Outline of Science is a systematic effort of acquiring knowledge—through observation and experimentation coupled with logic and Reasoning to find out what can be proved or not proved—and the knowledge thus acquired. The word "science" comes from the Latin word "scientia" meaning knowledge. A practitioner of science is called a "scientist". Modern science respects objective logical reasoning, and follows a set of core procedures or rules in order to determine the nature and underlying natural laws of the universe and everything in it.
Some scientists do not know of the rules themselves, but follow them through research policies. These procedures are known as the scientific method. Laws of Science are statements that describe or predict a range of phenomena behave as they appear to in nature. The term "law" has diverse usage in many cases: Scientific laws summarize and explain a large collection of facts determined by experiment , and are tested based on their ability to predict the results of future experiments.
They are developed either from facts or through mathematics , and are strongly supported by empirical evidence. It is generally understood that they reflect causal relationships fundamental to reality, and are discovered rather than invented. Data Driven Science is an interdisciplinary field about scientific methods , processes and systems to extract knowledge or insights from data in various forms , either structured or unstructured, similar to Knowledge Discovery in Databases KDD.
Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics , information science, and computer science, in particular from the subdomains of machine learning , classification , cluster analysis, data mining, databases , and visualization.
Fringe Science is an inquiry in an established field of study which departs significantly from mainstream theories in that field and is considered to be questionable by the mainstream. Fringe science may be either a questionable application of a scientific approach to a field of study or an approach whose status as scientific is widely questioned.
Natural Science Outline is a major branch of science that tries to explain, and predict, nature's phenomena based on empirical evidence. In natural science, hypothesis must be verified scientifically to be regarded as scientific theory. Validity , accuracy , and social mechanisms ensuring quality control , such as peer review and repeatability of findings, are amongst the criteria and methods used for this purpose.
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Natural science can be broken into 2 main branches: Each of these branches, and all of their sub-branches, are referred to as natural sciences. Natural Sciences Academy Naturalist environment Physical Science Outline is a branch of natural science that studies non-living systems , in contrast to Life Science.
It in turn has many branches, each referred to as a "physical science", together called the "physical sciences". However, the term "physical" creates an unintended, somewhat arbitrary distinction, since many branches of physical science also study biological phenomena and branches of chemistry such as organic chemistry. Protoscience involves the earliest eras of the history of science.
Involving the distinction between hard and soft sciences , in which various sciences or branches thereof are ranked according to methodological rigor. Materials Science Earth Science Outline all-embracing term for the sciences related to the planet Earth. It is also known as geoscience, the geosciences or the Earth sciences, and is arguably a special case in planetary science , the Earth being the only known life-bearing planet. Earth science is a branch of the physical sciences which is a part of the natural sciences. It in turn has many branches.
Formal Science Outline are branches of knowledge that are concerned with formal systems, such as those under the branches of: Unlike other sciences, the formal sciences are not concerned with the validity of theories based on observations in the real world, but instead with the properties of formal systems based on definitions and rules.
Social Science Outline is the branch of science concerned with society and human behaviors. Applied Science Outline is the branch of science that applies existing scientific knowledge to develop more practical applications, including inventions and other technological advancements. Science itself is the systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe.
Medicine is the science and practice of the diagnosis, treatment, and prevention of disease. Clinical Science Journal offers multi-disciplinary coverage and clinical perspectives to advance human health. The goal of translational medicine is to combine disciplines, resources, expertise, and techniques within these pillars to promote enhancements in prevention, diagnosis, and therapies.
The term translational refers to the "translation" of basic scientific findings in a laboratory setting into potential treatments for disease. Forensic Science is the application of science to criminal and civil laws , mainly—on the criminal side—during criminal investigation, as governed by the legal standards of admissible evidence and criminal procedure. Forensic scientists collect, preserve, and analyze scientific evidence during the course of an investigation.
While some forensic scientists travel to the scene of the crime to collect the evidence themselves, others occupy a laboratory role, performing analysis on objects brought to them by other individuals. Blackstone Discovery Philosophy of Science is a branch of philosophy concerned with the foundations, methods, and implications of science.
The central questions of this study concern what qualifies as science, the reliability of scientific theories, and the ultimate purpose of science. This discipline overlaps with metaphysics, ontology, and epistemology, for example, when it explores the relationship between science and truth. Holism Science is an approach to research that emphasizes the study of complex systems which aims to gain understanding of systems by dividing them into smaller composing elements and gaining understanding of the system through understanding their elemental properties. Open Science is the movement to make scientific research, data and dissemination accessible to all levels of an inquiring society, amateur or professional.
It encompasses practices such as publishing open research , campaigning for open access, encouraging scientists to practice open notebook science, and generally making it easier to publish and communicate scientific knowledge. A Search for Knowledge. Gathering Knowledge about Nature and Organizing and Condensing that Knowledge into testable laws and theories. Ability to produce Solutions in some Problem Domain. Research into questions posed by scientific Theories and Hypotheses. Empirical Research is research using empirical evidence. It is a way of gaining knowledge by means of direct and indirect Observation or experience.
Empiricism values such research more than other kinds. Empirical evidence the record of one's direct observations or experiences can be analyzed quantitatively or qualitatively. Through quantifying the evidence or making sense of it in qualitative form, a researcher can answer empirical questions, which should be clearly defined and answerable with the evidence collected usually called data. Research design varies by field and by the question being investigated. Many researchers combine qualitative and quantitative forms of analysis to better answer questions which cannot be studied in laboratory settings, particularly in the social sciences and in education.
Exploratory Research is research conducted for a problem that has not been clearly defined. It often occurs before we know enough to make conceptual distinctions or to posit an explanatory relationship. Exploratory research develops concepts more clearly, established priorities, develops operational definitions and improve the final research design. Exploratory research helps determine the best research design, data-collection method and selection of subjects.
It should draw definitive conclusions only with extreme caution. Given its fundamental nature, exploratory research often concludes that a perceived problem does not actually exist. Field Research is the collection of information outside a laboratory, library or workplace setting. The approaches and methods used in field research vary across disciplines. For example, biologists who conduct field research may simply observe animals interacting with their environments, whereas social scientists conducting field research may interview or observe people in their natural environments to learn their languages, folklore, and social structures.
Drug Research - Goals of Research PDF Research Proposal is a document proposing a research project, generally in the sciences or academia, and generally constitutes a request for sponsorship of that research. Proposals are evaluated on the cost and potential impact of the proposed research, and on the soundness of the proposed plan for carrying it out.
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Research proposals generally address several key points: What research question s will be addressed, and how they will be addressed. What cost and time will be required for the research. What prior research has been done on the topic. How the results of the research will be evaluated. How the research will benefit the sponsoring organization and other parties. Basic Research is scientific research aimed to improve scientific theories for improved understanding or prediction of natural or other phenomena. Applied research, in turn, uses scientific theories to develop technology or techniques to intervene and alter natural or other phenomena.
Primary Research involves the collection of original primary data by researchers. It is often undertaken after researchers have gained some insight into an issue by reviewing secondary research or by analyzing previously collected primary data. It can be accomplished through various methods, including questionnaires and telephone interviews in market research, or experiments and direct Observations in the physical sciences, amongst others.
The distinction between primary research and secondary research is crucial among market-research professionals. Open Research is to make clear accounts of the methodology freely available via the internet, along with any data or results extracted or derived from them. This permits a massively distributed collaboration, and one in which anyone may participate at any level of the project. Open Source Software Quantitative Research is the systematic empirical investigation of observable phenomena via statistical , mathematical or computational techniques.
The process of measurement is central to quantitative research because it provides the fundamental connection between empirical observation and mathematical expression of quantitative relationships. Original Research is research that is not exclusively based on a summary, review or synthesis of earlier publications on the subject of research.
This material is of a primary source character. The purpose of the original research is to produce new knowledge, rather than to present the existing knowledge in a new form e.
Gary G. Tibbetts
Secondary research is contrasted with primary research in that primary research involves the generation of data, whereas secondary research uses primary research sources as a source of data for analysis. A notable marker of primary research is the inclusion of a "methods" section, where the authors describe how the data was generated. Common examples of secondary research include textbooks, encyclopedias, news articles, review articles, and meta analyses.
When conducting secondary research, authors may draw data from published academic papers, government documents, statistical databases, and historical records. Social Research is a research conducted by social scientists following a systematic plan. Social research methodologies can be classified as quantitative or qualitative. Quantitative designs approach social phenomena through quantifiable evidence, and often rely on statistical analysis of many cases or across intentionally designed treatments in an experiment to create valid and reliable general claims.
Qualitative designs emphasize understanding of social phenomena through direct observation, communication with participants, or analysis of texts, and may stress contextual subjective accuracy over generality. Research and development is a component of Innovation and is situated at the front end of the Innovation life cycle. Research Institute is an establishment founded for doing research. Most scientific research is funded by government grants. Spending on basic research by all U. About 10 percent goes to federally funded labs operated by private contractors. For the first time in the post—World War II era, the federal government no longer funds a majority of the basic research carried out in the United States.
Funding of Science is a term generally covering any funding for scientific research, in the areas of both "hard" science and technology and social science. The term often connotes funding obtained through a competitive process, in which potential research projects are evaluated and only the most promising receive funding. Such processes, which are run by government, corporations or foundations, allocate scarce funds.
Most research funding comes from two major sources, corporations through research and development departments and government primarily carried out through universities and specialized government agencies; often known as research councils. Some small amounts of scientific research are carried out or funded by charitable foundations, especially in relation to developing cures for diseases such as cancer, malaria and AIDS. Prove it to be True. Prove it to be False.
How Necessary is it to Prove? What are your Priorities? Scientific Journal is a periodical publication intended to further the progress of science, usually by reporting new research. Academic publishing is the process of contributing the results of one's research into the literature, which often requires a peer-review process. Original scientific research published for the first time in scientific journals is called the primary literature.
Patents and technical reports, for minor research results and engineering and design work including computer software , can also be considered primary literature. Secondary sources include review articles which summarize the findings of published studies to highlight advances and new lines of research and books for large projects or broad arguments, including compilations of articles.
Tertiary sources might include encyclopedias and similar works intended for broad public consumption. Academic Journal is a periodical publication in which scholarship relating to a particular academic discipline is published. Academic journals serve as permanent and transparent forums for the presentation, scrutiny, and discussion of research. They are usually peer-reviewed or refereed. Content typically takes the form of articles presenting original research, review articles, and book reviews. Technical Report is a document that describes the process, progress, or results of technical or scientific research or the state of a technical or scientific research problem.
It might also include recommendations and conclusions of the research. Unlike other scientific literature, such as scientific journals and the proceedings of some academic conferences, technical reports rarely undergo comprehensive independent peer review before publication. They may be considered as grey literature. Where there is a review process, it is often limited to within the originating organization.
Similarly, there are no formal publishing procedures for such reports, except where established locally. Grey Literature are materials and research produced by organizations outside of the traditional commercial or academic publishing and distribution channels. Common grey literature publication types include reports annual, research, technical, project, etc. Organizations that produce grey literature include government departments and agencies, civil society or non-governmental organisations, academic centres and departments, and private companies and consultants.
Grey literature may be made available to the public, or distributed privately within organizations or groups, and may lack a systematic means of distribution and collection. The standard of quality, review and production of grey literature can vary considerably. Grey literature may be difficult to discover, access, and evaluate, but this can be addressed through the formulation of sound search strategies. Science Communication is the public communication of science-related topics to non-experts. This often involves professional scientists called "outreach" or "popularization" , but has also evolved into a professional field in its own right.
It includes science exhibitions, journalism, policy or media production. Science communication also includes communication between scientists for instance through scientific journals , as well as between scientists and non-scientists especially during public controversies over science and in citizen science initiatives. Science communication may generate support for scientific research or study, or to inform decision making, including political and ethical thinking. There is increasing emphasis on explaining methods rather than simply findings of science.
This may be especially critical in addressing scientific misinformation, which spreads easily because it is not subject to the constraints of scientific method. Science communicators can use entertainment and persuasion including humour, storytelling and metaphors. Scientists can be trained in some of the techniques used by actors to improve their communication. Scholarly Method is the body of principles and practices used by scholars to make their claims about the world as valid and trustworthy as possible, and to make them known to the scholarly public. It is the methods that systemically advance the teaching, research, and practice of a given scholarly or academic field of study through rigorous inquiry.
Scholarship is noted by its significance to its particular profession, and is creative, can be documented, can be replicated or elaborated, and can be and is peer-reviewed through various methods. Case Study is a research method involving an up-close, in-depth, and detailed examination of a subject of study the case , as well as its related contextual conditions. Evaluation is a systematic determination of a subject's merit, worth and significance, using criteria governed by a set of standards. The primary purpose of evaluation, in addition to gaining insight into prior or existing initiatives, is to enable reflection and assist in the identification of future change.
Third Party Testing drugs Diagnosis is the identification of the nature and cause of a certain phenomenon. Diagnosis is used in many different disciplines with variations in the use of logic, analytics, and experience to determine "cause and effect". In systems engineering and computer science, it is typically used to determine the causes of symptoms, mitigations, and solutions. Dissection is the dismembering of the body of a deceased animal or plant to study its anatomical structure.
Autopsy is used in pathology and forensic medicine to determine the cause of death in humans. It is carried out by or demonstrated to biology and anatomy students in high school and medical school. Less advanced courses typically focus on smaller subjects, such as small formaldehyde-preserved animals, while the more advanced courses normally use cadavers. Consequently, dissection is typically conducted in a morgue or in an anatomy lab. Predictions - Baseline Experiments Experiment is a procedure carried out to support, refute, or validate a hypothesis.
Experiments provide insight into cause-and-effect by demonstrating what outcome occurs when a particular factor is manipulated. Experiments vary greatly in goal and scale, but always rely on repeatable procedure and logical analysis of the results. There also exists natural experimental studies.
Randomized Experiment are the experiments that allow the greatest reliability and validity of statistical estimates of treatment effects. Randomization-based inference is especially important in experimental design and in survey sampling. Pilot Experiment is a small scale preliminary study conducted in order to evaluate feasibility, time, cost, adverse events, and effect size statistical variability in an attempt to predict an appropriate sample size and improve upon the study design prior to performance of a full-scale research project.
Pilot Studies , therefore, may not be appropriate for case studies. Quasi-Experiment is an empirical study used to estimate the causal impact of an intervention on its target population without random assignment. Quasi-experimental research shares similarities with the traditional experimental design or randomized controlled trial, but it specifically lacks the element of random assignment to treatment or control. Instead, quasi-experimental designs typically allow the researcher to control the assignment to the treatment condition, but using some criterion other than random assignment e.
Double Blind Design of Experiments is the design of any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associated with true experiments in which the design introduces conditions that directly affect the variation, but may also refer to the design of quasi-experiments, in which natural conditions that influence the variation are selected for observation. Natural Experiment is an empirical study in which individuals or clusters of individuals exposed to the experimental and control conditions are determined by nature or by other factors outside the control of the investigators, but the process governing the exposures arguably resembles random assignment.
Thus, natural experiments are observational studies and are not controlled in the traditional sense of a randomized experiment. Natural experiments are most useful when there has been a clearly defined exposure involving a well defined subpopulation and the absence of exposure in a similar subpopulation such that changes in outcomes may be plausibly attributed to the exposure.
In this sense, the difference between a natural experiment and a non-experimental observational study is that the former includes a comparison of conditions that pave the way for causal inference, but the latter does not. Natural experiments are employed as study designs when controlled experimentation is extremely difficult to implement or unethical, such as in several research areas addressed by epidemiology like evaluating the health impact of varying degrees of exposure to ionizing radiation in people living near Hiroshima at the time of the atomic blast and economics like estimating the economic return on amount of schooling in US adults.
Experimentalist is the philosophical belief that the way to truth is through experiments and empiricism. It is also associated with instrumentalism, the belief that truth should be evaluated based upon its demonstrated usefulness. Less formally, artists often pursue their visions through trial and error ; this form of experimentalism has been practiced in every field, from music to film and from literature to theatre. Thought Experiment considers some hypothesis , theory, or principle for the purpose of thinking through its consequences.
Given the structure of the experiment, it may not be possible to perform it, and even if it could be performed, there need not be an intention to perform it. The common goal of a thought experiment is to explore the potential consequences of the principle in question: Intuition Pump is a thought experiment structured to allow the thinker to use their intuition to develop an answer to a problem. Empiricism is a theory that states that knowledge comes only or primarily from Sensory Experience.
One of several views of epistemology, the study of human knowledge, along with rationalism and skepticism, empiricism emphasizes the role of empirical evidence in the formation of ideas, over the notion of innate ideas or traditions. Constructivist Epistemology is a branch in philosophy of science maintaining that scientific knowledge is constructed by the scientific community, who seek to Measure and construct models of the natural world. Natural science therefore consists of mental constructs that aim to explain sensory experience and measurements.
Scientific Control is an experiment or observation designed to minimize the effects of variables other than the independent variable. This increases the reliability of the results, often through a comparison between control measurements and the other measurements. Scientific controls are a part of the scientific method. Side by Side Comparisons - Pros and Cons. Multivariate Testing is hypothesis testing in the context of multivariate statistics.
An interesting consequence of this empirical realism about numbers is that measurement is not a representational activity, but rather the activity of approximating mind-independent numbers Michell Realist accounts of measurement are largely formulated in opposition to strong versions of operationalism and conventionalism, which dominated philosophical discussions of measurement from the s until the s.
In addition to the drawbacks of operationalism already discussed in the previous section, realists point out that anti-realism about measurable quantities fails to make sense of scientific practice. By contrast, realists can easily make sense of the notions of accuracy and error in terms of the distance between real and measured values Byerly and Lazara A closely related point is the fact that newer measurement procedures tend to improve on the accuracy of older ones.
If choices of measurement procedure were merely conventional it would be difficult to make sense of such progress. In addition, realism provides an intuitive explanation for why different measurement procedures often yield similar results, namely, because they are sensitive to the same facts Swoyer Finally, realists note that the construction of measurement apparatus and the analysis of measurement results are guided by theoretical assumptions concerning causal relationships among quantities.
The ability of such causal assumptions to guide measurement suggests that quantities are ontologically prior to the procedures that measure them. While their stance towards operationalism and conventionalism is largely critical, realists are more charitable in their assessment of mathematical theories of measurement. Brent Mundy and Chris Swoyer both accept the axiomatic treatment of measurement scales, but object to the empiricist interpretation given to the axioms by prominent measurement theorists like Campbell and Ernest Nagel ; Cohen and Nagel Rather than interpreting the axioms as pertaining to concrete objects or to observable relations among such objects, Mundy and Swoyer reinterpret the axioms as pertaining to universal magnitudes, e.
Moreover, under their interpretation measurement theory becomes a genuine scientific theory, with explanatory hypotheses and testable predictions. Despite these virtues, the realist interpretation has been largely ignored in the wider literature on measurement theory. Information-theoretic accounts of measurement are based on an analogy between measuring systems and communication systems. The accuracy of the transmission depends on features of the communication system as well as on features of the environment, i.
The accuracy of a measurement similarly depends on the instrument as well as on the level of noise in its environment. Conceived as a special sort of information transmission, measurement becomes analyzable in terms of the conceptual apparatus of information theory Hartley ; Shannon ; Shannon and Weaver Ludwik Finkelstein , and Luca Mari suggested the possibility of a synthesis between Shannon-Weaver information theory and measurement theory.
As they argue, both theories centrally appeal to the idea of mapping: If measurement is taken to be analogous to symbol-manipulation, then Shannon-Weaver theory could provide a formalization of the syntax of measurement while measurement theory could provide a formalization of its semantics. Information-theoretic accounts of measurement were originally developed by metrologists with little involvement from philosophers.
Metrologists typically work at standardization bureaus or at specialized laboratories that are responsible for the calibration of measurement equipment, the comparison of standards and the evaluation of measurement uncertainties, among other tasks. It is only recently that philosophers have begun to engage with the rich conceptual issues underlying metrological practice, and particularly with the inferences involved in evaluating and improving the accuracy of measurement standards Chang ; Boumans a: Further philosophical work is required to explore the assumptions and consequences of information-theoretic accounts of measurement, their implications for metrological practice, and their connections with other accounts of measurement.
Independently of developments in metrology, Bas van Fraassen He views measurement as composed of two levels: Measurement locates an object on a sub-region of this abstract parameter space, thereby reducing the range of possible states This reduction of possibilities amounts to the collection of information about the measured object. Since the early s a new wave of philosophical scholarship has emerged that emphasizes the relationships between measurement and theoretical and statistical modeling. According to model-based accounts, measurement consists of two levels: The central goal of measurement according to this view is to assign values to one or more parameters of interest in the model in a manner that satisfies certain epistemic desiderata, in particular coherence and consistency.
A central motivation for the development of model-based accounts is the attempt to clarify the epistemological principles underlying aspects of measurement practice. For example, metrologists employ a variety of methods for the calibration of measuring instruments, the standardization and tracing of units and the evaluation of uncertainties for a discussion of metrology, see the previous section. Traditional philosophical accounts such as mathematical theories of measurement do not elaborate on the assumptions, inference patterns, evidential grounds or success criteria associated with such methods.
As Frigerio et al. By contrast, model-based accounts take scale construction to be merely one of several tasks involved in measurement, alongside the definition of measured parameters, instrument design and calibration, object sampling and preparation, error detection and uncertainty evaluation, among others Other, secondary interactions may also be relevant for the determination of a measurement outcome, such as the interaction between the measuring instrument and the reference standards used for its calibration, and the chain of comparisons that trace the reference standard back to primary measurement standards Mari Although measurands need not be quantities, a quantitative measurement scenario will be supposed in what follows.
Two sorts of measurement outputs are distinguished by model-based accounts [JCGM As proponents of model-based accounts stress, inferences from instrument indications to measurement outcomes are nontrivial and depend on a host of theoretical and statistical assumptions about the object being measured, the instrument, the environment and the calibration process. Measurement outcomes are often obtained through statistical analysis of multiple indications, thereby involving assumptions about the shape of the distribution of indications and the randomness of environmental effects Bogen and Woodward Measurement outcomes also incorporate corrections for systematic effects, and such corrections are based on theoretical assumptions concerning the workings of the instrument and its interactions with the object and environment.
Systematic corrections involve uncertainties of their own, for example in the determination of the values of constants, and these uncertainties are assessed through secondary experiments involving further theoretical and statistical assumptions. Moreover, the uncertainty associated with a measurement outcome depends on the methods employed for the calibration of the instrument.
Calibration involves additional assumptions about the instrument, the calibrating apparatus, the quantity being measured and the properties of measurement standards Rothbart and Slayden ; Franklin ; Baird Finally, measurement involves background assumptions about the scale type and unit system being used, and these assumptions are often tied to broader theoretical and technological considerations relating to the definition and realization of scales and units. These various theoretical and statistical assumptions form the basis for the construction of one or more models of the measurement process.
Measurement is viewed as a set of procedures whose aim is to coherently assign values to model parameters based on instrument indications. Models are therefore seen as necessary preconditions for the possibility of inferring measurement outcomes from instrument indications, and as crucial for determining the content of measurement outcomes. As proponents of model-based accounts emphasize, the same indications produced by the same measurement process may be used to establish different measurement outcomes depending on how the measurement process is modeled, e.
As Luca Mari puts it,. Similarly, models are said to provide the necessary context for evaluating various aspects of the goodness of measurement outcomes, including accuracy, precision, error and uncertainty Boumans , a, , b; Mari b. Model-based accounts diverge from empiricist interpretations of measurement theory in that they do not require relations among measurement outcomes to be isomorphic or homomorphic to observable relations among the items being measured Mari Indeed, according to model-based accounts relations among measured objects need not be observable at all prior to their measurement Frigerio et al.
Instead, the key normative requirement of model-based accounts is that values be assigned to model parameters in a coherent manner. The coherence criterion may be viewed as a conjunction of two sub-criteria: The first sub-criterion is meant to ensure that the intended quantity is being measured, while the second sub-criterion is meant to ensure that measurement outcomes can be reasonably attributed to the measured object rather than to some artifact of the measuring instrument, environment or model.
How the Great Scientists Reasoned: The Scientific Method in Action by Gary G. Tibbetts
Taken together, these two requirements ensure that measurement outcomes remain valid independently of the specific assumptions involved in their production, and hence that the context-dependence of measurement outcomes does not threaten their general applicability. Besides their applicability to physical measurement, model-based analyses also shed light on measurement in economics. Like physical quantities, values of economic variables often cannot be observed directly and must be inferred from observations based on abstract and idealized models.
The nineteenth century economist William Jevons, for example, measured changes in the value of gold by postulating certain causal relationships between the value of gold, the supply of gold and the general level of prices Hoover and Dowell Taken together, these models allowed Jevons to infer the change in the value of gold from data concerning the historical prices of various goods.
The ways in which models function in economic measurement have led some philosophers to view certain economic models as measuring instruments in their own right, analogously to rulers and balances Boumans , c, , a, , a; Morgan Marcel Boumans explains how macroeconomists are able to isolate a variable of interest from external influences by tuning parameters in a model of the macroeconomic system.
This technique frees economists from the impossible task of controlling the actual system. As Boumans argues, macroeconomic models function as measuring instruments insofar as they produce invariant relations between inputs indications and outputs outcomes , and insofar as this invariance can be tested by calibration against known and stable facts. Another area where models play a central role in measurement is psychology. The measurement of most psychological attributes, such as intelligence, anxiety and depression, does not rely on homomorphic mappings of the sort espoused by the Representational Theory of Measurement Wilson These models are constructed from substantive and statistical assumptions about the psychological attribute being measured and its relation to each measurement task.
For example, Item Response Theory, a popular approach to psychological measurement, employs a variety of models to evaluate the validity of questionnaires. One of the simplest models used to validate such questionnaires is the Rasch model Rasch New questionnaires are calibrated by testing the fit between their indications and the predictions of the Rasch model and assigning difficulty levels to each item accordingly. The model is then used in conjunction with the questionnaire to infer levels of English language comprehension outcomes from raw questionnaire scores indications Wilson ; Mari and Wilson Psychologists are typically interested in the results of a measure not for its own sake, but for the sake of assessing some underlying and latent psychological attribute.
It is therefore desirable to be able to test whether different measures, such as different questionnaires or multiple controlled experiments, all measure the same latent attribute. A construct is an abstract representation of the latent attribute intended to be measured, and. Constructs are denoted by variables in a model that predicts which correlations would be observed among the indications of different measures if they are indeed measures of the same attribute. Several scholars have pointed out similarities between the ways models are used to standardize measurable quantities in the natural and social sciences.
Others have raised doubts about the feasibility and desirability of adopting the example of the natural sciences when standardizing constructs in the social sciences. As Anna Alexandrova points out, ethical considerations bear on questions about construct validity no less than considerations of reproducibility. Such ethical considerations are context sensitive, and can only be applied piecemeal. Examples of Ballung concepts are race, poverty, social exclusion, and the quality of PhD programs. Such concepts are too multifaceted to be measured on a single metric without loss of meaning, and must be represented either by a matrix of indices or by several different measures depending on which goals and values are at play see also Cartwright and Bradburn In a similar vein, Leah McClimans argues that uniformity is not always an appropriate goal for designing questionnaires, as the open-endedness of questions is often both unavoidable and desirable for obtaining relevant information from subjects.
Rather than emphasizing the mathematical foundations, metaphysics or semantics of measurement, philosophical work in recent years tends to focus on the presuppositions and inferential patterns involved in concrete practices of measurement, and on the historical, social and material dimensions of measuring. In the broadest sense, the epistemology of measurement is the study of the relationships between measurement and knowledge. Central topics that fall under the purview of the epistemology of measurement include the conditions under which measurement produces knowledge; the content, scope, justification and limits of such knowledge; the reasons why particular methodologies of measurement and standardization succeed or fail in supporting particular knowledge claims, and the relationships between measurement and other knowledge-producing activities such as observation, theorizing, experimentation, modelling and calculation.
In pursuing these objectives, philosophers are drawing on the work of historians and sociologists of science, who have been investigating measurement practices for a longer period Wise and Smith ; Latour The following subsections survey some of the topics discussed in this burgeoning body of literature. A topic that has attracted considerable philosophical attention in recent years is the selection and improvement of measurement standards. Generally speaking, to standardize a quantity concept is to prescribe a determinate way in which that concept is to be applied to concrete particulars.
This duality in meaning reflects the dual nature of standardization, which involves both abstract and concrete aspects. In Section 4 it was noted that standardization involves choices among nontrivial alternatives, such as the choice among different thermometric fluids or among different ways of marking equal duration. Appealing to theory to decide which standard is more accurate would be circular, since the theory cannot be determinately applied to particulars prior to a choice of measurement standard. A drawback of this solution is that it supposes that choices of measurement standard are arbitrary and static, whereas in actual practice measurement standards tend to be chosen based on empirical considerations and are eventually improved or replaced with standards that are deemed more accurate.
A new strand of writing on the problem of coordination has emerged in recent years, consisting most notably of the works of Hasok Chang , , and Bas van Fraassen These works take a historical and coherentist approach to the problem. Rather than attempting to avoid the problem of circularity completely, as their predecessors did, they set out to show that the circularity is not vicious.
Chang argues that constructing a quantity-concept and standardizing its measurement are co-dependent and iterative tasks. The pre-scientific concept of temperature, for example, was associated with crude and ambiguous methods of ordering objects from hot to cold. Thermoscopes, and eventually thermometers, helped modify the original concept and made it more precise.
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With each such iteration the quantity concept was re-coordinated to a more stable set of standards, which in turn allowed theoretical predictions to be tested more precisely, facilitating the subsequent development of theory and the construction of more stable standards, and so on. From either vantage point, coordination succeeds because it increases coherence among elements of theory and instrumentation.
It is only when one adopts a foundationalist view and attempts to find a starting point for coordination free of presupposition that this historical process erroneously appears to lack epistemic justification The new literature on coordination shifts the emphasis of the discussion from the definitions of quantity-terms to the realizations of those definitions. Examples of metrological realizations are the official prototypes of the kilogram and the cesium fountain clocks used to standardize the second. Recent studies suggest that the methods used to design, maintain and compare realizations have a direct bearing on the practical application of concepts of quantity, unit and scale, no less than the definitions of those concepts Tal forthcoming-a; Riordan As already discussed above Sections 7 and 8.
On the historical side, the development of theory and measurement proceeds through iterative and mutual refinements. On the conceptual side, the specification of measurement procedures shapes the empirical content of theoretical concepts, while theory provides a systematic interpretation for the indications of measuring instruments. This interdependence of measurement and theory may seem like a threat to the evidential role that measurement is supposed to play in the scientific enterprise.
After all, measurement outcomes are thought to be able to test theoretical hypotheses, and this seems to require some degree of independence of measurement from theory. This threat is especially clear when the theoretical hypothesis being tested is already presupposed as part of the model of the measuring instrument.
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To cite an example from Franklin et al. There would seem to be, at first glance, a vicious circularity if one were to use a mercury thermometer to measure the temperature of objects as part of an experiment to test whether or not objects expand as their temperature increases. Nonetheless, Franklin et al.
The mercury thermometer could be calibrated against another thermometer whose principle of operation does not presuppose the law of thermal expansion, such as a constant-volume gas thermometer, thereby establishing the reliability of the mercury thermometer on independent grounds. To put the point more generally, in the context of local hypothesis-testing the threat of circularity can usually be avoided by appealing to other kinds of instruments and other parts of theory.
A different sort of worry about the evidential function of measurement arises on the global scale, when the testing of entire theories is concerned. As Thomas Kuhn argues, scientific theories are usually accepted long before quantitative methods for testing them become available. The reliability of newly introduced measurement methods is typically tested against the predictions of the theory rather than the other way around.
Hence, Kuhn argues, the function of measurement in the physical sciences is not to test the theory but to apply it with increasing scope and precision, and eventually to allow persistent anomalies to surface that would precipitate the next crisis and scientific revolution. Note that Kuhn is not claiming that measurement has no evidential role to play in science. The theory-ladenness of measurement was correctly perceived as a threat to the possibility of a clear demarcation between the two languages.
Contemporary discussions, by contrast, no longer present theory-ladenness as an epistemological threat but take for granted that some level of theory-ladenness is a prerequisite for measurements to have any evidential power. Without some minimal substantive assumptions about the quantity being measured, such as its amenability to manipulation and its relations to other quantities, it would be impossible to interpret the indications of measuring instruments and hence impossible to ascertain the evidential relevance of those indications.
This point was already made by Pierre Duhem Moreover, contemporary authors emphasize that theoretical assumptions play crucial roles in correcting for measurement errors and evaluating measurement uncertainties. Indeed, physical measurement procedures become more accurate when the model underlying them is de-idealized, a process which involves increasing the theoretical richness of the model Tal This problem is especially clear when one attempts to account for the increasing use of computational methods for performing tasks that were traditionally accomplished by measuring instruments.
As Margaret Morrison and Wendy Parker forthcoming argue, there are cases where reliable quantitative information is gathered about a target system with the aid of a computer simulation, but in a manner that satisfies some of the central desiderata for measurement such as being empirically grounded and backward-looking. Such information does not rely on signals transmitted from the particular object of interest to the instrument, but on the use of theoretical and statistical models to process empirical data about related objects.
For example, data assimilation methods are customarily used to estimate past atmospheric temperatures in regions where thermometer readings are not available. These estimations are then used in various ways, including as data for evaluating forward-looking climate models. Antoine Lavoisier and Joseph Priestley both test the befuddling phlogiston theory: Michael Faraday discovers electromagnetic induction but fails to unify electromagnetism and gravitation: Max Planck, the first superhero of quantum theory, saves the universe from the ultraviolet catastrophe: Albert Einstein attacks the problem "Are atoms real?
Niels Bohr models the hydrogen atom as a quantized system with compelling exactness, but his later career proves that collaboration and developing new talent can become more significant than the groundbreaking research of any individual -- Conclusions, status of science, and lessons for our time. Conclusions from our biographies -- What thought processes lead to innovation?
Scientific Method in General Philosophy of Science. Find it on Scholar. Request removal from index. Google Books no proxy Setup an account with your affiliations in order to access resources via your University's proxy server Configure custom proxy use this if your affiliation does not provide a proxy. Carol Cleland - manuscript. Brad Wray - - Social Studies of Science