Uses and Practices of Computer Application in Demographic Research Regarding Nepal

The computer was used to model, simulate and analyze social phenomena in the research. This study aims to analyze the uses of computer software in demographic research in Nepal. Information was gathered from secondary sources from reports of GOs and NGOs, manuals and guidelines of various software packages. Information was taken from the United Nations software package, World Health Organization, United Nations Population Funds and software package of the U.S. Census Bureau. Other’s information was taken from the National Housing and Census and Demographic Health Survey of Nepal. To achieve the objective of this study data are analysed in a descriptive method. This study has focused on computer software mainly SPSS, Mort Park, CS Pro, and STATA. The roles of computer have been helpful in quantitative research. Its role is also important when the research is on a large sample. The computer assists the researcher throughout different phases in the quantitative research process. The major five phases of the research process are the conceptual phase, design and planning phase, empirical phase, analytic phase, and dissemination phase. The use of computer software is helpful in data entry, data editing, and data management. The data analysis software is supportive of processing and manipulating information and analyzing the relationship between variables. Therefore, the use of computer software can save money, time, and resources in research. In the case of Nepal, the Integrated Microcomputer Processing System (IMPS) was in the census in 1991. STATA and SPSS software packages were used for data editing, management, and tabulation of the census results. Open Data Kit platform and CAPI was used for data collection and the CS Pro software was used for data management in various demographic studies in Nepal.

What You Need to Know When Estimating Impact Functions with Panel Data for Demographic Research

The estimation of impact functions – that is the time-varying causal effect of a dichotomous treatment (e.g., marriage, divorce, parenthood) on outcomes (e.g., earnings, well-being, health) – has become a standard procedure in demographic applications. The basic methodology of estimating impact functions with panel data and fixed-effects regressions is now widely known. However, many researchers may not be fully aware of the methodological subtleties of the approach, which may lead to biased estimates of the impact function. In this paper, we highlight potential pitfalls and provide guidance on how to avoid these in practice. We demonstrate these issues with exemplary analyses, using data from the German Family Panel (pairfam) study and estimating the effect of motherhood on life satisfaction. * This article belongs to a special issue on “Identification of causal mechanisms in demographic research: The contribution of panel data”.

Képzési háttér és gyermekvállalási tervek

Összefoglaló. A tanulmány a népesedési folyamatok és az oktatási rendszer kölcsönhatásának értelmezési keretében mozog. Ezen belül a szülői végzettség egy lehetséges demográfiai következményével, a második gyermek vállalásának tervezésére gyakorolt hatásával foglalkozik. Az elemzés a KSH NKI Kohorsz ’18 Magyar Születési Kohorszvizsgálat várandósok körében gyűjtött adatain alapul. Az eredmények alapján az anyák második gyermek vállalására vonatkozó terveiben mindkét szülő végzettségének önálló hatása mutatható ki. A szülői iskolázottság szintje és a második gyermek tervezése között pozitív a kapcsolat. Legerősebb a felsőfokú végzettség hatása, mely mindkét szülő esetében növeli a második gyermek tervezésének esélyét. A szakképzettség alacsony szinten csökkenti, ugyanakkor a magasabb, középfok feletti szinteken növeli a gyermekvállalási kedvet. Az anyai tervekre az apa legmagasabb iskolai végzettsége is kimutatható hatással bír. Az eredmények oktatáspolitikai szempontú értelmezésében kiemelhető a magasabb végzettségi szintek gyermekvállalásra gyakorolt pozitív hatása, valamint a szakmunkásképzés felé irányítás potenciálisan kedvezőtlen demográfiai következményeinek azonosítása. Summary. The interpretive framework of the study is the interaction between demographic trends and the education system. Within this, it addresses a possible demographic consequence of parental education, i.e., its impact on the planning of having a second child. The analysis is based on the data of Cohort ’18 Growing Up In Hungary, collected among pregnant women by the Hungarian Demographic Research Institute in 2018 as a part of a Hungarian Birth Cohort Study. According to the results, the plans of the expecting mothers to have a second child significantly affected by the educational attainment. As regards directions, there is a linear positive relationship between the level of education and the odds of planning a second child. Higher educational background of both parents shows the strongest effect on the plans. Elementary vocational training works against planning chances, but upper vocational background increases the desire to have a second child. Educational attainment of the partner also has a significant effect on maternal childbearing plans. From the point of view of educational policy, the positive effect of higher education degree on having children can be highlighted. Potentially unfavorable demographic consequences of the direction towards vocational training can be also highlighted by the results.

Application of OLAP Technology in the Environment of Electronic Demography

The article proposes an approach to the development of an electronic demographic decision support system using technologies of Data Warehouse (DW) and Interactive Analytical Processing OLAP. This makes it possible to conduct high-level demographic research and provide support to decision-makers in demographic sphere. The article notes that demography is an interdisciplinary field of research and is defined as a complex science. Each industry of demography has many indicators. A sample list of these indicators is presented. The main characteristics of the DW, which should be taken into account when developing its architecture, are stated. Among these characteristics, one can find the main defining characteristics of Big Data — volume, velocity, variety, veracity, variability, visualization, value etc. For a more rational and efficient use of a large amount of information, taking into account its constant increase, to ensure the speed of execution of requests for a given system, it is proposed to use a Bus of Interconnected Data Marts (DM) as an architecture of DW. One of the advantages of using DM is that their use assumes distributed parallel data processing. This architecture allows for much faster results generation. It is based on the MapReduce distributed computing model and the Hadoop project. In addition, to effectively use large amounts of data, it is also proposed to use OLAP operations such as roll-up and drill-down, as well as fuzzy set theory, based on the technique of computing with words. The article also shows the practical application of interconnected DM. An OLAP cube is built on the basis of these DM. OLAP operations provide the ability to view cubes in different slices and provide aggregate data.