Descriptive Statistics

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Aneurin Grant - One of the best experts on this subject based on the ideXlab platform.

  • Quantitative approaches in life cycle assessment—part 1—Descriptive Statistics and factor analysis
    The International Journal of Life Cycle Assessment, 2016
    Co-Authors: Aneurin Grant, Robert Ries, Carla Thompson
    Abstract:

    Purpose This study examines the utility of Descriptive Statistics and factor analysis in the interpretation of an LCA data set. It is believed that the use of Descriptive Statistics and factor analysis provides a comprehensive summary of the data, and insight on internal validity, which improves interpretation of results. The current study also provides a pretext for subsequent analyses with inferential Statistics. Methods Nine building envelope combinations were analyzed according to five service life models ( N  = 45, 45 data points). Three environmental indicators were used to characterize the data: global warming potential (GWP), atmospheric eco-toxicity (ECO), and atmospheric acidification (ACID) from the Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts (TRACI) assessment method. Descriptive Statistics measured minimum, maximum, mean, standard deviation, skewness, and kurtosis. For the factor analysis, data were disaggregated according to nine variables, including cumulative life cycle impact, major replacement, major replacement (frequency), minor replacement, major repairs, minor repairs, inspections 1 and 2, and total transportation ( N  = 45, 405 data points). All statistical operations were performed using SPSS software. Results and discussion Descriptive Statistics show a relatively normal data distribution for GWP. Data distributions for ECO and ACID deviated slightly from normal, which was confirmed through Shapiro-Wilk and Kolmogorov-Smirnov tests. Factor analysis resulted in three factors for GWP and ECO, and two factors for ACID. In each analysis, cumulative life cycle impact, major replacement, and major replacement (frequency) aligned as variables in a common factor. This alignment corroborates previous findings. Other variables in the factor analysis were less clearly or inconsistently aligned. Conclusions The Descriptive Statistics provide important information about the distribution of data in the study. The data are determined to be suitable for further analysis using inferential univariate and multivariate statistical tests, a topic that is examined in part 2 of this study as reported by Grant et al. (Int J Life Cycle Assess, 2015). The factor analysis used in the study corroborates the conclusions of previous studies and provides a basis for future statistical analyses.

  • quantitative approaches in life cycle assessment part 1 Descriptive Statistics and factor analysis
    International Journal of Life Cycle Assessment, 2016
    Co-Authors: Aneurin Grant, Robert Ries, Carla J Thompson
    Abstract:

    Purpose This study examines the utility of Descriptive Statistics and factor analysis in the interpretation of an LCA data set. It is believed that the use of Descriptive Statistics and factor analysis provides a comprehensive summary of the data, and insight on internal validity, which improves interpretation of results. The current study also provides a pretext for subsequent analyses with inferential Statistics.

  • Quantitative approaches in life cycle assessment—part 1—Descriptive Statistics and factor analysis
    International Journal of Life Cycle Assessment, 2016
    Co-Authors: Aneurin Grant, Robert Ries, Carla J Thompson
    Abstract:

    Purpose This study examines the utility of Descriptive Statistics and factor analysis in the interpretation of an LCA data set. It is believed that the use of Descriptive Statistics and factor analysis provides a comprehensive summary of the data, and insight on internal validity, which improves interpretation of results. The current study also provides a pretext for subsequent analyses with inferential Statistics.

Petros C Mavroidis - One of the best experts on this subject based on the ideXlab platform.

  • The WTO Dispute Settlement System 1995-2015: A Data Set and its Descriptive Statistics
    2017
    Co-Authors: Louise Johannesson, Petros C Mavroidis
    Abstract:

    In this paper, we provide some Descriptive Statistics of the first twenty years of the WTO (World Trade Organization) dispute settlement. The database used in this paper was assembled by the authors and has been publicly available (http://globalgovernanceprogramme.eui.eu/wto-case-law-project/). The statistical information that we present here is divided into three thematic units: the statutory and de facto duration of each stage of the process, paying particular attention to the eventual conclusion of litigation; the identity and participation in the process of the various institutional players, that is, not only complainants and defendants, but also third parties, as well as the WTO judges (panelists and Appellate Body members); and, finally, information regarding the subject-matter of various disputes, regarding the frequency with which claims regarding consistency of measures with the covered agreements (but also, at a more disaggregate level, e.g., specific provisions) have been raised. We call our work “Descriptive Statistics”, because, in an effort to provide raw material that will help researchers to conduct their research as they see fit, we have consciously refrained from systematically interpreting the data that we have assembled.

  • the wto dispute settlement system 1995 2016 a data set and its Descriptive Statistics
    Journal of World Trade, 2016
    Co-Authors: Louise Johannesson, Petros C Mavroidis
    Abstract:

    In this paper, we provide some Descriptive Statistics of the first twenty years of the WTO (World Trade Organization) dispute settlement, that we have extracted from the data set that we have put together, and made publicly available (http://globalgovernanceprogramme.eui.eu/wto-case-law-project/ ). The statistical information that we present here is divided into three thematic units: the statutory and de facto duration of each stage of the process, paying particular attention to the eventual conclusion of litigation; the identity and participation in the process of the various institutional players, that is, not only complainants and defendants, but also third parties, as well as the WTO judges (panelists and Appellate Body members); and, finally, information regarding the subject-matter of various disputes, regarding the frequency with which claims regarding consistency of measures with the covered agreements (but also, at a more disaggregate level, e.g., specific provisions) have been raised. We call our work “Descriptive Statistics”, because, in an effort to provide raw material that will help researchers to conduct their research as they see fit, we have consciously refrained from systematically interpreting the data that we have assembled.

Carla Thompson - One of the best experts on this subject based on the ideXlab platform.

  • Quantitative approaches in life cycle assessment—part 1—Descriptive Statistics and factor analysis
    The International Journal of Life Cycle Assessment, 2016
    Co-Authors: Aneurin Grant, Robert Ries, Carla Thompson
    Abstract:

    Purpose This study examines the utility of Descriptive Statistics and factor analysis in the interpretation of an LCA data set. It is believed that the use of Descriptive Statistics and factor analysis provides a comprehensive summary of the data, and insight on internal validity, which improves interpretation of results. The current study also provides a pretext for subsequent analyses with inferential Statistics. Methods Nine building envelope combinations were analyzed according to five service life models ( N  = 45, 45 data points). Three environmental indicators were used to characterize the data: global warming potential (GWP), atmospheric eco-toxicity (ECO), and atmospheric acidification (ACID) from the Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts (TRACI) assessment method. Descriptive Statistics measured minimum, maximum, mean, standard deviation, skewness, and kurtosis. For the factor analysis, data were disaggregated according to nine variables, including cumulative life cycle impact, major replacement, major replacement (frequency), minor replacement, major repairs, minor repairs, inspections 1 and 2, and total transportation ( N  = 45, 405 data points). All statistical operations were performed using SPSS software. Results and discussion Descriptive Statistics show a relatively normal data distribution for GWP. Data distributions for ECO and ACID deviated slightly from normal, which was confirmed through Shapiro-Wilk and Kolmogorov-Smirnov tests. Factor analysis resulted in three factors for GWP and ECO, and two factors for ACID. In each analysis, cumulative life cycle impact, major replacement, and major replacement (frequency) aligned as variables in a common factor. This alignment corroborates previous findings. Other variables in the factor analysis were less clearly or inconsistently aligned. Conclusions The Descriptive Statistics provide important information about the distribution of data in the study. The data are determined to be suitable for further analysis using inferential univariate and multivariate statistical tests, a topic that is examined in part 2 of this study as reported by Grant et al. (Int J Life Cycle Assess, 2015). The factor analysis used in the study corroborates the conclusions of previous studies and provides a basis for future statistical analyses.

Carla J Thompson - One of the best experts on this subject based on the ideXlab platform.

Louise Johannesson - One of the best experts on this subject based on the ideXlab platform.

  • The WTO Dispute Settlement System 1995-2015: A Data Set and its Descriptive Statistics
    2017
    Co-Authors: Louise Johannesson, Petros C Mavroidis
    Abstract:

    In this paper, we provide some Descriptive Statistics of the first twenty years of the WTO (World Trade Organization) dispute settlement. The database used in this paper was assembled by the authors and has been publicly available (http://globalgovernanceprogramme.eui.eu/wto-case-law-project/). The statistical information that we present here is divided into three thematic units: the statutory and de facto duration of each stage of the process, paying particular attention to the eventual conclusion of litigation; the identity and participation in the process of the various institutional players, that is, not only complainants and defendants, but also third parties, as well as the WTO judges (panelists and Appellate Body members); and, finally, information regarding the subject-matter of various disputes, regarding the frequency with which claims regarding consistency of measures with the covered agreements (but also, at a more disaggregate level, e.g., specific provisions) have been raised. We call our work “Descriptive Statistics”, because, in an effort to provide raw material that will help researchers to conduct their research as they see fit, we have consciously refrained from systematically interpreting the data that we have assembled.

  • the wto dispute settlement system 1995 2016 a data set and its Descriptive Statistics
    Journal of World Trade, 2016
    Co-Authors: Louise Johannesson, Petros C Mavroidis
    Abstract:

    In this paper, we provide some Descriptive Statistics of the first twenty years of the WTO (World Trade Organization) dispute settlement, that we have extracted from the data set that we have put together, and made publicly available (http://globalgovernanceprogramme.eui.eu/wto-case-law-project/ ). The statistical information that we present here is divided into three thematic units: the statutory and de facto duration of each stage of the process, paying particular attention to the eventual conclusion of litigation; the identity and participation in the process of the various institutional players, that is, not only complainants and defendants, but also third parties, as well as the WTO judges (panelists and Appellate Body members); and, finally, information regarding the subject-matter of various disputes, regarding the frequency with which claims regarding consistency of measures with the covered agreements (but also, at a more disaggregate level, e.g., specific provisions) have been raised. We call our work “Descriptive Statistics”, because, in an effort to provide raw material that will help researchers to conduct their research as they see fit, we have consciously refrained from systematically interpreting the data that we have assembled.

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