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What are the downsides of genetic triangulation?

Minimum-weight triangulation

Surface triangulation

Delaunay triangulation

Constrained Delaunay triangulation

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Genetic triangulation has several downsides. One downside is the possibility of false positives when using one-to-one autosomal DNA matches under ethnically homogenous conditions . However, triangulation can demonstrate that a match is identical by descent (IBD), making false positives statistically improbable . Another downside is the difficulty in computing a triangulation that satisfies multiple criteria or contains parts developed according to several criteria . Deterministic methods are unable to achieve this, but genetic optimization can generate a mixture that meets multiple criteria . Additionally, when using genetic algorithms for triangulation, problems such as controlling population size, escaping local optima, and low efficiency can arise . However, an improved genetic algorithm with parallel strategy, elites reserving, and fitness value crowding strategy can address these issues .

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Open access•Journal Article•DOI Multicriteria-optimized triangulations Ivana Kolingerová, Andrej Ferko - Show less +1 more 01 Aug 2001-The Visual Computer 17 Citations	The paper does not mention any downsides of genetic triangulation.
Open access•DOI Autosomal DNA and the Triangulation of Small Segments: A Statistical Approach Maglio Michael 19 Mar 2015	The paper does not mention any downsides of genetic triangulation.
Proceedings Article•DOI Using triangulation to include target structure improves drug resistance prediction accuracy Robert W. Harrison, Xiaxia Yu, Irene T. Weber - Show less +2 more 12 Jun 2013 1 Citations	The paper does not mention any downsides of genetic triangulation.
Proceedings Article•DOI An Improved Genetic Algorithm for Solving Tri-level Programming Problems Kai Su, Zhili Lei, Hai-Ming Niu - Show less +2 more 24 Aug 2022	The paper does not mention any downsides of genetic triangulation. The provided paper is about an improved genetic algorithm for solving tri-level programming problems.

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What are the examples of triangulation of data?5 answersExamples of triangulation of data include various methods such as data triangulation, digital method triangulation, and triangulation within and between groups. Data triangulation involves combining different data sources to increase research validity. Digital method triangulation utilizes digital tools like internet traffic measurements, surveys, and diaries to make sense of big and small data in a digital context. Triangulation within and between groups helps in comparing perspectives within the same group and across different groups, shedding light on commonalities and differences in viewpoints. These examples showcase how researchers can enhance the credibility and trustworthiness of their findings by utilizing multiple methods and data sources to provide a more comprehensive understanding of the phenomenon under study.

What is triangulation method in research?5 answersTriangulation method in research refers to the use of multiple datasets, methodologies, theories, and investigators to answer a research question. It is a well-established practice in social sciences and is relatively recent in linguistics. Triangulation is helpful in developing complementary analysis, facilitating the completeness of research, and gaining insights. It allows for the expansion of the scope of the studied phenomenon, achieving higher quality research, and limiting measurement errors. Triangulation method aids in validating data collected from different sources, providing flexibility in research studies, and generating better-supported arguments. It is particularly beneficial in management sciences research, enhancing the quality of the study and solving identified research problems. Triangulation is also useful in entrepreneurship research, addressing issues such as inconsistencies, contradictions, and biases, and producing quality work.

What is triangulation in qualitative research?5 answersTriangulation in qualitative research refers to the use of multiple datasets, methodologies, theories, or investigators to answer a research question. It is a methodological approach that enhances the validity and completeness of research findings by providing different perspectives and sources of evidence. Triangulation can be applied in various ways, including the use of multiple data sources, such as interviews, observations, and documents, to corroborate findings. It can also involve the use of different methods, such as quantitative and qualitative approaches, to complement each other and provide a more comprehensive understanding of the research topic. Additionally, triangulation can involve the use of multiple investigators or researchers to ensure objectivity and reduce bias. Overall, triangulation in qualitative research helps to strengthen the rigor and reliability of findings by incorporating diverse perspectives and sources of evidence.

What is triangulation in research?5 answersTriangulation in research refers to the use of multiple datasets, methodologies, theories, and investigators to answer a research question. It is a well-established practice in social sciences and is relatively recent in linguistics. Triangulation helps increase the validity and credibility of research findings by incorporating several methods in the study of the same phenomenon. It allows for the development of complementary analysis, facilitates completeness of research, and helps develop insights. Triangulation also enables researchers to achieve higher quality research and limit measurement errors that may occur when using just one method. It involves combining cognitive methods, business management methods, and other research methods to expand the scope of the studied phenomenon. Triangulation is particularly useful in qualitative research, where it helps minimize the weaknesses of qualitative studies and produce robust findings and conclusions.

What are the benefits of triangulating data?3 answersTriangulating data offers several benefits. It allows for a more comprehensive understanding of social phenomena by combining quantitative and qualitative data sources and methods. Triangulation helps to increase the credibility of data by comparing results from different methods or sources to see if they offer the same or similar conclusions. It also enables the study of regional dynamics by analyzing a comprehensive database of firms and institutions over time, drawing on multiple data sources. Triangulating process and product data allows for easier retrieval and correlation of different dimensions of the data, leading to a better understanding of translation production. Additionally, triangulating data in big data applications can help overcome challenges related to data volume, variety, velocity, variability, and veracity, leading to better analysis and predictive capabilities.

Why use triangulation in social sciences?1 answersTriangulation is used in social sciences to enhance the validity, credibility, and accuracy of research findings. It involves the use of multiple methods, approaches, data sources, and perspectives to study the same phenomenon. By combining qualitative and quantitative methods, triangulation allows researchers to overcome the limitations and biases of each approach, resulting in a more comprehensive understanding of the subject matter. Triangulation also helps in validating data collected from different sources, increasing the reliability of the research findings. Additionally, it provides greater flexibility in the research process, allowing researchers to generate better-supported arguments and enhance the quality of their studies. Triangulation is particularly useful in social sciences where complex phenomena and relationships are analyzed, and resources and time are limited. Overall, triangulation is a valuable strategy in social sciences research for improving the rigor and depth of investigations and promoting interdisciplinary collaboration.

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