iv research methods: analytical approach aarne mämmelä drp-r... · requirements and therefore the...

1

VTT TECHNICAL RESEARCH CENTRE OF FINLAND

Aarne Mämmelä 23.9.2008 1

IV Research methods: Analytical approachIV Research methods: Analytical approach

AarneAarne MMäämmelmmelää

We will discuss the difference between research and development, and between science and engineering. Scientific research is divided into discovery and verification. The research starts with the definition of the problem. After collecting some data, a tentative solution that is also called a hypothesis or system model should be found, implying some kind of causal relationship or correlation between variables. We will present the conventional scientific, analytical, or reductive approach of research. Not all problems can be solved reductively. Therefore a complementary systems approach will be presented in a later session. However, the analysis or reduction has been the reason for the success of the western culture since the 1600’s after the remarkable work of Galileo, F. Bacon, Descartes, Newton, and many others. Reduction is based on the idea of dividing a large problem into several subproblems that are solved separately using induction, abduction, intuition, imagination, or any other methods of discovery. (Continued)



IV Research methods: Analytical approachIV Research methods: Analytical approach

AarneAarne MMäämmelmmelää

(Continued) Scientific theories are shown to be deductive and causal structures having internal and external coherence. Usually in engineering a construction of the system is implemented to show that the theory is “working”. The correspondence of the theory with reality is verified by using the hypothetico-deductive method where some conclusions are deductively derived from the hypothesis and they are compared with reality. In engineering research we start from performance criteria or performance requirements and therefore the hypothetico-deductive method must be slightly modified. Conceptual analysis will be emphasized before theories can be formed. Causality and correlation, deductive, inductive and abductivereasoning, strong inference, and the problem of induction will be explained in detail. Philosophical discussions will improve your awareness of our work.

2



Summary of my lectures Summary of my lectures

• A doctor must be able to discover new scientific knowledge independently (lecture 1)

• Existing knowledge in the literature is best found through bibliographies, citations, and databases (lecture 2)

• All documents are written using a hierarchical top-down approach (lecture 3)

• In this lecture I show that the actual research is a learning process where the opposite bottom-up approach (analytical approach) is used (lecture 4)

• In my last lecture I show that also top-down approach (systems approach) can be useful to foster creativity and support learning (lecture 5)



Research methods: Analytical approachResearch methods: Analytical approach

• Introduction

• Research & development

• Choosing a problem

• Formation of concepts and theories

• Order and creativity

• Empirical-inductive method

• Conclusions

3



IntroductionIntroduction

Intial data collection

(literature review)

Tentative solution(hypothesis)

Analysis/simulations/experiments

Theory/paper(new knowledge)

System model(prototype)

Problem



Journey of Exploration: Columbus Journey of Exploration: Columbus

[Suomalainen90][Suomalainen90]

• Problem: a new way to India

• Competing hypotheses: over the Atlantic (Spain), around Africa (Portugal)

4



Methodological Approaches and Research Methods

Actors approach

Systems approach

Analytical approach

Idiographic research

Constructive research

Nomothetic research

Methodological approaches

Research methods



Methodological Approaches [Arbnor97]

1. Analytical approach (reductive approach)• developed in the 1600’s (Galileo, Francis Bacon, Descartes, Newton)

• bottom-up approach, a whole is the sum of its parts

• knowledge is independent of observer

• interested in cause-effect relations in deterministic phenomena and correlations in stochastic phenomena

2. Systems approach (holistic approach)• developed in the 1950’s (Bertalanffy, Buckley, Churchman, Emery)

• top-down approach, a whole differs from the sum of its parts (synergy, emergence), environment has an important role

• knowledge is independent of observer

• interested also in producer-product relations and final causes (purposes)

3. Actors approach• developed in the 1970’s (Silverman, earlier work by Husserl, Weber, Schutz)

• top-down approach, actor is an active, reflective, and creative human being

• interested in understanding social wholes

• knowledge exists only as a social construction and is not independent of observers

5



Research Methods (Research Approaches) [Iivari91]

1. Nomothetic research (explains with general laws, e.g. in physics)

• Formal mathematical analysis

• Experiments (laboratory and field)

• Field studies and surveys

2. Constructive research (solution of the problem is constructed)

• Conceptual development

• Technical development

3. Idiographic research (describes unique events, e.g. in history)

• Case study

• Action research

Note 1. Action research can be classified also as nomothetic research.

Note 2. The term “method” refers to more concrete research principles than the term “approach”.

Note 3. Each method can be used in different methodological approaches.



Scientific Methods

1. Nomothetic research (in natural sciences and engineering): the aim is to find general causal laws to explain phenomena, theories are usually axiomatic (deductive) systems or sets of models

2.Constructive research (in engineering): the solution of the problem is not only shown to exist but it is constructed [Pagels88]

3. Idiographic (ideographic) research trying to provide all possible explanations of a particular case, for example in history [Nagel79]

radio waves

receiver where

waves are collected

waves

converted into

electro signals

computerreceived as signal

6



Additional Scientific Methods

1. Action research (in social sciences): the problem is solved by certain actions whose consequences are evaluated and new actions are specified (iterative improvement, trial and error)

2.Case study (in social sciences): an in-depth, longitudinal examination of a single instance or event, which is called a case

3.Questionnaire study (in social sciences): a series of questions are used for the purpose of gathering information, which is usually analyzed statistically



What is Research All About: Problem and HypothesisWhat is Research All About: Problem and Hypothesis

• No general systematic deductive methods exist to discover hypotheses

7



Question and Answer – Related Terminology



Research and Development [Jain97]Research and Development [Jain97]

• research: discover new knowledge (i.e., promote understanding by finding regularities and dynamical explanations [Gell-Mann94])

• basic research (no specific application in mind)

• applied research (ideas into operational form)

• development: systematic use of the existing knowledge

• research and development are closely related

• in research a prototype is often developed

8



Science, Technology and Engineering [Jain97]

• science: “organized knowledge of the physical or material world gained through observation and experimentation”[Random House79]

• technology: the ways we provide ourselves with the material objects of our civilization, application of scientific knowledge for practical ends in engineering, medicine, agriculture, etc.

• natural sciences and engineering sciences differ in the object of study

• natural sciences (also called “science”, inc. physics, chemistry, and biology): objects in the nature

• engineering sciences: objects (products, services, methods) not found in the nature, using results of mathematics and natural sciences



Difference of science and Difference of science and

engineeringengineering

• In science we develop a theory for a phenomenon in nature

• In engineering we start from requirements (needs) and our aim is to have a product that fits these requirements, a theory is needed to explain and predict the performance

9



Scientific MethodScientific Method

• scientific method is “a method of research, in which a problem is identified, relevant data are gathered, a hypothesis is formulated [= discovery], and the hypothesis is empirically tested [= verification]”[Random House99]

• Problem is a question proposed for solution or discussion

• Hypothesis is a provisional theory suggested as a solution to the problem: either a causal relationship or correlation between variables (correlation does not imply causality).



Criteria for Scientific Work

10



Criteria for Scientific Theories [Barbour97], [Wilson99], [Pagels88]

1. Agreement with data

• Falsifiability (hypothetico-deductive method)

• Repeatability and reproducibility

2. Coherence or unity

• Internal and external coherence (deductive structure)

3. Generality

• Parsimony or economy (Occam’s razor to find the simplest theory)

4. Fertility

• New implied discoveries



Critics Are Our FriendsCritics Are Our Friends

• The aim of criticism is to show weaknesses and finally improve the quality of the work in international competition

• Without criticism we would always compete in the “province league”

• Do not prevent criticism although you may become angry because criticism hurts

• Criticism must be objective, impersonal, and pertinent

• Ideally you should show what should be improved and how

• Start and finish with encouragement

11



NoveltyNovelty and and ParadigmParadigm

Paradigm is an unquestioned theory or set of beliefs, existing world-view (concept introduced by T. Kuhn in 1962) [Honderich05]. Novel results outside the present paradigm are often rejected by the scientific community.



Choosing a Problem [Loehle90]

• For success in research you need right problem, right timing, right approach [Hamming93], difficulty of problem and its likely payoff (not too easy, not too difficult)

• opportunities for you

• other person is wrong (show what is right)

• contradictory experiments

• terminological confusion

• more experience needed to solve problems

• discussions (most new ideas are generated by talking with others [Jain97])

• experiments (start them early, use experimental-inductive approach)

• literature (find out existing knowledge)

12



Induction and DeductionInduction and Deduction

• Deductive reasoning (inference):

All humans are mortal (assumption or premise 1)

Socrates is human (assumption or premise 2)

⇒Socrates is mortal (conclusion)

In deduction the conclusions are implied by the premises. The truth is necessarily preserved. There is no new information in the conclusions that would not be in the premises.

• Inductive reasoning (inference):

All crows observed so far have been black (assumption or premise)

⇒All crows are black (conclusion)

In induction the truth is not necessarily preserved. The conclusions include more information than the premises.



Induction and DeductionInduction and Deduction

Explanation

Properties

Explanation 1

Properties

Induction Deduction

Explanation

Truth preservedTruth not necessarily preserved

Synthesis Analysis

13



Scientific and Mathematical InductionScientific and Mathematical Induction

Scientific induction

• Scientific induction presented on the previous slides is incomplete: truth is not necessarily preserved

• Scientific induction is based on regularity of the world (in time and space), thus generalizations and predictions are possible

• Scientific induction should not be mixed with mathematical induction

Mathematical induction:

• Mathematical induction is a special case of complete induction, used in mathematical proofs

• Complete induction is induction where all the special cases of the generalization are enumerated, actually a form of deduction [Niiniluoto83]



Abduction and Strong InferenceAbduction and Strong Inference

• Abduction and deduction are opposites

• Strong inference tries to simulate abduction

14



Summary: Types of reasoningSummary: Types of reasoning

• Deduction (top-down): if the premises are true, the conclusion must be true, i.e., deduction preserves the truth (often proceeds from general to particular)

• Induction (bottom-up): the conclusion, though supported by the premises, does not follow from them necessarily, i.e., induction does not in general preserve the truth (often proceeds from particular to general)

• abduction: inference to the best explanation, Russell’s chicken example

• strong inference: use several competing hypotheses and select the best one, i.e., approximate for of abduction

• statistical inference (Bayesian inference): Bayes’ theorem is used to infer the probability that the hypothesis is true



Analysis and Synthesis, Deduction and Induction [Random House99], [Hall62], [Honderich05]

• Analysis (reduction): Separating of any material or abstract entity into its constituent elements.

• Synthesis: Combining of the constituent elements or separate material or abstract entities into a single or unified entity.

• Deduction: A form of inference; if the premises are true, the conclusion must be true, i.e., deduction preserves the truth (equivalent to analysis).

• Scientific induction: a form of inference in which the conclusion, though supported by the premises, does not follow from them necessarily, i.e., induction does not necessarily preserve the truth (equivalent to synthesis).

15



Criticism on Criticism on InductivismInductivism [Niiniluoto83][Niiniluoto83]

• Induction does not preserve the truth as deduction does (Hume’s problem of induction)

• Strictly speaking, we cannot verify but only falsify hypotheses (Popper’s “solution” to the problem of induction).

• Often the new theory (hypothesis) is in conflict with the old theory and thus cannot be induced from the old theory (Kuhn’s revolutionary science).

• Hypothetico-deductive method is presently seen as the standard view of scientific reasoning. There are no systematic methods to find hypotheses.

• Induction is needed in everyday life (based on regularities in space and time).



Formation of Concepts and TheoriesFormation of Concepts and Theories

Synthesis

Analysis

• We learn by induction (bottom up, generalization from examples to models) [Felder88]

• We present theories by deduction (top down, from models to results)

16



Concept Formation [Niiniluoto02] (1)Concept Formation [Niiniluoto02] (1)



Concept Formation [Honderich05] (2)

• Classes of definitions

• Ostensive definition (“pseudodefinition”, elementary terms are explained by examples to avoid an endless loop of definitions (Locke), “blue is the color of the sky”) [Rosenberg00]

• Dictionary definition (meaning explained as established in a language, “compexity refers to the number of parts and their interconnections”)

• Stipulative definition (definition by agreement, “complexity is measured by size/weight, delay, energy, and cost”)

• A definition names a wider class to which something belongs and distinguishing properties (“triangle is a closed plane figure [wider class] having three angles and three sides [distinguishing properties]”)

17



Comparison of Theories Comparison of Theories

[Honderich05], [Rosenberg00][Honderich05], [Rosenberg00]



Taxonomy of Theories [Rosenberg00]Taxonomy of Theories [Rosenberg00]

1. Axiomatic systems – ideal form of theory

• theorems are derived deductively (= analysis) from definitions and axioms

• two forms: Hilbertian axiomatic systems in formal sciences (mathematics, logic, set theory, computer science) and hypothetico-deductive systems in empirical sciences

2.Theories based on sets of theoretical models

• results are derived by analysis (deduction) from the definitions and assumptions of the model

• usually used in science and engineering, for example ideal gas, Bohr model of atom

18



Theoretical Model and IMRAD Structure Theoretical Model and IMRAD Structure

[Day98], [Rosenberg00][Day98], [Rosenberg00]

Introduction

Materials and

methods

Results

Discussion

Primitive

terms

Definitions

Model and

assumptionsRules of

analysis

Results

Rules of

verification



Models Classified by Structure [Chestnut65]

1. Physical (iconic) model

• Mechanical model, scaling is used (miniature models)

• Globe, still photo, double helix (DNA), wind tunnel

2. Theoretical (symbolic) model

• Conceptual models (qualitative): block diagrams, flow charts

• Mathematical models (quantitative): Represented by mathematical and logical symbols

• May be deterministic, statistical or a combination (regressive)

• Boolean algebra, probabilistic models

3. Analog model

• Transformation of analog properties

• Often electronic circuits are used as models

19



Methods of Solution in a Mathematical Model [Chestnut65]

1. Analytic methods

• Deduction is used in a theoretical model, usually desirable since an exact answer is derived, useful for relatively simple cases

2. Numerical or deterministic methods

• Iterative procedures used to find optimum or worst case state, for example Newton method

3. Monte Carlo simulation methods (random or stochastic sampling)

• Simulations are only descriptive, model is not “solved” or “optimized”, optimization can be done by varying the parameters or configuration



Deduction and Reduction [Rosenberg00] (1)

20



Deduction and Reduction [Rosenberg00] (2)

Newtonian mechanics (Newton, 1684, 1687)

Terrestrial mechanics

(Galileo, 1638)

Celestrial mechanics

(Kepler, 1619-21)

Theory of relativity (Einstein, 1905, 1915)

D R

RDD R

D = deductionR = reduction

Observations

D RDR



Hypothetico-Deductive (HD) System: Newtonian Mechanics [Rosenberg00], [Young04], [Nagel79]

A. Primitive (undefined) concepts: Mass m, space (length s or d), and time t.

B. Definitions: Velocity v = ds/dt, acceleration a = dv/dt = d2s/dt2, and impulse p = mv, G is contant of gravity.

C. Axioms (assumptions): 1. Law of inertia (F = 0), 2. Relationship of force and acceleration (F = dp/dt = ma), 3. Law of action and reaction F1 = -F2

(equivalent to conservation of momentum p = mv), 4. Law of gravitation F= Gm1m2/d

2.

D. Theorems (consequences, some examples):

I. Galileo’s terrestrial mechanics: 1. Projectiles follow the path of a parabola. 2. Period of a simple pendulum is proportional to the square root of the length of the wire and is independent of the weight of the pop. 3. Law of free fall: all bodies fall with an acceleration that is constant and independent of their weights.

II. Kepler’s celestrial mechanics: 1. Orbits of planets are elliptical. 2. A line from the sun to a planet sweeps out equal areas in equal times. 3. The squares of the periods of planets are directly proportional to the cubes of the major axes of their orbits.

21



Axiomatic SystemsAxiomatic Systems



Taxonomy of Axiomatic Systems [Niiniluoto02]

Hilbertian axiomatic systems

• used on formal sciences

• no interpretation made

• the axioms are assumptions and initially the theorems arehypotheses or conjectures, which are proved by deriving them deductively from axioms

• examples: logic, arithmetics, geometry, set theory, probability theory

Hypothetico-deductive systems

• used in empirical sciences

• interpretations made

• initially axioms arehypotheses that are verified indirectly by comparing the results with reality

• examples: Newtonian mechanics, quantum mechanics, hypothetico-deductive systems are used also in biology and social sciences

22



Scientific explanations [Rosenberg00]

1. Deductive explanation (deductive-nomological)

• every explanation includes a deductive argument containing at least one deterministic law and is empirically testable

• laws in science may be 1) causal (cause-effect relations), 2) static (Boyle’s law) or 3) dynamic (Galileo’s law of falling bodies) [Nagel79]

2. Statistical explanation (inductive-statistical)

• statistical generalizations instead of strict laws (for example quantum mechanics, statistical mechanics, opinion survey)

Note. Free will does not have a scientific explanation that all scientists could accept. Teleological explanations based on final causes (purpose) are suspected in the analytical approach. Not all laws in deductive explanation are causal although causality may be behind the law.



Scientific Method [Honderich05], [Pagels88]Scientific Method [Honderich05], [Pagels88]

• Scientific method is divided into two parts:

• Discovery: divide and conquer (reduction), iterative improvement, empirical-inductive method, systems approach (next lecture)

• Verification: hypothetico-deductive method

• Modern scientific method by Galileo includes mathematical analysis (coherence of the theory) and experiments (correspondence with reality), in engineering the solution must also be practical (pragmatic)

• Best theories are deductive systems (either axiomatic systems or causal theoretical models)

• Methods of discovery are controversial (no systematic method of discovery exists)

23



Creativity: On the Edge of Order and ChaosCreativity: On the Edge of Order and Chaos

• You must have something on which to build (order, systematic work) and something to move (chaos, flexibility)

• Ways to improve creativity: analogies, symmetries, relations, extremes, opposites

• Working habits: well defined problem, quiet time (“lazyness”), new environment, mental barriers avoided [Loehle90]

Order Chaos

Creativity



Creativity Can Be ImprovedCreativity Can Be Improved

• Define your problem carefully.

• Respect criticism and follow the instructions. Avoid group think by criticism.

• Follow ethical rules. Emphasize integrity.

• Appreciate publicity. Do not accept wildcat business (“hämärä puuha”).

• Respect independence and accept dissimilarity (diversity).

24



Sources of Knowledge [Honderich05]

• deductive and inductive reasoning

• experience, observation and experiment, improved instruments [Derry99]

• analogy [Bohm87], [Feynman98]

• abduction (Russel’s chicken [Deutsch98]), intuition, imagination, dream (Descartes: reductionism [Wilson99], Kekulé: benzene [Hudson92], Mendeleyev: periodic system of elements [Strathern00])

• patterns and discrepancies in data, serendipity [Derry02]

• wild guess [Losee01], brainstorming [Davis97], telepathy, clairvoyance, precognition, etc.



Discovery: Solution Finding Methods [Pahl07]

1. Conventional: use something that exists (literature, trade publications, exhibitions, catalogues, patents, natural systems, technical systems (reverse engineering), analogies, measurements)

2. Intuitive: brainstorming, problem defined, free discussion on solutions, criticism avoided

3.Discursive: complementary to intuitive methods, step-by-step approach, classification is used

4.Combining solutions: divide problems, tasks and functions

25



Discovery: Solution Finding Methods [Pahl07]



Theory Construction in ScienceTheory Construction in Science

• The finding of the hypothesis is a nonlinear process (it can be made systematic only in very specific cases)

• There are several possible hypotheses that are good approximations of the reality (for example Aristotle/Ptolemy, Galileo/Kepler, Newton, Einstein)

26



System Design in EngineeringSystem Design in Engineering

• Natural object is replaced by requirements that describe user needs.

• The requirements describe the properties of the expected system. The system is verified against the requirements, but finally validated in the field tests (perhaps not all needs are not included in the requirements).



Methods of DiscoveryMethods of Discovery

• Traditional methods of problem solving [Pagels88]

• Divide and conquer (reduction, break a large problem into simpler subproblems and try to solve them, and finally combine the solutions)

• Iterative improvement (guess a solution and then try to improve it)

• Traditional methods need a complementary “holistic”method that is also called systems approach (example: working of an airplane)

27



Reductive Method of Discovery [Wilson99], [Pagels88]Reductive Method of Discovery [Wilson99], [Pagels88]

• Break the problem down and then generalize the results (“divide and conquer”)

• “Practical people often balk at this approach [reduction, idealizations] since the idealized situations may be so far removed from those of use as to appear highly academic.” [Wilson90]

• We present the results explicitly by deduction (top down), but we learn through induction (bottom up)



EmpiricalEmpirical--Inductive Method of Discovery Inductive Method of Discovery

[Kragh02][Kragh02]

Induction

Synthesis

Analysis

• Problem is divided into subproblems (this is called reduction)

• Hypothesis (system model) is derived by using experience (often analogies used).

28



Iterative Method of Discovery [Pagels88]Iterative Method of Discovery [Pagels88]

• You must work iteratively since the problem and hypotheses are initially not very clear (a chicken and egg problem)

• In the beginning it is difficult to understand the literature

• Experience is gained by own experiments and discussions

• Reporting and publishing will improve the quality of research



Iterative Method of DiscoveryIterative Method of Discovery


(literature review)




Problem


(literature review)




Problem


(literature review)




Problem

29



Reality and Theory [Wohlin00]Reality and Theory [Wohlin00]

• In engineering a hypothesis (defined in system specifications) is usually an idea of the relationship between the cause and effect (defined in system requirements)

• Theoretical model is always only an approximation of observation in real world (prototypes include tacit knowledge [Leppälä03], e.g., Stradivarius violin)



RealityReality

Prototype

Parts Performance xx x x x

l

T

T

l

Theoretical

Experimental

Pendulum

String

Bob (bullet)

T periodl length

30



TheoryTheory

System model

PartsPerformance (statistics) x

x x x x

l

T

T

l

Theoretical

Experimental

Simplependulum

Mass-less string

Mass point

T periodl length

glT /2=Theory:

Assumptions:- small amplitude- no friction

Definitions:g is gravitationalacceleration (9.81 m/s2)



Reality and TheoryReality and Theory

System model

PartsPerformance

(statistics) xx x x x

l

T

T

l

Theoretical

Experimental

Simple

pendulum

Mass-less string

Mass point

T periodl length

glT /2=Theory:

Assumptions:- small amplitude

- no friction

Definitions:

g is gravitationalacceleration (9.81 m/s2)

Reality Theory

31



Division of a System into Parts and Properties Division of a System into Parts and Properties ––

Different Views [Honderich05]Different Views [Honderich05]

• system is a combination of parts forming a unitary whole

• complexity refers to number of parts and their relations

System

Parts and

relations

Properties

(observations )

System

Parts and

relations

Properties

(observations)

Reduction

Causality

System

Parts and

relations

Properties

(observations )



CausalityCausality

• Scientific theories are deterministic and deductive (relativitytheory) or probabilistic (quantum theory) [Nagel79].

• Scientific theories describe (question how?) but do not strictly speaking explain (question why?).

32



Experiment (Set of Tests) [Wohlin00]Experiment (Set of Tests) [Wohlin00]

• Tests are either deterministic or statistical

• One independent variable is changed and other independent variables are set at a fixed level

• In this way the factors affecting the system can be determined



Example: Factors Affecting PerformanceExample: Factors Affecting Performance

• Reductionism helps you to test your models.

• Use reference data from simplified analysis or literature.

33



Einstein’s Hypothetico-Deductive Method [Baeyer05]



HD method: Example [Pagels88]

Tests suggested by Einstein

1. Shift in the orbit of planet Mercury

2. Bending of light around the limb of sun

3. Slowing down of clocks in a gravitational field

34



HypotheticoHypothetico--Deductive Method of Deductive Method of

VerificationVerification

• The scientific verification method is called hypothetico-deductive method [Honderich05]: the theoretical model acts as a hypothesis, which is verified indirectly by comparing the results given by the theoretical model with the corresponding

experimental results given by the reality. (Deduction ⇒ internal coherence, verification ⇒ correspondence with reality.)



Structure of Science [Barbour97]

ConceptsTheories

Predictions

ImaginationAnalogiesModels

Theoriesinfluence

observation

Deduction

ObservationData

Reality

Input

Measurement

Input

Theory world

Real world

35



Typical mistakes of a doctoral student

• Research problem is not defined properly and the work is too wide. The hypothesis is not concrete enough. The student does not discuss with people with different backgrounds to find new ideas.

• Existing literature is either not studied or all time is used for studying literature. The student either does not present intermediate results at all or concentrates on writing comprehensive technical reports.

• The student does not concentrate on own original results. The start of writing papers is postponed to wait for better times. The student is all the time publishing at low-level conferences where the requirements are modest and thus the possible problems are not noticed.



History of Scientific Method

• Galileo Galilei (1564-1642)

• Francis Bacon (1561-1626)

• René Descartes (1596-1650)

• Isaac Newton (1642-1727)

36



BriefBrief HistoryHistory of of ScientificScientific MethodMethod [Losee01][Losee01]

• c. 585 BC Regularity of nature, deduction (Thales of Miletus)

• c. 400 BC Dialectic method (Socrates)

• C. 350 BC Plato

• c. 320 BC Induction and deduction, axiomatic system, classification (Aristotle)

• c. 200 BC Library at Alexandria

• 1011 Experimental method (Ibn al-Haytham or Alhazen or Alhacen)

• 1327 Occam’s razor: principle of parsimony (William of Occam)

• 1620 Empirical-inductive method (F. Bacon, Novum Organum)

• 1637 Reductionism (R. Descartes, Discourse on Method)

• 1638 Modern scientific method (G. Galilei, Two New Sciences)

• 1660 Scientific society (Royal Academy of London)

• c. 1665 Hypothetico-deductive method (R. Boyle), the term later suggested byW. Whewell

• 1687 I. Newton, Principia

• 1739 Problem of induction and causality (D. Hume)



BriefBrief HistoryHistory of of ScientificScientific MethodMethod [Losee01][Losee01]

• 1837 W. Whewell, The History of the Inductive Sciences

• 1840 W. Whewell, The Philosophy of the Inductive Sciences

• 1856 Positivism (A. Comte)

• 1878 Abduction: inference to the best explanation (C. S. Peirce)

• 1927 Uncertainty principle (W. Heisenberg)

• 1929 Logical positivism or logical empirism (Vienna Circle)

• 1931 Incompleteness theorem (K. Gödel)

• 1934 K. Popper, The Logic of Scientific Discovery, originally in German, falsification

• c. 1945 Analytical philosophy of science (Carnap, Hempel, Nagel, Quine)

• 1946 Electronic computer (ENIAC)

• 1962 T. Kuhn, The Structure of Scientific Revolutions, paradigms

• 1964 Strong inference: competitive hypotheses (J. R. Platt)

• 1966 Tacit knowing (M. Polanyi)

• 1970 Actors approach (D. Silverman, A. Schutz)

• 1976 Supercomputer (S. Cray)

37



Conclusions (1)Conclusions (1)


(literature review)




System model(prototype)

Problem




38




• use bottom-up (“inductive”) approach in research, which is essentially a learning process

• use top-down (deductive) approach in technical documents (reviews, monographs), this will make the presentation compact and easy to follow for experts (use IMRAD structure)

• use bottom-up approach in teaching (tutorials, textbooks), and integrate results by using the top-down approach

• remember that a doctoral thesis is not a textbook (writing a textbook is a large challenge), write the thesis for experts in the field



Conclusions (4): Iterative research methodConclusions (4): Iterative research method

39



Conclusions (5): Theory and practiceConclusions (5): Theory and practice

• A good research project emphasizes theoretical results (usually system models) and uses prototypes for verification and validation of the new results



AbbreviationsAbbreviations

• AWGN, additive white Gaussian noise

• DNA, deoxyribonucleic acid

• ENIAC, Electronic Numerical Integrator and Computer

• HD, hypothetico-deductive

• IMRAD, introduction, materials and methods, results, and discussion

40



References (1)References (1)

• I. Arbnor and B. Bjerke, Methodology for Creating Business Knowledge, 2nd ed. Sage Publications, 1997.

• I. G. Barbour, Religion and Science: Historical and Contemporary Issues. HarperSanFrancisco, 1997.

• H. C. von Baeyer, Informaatio: Tieteen uusi kieli [Information: The New Language of Science]. Terra Cognita, 2005.

• L. von Bertalanffy, General System Theory. George Braziller, 1998.

• D. Bohm and F. D. Beat, Science, Order and Creativity. Bantam Books, 1987.

• H. Chestnut, Systems Engineering Tools. New York: Wiley, 1965.

• M. Davis, Scientific Papers and Presentations. Academic Press, 1997.

• R. A. Day, How to Write and Publish a Scientific Paper, 5th ed. OxyxPress, 1998.

• G. N. Derry, What Science is and How It Works, reprint. Princeton University Press, 2002

• D. Deutsch, Fabric of Reality. Penguin, 1998.




• R. M. Felder and L. K. Silverman, “Learning and teaching styles in engineering education,” Engineering Education, pp. 674-681, April 1988.

• R. P. Feynman, The Meaning of It All. Perseus Publishing, 1998.

• M. Gell-Mann, The Quark and the Jaguar. W. H. Freeman and Company, 1994.

• A. D. Hall, A Methodology for Systems Engineering. Princeton, NJ: D. Van Nostrand Company, 1962.

• R. W. Hamming, “You and your research,” IEEE Potentials, pp. 37-40, October 1993.

• Honderich (Ed.), The Oxford Companion to Philosophy, 2nd ed. Oxford Univ Press, 2005.

• J. Hudson, The History of Chemistry. Chapman and Hall, 1992.

• J. Iivari, “A paradigmatic analysis of contemporary schools of IS development,” European Journal of Information Systems, vol. 1, no. 4, 1991, pp. 249-272.

41




• R. K. Jain and H. C. Triandis, Management of Research and Development Organizations. John Wiley & Sons, 1997.

• H. Kragh, Quantum Generations: A History of Physics in the Twentieth Century, reprint ed. Princeton University Press, 2002.

• K. Leppälä et al., Professional Virtual Design of Smart Products. IT Press, 2003.

• C. Loehle, “A guide to increased creativity in research - inspiration or perspiration?” BioScience, vol. 40, pp. 123-129, February 1990, limnology.wisc.edu/courses/zoo955/publications/Wk04_Research/Loehle_1990_Guide_to_Creativity.pdf.

• J. Losee, A Historical Introduction to the Philosophy of Science, 4th ed. Oxford Univ Press, 2001.

• E. Nagel, Structure of Science: Problems in the Logic of Scientific Explanation. Hackett Pub Co, 1979.

• I. Niiniluoto, Johdatus tieteenteoriaan: Käsitteen- ja teorianmuodostus, 3rd ed. Otava, 2002.

• I. Niiniluoto, Tieteellinen päättely ja selittäminen. Otava, 1983




• H. Pagels, The Dreams of Reason. Simon and Schuster, 1988.

• G. Pahl, W. Beitz, J. Feldhusen, and K.-H. Grote, Engineering Design: A Systematic Approach, 3rd ed. Springer, 2007.

• A. Rosenberg, Philosophy of Science. Routlegde, 2000.

• Random House Webster’s Concise College Dictionary. New York: Random House, 1999.

• P. Strathern, Mendeleyev‘s Dream: The Quest for the Elements. Thomas Dunne Books, 2001.

• Suomalainen tietosanakirja, vol. 4. Weilin+Göös, 1990, p. 351.

• E. B. Wilson, An Introduction to Scientific Research, rev. ed. Dover Publications, 1990.

• E. O. Wilson, Consilience: The Unity of Knowledge. Random House, 1999.

• C. Wohlin et al., Experimentation in Software Engineering: An Introduction, Springer, 1999.

• H. D. Young and R. A. Freedman, Sears and Zemansky’s University Physics with Modern Physics, 11th ed. Pearson - Addison Wesley, 2004.

iv research methods: analytical approach aarne mämmelä drp-r... · requirements and therefore the...

Documents