47972939 mb0050 research methodology rev

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Master of business administration semester 3

Research methodology

Q.1. Give examples of specific situations that would call for the followingtypes of research, explaining why a) Exploratory research b)Descriptive research c) Diagnostic research d) Evaluation research.

Ans. There are various objectives of research. It extends knowledge of humanbeings, social life and environment. The search is for answers for various

types of questions i.e. What, Where, When, How and Why of various

phenomena, and enlighten us. Research brings to light information during

the ordinary course of life. It establishes generalizations and general laws

and contributes to theory building in various fields of knowledge. Research

verifies and tests existing facts and theory and these help improving our

knowledge and ability to handle situations. General laws may enable us to

make reliable predictions of events yet to happen. Following are the some

specific situations that would call for the following types of research.

a) Exploratory Research

Exploratory Research is also known as formulative research, purpose of

which is formulating a problem for more precise investigation.

Following are some examples of situations for above research.

Reactions of strangers

Reactions of marginal individuals

Study of individuals who are in a transition from one stage to another.

Reactions of individuals from different social strategy.

Following are some methods in context of research design for study of

above situations.

The survey of concerning literature :

This happens to be the most simple and fruitful method of formulating the

research problem. Hypothesis stated by earlier workers may be reviewed

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and their usefulness be evaluated as a basis for further research. In this

way researcher should review and build upon the work already done by

others, but in cases where hypothesis has not been formulated his task is to

review the available material for deriving the relevant hypothesis from it.

Experience Survey:

It is the survey of people who have had practical experience with the survey

to be studied. The object is to obtain insight into the relationship between

variables and new ideas relating to the research problem. For such a survey

people who are competent and can contribute new ideas may be carefully

selected as respondents to ensure representation of different of experience.

The respondents selected can be interviewed by the investigator. Thus, an

experience survey may enable the researcher to define the problem more

concisely and help in formulation of research hypothesis. This survey may

as well provide information about the practical possibilities for doing different

types of research.

Analysis of insight stimulating examples:

This is a fruitful method for suggesting hypothesis for research. It is

particularly suitable in areas where there is little experience to serve as a

guide. It consists of the intensive study of the selected instances of the

phenomenon in which on is interested. For this purpose the existing records

may be examined the unstructured interviewing may take place or some

other approach may be adopted. Attitude of the investigator, the intensity of

the study and the ability of the researcher to draw together diverse

information into a unified interpretation are the main features which makethis method an appropriate procedure for evoking insights.

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b) Descriptive Research

Descriptive research studies are concerned with describing the

characteristics of certain individuals or a group e.g. studies concerning

whether certain variables are associated. It includes surveys and fact-finding enquiries of different kinds.

Examples of such research are frequency of shopping, preferences of

people. The major purpose of descriptive research is description of the

state of affairs as it exists at present. In social science and business

research we quite often use Research the term Ex post facto research for

descriptive research studies. The main characteristic of this method is that

the researcher has no control over the variables; he can only report what

has happened or what is happening. Most ex post facto research projects

are used for descriptive studies in which the researcher seeks to measure

such items. Ex post facto studies also include attempts by researchers to

discover causes even when they cannot control the variables. The methods

of research utilized in descriptive research are survey methods of all kinds,

including comparative and co-relational methods.

c) Diagnostic Research

Diagnostic research studies determine the frequency of with which

something occurs or its association with something else. e.g. studies

concerned with specific predictions, with narration of facts and

characteristics concerning individual, group or situation.

Best example of such Research is clinical research.

Such research follows case-study methods or indepth approaches to reachthe basic fundamental relations. Such studies usually go deep into the

causes of things or events that interest us, using very small samples and

very deep probing data gathering devices.

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d) Evaluation Research

It is a type of applied research. Such research aims at finding a solution for

an immediate problem facing a society or an industrial/business

organization.

Following are some examples of Evaluation Research

- Research aimed at certain conclusions (say, a solution) facing a

concrete social or business problem.

- Research to identify social, economic or political trends that may affect a

particular solution or the copy research (research to find out whether

certain communications will be read and understood) or the marketing

research.

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 differencebetween 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.

Ans. a) Difference between Null and Alternative hypothesis.

In the context of hypothesis testing, we often talk null and alternative

hypothesis. The hypothesis test is to formulate two statements, one that

describes our prediction and one that describes all the other possible

outcomes with respect to the hypothesized relationship. If we are to

compare method A with method B about is superiority and if we proceed on

the assumption that both methods are equally good, then the only other

possible outcome would be that variable A and variable B are not related.

Usually, we call the hypothesis that you support (our prediction) the

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alternative hypothesis, and we call the hypothesis that describes the

remaining possible outcomes the nullhypothesis. Symbolically presented

as: Null hypothesis = H0 and Alternative hypothesis = Ha.

Suppose we want to test the hypothesis that the population mean is equal tothe hypothesis mean ( 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: = H0 = 100. We have to be

careful here, though in some studies, our prediction might very well be that

there will be no difference or change. In this case, you are essentially trying

to find support for the null hypothesis and you are opposed to the

alternative.

The null hypothesis is always the simpler hypothesis and is generally

believed to be true. It is stated in terms of "no difference" (e.g. contains an =

sign). Examples of a null hypothesis are: "There is no difference in mean

weight between males and females" or "Patients' outcomes are no different

with or without the surgical procedure."

The alternative hypothesis represents the result that the experiment would

like to show. The alternative hypothesis can be stated in terms simple

inequality ("Male and female weights are different"), or can be stated in

terms of a result on one side or the other of the equals sign ("Females

weigh less than males").

b) Difference between Type 1 and Type 2 error.

In a hypothesis test, a type I error occurs when the null hypothesis is

rejected when it is in fact true; that is, H0 is wrongly rejected. For example,

in a clinical trial of a new drug, the null hypothesis might be that the newdrug is no better, on average, than the current drug; i.e.

H0: there is no difference between the two drugs on average.

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A type I error would occur if we concluded that the two drugs produced

different effects when in fact there was no difference between them. The

following table gives a summary of possible results of any hypothesis test:

DecisionReject Ho Accept Ho

Ho (True) Type I Error Right decision

Ho (False) Right decision Type II Error

A type I error is often considered to be more serious, and therefore more

important to avoid, than a type II error. The hypothesis test procedure is

therefore adjusted so that there is a guaranteed 'low' probability of rejecting

the null hypothesis wrongly; this probability is never 0. Thisprobabilityof a

type I error can be precisely computed as P (type I error) = significance

level =

The exact probability of a type II error is generally unknown. A type II error

occurs when the null hypothesis H0, is not rejected when it is in fact false.

For example, in a clinical trial of a new drug, the null hypothesis might be

that the new drug is no better, on average, than the current drug; i.e.

H0: there is no difference between the two drugs on average.

A type II error would occur if it was concluded that the two drugs produced

the same effect, i.e. there is no difference between the two drugs on

average, when in fact they produced different ones. A type II error is

frequently due to sample sizes being too small.

The probabilityof a type II error is generally unknown, but is symbolised

by and written as P (type II error) =

c) Difference between Two tailed and one tailed test.

Some hypotheses predict only that one value will be different from another,

without additionally predicting which will be higher. The test of such a

hypothesis is two-tailed because an extreme test statistic in either tail of

the distribution (positive or negative) will lead to the rejection of the null

hypothesis of no difference.

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Suppose that you suspect that a particular class's performance on a

proficiency test is not representative of those people who have taken the

test. The national mean score on the test is 74.

The research hypothesis is:The mean score of the class on the test is not 74.

Or in

notation:

Ha: 74

The null hypothesis is:

The mean score of the class on the test is 74.

In notation:H0: = 74As in the last example, you decide to use a 95 percent probability level for

the test. Both tests have a region of rejection, then, of five percent, or .05. Inthis example, however, the rejection region must be split between both tails

of the distribution.025 in the upper tail and .025 in the lower tailbecause

your hypothesis specifies only a difference, not a direction. You will reject

the null hypotheses of no difference if the class sample mean is either much

higher or much lower than the population mean of 74. In the previous

example, only a sample mean much lower than the population mean would

have led to the rejection of the null hypothesis.

Comparison of (a) a two-tailed test and (b) a one-tailedtest,

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at the same probability level (95%).The decision of whether to use a one- or a two-tailed test is important

because a test statistic that falls in the region of rejection in a one-tailed test

may not do so in a two-tailed test, even though both tests use the same

probability level. Suppose the class sample mean in your example was 77,

and its corresponding z-score was computed to be 1.80. In order to reject

the null hypothesis, the test statistic must be either smaller than 1.96 or

greater than 1.96. It is not, so you cannot reject the null hypothesis.

Suppose, however, you had a reason to expect that the class would perform

better on the proficiency test than the population, and you did a one-tailed

test instead. For this test, the rejection region of .05 would be entirely within

the upper tail. The critical z-value for a probability of .05 in the upper tail is

1.65. Your computed test statistic ofz= 1.80 exceeds the critical value and

falls in the region of rejection, so you reject the null hypothesis and say that

your suspicion that the class was better than the population was supported.

In practice, you should use a one-tailed test only when you have good

reason to expect that the difference will be in a particular direction. A two-

tailed test is more conservative than a one-tailed test because a two-tailedtest takes a more extreme test statistic to reject the null hypothesis.

d) Difference between Parametric and non-parametric tests.

Parametric statistics are statistics where the population is assumed to fit any

parameterized distributions (most typically the normal distribution).

Parametric inferential statistical methods are mathematical procedures for

statistical hypothesis testing which assume that the distributions of the

variables being assessed belong to known parameterized families of

probability distributions. In that case we speak of parametric model.

For example, analysis of variance (ANOVA) assumes that the underlying

distributions are normally distributed and that the variances of the

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distributions being compared are similar. The Pearson product-moment

correlation coefficient also assumes normality. While parametric techniques

are robust that is, they often retain considerable power to detect

differences or similarities even when these assumptions are violated somedistributions violate the assumptions so markedly that a non-parametric

alternative is more likely to detect a difference or similarity.

Non-parametric statistics is a branch of statistics concerned with non-

parametric statistical models and non-parametric inference, including non-

parametric statistical tests. Nonparametric methods are often referred to as

distribution free methods as they do not rely on assumptions that the data

are drawn from a given probability distribution. The term non-parametric

statistic can also refer to a statistic (a function on a sample) whose

interpretation does not depend on the population fitting any parametrized

distributions. Order statistics are one example of such a statistic that plays a

central role in many non-parametric approaches.

Q.3 Explain the difference between a causal relationship and correlation,

with an example of each. What are the possible reasons for acorrelation between two variables?

Ans. A relationship refers to the correspondence between two variables. In

correlation research, we do not influence any variables but only measure

them and look for relations between some set of variables, such as blood

pressure and cholesterol level. In experimental research, we manipulate

some variables and then measure the effects of this manipulation on other

variables. For example, a researcher might artificially increase blood

pressure and then record cholesterol level. Data analysis in experimental

research also comes down to calculating "correlations" between variables,

specifically, those manipulated and those affected by the manipulation.

However, experimental data may potentially provide qualitatively better

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information: only experimental data can conclusively demonstrate causal

relations between variables. For example, if we found that whenever we

change variable A then variable B changes, then we can conclude that "A

influences B." Data from correlational research can only be "interpreted" incausal terms based on some theories that we have, but correlational data

cannot conclusively prove causality.

Regardless of their type, two variables are related if the values of those

variables are distributed in a consistent manner. In other words, variables

are related if their values systematically correspond to each other for these

observations. Generally speaking, the ultimate goal of every research or

scientific analysis is to find relations between variables. The philosophy of

science teaches us that there is no other way of representing "meaning"

except in terms of relations between some quantities or qualities; either way

involves relations between variables. Thus, the advancement of science

must always involve finding new relations between variables. Correlational

research involves measuring such relations in the most straightforward

manner. However, experimental research is not any different in this respect.

For example, the above mentioned experiment comparing WCC in males

and females can be described as looking for a correlation between two

variables: Genderand WCC.

The two most elementary formal properties of every relation between

variables are the relation's (a) magnitude or size and (b) its reliability or

truthfulness.

a. The magnitude is much easier to understand and measure than the

reliability. For example, if every male in our sample was found to have ahigher WCC than any female in the sample, we could say that the

magnitude of the relation between the two variables (Genderand WCC)

is very high in our sample. In other words, we could predict one based

on the other.

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b. The reliability of a relation is a much less intuitive concept, but still

extremely important. It pertains to the "representativeness" of the result

found in our specific sample for the entire population. In other words, it

says how probable it is that a similar relation would be found if theexperiment was replicated with other samples drawn from the same

population.

Q.4 Briefly explain any two factors that affect the choice of a samplingtechnique. What are the characteristics of a good sample?

Ans. The researcher has to first identify the limiting factor or factors and must

judiciously balance the conflicting factors. There are various factors that

affect the choice of the sampling technique:

1. Purpose of the Survey

2. Measurability

3. Degree of Precision

4. Information about Population

5. The Nature of the Population

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

7. Financial resources

8. Time Limitation

9. Economy

Out of above, we can discuss two important factors as follows.

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.

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2. 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 aprobability sampling method. Then exploratory study with non-probability

sampling may be made to gain a better idea of population. After gaining

sufficient knowledge about the population through the exploratory study,

appropriate probability sampling design may be adopted.

The characteristics of a Good Sample are Representativeness, Accuracy,

Precision & Size.

Representativeness: a sample must be representative of the

population. Probability sampling technique yield representative sample.

Accuracy: accuracy is defined as the degree to which bias is absent

from the sample. An accurate sample is the one which exactly

represents the population.

Precision: the sample must yield precise estimate. Precision is

measured by standard error.

Size: a good sample must be adequate in size in order to be reliable.

Q 5. Select any topic for research and explain how you will use bothsecondary and primary sources to gather the required information.

Ans. The search for answers to research questions is called collection of data.

Data are facts, and other relevant materials, past and present, serving as

bases for study and analyses. The data needed for a social science

research may be broadly classified into (a) Data pertaining to human

beings, (b) Data relating to organization and (c) Data pertaining to territorial

areas.

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Primary Sources of Data

Primary sources are original sources from which the researcher directly

collects data that have not been previously collected e.g.., collection of data

directly by the researcher on brand awareness, brand preference, brandloyalty and other aspects of consumer behaviour from a sample of

consumers by interviewing them,. Primary data are first hand information

collected through various methods such as observation, interviewing,

mailing etc.

Advantage of Primary Data

It is original source of data

It is possible to capture the changes occurring in the course of time.

It flexible to the advantage of researcher.

Extensive research study is based of primary data

Disadvantage of Primary Data

o Primary data is expensive to obtain

o It is time consuming

o It requires extensive research personnel who are skilled.

o It is difficult to administer.

Use of primary sources for Collecting Data

Primary data are directly collected by the researcher from their original

sources. In this case, the researcher can collect the required date precisely

according to his research needs, he can collect them when he wants them

and in the form he needs them. But the collection of primary data is costly

and time consuming. Yet, for several types of social science research

required data are not available from secondary sources and they have to be

directly gathered from the primary sources.

In such cases where the available data are inappropriate, inadequate or

obsolete, primary data have to be gathered. They include: socio economic

surveys, social anthropological studies of rural communities and tribal

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communities, sociological studies of social problems and social institutions.

Marketing research, leadership studies, opinion polls, attitudinal surveys,

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

practice (KAP) studies, farm managements studies, business managementstudies etc.

There are various methods of data collection. A Method is different from a

Tool while a method refers to the way or mode of gathering data, a tool is

an instruments used for the method. For example, a schedule is used for

interviewing. The important methods are

(a) observation, (b) interviewing, (c) mail survey, (d) experimentation,

(e) simulation and (f) projective technique. Each of these methods is

discussed in detail in the subsequent sections in the later chapters.

Use of secondary sources for Collecting Data

These are sources containing data which have been collected and

compiled for another purpose. The secondary sources consists of readily

compendia and already compiled statistical statements and reports whose

data may be used by researchers for their studies e.g., census reports ,

annual reports and financial statements of companies, Statistical statement,

Reports of Government Departments, Annual reports of currency and

finance published by the Reserve Bank of India, Statistical statements

relating to Co-operatives and Regional Banks, published by the NABARD,

Reports of the National sample survey Organization, Reports of trade

associations, publications of international organizations such as UNO, IMF,

World Bank, ILO, WHO, etc., Trade and Financial journals newspapers etc.

Secondary sources consist of not only published records and reports, butalso unpublished records. The latter category includes various records and

registers maintained by the firms and organizations, e.g., accounting and

financial records, personnel records, register of members, minutes of

meetings, inventory records etc.

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Features of Secondary Sources

Though secondary sources are diverse and consist of all sorts of materials,

they have certain common characteristics.

First, they are readymade and readily available, and do not require thetrouble of constructing tools and administering them.

Second, they consist of data which a researcher has no original control over

collection and classification. Both the form and the content of secondary

sources are shaped by others. Clearly, this is a feature which can limit the

research value of secondary sources.

Finally, secondary sources are not limited in time and space. That is, the

researcher using them need not have been present when and where they

were gathered.

Use of Secondary Data

The second data may be used in three ways by a researcher. First, some

specific information from secondary sources may be used for reference

purpose. For example, the general statistical information in the number of

co-operative credit societies in the country, their coverage of villages, their

capital structure, volume of business etc., may be taken from published

reports and quoted as background information in a study on the evaluation

of performance of cooperative credit societies in a selected district/state.

Second, secondary data may be used as bench marks against which the

findings of research may be tested, e.g., the findings of a local or regional

survey may be compared with the national averages; the performance

indicators of a particular bank may be tested against the corresponding

indicators of the banking industry as a whole; and so on.Finally, secondary data may be used as the sole source of information for a

research project. Such studies as securities Market Behaviour, Financial

Analysis of companies, Trade in credit allocation in commercial banks,

sociological studies on crimes, historical studies, and the like, depend

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primarily on secondary data. Year books, statistical reports of government

departments, report of public organizations of Bureau of Public Enterprises,

Censes Reports etc, serve as major data sources for such research studies.

Advantages of Secondary Data

Secondary sources have some advantages:

1. Secondary data, if available can be secured quickly and cheaply.

Once their source of documents and reports are located, collection of

data is just matter of desk work. Even the tediousness of copying the

data from the source can now be avoided, thanks to Xeroxing facilities.

2. Wider geographical area and longer reference period may be

covered without much cost. Thus, the use of secondary data extends the

researchers space and time reach.

3. The use of secondary data broadens the data base from which

scientific generalizations can be made.

4. Environmental and cultural settings are required for the study.

5. The use of secondary data enables a researcher to verify the

findings bases on primary data. It readily meets the need for additional

empirical support. The researcher need not wait the time when additional

primary data can be collected.

Disadvantages of Secondary Data

The use of a secondary data has its own limitations.

1. The most important limitation is the available data may not meet

our specific needs. The definitions adopted by those who collected those

data may be different; units of measure may not match; and time periodsmay also be different.

2. The available data may not be as accurate as desired. To assess

their accuracy we need to know how the data were collected.

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3. The secondary data are not up-to-date and become obsolete

when they appear in print, because of time lag in producing them. For

example, population census data are published tow or three years later

after compilation, and no new figures will be available for another tenyears.

4. Finally, information about the whereabouts of sources may not be

available to all social scientists. Even if the location of the source is

known, the accessibility depends primarily on proximity. For example,

most of the unpublished official records and compilations are located in

the capital city, and they are not within the easy reach of researchers

based in far off places.

Q 6. Case Study: You are engaged to carry out a market survey on behalf ofa leading Newspaper that is keen to increase its circulation inBangalore City, in order to ascertain reader habits and interests.Develop a title for the study, define the research problem and theobjectives or questions to be answered by the study.

Ans: Title of the study: Choices in reading Newspaper.

Research problem: A research problem is the situation that causes theresearcher 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 andnotice 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.

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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 practicalproblems.

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.

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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 decadebetween 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 printmedia 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.

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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

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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 studentstaking 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.

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