mb0050 semester iii

8/3/2019 Mb0050 Semester III

1/37

1

MBA SEMESTER III

MB0050 Research Methodology- 4 Credits

(Book ID: B1206 )

Assignment Set- 1 (60 Marks)

Note: Each question carries 10 Marks. Answer all the questions

1. a. Differentiate between nominal, ordinal, interval and ratio scales, with an exampleof each. [ 5marks]

b. What are the purposes of measurement in social science research?

Ans:- A ordinal variable, is one where the order matters but not the differencebetween values. For example, you might ask patients to express the amount ofpain they are feeling on a scale of 1 to 10. A score of 7 means more pain that a

score of 5, and that is more than a score of 3. But the difference between the 7

and the 5 may not be the same as that between 5 and 3. The values simply

express an order. Another example would be movie ratings, from * to *****.

A interval variable is a measurement where the difference between two values ismeaningful. The difference between a temperature of 100 degrees and 90

degrees is the same difference as between 90 degrees and 80 degrees.

A ratio variable, has all the properties of an interval variable, and also has a cleardefinition of 0.0. When the variable equals 0.0, there is none of that variable.

Variables like height, weight, enzyme activity are ratio variables. Temperature,

expressed in F or C, is not a ratio variable. A temperature of 0.0 on either of those

scales does not mean 'no temperature'. However, temperature in degrees Kelvin

in a ratio variable, as 0.0 degrees Kelvin really does mean 'no temperature'.

Another counter example is pH. It is not a ratio variable, as pH=0 just means 1

molar of H+. and the definition of molar is fairly arbitrary. A pH of 0.0 does not

mean 'no acidity' (quite the opposite!). When working with ratio variables, but not

interval variables, you can look at the ratio of two measurements. A weight of 4

grams is twice a weight of 2 grams, because weight is a ratio variable. A

temperature of 100 degrees C is not twice as hot as 50 degrees C, becausetemperature C is not a ratio variable. A pH of 3 is not twice as acidic as a pH of 6,

because pH is not a ratio variable.

Empirical research in the social sciences requires both accurate and reliable measures.

Collection of data takes many forms in the social sciences including measurement of

perceptions, cognitions, opinions, and other latent constructs that cant be measured


2/37

2

directly. When it comes to quantification of people, perceptions, and events, there are 4

main types of measurement.

One mistake many social science researchers make is collecting data at a lower level

than is necessary. As it turns out, it is always possible to transform data from a higher

level to a lower level but never the other way around. Before you collect data for a

research study, consider carefully which of the 4 types of data you are collecting and

how you will use them once you have them.

4 Levels of Measurement

Nominal Scale

The nominal scale (also called dummy coding) simply places people, events,

perceptions, etc. into categories based on some common trait. Some data are naturally

suited to the nominal scale such as males vs. females, redheads vs. blondes vs.

brunettes, and African American vs. Asian. The nominal scale forms the basis for such

analyses as Analysis of Variance (ANOVA) because those analyses require that some

category is compared to at least one other category.

The nominal scale is the lowest form of measurement because it doesnt captureinformation about the focal object other than whether the object belongs or doesntbelong to a category; either you are a smoker or not a smoker, you attended college or

you didnt, a subject has some experience with computers, an average amount of

experience with computers, or extensive experience with computers. No data is capturedthat can place the measured object on any kind of scale say, for example, on a

continuum from one to ten. Coding of nominal scale data can be accomplished using

numbers, letters, labels, or any symbol that represents a category into which an object

can either belong or not belong.

Ordinal Scale

The ordinal scale has at least one major advantage over the nominal scale. The ordinal

scale contains all of the information captured in the nominal scale but it also ranks data

from lowest to highest. Rather than simply categorize data by placing an object eitherinto or not into a category, ordinal data give you some idea of where data lie in relation

to each other.

For example, suppose you are conducting a study on cigarette smoking and you capture

how many packs of cigarettes three smokers consume in a day. It turns out that the first


3/37

3

subject smokes one pack a day, the second smokes two packs a day, and the third

smokes ten packs a day. Using an ordinal scale, your data would look like this.

1. Ten packs a day smoker2. Two packs a day smoker3. One pack a day smoker

The ordinal scale rank orders the subjects by how many packs of cigarettes they smoke

in one day. Notice, however, that although you can use the ordinal scale to rank the

subjects, there is some important data missing; the first smoker occupies a rank the

same distance from the second smoker as the second smoker occupies a rank the same

distance from the third smoker. Consequently, no information exists in the ordinal scale

to indicate the distance one smoker is from the others except for the ranking. Richer

than nominal scaling, ordinal scaling still suffers from some information loss in the data.

Interval Scale

Unlike the nominal scale that simply places objects into or out of a category or the

ordinal scale that rank orders objects, the interval scale indicates the distance one object

is from another. In the social sciences, there is a famous example often taught to

students on this distinction.

Suppose you are near the shore of a lake and you see three tree stumps sticking out of

the water. Using the water as a reference point, it would be easy to measure which

stump rises highest out of the water. In this way, you can create a relative measure ofthe height of the stumps from the surface of the water. For example, the first stump may

breach the water by twenty-four centimeters, the second by twenty-six centimeters, and

the third by twenty-eight centimeters. Unlike the nominal and ordinal scales, you can

make relative distance measurements among objects using the interval scale.

However, the distance the stumps extend out of the water gives you no indication of

how long the stumps actually are. Its possible that the bottom of the lake is irregularmaking the tallest stump look tallest only in relation to the water. Using interval scaling,

you have no indication of the absolute length of the stumps. Still, the interval scale

contains richer information that the two lower levels of scaling.

Ratio Scale

The scale that contains the richest information about an object is ratio scaling. The ratio

scale contains all of the information of the previous three levels plus it contains an

absolute zero point. To use the example above, the ratio scale allows you to measure


4/37

4

the stumps from the bottom of the lake; the bottom of the lake represents the absolute

zero point.

The distinction between interval and ratio scales is an important one in the social

sciences. Although both can capture continuous data, you have to be careful not to

assume that the lowest possible score in your data collection automatically represents

an absolute zero point.

Take extraversion captured using a psychometrically sound survey instrument. The items

that capture this construct may range from zero to ten on the survey but there is no

guarantee that a score of zero on the survey places a subject at the absolute zero point

on the extraversion construct. Yes, you know that a subject with a score of eight on the

scale is more extraverted than someone with a score of seven, but those numbers only

exist for comparison between each other, not in comparison to some absolute score of

zero extraversion

[ 5 marks]

2. a. What are the sources from which one may be able to identify research problems?[ 5 marks]

b. Why literature survey is important in research? [ 5 marks]

Ans:- So how do researchers come up with the idea for a research project? Probably one

of the most common sources of research ideas is the experience ofpractical problems

in the field. Many researchers are directly engaged in social, health or human service

program implementation and come up with their ideas based on what they see

happening around them. Others aren't directly involved in service contexts, but work

with (or survey) people who are in order to learn what needs to be better understood.

Many of the ideas would strike the outsider as silly or worse. For instance, in health

services areas, there is great interest in the problem of back injuries among nursing staff.It's not necessarily the thing that comes first to mind when we think about the health

care field. But if you reflect on it for a minute longer, it should be obvious that nurses

and nursing staff do an awful lot of lifting in performing their jobs. They lift and push

heavy equipment, and they lift and push oftentimes heavy patients! If 5 or 10 out of

every hundred nursing staff were to strain their backs on average over the period of one

year, the costs would be enormous -- and that's pretty much what's happening. Even


5/37

5

minor injuries can result in increased absenteeism. Major ones can result in lost jobs and

expensive medical bills. The nursing industry figures that this is a problem that costs

tens of millions of dollars annually in increased health care. And, the health care industry

has developed a number of approaches, many of them educational, to try to reduce the

scope and cost of the problem. So, even though it might seem silly at first, many of

these practical problems that arise in practice can lead to extensive research efforts.

Another source for research ideas is the literature in your specific field. Certainly,

many researchers get ideas for research by reading the literature and thinking of ways

to extend or refine previous research. Another type of literature that acts as a source of

good research ideas is the Requests For Proposals (RFPs) that are published by

government agencies and some companies. These RFPs describe some problem that the

agency would like researchers to address -- they are virtually handing the researcher an

idea! Typically, the RFP describes the problem that needs addressing, the contexts in

which it operates, the approach they would like you to take to investigate to address theproblem, and the amount they would be willing to pay for such research. Clearly, there's

nothing like potential research funding to get researchers to focus on a particular

research topic.

And let's not forget the fact that many researchers simply think up their researchtopic

on their own. Of course, no one lives in a vacuum, so we would expect that the ideas

you come up with on your own are influenced by your background, culture, education

and experiences.

Is the study feasible?

Very soon after you get an idea for a study reality begins to kick in and you begin to

think about whether the study is feasible at all. There are several major considerations

that come into play. Many of these involve making tradeoffs between rigor and

practicality. To do a study well from a scientific point of view may force you to do

things you wouldn't do normally. You may have to control the implementation of your

program more carefully than you otherwise might. Or, you may have to ask program

participants lots of questions that you usually wouldn't if you weren't doing research. If

you had unlimited resources and unbridled control over the circumstances, you would

always be able to do the best quality research. But those ideal circumstances seldom

exist, and researchers are almost always forced to look for the best tradeoffs they can

find in order to get the rigor they desire.

There are several practical considerations that almost always need to be considered

when deciding on the feasibilityof a research project. First, you have to think about

how long the research will taketo accomplish. Second, you have to question whether


6/37

6

there are important ethical constraintsthat need consideration. Third, can you achieve

the needed cooperationto take the project to its successful conclusion. And fourth,

how significant are the costsof conducting the research. Failure to consider any of these

factors can mean disaster later.

The Literature Review

One of the most important early steps in a research project is the conducting of the

literature review. This is also one of the most humbling experiences you're likely to have.

Why? Because you're likely to find out that just about any worthwhile idea you will have

has been thought of before, at least to some degree. Every time I teach a research

methods course, I have at least one student come to me complaining that they couldn't

find anything in the literature that was related to their topic. And virtually every time

they have said that, I was able to show them that was only true because they only

looked for articles that were exactlythe same as their research topic. A literature reviewis designed to identify related research, to set the current research project within a

conceptual and theoretical context. When looked at that way, there is almost no topic

that is so new or unique that we can't locate relevant and informative related research.

Some tips about conducting the literature review. First, concentrate your efforts on

the scientificliterature. Try to determine what the most credible research journals are

in your topical area and start with those. Put the greatest emphasis on research journals

that use a blind review system. In a blind review, authors submit potential articles to a

journal editor who solicits several reviewers who agree to give a critical review of the

paper. The paper is sent to these reviewers with no identification of the author so thatthere will be no personal bias (either for or against the author). Based on the reviewers'

recommendations, the editor can accept the article, reject it, or recommend that the

author revise and resubmit it. Articles in journals with blind review processes can be

expected to have a fairly high level of credibility. Second, do the review earlyin the

research process. You are likely to learn a lot in the literature review that will help you in

making the tradeoffs you'll need to face. After all, previous researchers also had to face

tradeoff decisions.

What should you look for in the literature review? First, you might be able to find a

study that is quite similar to the one you are thinking of doing. Since all credible

research studies have to review the literature themselves, you can check their literature

review to get a quick-start on your own. Second, prior research will help assure that you

include all of the major relevant constructs in your study. You may find that other similar

studies routinely look at an outcome that you might not have included. If you did your

study without that construct, it would not be judged credible if it ignored a major


7/37

7

construct. Third, the literature review will help you to find and select appropriate

measurement instruments. You will readily see what measurement instruments

researchers use themselves in contexts similar to yours. Finally, the literature review will

help you to anticipate common problems in your research context. You can use the prior

experiences of other to avoid common traps and pitfalls.

Research is made in order to inform people with new knowledge or discovery. However,

it is not to be expected that everybody would willingly believe what you are tackling in

your whole research paper. Thus, what you can do to make your research more credible

will be to support them with other works which have spoken about the same topic that

you have for your research. This is where literature review comes in.

You can even have literature sources in works such as stories, comments, project,

speech, article, novel, poem, essay, program, theory, and others. This is why literature

review involves scanning the pages of any published literature like books, newspaper,

magazine, website, webpage, collection, paper, pamphlet, and the like where you maybe able to find any reference to the same topicthat you are researching on. This time,

literature does not exclusively refer to the poetic rendition of words, like that ofShakespeare alone.

There are many reasons why literature review is rendered as a significant part of any

researchor dissertation paper. You may ask what makes it as such if it is only supposed

to contain tidbits of other related works. Literature review is the part of the paper where

the researcher will be given the opportunity to strengthen your paper for you will be

citing what other reliable authors have said about your topic. This will prove that you are

not just writing about any random subject but that many others have also poured their

thoughts on the topic.You may also ask what makes literature review a necessary part of the paper. This

question can be answered by trying not to include the review in your paper. Obviously,

it affects the length of your paper but this is not the noticeable part. What would most

certainly be lacking is the fact that your paper, without the literature review, only

contains all of your opinions about the facts that you have discovered through your

research. Thus, how can you further convince the readers, in this case, the committee

who will scrutinize your paper? This is the need that is answered only by the literature

review. By the mere fact that you are using referencing by citing what more credible

people had said about the topic will build a stronger foundation for your paper.With a literature review, you need to establish a clear tie between the works that you

have cited and the topic that you are writingabout. You should be able to justify the

inclusion of a certain work in your review so as to make everything that you have

writtenuseful. The more you include useless points in your paper, the more that the

committee will think that you have not put in a lot of thinking into your paper.

Literature review is also unique from the rest of the paper. While you have to fill most of


8/37

8

the paper with your own analysis, in a literature review alone, you will have to write

purely about related works of other people.

3. a. What are the characteristics of a good research design? [ 5marks]b. What are the components of a research design? [ 5 marks]

Ans:- Much contemporary social research is devoted to examining whether a program,

treatment, or manipulation causes some outcome or result. For example, we might wish

to know whether a new educational program causes subsequent achievement scoregains, whether a special work release program for prisoners causes lower recidivism

rates, whether a novel drug causes a reduction in symptoms, and so on. Cook and

Campbell (1979) argue that three conditions must be met before we can infer that such

a cause-effect relation exists:

1. Covariation.Changes in the presumed cause must be related to changes in thepresumed effect. Thus, if we introduce, remove, or change the level of a

treatment or program, we should observe some change in the outcome

measures.

2. Temporal Precedence.The presumed cause must occur prior to the presumedeffect.

3. No Plausible Alternative Explanations.The presumed cause must be the onlyreasonable explanation for changes in the outcome measures. If there are other

factors which could be responsible for changes in the outcome measures we

cannot be confident that the presumed cause-effect relationship is correct.

In most social research the third condition is the most difficult to meet. Any number of

factors other than the treatment or program could cause changes in outcome measures.

Campbell and Stanley (1966) and later, Cook and Campbell (1979) list a number ofcommon plausible alternative explanations (or, threats to internal validity). For example,

it may be that some historical event which occurs at the same time that the program or

treatment is instituted was responsible for the change in the outcome measures; or,

changes in record keeping or measurement systems which occur at the same time as

the program might be falsely attributed to the program. The reader is referred to

standard research methods texts for more detailed discussions of threats to validity.


9/37


10/37

10

availability and sales of competing products. If there is no change in these

measures coincident with the onset of the advertising campaign, these threats

would be considerably minimized. Similarly, if one is studying the effects of

special mathematics training on math achievement scores of children, it might be

useful to observe everyday classroom behavior in order to verify that students

were not receiving any additional math training to that provided in the study.

3. By Design.Here, the major emphasis is on ruling out alternative explanations byadding treatment or control groups, waves of measurement, and the like. This

topic will be discussed in more detail below.

4. By Analysis.There are a number of ways to rule out alternative explanationsusing statistical analysis. One interesting example is provided by Jurs and Glass

(1971). They suggest that one could study the plausibility of an attrition or

mortality threat by conducting a two-way analysis of variance. One factor in this

study would be the original treatment group designations (i.e., program vs.

comparison group), while the other factor would be attrition (i.e., dropout vs.non-dropout group). The dependent measure could be the pretest or other

available pre-program measures. A main effect on the attrition factor would be

indicative of a threat to external validity or generalizability, while an interaction

between group and attrition factors would point to a possible threat to internal

validity. Where both effects occur, it is reasonable to infer that there is a threat to

both internal and external validity.

The plausibility of alternative explanations might also be minimized using

covariance analysis. For example, in a study of the effects of "workfare" programs

on social welfare case loads, one plausible alternative explanation might be thestatus of local economic conditions. Here, it might be possible to construct a

measure of economic conditions and include that measure as a covariate in the

statistical analysis. One must be careful when using covariance adjustments of

this type -- "perfect" covariates do not exist in most social research and the use

of imperfect covariates will not completely adjust for potential alternative

explanations. Nevertheless causal assertions are likely to be strengthened by

demonstrating that treatment effects occur even after adjusting on a number of

good covariates.

5. By Preventive Action.When potential threats are anticipated they can often beruled out by some type of preventive action. For example, if the program is a

desirable one, it is likely that the comparison group would feel jealous or

demoralized. Several actions can be taken to minimize the effects of these

attitudes including offering the program to the comparison group upon

completion of the study or using program and comparison groups which have


11/37

11

little opportunity for contact and communication. In addition, auditing methods

and quality control can be used to track potential experimental dropouts or to

insure the standardization of measurement.

The five categories listed above should not be considered mutually exclusive. The

inclusion of measurements designed to minimize threats to validity will obviously be

related to the design structure and is likely to be a factor in the analysis. A good

research plan should, where possible. make use of multiple methods for reducing

threats. In general, reducing a particular threat by design or preventive action will

probably be stronger than by using one of the other three approaches. The choice of

which strategy to use for any particular threat is complex and depends at least on the

cost of the strategy and on the potential seriousness of the threat.

Design Construction

Basic Design Elements. Most research designs can be constructed from four basic

elements:

1. Time.A causal relationship, by its very nature, implies that some time has elapsedbetween the occurrence of the cause and the consequent effect. While for some

phenomena the elapsed time might be measured in microseconds and therefore

might be unnoticeable to a casual observer, we normally assume that the cause

and effect in social science arenas do not occur simultaneously, In design

notation we indicate this temporal element horizontally - whatever symbol is

used to indicate the presumed cause would be placed to the left of the symbolindicating measurement of the effect. Thus, as we read from left to right in design

notation we are reading across time. Complex designs might involve a lengthy

sequence of observations and programs or treatments across time.

2. Program(s) or Treatment(s).The presumed cause may be a program ortreatment under the explicit control of the researcher or the occurrence of some

natural event or program not explicitly controlled. In design notation we usually

depict a presumed cause with the symbol "X". When multiple programs or

treatments are being studied using the same design, we can keep the programs

distinct by using subscripts such as "X1" or "X2". For a comparison group (i.e., one

which does not receive the program under study) no "X" is used.

3. Observation(s) or Measure(s). Measurements are typically depicted in designnotation with the symbol "O". If the same measurement or observation is taken at

every point in time in a design, then this "O" will be sufficient. Similarly, if the

same set of measures is given at every point in time in this study, the "O" can be

used to depict the entire set of measures. However, if different measures are


12/37

12

given at different times it is useful to subscript the "O" to indicate which

measurement is being given at which point in time.

4. Groups or Individuals. The final design element consists of the intact groups orthe individuals who participate in various conditions. Typically, there will be one

or more program and comparison groups. In design notation, each group is

indicated on a separate line. Furthermore, the manner in which groups are

assigned to the conditions can be indicated by an appropriate symbol at the

beginning of each line. Here, "R" will represent a group which was randomly

assigned, "N" will depict a group which was nonrandomly assigned (i.e., a

nonequivalent group or cohort) and a "C" will indicate that the group was

assigned using a cutoff score on a measurement.

ELEMENTS (Components) OF RESEARCH DESIGN

Choose among each component that offers several critical choices

HOW (Criteria for selecting)

What is the reason of the research?

To what extent the result will affect your decision?

Will it be used for a critical decision? (eg.like investment decision)(strategic or tactical or operational importance of the decision)


13/37

13

What is the required degree of precision and confidence?

Extent of generalization required.

Is there sufficient time available for such a research?

What is the budget allocated for the research?

SUMMARY: Ask the following question at every point of selection

Whether the benefits that result from a more sophisticated

design

to ensure accuracy, confidence, generalizability, etc.,

are

WORTH

the investment of more resources?

TYPES OFRESEARCH(Common

classification)

EXPLORATORYRESEARCH

DESCRIPTIVE

RESEARCH

CAUSALRESEARCH


14/37

14

PURPOSE OF THE

STUDY

EXPLORATORY DESCRIPTIVE HYPOTHESIS

TESTING

It is thepurpose rather than a technique that determines whether a study isexploratory, descriptive or causal.

TYPE OF THE INVESTIGATION

CORRELATIONAL CAUSAL COMPARISON (Difference)

TYPE OF INVESTIGATION - Causal versus non causal (correlational)

Correlational When the researcher is interested in delineating the important variablesthat are associated with the problem. Study can be conducted in the

natural setting where events occur without researcher interfering with

the variables. (No manipulation of independent variable)


15/37

15

Causal When the researcher want to delineate the cause of a problem Some

variables have to be manipulated and others controlled


16/37

16

Make EXPLORATORY Study

FOR

Obtaining better understanding of the phenomena

To define the problem more clearly- refer: what is a problem

Diagnosing a situation (hot issues in company, concern of employees, customers,suppliers, concern of company, sector, countries)

Discovering new ideas, seeking for new opportunities. (workers having suggestionsfor improvement like increasing production, reducing cost, customers suggestionsfor new products to develop.

THEN

Proceed with more rigorous research

Usually data is collected by interviews, observations and when the data reveal some

pattern regarding the phenomena of interest then

Theories are built - Hypothesis are developed

NOTs

Doing a study for the first time in a particular organization does not make the researchexploratory in nature

Purpose is to help formulate the problem and clarify concepts rather than developing aconclusive evidence

THREE GENERAL CATEGORIES OF EXPLORATORY RESEARCH


17/37

17

(Classified on the basis of data collection - information gathering)

EXPERIENCE SURVEYS - Discussions with knowledgeable people, both inside andoutside the company.

SECONDARY DATA ANALYSIS - Not only used for exploring (exploratory research butalso for descriptive and conclusive research.

CASE STUDIES PILOT STUDIES

1-FOCUS GROUP

2-PROJECTIVE METHODS

3-DEPTH INTERVIEWS


18/37

18

DESCRIPTIVE STUDY

Describing the characteristics of a

variable in a given situation

Like demographic characteristics

of individuals, like employees, customers

organizations like production levels, assets,

locations, sales, inventory levels,

suppliers, profits, financial ratios.

industries like economical, financial indicators

pertaining to the industries.

For the purpose of :

Understanding the characteristics of a group in a given situation Aid in thinking systematically about aspects in a given situation Offer ideas for further research Can be ultimately used for decision purposes.

Exploratory Qualitative data may help to understand the phenomena

Descriptive More quantitative data in terms of frequencies,

measures of central tendencies (mean, median, mode, quartiles,

percentiles)


19/37

19

measures of dispersion (standard deviation)

HYPOTHESIS TESTING

(refer to discussion on causal & correlational type of investigation)

Nature of certain relationships

Establish the differences among groups

Independence of two or more factors(variables)

Discussion of null (Ho) and alternative hypothesis (Ha)

Final Word on selection among exploratory, descriptive and hypothesis testing

Rigorness increase

Cost increase

STUDY SETTING

CONTRIVED - Artificial Setting


20/37

20

NONCONTRIVED - Natural environment where

events naturally occur

FIELD STUDY - A noncontrived setting with minimal researcher interference.

Correlational studies

FIELD EXPERIMENT- A noncontrived setting with researcher interference to a moderate

extent.

Cause -effect relationship

LAB EXPERIMENT - A contrived setting with researcher interference to the maximum

extent.

Cause- effect relationship

UNIT OF ANALYSIS

INDIVIDUALS ; DYADS (Wife- Husband, Superior-Subordinate); GROUPS;

DIVISIONS; INDUSTRY; COUNTRIES.

TIME HORIZON: CROSS SECTIONAL ; LONGITUDINAL

VALIDITY


21/37

21

INTERNAL VALIDITY : Refers to the extent of the confidence in the cause effect that

variable X causes variable Y.

EXTERNAL VALIDITY: Refers to the extent of generalizability of the results of a causal

study to other people, events or settings.

There is a trade-off between external validity and internal validity

For internal validity thight control

EXPLORATORY RESEARCH

Most exploratory research is qualitative rather than quantitative

Three interrelated purposes

Diagnosing a situation Screening alternatives concept

testing


22/37

22

Discovering new ideas

GENERAL CATEGORIES OF EXPLORATORY RESEARCH

(Classified on the basis of data collection - information gathering)

CATEGORIESEXPLANATIONS

EXPERIENCE SURVEYS

Discussions with knowledgeable people, both

inside and outside the company

SECONDARY DATA ANALYSIS

Not only used for exploring (exploratory

research) but also for descriptive and

conclusive research.

Preliminary review of data to clarify issues in

the early stages

CASE STUDIES

Investigating one or few situations similar to

the researchers problem and describing the

case situation

PILOT STUDIES

Small scale exploratory research technique

that uses sampling but does not apply

rigorous standards


23/37

23

PILOT STUDIES

FOCUS PROJECTIVE DEPTH INTERVIEW

FOCUS GROUP INTERVIEW

A-AdvantagesExplanations

SynergismCombined effort

Serendipity Opportunity to develop the idea to its full significance

Snowballing Chain of responses

Stimulation Stimulating, motivating the respondents to express.

Security Focus is on the group rather than the indivual

Spontaneity People speak if they have definite feelings

Specialization Group is interviewed by the highly trained interviewer

(moderator)

Scrunity Observed by several people. As the session can be tape recorded it

can be later examined in detail

Structure Moderator as a participant control the discussions.

Open and reopen the topics


24/37

24

Speed Interviewing more than one respondent at the same time

B-Group composition: 6 to 10 individuals; homogenous groups with similar

lifestyles, job classes, experience and communication skills

C - Environmental conditions: Site should be research agency, office conference room

and or a hotel (relaxed and natural)

D- The moderators Job

Everyone should get a chance to speak Promote interaction among members Should gain confidence of people Make them feel relaxed and eager to talk Manipulate the flow of the discussion to generate fresh ideas

E -Planning the focus group outline; Discussion guide

Written remarks and outline of topics that will be discussed

F - Focus group that use video conferencingremote locations


25/37

25

G - Focus group as diagnostic tools

Can be used to diagnose problems suggested by quantitative research.

Quantitative research may be short on why though it is possible to understand

what is happening.

H Shortcomings Dominant participant

I - Interactive media and on line research - is beginning to emerge

PROJECTIVE TECHNIQUES

An indirect means of questioning that enables a respondent to project the beliefs and

feelings on to a third party, onto an inanimate object or into a task situation

Word association test: Subject is presented with a list of words and asked torespond with the first word that comes to mind. Used to pretest words or ideas

to be used in questionnaire.

Sentence completion: e.g. A boss should not --------------

Third person technique : Respondent is asked why a third person (neighbor) doeswhat he or she does or what he or she thinks about an object, event, person,or

activity. Respondent is expected to transfer his or her attitutes to the third person.


26/37

26

Thematic apperception test picture frustration:A test consisting of a series of pictures shown to research subjects who are then asked

to provide a description of the pictures. The researcher analysis the content of these

descriptions in an effort to clarify a research problem.

DEPTH INTERVIEWS: A relatively unstructured, extensive interview used in the primary

stages of research process.

DATAINFORMATION

Recorded measures of certain

phenomena

A body of facts that are in format suitable for

decision making

GLOBAL INFIRMATION SYSTEMS AND THE INTERNET

An organized collection of computer hardware and software, data, and personnel

designed to capture, store, update, manipulate, analyse, and immeadiately display

information about worldwide business activity.

Decision support system is a computer based system that helps decision makers

confront problems through direct interaction with databases and analytical

software

Database is a collection of raw data or information arranged in a logical manner

and organized in aform that can be stored and processed by a computer.


27/37

27

Software : Spreadsheets and statistical software is important

EDI : Networks link one or more computers to share data and software.

Input management Computerized data archieves : Input includes all the

numerical text, voice and image data that enter data base systems. Input should

be managed otherwise garbage in - garbage out will occur.

Internet - Intranet Extranet host server

File transfer protocol (ftp): a software that allows users to establish an interactive file

transfer sessions with a remote hosts computer system so that the user can read and

download full-text versions of files from the remote system.

Search engines

SECONDARY DATA


28/37

28

ADVANTAGES DISADVANTAGES

Less expensive May not meet the researchers need

Obtained rapidly Format of the data may be inconvenient

Data conversion is necessary

Accuracy of the data should be verified -

Cross check

OBJECTIVE FOR SECONDARY DATA RESEARCH DESIGN

1- Fact Finding : Statistics, facts about the population

2- Model building: Specifying relationship between two or more variables.Developing descriptive or predictive equations. Eg; market share; estimation of

market potential


29/37

29

CLASSIFICATION OF SECONDARY DATA

Internal or proprietary data: Secondary data that are created, recorded, orgenerated by the organization

External sources:

Books and periodicals,

Government sources (DE, HAZNE) Stock market sources (MKB, SPK)

Chamber of commerce etc. TO, SO

4. a. Distinguish between Doubles sampling and multiphase sampling.[ 5 marks]

b. What is replicated or interpenetrating sampling? [ 5 marks]

Ans:- Sample size calculations for a continuous outcome require specification of the

anticipated variance; inaccurate specification can result in an underpowered or


30/37

30

overpowered study. For this reason, adaptive methods whereby sample size is

recalculated using the variance of a subsample have become increasingly popular. The

first proposal of this type (Stein, 1945, Annals of Mathematical Statistics 16, 243-258)

used all of the data to estimate the mean difference but only the first stage data to

estimate the variance. Stein's procedure is not commonly used because many peopleperceive it as ignoring relevant data. This is especially problematic when the first stage

sample size is small, as would be the case if the anticipated total sample size were small.

A more naive approach uses in the denominator of the final test statistic the variance

estimate based on all of the data. Applying the Helmert transformation, we show why

this naive approach underestimates the true variance and how to construct an unbiased

estimate that uses all of the data. We prove that the type I error rate of our procedure

cannot exceed alpha.

Double and multiple sampling plans were invented to give a questionable lot anotherchance. For example, if in double sampling the results of the first sample are not

conclusive with regard to accepting or rejecting, a second sample is taken. Application

of double sampling requires that a first sample of size n1 is taken at random from the

(large) lot. The number of defectives is then counted and compared to the first sample's

acceptance number a1 and rejection number r1. Denote the number of defectives in

sample 1 by d1 and in sample 2 by d2, then:

If d1 a1, the lot is accepted.

If d1 r1, the lot is rejected.

If a1 < d1 < r1, a second sample is taken.

If a second sample of size n2 is taken, the number of defectives, d2, is counted. The total

number of defectives is D2 = d1 + d2. Now this is compared to the acceptance number

a2 and the rejection number r2 of sample 2. In double sampling, r2 = a2 + 1 to ensure a

decision on the sample.

If D2 a2, the lot is accepted.

If D2 r2, the lot is rejected.

Replicated or Interpenetrating Sampling

It involves selection of a certain number of sub-samples rather than one full sample

from a population. All the sub-samples should be drawn using the same sampling

technique and each is a self-contained and adequate sample of the population.

Replicated sampling can be used with any basic sampling technique: simple or stratified,


31/37

31

single or multi-stage or single or multiphase sampling. It provides a simple means of

calculating the sampling error. It is practical. The replicated samples can throw light on

variable non-sampling errors. But disadvantage is that it limits the amount of

stratification that can be employed.

5. a. How is secondary data useful to researcher? [ 5 marks]b. What are the criteria used for evaluation of secondary data? [ 5 marks]

Ans:- Secondary data is information gathered for purposes other than the completion of

a research project. A variety of secondary information sources is available to the

researcher gathering data on an industry, potential product applications and the market

place. Secondary data is also used to gain initial insight into the research problem.

Secondary data is classified in terms of its source either internal or external. Internal, orin-house data, is secondary information acquired within the organization where research

is being carried out. External secondary data is obtained from outside sources.

The two major advantages of using secondary data in market research are time and cost

savings.

The secondary research process can be completed rapidly generally in 2 to 3week. Substantial useful secondary data can be collected in a matter of days by askillful analyst.

When secondary data is available, the researcher need only locate the source ofthe data and extract the required information.

Secondary research is generally less expensive than primary research. The bulk ofsecondary research data gathering does not require the use of expensive,

specialized, highly trained personnel.

Secondary research expenses are incurred by the originator of the information.There are also a number of disadvantages of using secondary data. These include:

Secondary information pertinent to the research topic is either not available, or isonly available in insufficient quantities.

Some secondary data may be of questionable accuracy and reliability. Evengovernment publications and trade magazines statistics can be misleading. For

example, many trade magazines survey their members to derive estimates of

market size, market growth rate and purchasing patterns, then average out these


32/37

32

results. Often these statistics are merely average opinions based on less than 10%

of their members.

Data may be in a different format or units than is required by the researcher. Much secondary data is several years old and may not reflect the current market

conditions. Trade journals and other publications often accept articles six months

before appear in print. The research may have been done months or even years

earlier.

As a general rule, a thorough research of the secondary data should be undertaken prior

to conducting primary research. The secondary information will provide a useful

background and will identify key questions and issues that will need to be addressed by

the primary research.

Internal data sources

Internal secondary data is usually an inexpensive information source for the company

conducting research, and is the place to start for existing operations. Internally

generated sales and pricing data can be used as a research source. The use of this data

is to define the competitive position of the firm, an evaluation of a marketing strategy

the firm has used in the past, or gaining a better understanding of the companys bestcustomers.

There are three main sources of internal data. These are:

1. Sales and marketing reports. These can include such things as:

Type of product/service purchased Type of end-user/industry segment Method of payment Product or product line Sales territory Salesperson Date of purchase Amount of purchase Price Application by product Location of end-user

2.Accounting and financial records. These are often an overlooked source of internal

secondary information and can be invaluable in the identification, clarification and


33/37

33

prediction of certain problems. Accounting records can be used to evaluate the success

of various marketing strategies such as revenues from a direct marketing campaign.

There are several problems in using accounting and financial data. One is the timeliness

factor it is often several months before accounting statements are available. Another isthe structure of the records themselves. Most firms do not adequately setup their

accounts to provide the types of answers to research questions that they need. For

example, the account systems should capture project/product costs in order to identify

the companys most profitable (and least profitable) activities.

Companies should also consider establishing performance indicators based on financial

data. These can be industry standards or unique ones designed to measure key

performance factors that will enable the firm to monitor its performance over a period

of time and compare it to its competitors. Some example may be sales per employee,

sales per square foot, expenses per employee (salesperson, etc.).

3. Miscellaneous reports. These can include such things as inventory reports, service calls,

number (qualifications and compensation) of staff, production and R&D reports. Also

the companys business plan and customer calls (complaints) log can be useful sourcesof information.

External data sources

There is a wealth of statistical and research data available today. Some sources are:

Federal government Provincial/state governments Statistics agencies Trade associations General business publications Magazine and newspaper articles Annual reports Academic publications Library sources Computerized bibliographies Syndicated services.

A good place to start your search is the local city, college or university library. Most

reference librarians are very knowledgeable about what data is available, or where to

look to find it. Also contact government libraries and departments for research

reports/publications they may have done.


34/37

34

On many occasions, researchers must choose from two or more sources of data. The

choice should be guided by the determination of which data score highest on the

following considerations:

Pertinency: To be usable, the data must have the same units of measurement specified

in the project, must be applicable to the periods of time in question, and must be

derived from the universe. Classes of data must be constructed in the same way as in

the project.

Who collected and Published the Data and Why: In evaluating secondary data, the

researcher must examine the organization that collected the data and the purposes for

which they were published. An organization that makes the collection and publication of

data its chief functions is apt to furnish accurate data. Obviously, the success of such a

firm depends on the long-run satisfaction of its client that the information supplied is

accurate.

The ability of an organization to procure the wanted information is a pivotal

consideration. This often reduces itself to a matter of authority and prestige. The US

Bureau of Internal Revenue, for instance, can obtain accurate information about income

more easily than any private firm simply because it has legal authority to do so.

When feasible, the capabilities and motivation of the individuals responsible for the data

collection should also be appraised. Reputation, experience, and degree of

independence on the particular project are all genuine considerations in assessing the

reliability of an expert. An individual working for an independent research agencywould be more likely to turn out an accurate report than the same individual working or

an organization committed to one side of a question.

Discovering the purpose for which data are published is mandatory for an adequate

evaluation of secondary data. Data published to promote the interest of a particular

group, whether political commercial or social are suspect. At the same time, not all data

credited to sources with an axe to grind should be dismissed out of hand. Nevertheless

information so procured should always be handled with care.

Data Collection Methods: If a source fails to give a detailed description of its method ofdata collection, researchers should be hesitant about using the information provided. All

too, often shyness about revealing the procedures used to collect data suggest the

employment of inadequate methods. Most primary sources, however, describe their

methods, even if only briefly.


35/37

35

When the methodology is described, researchers should subject it to a painstaking

examination. Even if the procedures appear sound, caution must be exercised because

weaknesses tend to be camouflaged. Searching questions must be answered positively

before the data can be used. If a sample was used, was it selected objectively? Was it

large enough particularly for the sub-samples? Was it chosen from the universe of

interest? Was the questionnaire adequate or getting the desired information? What kind

of supervision was exercised over the people who actually collected the data? Were any

checks made on the accuracy of the field workers results?

General Evidences of Careful Work: An indispensable point of evaluation is the general

evidence that the data have been collected and processed carefully. Is the information

presented in a well-organized manner? Are the tables constructed properly, and are

they consistent within and among themselves? Are the conclusions supported by the

data.

Conflicting Data: If several sources of data relating to a researchers problem areavailable, the data can be submitted to a quality control analysis of the sort applied in

production. After dividing the data into good and poor on the basis of criteria likethose mentioned above, correlations on points of interest can be run between the two

groups, and statistical tests can be made. In projects that rely heavily on secondary data,

this technique is particularly valuable.

6. What are the differences between observation and interviewing as methods ofdata collection? Give two specific examples of situations where either observation

or interviewing would be more appropriate.

[10 marks].

Ans:- Observation vs Interviewing as Methods of Data Collection

Collection of data is the most crucial part of any research project as the success or

failure of the project is dependent upon the accuracy of the data. Use of wrong methodsof data collection or any inaccuracy in collecting data can have significant impact on the

results of a study and may lead to results that are not valid. There are many techniques

of data collection along a continuum and observation and interviewing are two of the

popular methods on this continuum that has quantitative methods at one end while

qualitative methods at the other end. Though there are many similarities in these two


36/37

36

methods and they serve the same basic purpose, there are differences that will be

highlighted in this article.

Observation

Observation, as the name implies refers to situations where participants are observedfrom a safe distance and their activities are recorded minutely. It is a time consuming

method of data collection as you may not get the desired conditions that are required

for your research and you may have to wait till participants are in the situation you want

them to be in. Classic examples of observation are wild life researchers who wait for the

animals of birdsto be in a natural habitat and behave in situations that they want to

focus upon. As a method of data collection, observation has limitations but produces

accurate results as participants are unaware of being closely inspected and behave

naturally.

Interviewing

Interviewing is another great technique of data collection and it involves asking

questions to get direct answers. These interviews could be either one to one, in the form

of questionnaires, or the more recent form of asking opinions through internet.

However, there are limitations of interviewing as participants may not come up with true

or honest answers depending upon privacy level of the questions. Though they try to be

honest, there is an element of lie in answers that can distort results of the project.

Though both observation and interviewing are great techniques of data collection, theyhave their own strengths and weaknesses. It is important to keep in mind which one of

the two will produce desired results before finalizing.

Observation vs Interviewing

Data collection is an integral part of any research and various techniques are employedfor this purpose.

Observation requires precise analysis by the researcher and often produces most

accurate results although it is very time consuming

Interviewing is easier but suffers from the fact that participants may not come up withhonest replies.
http://www.differencebetween.com/category/science-nature/nature/animals/http://www.differencebetween.com/category/science-nature/nature/animals/http://www.differencebetween.com/category/science-nature/nature/birds/http://www.differencebetween.com/category/science-nature/nature/birds/http://www.differencebetween.com/category/technology/it/internet/http://www.differencebetween.com/category/technology/it/internet/http://www.differencebetween.com/category/technology/it/internet/http://www.differencebetween.com/category/science-nature/nature/birds/http://www.differencebetween.com/category/science-nature/nature/animals/


37/37

mb0050 semester iii

Documents