The use of rank order data in segmentation analysis: A case study of Bruce Country, Ontario, Canada

2011 ◽  
Vol 17 (2) ◽  
pp. 105-114 ◽  
Author(s):  
Wayne W. Smith ◽  
Stephen L.J. Smith
Keyword(s):  
Rank Order ◽  
Rank Order Data ◽  
Segmentation Analysis

scholarly journals Instructors’ ideas on the use of code-switching in EFL in Turkey: A case study

2017 ◽  
Vol 7 (1) ◽  
pp. 9-17
Author(s):  
Engin Evrim Onem
Keyword(s):  
Rank Order ◽  
Relevant Literature ◽  
Code Switching ◽  
State Universities ◽  
Future Research ◽  
Efl Teachers ◽  
Language Classrooms ◽  
Foreign Language Instructors ◽  
Teaching Ideas

Abstract   Code switching is a very common phenomenon in EFL language classrooms. The goal of this case study is to find out the possible reasons why EFL instructors employ code switching in ELT classes in Turkey. To achieve this, a brief questionnaire composed of the most common seven reasons mentioned in the relevant literature on code switching in language classrooms was compiled by the researcher and administered to ten EFL teachers working at different state universities in Turkey. The participants were asked to rank order the reasons from the most ideal to the least ideal purpose of employing code switching in classrooms for themselves and were later asked to write the reasons for their choices. It was found that “leaving no confusion about the topic” was the most common reason for the participants and the teachers who prioritized that reason seemed to have similar ideas about employing code switching in EFL. Discussion of the results and implications for future research are presented. Keywords: Code switching, language teaching, ideas about code switching, foreign language instructors.    

scholarly journals CASUALIZATION & EMPLOYEE MORALE IN THE OIL INDUSTRY: A CASE STUDY OF SHELL COMPANIES IN NIGERIA

2020 ◽  
pp. 17-31
Author(s):  
Omoruyi Eke ◽  
Chima Onuoha
Keyword(s):  
Social Science ◽  
Rank Order ◽  
Sampling Technique ◽  
Oil Industry ◽  
Work Conditions ◽  
Employee Morale ◽  
Spearman Rank Order Correlation ◽  
Spearman Rank ◽  
Convenience Sampling

This study empirically investigates the nexus between Casualization and Employee Morale in the Oil industry, using Shell Companies in Nigeria (SCiN) as a case study. The ‘Convenience Sampling Technique’ was used to assess the sample size of 200 employees. Data was analyzed via Spearman Rank Order Correlation Coefficient, with the aid of Statistical Package for Social Science (SPSS) Version 27. The Findings revealed that: Casualization is significantly related to Employee Morale. It was concluded that all dimensions of the exogenous variable should be encouraged. All of which is to achieve high employee morale. Thus, the study recommends that: Management should allow casual workers access to certain perks and benefits such as: health benefits, performance bonuses, transportation allowances, etc. and they also should be allowed to have a workers' union for collective bargaining, sustained compensation policies, work on reducing stigmatization and focus on improving work conditions in order to achieve ‘high’ employee morale.

Scaling Replicated Conditional Rank-Order Data

1975 ◽  
Vol 6 ◽  
pp. 129 ◽  
Author(s):  
Forrest W. Young
Keyword(s):  
Rank Order ◽  
Rank Order Data

scholarly journals Postprocessing ensemble forecasts of vertical temperature profiles

2020 ◽  
Vol 6 (1) ◽  
pp. 45-60
Author(s):  
David Schoenach ◽  
Thorsten Simon ◽  
Georg Johann Mayr
Keyword(s):  
Rank Order ◽  
Probability Distributions ◽  
Ensemble Prediction ◽  
Order Structure ◽  
Temperature Profiles ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Atmospheric Observations ◽  
Prediction Systems

Abstract. Weather forecasts from ensemble prediction systems (EPS) are improved by statistical models trained on past EPS forecasts and their atmospheric observations. Recently these corrections have moved from being univariate to multivariate. The focus has been on (quasi-)horizontal atmospheric variables. This paper extends the correction methods to EPS forecasts of vertical profiles in two steps. First univariate distributional regression methods correct the probability distributions separately at each vertical level. In the second step copula coupling re-installs the dependence among neighboring levels by using the rank order structure of the EPS forecasts. The method is applied to EPS data from the European Centre for Medium-Range Weather Forecasts (ECMWF) at model levels interpolated to four locations in Germany, from which radiosondes are released to measure profiles of temperature and other variables four times a day. A winter case study and a summer case study, respectively, exemplify that univariate postprocessing fails to preserve stable layers, which are crucial for many atmospheric processes. Quantile resampling and a resampling that preserves the relative distance between individual EPS members improve the calibration of the raw forecasts of the temperature profiles as shown by rank histograms. They also improve the multivariate metrics of energy score and variogram score and retain the stable layers. Improvements take place over all times of the day and all seasons. They are largest within the atmospheric boundary layer and for shorter lead times.

scholarly journals Using Red List species in designating protection status to forest areas: a case study on the problem of spatio-temporal dynamics

2020 ◽  
Vol 29 (11-12) ◽  
pp. 3429-3443
Author(s):  
Lise Tingstad ◽  
John-Arvid Grytnes ◽  
Magne Sætersdal ◽  
Ivar Gjerde
Keyword(s):  
Temporal Dynamics ◽  
Rank Order ◽  
Common Species ◽  
Red List ◽  
Red List Species ◽  
Polypore Fungi ◽  
Temporal Turnover ◽  
Spatio Temporal ◽  
Selection Of

Abstract Red-listed species are often used as target species in selection of sites for conservation. However, limitations to their use have been pointed out, and here we address the problem of expected high spatio-temporal dynamics of red-listed species. We used species data (vascular plants, bryophytes, macrolichens and polypore fungi) from two inventories 17 years apart to estimate temporal turnover of red-listed and non-red-listed species in two forest areas (147 and 195 ha) and of plots (0.25 ha) within each area. Furthermore, we investigated how turnover of species affected the rank order of plots regarding richness of red-listed species, using two different national Red List issues (1998 and 2015). In both study areas, temporal turnover was substantial, despite minor changes in the overall number of species. At plot level, temporal turnover in red-listed species was higher than in non-red-listed species, but similar to non-red-listed species of the same frequency of occurrence. Adding the effect of changing identities of species red-listed according to the two Red List issues, further increased the estimated spatio-temporal dynamics. Recorded spatio-temporal turnover also resulted in substantial changes in the rank order of plots regarding richness of red-listed species. Using rare red-listed species for site selection may therefore be accompanied by a higher loss of conservation effectiveness over time than for more common species, and particularly at finer scales.

Evaluation of System Performance from Rank-Order Data

1964 ◽  
Vol 6 (5) ◽  
pp. 533-536 ◽  
Author(s):  
Luther W. Rook
Keyword(s):  
Input Data ◽  
Rank Order ◽  
Data Gathering ◽  
Approximate Model ◽  
System Failure ◽  
Order Form ◽  
Rank Order Data ◽  
System Information ◽  
Slight Amount ◽  
Evaluation Of System Performance

An approximate model is proposed for predicting the rank-order of system failure probabilities. This approximate model, based on a previous exact one, uses rank-order input data. Rank-order form simplifies data gathering while sacrificing only a slight amount of rigor. Further, a wider range of informants may be used to obtain useful system information than when numerical probabilities must be requested.

IMPROVED COMMON WEIGHT DEA-BASED DECISION APPROACH FOR ECONOMIC AND FINANCIAL PERFORMANCE ASSESSMENT

2019 ◽  
Vol 26 (2) ◽  
pp. 430-448
Author(s):  
E. Ertugrul Karsak ◽  
Nazli Goker
Keyword(s):  
Decision Making ◽  
Financial Performance ◽  
Performance Assessment ◽  
Rank Order ◽  
Multiple Criteria Decision Making ◽  
Data Envelopment ◽  
Step Size ◽  
Decision Making Unit ◽  
Common Weight

Economic and financial performance assessment possesses an important role for efficient usage of available resources. In this study, a novel common weight multiple criteria decision making (MCDM) approach based on data envelopment analysis (DEA) is presented to identify the best performing decision making unit (DMU) accounting for multiple inputs as well as multiple outputs. The robustness of the developed model, which provides a rank-order with enhanced discriminatory characteristics and improved weight dispersion, is illustrated by two case studies that aim to provide economic and financial performance assessment. The first study presents an evaluation of Morgan Stanley Capital International emerging markets, whereas the second case study ranks the Turkish deposit banks using the proposed methodology as well as providing a comparative evaluation with several other approaches addressed in earlier works. The results indicate that the introduced approach guarantees to identify the best performing DMU without including a discriminating parameter requiring an arbitrary step size value in model formulation while also achieving an improved weight dispersion for inputs and outputs.

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