    Updated May 30, 2013
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    Anthony Damico (2013). Current Population Survey (CPS) [Dataset]. http://doi.org/10.7910/DVN/AK4FDD
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 30, 2013
    Dataset provided by
    Harvard Dataverse
    Authors
    Anthony Damico
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

  13. SPSS Raw Data.sav

    • figshare.com
    bin
    Updated Sep 23, 2025
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    James Pulverenti (2025). SPSS Raw Data.sav [Dataset]. http://doi.org/10.6084/m9.figshare.30183445.v1
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    binAvailable download formats
    Dataset updated
    Sep 23, 2025
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    James Pulverenti
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    SPSS Data GAI Evaluations

  14. f

    SPSS data set.

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    Updated Dec 15, 2023
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    Manaf, Rosliza Abdul; Ismail, Suriani; Al-Oseely, Sarah (2023). SPSS data set. [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001089470
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    Dataset updated
    Dec 15, 2023
    Authors
    Manaf, Rosliza Abdul; Ismail, Suriani; Al-Oseely, Sarah
    Description

    IntroductionCervical cancer is a significant public health problem for women worldwide. It is the fourth most frequent cancer in women globally. While early detection of cancerous lesions through screening tests leads to a better prognosis and a better chance of being cured, the number of people who go for screening is still low, especially for groups that are marginalized, like immigrant women.ObjectiveThe purpose of this study was to identify cervical cancer screening practices and factors influencing screening status among Yemeni immigrant women living in the Klang Valley, Malaysia.MethodA cross-sectional study among 355 randomly selected respondents between the ages of 20 and 65 was conducted through an online survey. A questionnaire was sent directly to the participants via WhatsApp. The analysis was conducted using SPSS 25 with a significance level of 0.05. It included descriptive analysis, chi-square and multiple logistic regression.ResultsThe response rate was 59%, with the majority of the respondents being married and between the ages of 35 and 49. Screening was reported at 23.1% in the previous three years. The final model revealed that age group 50–65 years (AOR = 5.39, 95% CI: 1.53–18.93), insurance status (AOR 2.22, 95% CI = 1.15–4.3), knowledge (AOR = 6.67, 95% CI = 3.45–12.9), access to health care facilities (AOR = 4.64, 95% CI = 1.29–16.65), and perceived barriers (AOR = 2.5, 95% CI = 1.3–4.83) were significant predictors of cervical screening uptake among Yemeni immigrant women in Malaysia (p<0.05).ConclusionAccording to the results, cervical cancer screening was found to be low among Yemeni immigrant women. The predictors were age group 50–65 years, insurance status, knowledge, access to health care facilities and perceived barriers. Efforts to enhance immigrant women’s participation in cervical cancer screening must tackle barriers to access to healthcare services as well as expand cervical cancer screening education programs.

  15. f

    Spss data

    • datasetcatalog.nlm.nih.gov
    • figshare.com
    Updated Feb 13, 2023
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    Qi, Yingao (2023). Spss data [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001110644
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    Dataset updated
    Feb 13, 2023
    Authors
    Qi, Yingao
    Description

    Raw data from a study on the regulation of milk fat synthesis by sodium acetate via activation of the GPR41/GPR43 signaling pathway

  16. SPSS Data File of the Study

    • figshare.com
    bin
    Updated Jan 19, 2016
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    Lawrence k. Ma (2016). SPSS Data File of the Study [Dataset]. http://doi.org/10.6084/m9.figshare.1243747.v1
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    binAvailable download formats
    Dataset updated
    Jan 19, 2016
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Lawrence k. Ma
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This is the raw data file with which the analysis of the paper entitled ' Gratefully received, gratefully repaid: the role of perceived fairness in cooperative interactions' was carried out. Please refer to the dataset explanatory document (http://figshare.com/articles/Explanatory_Document_for_the_SPSS_Data_File_of_the_Study/1243735) for details.

  17. m

    RAW DATA SPSS AND QUESTIONNAIRE (MALAY AND ENGLISH VERSION)

    • data.mendeley.com
    Updated Nov 17, 2023
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    Rahman Latip (2023). RAW DATA SPSS AND QUESTIONNAIRE (MALAY AND ENGLISH VERSION) [Dataset]. http://doi.org/10.17632/3j83sv5zdv.1
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    Dataset updated
    Nov 17, 2023
    Authors
    Rahman Latip
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The data set provides insightful information based on a survey related to the socioeconomic demographics of drug-free parents living in high-risk environments and their involvement in drug prevention programs. The survey involved 200 families living in high-risk drug environments located in East Coast states of Peninsular Malaysia. The data includes a significant group of variables (A) socioeconomic demographic including; gender, age, race, marital status, place of current residents, number of children, number of children in primary school, number of children in secondary school, number of working children, number of children with disabilities, number of household members, level of education and employment categories, (B) parents involvement in drug prevention programs organised by various agencies including National Anti-Drug Agency, Ministry of Education, Ministry of Youth and Sport, The National Population and Family Development Board (LPPKN), Department of Information (Ministry of Communications and Multimedia), Department of National Unity and National Integration (Ministry of National Unity) and Non-Government Organisation (NGOs), parents’ interest towards drug prevention program and reasons for involvement. Each question regarding parent involvement is rated on a nominal scale in such a way that scores are given for 'Yes' for parents who involved in drug prevention programs and 'No' for parents who never involved in any drug prevention programs. Further question for parents’ interest towards drug prevention programs is rated by Yes because of seek new information, increase knowledge, availability and No because of commitment and health condition

  18. SPSS spreadsheet data

    • zenodo.org
    Updated Oct 8, 2024
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    HAMZA EL GHAZALI; HAMZA EL GHAZALI (2024). SPSS spreadsheet data [Dataset]. http://doi.org/10.5281/zenodo.13902598
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    Dataset updated
    Oct 8, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    HAMZA EL GHAZALI; HAMZA EL GHAZALI
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This is a dataset analysis regarding our previous research and the current research. it is the result of our observations over 3 years of monitoring and is provided briefly within our 1st publication: https://doi.org/10.5281/zenodo.10407923.

  19. m

    Questionnaire data on land use change of Industrial Heritage: Insights from...

    • data.mendeley.com
    Updated Jul 20, 2023
    + more versions
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    Arsalan Karimi (2023). Questionnaire data on land use change of Industrial Heritage: Insights from Decision-Makers in Shiraz, Iran [Dataset]. http://doi.org/10.17632/gk3z8gp7cp.2
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    Dataset updated
    Jul 20, 2023
    Authors
    Arsalan Karimi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Iran, Shiraz
    Description

    The survey dataset for identifying Shiraz old silo’s new use which includes four components: 1. The survey instrument used to collect the data “SurveyInstrument_table.pdf”. The survey instrument contains 18 main closed-ended questions in a table format. Two of these, concern information on Silo’s decision-makers and proposed new use followed up after a short introduction of the questionnaire, and others 16 (each can identify 3 variables) are related to the level of appropriate opinions for ideal intervention in Façade, Openings, Materials and Floor heights of the building in four values: Feasibility, Reversibility, Compatibility and Social Benefits. 2. The raw survey data “SurveyData.rar”. This file contains an Excel.xlsx and a SPSS.sav file. The survey data file contains 50 variables (12 for each of the four values separated by colour) and data from each of the 632 respondents. Answering each question in the survey was mandatory, therefor there are no blanks or non-responses in the dataset. In the .sav file, all variables were assigned with numeric type and nominal measurement level. More details about each variable can be found in the Variable View tab of this file. Additional variables were created by grouping or consolidating categories within each survey question for simpler analysis. These variables are listed in the last columns of the .xlsx file. 3. The analysed survey data “AnalysedData.rar”. This file contains 6 “SPSS Statistics Output Documents” which demonstrate statistical tests and analysis such as mean, correlation, automatic linear regression, reliability, frequencies, and descriptives. 4. The codebook “Codebook.rar”. The detailed SPSS “Codebook.pdf” alongside the simplified codebook as “VariableInformation_table.pdf” provides a comprehensive guide to all 50 variables in the survey data, including numerical codes for survey questions and response options. They serve as valuable resources for understanding the dataset, presenting dictionary information, and providing descriptive statistics, such as counts and percentages for categorical variables.

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    1. data all field studies SPSS.sav

    • psycharchives.org
    Updated Aug 5, 2022
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    (2022). 1. data all field studies SPSS.sav [Dataset]. https://psycharchives.org/en/item/5bb80531-2812-4a0a-9b75-b396c8543d34
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    Dataset updated
    Aug 5, 2022
    License

    https://doi.org/10.23668/psycharchives.4988https://doi.org/10.23668/psycharchives.4988

    Description

    Citizen Science (CS) projects play a crucial role in engaging citizens in conservation efforts. While implicitly mostly considered as an outcome of CS participation, citizens may also have a certain attitude toward engagement in CS when starting to participate in a CS project. Moreover, there is a lack of CS studies that consider changes over longer periods of time. Therefore, this research presents two-wave data from four field studies of a CS project about urban wildlife ecology using cross-lagged panel analyses. We investigated the influence of attitudes toward engagement in CS on self-related, ecology-related, and motivation-related outcomes. We found that positive attitudes toward engagement in CS at the beginning of the CS project had positive influences on participants’ psychological ownership and pride in their participation, their attitudes toward and enthusiasm about wildlife, and their internal and external motivation two months later. We discuss the implications for CS research and practice. Dataset for: Greving, H., Bruckermann, T., Schumann, A., Stillfried, M., Börner, K., Hagen, R., Kimmig, S. E., Brandt, M., & Kimmerle, J. (2023). Attitudes Toward Engagement in Citizen Science Increase Self-Related, Ecology-Related, and Motivation-Related Outcomes in an Urban Wildlife Project. BioScience, 73(3), 206–219. https://doi.org/10.1093/biosci/biad003: Data (SPSS format) collected for all field studies

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U.S. Department of State (2021). PISA Data Analysis Manual: SPSS, Second Edition [Dataset]. https://catalog.data.gov/dataset/pisa-data-analysis-manual-spss-second-edition
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Data from: PISA Data Analysis Manual: SPSS, Second Edition

Related Article
Explore at:
Dataset updated
Mar 30, 2021
Dataset provided by
United States Department of Statehttp://state.gov/
Description

The OECD Programme for International Student Assessment (PISA) surveys collected data on students’ performances in reading, mathematics and science, as well as contextual information on students’ background, home characteristics and school factors which could influence performance. This publication includes detailed information on how to analyse the PISA data, enabling researchers to both reproduce the initial results and to undertake further analyses. In addition to the inclusion of the necessary techniques, the manual also includes a detailed account of the PISA 2006 database and worked examples providing full syntax in SPSS.

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