Business Research: Principles and Processes MGMT6791 · 2016-12-12 · Operationalization Theory...

©Mazzarol 2016 all rights reserved

Business Research: Principles

and Processes MGMT6791 Workshop 1C: The Nature of Research & Scientific Method

Professor Tim Mazzarol – UWA Business School

UWA Business School DBA Program [email protected] MGMT6791



Prediction in scientific research

“Prediction in research fulfils one of the

basic desires of humanity, to discern the

future and know what fate holds. Such

foresight used to involve studying the

stars or looking at the entrails of

animals.”

• Examples of Prediction in Research:

– Medicine • Ali Razi location of hospital in Bagdad.

• Ignaz Semmelweis hand washing in Vienna.

– Physics • Albert Einstein & Stephen Hawking in theoretical physics.

– Astronomy • Urbain Le Verrier & John Couch Adams discovery of

Neptune.

– Archaeology • Harald Schliemann’s discovery of Troy.

Source: Shuttleworth 2008


The rise of predictive science

Hypotheses

Predictions Test of

Predictions

Deduction

Observation

Induction


Predictive science is being driven

in part by the increasing power of

computers that allow more

complex modelling.

This can be seen in econometric

modelling and climate science.

Governments and the general

public want predictions to help

them plan and develop policies.

Yet predictions can be wrong (e.g.

Global Financial Crisis).

Or highly politically contentious

(e.g. Global Climate Change).


Video – Prediction in research


Group Exercise

• Read the article by Shuttleworth and watch the “Climate Change” video.

• In groups discuss the following:

– Why has the debate over global warming “become a shambles”?

– Have climate scientists strayed too fare from the “Scientific Method” in reaching their predictions?

– If the majority of scientists believe that climate change is real, why has it been so difficult for them to win the debate?

– What does this tell us about the risks of predictive science?


Conceptual variables

• “Conceptual variables are often expressed in general, theoretical, qualitative, or subjective terms and are important in the hypothesis building process.”

• The research hypothesis “H¹” is usually developed from the conceptual variables.

• To measure conceptual variables: – You need an objective definition.

– Validated measurement instruments.

– Theoretically supported operational variables.

– A consensus for all the above.



Example of conceptual variable

measurement

• Step 1 – Conceptual variable:

– The effect of a nicotine patch is poorer

among people who lack the mental

determination to quit smoking.

• Step 2 – Define what is meant by:

– “effect of a nicotine patch”, and “mental

determination”.

• Step 3 – Decide on measurement

scale:

– Effect of nicotine patch • (nominal) “Yes” or “No” .

• (ordinal) “none/moderate/high”.

• Based on potency of patch.

– Mental determination • (nominal) “Present” or “Absent” .

• (ordinal) “none/moderate/high”.

Source: Hani 2009


Fuzzy concepts to operationalisation

• Operationalization starts with “fuzzy

concepts” (conceptual variables)

and sets out exact definitions for

each variable, increasing the quality

of the results, and improving the

robustness of the research design.

• Fuzzy Concepts

– Vague conceptual ideas that

lack clarity or require validation.

– Require clear definition to

enable accurate replication in

the research process.



Real and nominal concepts

Concepts can be classified into two types: Real and Nominal

“Real Concept”

Describes the concept’s

characteristics of the

concept.

• Golf club

• Woman

• Golf Ball

• Golf course

Changing the name of the

concept will not change

the characteristics.

Well understood and

observable.

“Nominal Concept”

Has no definitive nature.

Both the name and its

characteristics are

arbitrary.

• Democracy

• Justice

• Peace

• Love

• Tyranny

• Mercy

Subject to value systems

and bias. Need careful

definition.


Higher and lower order concepts

Concepts can be classified into a hierarchy higher and lower order

• Animals

• Terrestrial

• Mammals

• Quadrupeds

• Dogs

• Domesticated Dogs

• Sheep Dogs

• Kelpies

• Red Kelpies

High Order

Low Order

A Sheep Dog

Concept Hierarchy

• Animals

• Terrestrial

• Reptiles

• Limbless

• Venomous

• Diurnal

• Serpent

• Dugite

• Coastal Dugite

A Dugite Snake

Concept Hierarchy


Group Exercise

• In groups discuss the following:

– Problem 1: Defining real and nominal concepts

– Problem 2: Concept identification

– Problem 3: Higher and lower order concepts


Operationalization

Theory

Hypotheses

Observations of the world

Empirical Generalisations

Deduction

Operationalization

Induction


Operationalization is the process

of strictly defining variables into

measurable factors.

The process defines “fuzzy”

concepts and allows them to be

measured, empirically and

quantitatively.

Analysis


Importance of operationalization

• Case of the Mars Climate Orbiter

– 1998 NASA sent the Mars Climate Orbiter to

space.

– Mission – to study the climate of Mars, the

atmosphere and planet surface.

– 1999 all communication with the space craft was

lost as it entered Mars orbit.

– Cause of failure – mismatched ground based

software that used empirical measures rather that

metric measures. Put the spaceship into the

wrong angle for orbit leading to it entering the

atmosphere and burning up.

– Lessons:

• Good operationalization requires clear

definition and agreement over use of the right

measurement methods for a study.

• This allows all researchers to adopt the same

standards and follow a similar methodology.

• Failure to identify the common units of

measure resulted in a disaster.



The operationalization process

Source: Experiment-Resources.com

Questions about

the big picture Population

Hypothetical

Theories/Concepts

Theory Literature

Review &

Scientist’s

Thoughts

Defining a

Research

Problem

Sampling Choosing

Indicators

Creating

Study

Testable, narrow

Hypothesis (Prediction)

Sample Observable,

Measurable Variables

Specific Situation Da

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on

Ch

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g

De

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Me

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Va

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Different research methods

• Experimental Research Methods – Common in physical and life sciences as well

as psychology.

– Typically quantitative using interval or ratio scales.

– Usually has experimental and control groups.

– Often viewed as “true science”.

– Can by difficult to do with need for rigorous design, equipment and labs.

– Need to control variables is a key challenge.

– Requires replication and falsification.

• Opinion Based Research Methods – Common in social science.

– Can be qualitative and quantitative with ordinal or interval scales.

– Less expensive than experimental design.

– Less precise than the experimental design.

– Can involve “experimental” type studies that allow for control groups.

– Allows for replication and falsification.



Video – Experimental design


Different research methods

• Observational Research Methods

– Common in social science but also used in behavioural science, anthropology.

– Often dismissed as not robust and “quasi-experimental” in nature.

– Use nominal and ordinal scales plus researcher coding of qualitative data.

– Examples:

• Case Studies

• Focus Groups

• Interviews

• Grounded Studies

– Difficult to replicate or falsify.

– Useful to explore phenomena prior to more rigorous methodology.

– Commonly used in business research to help develop theory.

– Major problem is identifying cause and effect.



Video – Cause and effect


Measurement in research

• Nominal Measures

– Numbers arbitrarily assigned to variables to enable easier manipulation.

– Example:

• Numbering focus groups = “In group 1 we found...”

– Useful only as a point of reference or labelling.

• Ordinal Measures

– Numbering system that has meaning and can be statistically analysed.

– Example:

• Likert scales “1=strongly disagree...5=strongly agree”

– Allows numerical analysis but is lacking in precision.

• Interval Measures

– Measures that have scales with an arbitrary zero point.

– Example:

• Celsius or Fahrenheit temperature scales.

– Allow more precision and scales can be divisible.

– However, scales can have negative measures (e.g. -5ºC).

• Ratio Measures

– Measures that have true zero points and no negative values.

– Example:

• Weight in kilograms.

– Allow most precision and have no relationship of scale,

• e.g. 100 KG being twice as heavy as 50 KG.



The scientific method

Develop research questions

Review previous research evidence (literature review)

Construct hypotheses

Test hypotheses by experiment or observation

Analyse data and draw conclusions

Were the hypotheses

correct?

The “Scientific Method” is a

standard process for how to

undertake research that aims

to discover new knowledge.


Scientific observation

• Scientific observation is the central element of scientific method or process. The core skill of a scientist is to make observations.

• Observations: – Any information, data or knowledge from the outside world

received via our senses, or scientific instruments.

– A description of what you see.

• Inferences: – Assumptions made by us from the observations we make.

– Care must be taken in making inferences as you may not interpret the data correctly.

• Types of observations: – Qualitative – subjective and less precise.

– Quantitative – uses numbers and is objective.



Video – Observation and inference


Group Exercise

• In groups:

– Read the exercise.

– Address the questions.

– What does this tell you about observation and inference?


End of presentation

Business Research: Principles and Processes MGMT6791 · 2016-12-12 · Operationalization Theory...

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