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Competent researchers and astute managers alike practice thinking habits that reflect sound reasoning – finding correct premises, testing the connections between their facts and assumptions, making claims based on adequate evidence.

Empirical testing or empiricism is said to denote observations and propositions based on sensory experience and/or derived from such experience by methods of inductive logic, including mathematics and statistics.

The essential tenets of the scientific methods are:

Direct observation of phenomena
Clearly defined variables, methods, and procedures
Empirically testable hypothesis
The ability to rule out rival hypothesis
Statistical rather than linguistic justification of conclusions.

The self-correcting process.

The scientific method, and scientific inquiry generally, is described as a puzzle-solving activity. One approach to assessing the validity of conclusions about observable events for the researcher:

Encounters a curiosity, doubt, barrier, suspicion, or obstacle. Struggles to state the problem – asks questions, contemplates existing knowledge, gathers facts, and moves from an emotional to an intellectual confrontation with the problem. Proposes a hypothesis, a plausible explanation, to explain the facts that are believe to be logically related to the problem. Deduces outcomes or consequences of the hypothesis-attempts to discover what happens if the results are in the opposite direction of that predicted or if the results support the expectations.

Formulates several rival hypotheses.

Devises and conducts a crucial empirical test with various possible outcomes, each of which selectively excludes one or more hypotheses. Draws a conclusion (an inductive inference) based on acceptance or rejection of the hypotheses. Feeds information back into the original problem, modifying it according to the strength of the evidence.

Sound Reasoning for Useful Answers
Communicate our meaning with two types of discourse;

Exposition consists of statements that describe without attempting to explain. Argument allows us to explain, interpret, defend, challenge, and explore meaning. Two types of argument of great importance to research are deduction and induction.

Deduction
Deduction is a form of argument that purports to be conclusive-the conclusion must necessarily follow from the reasons given. These reasons are said to imply the conclusion and represent a proof. A deduction must be both true and valid in order to be correct.

Example:

(Premise 1) All employees at BankOne can be trusted to observe the ethical code. (Premise 2) Sara is an employee of BankOne
(Premise 3) Sara can be trusted to observe the ethical code.

The conclusion in this case must be based on our confidence in Sara as an individual rather than a general premise that all employees of BankOne are ethical.

Induction

There is no such strength of relationship between reasons and conclusions in induction. In induction you draw a conclusion from one or more particular facts or pieces of evidence. The conclusion explains the facts, and the facts explain the conclusion. For example the following hypotheses may be used to explain why even after a firm spends $1 million on a regional promotional campaign, sales do not increase; Regional retailers did not have sufficient stock to fill customer requests during the promotional period. A strike by the employees of the trucking firm prevented stocks from arriving in time for the promotion to be effective. A category-five hurricane closed all our retail locations in the region for the 10 days during the promotion.

The inductive conclusion is an inferential jump beyond the evidence presented- that is, although one conclusion explains the facts of no sales increase, other conclusions also can explain the fact.

Combining Induction and Deduction

Induction and deduction are used together in research reasoning. Induction occurs when we observe a fact and ask, “Why is this?” The hypothesis for answering this question is plausible if it explains the event or condition that prompted the question. Deduction is the process by which we test whether the hypothesis is capable of explaining the fact. Example:

You promote a product but sales don’t increase. (FACT)

You ask the question “Why didn’t sales increase?) (INDUCTION) You infer a conclusion (hypothesis) to answer the question: The promotion was poorly executed. (HYPOTHESIS) You use this hypothesis to conclude (deduce) that sales will not increase during a poorly executed promotion. You know that from experience that ineffective promotion will not increase sales. (DEDUCTION)

In most research, the process may be more complicated than these examples suggest. For instance, we often develop multiple hypotheses by which to
explain the problem in question. Then we design a study to test all the hypotheses at once. Not only is this more efficient, but it is also a good way to reduce the attachment of the researcher for any given hypothesis.

The Language of Research

We do research when we seek to know what is in order to understand, explain, and predict phenomena. Questions require the use of concepts, constructs, and definitions.

Concepts

A concept is a generally accepted collection of meaning or characteristics associated with certain events, objects, conditions, situations, and behaviors. Classifying and categorizing objects or events that have common characteristics beyond any single observation creates concepts. We use numerous concepts daily in our thinking, conversing, and other activities.

Importance to Research

In research, special problems grow out of the need for concept precision and inventiveness. We design hypotheses using concepts. We devise measurement concepts by which to test these hypothetical statements. We gather data using these measurement concepts. The success of the research hinges on how clearly it is conceptualized and how well others understand the concept we use. The challenge is to develop concepts that others will clearly understand.

Constructs

Concepts have progressive levels of abstractions. Table is an objective concept. We can point to a table, and we have images of the characteristics of all the tables in our mind. An abstraction like personality is much more difficult to visualize. Such abstract concepts are often called constructs. A construct is an image or abstract idea specifically invented for a given research and or theory building purpose.

Definitions

Confusion about the meaning of concepts can destroy a research study’s value without the researcher or client knowing it. In dictionary definition, a concept is defined with a synonym. For example, a customer is defined as a patron and vice versa.

An Operational Definition is a definition stated in terms of specific criteria for testing or measurement. These terms must refer to empirical standards. The specifications and procedures must be so clear that any competent person using them would classify the object in the same way.

Definitional or Operational definition must provide an understanding and measurement of concepts.

Variables

In research, variable is a symbol of an event, act, characteristic, trait, or attribute that can be measured and to which we assign categorical values. Income, temperature, age, or a test score are examples of continuous variables. Researchers are most interested in relationships among variables. For example, does a newspaper coupon (independent variable) influence product purchase (dependent variable). Predicator variable is also used as a synonym for independent variable. Criterion variable is used synonymously with dependent variable.

Moderating Variables

IV causes the DV to occur. For simple relationships, all other variables are considered extraneous and are ignored. A moderating variable is a second independent variable that is included because it is believed to have a significant contributory or contingent effect on the originally stated IV-DV relationship. For example: The switch to commission from a salary compensation system (IV) will lead to increased sales productivity (DV) per worker, especially among younger workers (MV).

Extraneous Variables

An almost infinite number of EV’s exist that might conceivably affect a given relationship. These variables have little or no effect on a given situation and can be safely ignored. However, there may be other EV’s to consider as possible confounding variables. For example: With new customers (EV), a switch to commission from a salary compensation system (IV) will lead to increased sales productivity (DV) per worker, especially among younger workers (MV).

Intervening Variables

IVV may be defined as that factor which theoretically affects the observed phenomenon but cannot be seen, measured, or manipulated; its effects must be inferred from the effects of the independent and moderator variables on the observed phenomenon. For example: The switch to a commission compensation system (IV) will lead to higher sales productivity (DV) by increasing overall compensation (IVV).

Propositions and Hypotheses
A proposition is a statement about observable phenomena (concepts) that may be judged as true or false. When a proposition is formulated for empirical testing, we call it a hypothesis.

Descriptive Hypotheses
Descriptive hypotheses state the existence, size, form, or distribution of some variable. For example: In Detroit (case), our potato chip market share (variable) stands at 13.7 percent. Advantages to DH:

It encourages researchers to crystallize their thinking about the likely relationships to be found. It encourages them to think about the implications of a supported or rejected finding. It is useful for testing statistical significance.

Relational Hypotheses

RH describe a relationship between two variables with respect to some case. For example, “Foreign (variable) cars are perceived by American consumers (case) to be of better quality (variable) than domestic cars.” The nature of the relationship between the two variables is not specified.

Correlational hypotheses state that the variables occur together in some specified manner without implying that one causes the other. For example: Young women (under 35 years of age) purchase fewer units of our products than women who are 35 years of age or older.

Explanatory (causal) hypotheses has an implication that the existence of or a change in one variable causes or leads to a change in the other variable. For example: An increase in family income (IV) leads to an increase in the percentage of income saved (DV).

The Role of the Hypothesis

In research, a hypothesis serves several important functions: It guides the direction of the study. It defines facts that are relevant and those that are not. It suggests which form of research design is likely to be most appropriate. It provides a framework for organizing the conclusions that result.

Example: Husbands and wives agree in their perceptions of their respective roles in purchase decisions. The hypothesis specifies who shall be studied (married couples), in what context they shall be studied (their consumer decision making), and what shall be studied (their individual perceptions of their roles).

A strong hypothesis should fulfill three conditions: Adequate for its purpose, Testable, Better than rivals.

Theory

A theory is a set of systematically interrelated concepts, definitions, and propositions that are advanced to explain and predict phenomena (facts). We have many theories and use them continually to explain or predict what goes around us.

Model

A model is defined as a representation of a system that is constructed to study some aspect of that system or the system as a whole. Models differ from theories in that a theory’s role is explanation whereas a model’s role is representation.

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