Integrated Scientific Method 

 is a model that will help you understand 
and teach the methods of science.
 
 


by Craig Rusbult, Ph.D.

Before you explore this page — which is a
condensed overview of the first half of my
Ph.D. dissertation that will help you understand
the complexity of scienceyou can focus on
the simplicity of science in my Introduction to
Scientific Method
.  It begins with a summary:

 
You can understand and enjoy the adventure of science, because the thinking used in science is not strange and mysterious, it's the same thinking you use in daily life.  In scientific logic, as in daily life, you use REALITY CHECKS to decide whether "the way you think the world is" matches "the way the world really is."  In the central activity of modern scientific method, OBSERVATIONS (from an Experiment) and PREDICTIONS (based on a Theory) are compared in a REALITY CHECK that is one test of quality for this Theory.
 

what it is:
Integrated Scientific Method (ISM) is a model of scientific action.
It is a synthesis of ideas — mainly from scientists and philosophers,
but also from sociologists, psychologists, historians, and myself — that
describes the activities of scientists: what they think about and what they do.
It shows how the mutually supportive skills of creativity and critical thinking
are intimately integrated in the problem-solving methods used by scientists.

and
what it is not
:
Because I agree with the consensus of scholars that no single "method" is used
by all scientists at all times, I am not trying to define The Scientific Method.
Therefore, it's more accurate (and more useful) to view ISM
as a roadmap that shows possibilities for creative wandering,
not a rigorous flowchart for describing a predictable sequence.

What is it for?

Scientific Method in Education

Integrated Scientific Method describes what scientists do, but its main function is to improve science education, to help students learn scientific method — what it is and how they can do it.

Practical Applications in Science Education
In all schools — public, private, and home, from K-12 through college — teachers are wondering whether "scientific method" exists and how scientific thinking skills can be taught in the classroom.  Teachers want to offer a quality education that includes science concepts and also thinking skills, so students will be motivated to think and will learn how to think more often and more effectively, with enthusiasm and skill.  A variety of questions — about scientific method and much more — are explored in the area for thinking skills education, and you can see what's available in an overview-sitemap for pages that include An Introduction to Design Method for Problem Solving in Education & Life and Developing an Education Curriculum for Thinking Skills and Problem Solving and pages by other authors about problem solving by using design method & scientific method.

Visual Exploration:
Study the two ISM-diagrams below (simplified & full) and
think about the visual symbolism in the shapes & colors, arrows & words.
(if you want, click on any part of the diagram to learn about it)
And I suggest opening this page in a new window (by clicking this link) so,
by re-sizing and moving the windows, you can look at the diagram(s) while
you're reading the text, to help you combine the visual & verbal information.


   Scientific Method - Overview


spacer gif (blank, with no content)

spacer gif (blank, with no content)Scientific Method
 

      Here is a quick summary of ISM (a model for Integrated Scientific Method), focusing on the symbolism for the shapes and colors you see above:
      In the eight ovals are major activities of science:  generate and evaluate actions, generate and evaluate theories, generate and evaluate experiments, do thought-experiments and physical experiments.
      Comparing the results of a mental experiment and physical experiment produces Hypothetico-Deductive logic (combining yellow-and-green information, predictions and observations) in the two H-D boxes.
      Three types of evaluation criteria (light blue boxes) influence theory evaluation (blue oval).
      The intimate connections between generation (red) and evaluation (blue) are symbolized by purple (red plus blue makes purple*) as a reminder of the continual interplay between creative thinking and critical thinking to make productive thinking.   {*with pigments}
      The activities of scientists are motivated-and-guided by goals (gold).

Different parts of the visual representation of ISM (above) have
links (find them by running your mouse around the diagram) taking
you to different parts of the verbal representation of ISM (below)
which contains nine sections that — since they are not "steps in
a fixed process" — can be explored in any order you want:

1. Hypothetico-Deductive Logic, and
    Empirical Factors in Theory Evaluation
2. Conceptual Factors in Theory Evaluation
3. Cultural-Personal Factors in Theory Evaluation
4. Theory Evaluation    and    5. Theory Generation
6. Experimental Design (Generation-and-Evaluation)  
7. Problem-Solving Projects  
8. Thought Styles  
9. Mental Operations

In the text that follows,
bold red print is used for an element in the ISM-diagram, and
non-bold red print shows concepts that are not in the diagram.

an integrative model of Scientific Method

1. Hypothetico-Deductive Logic, and
Empirical Factors in Theory Evaluation

        This tour of ISM (Integrated Scientific Method) begins with hypothetico-deductive logic, the foundation for modern science that provides a "reality check" to guide the invention, evaluation, and revision of theories.
        In ISM an experimental system (for a controlled experiment or field study) is defined as everything involved in an experiment, including what is being studied, what is done to it, and the observers (which can be human or mechanical).  When a physical experiment is done with the experimental system, observation detectors are used to obtain observations.
        A theory is a humanly constructed representation intended to describe and/or explain the observed phenomena in a specified domain of nature.  By using a general domain-theory (which is claimed to be valid for all experimental systems in a domain, and involves a theory plus a foundation of supplementary theories) combined with a specific system-theory (about the characteristics of one experimental system), scientists construct an explanatory model that is a simplified representation of the system's composition (what it is) and operation (what it does).  After defining an explanatory model (for composition-and-operation, made by applying a general domain-theory to a specific experimental system that has been characterized by a system-theory), a thought experiment can be done by asking, "IF this model is true, THEN what will occur?", thereby using deductive logic to make predictions.
        Or, based on a descriptive model that is limited to observable properties and their relationships, scientists can make predictions by using inductive logic, by making a deductive generalization that "IF this situation is similar (or identical) to previous situations, THEN we should expect a result that is similar (or identical)."
        Usually, predictions (and evaluations) are based on logic that is both deductive and inductive.

Most of this page was written in 1997.  In 2006, I said, in a section about "Designing of Scientific Theories" in An Introduction to Scientific Method,
      In their daily work, scientists rarely design large-scale generalized mega-theories, such as the theories of gravity, invariance, or evolution developed by Newton, Einstein, or Darwin.  Instead, they typically are applying generalized theories that already are accepted, in their study of particular experimental systems for which they are designing small-scale specialized sub-theories.
      Even more commonly, scientists design experiments (Part 6) based on mega-theories and sub-theories that already are accepted, with the goal of simply making observations so they can learn more about nature.

        The dual-parallel shape of the hypothetico-deductive "box" (whose 4 corners are defined by the model and system, predictions and observations) symbolizes two parallel relationships.  The left-side process (done by mentally running a theory-based model) parallels the right-side process (done by physically running a real-world experimental system).  There is also a parallel between the top and bottom of the box.
      At the top, a hypothesis is a claim that the model and system are similar in some respects and to some degree of accuracy.
      At the bottom of the box is a logical comparison of predictions (by the model) and observations (of the system);  this comparison is used to evaluate the hypothesis, based on the logic that the degree of agreement between predictions and observations usually is related to the degree of similarity between model and system.  But a theory can be false even if its predictions agree with observations, so it is necessary to supplement this "agreement logic" with another criterion, the degree of predictive contrast, by asking "How much contrast exists between the predictions of this theory and the predictions of plausible alternative theories?" in an effort to consider the possibility that two or more theories could make the same correct predictions for this system.
        Estimates for these two evaluation criteria (degree of agreement and degree of predictive contrast) are combined to form an empirical evaluation of current hypothesis.  This evaluation and the analogous empirical evaluations of previous hypotheses (that are based on the same theory as the current hypothesis, so both previous-and-current can be used to evaluate this theory) are empirical factors that influence evaluation of the theory.     ISM-diagrams    details for Part 1 and more
2. Conceptual Factors in Theory Evaluation
        In ISM the conceptual factors that influence theory evaluation are split into internal characteristics and external relationships.
        Scientists expect a logical internal consistency between a theory's own components.  And when evaluating a theory's logical structure, one common criteria is simplicity, which is achieved by postulating a minimum number of logically interconnected theory-components.  Also, in each field of science there are expectations for the types of entities and actions that should (and should not) be included in a theory.  These "expectations about components" can be explicit or implicit, due to scientists' beliefs about ontology (what exists) or utility (what is useful).
        The external relationships between theories (including both scientific and cultural-personal theories) can involve an overlapping of domains or a sharing of theory components.  Theories with domains that overlap are in direct competition because they claim to explain the same systems.  Theories with shared components often provide support for each other, and can help to unify our understanding of the domains they describe.  There is some similarity between the logical structures for a theory (composed of smaller components) and for a mega-theory (composed of smaller theories), and many conceptual criteria can be applied to either internal structure (within a theory) or external relationships (between theories in a mega-theory).    details for Part 2 and more
3. Cultural-Personal Factors in Theory Evaluation
        During all activities of science, including theory evaluation, scientists are influenced by cultural-personal factors.  These factors include psychological motives and practical concerns (such as intellectual curiosity, and desires for self esteem, respect from others, financial security, and power), metaphysical worldviews (that form the foundation for some criteria used in conceptual evaluation), ideological principles (about "the way things should be" in society), and opinions of authorities (who are acknowledged due to expertise, personality, and/or power).
        These five factors interact with each other, and operate in a complex social context that involves individuals, the scientific community, and society as a whole.  Science and culture are mutually interactive, with each affecting the other.  The effects of culture, on both the process of science and the content of science, are summarized at the top of the ISM diagram: "scientific activities... are affected by culturally influenced thought styles."   8. Thought Styles
        Some cultural-personal influence is due to a desire for personal consistency between ideas, between actions, and between ideas and actions.  For example, scientists are more likely to accept a scientific theory that is consistent with their metaphysical and ideological theories.  In the diagram this type of influence appears as a conceptual factor, external relationships... with cultural-personal theories.
        All of these cultural-personal factors vary in different areas of science and in communities within each area, and for different individuals, so the types and amounts of resulting influences (on the process of science and the content of science) vary widely. {debates about cultural-personal factors & thought styles}     ISM-diagrams    details for Part 3 and more
4. Theory Evaluation
        A theory is evaluated in association with supplementary theories, and relative to alternative theories.  Inputs for evaluating a theory come from evaluation factors (empirical & conceptual & cultural-personal), with the relative weighting of factors varying from one science-situation to another.  The immediate output of theory evaluation is a theory status that is an estimate of a theory's plausibility (whether it seems likely to be true) and/or usefulness (for stimulating scientific research or solving problems).  Based on their estimate of a theory's status — which can be anywhere along a range from very low to very high, since in ISM the word "theory" doesn't imply that support is weak or strong — scientists can decide to retain this theory with no revisions, revise it to generate a modified (new?) theory, or reject it.  When a theory is retained after evaluation, its status can be increased, decreased, or unchanged.  A theory can be retained for the purpose of pursuit (to serve as a basis for further research) and/or acceptance (as a proposed explanation, for being treated as if it were true).  According to formal logic it is impossible to prove a theory is either true or false, but scientists have developed analytical methods that encourage them to claim a "rationally justified confidence" for their conclusions about status.  Each theory has two types of status: its own intrinsic status, and a relative status that is defined by asking "What is the overall appeal of this theory compared with alternative theories?"    details for Part 4 and more
5. Theory Generation
        Generating a theory can involve selecting an old theory or, if necessary, inventing a new theory.  The process of inventing a new theory usually occurs by revising an existing "old theory."   Some strategies for invention are:  split an old theory into components that can be modified or recombined in new ways;  borrow components (or logical stru cture) from other theories;  generalize an old theory, as-is or modified, into a new domain;  or apply the logic of internal consistency to build on the foundation of a few assumed axiom-components.  Often, a creative analysis of data (to search for patterns) is a key step in constructing a theory.
        Theory generation is guided by evaluation factors that are cultural-personal, conceptual, and empirical.  There is a close relationship between the generation and evaluation of a theory.  { Similarly, the generation and evaluation of an action (such as designing or executing an experiment) are closely related. }
        Empirical guidance is used in the creative-and-critical process of retroduction — a thinking strategy in which the goal is to generate (to propose by selection or invention) a theory whose predictions will match known observations.  If there is data from several experiments, retroduction can aim for a theory whose predictions are consistent with all known data.  During retroduction a scientist, curious about puzzling observations and motivated to find an explanation, can adjust either of the two sources used to construct a model: a general domain-theory (that applies to all systems in a domain) and a specific system-theory (about the characteristics of one system).  Usually, a scientific "inference to the best explanation" involves a creative use of logic that is both inductive and deductive.
        With retroduction or hypothetico-deduction (which are similar, except that in retroduction a model is proposed after the observations are known), similar logical limitations apply.  Even if a theory correctly predicts the observations, plausible alternative theories might make the same correct predictions, so with either retroduction or hypothetico-deduction there is a cautious conclusionIF system-and-observations, THEN MAYBE model (and theory).  This caution contrasts with the definite conclusion of deductive logic:  IF theory-and-model, THEN prediction.       ISM-diagrams    details for Part 5 and more
6. Experimental Design (Generation-and-Evaluation)
        In ISM an "experiment" is defined broadly to include both controlled experiments and field studies.  Three arrows point toward generate experiment, showing inputs from theory evaluation (which can motivate-and-guide a designing of experiments), gaps in system-knowledge (that can be filled by experimentation, and provide motivation) and "do thought experiments..." (to facilitate the process of design).  The result of experimental design (which combines generating an experiment with evaluating an experiment) is a "real-world experimental system" that can be used for hypothetico-deductive logic.
        Sometimes experiments are done just to see what will happen, but an experiment is often designed to accomplish a specific goal.  For example, an experiment (or a cluster of related experiments) can be done to gather information about a system or experimental technique, to resolve anomaly, to provide support for an argument, or to serve as a crucial experiment that can distinguish between competing theories.  To facilitate the collection and interpretation of data for each goal, logical strategies are available.  When using these strategies, scientists can think ahead to questions that will be raised during evaluation, regarding issues such as sample size and representativeness, the adequacy of controls, and the effects of random errors and systematic errors.
        Often, new opportunities for experimenting (and theorizing) emerge from a change in the status quo.  For example, opportunities for field studies may arise from new events (such as an ozone hole) or new discoveries (of old dinosaur bones,...).  A new theory may stimulate experiments to test and develop the theory, or to explore its application for a variety of systems.  Or a new observation technology may allow new types of experimental systems.  When an area of science opens up due to any of these changes, opportunities for research are produced.  To creatively take advantage of these opportunities requires an open-minded awareness that can imagine a wide variety of possibilities.
        Thought-experiments, done to quickly explore a variety of possibilities, can help scientists evaluate potential experimental systems and decide which ones are worthy of further pursuit with physical experiments that typically require larger investments of time and money.
        Thought-experiments play a key role in three parts of ISM: in experimental design, retroduction, and hypothetico-deduction.  In each case a prediction is produced from a theory by using deductive logic, but there are essential differences in timing and objectives.  And sometimes mental experiments are done for their own sake, to probe the implications of a theory by deductively exploring systems that may be difficult or impossible to attain physically.    details for Part 6 and more
7. Problem-Solving Projects
        The activities of science usually occur in a context of problem solving, which can be defined as "an effort to convert an actual current state into a desired future state" or, more simply, "converting a NOW-state into a GOAL-state."  If the main goal of science is knowledge about nature, the main goal of scientific research is improved knowledge, which includes observations of nature and interpretations of nature.  Before and during problem formulation, scientists prepare by learning (through active reading and listening) the current now-state of knowledge for a selected area, including observations, theories, and experimental techniques.  Critical evaluation of this now-state may lead to recognizing a gap in the current knowledge, and imagining a potential future state with improved knowledge.  When scientists decide to pursue a solution for a science problem (characterized by deciding what to study and how to study it) this becomes the focal point for a problem-solving project.
        Problem formulation — by defining a problem that is original, significant, and can be solved using available resources — is an essential activity in science.  During research a mega-problem (the attempt by science to understand all of nature) is narrowed to a problem (of trying to answer specific questions about one area of nature) and then to sub-problems and specific actions.  In an effort to solve a problem, scientists generate, evaluate, and execute actions that involve observation (generate and do experiments, collect data) or interpretation (analyze data, generate and evaluate theories);  action generation and action evaluation, done for the purpose of deciding what to do, when, and how, is guided by the goal-state (which serves as an aiming point in searching for a solution) and by an awareness of the constantly changing now-state.  Evaluation of actions [or theories] can involve persuasion that is internally oriented (within a research group) or externally oriented (to convince others).     ISM-diagrams    details for Part 7 and more
8. Thought Styles
        All activities in science, mental and physical, are affected by thought styles that are influenced by cultural-personal factors, operate at the levels of individuals and sub-communities and communities, and involve both conscious choices and unconscious assumptions.  A collective thought style includes the shared beliefs, among a group of scientists, about "what should be done and how it should be done."
      Thought styles affect the types of theories generated and accepted, and the problems formulated, experiments done, and techniques for interpreting data.  There are mutual influences between thought styles and the procedural "rules of the game" that are developed by a community of scientists, operating in a larger social context, to establish and maintain certain types of institutions and reward systems, styles of presentation, attitudes toward competition and cooperation, and relationships between science, technology and society.  Decisions about which problem-solving projects to pursue — decisions (made by scientists and by societies) that are heavily influenced by thought styles — play a key role in the two-way interactions between society and science by determining the allocation of societal resources (for science as a whole, and for areas within science, and for individual projects) and the returns (to society) that may arise from investments in scientific research.
      Thought styles affect the process and content of science in many ways, but this influence is not the same for all science, because thought styles vary between fields (and within fields), and change with time.    details for Part 8 and more
9. Mental Operations
        The mental operations used in science can be summarized as "motivation and memory, creativity and critical thinking."  Motivation inspires effort.  And memory — with information in the mind or in "external storage" such as notes or a book or a computer file — provides raw materials (theories, experimental techniques, known observations,...) for creativity and critical thinking.  At its best, productive thinking (in science or in other areas of life) combines knowledge with creative/critical thinking.  Ideally, an effective productive thinker will have the ability to be fully creative and fully critical, and will know, based on logic and intuition, what blend of cognitive styles is likely to be productive in each situation.     ISM-diagrams    details for Part 9 and more
 

note:  Unfortunately, there is a lack of consistency in the
terms used to describe scientific method.  Some terms have
many meanings, and some meanings are known by many names.
This makes precise communication difficult, but in ISM I have tried
to be internally consistent and (to the extent this is possible) also
externally consistent with commonly used terms and meanings,
as discussed in Terminology: Coping with Confusion.

For a discussion of ISM that is more complete and precise,
you can read A Detailed Examination of Scientific Method
which is a condensed version (but much less condensed
than in this page) of the half of my PhD dissertation.
{ Developing ISM was the first part of my PhD project;
using it for instructional analysis was the second part. }

This "details" page also includes references for
some sources (and stimulaters) of the ideas in ISM,
such as Ronald Giere's "box" for hypothetico-deductive logic.

  

This website for Whole-Person Education has TWO KINDS OF LINKS:
an ITALICIZED LINK keeps you inside a page, moving you to another part of it, and
 a NON-ITALICIZED LINK opens another page.  Both keep everything inside this window, 
so your browser's BACK-button will always take you back to where you were.

 

An Introduction to Scientific Method
based on logical Reality-Checks to test
whether "the way you think it is"
matches "the way it really is."


Strategies for Problem Solving:
Using Creativity and Critical Thinking in
 SCIENCE, DESIGN, and EDUCATION 

Teaching Scientific Method in Education

An Introduction to Design Method
how to design a product, strategy, or theory
(this includes almost everything we do in life!)

Motivations (and strategies) for Learning
goal-directed personal motives for learning;  teamwork;
how a friend learned to weld, and how I didn't learn to ski

Aesop's Activities for Goal-Directed Education
a creative coordinating of goals and activities will
help students gain experience and learn from it


The area of THINKING SKILLS has sub-areas of
CREATIVE THINKING in Education and Life
CRITICAL THINKING in Education and Life

PROBLEM SOLVING in Education and Life


 

This page, written by Craig Rusbult (copyright © 1997, all rights reserved), is
http://www.asa3.org/ASA/education/think/science.htm