1. Thinking Like A Researcher
Chapter 2
Thinking Like A Researcher

2. Learning Objectives
The need for sound reasoning to enhance business research results
The terminology used by professional researchers employing scientific thinking
What you need to formulate a solid research hypothesis

3. The Scientific Method Direct observation Clearly defined variables
Clearly defined methods
Empirically testable
Elimination of alternatives
Statistical justification
Self-correcting process
Good business research is based on sound reasoning because reasoning is essential for producing scientific results. This slide introduces the scientific method and its essential tenets. The scientific method guides our approach to problem-solving.
An important term in the list is empirical. Empirical testing denotes observations and propositions based on sensory experiences and/or derived from such experience by methods of inductive logic, including mathematics and statistics. Researchers using this approach attempt to describe, explain, and make predictions by relying on information gained through observation.
The scientific method is described as a puzzle-solving activity.

4. Researchers Encounter problems State problems Propose hypotheses
Deduce outcomes
Formulate rival hypotheses
Devise and conduct empirical tests
Draw conclusions
The steps followed by business researchers to approach a problem are presented in the slide.

5. Synovate Curiosity is necessary to be a good business researcher
This ad from Synovate reinforces the notion that researchers must be curious. Students can see the Synovate website at

6. Sound Reasoning Types of Discourse Exposition Argument Deduction
Exposition consists of statements that describe without attempting to explain.
Argument allows us to explain, interpret, defend, challenge, and explore meaning. There are two types of argument: deduction and induction.
Deduction is a form of reasoning in which the conclusion must necessarily follow from the premises given. The next slide provides an example of a deductive argument.
Induction is a form of reasoning that draws a conclusion from one or more particular facts or pieces of evidence. Slide 2-8 illustrates an inductive argument.
Deduction
Induction

7. Deductive Reasoning
Inner-city household interviewing is especially difficult and expensive
This survey involves substantial inner-city household interviewing
This slide provides an example of a deductive argument.
The interviewing in this survey will be especially difficult and expensive
© 2002 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin

8. Inductive Reasoning
Why didn’t sales increase during our promotional event?
Regional retailers did not have sufficient stock to fill customer requests during the promotional period
A strike by employees prevented stock from arriving in time for promotion to be effective
A hurricane closed retail outlets in the region for 10 days during the promotion
This slide provides an example of an inductive argument.

9. Exhibit 2-1 Why Didn’t Sales Increase?
Deduction
Induction and deduction can be used together in research reasoning. Induction occurs when we observe a fact and ask, “Why is this?” In answer to this question, we advance a tentative explanation or hypothesis. The hypothesis is plausible if it explains the event or condition (fact) that prompted the question. Deduction is the process by which we test whether the hypothesis is capable of explaining the fact. Exhibit 2-1 illustrates this process.

10. Exhibit 2-2 Tracy’s Performance

11. Language of Research Terms used in research Concepts Constructs
Conceptual
schemes
Models
Operational
definitions
Terms used
in research
Several terms are used by researchers to converse about applied and theoretical business problems.
A concept is a bundle of meanings or characteristics associated with certain concrete, unambiguous events, objects, conditions, or situations. The importance of conceptualization is discussed in the following slide.
A construct is a definition specifically invented to represent an abstract phenomena for a given research project. Exhibit 2-3, a depiction of job redesign constructs, is provided in Slide 2-13.
A conceptual scheme is the interrelationship between concepts and constructs.
An operational definition defines a variable in terms of specific measurement and testing criteria. An example of an operational definition is provided in Slide 2-14.
A variable is used as a synonym for the construct being studied. Slides 2-15 through 2-20 expand on different types of variables.
A proposition is a statement about observable phenomena that may be judged as true or false. (Slide 2-21)
A hypothesis is a proposition formulated for empirical testing. (Slides 2-22 through 2-25)
A theory is a set of systematically interrelated concepts, definitions, and propositions that are advanced to explain or predict phenomena. Slide 2-26 shows an example of a theory.
A model is a representation of a system constructed to study some aspect of that system. Slide 2-27 shows an example of a model.
Theory
Variables
Propositions/
Hypotheses

12. Clear conceptualization
Language of Research
Clear conceptualization
of concepts
Success of
Research
Shared understanding
of concepts
We must attempt to measure concepts in a clear manner that others can understand. If concepts are not clearly conceptualized and measured, we will receive confusing answers.

13. Exhibit 2-3 Job Redesign Constructs and Concepts
Exhibit 2-3 illustrates some of the concepts and constructs relevant to job redesign. The concepts at the bottom of the exhibit (format accuracy, manuscript errors, and keyboarding speed) are the most concrete and easily measured. Keyboarding speed is one just concept in the group that defines a construct that the human resource analyst calls Presentation Quality. It is not directly observable like keyboarding speed. It is a term used to communicate (a label) the combination of meanings presented by the three concepts.
Concepts at the next level are vocabulary, syntax, and spelling. As they are related, the analyst groups them into a construct she calls language skill. Language skills is placed at a higher level of abstraction in the exhibit because two of the concepts that comprise it, vocabulary and syntax, are more difficult to observe and measure.
The construct of job interest is not yet measured nor are its components specified. Researchers often refer to such constructs as hypothetical constructs because they are inferred only from the data—they are presumed to exist but no measure tests whether such constructs actually exist.
If research shows the concepts and constructs in this example to be interrelated, and if the connections can be supported, then the analyst has the beginning of a conceptual scheme.
One exercise you can try is to have students attempt to identify the concepts/constructs in the hypothetical construct…job interest, and discuss which elements are truly measurable…and how.
3-13

14. Operational Definitions
How can we define the variable “class level of students”?
Freshman
Sophomore
Junior
Senior
< 30 credit hours
30-50 credit hours
60-89 credit hours
> 90 credit hours
Operational definitions are definitions stated in terms of specific criteria for testing or measurement. The specifications must be so clear that any competent person using them would classify the objects in the same way.
If a study of college students required classifying students by class level, a definition of each category would be necessary. Students could be grouped by class level based on self-report, number of years in school, or number of credit hours completed. Credit hours is the most precise measure.

15. What Is A Variable? Variable Event Act Characteristic Trait
In practice, the term variable is used as a synonym for the property being studied. In this context, a variable is a symbol of an event, act, characteristic, trait, or attribute that can be measured and to which we assign categorical values. The different types of variables are presented on the following slides.

16. Religious affiliation
Types of Variables
Dichotomous
Male/Female
Employed/ Unemployed
Discrete
Ethnic background
Educational level
Religious affiliation
For the purposes of data entry and analysis, we assign numerical values to a variable based on that variable’s properties. Dichotomous variables have only two values that reflect the absence or presence of a property. Variables also take on values representing added categories such as demographic variables. All such variables are said to be discrete since only certain values are possible. Continuous variables take on values within a given range or, in some cases, an infinite set.
Continuous
Income
Temperature
Age

17. Exhibit 2-4 Independent and Dependent Variables
Independent Variable (IV)
Predictor
Presumed cause
Stimulus
Predicted from…
Antecedent
Manipulated
Dependent Variable (DV)
Criterion
Presumed effect
Response
Predicted to….
Consequence
Measured outcome
Exhibit 2-4 presents the commonly used synonyms for independent and dependent variables.
An independent variable is the variable manipulated by the researcher to cause an effect on the dependent variable.
The dependent variable is the variable expected to be affected by the manipulation of an independent variable.

18. Moderating Variables (MV)
The switch to commission from a salary compensation system (IV) will lead to increased sales productivity (DV) per worker, especially among younger workers (MV).
The loss of mining jobs (IV) leads to acceptance of higher-risk behaviors to earn a family-supporting income (DV) – particularly among those with a limited education (MV).
Moderating variables are variables that are believed to have a significant contributory or contingent effect on the originally stated IV-DV relationship. Whether a variable is treated as an independent or as a moderating variable depends on the hypothesis. Examples of moderating variables are shown in the slide.

19. Extraneous Variables (EV)
With new customers (EV-control), a switch to commission from a salary compensation system (IV) will lead to increased sales productivity (DV) per worker, especially among younger workers (MV).
Among residents with less than a high school education (EV-control), the loss of jobs (IV) leads to high-risk behaviors (DV), especially due to the proximity of the firing range (MV).
Extraneous variables are variables that could conceivably affect a given relationship. Some can be treated as independent or moderating variables or assumed or excluded from the study. If an extraneous variable might confound the study, the extraneous variable may be introduced as a control variable to help interpret the relationship between variables. Examples are given in the slide.

20. Intervening Variables (IVV)
The switch to a commission compensation system (IV) will lead to higher sales productivity (DV) by increasing overall compensation (IVV).
A promotion campaign (IV) will increase savings activity (DV), especially when free prizes are offered (MV), but chiefly among smaller savers (EV-control). The results come from enhancing the motivation to save (IVV).
An intervening variable is a factor that affects the observed phenomenon but cannot be measured or manipulated. It is a conceptual mechanism through which the IV and MV might affect the DV.

21. Propositions and Hypotheses
Brand Manager Jones (case) has a higher-than-average achievement motivation (variable).
Brand managers in Company Z (cases) have a higher-than-average achievement motivation (variable).
Generalization
A proposition is a statement about observable phenomena that may be judged as true or false. A hypothesis is a proposition formulated for empirical testing. A case is the entity or thing the hypothesis talks about. When the hypothesis is based on more than one case, it would be a generalization. Examples are provided in the slide.

22. Hypothesis Formats Descriptive
In Detroit, our potato chip market share stands at 13.7%.
American cities are experiencing budget difficulties.
Research Question
What is the market share for our potato chips in Detroit?
Are American cities experiencing budget difficulties?
A descriptive hypothesis is a statement about the existence, size, form, or distribution of a variable. Researchers often use a research question rather than a descriptive hypothesis. Examples are provided in the slide. Either format is acceptable, but the descriptive hypothesis has three advantages over the research question.
Descriptive hypotheses encourage researchers to crystallize their thinking about the likely relationships.
Descriptive hypotheses encourage researchers to think about the implications of a supported or rejected finding.
Descriptive hypotheses are useful for testing statistical significance.

23. Relational Hypotheses
Correlational
Young women (under 35) purchase fewer units of our product than women who are older than 35.
The number of suits sold varies directly with the level of the business cycle.
Causal
An increase in family income leads to an increase in the percentage of income saved.
Loyalty to a grocery store increases the probability of purchasing that store’s private brand products.
A relational hypothesis is a statement about the relationship between two variables with respect to some case. Relational hypotheses may be correlational or explanatory (causal).
A correlational hypothesis is a statement indicating that variables occur together in some specified manner without implying that one causes the other.
A causal hypothesis is a statement that describes a relationship between two variables in which one variable leads to a specified effect on the other variable.

24. The Role of Hypotheses Guide the direction of the study
Identify relevant facts
Suggest most appropriate research design
Provide framework for organizing resulting conclusions
This slide presents the functions served by hypotheses.

25. Characteristics of Strong Hypotheses
Adequate A
Strong
Hypothesis Is
Testable
The conditions for developing a strong hypothesis are more fully developed in Exhibit 2-5.
Better
than rivals

26. Exhibit 2-6 Theory of the Product Life Cycle
What is the difference between theories and hypotheses?
Theories tend to be complex, abstract, and involve multiple variables.
Hypotheses tend to be simple, limited-variable statements involving concrete instances.
A theory is a set of systematically interrelated concepts, definitions, and propositions that are advanced to explain or predict phenomena. To the degree that our theories are sound and fit the situation, we are successful in our explanations and predictions.
The product life cycle, shown in Exhibit 2-6, is an example of a theory.

27. Exhibit 2-7 A Distribution Network Model
A model is a representation of a system constructed to study some aspect of that system or the system as a whole.
Models versus Theories
a model’s role is to represent or describe
A theory’s role is to explain.
Models in business research may be descriptive, predictive, and normative.
Descriptive models are used for complex systems because they allow for the visualization of numerous variables and relationships.
Predictive models forecast future events and facilitate business planning.
Normative models are used for control, because they indicate necessary actions.
Exhibit 2-7, shown in the slide, is a distribution network model called a maximum flow model used in management science. In this example, a European manufacturer of automobiles needs an increased flow of shipping to its Los Angeles distribution center to meet demand. However the primary distribution channel is saturated and alternatives must be sought.
Models allow researchers to specify hypotheses that characterize present or future conditions: the effect of advertising on consumer awareness or intention to purchase, brand switching behavior, an employee training program, or other aspects of business.

28. Exhibit 2-8 The Role of Reasoning
Business models are developed through the use of inductive and deductive reasoning.
As illustrated in Exhibit 2-8, a business model may originate from empirical observations about market behavior based on researched facts and relationships among variables.
Inductive reasoning allows the modeler to draw conclusions from the facts or evidence in planning the dynamics of the model. The modeler may also use existing theory, managerial experience or judgment, or facts.

29. Live and Learn Source: Cooper R. D. & Schindler, S. P.
(2006). Business Research Methods
9e. McGraw.Hill