scholarly journals An Excel program for calculating statistics presented in Marfak et al.’s article ‘Improved RIDIT statistic approach provides more intuitive and informative interpretation of EQ-5D Data’

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Abdelghafour Marfak ◽  
Ibtissam Youlyouz-Marfak
Keyword(s):  
Statistical Analyses ◽  
Statistic Approach

AbstractThe objective is to present and share an Excel program that we have developed to perform statistical analyses based on the Improved RIDIT approach of Marfak et al.’s article ‘Improved RIDIT statistic approach provides more intuitive and informative interpretation of EQ-5D Data’.

Structuring Situational Information

2015 ◽  
Vol 20 (3) ◽  
pp. 176-189 ◽  
Author(s):  
John F. Rauthmann
Keyword(s):  
Statistical Analyses ◽  
Current Work ◽  
Common Language ◽  
Road Map ◽  
Taxonomic Approach ◽  
Situational Information

Abstract. There is as yet no consensually agreed-upon situational taxonomy. The current work addresses this issue and reviews extant taxonomic approaches by highlighting a “road map” of six research stations that lead to the observed diversity in taxonomies: (1) theoretical and conceptual guidelines, (2) the “type” of situational information studied, (3) the general taxonomic approach taken, (4) the generation of situation pools, (5) the assessment and rating of situational information, and (6) the statistical analyses of situation data. Current situational taxonomies are difficult to integrate because they follow different paths along these six stations. Some suggestions are given on how to spur integrated taxonomies toward a unified psychology of situations that speaks a common language.

PENGARUH KEPEMIMPINAN TRANSFORMASIONAL, MOTIVASI DAN KEPUASAN KERJA TERHADAP KINERJA KARYAWAN PT. ADYA TOURS

2017 ◽  
Vol 19 (1) ◽  
pp. 23
Author(s):  
Ahmad Gunawan
Keyword(s):  
Quantitative Research ◽  
Total Population ◽  
Rating Scale ◽  
Research Data ◽  
Positive Direction ◽  
Leadership Motivation ◽  
Multiple Determination ◽  
Strongly Agree ◽  
Transformation Leadership ◽  
Statistic Approach

Transformation Leadership, Motivation and Satisfiction are the three factors of a few relatively large factors suspected to influence Performance on the PT. Adya Tours. These research aimed to determine the effect of Transformation Leadership, Motivation and Satisfiction toward Performance on the PT. Adya Tours.Research conducted at the PT. Adya Tours by taking 71 employees as the research sample, calculated using the Slovin formula of the total population of 240 employees  at  the  margin  of  error  of  10%.  Data  were collected by questionnaire instruments covered by the five rating scale from strongly disagree to strongly agree. Quantitative research was conducted by describing and analyzing research data. The multiple linier regression analysis and multiple determination coeficient are the statistic approach to data analysis.The study produced four major findings consistent with the hypothesis put forward, that are: 1) Transformation Leadership has a significant effect on Performance  in  a  positive  direction;  2)  Motivation  has  a  significant  effect  on Performance in a positive direction; 3) Satisfiction has a significant effect on Performance in a positive direction; 4) Transformation Leadership, Motivation and Satisfiction simultaneously influence 92.70% Performance variability.Base on the research finding, in order to increase Performance can be done by increasing Transformation Leadership, Motivation and Satisfiction. Kata kunci:Transformation Leadership, Motivation, Satisfaction, Performance

Integrating Visual and Bayesian Statistical Analyses in Single Case Experimental Research to Evaluate the Effectiveness and Magnitude of a Comprehensive Behavioral Intervention

2018 ◽  
Author(s):  
Prathiba Natesan ◽  
Smita Mehta
Keyword(s):  
Behavioral Intervention ◽  
Effect Size ◽  
Rate Ratio ◽  
Visual Analysis ◽  
Single Case ◽  
Small Sample ◽  
Statistical Analyses ◽  
Data Types ◽  
Ratio Effect ◽  
Small Sample Sizes

Single case experimental designs (SCEDs) have become an indispensable methodology where randomized control trials may be impossible or even inappropriate. However, the nature of SCED data presents challenges for both visual and statistical analyses. Small sample sizes, autocorrelations, data types, and design types render many parametric statistical analyses and maximum likelihood approaches ineffective. The presence of autocorrelation decreases interrater reliability in visual analysis. The purpose of the present study is to demonstrate a newly developed model called the Bayesian unknown change-point (BUCP) model which overcomes all the above-mentioned data analytic challenges. This is the first study to formulate and demonstrate rate ratio effect size for autocorrelated data, which has remained an open question in SCED research until now. This expository study also compares and contrasts the results from BUCP model with visual analysis, and rate ratio effect size with nonoverlap of all pairs (NAP) effect size. Data from a comprehensive behavioral intervention are used for the demonstration.

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