The Principles of Experimental Research

The Principles of Experimental Research

1st Edition - December 1, 2005

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  • Author: K Srinagesh
  • eBook ISBN: 9780080497815

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The need to understand how to design and set up an investigative experiment is nearly universal to all students in engineering, applied technology and science, as well as many of the social sciences. Many schools offer courses in this fundamental skill and this book is meant to offer an easily accessible introduction to the essential tools needed, including an understanding of logical processes, how to use measurement, the do’s and don’ts of designing experiments so as to achieve reproducible results and the basic mathematical underpinnings of how data should be analyzed and interpreted. The subject is also taught as part of courses on Engineering statistics, Quality Control in Manufacturing, and Senior Design Project, in which conducting experimental research is usually integral to the project in question.

Key Features

* Covers such essential fundamentals as "definitions," "quantification," and standardization of test materials

* Shows students and professionals alike how to plan an experiment—from how to frame a proper Hypothesis to designing an experiment to accurately reflect the nature of the problem to "designing with factors."

* Includes a separate section on the use of Statistics in Experimental Research, including overview of probability and statistics, as well as Randomization, Replication and Sampling, as well as proper ways to draw statistical inferences from experimental data.


Undergraduate and first-year graduate students in most engineering disciplines taking required or optional course in “Design of Experiments,” “Senior Design Project,” “Capstone Design Project,” “Engineering Statistics,” and other course related to experimental research, data analysis and statistical inference.

Table of Contents

  • 1 Experimental Research in Science: Its Name and Nature
    1.1 Defining Science
    1.2 Science: Play or Profession
    1.3 Science and Research
    1.4 Varieties of Experimental Research
    1.5 Conventional Researchers
    1.6 Bibliography

    2 The Importance of Definitions
    2.1 Toward Definition
    2.2 Defining "Definition"
    2.3 Common Terms Used in Definitions
    2.4 Varieties of Definitions
    2.4.1 A. Direct and B. Indirect Definitions
    2.4.2 C. Informal and D. Formal Definitions
    2.4.3 E. Lexical and F. Stipulated Definitions
    2.4.4 G. Nominal and H. Real Definitions
    2.4.5 J. Definitions by Denotation
    2.4.6 K. Ostensive Definitions
    2.4.7 L. Definitions by Genus and Difference
    2.5 Need for Definitions
    2.6 What Definitions Should and Should Not Do
    2.7 References
    2.8 Bibliography

    3 Aspects of Quantification
    3.1 Quantity and Quality
    3.2 The Uses of Numbers
    3.3 An Intellectual Close-up of Counting
    3.4 The Process of Measurement
    3.5 Quantities and Measurements
    3.6 Derived Quantities
    3.7 Units for Measurement
    3.8 Fundamental Quantities and Dimensions
    3.9 Dimensional Analysis
    3.10 Accuracy versus Approximation
    3.11 Bibliography

    4 The Purpose and Principles Involved in Experimenting
    4.1 The Purpose of Experimenting
    4.2 Cause and Effect
    4.3 Pertinence and Forms of Cause
    4.4 Mill’s Methods of Experimental Inquiry
    4.4.1 Method of Agreement
    4.4.2 Method of Difference
    4.4.3 Joint Methods of Agreement and Difference
    4.4.4 Method of Residue
    4.4.5 Method of Concomitant Variation
    4.5 Planning for the Experiment
    4.6 Standardization of Test Material(s)
    4.7 Reproducibility
    4.8 Number of "Experiments"
    4.9 References
    4.10 Bibliography

    Part II: Planning the Experiments

    5 Defining the Problem for Experimental Research
    5.1 To Define a Problem
    5.2 Relation of the Problem to Resources
    5.3 Relevance of the Problem
    5.4 Extent of the Problem
    5.5 Problem: Qualitative or Quantitative?
    5.6 Can the Problem Be Reshaped?
    5.7 Proverbs on Problems
    5.8 At the beginning
    5.9 In Progress
    5.10 At the End
    5.11 References
    5.12 Bibliography

    6 Stating the Problem as a Hypothesis
    6.1 The Place of Hypothesis in Research
    6.2 Desirable Qualities of Hypotheses
    6.3 Bibliography

    7 Designing Experiments to Suit Problems
    7.1 Several Problems, Several Causes
    7.2 Treatment Structures
    7.2.1 Placebo
    7.2.2 Standard Treatment
    7.2.3 “Subject-and-Control” Group Treatment
    7.2.4 Paired Comparison Treatment
    7.2.5 Varying the Amount of One of the Two Factors
    7.3 Many Factors at Many Levels, but One Factor at a Time
    7.4 Factorial Design, the Right Way
    7.5 Too Many Factors on Hand?
    7.6 "Subjects-and-Controls" Experiments
    7.6.1 Varieties within Subjects and Controls: Paired Comparison
    7.6.2 Experiments with Humans
    7.7 Combined Effect of Many Causes
    7.8 Unavoidable (“Nuisance”) Factors
    7.9 Bibliography

    8 Dealing with Factors
    8.1 Designing Factors
    8.2 Experiments with Designed Factors
    8.3 Matrix of Factors
    8.3.1 More Than Three Factors
    8.4 Remarks on Experiments with Two-Level Factors
    8.5 Response of Multifactor Experiments
    8.6 Experiments with More Factors, Each at Two Levels
    8.7 Fractional Factorials
    8.8 Varieties of Factors
    8.8.1 Quantitative versus Qualitative Factors
    8.8.2 Random versus Fixed Factors
    8.8.3 Constant and Phantom Factors
    8.8.4 Treatment and Trial Factor
    8.8.5 Blocking and Group Factors
    8.8.6 Unit Factor
    8.9 Levels of Factors
    8.9.1 Levels of Quantitative Factors
    8.9.2 Levels of Qualitative Factors
    8.10 Bibliography

    9 Factors at More Than Two Levels
    9.1 Limitations of Experiments with Factors at Two Levels
    9.2 Four-Level Factorial Experiments
    9.2.1 Main Effects and Interactions
    9.3 Interactions
    9.4 Main Effects
    9.5 More on Interactions
    9.6 More Factors at More Than Two Levels
    9.6.1 Fractional Factorial with Three-Level Factors
    9.7 Bibliography

    Part III: The Craft Part of Experimental Research

    10 Searching through Published Literature
    10.1 Researcher and Scholar
    10.2 Literature in Print
    10.3 Overdoing?
    10.4 After the Climb
    10.5 Bibliography

    11 Building the Experimental Setup
    11.1 Diversity to Match the Need
    11.2 Designing the Apparatus
    11.2.1 Seeking Advice
    11.3 Simplicity, Compactness, and Elegance
    11.4 Measuring Instruments
    11.5 Calibration
    11.6 Researcher as Handyman
    11.7 Cost Considerations
    11.8 Bibliography

    Part IV: The Art of Reasoning in Scientific Research

    12 Logic and Scientific Research
    12.1 The Subject, Logic
    12.2 Some Terms in Logic
    12.3 Induction versus Deduction
    12.4 References
    12.5 Bibliography

    13 Inferential Logic for Experimental Research
    13.1 Inferential Logic and Experimental Research
    13.2 Logical Fallacies
    13.2.1 Fallacies of Ambiguity
    13.2.2 Fallacies of Irrelevance
    13.3 Argument
    13.3.1 Categorical Propositions
    13.3.2 Forms of Categorical Propositions
    13.3.3 Conventions, Symbolism, and Relations among Categorical
    13.4 Diagrammatic Representation of [AQ: Categorical?]Propositions
    13.5 Categorical Syllogisms
    13.5.1 Structures of Syllogisms
    13.5.2 Validity of Syllogisms
    13.5.3 Venn Diagrams for Testing Syllogisms
    13.6 Ordinary Language and Arguments
    13.7 References
    13.8 Bibliography

    14 Use of Symbolic Logic
    14.1 The Need for Symbolic Logic
    14.2 Symbols in Place of Words
    14.3 Conjunction
    14.4 Truth Tables
    14.5 Disjunction
    14.6 Negation
    14.7 Conditional Statements
    14.8 Material Implication
    14.9 Punctuation in Symbolic Logic
    14.10 Equivalence: "Material" and "Logical"
    14.10.2 Logical Equivalence
    14.11 Application of Symbolic Logic
    14.11.1 Ordinary Language to Symbolic Language
    14.12 Validity of Arguments
    14.13 Reference
    14.14 Bibliography

    Part V: Probability and Statistics for Experimental Research

    15 Introduction to Probability and Statistics
    15.1 Relevance of Probability and Statistics in Experimental Research
    15.2 Defining the Terms: Probability and Statistics
    15.2.1 Probability
    15.2.2 Statistics
    15.3 Relation between Probability and Statistics
    15.4 Philosophy of Probability
    15.5 Logic of Probability and Statistics
    15.6 Quantitative Probability
    15.6.1 Relative Frequency Theory
    15.7 Nature of Statistics
    15.8 Measures of Central Tendency (Average)
    15.8.1 Arithmetic Average (Sample Mean)
    15.8.2 Weighted Mean
    15.8.3 Median
    15.8.4 Mode
    15.9 Measures of Dispersion
    15.9.1 Range
    15.9.2 Mean Deviation
    15.9.3 Coefficient of Dispersion
    15.9.4 Standard Deviation
    15.10 Tabular Presentations of Statistical Data
    15.11 Grouping the Data
    15.12 Graphical Presentations of Data
    15.12.1 Histogram
    15.12.2 Frequency Polygon
    15.12.3 Cumulative Frequency Distribution
    15.13 Normal Distribution Curve
    15.14 Frequency Distributions That Are Not Normal
    15.15 References
    15.16 Bibliography

    16 Randomization, Replication, and Sampling
    16.1 Need for Randomization
    16.2 Applications of Randomization
    16.3 Methods of Randomization
    16.4 Meaning of Randomization
    16.5 Replication
    16.6 Samples and Sampling
    16.7 Notions of Set
    16.8 Permutations and Combinations
    16.8.1 Permutations
    16.8.2 Combinations
    16.9 Quantitative Statement of Randomization
    16.10 Sampling Methods
    16.10.1 Simple Random Sampling
    16.10.2 Cluster Sampling
    16.10.3 Stratified Sampling
    16.10.4 Systematic Sampling
    16.10.5 Multistage Sampling
    16.11 Bibliography

    17 Further Significance of Samples
    17.1 Inference from Samples
    17.2 Theoretical Sampling Distribution of X
    17.3 Central Limit Theorem
    17.4 Standard Normal Distribution
    17.5 Frequency Distribution and Probability Function
    17.6 Standard Normal Curve
    17.7 Questions/Answers Using the APSND Table
    17.8 Bibliography

    18 Planning the Experiments in Statistical Terms
    18.1 Guiding Principles
    18.2 Some Preliminaries for Planned Experiments
    18.2.1 Sample Size
    18.2.2 Minimum Acceptable Improvement
    18.3 Null and Alternate Hypotheses
    18.3.1 Null Hypothesis in an Experiment
    18.3.2 Alternate Hypothesis
    18.3.3 Risks Involved: a and b Errors
    18.3.4 Sample Mean X: Its Role in the Design
    18.3.5 Hypotheses Based on Other Parameters
    18.4 Accepting (or Rejecting) Hypotheses: Objective Criteria
    18.5 Procedures for Planning the Experiments
    18.5.1 Criterion Values
    18.6 Other Situation Sets
    18.7 Operating Characteristic Curve
    18.8 Sequential Experimenting
    18.9 Concluding Remarks on the Procedures
    18.10 Bibliography

    19 Statistical Inference from Experimental Data
    19.1 The Way to Inference
    19.2 Estimation (From Sample Mean to Population Mean)
    19.2.1 Interval Estimation
    19.2.2 Variations in Confidence Interval
    19.2.3 Interval Estimation of Other Parameters
    19.3 Testing of Hypothesis
    19.4 Regression and Correlation
    19.4.1 Regression Analysis
    19.4.2 Measuring the Goodness of Regression
    19.4.3 Correlation Coefficient
    19.5 Multiple Regression
    19.6 Bibliography

Product details

  • No. of pages: 432
  • Language: English
  • Copyright: © Butterworth-Heinemann 2005
  • Published: December 1, 2005
  • Imprint: Butterworth-Heinemann
  • eBook ISBN: 9780080497815

About the Author

K Srinagesh

Affiliations and Expertise

Professor, Mechanical Engineering, University of Massachusetts, Dartmouth

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