2. Introduction of the course
This course aims to introduce graduate students to a
scientific approach to the study of management, marketing
and international business issues.
The focus is on equipping students with the fundamental
knowledge and skills for undertaking both quantitative and
qualitative research and to critically evaluate research
conducted by others.
3. Introduction of the course
Three predominant approaches used in management research:
The quantitative empirical approach, in which the design provides for
obtaining data that can be treated by rigorous statistical analysis. In this,
hypothesis testing and multivariate analysis procedures are used. This is
predominant in marketing management, organization theory and decision theory.
The mathematical modelling approaches, use mainly logical thinking and
operations research models in decision making situations, especially in the areas
of operations, manufacturing, logistics, inventory and finance.
The qualitative analysis approach of organizational theorists who emphasize
studying and understanding particular problems and situations based on
voluminous empirical data.
Our
Focus in
this
Course
4. Introduction of the course
I will teach the first four weeks of the course.
My four-week course is divided into four interrelated
segments:
Overview of Research in management
Problem conceptualization
Measurement Design
Sample Design
5. Overview of Research in management
Scientific research can be thought of as a critical enquiry in the cyclical process of idea
generation, exploration, investigation, and verification.
In the idea generation stage, the scientist out of his/her own experience in research, or as a
result of an observation, may conceive an idea or develop a concept. In order to check the
validity of such a concept arrived at subjectively, it becomes necessary for him/her to initially
explore a little bit more the phenomenon to which the concept relates.
The nature of this exploration is cyclic, but at this stage the attitude is one of inquisitiveness,
investigation and verification. Processes tend to become tentative and not rigorous. Once the
idea is accepted as tentatively valid, further investigation will be necessary.
The researcher will lay down the detailed steps of the investigation, accurate data will be
collected, and statistical or logical procedures will be used to verify the idea or concept in the
form of a declaration, a hypothesis, or a model.
6. Overview of Research in management
Research Process
A typical research process has the following stages:
research problem identification;
research problem definition;
theoretical framework (that is, identification of variables and development of
hypothesis or model);
research design;
data collection;
analysis of data and reporting
8. Research problem identification:
Sources of a research problem are:
(1) A manager who is faced with a problem to be solved or who needs
improvement in some aspect of his decision-making.
(2)Research literature, consisting of theses, research journal articles, books,
general observations in conferences and seminars, and opinions of experts in
the field of interest.
(3) Considerable personal experience of the above researcher in the field of
research interest.
(4) A scientific observation of a phenomenon or a managerial set up.
Overview of Research in management
In universities, we mainly find research problems from (2) and (4)
9. Overview of Research in management
Research problem definition :
The tentative and general statements of problems obtained in the
identification phase are converted into researchable questions and
propositions.
Clear and unambiguous statements of the problem are made and
the information required for research is stated.
10. Overview of Research in management
Theoretical framework:
Variables required for solving the problem are identified, partly
from literature and partly by the researcher for defining the
problem.
The problem is related to the existing research-theories,
constructs, and hypothesis in a theoretical framework that will
ensure step-by-step progress of knowledge (as in pure research)
or a strong basis for the current problem solving (as in applied
research).
11. Overview of Research in management
Theoretical framework:
Tentative hypothesis/models development: The problem
definitions/propositions are converted into hypotheses or
models, which are in testable form to ascertain whether they
can be verified statistically or are feasible for solution
procedures.
Hypothesis and theoretical framework are termed research
problem formulation.
12. Overview of Research in management
Theoretical framework:
The generation of hypotheses is the most difficult phase of
research. It is primarily a creative process and, therefore
attempting well structured approaches to this would be self
defeating.
A broad problem is stated on the basis of a limited observation or
a quick review of research literature as well as secondary sources
of data, discussions with executives, and so on.
13. Overview of Research in management
Research design:
This involves the following steps:
(i) Determining the type of research to be carried out for data
collection-secondary data, experiment, ex post facto, or model building;
(ii) Selection of the measurement and scaling of the variables that is,
whether questionnaires, or observations or interview techniques are
used:
(iii) Selection of the representative sample: specification of how many
respondents, and what kind of respondents or objects to measure;
(iv) Selection of the appropriate method/techniques of analysis of data;
(v) Preparation of a research proposal.
14. Overview of Research in management
Data collection:
Data is collected as per the sampling plan using the instrument
developed as per the specification in the design phase
15. Overview of Research in management
Data preparation and analysis:
The raw data collected in the earlier step is converted into data
usable for research by carrying out coding, transformation, and
performing descriptive analysis, as required.
This converted data is used for verifying hypothesis, deriving
significant relationships, or testing models, as required, and
inferences are drawn from the study and results are analyzed.
16. Overview of Research in management
Reporting results:
The results obtained in the research are presented in the form of a
written report, thesis, or in an oral presentation.
18. Now we will go into details!
1. RESEARCH PROBLEM DEFINITION is generating hypothesis in
quantitative empirical study.
Generation of Hypotheses
Usually tentative propositions or declarative statements
regarding the description or relations are first generated,
which are later converted into hypotheses.
Hypotheses are built on the basis of theoretical work
that has already been done. Examples of such work are
the existence of a research gap (mainly concluded from
literature review), an unanswered research question, or
an unsolved research problem.
19. Generation of Hypotheses
It should be ensured that hypotheses are well grounded in
the cultural setting of the decision-making system. Analogy
with problems in other disciplines is a useful method for
generating hypotheses. Before generating hypothesis, it is
necessary to identify the relevant variables related to the
problem.
A list of variables generally used in hypothesis generation
is given below:
Independent variable (resultant variable): A manipulated variable in an
experiment (treatment).
Explanatory (causal) variable: Independent variable that influences the
dependent variable.
Dependent variable (criterion variable): The effect in an experiment.
20. Generation of Hypotheses
A list of variables generally used in hypothesis
generation is given below:
Extraneous variable (non-observable): Independent variable
other than the one manipulated in an experiment, (independent
variables that are not related to the purpose of the study), which
affects the result. Unless controlled, they become sources of
errors.
Moderating variable: Values that are not variable, which
directly influence the dependent variable but modify or moderate
the influence on one or more independent variables on the
dependent variables.
21. Generation of Hypotheses
A list of variables generally used in hypothesis
generation is given below:
Mediating variable: Values that affect the relationship between
independent and dependent variable but is not causal with respect
to dependent variables
Discrete variable: Values that the variable can take are non-
continuous (for example, integer variable).
Continuous variable: Values that the variable can take are
continuous
Dummy variable: Used in algebraic manipulations. but is a
variable in a technical sense only.
22. 2.Research Design
The research design phase deals with the detailing of procedures
that will be adopted to carry out the research study. The kind of
research that is carried out, whether the study is carried out in the
field or in the laboratory, are decided. The details of data
collection procedures and the schedule of analytical procedures to
be used in order to accomplish the research objectives (set in the
earlier stages of research process) are also dealt with in research
design
23. Research Design
The empirical study may generate data for the purpose of hypothesis
testing or verification of a conceptual model related to a decision.
There are two broad ways in which the research may proceed:
A. Hypotheses are tested using statistical hypothesis testing procedures and
accepted or rejected.
B. A hypothesis or a conceptual model may lead to a relational statistical model.
The statistical relational model will be analyzed using bivariate or multivariate statistical
procedures, as required, and the relation is established and validated using the data generated.
When the hypothesis or objective of the study is to explore the dimensions of a construct,
then the data generated will be subject to factor analysis or cluster analysis to obtain them.
24. A truly scientific research is believed by most physical science researchers to
be one of experimentation. Experimental type of research will be carried out
when the problem clarity is very high and the casual relationships between
variables are investigated rigorously. When the problem clarity is low, usually
exploratory kind of research will be undertaken. In exploration the researcher
may try to discover factors and authenticate the variables with which he
starts the research, thereby, obtaining a greater understanding of the
phenomena, whereas when the problem clarity is comparatively moderate, a
field study may be attempted. The study is usually carried out with the help
of a questionnaire, which seeks to get information to test relationships
between variables. Correlational analyses are the popular mode of analysis of
field study. But sometimes casual relationships are also obtained using
statistical procedures, wherein the researcher is a little more confident and
well supported by earlier research. The point is that a research design may
combine many of these features.
25. RESEARCH DESIGN PROCESS
Once the objective of research is clear, the research process enters the research
design phase.
In this phase, the researcher will have to detail a plan in which alternatives are
going to be chosen at each of the following stages of research.
1. Selection of the type of research
2. Selection of the measures and the measurement techniques.
3. The kind and the number of subjects sampled, that is, sample design.
4. Selection of the data collection procedures.
5. The selection of methods of analysis of data.
Our focus in
this course
28. Measurement and Measurement Techniques
Measurement is defined as the assignment of numbers to
characteristics of objects, persons states, or events according to
rules (Tull & Hawkins, 1987).
The most critical aspect of measurement is the development of
rules for assigning numbers to the characteristics. This problem is
particularly tricky and difficult in social science and
organizational research in which the definition of concepts and
variables are often neither easy nor direct.
29. Measurement is assignment of numbers to characteristics of objects, persons,
states, or events according to rules. There are four measurement techniques:
questionnaires, attitude scales, observation sheets and depth interview
schedules.
Questionnaire:
This is a set of questions, used as an instrument for seeking relevant
information directly from respondents. The questions pertain to one or more
of characteristics of the respondent, like behavior, demographic
characteristic, knowledge, opinions, attitudes, beliefs, and feelings.
Generally a question or a set of questions represents a variable used in
research. These are usually specially designed for a particular research and
then suitably validated before use.
However, in many studies standard inventories/tests designed and tested by
others may also be used.
30. Measurement is assignment of numbers to characteristics of objects, persons,
states, or events according to rules. There are four measurement techniques:
questionnaires, attitude scales, observation sheets and depth interview
schedules.
Attitude scales:
These scales elicit self-reports of beliefs and feelings towards an
object.
There are different types of attitude scales:
(i) Rating Scales that require the respondent to place the object at some
point on a continuum that is numerically ordered;
(ii) Composite scales require a respondent to express a degree of belief
with regard to several attributes of an object;
(iii) multidimensional scales and scales developed using conjoint analysis
are mathematically developed scales to be used for inferring specific
aspects of an individual's attitude towards an object as against direct
evaluation of the respondents (as in the first two scaling methods).
31. Measurement is assignment of numbers to characteristics of objects, persons,
states, or events according to rules. There are four measurement techniques:
questionnaires, attitude scales, observation sheets and depth interview
schedules.
Observation:
This is the direct examination of behavior or results of
behavior.
Depth interviews:
These are interviews in which individuals are made to
express their feelings freely and without fear of dispute or
disapproval. The details are recorded in specifically
designed sheets.
32. Errors of measurement
A number of errors tend to vitiate a measurement. The researcher has to ensure
that the desired accuracy levels are achieved by conducting suitable tests.
The errors in measurement can be systematic or variable. Systematic errors,
which are consistent, constitute the bias in measurement. Validity refers to bias
and is the degree to which measurement is free from systematic error.
The variable error is associated with each replication of measurement and the
term reliability refers to variable errors. It is defined as the extent to which a
measurement is free of variable errors.
Therefore, unless a pre-evaluated and reliable instrument is used for data
collection, the validity and reliability of a measurement technique or
instrument designed by the researcher must be established.
33. The next step after measurement is Selection of Sample
In most cases of research, sampling is needed. Sampling is a necessary and an
inescapable part of any human activity like purchasing commodities, selection
of a television program to watch or even a book to read.
If the population is small enough, instead of sampling a census can be carried
out.
But usually, populations are large and there is limited time and resources
available with the researcher for data collection. Therefore. selecting a sample
becomes necessary.
34. Selection of Sample
In spite of statistical methods being used in the selection of a
sample, judgment is central to all stages of sampling.
Sampling designs are aimed at two major objectives:
(i) the sample is representative of the population;
(ii) the size of the sample is adequate to get the desired accuracy.
35. Selection of Sample
In general, the sampling process consists of:
A definition of the target population in terms of elements, sampling units. domain, and
period;
Specification of a frame of sampling if probability sampling is used (for example,
telephone directory, map, or listings);
Specifying sampling units (for example, a firm, department, group, or an individual that
is addressed in the sample);
The sampling method (for example, probability versus non-probability, single versus
cluster, stratified versus non-stratified, single stage versus multistage);
Determination of sample size, which is the number of elements in the sample, using
statistical methods but often moderated by judgment based on other considerations like
availability, cost, and accessibility;
Implementation of the sampling plan by ensuring the various controls required in the
field to attain the sampling objectives and by contacting the sample members
36. The next step is Selection of Data Collection Procedures
Data collection will involve the development of the instruments for data
collection, identification of sources of data, and the context in which the
sampling has to be done. The sources of data are usually people and existing
records.
To get information from people, it is either necessary to use interviews, where
the information may be given readily, or questionnaires, where the information
may have to be given after careful reflection on the part of the respondent.
There are several procedures of data collection available to the researcher.
Depending on the problem, he may choose one or a combination of more than
one procedure
37. Selection of Data Collection Procedures
There are two sources of data-secondary data and primary data.
Secondary data:
This kind of data is generated for purposes other than for solving the problem under
study.
There are three methods of obtaining secondary data:
1. The data is available in published research journals, reports, and books open to the public
in libraries.
2. Search of data generated within the organization through reports, log books, records of
unions, minutes of meetings, proceedings, accounting documents, home journals, and so on.
3. Computer search of databases and the World Wide Web.
38. Selection of Data Collection Procedures
There are two sources of data-secondary data and primary data.
Primary data:
The procedures used for collecting primary data in a research study are:
(a) questionnaire mail surveys;
(b) interviews of several kinds;
(c) observation of phenomena/subject;
(d) special techniques like video/audio recording/projective methods.
In general a researcher may use a pure strategy (one single type research) or a
combination of a few types as in mixed design. For example, cross-sectional research
may be repeated at many points of time in a longitudinal study. An exploration may be
used before a descriptive study or a field study or an experiment.
39. The next step is Selection of Methods of Analysis
Data analysis deals with the conversion of a series of data gathered into information
statements:
(i)which descriptively state the information in terms of means, percentages, classification or
distribution.
(ii)which make assertions about relationships conjectured prior to data collection.
(iii)which provide estimates for the purposes of prediction.
The selection of methods or techniques of analysis must generally precede the collection of
data in any good research. Dummy data (intuitional responses) may be used with the
designed instrument and subjected to analysis as per the selected methods to test whether the
results provide the desired information for the solution of the problem at hand.
There are a large number of statistical methods available for analysing the research data
collected.
40. Selection of Methods of Analysis
Data analysis methods in general
Data analysis aims at the levels of variables and their variability when a single variable
is used in the analysis. Univariate hypothesis testing is a typical analysis. An important
aspect is when small samples of data are used, non-parametric tests are used, and
parametric tests are used on large samples.
It aims at associations in the case of two variables. Correlation and regression analysis
are performed and the significance of regression or correlation coefficient are tested to
confirm the results.
It aims at dependence relationships (in general a set of independent variables) or
interdependence relationships (a set of independent variables are present but there is no
dependent variable). The former are more useful in establishing relationships and the
latter in developing concepts and constructs.
41. Selection of Methods of Analysis
Data analysis methods in general
Four basic procedures are involved. They are as follows:
A. Data reduction
This includes the following steps:
1. Field controls to minimize errors in data collection
2. Editing to ensure readable and accurate data
3. Coding to categories the edited data
4. Transferring data to usable media, like tapes
5. Generation of new variables by aggregation. scale changing, and data transformation
and
6. Calculating summary statistics like the mean, standard deviation, proportion, and so
on.
42. Selection of Methods of Analysis
Data analysis methods in general
Four basic procedures are involved. They are as follows:
B. Hypothesis testing
This includes:
1. Hypothesis testing requiring interval data; tests for single sample-sample mean
and sample proportions; tests for multiple samples involving differences in means,
difference in proportions of both independent and related samples.
2. Hypothesis testing using ordinal data.
3. Hypothesis testing using nominal data.
4. Multivariate hypothesis testing, including hypothesis tests of difference between
groups to test interaction effects, for example, ANOVA in experimental situations.
43. Selection of Methods of Analysis
Data analysis methods in general
Four basic procedures are involved. They are as follows:
C. Bivariate measures of association
This constitutes:
1. Simple correlation and regression analysis using ratio/interval data.
2. Rank correlation analysis using ordinal data.
3. Contingency coefficient determination for nominal data.
44. Selection of Methods of Analysis
Data analysis methods in general
Four basic procedures are involved. They are as follows:
D. Multivariate measures of association
The key questions to be answered in order to make the choice of a particular
technique are:
(1) The number of independent variables (two or more).
(2) Whether there is a dependent variable.
(3) The level of measurement: nominal (category) ordinal (Ranked) or Internal or ratio,
with respect to independent variables in the analysis of dependence relationships.
(4) The level of measurement, nominal, ordinal or interval of the independent variables
whose interdependence is to be analyzed.
45. Selection of Methods of Analysis
Data analysis methods in general
Four basic procedures are involved. They are as follows:
D. Multivariate measures of association
A brief outline of each technique is given below.
Multiple regression analysis examines relationships between two or more intervally scaled predictor
variables and one intervally scaled criterion variable (ordinal data that are near interval can also be used).
This is an extension of the bivariate regression analysis.
Discriminant analysis is used in place of regression analysis when the criterion variable is nominally
scaled and the predictor variables are intervally scaled. The objective is to group the criterion variables
into two or more categories like good or bad, high risk or low risk, and so on.
Path analysis is a technique for refining causal relationships in theory building or understanding
influencing factors. It uses a series of regression analyses conducted simultaneously to determine if a set
of proposed causal relationships exist in a sample data.
Factor analysis is helpful in summarizing a large number of original variables into a smaller number of
factors (synthetic variables) in order to achieve parsimony in representing phenomena. It can help (i) in
determining underlying dimensions of data，(ii) in condensing and simplifying data, and (iii) in
hypothesis testing and the structuring of data, that is, if a set of variables come from a specified factor. It
doe: not use criterion and predictor variables or their relationships. It determines the relationship among a
set of variables.
Cluster analysis is useful in segregating objects into groups such that the groups are relatively
homogeneous. Examples are grouping of products and market segmentation.
