Research logy Answer

8/14/2019 Research logy Answer

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1. Explain the different types of research.

Answer.

Different Types of Research

Research can be conducted in a number of different ways for many different purposes.

Most research conducted today is in the corporate sector, and the reasons for this should

be obvious.

Survey Research

Developed since the 1930's, survey research seeks to identify what large numbers of

people (mass) think or feel about certain things. It is used extensively in politics and

marketing (such as TV advertising). Examples of survey research are; Public opinion

polls, Mail Surveys, Telephone Surveys, and Consumer Surveys (in the Mall).

An amazing fact about survey research is that the amount of error (expressed as plus and

minus a certain percentage) is determined by the sample size (the number of people

surveyed). Most opinion polls use a sample size of around 1500, which has a margin of

error of 3%. Using a larger sample size than 1500 gives a slight reduction in the error

margin, and using a smaller sample size than 1500 significantly increases the error

margin.

Increasingly we find the Internet being used to conduct survey research, with the use of

opinion polls and questionnaires. This is due to the ease of creating on-line questionnaires

and the power of analyzing the data in real-time by the use of powerful database servers.

Focus Groups

A focus group gathers in-depth information by interviewing six to twelve experts in an

informal discussion that lasts one to two hours. An experienced interviewer gathers

opinions of the group. Typical Uses of Focus Groups are; Gauging consumer reaction to


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products (such as the pre-release of a movie or product), Understanding why consumers

buy or don't buy certain products, Identifying the use of products and services

Case Studies

A case study looks at existing information that is readily available and attempts to draw

conclusions from this information. Information has been collected and stored from a wide

variety of sources about many issues. Much of this information is readily available in the

public domain.

The researcher begins with a number of research questions. For instance, a researcher

might want to look at the impact of information technology in South Africa since the

overthrow of white rule. The researcher will identify information in the public domain,

such as tele-density, number of Internet hosts, and other data. From this information, the

researcher will draw qualitative conclusions. Case studies allow existing information to

be reused in new ways that the original collectors of the data did not envisage.

Delphi MethodThe Delphi method surveys the opinions of "expert panels". The research is conducted in

three rounds, where the information is gathered, refined and then feed back to the expert

participants. The feedback stage allows issues to be sharpened and helps to highlight the

major issues involved. Round one involves an in-house panel that seeks to identify the

research questions for the experts. It also conducts a pre-test of the survey. The second

round involves an examination of the issues by the expert panel. At this stage, additional

items are often suggested that may have been overlooked. The panel responses and

additional items are combined to the original questionnaire. In the third round, the panel

makes judgments on the items. This sometimes takes the form of a multiple-choice

questionnaire.


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Content Analysis

Content analysis is often used in quantitative research to study trends or occurrences of

information. During World War II, the allies monitored the number and types of songs

played on European radio stations. By comparing the music played on German radio to

that of other radio stations in occupied territory, the allies were able to measure the

changes in troop concentration on the continent. Other early uses of content analysis were

attempts to verify authorship of documents. Knowing that certain authors chose certain

words, many scholars attempted to show that some documents had undergone a process

or redaction or rewriting by subsequent editors.

Content analysis is heavily involved in the use of television. A common exercise is to use

content analysis to measure the incidences of violence on television or in print.


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2. Discuss the criteria of good research problem.

Answer:

The selection of one appropriate researchable problem out of the identified problems

requires evaluation of those alternatives against certain criteria, which is grouped into

both internal and external criteria. Internal criteria involve those values internal to the

researcher that may make a research problem a success. The internal criteria consist of;

Researchers interest. The problem should interest the researcher and be a challenge to

him. Without interest and curiosity, one may not develop sustainable perseverance. Even

a small difficulty may become an excuse for discontinuing the study. Interest in a problem depends upon the researchers educational background, experience, outlook and

sensitivity.

The researchers competence matters in selecting a research problem. A researcher must

be competent to plan and carry out a study of the problem. One must have the ability to

grasp and deal with it. One must possess adequate knowledge of the subject matter,

relevant methodology and statistical procedures.

A researchers own resource matters in selecting a good research problem. In cases

where a researcher is to do a research on his own, consideration of a researchers

financial competence must be given due consideration. If it is beyond his means, he will

not be able to complete the work, unless he gets some external financial support. Time

resource is more important than finance. Research is a time-consuming process; hence it

should be efficiently utilized.

Whilst the external criteria is concerned with the unique tenets of a problem it self that

can make it a success to a researcher. This external criterion involves the following

tenets;


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The research-ability of the problem. The problem should be researchable, viz; amendable

for finding answers to the questions involved in it through scientific method. To be

researchable, a question must be one for which observation or other data collection in the

real world can provide the answer.

Importance and urgency of the problem matters a lot. When selecting a research problem,

the relative importance and urgency must be given due consideration. An urgent and

important problem should be selected first ahead of others.

Novelty of the problem. The problem must be novel. One should not spend time

researching a problem which has already been studied. How ever I certain cases,

replication may be called for in order to ascertain the validity and reliability of thefindings.

The usefulness and social relevance of a problem matters a lot. The findings should make

significance contribution to the existing body of knowledge. Research should provide

solutions to practical problems. It should be socially relevant and feasible.

For each identified problem, evaluation must be done in terms of the above internal and

external criteria, the most appropriate of which must be selected by the researcher.


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3. Describe the procedure used to test the hypothesis

Answer:

Procedure for hypothesis testing refers to all those steps that are undertaken before

making a choice between two actions viz; rejection and acceptance of a null hypothesis.

The various steps involved in hypothesis testing include the following;

Making a formal statement. Here the null hypothesis (Ho), and the alternative hypothesis

(Ha) is clearly stated. H0 is a statement of no difference. This is the hypothesis that the

researcher hopes to reject. H1 opposes H0. We retain the premise of the null hypothesis

until proved otherwise. This has a basis in quasi-deduction and is analogous to the

presumption of innocence in a criminal trial.

Selecting a significant level; the hypothesis is tested on a pre-determined level of

significance. If we wish to reach a yes-or-no decision, fixed level testing must be

pursued. (This is not always necessary, and is sometimes unwise.) To pursue fixed-level

testing, we set an error threshold for the decision. The error threshold, called alpha (a), is

the probability the researcher is willing to take of incorrectly rejecting a true H0. For

example, the researcher may be willing to take a 1% chance of incorrectly rejecting a true

H0. In such instances, a = .01.

Deciding the distribution to use; a test statistic is calculated. There are different test

statistics depending on the data being tested and question being asked. We have tests of

single means. For single means tests, the null hypothesis is H0:: = some value and the

test statistic is either a z - stat or t - stat.

Selecting a random sample and computing an appropriate value from the sample data

concerning the test statistic utilizing the relevant distribution. In other words, draw a

sample to furnish empirical data.


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Calculating of the probability; one has to calculate the probability that the sample result

would diverge as widely as it has from expectations, if the null hypothesis were in fact

true.

Comparing the probability; we convert the test statistic to a p value by placing the test

statistic on its appropriate probability distribution and determine the area under the curve

beyond the test statistic. With fixed-level testing, the p value is compared to the " level

and this simple decision rule is applied: When p # a, H 0 is rejected. When p > a, H 0 is

retained. With flexible significance testing, the p value answers the question: If the null

hypothesis were true, what is the probability of observing the current test statistic or a test

statistic that is more extreme than the current test statistic?

Thus, the smaller p value, the better the evidence against H 0. As an initial rule-of-thumbwe might say that we ought to take note of any p value approaching .05 (or less). In the

parlance of statistics, such findings denote statistical significance.


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4. Write a note on experimental design

Answer:

An experiment deliberately imposes a treatment on a group of objects or subjects in the

interest of observing the response. This differs from an observational study, which

involves collecting and analyzing data without changing existing conditions. Because the

validity of an experiment is directly affected by its construction and execution, attention

to experimental design is extremely important.

Experimentation include; treatment - something that researchers administer to

experimental units, a factor - of an experiment is a controlled independent variable; a

variable whose levels are set by the experimenter.

In experimental Design, the concern is with the analysis of data generated from an

experiment. The time and effort taken to organize the experiment properly to ensure that

the right type of data, and enough of it, is available to answer the questions of interest as

clearly and efficiently as possible, is what experimental design entails.

The specific questions that the experiment is intended to answer must be clearly

identified before carrying out the experiment. We should also attempt to identify known

or expected sources of variability in the experimental units since one of the main aims of

a designed experiment is to reduce the effect of these sources of variability on the

answers to questions of interest. That is, we design the experiment in order to improve

the precision of our answers.

Experimental design is premised on certain principles as enumerated by professor

fisher.

The principle of local control. Under control, the extraneous factors, the known source

of variability, is made to vary deliberately over as wide a range as necessary and this

needs to be done in such a way that the variability it causes can be measured and hence

eliminated from the experimental error.


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Suppose a farmer wishes to evaluate a new fertilizer. She uses the new fertilizer on one

field of crops (A), while using her current fertilizer on another field of crops (B). The

irrigation system on field A has recently been repaired and provides adequate water to all

of the crops, while the system on field B will not be repaired until next season. She

concludes that the new fertilizer is far superior.

The problem with this experiment is that the farmer has neglected to control for the effect

of the differences in irrigation. This leads to experimental bias, the favoring of certain

outcomes over others. To avoid this bias, the farmer should have tested the new fertilizer

in identical conditions to the control group, which did not receive the treatment. Without

controlling for outside variables, the farmer cannot conclude that it was the effect of the

fertilizer, and not the irrigation system, that produced a better yield of crops.

Another type of bias that is most apparent in medical experiments is the placebo effect.

Since many patients are confident that a treatment will positively affect them, they react

to a control treatment which actually has no physical affect at all, such as a sugar pill. For

this reason, it is important to include control, or placebo, groups in medical experiments

to evaluate the difference between the placebo effect and the actual effect of the

treatment.

The principle of randomization.

Because it is generally extremely difficult for experimenters to eliminate bias using only

their expert judgment, the use of randomization in experiments is called for. In a

randomized experimental design, objects or individuals are randomly assigned to an

experimental group. Using randomization is the most reliable method of creating

homogeneous treatment groups, without involving any potential biases or judgments.

There are several variations of randomized experimental designs, two of which are

briefly discussed below.

Completely Randomized Design

In a completely randomized design, objects or subjects are assigned to groups completely

at random.

Randomized Block Design


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If an experimenter is aware of specific differences among groups of subjects or objects

within an experimental group, he or she may prefer a randomized block design to a

completely randomized design.

Replication is yet another principle in experimental design.

To improve the significance of an experimental result, replication, the repetition of an

experiment on a large group of subjects, is required. If a treatment is truly effective, the

long-term averaging effect of replication will reflect its experimental worth. If it is not

effective, then the few members of the experimental population who may have reacted to

the treatment will be negated by the large numbers of subjects who were unaffected by it.

Replication reduces variability in experimental results, increasing their significance and

the confidence level with which a researcher can draw conclusions about an experimentalfactor.


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5. Elaborate the ways of making a case study effective.

A case study looks at existing information that is readily available and attempts to draw

conclusions from this information. Information has been collected and stored from a wide

variety of sources about many issues. Much of this information is readily available in the

public domain.

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. Whats more, writing case studies without

careful planning usually results in suboptimal results. Savvy case study writers increase

their chances of success by following these 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.

Request high-level customer involvement. Early in the process, recommend that a high-

level manager or executive sign their name to the document. Including such a name and

title on the case study increases its credibility, and can benefit the manager as well, in the

form of recognition for a job well done.


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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.


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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.

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 betweenan 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.


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Quota sampling; Quota sampling is a non-probability sampling technique wherein the

researcher ensures equal or proportionate representation of subjects depending on which trait

is considered as basis of the quota. For example, if basis of the quota is college year level and

the researcher needs equal representation, with a sample size of 100, he must select 25 1 st

year students, another 25 2 nd year students, 25 3 rd year and 25 4 th year students. The bases of

the quota are usually age, gender, education, race, religion and socioeconomic status.

Judgmental sampling; Judgmental sampling is more commonly known as purposive

sampling. In this type of sampling, subjects are chosen to be part of the sample with a

specific purpose in mind. With judgmental sampling, the researcher believes that some

subjects are fit for the research compared to other individuals. This is the reason why they are

purposively chosen as subjects.

Snowball sampling; Snowball sampling is usually done when there is a very small population

size. In this type of sampling, the researcher asks the initial subject to identify another

potential subject who also meets the criteria of the research. The downside of using a

snowball sample is that it is hardly representative of the population.

Self-Selection; this technique is self-explanatory respondents themselves decide whether to

take part in the survey or not.
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Research logy Answer