Q Methodological Approach to Types of Perception of Childcare Policy of the Childcare Center Director

2021 ◽  
Vol 26 (3) ◽  
pp. 465-481
Author(s):  
Hee-jung Chung ◽  
Ki-jung Kang ◽  
Kyoung-mi Kim
2006 ◽  
Vol 18 (4) ◽  
pp. 160-173 ◽  
Author(s):  
Maria Senokozlieva ◽  
Oliver Fischer ◽  
Gary Bente ◽  
Nicole Krämer

Abstract. TV news are essentially cultural phenomena. Previous research suggests that the often-overlooked formal and implicit characteristics of newscasts may be systematically related to culture-specific characteristics. Investigating these characteristics by means of a frame-by-frame content analysis is identified as a particularly promising methodological approach. To examine the relationship between culture and selected formal characteristics of newscasts, we present an explorative study that compares material from the USA, the Arab world, and Germany. Results indicate that there are many significant differences, some of which are in line with expectations derived from cultural specifics. Specifically, we argue that the number of persons presented as well as the context in which they are presented can be interpreted as indicators of Individualism/Collectivism. The conclusions underline the validity of the chosen methodological approach, but also demonstrate the need for more comprehensive and theory-driven category schemes.


2010 ◽  
Vol 72 (08/09) ◽  
Author(s):  
A Ahmad ◽  
R Krumkamp ◽  
S Mounier-Jack ◽  
R Reintjes ◽  
R Coker

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.


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