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Nicolas Glady - One of the best experts on this subject based on the ideXlab platform.

  • Modeling the costs of trade finance during the financial crisis of 2008 2009 an application of dynamic Hierarchical Linear Model
    International Conference Information Processing, 2016
    Co-Authors: Ashwin Malshe, Shantanu Mullick, Nicolas Glady
    Abstract:

    The authors propose a dynamic Hierarchical Linear Model (DHLM) to study the variations in the costs of trade finance over time and across countries in dynamic environments such as the global financial crisis of 2008–2009. The DHLM can cope with challenges that a dynamic environment entails: nonstationarity, parameters changing over time and cross-sectional heterogeneity. The authors employ a DHLM to examine how the effects of four macroeconomic indicators – GDP growth, inflation, trade intensity and stock market capitalization - on trade finance costs varied over a period of five years from 2006 to 2010 across 8 countries. We find that the effect of these macroeconomic indicators varies over time, and most of this variation is present in the year preceding and succeeding the financial crisis. In addition, the trajectory of time-varying effects of GDP growth and inflation support the “flight to quality” hypothesis: cost of trade finance reduces in countries with high GDP growth and low inflation, during the crisis. The authors also note presence of country-specific heterogeneity in some of these effects. The authors propose extensions to the Model and discuss its alternative uses in different contexts.

  • Modeling the costs of trade finance during the financial crisis of 2008 2009 an application of dynamic Hierarchical Linear Model
    Social Science Research Network, 2016
    Co-Authors: Ashwin Malshe, Shantanu Mullick, Nicolas Glady
    Abstract:

    The authors propose a dynamic Hierarchical Linear Model (DHLM) to study the variations in the costs of trade finance across multiple countries during the global financial crisis of 2008-2009. Specifically, they examine how the impact of a set of four macroeconomic indicators on trade finance costs varied in and around the financial crisis. They find that countries with higher GDP growth faced lower costs of trade finance and countries with higher trade intensity (Trade/GDP) experienced higher trade finance costs in 2009 and 2010. Somewhat surprisingly, the countries with more stock market capitalization compared to GDP also faced higher costs of trade finance during and post crisis. Finally, inflation had a weak statistically significant impact on trade finance costs in 2009. The authors propose extensions to the Model and discuss its alternative uses in different contexts.

Christopher C. Cheatham - One of the best experts on this subject based on the ideXlab platform.

  • Hierarchical Linear Model: Thinking Outside the Traditional Repeated-Measures Analysis-of-Variance Box
    Journal of athletic training, 2015
    Co-Authors: Monica R. Lininger, Jessaca Spybrook, Christopher C. Cheatham
    Abstract:

    Longitudinal designs are common in the field of athletic training. For example, in the Journal of Athletic Training from 2005 through 2010, authors of 52 of the 218 original research articles used longitudinal designs. In 50 of the 52 studies, a repeated-measures analysis of variance was used to analyze the data. A possible alternative to this approach is the Hierarchical Linear Model, which has been readily accepted in other medical fields. In this short report, we demonstrate the use of the Hierarchical Linear Model for analyzing data from a longitudinal study in athletic training. We discuss the relevant hypotheses, Model assumptions, analysis procedures, and output from the HLM 7.0 software. We also examine the advantages and disadvantages of using the Hierarchical Linear Model with repeated measures and repeated-measures analysis of variance for longitudinal data.

Bette Chambers - One of the best experts on this subject based on the ideXlab platform.

  • final reading outcomes of the national randomized field trial of success for all
    American Educational Research Journal, 2007
    Co-Authors: Geoffrey D Borman, Robert E Slavin, Alan C K Cheung, Anne Chamberlain, Nancy A Madden, Bette Chambers
    Abstract:

    Using a cluster randomization design, schools were randomly assigned to implement Success for All, a comprehensive reading reform Model, or control methods. This article reports final literacy outcomes for a 3-year longitudinal sample of children who participated in the treatment or control condition from kindergarten through second grade and a combined longitudinal and in-mover student sample, both of which were nested within 35 schools. Hierarchical Linear Model analyses of all three outcomes for both samples revealed statistically significant school-level effects of treatment assignment as large as one third of a standard deviation. The results correspond with the Success for All program theory, which emphasizes both comprehensive school-level reform and targeted student-level achievement effects through a multi-year sequencing of literacy instruction.

  • the national randomized field trial of success for all second year outcomes
    American Educational Research Journal, 2005
    Co-Authors: Geoffrey D Borman, Robert E Slavin, Alan C K Cheung, Anne Chamberlain, Nancy A Madden, Bette Chambers
    Abstract:

    This article reports literacy outcomes for a 2-year longitudinal student sample and a combined longitudinal and “in-mover” (i.e., those students who moved into the study schools between the initial pretest and the second-year posttest) sample, both of which were nested within 38 schools. Through the use of a cluster randomization design, schools were randomly assigned to implement Success for All or control methods. Hierarchical Linear Model analyses involving the longitudinal sample revealed statistically significant school-level effects of assignment to Success for All on three of the four literacy outcomes measured. Effects were as large as one quarter of a standard deviation—a learning advantage relative to controls exceeding half of a school year. Impacts for the combined longitudinal and in-mover sample were smaller in magnitude and more variable. The results correspond with the Success for All program theory, which targets school-level reform through multiyear sequencing of intensive literacy instr...

  • success for all first year results from the national randomized field trial
    Educational Evaluation and Policy Analysis, 2005
    Co-Authors: Geoffrey D Borman, Robert E Slavin, Alan C K Cheung, Anne Chamberlain, Nancy A Madden, Bette Chambers
    Abstract:

    This article reports first-year achievement outcomes of a national randomized evaluation of Success for All, a comprehensive reading reform Model. Forty-one schools were recruited for the study and were randomly assigned to implement Success for All or control methods. No statistically significant differences between experimental and control groups were found in regard to pretests or demographic characteristics. Hierarchical Linear Model analyses revealed a statistically significant school-level effect of assignment to Success for All of nearly one quarter of a standard deviation-or more than 2 months of additional learning-on individual Word Attack test scores, but there were no schoollevel differences on the three other posttest measures assessed. These results are similar to those of earlier matched experiments and correspond with the Success for All program theory.

Juh Hyun Shin - One of the best experts on this subject based on the ideXlab platform.

  • Application of repeated-measures analysis of variance and Hierarchical Linear Model in nursing research
    Nursing Research, 2009
    Co-Authors: Juh Hyun Shin
    Abstract:

    OBJECTIVE: The aims of this study were to describe how repeated-measures analysis of variance (ANOVA) and the Hierarchical Linear Model (HLM) are used to evaluate intervention effect and to compare these methods, especially in relation to their requirements regarding assumptions, number of repeated measures, completeness of repeated measures, and equal intervals between measurements. APPROACH: Alzheimer's Disease Assessment Scale (ADAS) data sets (101 residents in 14 nursing homes, five times) were analyzed to explain differences between repeated-measures ANOVA and the HLM. RESULTS: More detailed information is available when HLM is used. For example, repeated-measures ANOVA showed that there is a statistically significant difference on overall mean ADAS scores between the married and nonmarried groups. The HLM analysis showed more detailed information; the ADAS score of the married group was higher by 6.4 than that of the nonmarried group on the adjusted average ADAS scores (during the whole data collection period). Repeated-measures ANOVA does not provide results on the within-subject changes with days. The HLM provides the specific conclusion that ADAS scores were increased by the one unit of "days" variable (0.017) when days were included. DISCUSSION: Hierarchical Linear Model is a powerful statistical method that can be applied to longitudinal research to evaluate an intervention at multiple levels. The major differences between the repeated-measures ANOVA and the HLM can be summarized as follows: The HLM (a) has less strict assumptions, (b) has more flexible data requirements (dealing with the missing data), and (c) stresses individual change over group differences. More stringent assumptions should be satisfied in repeated-measures ANOVA than in the HLM. The HLM may resolve important statistical issues that have existed in repeated-measures ANOVA. The HLM has more flexible data requirements in that it (a) can be utilized when the measurement data collection points are unequal and (b) may be used when researchers do not have data for all follow-up points, whereas the repeated-measures ANOVA requires a fixed time series design (equal interval, equal number of time points).

Yinkang Zhou - One of the best experts on this subject based on the ideXlab platform.

  • analysis of influencing factors of cultivated land fragmentation based on Hierarchical Linear Model a case study of jiangsu province china
    Land Use Policy, 2021
    Co-Authors: Xiaobin Jin, Jing Liu, Yinkang Zhou
    Abstract:

    Abstract Cultivated land fragmentation (CLF) is a common phenomenon of land use in the world, which has an essential effect on agricultural production. Given the limitations of single level in the previous study on the influencing factors of CLF, this study utilizes Hierarchical Linear Model (HLM) to explore the influencing factors of CLF from township and county levels in Jiangsu Province, and puts forward policy suggestions for relieving the CLF. The results indicate that the spatial distribution of CLF shows obvious characteristics in Jiangsu, which gradually increases from north to south. Besides, Hierarchical structure exists in the CLF, and 43 % of the differences in CLF come from townships, 57 % from counties. Furthermore, the CLF is affected by multilevel factors, average patch area, GDP, the proportion of secondary and tertiary industries, population density, and road accessibility are the dominant factors at the township level, which explain 76 % of CLF affected by townships, and the main factors at the county level include average patch area, GDP, land use intensity, and urbanization rate, which explain 64 % of CLF affected by counties. Ultimately, a positive interaction with county-level overall guidance and township-level specific implementation should be established to alleviate the CLF. Specifically, at the county level, we should scientifically formulate overall planning of land use, and arrange land consolidation projects according to local conditions, readjust and optimize the economic structure, coordinate kinds of land demands and revitalize inefficient urban land. At the township level, while strictly implementing county-level policies, we should also promote land consolidation projects focusing on basic farmland construction and rural residential land readjustment, and pay attention to the overall layout of agricultural production factors, optimize industrial structure, and orderly transfer rural surplus laborers. This study provides a new perspective for the research on the influencing factors of CLF, and also has important guiding effect on formulating land policies to alleviate the CLF and accelerate sustainable utilization of cultivated land.