Master of Business Administration – MBA Semester 3rd core

MB0050 Research Methodology - 4 Credits

Assignment Set- 1

60 Marks

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

Q1. Give examples of specific situations that would call for the following types of

research, explaining why – a) Exploratory research b) Descriptive research c)

Diagnostic research d) Evaluation research. (10 marks)

Ans:

a) Exploratory Research

Exploratory research is a type of research conducted for a problem that has not been

clearly defined. Exploratory research helps determine the best research design, data

collection method and selection of subjects. It should draw definitive conclusions only

with extreme caution. Given its fundamental nature, exploratory research often

concludes that a perceived problem does not actually exist.

Exploratory research often relies on secondary research such as reviewing available

literature and/or data, or qualitative approaches such as informal discussions with

consumers, employees, management or competitors, and more formal approaches

through in-depth interviews, focus groups, projective methods, case studies or pilot

studies. The Internet allows for research methods that are more interactive in nature.

For example, RSS feeds efficiently supply researchers with up-to-date information;

major search engine search results may be sent by email to researchers by services

such as Google Alerts; comprehensive search results are tracked over lengthy periods

of time by services such as Google Trends; and websites may be created to attract

worldwide feedback on any subject.

The results of exploratory research are not usually useful for decision-making by

themselves, but they can provide significant insight into a given situation. Although

the results of qualitative research can give some indication as to the "why", "how" and

"when" something occurs, it cannot tell us "how often" or "how many".

Exploratory research is not typically generalizable to the population at large.

It is also known as formulative research. It is preliminary study of an unfamiliar

problem about which the researcher has little or no knowledge. It is ill-structured and

much less focused on pre-determined objectives. It usually takes the form of a pilot

study. The purpose of this research may be to generate new ideas, or to increase the

researcher’s familiarity with the problem or to make a precise formulation of the

problem or to gather information for clarifying concepts or to determine whether it is

feasible to attempt the study. Katz conceptualizes two levels of exploratory studies.

“At the first level is the discovery of the significant variable in the situations; at the

second, the discovery of relationships between variables.”

b) Descriptive Research

Descriptive research, also known as statistical research, describes data and

characteristics about the population or phenomenon being studied. Descriptive

research answers the questions who, what, where, when and how...

Although the data description is factual, accurate and systematic, the research cannot

describe what caused a situation. Thus, Descriptive research cannot be used to create

a causal relationship, where one variable affects another. In other words, descriptive

research can be said to have a low requirement for internal validity.

The description is used for frequencies, averages and other statistical calculations.

Often the best approach, prior to writing descriptive research, is to conduct a survey

investigation. Qualitative research often has the aim of description and researchers

may follow-up with examinations of why the observations exist and what the

implications of the findings are.

In short descriptive research deals with everything that can be counted and studied.

But there are always restrictions to that. Your research must have an impact to the

lives of the people around you. For example, finding the most frequent disease that

affects the children of a town. The reader of the research will know what to do to

prevent that disease thus, more people will live a healthy life.

It is a fact-finding investigation with adequate interpretation. It is the simplest type of

research. It is more specific than an exploratory research. It aims at identifying the

various characteristics of a community or institution or problem under study and also

aims at a classification of the range of elements comprising the subject matter of

study. It contributes to the development of a young science and useful in verifying

focal concepts through empirical observation. It can highlight important

methodological aspects of data collection and interpretation. The information obtained

may be useful for prediction about areas of social life outside the boundaries of the

research. They are valuable in providing facts needed for planning social action

program.

c) Diagnostic Research

It is similar to descriptive study but with a different focus. It is directed towards

discovering what is happening, why it is happening and what can be done about. It

aims at identifying the causes of a problem and the possible solutions for it. It may

also be concerned with discovering and testing whether certain variables are

associated. This type of research requires prior knowledge of the problem, its

thorough formulation, clear-cut definition of the given population, adequate methods

for collecting accurate information, precise measurement of variables, statistical

analysis and test of significance.

d) Evaluation Research

It is a type of applied research. It is made for assessing the effectiveness of social or

economic programmes implemented or for assessing the impact of developmental

projects on the development of the project area. It is thus directed to assess or appraise

the quality and quantity of an activity and its performance, and to specify its attributes

and conditions required for its success. It is concerned with causal relationships and is

more actively guided by hypothesis. It is concerned also with change over time.

Q 2.In the context of hypothesis testing, briefly explain the difference between a) Null

and alternative hypothesis b) Type 1 and type 2 error c) Two tailed and one tailed test

d) Parametric and non parametric tests. (10 marks)

Ans:

a) Null Hypothesis and Alternative Hypothesis

In the context of statistical analysis, we often talk null and alternative hypothesis. If

we are to compare method A with method B about its superiority and if we proceed

on the assumption that both methods are equally good, then this assumption is termed

as null hypothesis. As against this, we may think that the method A is superior, it is

alternative hypothesis. Symbolically presented as:

Null hypothesis = H0 and Alternative hypothesis = Ha

Suppose we want to test the hypothesis that the population mean is equal to the

hypothesis mean (µ H0) = 100. Then we would say that the null hypotheses are that

the population mean is equal to the hypothesized mean 100 and symbolical we can

express as: H0: µ= µ H0=100

If our sample results do not support these null hypotheses, we should conclude that

something else is true. What we conclude rejecting the null hypothesis is known as

alternative hypothesis. If we accept H0, then we are rejecting Ha and if we reject H0,

then we are accepting Ha. For H0: µ= µ H0=100, we may consider three possible

alternative hypotheses as follows:

Alternative

Hypothesis To be read as follows

Ha: µ≠µ H0

(The alternative hypothesis is that the population mean is

not equal to 100 i.e., it may be more or less 100)

Ha: µ>µ H0

(The alternative hypothesis is that the population mean is

greater than 100)

Ha: µ< µ H0

(The alternative hypothesis is that the population mean is

less than 100)

The null hypothesis and the alternative hypothesis are chosen before the sample is

drawn (the researcher must avoid the error of deriving hypothesis from the data he

collects and testing the hypothesis from the same data). In the choice of null

hypothesis, the following considerations are usually kept in view:

Alternative hypothesis is usually the one which wishes to prove and the null

hypothesis are ones that wish to disprove. Thus a null hypothesis represents

the hypothesis we are trying to reject, the alternative hypothesis represents all

other possibilities.

If the rejection of a certain hypothesis when it is actually true involves great

risk, it is taken as null hypothesis because then the probability of rejecting it

when it is true is α (the level of significance) which is chosen very small.

Null hypothesis should always be specific hypothesis i.e., it should not state

about or approximately a certain value.

Generally, in hypothesis testing we proceed on the basis of null hypothesis,

keeping the alternative hypothesis in view. Why so? The answer is that on

assumption that null hypothesis is true, one can assign the probabilities to

different possible sample results, but this cannot be done if we proceed with

alternative hypothesis. Hence the use of null hypothesis (at times also known

as statistical hypothesis) is quite frequent.

 

b) Type 1 and type 2 error

In the context of testing of hypothesis there are basically two types of errors that

researchers make. We may reject H0 when H0 is true & we may accept H0 when it is

not true. The former is known as Type I & the later is known as Type II. In other

words, Type I error mean rejection of hypothesis which should have been accepted &

Type II error means accepting of hypothesis which should have been rejected. Type I

error is donated by α (alpha), also called as level of significance of test; and Type II

error is donated by β(beta).

 Decision

Accept H0 Reject H0

H0 (true) Correct decision Type I error (α error)

Ho (false)Type II error (β

error)Correct decision

The probability of Type I error is usually determined in advance and is understood as

the level of significance of testing the hypothesis. If type I error is fixed at 5%, it

means there are about chances in 100 that we will reject H0 when H0 is true. We can

control type I error just by fixing it at a lower level. For instance, if we fix it at 1%,

we will say that the maximum probability of committing type I error would only be

0.01.

But with a fixed sample size, n when we try to reduce type I error, the probability of

committing type II error increases. Both types of errors can not be reduced

simultaneously. There is a trade-off in business situations, decision-makers decide the

appropriate level of type I error by examining the costs of penalties attached to both

types of errors. If type I error involves time & trouble of reworking a batch of

chemicals that should have been accepted, where as type II error means taking a

chance that an entire group of users of this chemicals compound will be poisoned,

then in such a situation one should prefer a type I error to a type II error means taking

a chance that an entire group of users of this chemicals compound will be poisoned,

then in such a situation one should prefer a type II error. As a result one must set very

high level for type I error in one’s testing techniques of a given hypothesis. Hence, in

testing of hypothesis, one must make all possible effort to strike an adequate balance

between Type I & Type II error.

C) Two Tailed Test & One Tailed Test

In the context of hypothesis testing these two terms are quite important and must be

clearly understood. A two-tailed test rejects the null hypothesis if, say, the sample

mean is significantly higher or lower than the hypnotized value of the mean of the

population. Such a test inappropriate when we haveH0: µ= µ H0 and Ha: µ≠µ H0 which

may µ>µ H0 or µ<µ H0. If significance level is % and the two-tailed test to be applied,

the probability of the rejection area will be 0.05 (equally split on both tails of curve as

0.025) and that of the acceptance region will be 0.95. If we take µ = 100 and if our

sample mean deviates significantly from µ, in that case we shall accept the null

hypothesis. But there are situations when only one-tailed test is considered

appropriate. A one-tailed test would be used when we are to test, say, whether the

population mean in either lower than or higher than some hypothesized value.

d) Parametric and non parametric tests

The hypothesis testing determines the validity of the assumption (technically

described as null hypothesis) with a view to choose between the conflicting

hypotheses about the value of the population hypothesis about the value of the

population of a population parameter. Hypothesis testing helps to secede on the basis

of a sample data, whether a hypothesis about the population is likely to be true or

false. Statisticians have developed several tests of hypothesis (also known as tests of

significance) for the purpose of testing of hypothesis which can be classified as:

Parametric tests or standard tests of hypothesis ;

Non Parametric test or distribution – free test of the hypothesis.

Parametric tests usually assume certain properties of the parent population from

which we draw samples. Assumption like observations come from a normal

population, sample size is large, assumptions about the population parameters like

mean, variants etc must hold good before parametric test can be used. But there are

situation when the researcher cannot or does not want to make assumptions. In such

situations we use statistical methods for testing hypothesis which are called non

parametric tests because such tests do not depend on any assumption about the

parameters of parent population. Besides, most non-parametric test assumes only

nominal or original data, where as parametric test require measurement equivalent to

at least an interval scale. As a result non-parametric test needs more observation than

a parametric test to achieve the same size of Type I & Type II error.

Q3. Explain the difference between a causal relationship and correlation, with an

example of each. What are the possible reasons for a correlation between two

variables? (10 marks)

Ans:

Economic & business variables are related. For instance, demand & supply of a

commodity is related to its price. Demand for a commodity increases as price falls.

Demand for a commodity decreases as its price rises. We say demand & price are

inversely related or negatively correlated. But sellers supply more of a commodity

when its price rises. Supply of the commodity decreases when its price falls. We say

supply & price are directly related or positively co-related. Thus, correlation indicates

the relationship between two such variables in which changes in the value of one

variable is accompanies with a change in the value of other variable.

According to L.R.Connor, “ if two or more quantities vary in sympathy so that

movements in the one tend to be accompanied by corresponding movements in the

others(s) they are said to be correlated.

W.I.King defined “Correlation means that between two series or groups of data, there

exists some casual connection.”

The definitions make it clear that the term correlation refers to the study of

relationship between two or more variables. Correlation is a statistical device, which

studies the relationship between two variables. If two variables are said to be

correlated, change in the value of one variable results in a corresponding change in

the value of the other variable. Heights & weights of a group of people, age of

husbands & wives etc., are examples of bi-variant data that change together.

Correlation and Causation

Although, the term correlation is sued in the sense of mutual dependence of two or

more variable, it is not always necessary that they have cause & effect relation. Even

a high degree of correlation between two variables does not necessarily indicate a

cause & effect relationship between them.

The main difference between cause and correlation is the strength and degree to

which two things are related and the certainty with which anyone can establish a

causal relationship. Essentially when you say one thing causes another, you are saying

that there is a direct line between that one thing and the result. Cause means that an

action will always have a predictable reaction.

When you define correlation, the terms cause and correlation become easier to

understand. If you see a correlation between two things, you can see that there is a

relationship between those two things. One thing doesn’t necessarily result in the

other thing occurring, but it may increase likelihood that something will occur.

Understanding the difference of cause and correlation can be helped by an example.

You can, perhaps, examine the statement: “Violent video games cause violent

behavior.” According to all research on this matter, this statement is not true, due to

the use of the word causes in the sentence. Research has shown that violent video

games may influence violent behavior.

It also shows that a number of different factors may be responsible for a person being

violent, among them, poorer socioeconomic status, mental illness, abusive childhoods,

and bad parenting. You cannot say violent video games are the cause of violence. In

order to make the above statement, you’d have to be able to prove that everyone who

ever played a violent video game subsequently exhibited violence.

Instead, what you can say, and what has been studied, is the correlation between

violent video games and violent behavior. Researchers have shown that there is a

connection/correlation there. Such games may influence others to act in more

aggressive ways but they are not the sole factor and sometimes not even a factor for

predicting violence. Thus there’s a correlation there, which should be considered, but

there is no cause factor. Plenty of people were violent, prior to the advent of video

games, thus if you’re deciding between cause and correlation here, you must choose

correlation.

In some ways, it can be almost impossible, except in extremely controlled

circumstances to say any one thing causes something else, especially when you’re

dealing with human health or behavior. You can, in limited ways, make blanket

cause/effect statements about some things. For example, heating water to a certain

temperature causes it to boil. This is a specific cause/effect relationship that no one

would dispute.

While all relationships tell about the correspondence between two variables, there is a

special type of relationship that holds that the two variables are not only in

correspondence, but that one causes the other. This is the key distinction between a

simple correlational relationship and a causal relationship. A correlational

relationship simply says that two things perform in a synchronized manner. For

instance, we often talk of a correlation between inflation and unemployment. When

inflation is high, unemployment also tends to be high. When inflation is low,

unemployment also tends to be low. The two variables are correlated. But knowing

that two variables are correlated does not tell us whether one causes the other. We

know, for instance, that there is a correlation between the number of roads built in

Europe and the number of children born in the United States. Does that mean that is

we want fewer children in the U.S., we should stop building so many roads in

Europe? Or, does it mean that if we don't have enough roads in Europe, we should

encourage U.S. citizens to have more babies? Of course not. (At least, I hope not).

While there is a relationship between the number of roads built and the number of

babies, we don't believe that the relationship is a causal one. This leads to

consideration of what is often termed the third variable problem. In this example, it

may be that there is a third variable that is causing both the building of roads and the

birthrate, that is causing the correlation we observe. For instance, perhaps the general

world economy is responsible for both. When the economy is good more roads are

built in Europe and more children are born in the U.S. The key lesson here is that you

have to be careful when you interpret

correlations. If you observe a

correlation between the number of

hours students use the computer to

study and their grade point averages

(with high computer users getting

higher grades), you cannot assume that

the relationship is causal: that

computer use improves grades. In this

case, the third variable might be socioeconomic status -- richer students who have

greater resources at their disposal tend to both use computers and do better in their

grades. It's the resources that drives both use and grades, not computer use that causes

the change in the grade point average.

Correlation between two variables can be due to following reasons:

Cause & effect relationship: Heat & temperature are cause & effect variable.

Heat is the cause of temperature. Higher the heat, higher will be the

temperature.

Both the correlated variables are being affected by a third variable. For

instance, price of rice & price of sugar are affected by rainfall. Here there may

not be any cause & effect relation between price of rice & price of sugar.

Related variable may be mutually affecting each other so that none of them is

either a cause or an effect. Demand may be the result of price. There are cases

when price rise due to increased demand.

The correlation may be due to chance. For instance, a small sample may show

correlation between wages & productivity. That is higher wage leading to

lower productivity. In real life it need not be true. Such correlation is due to

chance.

There might be a situation of nonsense or spurious correlation between and

two variables. For instance, relationship between number of divorces &

television exports may be correlated. There cannot be any relationship

between divorce & exports of television.

The above points make it clear that correlation is only a statistical relationship & it

does not necessarily signify a cause & effect relationship between the variable.

Q4. Briefly explain any two factors that affect the choice of a sampling technique.

What are the characteristics of a good sample? (10 marks)

Ans:

The difference between non-probability and probability sampling is that non-

probability sampling does not involve random selection and probability sampling

does. Does that mean that non-probability samples aren't representative of the

population? Not necessarily. But it does mean that non-probability samples cannot

depend upon the rationale of probability theory. At least with a probabilistic sample,

we know the odds or probability that we have represented the population well. We are

able to estimate confidence intervals for the statistic. With non-probability samples,

we may or may not represent the population well, and it will often be hard for us to

know how well we've done so. In general, researchers prefer probabilistic or random

sampling methods over non probabilistic ones, and consider them to be more accurate

and rigorous. However, in applied social research there may be circumstances where

it is not feasible, practical or theoretically sensible to do random sampling. Here, we

consider a wide range of non-probabilistic alternatives.

We can divide non-probability sampling methods into two broad types: 

Accidental or purposive.

Most sampling methods are purposive in nature because we usually approach

the sampling problem with a specific plan in mind. The most important distinctions

among these types of sampling methods are the ones between the different types of

purposive sampling approaches.

Accidental, Haphazard or Convenience Sampling

One of the most common methods of sampling goes under the various titles

listed here. I would include in this category the traditional "man on the street" (of

course, now it's probably the "person on the street") interviews conducted frequently

by television news programs to get a quick (although non representative) reading of

public opinion. I would also argue that the typical use of college students in much

psychological research is primarily a matter of convenience. (You don't really believe

that psychologists use college students because they believe they're representative of

the population at large, do you?). In clinical practice, we might use clients who are

available to us as our sample. In many research contexts, we sample simply by asking

for volunteers. Clearly, the problem with all of these types of samples is that we have

no evidence that they are representative of the populations we're interested in

generalizing to -- and in many cases we would clearly suspect that they are not.

Purposive Sampling

In purposive sampling, we sample with a purpose in mind. We usually would

have one or more specific predefined groups we are seeking. For instance, have you

ever run into people in a mall or on the street who are carrying a clipboard and who

are stopping various people and asking if they could interview them? Most likely they

are conducting a purposive sample (and most likely they are engaged in market

research). They might be looking for Caucasian females between 30-40 years old.

They size up the people passing by and anyone who looks to be in that category they

stop to ask if they will participate. One of the first things they're likely to do is verify

that the respondent does in fact meet the criteria for being in the sample. Purposive

sampling can be very useful for situations where you need to reach a targeted sample

quickly and where sampling for proportionality is not the primary concern. With a

purposive sample, you are likely to get the opinions of your target population, but you

are also likely to overweight subgroups in your population that are more readily

accessible.

All of the methods that follow can be considered subcategories of purposive

sampling methods. We might sample for specific groups or types of people as in

modal instance, expert, or quota sampling. We might sample for diversity as in

heterogeneity sampling. Or, we might capitalize on informal social networks to

identify specific respondents who are hard to locate otherwise, as in snowball

sampling. In all of these methods we know what we want -- we are sampling with a

purpose.

Modal Instance Sampling

In statistics, the mode is the most frequently occurring value in a distribution. In

sampling, when we do a modal instance sample, we are sampling the most frequent

case, or the "typical" case. In a lot of informal public opinion polls, for instance, they

interview a "typical" voter. There are a number of problems with this sampling

approach. First, how do we know what the "typical" or "modal" case is? We could say

that the modal voter is a person who is of average age, educational level, and income

in the population. But, it's not clear that using the averages of these is the fairest

(consider the skewed distribution of income, for instance). And, how do you know

that those three variables -- age, education, income -- are the only or even the most

relevant for classifying the typical voter? What if religion or ethnicity is an important

discriminator? Clearly, modal instance sampling is only sensible for informal

sampling contexts.

Expert Sampling

Expert sampling involves the assembling of a sample of persons with known or

demonstrable experience and expertise in some area. Often, we convene such a

sample under the auspices of a "panel of experts." There are actually two reasons you

might do expert sampling. First, because it would be the best way to elicit the views

of persons who have specific expertise. In this case, expert sampling is essentially just

a specific sub case of purposive sampling. But the other reason you might use expert

sampling is to provide evidence for the validity of another sampling approach you've

chosen. For instance, let's say you do modal instance sampling and are concerned that

the criteria you used for defining the modal instance are subject to criticism. You

might convene an expert panel consisting of persons with acknowledged experience

and insight into that field or topic and ask them to examine your modal definitions

and comment on their appropriateness and validity. The advantage of doing this is that

you aren't out on your own trying to defend your decisions -- you have some

acknowledged experts to back you. The disadvantage is that even the experts can be,

and often are, wrong.

Quota Sampling

In quota sampling, you select people non-randomly according to some fixed quota.

There are two types of quota sampling: proportional and non proportional.

In proportional quota sampling you want to represent the major characteristics of

the population by sampling a proportional amount of each. For instance, if you know

the population has 40% women and 60% men, and that you want a total sample size

of 100, you will continue sampling until you get those percentages and then you will

stop. So, if you've already got the 40 women for your sample, but not the sixty men,

you will continue to sample men but even if legitimate women respondents come

along, you will not sample them because you have already "met your quota." The

problem here (as in much purposive sampling) is that you have to decide the specific

characteristics on which you will base the quota. Will it be by gender, age, education

race, religion, etc.?

Non-proportional quota sampling is a bit less restrictive. In this method, you

specify the minimum number of sampled units you want in each category. Here,

you're not concerned with having numbers that match the proportions in the

population. Instead, you simply want to have enough to assure that you will be able to

talk about even small groups in the population. This method is the non-probabilistic

analogue of stratified random sampling in that it is typically used to assure that

smaller groups are adequately represented in your sample.

Heterogeneity Sampling

We sample for heterogeneity when we want to include all opinions or views, and we

aren't concerned about representing these views proportionately. Another term for this

is sampling for diversity. In many brainstorming or nominal group processes

(including concept mapping), we would use some form of heterogeneity sampling

because our primary interest is in getting broad spectrum of ideas, not identifying the

"average" or "modal instance" ones. In effect, what we would like to be sampling is

not people, but ideas. We imagine that there is a universe of all possible ideas relevant

to some topic and that we want to sample this population, not the population of people

who have the ideas. Clearly, in order to get all of the ideas, and especially the

"outlier" or unusual ones, we have to include a broad and diverse range of

participants. Heterogeneity sampling is, in this sense, almost the opposite of modal

instance sampling.

Snowball Sampling

In snowball sampling, you begin by identifying someone who meets the criteria for

inclusion in your study. You then ask them to recommend others who they may know

who also meet the criteria. Although this method would hardly lead to representative

samples, there are times when it may be the best method available. Snowball

sampling is especially useful when you are trying to reach populations that are

inaccessible or hard to find. For instance, if you are studying the homeless, you are

not likely to be able to find good lists of homeless people within a specific

geographical area. However, if you go to that area and identify one or two, you may

find that they know very well whom the other homeless people in their vicinity are

and how you can find them.

Characteristics of good Sample: The decision process is a complicated one. The

researcher has to first identify the limiting factor or factors and must judiciously

balance the conflicting factors. The various criteria governing the choice of the

sampling technique are:

1. Purpose of the Survey: What does the researcher aim at? If he intends to

generalize the findings based on the sample survey to the population, then

an appropriate probability sampling method must be selected. The choice

of a particular type of probability sampling depends on the geographical

area of the survey and the size and the nature of the population under

study.

2.Measurability: The application of statistical inference theory requires

computation of the sampling error from the sample itself. Only probability

samples allow such computation. Hence, where the research objective

requires statistical inference, the sample should be drawn by applying

simple random sampling method or stratified random sampling method,

depending on whether the population is homogenous or heterogeneous.

3.Degree of Precision: Should the results of the survey be very precise, or

could even rough results serve the purpose? The desired level of precision

is one of the criteria for sampling method selection. Where a high degree

of precision of results is desired, probability sampling should be used.

Where even crude results would serve the purpose (E.g., marketing

surveys, readership surveys etc), any convenient non-random sampling

like quota sampling would be enough.

4. Information about Population: How much information is available about

the population to be studied? Where no list of population and no

information about its nature are available, it is difficult to apply a

probability sampling method. Then an exploratory study with non-

probability sampling may be done to gain a better idea of the population.

After gaining sufficient knowledge about the population through the

exploratory study, an appropriate probability sampling design may be

adopted.

5. The Nature of the Population: In terms of the variables to be studied, is

the population homogenous or heterogeneous? In the case of a

homogenous population, even simple random sampling will give a

representative sample. If the population is heterogeneous, stratified

random sampling is appropriate.

6. Geographical Area of the Study and the Size of the Population: If the

area covered by a survey is very large and the size of the population is

quite large, multi-stage cluster sampling would be appropriate. But if the

area and the size of the population are small, single stage probability

sampling methods could be used.

7. Financial Resources: If the available finance is limited, it may become

necessary to choose a less costly sampling plan like multistage cluster

sampling, or even quota sampling as a compromise. However, if the

objectives of the study and the desired level of precision cannot be attained

within the stipulated budget, there is no alternative but to give up the

proposed survey. Where the finance is not a constraint, a researcher can

choose the most appropriate method of sampling that fits the research

objective and the nature of population.

8. Time Limitation: The time limit within which the research project should

be completed restricts the choice of a sampling method. Then, as a

compromise, it may become necessary to choose less time consuming

methods like simple random sampling, instead of stratified

sampling/sampling with probability proportional to size; or multi-stage

cluster sampling, instead of single-stage sampling of elements. Of course,

the precision has to be sacrificed to some extent.

9. Economy: It should be another criterion in choosing the sampling method.

It means achieving the desired level of precision at minimum cost. A

sample is economical if the precision per unit cost is high, or the cost per

unit of variance is low. The above criteria frequently conflict with each

other and the researcher must balance and blend them to obtain a good

sampling plan. The chosen plan thus represents an adaptation of the

sampling theory to the available facilities and resources. That is, it

represents a compromise between idealism and feasibility. One should use

simple workable methods, instead of unduly elaborate and complicated

techniques.

Q5. Select any topic for research and explain how you will use both secondary and

primary sources to gather the required information. (10 marks)

Ans:

Primary Sources of Data

Primary sources are original sources from which the researcher directly collects data

that has not been previously collected, e.g., collection of data directly by the

researcher on brand awareness, brand preference, and brand loyalty and other aspects

of consumer behavior, from a sample of consumers by interviewing them. Primary

data is first hand information collected through various methods such as surveys,

experiments and observation, for the purposes of the project immediately at hand.

The advantages of primary data are –

It is unique to a particular research study

It is recent information, unlike published information that is already

available

The disadvantages are –

It is expensive to collect, compared to gathering information from

available sources

Data collection is a time consuming process

It requires trained interviewers and investigators

2 Secondary Sources of Data

These are sources containing data, which has been collected and compiled for another

purpose. Secondary sources may be internal sources, such as annual reports, financial

statements, sales reports, inventory records, minutes of meetings and other

information that is available within the firm, in the form of a marketing information

system. They may also be external sources, such as government agencies (e.g. census

reports, reports of government departments), published sources (annual reports of

currency and finance published by the Reserve Bank of India, publications of

international organizations such as the UN, World Bank and International Monetary

Fund, trade and financial journals, etc.), trade associations (e.g. Chambers of

Commerce) and commercial services (outside suppliers of information).

Methods of Data Collection:

The researcher directly collects primary data from its original sources. In this case, the

researcher can collect the required data precisely according to his research needs and

he can collect them when he wants and in the form that he needs it. But the collection

of primary data is costly and time consuming. Yet, for several types of social science

research, required data is not available from secondary sources and it has to be

directly gathered from the primary sources.

Primary data has to be gathered in cases where the available data is inappropriate,

inadequate or obsolete. It includes: socio economic surveys, social anthropological

studies of rural communities and tribal communities, sociological studies of social

problems and social institutions, marketing research, leadership studies, opinion polls,

attitudinal surveys, radio listening and T.V. viewing surveys, knowledge-awareness

practice (KAP) studies, farm management studies, business management studies etc.

There are various methods of primary data collection, including surveys, audits and

panels, observation and experiments.

1 Survey Research

A survey is a fact-finding study. It is a method of research involving collection of data

directly from a population or a sample at a particular time. A survey has certain

characteristics:

It is always conducted in a natural setting. It is a field study.

It seeks responses directly from the respondents.

It can cover a very large population.

It may include an extensive study or an intensive study

It covers a definite geographical area.

A survey involves the following steps -

Selection of a problem and its formulation

Preparation of the research design

Operation concepts and construction of measuring indexes and scales

Sampling

Construction of tools for data collection

Field work and collection of data

Processing of data and tabulation

Analysis of data

Reporting

There are four basic survey methods, which include:

Personal interview

Telephone interview

Mail survey and

Fax survey

Personal Interview

Personal interviewing is one of the prominent methods of data collection. It may be

defined as a two-way systematic conversation between an investigator and an

informant, initiated for obtaining information relevant to a specific study. It involves

not only conversation, but also learning from the respondent’s gestures, facial

expressions and pauses, and his environment.

Interviewing may be used either as a main method or as a supplementary one in

studies of persons. Interviewing is the only suitable method for gathering information

from illiterate or less educated respondents. It is useful for collecting a wide range of

data, from factual demographic data to highly personal and intimate information

relating to a person’s opinions, attitudes, values, beliefs, experiences and future

intentions. Interviewing is appropriate when qualitative information is required, or

probing is necessary to draw out the respondent fully. Where the area covered for the

survey is compact, or when a sufficient number of qualified interviewers are

available, personal interview is feasible.

Interview is often superior to other data-gathering methods. People are usually more

willing to talk than to write. Once rapport is established, even confidential

information may be obtained. It permits probing into the context and reasons for

answers to questions.

Interview can add flesh to statistical information. It enables the investigator to grasp

the behavioral context of the data furnished by the respondents. It permits the

investigator to seek clarifications and brings to the forefront those questions, which

for some reason or the other the respondents do not want to answer. Interviewing as a

method of data collection has certain characteristics. They are:

1. The participants – the interviewer and the respondent – are strangers; hence,

the investigator has to get himself/herself introduced to the respondent in an

appropriate manner.

2. The relationship between the participants is a transitory one. It has a fixed

beginning and termination points. The interview proper is a fleeting,

momentary experience for them.

3. The interview is not a mere casual conversational exchange, but a

conversation with a specific purpose, viz., obtaining information relevant to a

study.

4. The interview is a mode of obtaining verbal answers to questions put verbally.

5. The interaction between the interviewer and the respondent need not

necessarily be on a face-to-face basis, because the interview can also be

conducted over the telephone.

6. Although the interview is usually a conversation between two persons, it need

not be limited to a single respondent. It can also be conducted with a group of

persons, such as family members, or a group of children, or a group of

customers, depending on the requirements of the study.

7. The interview is an interactive process. The interaction between the

interviewer and the respondent depends upon how they perceive each other.

8. The respondent reacts to the interviewer’s appearance, behavior, gestures,

facial expression and intonation, his perception of the thrust of the questions

and his own personal needs. As far as possible, the interviewer should try to be

closer to the social-economic level of the respondents.

9. The investigator records information furnished by the respondent in the

interview. This poses a problem of seeing that recording does not interfere

with the tempo of conversation.

10. Interviewing is not a standardized process like that of a chemical technician; it

is rather a flexible, psychological process.

3 Telephone Interviewing Telephone interviewing is a non-personal method of data

collection. It may be used as a major method or as a supplementary method. It will be

useful in the following situations:

1. When the universe is composed of those persons whose names are listed in

telephone directories, e.g. business houses, business executives, doctors and

other professionals.

2. When the study requires responses to five or six simple questions, e.g. a radio

or television program survey.

3. When the survey must be conducted in a very short period of time, provided

the units of study are listed in the telephone directory.

4. When the subject is interesting or important to respondents, e.g. a survey

relating to trade conducted by a trade association or a chamber of commerce, a

survey relating to a profession conducted by the concerned professional

association.

5. When the respondents are widely scattered and when there are many call

backs to make.

4 Group Interviews A group interview may be defined as a method of collecting

primary data in which a number of individuals with a common interest interact with

each other. In a personal interview, the flow of information is multi dimensional. The

group may consist of about six to eight individuals with a common interest. The

interviewer acts as the discussion leader. Free discussion is encouraged on some

aspect of the subject under study. The discussion leader stimulates the group members

to interact with each other. The desired information may be obtained through self-

administered questionnaire or interview, with the discussion serving as a guide to

ensure consideration of the areas of concern. In particular, the interviewers look for

evidence of common elements of attitudes, beliefs, intentions and opinions among

individuals in the group. At the same time, he must be aware that a single comment by

a member can provide important insight. Samples for group interviews can be

obtained through schools, clubs and other organized groups.

5 Mail Survey The mail survey is another method of collecting primary data. This

method involves sending questionnaires to the respondents with a request to complete

them and return them by post. This can be used in the case of educated respondents

only. The mail questionnaires should be simple so that the respondents can easily

understand the questions and answer them. It should preferably contain mostly closed-

ended and multiple choice questions, so that it could be completed within a few

minutes. The distinctive feature of the mail survey is that the questionnaire is self-

administered by the respondents themselves and the responses are recorded by them

and not by the investigator, as in the case of personal interview method. It does not

involve face-to-face conversation between the investigator and the respondent.

Communication is carried out only in writing and this requires more cooperation from

the respondents than verbal communication. The researcher should prepare a mailing

list of the selected respondents, by collecting the addresses from the telephone

directory of the association or organization to which they belong. The following

procedures should be followed - a covering letter should accompany a copy of the

questionnaire. It must explain to the respondent the purpose of the study and the

importance of his cooperation to the success of the project. Anonymity must be

assured. The sponsor’s identity may be revealed. However, when such information

may bias the result, it is not desirable to reveal it. In this case, a disguised

organization name may be used. A self-addressed stamped envelope should be

enclosed in the covering letter.

After a few days from the date of mailing the questionnaires to the respondents, the

researcher can expect the return of completed ones from them. The progress in return

may be watched and at the appropriate stage, follow-up efforts can be made.

The response rate in mail surveys is generally very low in developing countries like

India. Certain techniques have to be adopted to increase the response rate. They are:

1. Quality printing: The questionnaire may be neatly printed on quality light

colored paper, so as to attract the attention of the respondent.

2. Covering letter: The covering letter should be couched in a pleasant style, so

as to attract and hold the interest of the respondent. It must anticipate

objections and answer them briefly. It is desirable to address the respondent by

name.

3. Advance information: Advance information can be provided to potential

respondents by a telephone call, or advance notice in the newsletter of the

concerned organization, or by a letter. Such preliminary contact with potential

respondents is more successful than follow-up efforts.

4. Incentives: Money, stamps for collection and other incentives are also used to

induce respondents to complete and return the mail questionnaire.

5. Follow-up-contacts: In the case of respondents belonging to an organization,

they may be approached through someone in that organization known as the

researcher.

6. Larger sample size: A larger sample may be drawn than the estimated sample

size. For example, if the required sample size is 1000, a sample of 1500 may

be drawn. This may help the researcher to secure an effective sample size

closer to the required size.

Q6. Case Study: You are engaged to carry out a market survey on behalf of a leading

Newspaper that is keen to increase its circulation in Bangalore City, in order to

ascertain reader habits and interests. Develop a title for the study; define the research

problem and the objectives or questions to be answered by the study. (10 marks)

Ans:

Title: Newspaper reading choices

Research problem: A research problem is  the situation that causes the researcher to

feel apprehensive, confused and ill at ease.  It is the demarcation of a problem area

within a certain context involving the WHO or WHAT, the WHERE, the WHEN and

the WHY of the problem situation.

There are many problem situations that may give rise to research.   Three sources

usually contribute to problem identification.  Own experience or the experience of

others may be a source of problem supply.  A second source could be scientific

literature.  You may read about certain findings and notice that a certain field was not

covered.  This could lead to a research problem.  Theories could be a third source. 

Shortcomings in theories could be researched.

Research can thus be aimed at clarifying or substantiating an existing theory, at

clarifying contradictory findings, at correcting a faulty methodology, at correcting the

inadequate or unsuitable use of statistical techniques, at reconciling conflicting

opinions, or at solving existing practical problems

Types of questions to be asked :For more than 35 years, the news about newspapers

and young readers has been mostly bad for the newspaper industry. Long before any

competition from cable television or Nintendo, American newspaper publishers were

worrying about declining readership among the young.

As early as 1960, at least 20 years prior to Music Television (MTV) or the Internet,

media research scholars1 began to focus their studies on young adult readers'

decreasing interest in newspaper content. The concern over a declining youth market

preceded and perhaps foreshadowed today's fretting over market penetration. Even

where circulation has grown or stayed stable, there is rising concern over penetration,

defined as the percentage of occupied households in a geographic market that are

served by a newspaper.2 Simply put, population growth is occurring more rapidly

than newspaper readership in most communities.

This study looks at trends in newspaper readership among the 18-to-34 age group and

examines some of the choices young adults make when reading newspapers.

One of the underlying concerns behind the decline in youth newspaper reading is the

question of how young people view the newspaper. A number of studies explored

how young readers evaluate and use newspaper content.

Comparing reader content preferences over a 10-year period, Gerald Stone and

Timothy Boudreau found differences between readers ages 18-34 and those 35-

plus.16 Younger readers showed increased interest in national news, weather, sports,

and classified advertisements over the decade between 1984 and 1994, while older

readers ranked weather, editorials, and food advertisements higher. Interest in

international news and letters to the editor was less among younger readers, while

older readers showed less interest in reports of births, obituaries, and marriages.

David Atkin explored the influence of telecommunication technology on newspaper

readership among students in undergraduate media courses.17 He reported that

computer-related technologies, including electronic mail and computer networks,

were unrelated to newspaper readership. The study found that newspaper subscribers

preferred print formats over electronic. In a study of younger, school-age children,

Brian Brooks and James Kropp found that electronic newspapers could persuade

children to become news consumers, but that young readers would choose an

electronic newspaper over a printed one.18

In an exploration of leisure reading among college students, Leo Jeffres and Atkin

assessed dimensions of interest in newspapers, magazines, and books,19 exploring the

influence of media use, non-media leisure, and academic major on newspaper content

preferences. The study discovered that overall newspaper readership was positively

related to students' focus on entertainment, job / travel information, and public affairs.

However, the students' preference for reading as a leisure-time activity was related

only to a public affairs focus. Content preferences for newspapers and other print

media were related. The researchers found no significant differences in readership

among various academic majors, or by gender, though there was a slight correlation

between age and the public affairs readership index, with older readers more

interested in news about public affairs.

Methodology

Sample

Participants in this study (N=267) were students enrolled in 100- and 200-level

English courses at a midwestern public university. Courses that comprise the

framework for this sample were selected because they could fulfill basic studies

requirements for all majors. A basic studies course is one that is listed within the core

curriculum required for all students. The researcher obtained permission from seven

professors to distribute questionnaires in the eight classes during regularly scheduled

class periods. The students' participation was voluntary; two students declined. The

goal of this sampling procedure was to reach a cross-section of students representing

various fields of study. In all, 53 majors were represented.

Of the 267 students who participated in the study, 65 (24.3 percent) were male and

177 (66.3 percent) were female. A total of 25 participants chose not to divulge their

genders. Ages ranged from 17 to 56, with a mean age of 23.6 years. This mean does

not include the 32 respondents who declined to give their ages. A total of 157

participants (58.8 percent) said they were of the Caucasian race, 59 (22.1 percent)

African American, 10 (3.8 percent) Asian, five (1.9 percent) African/Native

American, two (.8 percent) Hispanic, two (.8 percent) Native American, and one (.4

percent) Arabic. Most (214) of the students were enrolled full time, whereas a few

(28) were part-time students. The class rank breakdown was: freshmen, 45 (16.9

percent); sophomores, 15 (5.6 percent); juniors, 33 (12.4 percent); seniors, 133 (49.8

percent); and graduate students, 16 (6 percent).

Procedure

After two pre-tests and revisions, questionnaires were distributed and collected by the

investigator. In each of the eight classes, the researcher introduced herself to the

students as a journalism professor who was conducting a study on students' use of

newspapers and other media. Each questionnaire included a cover letter with the

researcher's name, address, and phone number. The researcher provided pencils and

was available to answer questions if anyone needed further assistance. The average

time spent on the questionnaires was 20 minutes, with some individual students taking

as long as an hour. Approximately six students asked to take the questionnaires home

to finish. They returned the questionnaires to the researcher's mailbox within a couple

of day.

Master of Business Administration – MBA Semester 3rd core

MB0050 Research Methodology - 4 Credits

Assignment Set- 2

60 Marks

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

Q1. Discuss the relative advantages and disadvantages of the different methods of

distributing questionnaires to the respondents of a study. (10 marks)

Ans:

There are some alternative methods of distributing questionnaires to the respondents.

They are:

1) Personal delivery,

2) Attaching the questionnaire to a product,

3) Advertising the questionnaire in a newspaper or magazine, and

4) News-stand inserts.

Personal delivery: The researcher or his assistant may deliver the questionnaires to

the potential respondents, with a request to complete them at their convenience. After

a day or two, the completed questionnaires can be collected from them. Often referred

to as the self-administered questionnaire method, it combines the advantages of the

personal interview and the mail survey. Alternatively, the questionnaires may be

delivered in person and the respondents may return the completed questionnaires

through mail.

Attaching questionnaire to a product: A firm test marketing a product may attach a

questionnaire to a product and request the buyer to complete it and mail it back to the

firm. A gift or a discount coupon usually rewards the respondent.

Advertising the questionnaire: The questionnaire with the instructions for

completion may be advertised on a page of a magazine or in a section of newspapers.

The potential respondent completes it, tears it out and mails it to the advertiser. For

example, the committee of Banks Customer Services used this method for collecting

information from the customers of commercial banks in India. This method may be

useful for large-scale studies on topics of common interest. Newsstand inserts: This

method involves inserting the covering letter, questionnaire and self addressed reply-

paid envelope into a random sample of newsstand copies of a newspaper or magazine.

Advantages and Disadvantages:

The advantages of Questionnaire are:

this method facilitates collection of more accurate data for longitudinal studies than

any other method, because under this method, the event or action is reported soon

after its occurrence.

this method makes it possible to have before and after designs made for field based

studies. For example, the effect of public relations or advertising campaigns or

welfare measures can be measured by collecting data before, during and after the

campaign.

the panel method offers a good way of studying trends in events, behavior or

attitudes. For example, a panel enables a market researcher to study how brand

preferences change from month to month; it enables an economics researcher to study

how employment, income and expenditure of agricultural laborers change from month

to month; a political scientist can study the shifts in inclinations of voters and the

causative influential factors during an election. It is also possible to find out how the

constituency of the various economic and social strata of society changes through

time and so on.

A panel study also provides evidence on the causal relationship between variables.

For example, a cross sectional study of employees may show an association between

their attitude to their jobs and their positions in the organization, but it does not

indicate as to which comes first - favorable attitude or promotion. A panel study can

provide data for finding an answer to this question.

It facilities depth interviewing, because panel members become well acquainted

with the field workers and will be willing to allow probing interviews.

The major limitations or problems of Questionnaire method are:

this method is very expensive. The selection of panel members, the payment of

premiums, periodic training of investigators and supervisors, and the costs involved in

replacing dropouts, all add to the expenditure.

it is often difficult to set up a representative panel and to keep it representative.

Many persons may be unwilling to participate in a panel study. In the course of the

study, there may be frequent dropouts. Persons with similar characteristics may

replace the dropouts. However, there is no guarantee that the emerging panel would

be representative.

A real danger with the panel method is “panel conditioning” i.e., the risk that

repeated interviews may sensitize the panel members and they become untypical, as a

result of being on the panel. For example, the members of a panel study of political

opinions may try to appear consistent in the views they express on consecutive

occasions. In such cases, the panel becomes untypical of the population it was

selected to represent. One possible safeguard to panel conditioning is to give members

of a panel only a limited panel life and then to replace them with persons taken

randomly from a reserve list.

the quality of reporting may tend to decline, due to decreasing interest, after a panel

has been in operation for some time. Cheating by panel members or investigators may

be a problem in some cases.

Q2. In processing data, what is the difference between measures of central tendency

and measures of dispersion? What is the most important measure of central tendency

and dispersion? (10 marks)

Ans:

Measures of Central tendency:

Arithmetic Mean

The arithmetic mean is the most common measure of central tendency. It simply the

sum of the numbers divided by the number of numbers. The symbol m is used for the

mean of a population. The symbol M is used for the mean of a sample. The formula

for m is shown below: m=

ΣX

N

Where ΣX is the sum of all the numbers in the numbers in the sample and N is the

number of numbers in the sample. As an example, the mean of the numbers

1+2+3+6+8=

20

5

=4 regardless of whether the numbers constitute the entire population or just a sample

from the population.

The table, Number of touchdown passes, shows the number of touchdown (TD)

passes thrown by each of the 31 teams in the National Football League in the 2000

season. The mean number of touchdown passes thrown is 20.4516 as shown below.

m=

ΣX

N

=

634

31

=20.4516

37 33 33 32 29 28 28 23

22 22 22 21 21 21 20 20

19 19 18 18 18 18 16 15

14 14 14 12 12 9 6

Table 1: Number of touchdown passes

Although the arithmetic mean is not the only "mean" (there is also a geometric mean),

it is by far the most commonly used. Therefore, if the term "mean" is used without

specifying whether it is the arithmetic mean, the geometric mean, or some other mean,

it is assumed to refer to the arithmetic mean.

Median

The median is also a frequently used measure of central tendency. The median is the

midpoint of a distribution: the same number of scores is above the median as below it.

For the data in the table, Number of touchdown passes, there are 31 scores. The 16th

highest score (which equals 20) is the median because there are 15 scores below the

16th score and 15 scores above the 16th score. The median can also be thought of as

the 50th percentile.

Let's return to the made up example of the quiz on which you made a three discussed

previously in the module Introduction to Central Tendency and shown in Table 2.

Student Dataset 1 Dataset 2 Dataset 3

You 3 3 3

John's 3 4 2

Maria's 3 4 2

Shareecia's 3 4 2

Luther's 3 5 1

Table 2: Three possible datasets for the 5-point make-up quiz

For Dataset 1, the median is three, the same as your score. For Dataset 2, the median

is 4. Therefore, your score is below the median. This means you are in the lower half

of the class. Finally for Dataset 3, the median is 2. For this dataset, your score is

above the median and therefore in the upper half of the distribution.

Computation of the Median: When there is an odd number of numbers, the median

is simply the middle number. For example, the median of 2, 4, and 7 is 4. When there

is an even number of numbers, the median is the mean of the two middle numbers.

Thus, the median of the numbers 2, 4, 7, 12 is

4+7

2

=5.5.

Mode

The mode is the most frequently occurring value. For the data in the table, Number of

touchdown passes, the mode is 18 since more teams (4) had 18 touchdown passes

than any other number of touchdown passes. With continuous data such as response

time measured to many decimals, the frequency of each value is one since no two

scores will be exactly the same (see discussion of continuous variables). Therefore the

mode of continuous data is normally computed from a grouped frequency distribution.

The Grouped frequency distribution table shows a grouped frequency distribution for

the target response time data. Since the interval with the highest frequency is 600-700,

the mode is the middle of that interval (650).

Range Frequency

500-600 3

600-700 6

700-800 5

800-900 5

900-1000 0

1000-1100 1

Table 3: Grouped frequency distribution

Measures of Dispersion: A measure of statistical dispersion is a real number that is

zero if all the data are identical, and increases as the data becomes more diverse. It

cannot be less than zero.

Most measures of dispersion have the same scale as the quantity being measured.

In other words, if the measurements have units, such as metres or seconds, the

measure of dispersion has the same units. Such measures of dispersion include:

Standard deviation

Interquartile range

Range

Mean difference

Median absolute deviation

Average absolute deviation (or simply called average deviation)

Distance standard deviation

These are frequently used (together with scale factors) as estimators of scale

parameters, in which capacity they are called estimates of scale.

All the above measures of statistical dispersion have the useful property that they are

location-invariant, as well as linear in scale. So if a random variable X has a

dispersion of SX then a linear transformation Y = aX + b for real a and b should have

dispersion SY = |a|SX.

Other measures of dispersion are dimensionless (scale-free). In other words, they

have no units even if the variable itself has units. These include:

Coefficient of variation

Quartile coefficient of dispersion

Relative mean difference, equal to twice the Gini coefficient

There are other measures of dispersion:

Variance (the square of the standard deviation) — location-invariant but not

linear in scale.

Variance-to-mean ratio — mostly used for count data when the term

coefficient of dispersion is used and when this ratio is dimensionless, as count

data are themselves dimensionless: otherwise this is not scale-free.

Some measures of dispersion have specialized purposes, among them the Allan

variance and the Hadamard variance.

For categorical variables, it is less common to measure dispersion by a single number.

See qualitative variation. One measure that does so is the discrete entropy.

Sources of statistical dispersion

In the physical sciences, such variability may result only from random measurement

errors: instrument measurements are often not perfectly precise, i.e., reproducible.

One may assume that the quantity being measured is unchanging and stable, and that

the variation between measurements is due to observational error.

In the biological sciences, this assumption is false: the variation observed might be

intrinsic to the phenomenon: distinct members of a population differ greatly. This is

also seen in the arena of manufactured products; even there, the meticulous scientist

finds variation.The simple model of a stable quantity is preferred when it is tenable.

Each phenomenon must be examined to see if it warrants such a simplification.

Q3. What are the characteristics of a good research design? Explain how the research

design for exploratory studies is different from the research design for descriptive and

diagnostic studies. (10 marks).

Ans:

Good research design: 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 score gains, 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 the

presumed 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 presumed

effect.

3. No Plausible Alternative Explanations. The presumed cause must be the

only reasonable 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 of common 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.

This paper is primarily heuristic in purpose. Standard social science methodology

textbooks (Cook and Campbell 1979; Judd and Kenny, 1981) typically present an

array of research designs and the alternative explanations, which these designs rule

out or minimize. This tends to foster a "cookbook" approach to research design - an

emphasis on the selection of an available design rather than on the construction of an

appropriate research strategy. While standard designs may sometimes fit real-life

situations, it will often be necessary to "tailor" a research design to minimize specific

threats to validity. Furthermore, even if standard textbook designs are used, an

understanding of the logic of design construction in general will improve the

comprehension of these standard approaches. This paper takes a structural approach to

research design. While this is by no means the only strategy for constructing research

designs, it helps to clarify some of the basic principles of design logic.

Minimizing Threats to Validity

Good research designs minimize the plausible alternative explanations for the

hypothesized cause-effect relationship. But such explanations may be ruled out or

minimized in a number of ways other than by design. The discussion, which follows,

outlines five ways to minimize threats to validity, one of which is by research design:

1. By Argument. The most straightforward way to rule out a potential threat to

validity is to simply argue that the threat in question is not a reasonable one.

Such an argument may be made either a priori or a posteriori, although the

former will usually be more convincing than the latter. For example,

depending on the situation, one might argue that an instrumentation threat is

not likely because the same test is used for pre and post test measurements and

did not involve observers who might improve, or other such factors. In most

cases, ruling out a potential threat to validity by argument alone will be

weaker than the other approaches listed below. As a result, the most plausible

threats in a study should not, except in unusual cases, be ruled out by

argument only.

2. By Measurement or Observation. In some cases it will be possible to rule

out a threat by measuring it and demonstrating that either it does not occur at

all or occurs so minimally as to not be a strong alternative explanation for the

cause-effect relationship. Consider, for example, a study of the effects of an

advertising campaign on subsequent sales of a particular product. In such a

study, history (i.e., the occurrence of other events which might lead to an

increased desire to purchase the product) would be a plausible alternative

explanation. For example, a change in the local economy, the removal of a

competing product from the market, or similar events could cause an increase

in product sales. One might attempt to minimize such threats by measuring

local economic indicators and the 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

by adding 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 explanations

using 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 caseloads, one plausible alternative explanation

might be the status 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 some type of

preventive action can often rule them out. 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

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

elapsed between 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 symbol indicating 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 or

treatment 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

design notation 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 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

or the 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 nonrandom 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.

Q4. How is the Case Study method useful in Business Research? Give two specific

examples of how the case study method can be applied to business research. (10

marks)

Ans:

While case study writing may seem easy at first glance, developing an effective case

study (also called a success story) is an art.  Like other marketing communication

skills, learning how to write a case study takes time.  What’s more, writing case

studies without careful planning usually results in sub optimal results?

Savvy case study writers increase their chances of success by following these ten

proven techniques for writing an effective case study:

Involve the customer throughout the process. Involving the customer throughout

the case study development process helps ensure customer cooperation and approval,

and results in an improved case study. Obtain customer permission before writing the

document, solicit input during the development, and secure approval after drafting the

document.

Write all customer quotes for their review. Rather than asking the customer

to draft their quotes, writing them for their review usually results in more

compelling material.

Case Study Writing Ideas

Establish a document template. A template serves as a roadmap for the case

study process, and ensures that the document looks, feels, and reads

consistently. Visually, the template helps build the brand; procedurally, it

simplifies the actual writing. Before beginning work, define 3-5 specific

elements to include in every case study, formalize those elements, and stick to

them.

Start with a bang. Use action verbs and emphasize benefits in the case study

title and subtitle.  Include a short (less than 20-word) customer quote in larger

text.  Then, summarize the key points of the case study in 2-3 succinct bullet

points.  The goal should be to tease the reader into wanting to read more.

Organize according to problem, solution, and benefits. Regardless of

length, the time-tested, most effective organization for a case study follows the

problem-solution-benefits flow.  First, describe the business and/or technical

problem or issue; next, describe the solution to this problem or resolution of

this issue; finally, describe how the customer benefited from the particular

solution (more on this below). This natural story-telling sequence resonates

with readers.

Use the general-to-specific-to-general approach. In the problem section,

begin with a general discussion of the issue that faces the relevant industry.

Then, describe the specific problem or issue that the customer faced.  In the

solution section, use the opposite sequence.  First, describe how the solution

solved this specific problem; then indicate how it can also help resolve this

issue more broadly within the industry.  Beginning more generally draws the

reader into the story; offering a specific example demonstrates, in a concrete

way, how the solution resolves a commonly faced issue; and concluding more

generally allows the reader to understand how the solution can also address

their problem.

Quantify benefits when possible. No single element in a case study is more

compelling than the ability to tie quantitative benefits to the solution. For example,

“Using Solution X saved Customer Y over $ZZZ, ZZZ after just 6 months of

implementation;” or, “Thanks to Solution X, employees at Customer Y have realized

a ZZ% increase in productivity as measured by standard performance indicators.”

Quantifying benefits can be challenging, but not impossible. The key is to present

imaginative ideas to the customer for ways to quantify the benefits, and remain

flexible during this discussion.  If benefits cannot be quantified, attempt to develop a

range of qualitative benefits; the latter can be quite compelling to readers as well.

Use photos. Ask the customer if they can provide shots of personnel, ideally

using the solution. The shots need not be professionally done; in fact, “homegrown”

digital photos sometimes lead to surprisingly good results and often appear more

genuine. Photos further personalize the story and help form a connection to readers.

Reward the customer. After receiving final customer approval and finalizing

the case study, provide a pdf, as well as printed copies, to the customer.  Another idea

is to frame a copy of the completed case study and present it to the customer in

appreciation for their efforts and cooperation.

Writing a case study is not easy. Even with the best plan, a case study is doomed to

failure if the writer lacks the exceptional writing skills, technical savvy, and

marketing experience that these documents require.  In many cases, a talented writer

can mean the difference between an ineffective case study and one that provides the

greatest benefit. If a qualified internal writer is unavailable, consider outsourcing the

task to professionals who specialize in case study writing.

Q5. What are the differences between observation and interviewing as methods of

data collection? Give two specific examples of situations where either observation or

interviewing would be more appropriate. ( 10 marks)

Ans:

Observation means viewing or seeing. Observation may be defined as a systematic

viewing of a specific phenomenon on its proper setting for the specific purpose of

gathering data for a particular study. Observation is classical method of scientific

study.

The prerequisites of observation consist of:

Observations must be done under conditions, which will permit accurate

results. The observer must be in vantage point to see clearly the objects to be

observed. The distance and the light must be satisfactory. The mechanical

devices used must be in good working conditions and operated by skilled

persons.

Observation must cover a sufficient number of representative samples of the

cases.

Recording should be accurate and complete.

The accuracy and completeness of recorded results must be checked. A

certain number of cases can be observered again by another observer/another

set of mechanical devices as the case may be. If it is feasible two separate

observers and set of instruments may be used in all or some of the original

observations. The results could then be compared to determine their accuracy

and completeness.

Advantages of observation

o The main virtue of observation is its directness it makes it possible to

study behavior as it occurs. The researcher needs to ask people about

their behavior and interactions he can simply watch what they do and

say.

o Data collected by observation may describe the observed phenomena

as they occur in their natural settings. Other methods introduce

elements or artificiality into the researched situation for instance in

interview the respondent may not behave in a natural way. There is no

such artificiality in observational studies especially when the observed

persons are not aware of their being observed.

o Observations in more suitable for studying subjects who are unable to

articulate meaningfully e.g. studies of children, tribal animals, birds

etc.

o Observations improve the opportunities for analyzing the contextual

back ground of behavior. Furthermore verbal resorts can be validated

and compared with behavior through observation. The validity of what

men of position and authority say can be verified by observing what

they actually do.

o Observations make it possible to capture the whole event as it occurs.

For example only observation can be providing an insight into all the

aspects of the process of negotiation between union and management

representatives.

o Observation is less demanding of the subjects and has less biasing

effect on their conduct than questioning.

o It is easier to conduct disguised observation studies than disguised

questioning.

o Mechanical devices may be used for recording data in order to secure

more accurate data and also of making continuous observations over

longer periods.

Interviews are a crucial part of the recruitment process for all Organisations. Their

purpose is to give the interviewer(s) a chance to assess your suitability for the role and

for you to demonstrate your abilities and personality. As this is a two-way process, it

is also a good opportunity for you to ask questions and to make sure the organisation

and position are right for you.

Interview format

Interviews take many different forms. It is a good idea to ask the organisation in

advance what format the interview will take.

Competency/criteria based interviews - These are structured to reflect the

competencies or qualities that an employer is seeking for a particular job,

which will usually have been detailed in the job specification or advert. The

interviewer is looking for evidence of your skills and may ask such things as:

‘Give an example of a time you worked as part of a team to achieve a common

goal.’

The organisation determines the selection criteria based on the roles they are

recruiting for and then, in an interview, examines whether or not you have

evidence of possessing these.

Recruitment Manager, The Cooperative Group

Technical interviews - If you have applied for a job or course that requires

technical knowledge, it is likely that you will be asked technical questions or

has a separate technical interview. Questions may focus on your final year

project or on real or hypothetical technical problems. You should be prepared

to prove yourself, but also to admit to what you do not know and stress that

you are keen to learn. Do not worry if you do not know the exact answer -

interviewers are interested in your thought process and logic.

Academic interviews - These are used for further study or research positions.

Questions are likely to center on your academic history to date.

Structured interviews - The interviewer has a set list of questions, and asks

all the candidates the same questions.

Formal/informal interviews - Some interviews may be very formal, while

others will feel more like an informal chat about you and your interests. Be

aware that you are still being assessed, however informal the discussion may

seem.

Portfolio based interviews - If the role is within the arts, media or

communications industries, you may be asked to bring a portfolio of your

work to the interview, and to have an in-depth discussion about the pieces you

have chosen to include.

Senior/case study interviews - These ranges from straightforward scenario

questions (e.g. ‘What would you do in a situation where…?’) to the detailed

analysis of a hypothetical business problem. You will be evaluated on your

analysis of the problem, how you identify the key issues, how you pursue a

particular line of thinking and whether you can develop and present an

appropriate framework for organising your thoughts.

Specific types of interview

The Screening Interview

Companies use screening tools to ensure that candidates meet minimum qualification

requirements. Computer programs are among the tools used to weed out unqualified

candidates. (This is why you need a digital resume that is screening-friendly. See our

resume center for help.) Sometimes human professionals are the gatekeepers.

Screening interviewers often have honed skills to determine whether there is anything

that might disqualify you for the position. Remember-they does not need to know

whether you are the best fit for the position, only whether you are not a match. For

this reason, screeners tend to dig for dirt. Screeners will hone in on gaps in your

employment history or pieces of information that look inconsistent. They also will

want to know from the outset whether you will be too expensive for the company.

Some tips for maintaining confidence during screening interviews:

Highlight your accomplishments and qualifications.

Get into the straightforward groove. Personality is not as important to the

screener as verifying your qualifications. Answer questions directly and

succinctly. Save your winning personality for the person making hiring

decisions!

Be tactful about addressing income requirements. Give a range, and try to

avoid giving specifics by replying, "I would be willing to consider your best

offer."

If the interview is conducted by phone, it is helpful to have note cards with

your vital information sitting next to the phone. That way, whether the

interviewer catches you sleeping or vacuuming the floor, you will be able to

switch gears quickly.

The Informational Interview

On the opposite end of the stress spectrum from screening interviews is the

informational interview. A meeting that you initiate, the informational interview is

underutilized by job-seekers who might otherwise consider themselves savvy to the

merits of networking. Job seekers ostensibly secure informational meetings in order to

seek the advice of someone in their current or desired field as well as to gain further

references to people who can lend insight. Employers that like to stay apprised of

available talent even when they do not have current job openings, are often open to

informational interviews, especially if they like to share their knowledge, feel

flattered by your interest, or esteem the mutual friend that connected you to them.

During an informational interview, the jobseeker and employer exchange information

and get to know one another better without reference to a specific job opening. 

This takes off some of the performance pressure, but be intentional nonetheless:

Come prepared with thoughtful questions about the field and the company.

Gain references to other people and make sure that the interviewer would be

comfortable if you contact other people and use his or her name.

Give the interviewer your card, contact information and resume.

Write a thank you note to the interviewer.

The Directive Style

In this style of interview, the interviewer has a clear agenda that he or she follows

unflinchingly. Sometimes companies use this rigid format to ensure parity between

interviews; when interviewers ask each candidate the same series of questions, they

can more readily compare the results. Directive interviewers rely upon their own

questions and methods to tease from you what they wish to know. You might feel like

you are being steam-rolled, or you might find the conversation develops naturally.

Their style does not necessarily mean that they have dominance issues, although you

should keep an eye open for these if the interviewer would be your supervisor.

Either way, remember:

Flex with the interviewer, following his or her lead.

Do not relinquish complete control of the interview. If the interviewer does

not ask you for information that you think is important to proving your

superiority as a candidate, politely interject it.

The Meandering Style

This interview type, usually used by inexperienced interviewers, relies on you to lead

the discussion. It might begin with a statement like "tell me about yourself," which

you can use to your advantage. The interviewer might ask you another broad, open-

ended question before falling into silence. This interview style allows you tactfully to

guide the discussion in a way that best serves you.

The following strategies, which are helpful for any interview, are particularly

important when interviewers use a non-directive approach:

Come to the interview prepared with highlights and anecdotes of your skills,

qualities and experiences. Do not rely on the interviewer to spark your

memory-jot down some notes that you can reference throughout the interview.

Remain alert to the interviewer. Even if you feel like you can take the driver's

seat and go in any direction you wish, remain respectful of the interviewer's

role. If he or she becomes more directive during the interview, adjust.

Ask well-placed questions. Although the open format allows you significantly

to shape the interview, running with your own agenda and dominating the

conversation means that you run the risk of missing important information

about the company and its needs.

Q6. Case Study: You are engaged to carry out a market survey on behalf of a leading

Newspaper that is keen to increase its circulation in Bangalore City, in order to

ascertain reader habits and interests. What type of research report would be most

appropriate? Develop an outline of the research report with the main sections.(10

marks).

Ans:

There are four major interlinking processes in the presentation of a literature review:

1. Critiquing rather than merely listing each item a good literature review is led

by your own critical thought processes - it is not simply a catalogue of what

has been written.

Once you have established which authors and ideas are linked, take each

group in turn and really think about what you want to achieve in presenting

them this way. This is your opportunity for showing that you did not take all

your reading at face value, but that you have the knowledge and skills to

interpret the authors' meanings and intentions in relation to each other,

particularly if there are conflicting views or incompatible findings in a

particular area.

Rest assured that developing a sense of critical judgment in the literature

surrounding a topic is a gradual process of gaining familiarity with the

concepts, language, terminology and conventions in the field. In the early

stages of your research you cannot be expected to have a fully developed

appreciation of the implications of all findings.

As you get used to reading at this level of intensity within your field you will

find it easier and more purposeful to ask questions as you read:

o What is this all about?

o Who is saying it and what authorities do they have?

o Why is it significant?

o What is its context?

o How was it reached?

o How valid is it?

o How reliable is the evidence?

o What has been gained?

o What do other authors say?

o How does it contribute?

o So what?

2. Structuring the fragments into a coherent body through your reading and

discussions with your supervisor during the searching and organising phases

of the cycle, you will eventually reach a final decision as to your own topic

and research design.

As you begin to group together the items you read, the direction of your

literature review will emerge with greater clarity. This is a good time to

finalise your concept map, grouping linked items, ideas and authors into firm

categories as they relate more obviously to your own study.

Now you can plan the structure of your written literature review, with your

own intentions and conceptual framework in mind. Knowing what you want to

convey will help you decide the most appropriate structure.

A review can take many forms; for example:

o An historical survey of theory and research in your field

o A synthesis of several paradigms

o A process of narrowing down to your own topic

It is likely that your literature review will contain elements of all of these.

As with all academic writing, a literature review needs:

o An introduction

o A body

o A conclusion

The introduction sets the scene and lays out the various elements that are to be

explored.

The body takes each element in turn, usually as a series of headed sections and

subsections. The first paragraph or two of each section mentions the major

authors in association with their main ideas and areas of debate. The section

then expands on these ideas and authors, showing how each relates to the

others, and how the debate informs your understanding of the topic. A short

conclusion at the end of each section presents a synthesis of these linked ideas.

The final conclusion of the literature review ties together the main points from

each of your sections and this is then used to build the framework for your

own study. Later, when you come to write the discussion chapter of your

thesis, you should be able to relate your findings in one-to-one correspondence

with many of the concepts or questions that were firmed up in the conclusion

of your literature review.

3. Controlling the 'voice' of your citations in the text (by selective use of direct

quoting, paraphrasing and summarizing)

You can treat published literature like any other data, but the difference is that

it is not data you generated yourself.

When you report on your own findings, you are likely to present the results

with reference to their source, for example:

o 'Table 2 shows that sixteen of the twenty subjects responded

positively.'

When using published data, you would say:

o 'Positive responses were recorded for 80 per cent of the subjects (see

table 2).'

o 'From the results shown in table 2, it appears that the majority of

subjects responded positively.'

In these examples your source of information is table 2. Had you found the

same results on page 17 of a text by Smith published in 1988, you would

naturally substitute the name, date and page number for 'table 2'. In each case

it would be your voice introducing a fact or statement that had been generated

somewhere else.

You could see this process as building a wall: you select and place the 'bricks'

and your 'voice' provides the ‘mortar’, which determines how strong the wall

will be. In turn, this is significant in the assessment of the merit and rigor of

your work.

There are three ways to combine an idea and its source with your own voice:

o Direct quote

o Paraphrase

o Summary

In each method, the author's name and publication details must be associated

with the words in the text, using an approved referencing system. If you don't

do this you would be in severe breach of academic convention, and might be

penalized. Your field of study has its own referencing conventions you should

investigate before writing up your results.

Direct quoting repeats exact wording and thus directly represents the author:

o 'Rain is likely when the sky becomes overcast'.

If the quotation is run in with your text, single quotation marks are used to

enclose it, and it must be an identical copy of the original in every respect.

Overuse or simple 'listing' of quotes can substantially weaken your own

argument by silencing your critical view or voice.

Paraphrasing is repeating an idea in your own words, with no loss of the

author's intended meaning:

o As Smith (1988) pointed out in the late eighties, rain may well be

indicated by the presence of cloud in the sky.

Paraphrasing allows you to organize the ideas expressed by the authors

without being rigidly constrained by the grammar, tense and vocabulary of the

original. You retain a degree of flexibility as to whose voice comes through

most strongly.

Summarizing means to shorten or crystallize a detailed piece of writing by

restating the main points in your own words and in the order in which you

found them. The original writing is 'described' as if from the outside, and it is

your own voice that is predominant:

o Referring to the possible effects of cloudy weather, Smith (1988)

predicted the likelihood of rain.

o Smith (1988) claims that some degree of precipitation could be

expected as the result of clouds in the sky: he has clearly discounted

the findings of Jones (1986).

4. Using appropriate language

Your writing style represents you as a researcher, and reflects how you are

dealing with the subtleties and complexities inherent in the literature.

Once you have established a good structure with appropriate headings for your

literature review, and once you are confident in controlling the voice in your

citations, you should find that your writing becomes more lucid and fluent

because you know what you want to say and how to say it.

The good use of language depends on the quality of the thinking behind the

writing, and on the context of the writing. You need to conform to discipline-

specific requirements. However, there may still be some points of grammar

and vocabulary you would like to improve. If you have doubts about your

confidence to use the English language well, you can help yourself in several

ways:

o Ask for feedback on your writing from friends, colleagues and

academics

o Look for specific language information in reference materials

o Access programs or self-paced learning resources which may be

available on your campus

Grammar tips - practical and helpful

The following guidance on tenses and other language tips may be useful.

Which tense should I use?

Use present tense:

o For generalizations and claims:

The sky is blue.

o To convey ideas, especially theories, which exist for the reader at the

time of reading:

I think therefore I am.

o For authors' statements of a theoretical nature, which can then be

compared on equal terms with others:

Smith (1988) suggests that...

o In referring to components of your own document:

Table 2 shows...

Use present perfect tense for:

o Recent events or actions that are still linked in an unresolved way to

the present:

Several studies have attempted to...

Use simple past tense for:

o Completed events or actions:

Smith (1988) discovered that...

Use past perfect tense for:

o Events which occurred before a specified past time:

Prior to these findings, it had been thought that...

Use modals (may, might, could, would, should) to:

o Convey degrees of doubt

This may indicate that ... this would imply that...

Other language tips

o Convey your meaning in the simplest possible way. Don't try to use an

intellectual tone for the sake of it, and do not rely on your reader to

read your mind!

o Keep sentences short and simple when you wish to emphasise a point.

o Use compound (joined simple) sentences to write about two or more

ideas which may be linked with 'and', 'but', 'because', 'whereas' etc.

o Use complex sentences when you are dealing with embedded ideas or

those that show the interaction of two or more complex elements.

o Verbs are more dynamic than nouns, and nouns carry information

more densely than verbs.

o Select active or passive verbs according to whether you are

highlighting the 'doer' or the 'done to' of the action.

o Keep punctuation to a minimum. Use it to separate the elements of

complex sentences in order to keep subject, verb and object in clear

view.

o Avoid densely packed strings of words, particularly nouns.

The total process

The story of a research study

Introduction

I looked at the situation and found that I had a question to ask about it. I wanted to

investigate something in particular.

Review of literature

So I read everything I could find on the topic - what was already known and said and

what had previously been found. I established exactly where my investigation would

fit into the big picture, and began to realise at this stage how my study would be

different from anything done previously.

Methodology

I decided on the number and description of my subjects, and with my research

question clearly in mind, designed my own investigation process, using certain known

research methods (and perhaps some that are not so common). I began with the broad

decision about which research paradigm I would work within (that is,

qualitative/quantitative, critical/interpretive/ empiricist). Then I devised my research

instrument to get the best out of what I was investigating. I knew I would have to

analyse the raw data, so I made sure that the instrument and my proposed method(s)

of analysis were compatible right from the start. Then I carried out the research study

and recorded all the data in a methodical way according to my intended methods of

analysis. As part of the analysis, I reduced the data (by means of my preferred form of

classification) to manageable thematic representation (tables, graphs, categories, etc).

It was then that I began to realise what I had found.

Findings/results

What had I found? What did the tables/graphs/categories etc. have to say that could be

pinned down? It was easy enough for me to see the salient points at a glance from

these records, but in writing my report, I also spelled out what I had found truly

significant to make sure my readers did not miss it. For each display of results, I wrote

a corresponding summary of important observations relating only elements within my

own set of results and comparing only like with like. I was careful not to let my own

interpretations intrude or voice my excitement just yet. I wanted to state the facts -

just the facts. I dealt correctly with all inferential statistical procedures, applying tests

of significance where appropriate to ensure both reliability and validity. I knew that I

wanted my results to be as watertight and squeaky clean as possible. They would

carry a great deal more credibility, strength and thereby academic 'clout' if I took no

shortcuts and remained both rigorous and scholarly.

Discussion

Now I was free to let the world know the significance of my findings. What did I find

in the results that answered my original research question? Why was I so sure I had

some answers? What about the unexplained or unexpected findings? Had I interpreted

the results correctly? Could there have been any other factors involved? Were my

findings supported or contested by the results of similar studies? Where did that leave

mine in terms of contribution to my field? Can I actually generalise from my findings

in a breakthrough of some kind, or do I simply see myself as reinforcing existing

knowledge? And so what, after all? There were some obvious limitations to my study,

which, even so, I'll defend to the hilt. But I won't become over-apologetic about the

things left undone, or the abandoned analyses, the fascinating byways sadly left

behind. I have my memories...

Conclusion

We'll take a long hard look at this study from a broad perspective. How does it rate?

How did I end up answering the question I first thought of? The conclusion needs to

be a few clear, succinct sentences. That way, I'll know that I know what I'm talking

about. I'll wrap up with whatever generalizations I can make, and whatever

implications have arisen in my mind as a result of doing this thing at all. The more

you find out, the more questions arise. How I wonder what you are ... how I speculate.

OK, so where do we all go from here?

Three stages of research

1. Reading

2. Research design and implementation

3. Writing up the research report or thesis

Use an active, cyclical writing process: draft, check, reflect, revise, redraft.

Establishing good practice

1. Keep your research question always in mind.

2. Read widely to establish a context for your research.

3. Read widely to collect information, which may relate to your topic,

particularly to your hypothesis or research question.

4. Be systematic with your reading, note-taking and referencing records.

5. Train yourself to select what you do need and reject what you don't need.

6. Keep a research journal to reflect on your processes, decisions, state of mind,

changes of mind, reactions to experimental outcomes etc.

7. Discuss your ideas with your supervisor and interested others.

8. Keep a systematic log of technical records of your experimental and other

research data, remembering to date each entry, and noting any discrepancies or

unexpected occurrences at the time you notice them.

9. Design your research approaches in detail in the early stages so that you have

frameworks to fit findings into straightaway.

10. Know how you will analyse data so that your formats correspond from the

start.

Keep going back to the whole picture. Be thoughtful and think ahead about the way

you will consider and store new information as it comes to light.