Statistical Analysis - Dan Remenyi - Chapter Summary

Ph.D Research Methodology - Statistical Analysis


Mathematics and Statistics are  important for the analysis and interpretation of evidence in the business and management world. They enable us to deal with and to solve problems that otherwise would be quite intractable.

Representing Evidence
Evidence may be represented by graphical summaries such as bar charts and histograms, tabular summaries such as one-way and two-way relative frequency tables and by numerical summaries such as the mean and the standard deviation.

Bar Charts and Histograms
Familiar to everyone, is to use bar charts or frequency histograms.  A chart is drawn in which the height of each bar is proportional to the frequency with which that outcome occurs or, by dividing each bar by the total number of observed events, to estimate the probability with which that outcome occurs.

Measures of distribution
Distributions can be summaries in terms of certain key characteristics.  Range and quartiles are other dimensions that are sometimes used.

The Mean
The most common measure of location is the mean.

The Median
Another measure of location is the median, which is the measurement that falls in the middle of the distribution so that there are as many items below it as above it.

Standard Deviation

Range
The range would then simply correspond to the largest value minus the smallest value.  The lower corresponding to a point below which one quarter of the points lie (the lower quartile) and the other to a point above which one quarter of the points lie( the upper quartile).

Distributions
Important, distributions which arise in statistics.   The first is the binomial distribution, which is the case whenever there are only two possible outcomes: heads or tails, true of false, girls or boys, and so on.  The Poisson distribution is the limiting case of the binomial distribution when the probability of one of the outcomes is very small.

But the most important of all is the Normal distribution in which the distribution of outcomes follows the familiar bell-shaped curve.



Testing Hypotheses

The hypothesis of the thesis is many times tested using statistical tests of hypothesis.
A null hypothesis is stated and an alternate hypothesis is stated. One of them is accepted. Technical it is said
that the null hypothesis has not been disproved or disproved.

Type I and Type II Errors
The null hypothesis can be rejected when it is true (Type I) or be accepted when it is false (Type II). A Type I error is small – this is referred to as the significance level of the test.  5 per cent and 1 per cent it is given one star, between 1 per cent and 0.1 per cent two stars; and below 0.1 per cent three stars.

In order to determine the probability of making a Type II error, is specified as the power of the test.   At the 5 per cent significance level and with 90 per cent power.

Comparisons
m1 and S1, m2 and S2  then the difference in the means is d = m1- m2

 and the standard error of the difference is:
                       e =  S²1  + S² 2
So a null hypothesis is made that the true value of d is equal to zero and the d  calculated should exceed 1.96 x e with less than 5 per cent probability.

Gossett showed that even for small numbers of evidence points it is still possible to test the ratio of d/e, and he provided what is now called Student’s t –distribution which is used instead of the normal distribution.

Paired and Unpaired t-Tests
If it is possible to make both measurements on the same person, organisation or sampling unit, a more powerful test can be developed. Instead of testing the difference between the means, the difference between each pair of means is calculated and then the standard deviations of the mean of the differences is calculated.  This is called a paired t-test since it has been possible to treat the evidence points in pairs.


Tests of Association


Regression
For example, to ascertain if more beer is sold when the weather is hot the first step would be to plot a graph of the amount of beer sold against the temperatures.
A straight line could be drawn that is considered to ‘best fit’ the evidence and secondly it enables the error in the slope to be determined so that it can be seen if the slope differs significantly from zero.

Y = a + bx   (5)





Factor Analysis
For data reduction and the exploration of underlying dimensions.  It is therefore a technique that can be used to provide a parsimonious description of complex multi-faceted intangible concept such as the quality of service or the relationship between individuals in an organisation.



Consult the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy as it offers some idea of how relevant the factor analysis is for the evidence being used.  The rule for the use of this statistic is that if the KMO is less than 0.50 there is no value in proceeding with the technique.  The greater the value of the KMO the more effective the factor analysis is likely to be.


Examine the eigen-values.  Only factors with an eigen-value of greater than one are used in the analysis will explain more variability than any one of the original variables on their own.

Study the rotated factor matrix.  Examine each factor separately, looking for the input variables that influence the factor, which have a loading of 0.5 or more.

Attempt to combine the meaning of the variables identified in 3 above into an underlying factor or super-variable which will explain the combined effect of these individual variables, what is being sought is a relatively simple description of the complex effect of several of the original variables.


Correspondence Analysis

Correspondence analysis is a multivariate analysis technique that can be used to analyse and interpret cross-tabulations of categorical data.  The only constraint on the cell entries in the contingency table is that they be non negative.

The main output from a correspondence analysis is a graphical display that is a simultaneous plot of the rows and columns of the contingency table in a space of two or more dimensions.  Those rows with similar profiles are plotted ‘close’ together, as are columns with similar profiles.

The number of dimensions needed for  a perfect representation of a contingency table is given by the minimum of (R-1) and (C-1), which for a large contingency table will clearly not be helpful.
The ANACOR program within the SPSS package can be used to perform a correspondence analysis.



Very detailed description of Statistics used in Research Studies

Handbook of Chemometrics and Qualimetrics, Part 1

Elsevier, 12-Dec-1997 - Technology & Engineering - 886 pages
https://books.google.co.in/books?id=jF0QhuxXeIwC

Handbook of Chemometrics and Qualimetrics, Part 2

Elsevier, 04-Dec-1998 - Science - 876 pages
https://books.google.co.in/books?id=lWpMrQ3WLv8C



Updated 28 May 2017, 1 June 2013

Essays on Research Methodology - Dinesh Hegde - Book Information

Essays on Research Methodology


Dinesh S. Hegde
Springer, 03-Jun-2015 - Business & Economics - 234 pa

The book presents a collection of essays addressing a perceived need for persistent and logical thinking, critical reasoning, rigor and relevance on the part of researchers pursuing their doctorates. Accordingly, eminent experts have come together to consider these significant aspects of the research process, which result in different knowledge claims in different fields or subject areas. An attempt has been made to find a common denominator across diverse management disciplines, so that the broadest range of researchers can benefit from the book. The topics have been carefully chosen to cover problem formulation, contextualizing, soft & hard modeling, qualitative and quantitative analysis and ethical issues, in addition to the design of experiments and survey-based research.

The distinguishing feature of this book is that it recognizes the diverse backgrounds of scholars from different interdisciplinary areas as well as their varying needs with regard to modeling, observations, measurements, aggregation, data analyses, etc. After all, researchers are expected to deepen our understanding, expand on existing information, introduce fresh insights, present new evidence and/or disprove accepted theories, hypotheses etc. More importantly, the book cautions against the over-reliance on software packages and mechanical interpretation of results based on the size, sign and significance of the coefficients obtained. Instead, the focus is on the underlying theories, hypotheses and relationships and on establishing new ones. In doing so, due care is taken to clearly enunciate what exactly constitutes a knowledge claim and what is methodology as distinct from methods, tools and techniques.

https://books.google.co.in/books?id=dfbLCQAAQBAJ

Phenomenology - Blog Book - Table of Contents

Phenomenology: The Non-Positivist Approach - Dan Remenyi - Chapter Summary

Phenomenology: An Introduction

Phenomenology - An Approach to Psychology


Hermeneutic Phenomenology

Transcendental Phenomenology



Management Thesis Using Phenomenology or Qualitative Research Methods


Phenomenology - Blog Book - References



Book Information Pages

The Phenomenology Reader - Dermot Moran - Book Information

Phenomenology - Blog Book - References

References

1. Brooks, D. (2008). The behavioral revolution. The New York Times, October 27, pp A. 31.

2. Coomer, D.L., & Hultgren, F.H. (1989). Considering alternatives: an invitation to dialog and question. In D.L. Commer & F.H. Hultgren (Eds), Alternative modes of inquiry. Washington DC: American Home Economics Association, Teacher Education Section.

3. Courtenay B.C., Merriam, S.B. & Reeves, P.M. (1998). The centrality of meaning-making in transformational learning: how HIV positive adults make sense of their lives, Adult Education Quarterly, 48 (2), pp. 65-84.

4. Denzin, N.A. & Lincoln, Y.S. (1994). Introduction: entering the field of interpretive research. In N.K. Denzin and Y.S. Lincoln (Eds.), Handbook of interpretive research (pp. 1-17). Thousand Oaks, CA: Sage publications.

5. Derman, E. & Wilmott, P. (2009). Perfect models imperfect world. Businessweek, January 12, pp. 59-60.

8. Enrich, L. (2005). Revisiting phenomenology: it’s potential for management research. In proceedings challenges or organizations in global markets. British Academy of Management Conference, pp. 1-13.

11. Giorgi, A. (1997). Theory, practice, and evaluation of the phenomenological method as a interpretive research procedure, Journal of Phenomenological Psychology, 28 (2), pp. 235-260.

12. Hirshleifer, D. (2001). Investor psychology and asset pricing. The Journal of Finance, 56 (4).

13. Hirshleifer, D., Teoh, S.H. (2003). Herd behavior and cascading in capital markets: a review and synthesis, European Financial Management, 9 (1), pp.25-66.

14. Hultgren, F.H. (1989). Introduction to Interpretive Inquiry. In F.H. Hultgren & D.L. Coomer (Eds). Alternative modes of inquiry. Washington D.C., American Home Economics Association, Teacher Education Section, pp. 283-290.

15. Kane, E.J. (1989) Changing incentives facing financial-services regulators, Journal of Financial Services Research, 2, (3), pp. 265-274

16. Lewis, M. (2008). The End. Portfolio.com, December.

17. Lohr, S. (2008). In modeling risk, the human factor was left out, The New York Times, November 4, pp. B1.

18. McClelland, J. (1995). Sending children to kindergarten: a phenomenological study of mother’s experiences, Family Relations, 44 (2).

19. Phenomenological Research and its Potential for Understanding Financial Models, Michael S Wilson, USA

20. Polkinghorne, D. (1989). Methodology for the human sciences: systems of inquiry. Albany, NY: University of New York Press.

26. Van Manen, M. (2001). Researching Lived Experience. Human Science for an Action Sensitive Pedagogy (2nd ed.). Alberta, Canada: Althouse Press.

27. Ehrich, Lisa (2005) Revisiting phenomenology: its potential for management research. In Proceedings Challenges or organisations in global markets, British Academy of Management
Conference, pages pp. 1-13, Said Business School, Oxford University.

28. Sebastian Reiter, Glenn Stewart and Christian Bruce,  A Strategy for Delayed Research Method Selection: Deciding between Grounded Theory and Phenomenology, The Electronic journal of Business Research Methods, Vol-9, Issue-1, 2011,pp 35-46

Introduction to Qualitative Research Methods: A Guidebook and Resource - Book Information

Introduction to Qualitative Research Methods: A Guidebook and Resource

Steven J. Taylor, Robert Bogdan, Marjorie DeVault

John Wiley & Sons, 04-Sep-2015 - Psychology - 416 pages


An informative real-world guide to studying the "why" of human behavior

Introduction to Qualitative Research Methods is a practical, comprehensive guide to the collection and presentation of qualitative data. This book describes the entire research process — from design through writing — illustrated by examples of real, complete qualitative work that clearly demonstrates how methods are used in actual practice. This updated fourth edition includes  new case studies, with additional coverage of mixed methods, non-sociological settings, funding, and a sample interview guide. The studies profiled are accompanied by observation field notes, and the text includes additional readings for both students and instructors. This guide provides you a real-world practitioner's view of how qualitative research is handled every step of the way.

Many different disciplines rely on qualitative research as a method of inquiry, to gain an in-depth understanding of human behavior and the governing forces behind it.

Understand the strengths and limitations of qualitative data
Learn how experts work around common methodological issues
Compare actual field notes to the qualitative studies they generated
Examine the full range of qualitative methods throughout the research process


Whether you're doing research in sociology, psychology, marketing, or any number of other fields,  having human behavior as an important component, human behavior is the central concern of research. "What drives human behavior?" is the question That's what qualitative research helps to explain. Introduction to Qualitative Research Methods gives you the foundation you need to begin seeking answers.
https://books.google.co.in/books?id=RkCCCgAAQBAJ

Understanding Management Research: An Introduction to Epistemology - Phil Johnson, Joanne Duberley - Book Information

Understanding Management Research: An Introduction to Epistemology


Phil Johnson, Joanne Duberley
SAGE, 28-Sep-2000 - Business & Economics - 224 pages


'These sections represent the clearest rendition yet of these subjects, with difficult concepts introduced in a digestible form for the neophytic (or not so neophytic) researcher. Whilst in a book this size not every argument can be presented, there is ample extra material to be found to encourage further engagement... At the end of each chapter, there is a very useful Further Reading section provided by the authors, which gives useful guidelines.

Understanding Management Research provides an overview of the principal epistemological debates in social science and how these lead to and are expressed in different ways of conceiving and undertaking organizational research. For researchers and students who are increasingly expected to adopt a reflexive understanding of their own epistemological position, the authors present a concise, accessible guide to the different perspectives available and their implications for research output.

All students undertaking empirical research for theses and dissertations will find this book helps them comprehend the key ongoing debates and engage with their own pre-understandings when trying to make sense of management and organizations.

https://books.google.co.in/books?id=Do-v6tfJAjoC

Deductive Theory Building and Inductive Theory Building

Developing Theory from Observations - Creativity in Inductive Thinking - Research Methodology


Scientific Research is theory building.

Theory is developed for the set of observations. The process involved is inductive thinking. There is creativity involved in theory building. The concrete or specific observations are to be described by general concepts. Theory connects the concepts.

In developing the concept from a practical instance or observation some assumptions are employed and a rigorous description of the concept is developed. Further assumptions are used to develop theory. Model building is also theory development only. Model building used to solve practical problems also involves assumptions that bring the reality to close to the existing problem solving theories. From the set of assumptions, the theory is developed. This is termed as deductive approach to theory building.

In grounded theory method, Glaser and Strauss recommend theory building from the evidence only without building any model and then developing theory. They criticize model based theory building as too distant from the evidence on which it was supposed to be based. Hence, the likelihood of the theory failing in test is high.


Illustrations of Assumptions and Theory Building

Modigliani and Miller Capital Structure Theory

Assumptions

1. Perfect capital market: Information is freely available, there is no asymmetry, transactions are costless; there are no bankruptcy costs, securities are infinitely divisible.
2. Rational Investors and Managers: Investors rationally choose a combination of risk and return that is most advantageous to them. Managers act in the interests of shareholders.
3. Homogeneous expectations: Investors hold identical expecations about future operating earnings.
4. Equivalent risk classes: Firms can be grouped into 'equivalent risk classes' on the basis of their business risk.
5. Absence of Taxes: There is no corporate income tax.

MM Proposition I
The value of a firm is equal to its expected operating income divided by the discount rate appropriate to its risk class. It is independent of its capital structure.

MM Proposition II
The expected return on equity is equal to the expected rate of return on assets, plus a premium. The premium is equal to the debt-equity ratio times the difference between the expected return on assets and the expected return on debt.

(Source: Prasanna Chandra, Financial Management: Theory and Practice, Fifth Edition, Tata McGraw-Hill Pub. Co. Ltd, New Delhi, 2001. pp.417-24.)


Theory of Collisions (Physics)

Assumptions

The masses are moving on a frictionless surface.
The masses are perfectly elastic bodies (or they are connected by massless springs).

(Reference: H.C. Verma, Concepts of Physics Part 1, Bharati Bhawan, New Delhi, 1993 (Second reprint of revised edition 2007), p.145.


Article originally published at Knol 2657

List of Articles on the Topic


Volume 14, No. 1, Art. 25 – January 2013
Theory Building in Qualitative Research: Reconsidering the Problem of Induction

Pedro F. Bendassolli

Abstract: The problem of induction refers to the difficulties involved in the process of justifying experience-based scientific conclusions. More specifically, inductive reasoning assumes a leap from singular observational statements to general theoretical statements. It calls into question the role of empirical evidence in the theory-building process. In the philosophy of science, the validity of inductive reasoning has been severely questioned since at least the writings of David HUME. At the same time, induction has been lauded as one of the main pillars of qualitative research methods, and its identity as such has consolidated to the detriment of hypothetical-deductive methods. This article proposes reviving discussion on the problem of induction in qualitative research. It is argued that qualitative methods inherit many of the tensions intrinsic to inductive reasoning, such as those between the demands of empiricism and of formal scientific explanation, suggesting the need to reconsider the role of theory in qualitative research.
http://www.qualitative-research.net/index.php/fqs/article/view/1851/3497

Full paper available


Updated   14 September 2016,  24 August 2016,  10 December 2012