Is the Life Cycle Model or Ono’s Model Most Suitable for the Japanese? Analysis by Time-Series Data and Surveys of Lottery Purchase and Large Prizewinners

2014 ◽  
pp. 137-155
Author(s):  
Junmin Wan
Keyword(s):  
Time Series ◽  
Life Cycle ◽  
Time Series Data ◽  
Series Data ◽  
Life Cycle Model ◽  
Cycle Model

scholarly journals Impact of repeated hospital accreditation surveys on quality and reliability, an 8-year interrupted time series analysis

BMJ Open ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. e024514 ◽  
Author(s):  
Subashnie Devkaran ◽  
Patrick N O’Farrell ◽  
Samer Ellahham ◽  
Randy Arcangel
Keyword(s):  
Time Series ◽  
Life Cycle ◽  
Interrupted Time Series ◽  
Quality Measures ◽  
Simple Random Sample ◽  
Regression Equations ◽  
Life Cycle Model ◽  
Cycle Model ◽  
Hospital Accreditation ◽  
The Impact

ObjectiveTo evaluate whether hospital re-accreditation improves quality, patient safety and reliability over three accreditation cycles by testing the accreditation life cycle model on quality measures.DesignThe validity of the life cycle model was tested by calibrating interrupted time series (ITS) regression equations for 27 quality measures. The change in the variation of quality over the three accreditation cycles was evaluated using the Levene’s test.SettingA 650-bed tertiary academic hospital in Abu Dhabi, UAE.ParticipantsEach month (over 96 months), a simple random sample of 10% of patient records was selected and audited resulting in a total of 388 800 observations from 14 500 records.Intervention(s)The impact of hospital accreditation on the 27 quality measures was observed for 96 months, 1-year preaccreditation (2007) and 3 years postaccreditation for each of the three accreditation cycles (2008, 2011 and 2014).Main outcome measure(s)The life cycle model was evaluated by aggregating the data for 27 quality measures to produce a composite score (YC) and to fit an ITS regression equation to the unweighted monthly mean of the series.ResultsThe results provide some evidence for the validity of the four phases of the life cycle namely, the initiation phase, the presurvey phase, the postaccreditation slump and the stagnation phase. Furthermore, the life cycle model explains 87% of the variation in quality compliance measures (R2=0.87). The best-fit ITS model contains two significant variables (β1 and β3) (p≤0.001). The Levene’s test (p≤0.05) demonstrated a significant reduction in variation of the quality measures (YC) with subsequent accreditation cycles.ConclusionThe study demonstrates that accreditation has the capacity to sustain improvements over the accreditation cycle. The significant reduction in the variation of the quality measures (YC) with subsequent accreditation cycles indicates that accreditation supports the goal of high reliability.

Age, Generations, and Participation

2017 ◽  
Author(s):  
Russell J. Dalton
Keyword(s):  
United States ◽  
Time Series ◽  
Life Cycle ◽  
Millennial Generation ◽  
Time Series Data ◽  
Direct Action ◽  
The United States ◽  
Series Data ◽  
Online Participation ◽  
Modes Of Action

Political scientists debate whether the Millennial generation is disengaging from politics in contemporary democracies. The ISSP surveys show that the generational decline in participation is largely limited to voting and other forms of partisan activity. At the same time, younger citizens are often more engaged in non-electoral activities, such as direct action, protest, and online participation. Time-series data for the United States disentangles the effects of life-cycle changes and generations. More recent generations display a clear decline in voting across the 1967–2014 period. In contrast, life-cycle increases in participation are more common for non-electoral activity. Both factors influence participation but in contrasting ways for different modes of action.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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