Friday, May 26, 2017

Action Research

French and Bell (1978) defined it as:  The process of systematically collecting research data about an ongoing system relative to some objective, goal or need of that system; feeding these data back into the system, taking action by altering selected variable within the system based both on the data and on hypotheses; and evaluating the results of the actions by collecting more data.

Action research essentially involves:

* taking a static picture of the organisational situation
* formulating a hypotheses based on the picture
* the manipulation of variables in control of the researcher
* taking and evaluating a second static picture of the situation.

 Doing Research in Business and Management - Dan Remenyi et al.

"Action research...aims to contribute both to the practical concerns of people in an immediate problematic situation and to further the goals of social science simultaneously.  Thus, there is a dual commitment in action research to study a system and concurrently to collaborate with members of the system in changing it in what is together regarded as a desirable direction.  Accomplishing this twin goal requires the active collaboration of researcher and client, and thus it stresses the importance of co-learning as a primary aspect of the research process."
Editors, Action Reseach Journal
Ethnographic Action Research - 9 projects - Report on use of ICT in Poverty Reduction
Action Research Project - Zero budget natural farming

The SAGE Encyclopedia of Action Research

David Coghlan, Mary Brydon-Miller
SAGE, 11-Aug-2014 - Social Science - 904 pages

Action research is a term used to describe a family of related approaches that integrate theory and action with a goal of addressing important organizational, community, and social issues together with those who experience them. It focuses on the creation of areas for collaborative learning and the design, enactment and evaluation of liberating actions through combining action and research, reflection and action in an ongoing cycle of cogenerative knowledge. While the roots of these methodologies go back to the 1940s, there has been a dramatic increase in research output and adoption in university curricula over the past decade. This is now an area of high popularity among academics and researchers from various fields—especially business and organization studies, education, health care, nursing, development studies, and social and community work.

The SAGE Encyclopedia of Action Research brings together the many strands of action research and addresses the interplay between these disciplines by presenting a state-of-the-art overview and comprehensive breakdown of the key tenets and methods of action research as well as detailing the work of key theorists and contributors to action research.

Qualimetrics is an approach discussed in the above encyclopedia. There is a full book available on qualimetrics

The Qualimetrics Approach: Observing the Complex Object

Henri Savall, VĂ©ronique Zardet
IAP, 2011 - Business & Economics - 387 pages

Edited by Henri Savall and Veronique Zardet, Institut de Socio-Economie des Entreprises et des Organisations A volume in Research in Management Consulting Series Editor Anthony F. Buono, Bentley University The impetus for this work emerged from Savall's belief that there is a doubleloop interaction between social and economic factors in organizations, between behaviors and structures, and between the quality of life in organizations and their economic performance. When managers underestimate this dynamic interaction, the resulting tension ultimately manifests in lowered performance and increased costs, what he refers to as the "hidden costs" of organizational life. Only by delving into the depths of these organizational dynamics can we hope to fully understand - and create the basis for improving - organizational performance. The Qualimetrics Approach presents a different and challenging way of thinking about analyzing organizations, one that draws together quantitative information, financial analysis and qualitative insights into organizational dynamics. As Savall and Zardet argue, to gain a true understanding of what is happening in organizations, intervener-researchers must focus on all three perspectives, as ignoring any one of them will lead to incomplete understandings. Their approach underscores the importance of using qualitative data to validate quantitative depictions ("the numbers") of organizational performance in understanding the construction of financial statements. The strength of Savall and Zardet's approach is that it pushes us to go deeper, to fully understand the narratives underlying the numbers and the social construction of our financial assessments."

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Updated   28 May 2017,  28 Jan 2016,  7 Jan 2012

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

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).

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.

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

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

Handbook of Chemometrics and Qualimetrics, Part 2

Elsevier, 04-Dec-1998 - Science - 876 pages

Updated 28 May 2017, 1 June 2013

Thursday, May 4, 2017

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.