Three aspects of an empirical effect: statistical, theoretical, and practical aspect

A transparent evaluation of an empirical effect’s relevance is based on the size of effect (statistical aspect), a theoretical construct’s ability to adequately predict the effect (theoretical aspect), and the effect’s practical utility (practical aspect). In behavioral science publications, however, all three aspects are often found conflated. Already if only the practical aspect is evaluated independently of the other two aspects, disagreements about the effect’s relevance turn out to be resolvable. And, if also the statistical aspect is evaluated independently of the theoretical aspect, then the ‘smallest effect of interest’ turns out to be much larger when predicting an effect (statistical aspect) as opposed to explaining it (theoretical aspect). Crucially, behavioral science publications today typically report either small, homogenous empirical effects or large, heterogeneous ones. This pattern greatly impairs the prospects for theory construction in behavioral science, because an empirically adequate theoretical construct would have to predict a larger and more homogenous empirical effect than can be observed.

Higher Order Thinking Skills for Improved Learning Outcomes Among Indonesian Students: A Blended Web Mobile Learning (BWML) Model

The purpose of this research was to produce a model that can be used as a reference in implementing hybrid learning and Problem Based Learning (PBL) in learning with the MoLearn application. This research was included in Research and Development/R&D type by developing the right learning model for the MoLearn application. The Blended Web Mobile Leaning (BWML) model test was conducted related to its validity and practicality. The result of research showed (1) quality test with average content validity (3.72) statistically in (rα = 0.25) and reliability in (α = 81), average construct validity (3.74), with validity of each aspect statistically in (rα = 0.20) and reliability in (α = 0.75). It can be concluded that the BWML model is qualified (valid in content and constructs, and reliable) by experts. (2) The feasibility test (practically used by students) had an average of 3.23 with a statistical aspect validity in rα = 0.91 and reliability in α = 0.99 This can mean that students stated that the model has novelty and is easy to use. The implication of this research is that a quality BWML model can be used to improve HOTs-based learning outcomes. Further research can be focused on looking at the effectiveness of the BWML model for improving HOTs-based learning outcomes

Differentiation of Public Opinion on Law Enforcement of Federal Laws: a Statistical Aspect

In order to identify the level of awareness and satisfaction of citizens with the measu­res taken to regulate public relations in specific areas of legislation, monitoring of the enforcement of existing and newly introduced laws is used. Monitoring is based on a sociological survey and statistical methodology. The first explores the subjective perception of the population of the effectiveness of the mechanism of public administration. With the correct formation of the sample, the data obtained using statistical inference can be extended to the entire population and the general patterns of the process under study can be investigated. The authors carried out a sociological survey on the enforcement of four federal laws. In addition to the obtained section of public opinion on the awareness and satisfaction of citizens with the functioning of the laws under study, the paper also presents an analysis of the territorial differentiation of public opinion on law enforcement, the uneven development of civic activity in the regional context, and the development of civil society in Russia.

Evaluation of the Spatial Pattern of the Resolution-Enhanced Thermal Data for Urban Area

With the development of urbanization, land surface temperature (LST), as a vital variable for the urban environment, is highly demanded by urban-related studies, especially the LST with both fine temporal and spatial resolutions. Thermal sharpening methods have been developed just under this demand. Until now, there are some thermal sharpening methods proposed especially for urban surface. However, the evaluation of their accuracy still stopped at the level that only considers the statistical aspect, but no spatial information has been included. It is widely acknowledged that the spatial pattern of the thermal environment in an urban area is relatively critical for urban-related studies (e.g., urban heat island studies). Thus, this paper chose three typical methods from the limited number of thermal sharpening methods designed for the urban area and made a comparison between them, together with a newly proposed thermal sharpening method, superresolution-based thermal sharpener (SRTS). These four methods are analyzed by data from different seasons to explore the seasoning impact. Also, the accuracy for different land covers is explored as well. Furthermore, accuracy evaluation was not only taken by statistical variables which are commonly used in other studies; evaluation of the spatial pattern, which is equally important for urban-related studies, was also carried out. This time, the spatial pattern not only was analyzed qualitatively but also has been quantified by some variables for the comparison of accuracy. It is found that all methods obtained lower accuracies for data in winter than for data in other seasons. Linear water features and areas along it are difficult to be detected correctly for most methods.

Perlukah menghitung ukuran sampel?

Sample size is an issue worth-considering but not the most essential thing to fulfil for a good research. A much more crucial cause of concern to any research is the validity of inference a research is drawing, i.e. the extent to which the research is able to control systematic error that stems from bias and confounding. Sample size refers to random error; it does not address nor correct systematic error. The larger sample size, the less random error, the more precise estimates a research can yield about difference/ association/ effect of a variable(s). Most of the assignment of values in any sample size formula is arbitrary. As such, the product of estimating sample size, regardless of the formula being used, cannot be viewed as an absolute end; the actual sample size can be larger or smaller than the estimated one. Beyond statistical aspect, several other important factors should be considered when estimating sample size, such as ethics, cost, and the amount oftime available for conducting the research.