Identify And Evaluate Three Current Basic Research Examples

Identify And Evaluate Three 3 Examples Of Current Basic Research

1. Identify and evaluate three (3) examples of current basic research.

2. Discuss the meaning of the word “theory” in science and describe how it differs from hypothesis. Please, substantiate your explanation with examples.

3. Discuss the most important differences between positivism and pragmatism.

4. Explain under what circumstances each of following sampling techniques is appropriate: 1. Simple random sampling 2. Stratified random sampling 3. Systematic random sampling 4. Clustered random sampling.

Paper For Above instruction

Basic research, also known as fundamental or pure research, is driven by a scientist's curiosity or interest in a specific phenomenon. Its primary goal is to expand knowledge and understanding without immediate application in mind. In contemporary settings, several examples of basic research are prominently shaping scientific and technological advancements.

Firstly, research in quantum mechanics continues to be a fertile ground for basic scientific exploration. For instance, recent studies delve into quantum entanglement and superposition states, with researchers probing the fundamental principles of quantum behavior. These studies, such as the experiments conducted by the National Institute of Standards and Technology (NIST) on entangled photons, aim to understand the foundational elements of the universe. They do not target immediate applications but seek to deepen our understanding of quantum phenomena, which later can lead to groundbreaking technologies like quantum computers.

Secondly, research in neuroscience, particularly in understanding the human brain's complex functions, epitomizes basic research. Projects such as the Human Connectome Project aim to map neural pathways and comprehend brain connectivity. This research is vital in revealing how the brain processes information and maintains cognitive functions. Although its immediate practical applications may seem indirect, such foundational knowledge is critical for future advancements in medical treatments for neurological disorders and mental health issues.

Thirdly, in the field of materials science, the discovery and characterization of novel materials like graphene are exemplary of current basic research. While graphene's potential for various applications like electronics and energy storage is well-known, ongoing fundamental research focuses on understanding its properties at the atomic level. Studies such as those conducted by graphene research labs worldwide analyze electron mobility, strength, and flexibility without specific application goals at the outset. This foundational understanding paves the way for innovative applications decades later.

Understanding the nature of scientific inquiry involves differentiating between theories and hypotheses. In science, a theory is a well-substantiated explanation of some aspect of the natural world that is based on a body of evidence and has stood rigorous testing over time. For example, the theory of evolution by natural selection explains the diversity of life based on extensive fossil, genetic, and observational evidence. Theories are comprehensive frameworks that integrate multiple hypotheses and experimental results.

In contrast, a hypothesis is a specific, testable prediction about the outcome of an experiment or observation. Hypotheses are narrower in scope and are formulated based on prior knowledge or theories. For example, a hypothesis might state that "Plants grown with blue light will grow taller than those with red light." This prediction can be tested through experiments, and its validation or refutation contributes to the broader theoretical understanding.

Positivism and pragmatism are two philosophical approaches concerning the nature of knowledge and scientific inquiry. Positivism emphasizes observable, measurable phenomena and advocates that knowledge should be derived from empirical evidence obtained through the scientific method. It asserts that scientific knowledge is objective and verifiable, leading to general laws. For example, Newton’s laws of motion were derived through positivist principles, emphasizing observable and testable data.

On the other hand, pragmatism focuses on the practical application of ideas and the usefulness of theories in solving real-world problems. It is more flexible regarding the sources of knowledge, often integrating empirical evidence with experiential and contextual factors. A pragmatist might prioritize the outcomes or effects of a theory in specific situations rather than its absolute empirical truth. An example is the use of educational theories in classroom settings, where the effectiveness of teaching methods is judged based on student engagement and learning outcomes rather than strict observable phenomena alone.

Sampling techniques are essential tools in research design, each suitable under particular circumstances. Simple random sampling involves selecting a subset of individuals from the population where each member has an equal chance of being chosen. It is appropriate when the population is homogeneous, and the researcher aims for unbiased representation, such as surveying students in a university.

Stratified random sampling divides the population into subgroups or strata based on specific characteristics (e.g., age, gender) and then randomly samples from each stratum proportionally. This technique is ideal when the researcher wants to ensure representation of all subgroups to increase precision, for example, when studying opinions across different socioeconomic groups.

Systematic random sampling involves selecting every kth individual from a list after a random starting point. It is suitable when the population list is ordered and the researcher seeks an efficient sampling method, such as selecting every 10th person in a phone directory.

Clustered random sampling partitions the population into clusters or groups (e.g., neighborhoods), randomly selects a few clusters, and then surveys all individuals within those clusters. This method is appropriate when the population is spread over a wide geographic area, and consolidating data collection within selected clusters is logistically efficient, such as in rural community studies.

References

• Babbie, E. (2015). The Practice of Social Research. Cengage Learning.
• Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications.
• Gage, R. (2019). Basic Research: Definition, Methods & Examples. Study.com.
• Hempel, C. (1965). Aspects of scientific explanation and other essays. Free Press.
• Kuhn, T. S. (1962). The Structure of Scientific Revolutions. University of Chicago Press.
• Popper, K. R. (1959). The Logic of Scientific Discovery. Routledge.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.
• Thompson, B. (2012). Survey Methods. Wiley-Blackwell.
• Yin, R. K. (2018). Case Study Research and Applications: Design and Methods. Sage Publications.
• Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2010). Business Research Methods. Cengage Learning.