The document defines and describes 13 processes involved in scientific thinking: observation, measurement, classification, quantification, inferring, predicting, relationships, communication, interpreting data, controlling variables, operational definitions, hypothesizing, and experimenting. It provides examples and explanations of each process, emphasizing their analytical and systematic nature. Key aspects include objectively gathering data, identifying patterns and associations, reducing systems to interacting variables, and systematically evaluating hypotheses through experimentation.

1. 1. Observation
This is the most fundamental of all of the processes. Observation may be defined as
the gathering of information through the use of any one, or combination of the five
basic senses; sight, hearing, touch, taste, and smell.
The term observation may also be used to express the result of observing. In other
words one might observe and, as a result, gather observations. These observations can
also be called data or facts.
Observation should suggest objectivity as opposed to the expression of opinion. For
example, "John is a bad boy" is not an observation. On the other hand, "John exhibits
behavior that we characterize as bad" is an observation. "John is throwing Mary out of
the window" is also an observation.
Skilled observers seem to proceed from general perceptions of a system to more
specific ones so the nature of skilled observing can be thought of as analytical.
Systems are first observed as a whole then analyzed for subsystem information.
Subsequently, subsystems can be treated as a whole and subjected to further analysis
in an ever tightening spiral. Technology can be used to amplify the senses, which
provides for even more analysis. A microscope, for example, is a technology that
allows us to see things that are too small to be seen with the unaided eye.
In summary, observation is an objective process of gathering data through the use of
one's senses applied in an analytical way.
2. Measurement
Measurement is an observation made more specific by comparing some attribute of a
system to a standard of reference. An example is when the length of an object is
expressed in terms of the length of a meter or when the mass of an object is expressed
by referring to a standard such as a gram. Measurement and observation are the only
process skills that are actually two forms of the same thing.
There are many standards that can be employed to make observations more precise.
For instance, academic scholarship can be expressed as a grade. When one receives an
"A" or a "C" in a course one's performance has been measured relative to a standard.
In a similar fashion, a four star restaurant is a measure of quality.
As one can see from these examples, a measurement can range from highly concrete
and universal to rather conditional. Observing that a stick is 27 centimeters long
requires little interpretation. The meaning is rigid and understood by anyone,

2. anywhere who is familiar with the metric system. On the other hand, being an "A"
student may require considerable interpretation with meaning highly dependent upon
circumstance. And, of course, with respect to restaurants, "Charlie's Four Star Chili
Dog Heaven" may be just that to some.
The nature of this process entails the description of some system attribute by
comparison to a standard of reference.
3. Classification
Classification is the process of grouping objects on the basis of observable traits.
Objects that share a given characteristic can be said to belong to the same set. The
process is somewhat arbitrary depending upon the identifying trait selected.
This is an important process to science because of an underlying assumption that
kinship in one regard may entail kinship in others. Science assumes that to a large
degree the universe is consistent with it's laws holding true everywhere. Therefore, if
a set of objects share one thing in common they may well share other attributes.
Also there is the notion of realness or depth. This means that the more characteristic a
trait is of a particular system the closer the kinship of those sharing the trait. For
example, consider the idea of a marble. What makes a marble a marble? Is color a
fundamental component of being a marble? We could, of course, classify objects on
the basis of color but is that a deep characteristic? Because some marbles are red does
it follow that all red objects are marbles? The issue here is that some traits are more
expressive of the essence of the system than are other shared traits. In most instances
we should seek to classify on the basis of traits that are essential to the idea of the set.
The nature of the skill of classification is two fold. First, one must be able to identify
traits and, second, one must select traits that express the deeper essence of the system.
4. Quantification
Quantification refers to the process of using numbers to express observations rather
than relying only on qualitative descriptions.
The process has two major values. First, by expressing something in numerical terms
the need for translation of verbal meaning is reduced. Second, the use of numbers
allows mathematical logic to be applied to attempts to explore, describe and
understand nature.

3. For example, consider a situation where one might try to describe the various hair
colors of students in a classroom. Try making an accurate and complete description
using only qualitative terms. At best we might develop groupings based on generic
names such as brunette and blonde (I am sure you will recognize these as an example
of classification, as described above). The problem we must deal with is that terms
such as brunette and blonde are not absolute. Some brunettes are obviously darker
than others and some blondes are clearly lighter than others and we need a scheme
that will allow us to express such variation. Numbers will allow us to do that. For
example, suppose Sally's hair is the darkest and Jeff's is the lightest. If we assigned a
number such as 10 to Sally and 1 to Jeff a range has been developed within which all
other shades must fall. Incidentally, the range could be reversed with Sally being
assigned the 1 and Jeff the 10. It really doesn't matter and the scheme would work just
as well. Either way, by defining color as a number the arithmetic logic of sequencing
can be applied to the problem. In so doing, we find that all observers of hair color are
playing by the same rules. Everyone is accepting the quantitative logic so that there is
no question that haircolor #7 must fall somewhere, probably midway, between #6 and
#8. This leaves a lot of room for describing very subtle differences. For instance, we
can have some idea of the color difference between a 6.9 and a 7.2 but try describing
that difference in qualitative terms.
Consequently the nature of the skill of quantification is one of application where one
seeks precision of expression by transferring the logic of mathematics to qualitative
problems.
5. Inferring
Inferring is an inventive process in which an assumption of cause is generated to
explain an observed event. This is a very common function and is influenced by
culture and personal theories of nature.
Inferences can also influence actions. For example, suppose two students receive a
poor grade on some project. One student observes the poor grade and infers that the
reason he received it was because the teacher does not like him. The second student
infers that he did not spend enough time on the project. Would you expect these two
students to respond to the poor grade in the same way? In both cases the event was the
same but different inferences about the cause of the event would likely lead to very
different responses.
The nature of this process is inventive within the parameters of cosmology and
culture.
6. Predicting

4. This process deals with projecting events based upon a body of information. One
might project in a future tense, a sort of trend analysis, or one might look for an
historical precedent to a current circumstance. In either case, the prediction emerges
for a data base rather than being just a guess. A guess is not a prediction. By
definition, predictions must also be testable. This means that predictions are accepted
or rejected based upon observed criteria. If they are not testable they are not
predictions.
It is not unusual to find that a data base is not available for a particular system. In such
cases predictions about that system are not possible. The first step in understanding
such a mystery system would be to observe it as objectively as possible with the goal
being to acquire the data base necessary to develop predictions.
The nature of the skill of predicting is to be able to identify a trend in a body of data
and then to project that trend in a way that can be tested.
7. Relationships
The process skill of relationships deals with the interaction of variables. This
interaction can be thought of as a kind of influence--counter influence occurring
among a system's variables.
Relationships can occur in multiple or single dimensions. An example of a multiple
dimension relationship is speed with distance and time representing the two
dimensions. Single dimension relationships can only be expressed relative to
something else as in the location in space of some object. It's location can only be
expressed with relative terms such as over, under, near, far, etc.
Of course the notion of relationships can be extended into more abstract areas such as
values, friendships, marriage, love, and growth, for examples.
The inherent nature of this skill is that it requires analytical thought in which one
seeks to dissect cause from effect. The causal elements are the system's variables and
the effect is the resulting interaction.
8. Communication
This process actually refers to a group of skills, all of which represent some form of
systematic reporting of data. The most common examples include data display tables,
charts and graphs. The process is conceptually fairly simple and is frequently based
upon some type of two or three dimensional matrix with the axes representing the
system variables and the cells of the matrix representing the interactions.

5. The purpose of the communication skills is to represent information in such a way that
the maximum amount of data can be reviewed with an eye toward discovering
inherent patterns of association.
The inherent nature of this process skill involves the ability to see and, consequently,
represent information as the interplay among influencing variables.
9. Interpreting data
This process refers to the intrinsic ability to recognize patterns and associations within
bodies of data. Obviously there is a direct contribution of the previous process,
communication, to interpreting data. The better the data is represented the more likely
one will detect associations within the data.
Interpretation probably requires creative thinking that results in the invention of
conceptual umbrellas that can encompass the data.
10. Controlling variables
This process is also a kind of group process because one may engage in several
different behaviors in an attempt to control variables. In general, this skill is any
attempt to isolate a single influent of a system so that it's role can be inferred. The
process is an attempt to achieve a circumstance or condition in which the impact of
one variable is clearly exposed. The use of experimental and control circumstances,
standardizing procedures and repeated measures are only a few of the ways in which
variables might be controlled.
Understanding the nature of the skill requires analytical thinking in which the system
under study can be reduced to a set of interacting components. The next step is to
establish some circumstance that allows the scientist to observe one component in
isolation.
11. Operational definitions
An operational definition is one that is made in measurable, or observable terms. An
operational definition should not require interpretation of meaning nor is it relative.
The meaning of the defined term must be explicit and limited to the parameters
established for the definition.
An operational definition is primarily a research tool and related to the concern for
controlling variables. The major function of operational definitions is to establish the

6. parameters of an investigation or conclusion in an attempt to gain a higher degree of
objectivity.
Consider this example. An investigator suggests that by applying some treatment a
class of students will become more intelligent. The problem here lies with the word
intelligent. What does it mean? And, more to the point, what does the investigator
mean with the word? In order to evaluate the treatment intelligence must be defined in
a very clear way. Perhaps, in this case, defining intelligence as a score on an IQ test
makes sense. Such a definition (intelligence = IQ score) would be an excellent
example of an operational definition.
In terms of the nature of the skill, we are again dealing with analytical issues. An
individual who is skillful a making operational definitions is one who can engage in
reductionistic thinking that defines phenomena as a collection of components which
interact.
12. Hypothesizing
Hypothesizing is, again, an intrinsic and creative mental process rather than a more
straight forward and obvious behavior. Consequently, developing this ability is
probably less a product of linear training but more a function of intuitive thinking that
emerges from experience.
Defined, an hypothesis in a response, or potential solution, to a specific research
question, or problem. For our purposes we will insist upon a rather rigid use of the
term and will restrict it to the second step in the classical scientific algorithm as
outlined in the next process.
The kind of hypothesis one produces is also heavily dependent upon one's world view.
For instance consider the individual whose world view is based upon
anthropomorphic and supernatural beliefs. This person is likely to develop
anthropomorphic and supernatural hypotheses in response to questions so disasters
become a function of angered gods and good times result from happier gods. A result
of western science has been to replace the supernatural worldview with one steeped in
the physics of Newton and the philosophy of Descartes. This has lead to an industrial
age cosmology characterized by cause and effect and the separateness of the observed
from the observer. Therefore current explanations (or hypotheses) are more likely to
take the form of a causal chain forged link by link by observations which seem to lead
inevitably to a conclusion.
The nature of the skill is to recognize that objectively gathered observations are
justified into an explanation as a result of having an operational cosmology, or

7. worldview. Secondly, a good hypothesizer recognizes that explanations are inventions
rather than discoveries and subject to rejection based upon facts. Beyond this no one
is really sure how hypotheses are actually generated. No one really knows what goes
on in the mind that results in the hypothesis but it seems reasonable to suspect that
information, perceptions, and ideas are being combined and recombined until a
particular combination seems to make sense.
13. Experimenting
This process is a systematic approach to solving a problem. Usually experimenting is
synonymous with the algorithm called scientific method which follows these five
basic steps:
PROBLEM---->HYPOTHESIS---->PREDICTIONS---->TEST OF PREDICTIONS--
-->EVALUATION OF HYPOTHESIS
In experimentation each step emerges from the previous one. The purpose of the
process is to judge the extent to which an hypothesis might be true and to set a
standard whereby that judgement is made. Consequently, scientists tend to think in
terms of probabilities of truth rather than absolute correctness.
As a term, experimenting is frequently used in a much broader way than described
here. It is not unusual to hear teachers applying the term to any activity or
demonstration but, strictly speaking, experimentation should be reserved for the
process of systematically evaluating hypotheses.