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

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1 Business Research: Principles and Processes MGMT6791 Workshop 1C: The Nature of Research & Scientific Method Professor Tim Mazzarol UWA Business School MGMT6791 UWA Business School DBA Program

2 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

3 The rise of predictive science Hypotheses Predictive science is being driven in part by the increasing power of computers that allow more complex modelling. Induction Test of Predictions Predictions Deduction 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). Observation Source: Shuttleworth 2008

4 Video Prediction in research

5 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?

6 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. Source: Shuttleworth 2008

7 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

8 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. Source: Shuttleworth 2008

9 Real and nominal concepts Concepts can be classified into two types: Real and Nominal 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. 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.

10 Higher and lower order concepts Concepts can be classified into a hierarchy higher and lower order A Sheep Dog Concept Hierarchy Animals Terrestrial Mammals Quadrupeds Dogs Domesticated Dogs Sheep Dogs Kelpies Red Kelpies High Order Low Order A Dugite Snake Concept Hierarchy Animals Terrestrial Reptiles Limbless Venomous Diurnal Serpent Dugite Coastal Dugite

11 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

12 Operationalization Theory Induction Empirical Generalisations Analysis Deduction Hypotheses Operationalization 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. Observations of the world Source: Shuttleworth 2008

13 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 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. Source: Shuttleworth 2008

14 Data Collection Choosing Design & Method Conceptual Variables The operationalization process 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 Source: Experiment-Resources.com

15 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. Source: Shuttleworth 2008

16 Video Experimental design

17 Different research methods Observational Research Methods Common in social science but also used in behavioural science, anthropology. Often dismissed as not robust and quasiexperimental 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. Source: Shuttleworth 2008

18 Video Cause and effect

19 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. Source: Shuttleworth 2008

20 The scientific method Develop research questions Were the hypotheses correct? Review previous research evidence (literature review) The Scientific Method is a standard process for how to undertake research that aims to discover new knowledge. Analyse data and draw conclusions Construct hypotheses Test hypotheses by experiment or observation

21 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. Source: Shuttleworth 2008

22 Video Observation and inference

23 Group Exercise In groups: Read the exercise. Address the questions. What does this tell you about observation and inference?

24 End of presentation

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