Stop 4: Core Concepts in Science

I skip the foundation of science and jump into the science in the context of doing research.?

4.1 ? ? ? Empirical Research

Empirical research is a type of research approach that is based on observed and measured phenomena. It derives knowledge from actual experience rather than from theory or belief. The key component of empirical research is the collection of data that can be analyzed and used to answer research questions, test hypotheses, or prove theories. Empirical research can be conducted using either qualitative or quantitative methods and can use a variety of research designs, such as experimental or observational setups.

This involves controlled experiments to determine causality. Empirical research is a type of research approach that is based on observed and measured phenomena. It derives knowledge from actual experience rather than from theory or belief. The key component of empirical research is the collection of data that can be analyzed and used to answer research questions, test hypotheses, or prove theories. Empirical research can be conducted using either qualitative or quantitative methods and can use a variety of research designs, such as experimental or observational setups.

Empirical research is a type of research methodology that involves the collection of data through observation and experimentation to answer research questions, test hypotheses, or prove theories. The term "empirical" refers to information gained by means of observation, experience, or experiments. Empirical research aims to gather data from the real world, rather than relying solely on theoretical constructs or opinions.

4.1.1? ? ? Breadth of Empirical Research

Disciplines: Empirical research spans a wide range of disciplines, including natural sciences, social sciences, and even some areas of the humanities.

Data Types: It can involve both quantitative data (numeric) and qualitative data (non-numeric, such as words or images).

Research Design: Can be experimental (with controlled variables) or non-experimental (observational, correlational, or case studies).

Setting: May be conducted in laboratory settings, in natural environments, or through surveys and interviews.

4.1.2? ? ? Depth of Empirical Research

4.1.2.1? Formulating Research Questions/Hypotheses:

Before data collection begins, researchers must identify what they intend to study, usually framed as research questions or hypotheses.

4.1.2.2? Literature Review: (see 2.2.1.3)

Researchers review existing literature to understand what is already known about the topic and to justify the need for the new study.

4.1.2.3? Research Design:

Researchers choose the appropriate methodology for data collection. This could be an experiment, a survey, a case study, etc.

4.1.2.4? Sampling:

Researchers decide how to select participants or units of analysis.

4.1.2.5? Data Collection:

Researchers collect data through observations, experiments, surveys, interviews, etc.

4.1.2.6? Data Analysis:

The collected data are analyzed using statistical or qualitative methods to identify patterns, relationships, or to test hypotheses.

4.1.2.7? Interpretation:

The findings are interpreted in the context of the original research questions or hypotheses, and conclusions are drawn.

4.1.2.8? Presentation:

Results are typically published in peer-reviewed journals, presented at conferences, or otherwise disseminated.

4.1.2.9? Evaluation and Replication:

The research process and findings are evaluated for their validity and reliability. In some cases, other researchers may attempt to replicate the study to verify the findings.

4.1.3? ? ? Alternative Names of "Empirical Research" in Literature

4.1.3.1? Experimental Research:

Sometimes empirical research conducted in controlled settings is specifically referred to as experimental research.

4.1.3.2? Observational Research:

If the research involves observing phenomena without manipulating any variables, it might be termed observational research.

4.1.3.3? Field Research:

Empirical research conducted in natural settings is sometimes called field research.

4.1.3.4? Primary Research:

This term is sometimes used to distinguish empirical research (where you collect original data) from secondary research (where you analyze existing data or publications).

4.1.3.5? Empirical Studies:

A common term used to refer to individual instances of empirical research.

4.1.3.6? Data-Driven Research:

This is another term that emphasizes the importance of data collection in the research process.

4.1.4? ? ? Types of Non-Empirical Research

?

NOTE: Descriptive tidak membutuhkan teori?

4.1.4.1? Theoretical Research:

Involves the development and testing of theories or frameworks, often without new empirical data.

4.1.4.2? Conceptual Research:

Research focused on abstract ideas or theory, often using existing literature.

4.1.4.3? Mathematical Research:

Using mathematical theorems and algorithms to solve problems.

4.1.4.4? Philosophical Research:

Encompasses a range of non-empirical methodologies, including conceptual analysis, thought experiments, and logical argumentation.

4.1.4.5? Narrative Research:

Often seen in disciplines like history or literature, this involves the study of texts, documents, or artifacts, often to construct or understand a narrative.

4.1.4.6? Legal Research:

Involves studying laws, legal policies, and constitutions, often through analysis of legal texts and rulings.

4.1.4.7? Critical Research:

A type of research often seen in the humanities, involving the critique or deconstruction of texts, ideologies, or societal structures.

4.1.5? ? ? Bridge between Empirical and Non-Empirical Research

4.1.5.1? Methodological Pluralism:

Some research questions may benefit from both empirical and non-empirical methods. For instance, a study in the philosophy of mind might include both philosophical argumentation and empirical data from neuroscience.

4.1.5.2? Interdisciplinary Research:

Increasingly, research questions require a variety of approaches. For example, climate science involves not only empirical data collection and computer modeling but also ethical and policy considerations that may be explored through non-empirical methods.

4.1.5.3? Validity and Generalizability:

Both empirical and non-empirical research methods have different strengths and weaknesses when it comes to issues like validity, replicability, and generalizability, which can be complementary.

Understanding the nuances of these various types of research can equip you better for advanced study, whether in a PhD program or other research endeavors.

4.2 ? ? ? Scientific Method

4.2.1? ? ? Core Steps of the Scientific Method:

4.2.1.1? Observation:

The first step often involves observing a phenomenon or set of phenomena, which leads to a research question. Example: Observing that certain plants grow better in specific types of soil.

4.2.1.2? Question Formation:

From observations, researchers formulate a question that they seek to answer. Example: Why do some plants grow better in acidic soil?

4.2.1.3? Literature Review: Finding the Gaps

Before proceeding, researchers usually examine existing literature to understand what is already known about the topic. The goal is to find a research gap.? Example: Reading scientific papers about soil acidity and plant growth.

4.2.1.3.1? ? Evidence Gap:

·? ? ? This involves evaluating existing scientific studies and evidence to identify what areas lack sufficient information, which is a fundamental part of research methodology.

4.2.1.3.2? ? Knowledge Gap:

·? ? ? Like an evidence gap but broader(Rifkin, 2014), it involves not just scientific evidence but also theoretical or conceptual knowledge. It is intrinsically related to the philosophy of science, but it's an operational aspect that fits under methodology.

4.2.1.3.3? ? Practical-Knowledge Gap:

·? ? ? This is related to the applicability of research, so it involves a level of methodological consideration.(Tahir & Rithmire, 2017)

4.2.1.3.4? ? Methodological Gap:

·? ? ? By its very name, it pertains to methods and would, therefore, fit under "Research Methodology."

4.2.1.3.5? ? Empirical Gap:

·? ? ? This is focused on the lack of empirical studies or data in a particular area, and thus it pertains to methods for data collection and analysis.

4.2.1.3.6? ? Theoretical Gap:

·? ? ? While this is closely aligned with the philosophy of science and the development of scientific theories, identifying such a gap is part of research planning and therefore fits under methodology.

4.2.1.3.7? ? Population Gap:

·? ? ? This involves identifying the lack of research involving specific populations, another methodological consideration when planning research.

?

Identifying and understanding these gaps is essential for informing the design, focus, and methods of a research project, making them central to the methodology.?

4.2.1.4? Hypothesis:

Based on the research question and literature review, a testable hypothesis is formed. Example: Acid-loving plants have a specific enzyme that allows them to better absorb nutrients in acidic conditions.

4.2.1.5? Experiment Design:

A controlled experiment is designed to test the hypothesis. Example: Setting up a controlled environment where the soil's acidity is the only variable being changed.

4.2.1.6? Data Collection:

The experiment is conducted, and data are collected. Example: Measuring the growth rates of plants in different soil types.

4.2.1.7? Data Analysis:

The collected data are analyzed, often using statistical methods. Example: Running statistical tests to determine if the differences in growth rates are significant.

4.2.1.8? Conclusion:

Based on the data analysis, a conclusion is drawn, which either supports or refutes the hypothesis. Example: The hypothesis that certain plants grow better in acidic soil because of a specific enzyme is supported or refuted.

4.2.1.9? Publication and Peer Review:

The findings are then published in scientific journals, where they are peer-reviewed by other experts. Example: Submitting the research paper to a journal focused on plant biology.

4.2.1.10 ? ? ? ? ? ? ? ? Replication and Extension:

Other scientists may attempt to replicate the study to verify its findings or extend it by applying similar methods to different variables. Example: Other researchers might test the enzyme's presence in other acid-loving plants.

4.2.2? ? ? Importance in Science:

Understanding the scientific method is fundamental to conducting research in science. The approach ensures a level of standardization, which makes the results of research more reliable. The scientific method's rigorous demands for testing and proof make it a powerful tool for advancing knowledge.

4.2.3? ? ? Limitations:

Although highly effective, the scientific method is not without limitations. It's most applicable for questions that are empirically testable. Ethical considerations can also limit the types of experiments that can be conducted. Furthermore, no method can eliminate the potential for human error or bias.?

4.2.4? ? ? Step by Step Philosophy in Scientific Method

The scientific method doesn't operate in a philosophical vacuum. Various philosophical doctrines underpin the process, shaping its methodology and interpretation of findings. Here's a look at the philosophical underpinnings:

4.2.4.1? Empiricism

Concept: The belief that knowledge is primarily gained through sensory experience.

Role in the Scientific Method: Empiricism underscores the need for observation and experiment. The method begins with observation and proceeds through a cycle of hypothesis and experiment to test the hypothesis against the empirical world.

Example: The focus on gathering empirical data, such as measuring plant growth rates in different soil types.

4.2.4.2? Falsifiability

Concept: Proposed by philosopher Karl Popper, falsifiability is the idea that scientific theories should be testable and potentially falsifiable.

Role in the Scientific Method: Hypotheses are crafted to be falsifiable, enabling a rigorous test of their validity.

Example: The hypothesis that a specific enzyme allows certain plants to grow better in acidic soil is falsifiable through experimental testing.

Special Note: Karl Popper was an Austrian-British philosopher of science who is known for his contributions to the philosophy of science, particularly the concept of falsifiability. Popper argued that a scientific theory can never be proven true, but it can be falsified through empirical testing. According to Popper's theory of falsification, a scientific theory must be testable and open to being proven false. This means that a scientific theory must make predictions that can be tested through observation and experimentation. If the predictions are not supported by the evidence, then the theory can be falsified and must be modified or abandoned. Popper's emphasis on falsifiability has been influential in the philosophy of science, as it emphasizes the importance of empirical testing and the possibility of scientific progress through the rejection of false or flawed theories. However, some critics have argued that Popper's concept of falsification is too strict and does not account for the role of confirmation and corroboration in scientific inquiry.

Despite the criticisms, Popper's ideas have had a lasting impact on the philosophy of science and continue to shape debates about the nature of scientific reasoning and the criteria for scientific knowledge.

4.2.4.3? Induction and Deduction and Abduction

Concept: Methods of reasoning where induction infers general principles from specific observations, and deduction applies general principles to predict specific outcomes.

Role in the Scientific Method: Induction is often used in the initial stages to form a hypothesis, while deduction is used to design experiments that will test the hypothesis.

Example: Inductively forming a hypothesis based on observations (e.g., certain plants grow better in specific soils) and then deducing what outcomes to expect in a controlled experiment.

4.2.4.4? Objectivity and Subjectivity

Concept: Objectivity refers to the concept of perceiving things as true and unbiased, whereas subjectivity acknowledges the role of personal interpretation and influence.

Role in the Scientific Method: Strives for objectivity in data collection and interpretation but acknowledges the potential for subjective biases.

Example: Using blinding techniques in experiments to minimize subjective biases.

4.2.4.5? Paradigms and Scientific Revolutions

Concept: Proposed by Thomas Kuhn, a paradigm is a set of practices that define a scientific discipline during a particular period. Paradigms can shift during scientific revolutions.

Role in the Scientific Method: A prevailing paradigm often shapes the kinds of questions asked and the methods used to answer them. Scientific revolutions may require the scientific method to adapt or evolve.

Example: The shift from a Newtonian paradigm to an Einsteinian one in physics involved re-evaluating the methods used to investigate physical phenomena.

4.2.4.6? Rationalism

Concept: The belief that reason is a primary source of knowledge, distinct from sensory experience.

Role in the Scientific Method: Rationalism often guides the theoretical aspects of science, including the logical structuring of hypotheses and the interpretation of data.

Example: Using logical reasoning to make sense of data and formulating theories that explain observed phenomena.

By understanding these philosophical underpinnings, you gain a deeper comprehension of the complexities and limitations of the scientific method, allowing for a more nuanced approach to your own research.

?

4.3 ? ? ? Models and Theories (Philosophy of Science, Epistemology)

The focus would be on explaining how scientists use models and theories to understand and explain natural phenomena. Both models and theories are central to the scientific method, serving as tools for both explanation and prediction.? Models and theories serve as frameworks for understanding the natural world. They are simplifications of reality that capture essential features of phenomena for the purpose of explanation or prediction.?

4.3.1? ? ? Scientific Models

Concept: Scientific models are representations that capture aspects of the object or phenomenon under study, often simplifying complex systems.

Example: The Bohr model of the atom simplifies atomic structure to help explain electron orbits, even though it doesn't capture the full complexity of quantum mechanics.

4.3.2? ? ? Types of Models

Concept: Explains the different types of models used in science, such as conceptual, mathematical, and computational models.

Example: Climate models used in meteorology are computational and incorporate various factors like temperature, humidity, and wind patterns to predict weather.

4.3.3? ? ? Role of Theories

Concept: Scientific theories are overarching frameworks that explain a broad range of phenomena and are supported by a large body of evidence.

Example: The theory of evolution by natural selection explains a wide range of biological phenomena, from the fossil record to DNA sequences.

4.3.4? ? ? Distinction Between Models and Theories

Concept: Explores the differences between models and theories, particularly their scope and levels of abstraction.

Example: Newton's law of gravitation is a theory that explains a wide range of phenomena, whereas the model of Earth as a perfect sphere is a simplification used for specific calculations.

4.3.5? ? ? Validation and Falsification

Concept: Discusses how models and theories are validated or falsified through empirical testing.

Example: Einstein's theory of general relativity was validated during the solar eclipse of 1919 when light from stars was observed to bend around the Sun.

4.3.6? ? ? Limits and Assumptions

Concept: Discusses the limitations and assumptions inherent in models and theories.

Example: The ideal gas law model assumes no interactions between gas particles, which is not true under conditions of high pressure and low temperature.

4.3.7? ? ? Role in Scientific Explanation

Concept: Elaborates on how models and theories serve as tools for scientific explanation, unifying disparate phenomena under a common framework.

Example: Quantum field theories in physics provide a unifying framework for understanding electromagnetism, weak, and strong forces.

4.3.8? ? ? Ethical and Social Implications

Concept: Discusses the ethical and social implications of scientific models and theories, such as how they influence public policy or ethical considerations.

Example: Epidemiological models of disease spread can inform public health policies but also raise ethical questions about individual freedom and collective responsibility

?

Understanding "Models and Theories" will provide a comprehensive view of how science organizes and interprets data to construct reliable knowledge about the world. This understanding is essential for anyone engaged in scientific research or those interpreting scientific findings.

4.4 ? ? ? Realism (Metaphysics, Philosophy of Science)

The focus would be on the philosophical and methodological stance that considers scientific theories as describing objective features of the world. Realism in the context of science holds that the entities and phenomena described by scientific theories exist independently of human thought or perception. Realism contrasts with anti-realism, which argues that scientific theories are merely useful instruments for predicting sensory experience.

4.4.1? ? ? Ontological Realism

Concept: The belief that entities like atoms, electrons, and perhaps even things like fields, really do exist, whether we can observe them directly.

Example: The existence of atoms was postulated long before they could be observed, and this belief enabled further scientific progress.

4.4.2? ? ? Epistemic Realism

Concept: The idea that science can give us true, or at least approximately true, beliefs about those entities.

Example: The law of universal gravitation allows for incredibly accurate predictions, suggesting that it represents some true aspects of the world.

4.4.3? ? ? Instrumentalism vs Realism

Concept: Instrumentalism suggests that scientific theories are useful tools for explaining and predicting phenomena but doesn't commit to the actual existence of the entities involved in those theories.

Example: For an instrumentalist, the model of electrons orbiting an atomic nucleus is merely a useful fiction for predicting the results of experiments.

4.4.4? ? ? Scientific Realism and Scientific Practice

Concept: Discuss how the realist perspective impacts the conduct of actual science—how hypotheses are formed, how experiments are designed, etc.

Example: Researchers in quantum mechanics often hold realist views about quantum states, which impacts the types of experiments they design and how they interpret their data.

4.4.5? ? ? Challenges to Realism

Concept: Overview of key challenges to realism, such as the "Pessimistic Meta-Induction" from the history of science.

Example: Past scientific theories, like the phlogiston theory of combustion, were found to be wrong, which some argue should make us cautious about claiming current theories are approximately true.

4.4.6? ? ? Implications for Interdisciplinary Research

Concept: How realism as a guiding principle might affect research that spans multiple scientific disciplines.

Example: A realist stance may facilitate more effective interdisciplinary communication because it assumes that each discipline is tapping into the same underlying reality.

Understanding "Realism" and its nuances can significantly impact how you view the role of science in acquiring knowledge and how you might approach your scientific inquiries.

4.5 ? ? ? Objectivity (Epistemology, Philosophy of Science)

The primary focus would be on explaining the philosophical belief and methodological approach that scientific inquiry should aim to minimize bias and subjectivity to uncover universal truths. Objectivity refers to the concept that scientific observations and interpretations should be free from personal bias and should be conducted as impartially as possible.

4.5.1? ? ? Observational Objectivity

Concept: This deals with the belief that it's possible to make observations of the world that are not tainted by personal or societal beliefs.

Example: Using a thermometer to measure temperature, as opposed to subjectively deciding if something feels hot or cold.

4.5.2? ? ? Interpretative Objectivity

Concept: The idea that researchers can interpret data without personal biases affecting their judgment.

Example: A scientist interpreting data from a clinical trial should do so based on statistical validity, regardless of their personal belief in the efficacy of the drug being tested.

4.5.3? ? ? Methodological Objectivity

Concept: This form of objectivity pertains to the standardization of research methods to ensure reproducibility and comparability across different researchers and settings.

Example: Following a peer-reviewed protocol for a chemistry experiment to ensure that the results can be compared to other studies.

4.5.4? ? ? Objectivity and Subjectivity

Concept: An exploration of the ongoing debate about whether complete objectivity is possible, or whether all science is ultimately "theory-laden" or influenced by human assumptions.

Example: The argument that all observations are influenced by the theoretical framework of the observer, thus introducing some level of subjectivity.

4.5.5? ? ? Challenges to Objectivity

Concept: Discussion of criticisms and challenges to the concept of objectivity in science, including arguments from feminist epistemology, social constructivism, and postmodernism.

Example: The argument that science, being a human endeavor, can never be entirely free from social and cultural biases.

4.5.6? ? ? Role of Peer Review

Concept: How peer review serves as a corrective mechanism to ensure objectivity in scientific research.

Example: Manuscripts submitted to scientific journals are often reviewed by multiple experts in the field to ensure that the research meets the standards of objectivity.

4.5.7? ? ? Ethical Considerations

Concept: The ethical implications of striving for objectivity in scientific research, such as the prevention of fraudulent data manipulation.

Example: The retraction of published scientific papers that were found to contain fabricated or manipulated data.

?

Understanding the concept of "Objectivity" can give you a clearer view of how science aims to arrive at reliable knowledge and the various challenges that this ideal faces.