MBAR Research Methodology for Management Decision

MBA-206

1) Explain quantitative and qualitative research.

ANS- Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. Qualitative is primarily used to discover and gain an in-depth understanding of individual experiences, thoughts, opinions, and trends, and to dig deeper into the problem at hand.

2) What is probability sampling?

ANS- A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. Humans have long practiced various forms of random selection, such as picking a name out of a hat, or choosing the short straw. These days, we tend to use computers as the mechanism for generating random numbers as the basis for random selection.

3) What is a Hypothesis?

ANS- It is a testable statement about the relationship between two or more variables or a proposed explanation for some observed phenomenon. In a scientific experiment or study, the hypothesis is a brief summation of the researcher's prediction of the study's findings, which may be supported or not by the outcome. Hypothesis testing is the core of the scientific method.

4) Explain graphic rating scale?

ANS- Graphic Rating Scale is a type of performance appraisal method. In this method traits or behaviours that are important for effective performance are listed out and each employee is rated against these traits. The rating helps employers to quantify the behaviours displayed by its employees.

5) What is ANOVA?

ANS- ANOVA or Analysis of Variance is a group of statistical models to test if there exists a significant difference between means. It tests whether the means of various groups are equal or not. In ANOVA, the variance observed in a particular variable is partitioned into different components based on the sources of variation

Q. 2 Explain the research process in detail.

ANS- Dissertation markers expect the explanation of research process to be included in Methodology chapter. A typical research process comprises the following stages:

 1. Selecting the research area. You are expected to state that you have selected the research area due to professional and personal interests in the area and this statement must be true. The importance of this first stage in the research process is often underestimated by many students. If you find research area and research problem that is genuinely interesting to you it is for sure that the whole process of writing your dissertation will be much easier. Therefore, it is never too early to start thinking about the research area for your dissertation.

2. Formulating research aim, objectives and research questions or developing hypotheses. The choice between the formulation of research questions and the development of hypotheses depends on your research approach as it is discussed further below in more details. Appropriate research aims and objectives or hypotheses usually result from several attempts and revisions and these need to be mentioned in Methodology chapter. It is critically important to get your research questions or hypotheses confirmed by your supervisor before moving forward with the work.

3. Conducting the literature review. Literature review is usually the longest stage in the research process. Actually, the literature review starts even before the formulation of research aims and objective; because you have to check if exactly the same research problem has been addressed before. Nevertheless, the main part of the literature review is conducted after the formulation of research aim and objectives. You have to use a wide range of secondary data sources such as books, newspapers, magazines, journals, online articles etc.

4. Selecting methods of data collection. Data collection method(s) need to be selected on the basis of critically analyzing advantages and disadvantages associated with several alternative data collection methods. In studies involving primary data collection, in-depth discussions of advantages and disadvantages of selected primary data collection method(s) need to be included in methodology.

5. Collecting the primary data. Primary data collection needs to be preceded by a great level of preparation and pilot data collection may be required in case of questionnaires. Primary data collection is not a compulsory stage for all dissertations and you will skip this stage if you are conducting a desk-based research.

6. Data analysis. Analysis of data plays an important role in the achievement of research aim and objectives. Data analysis methods vary between secondary and primary studies, as well as, between qualitative and quantitative studies.

7. Reaching conclusions. Conclusions relate to the level of achievement of research aims and objectives. In this final part of your dissertation you will have to justify why you think that research aims and objectives have been achieved. Conclusions also need to cover research limitations and suggestions for future research.

8. Completing the research. Following all of the stages described above, and organizing separate chapters into one file leads to the completion of the first draft. The first draft of your dissertation needs to be prepared at least one month before the submission deadline. This is because you will need to have sufficient amount of time to address feedback of your supervisor.

Q.4 Explain the significance of a research report and narrate the various steps involved read and writing such a report.

ANS- SIGNIFICANCE OF REPORT WRITING Research report is considered a major component of the research study for the research task remains incomplete tillthe report has been presented and/or written. As a matter of fact even the most brilliant hypothesis, highly well designed and conducted research study, and the most striking generalizations and findings are of little value unless they are effectively communicated to others. The purpose of research is not well served unless the findings are made known to others. Research results must invariably enter the general store of knowledge. All this explains the significance of writing research report. There are people who do not consider writing of report as an integral part of the research process. But the general opinionis in favour of treating the presentation of research results or the writing of report as part and parcel of the research project. Writing of report is the last step in a research study and requires a set of skills somewhat different from those called for in respect of the earlier stages of research. This task should be accomplished by the researcher with utmost care; he may seek the assistance and guidance of experts for the purpose. Different Steps In Writing Report Research reports are the product of slow, painstaking, accurate inductive work. The usual steps involved in writing report are: 1. logical analysis of the subject-matter; 2. preparation of the final outline; 3. preparation of the rough draft; 4. rewriting and polishing; 5. preparation of the final bibliography; and 6. writing the final draft. Though all these steps are self explanatory, yet a brief mention of each one of these will be appropriate for better understanding. Logical analysis of the subject matter: It is the first step which is primarily concerned with the development of a subject. There are two ways in which to develop a subject (a) logically and (b) chronologically. The logical development is made on the basis of mental connections and associations between the one thing and another by means of analysis. Logical treatment often consists in developing the material from the simple possible to the most complex structures. Chronological development is based on a connection or sequence in time or occurrence. The directions for doing or making something usually follow the chronological order. Preparation of the final outline: It is the next step in writing the research report “Outlines are the framework upon which long written works are constructed. They are an aid to the logical organisation of the material and a reminder of the points to be stressed inthe report.”

Internal Assignment No. 2

Q. 1.    Answer all the questions:

1) What is descriptive research?

ANS- Descriptive research does not fit neatly into the definition of either quantitative or qualitative research methodologies, but instead it can utilize elements of both, often within the same study. The term descriptive research refers to the type of research question, design, and data analysis that will be applied to a given topic. Descriptive statistics tell what is, while inferential statistics try to determine cause and effect.

2) Explain non-probability sampling.

ANS- Non-probability sampling is a sampling technique where the odds of any member being selected for a sample cannot be calculated. It’s the opposite of probability sampling, where you can calculate the odds. In addition, probability sampling involves random selection, while non-probability sampling does not–it relies on the subjective judgement of the researcher.

3) What is multivariate techniques?

ANS- Multivariate techniques are statistical methods that measure relationships among variables. They attempt to model reality where each situation, product or decision involves more than a single variable. For example, the decision to purchase a car may take into consideration price, safety features, color and functionality. Modern society has collected masses of data in every field, but the ability to use that data to obtain a clear picture of what is going on and make intelligent decisions is still a challenge.

4) Define Null hypothesis and alternative hypothesis.

ANS- A null hypothesis is a hypothesis that says there is no statistical significance between the two variables in the hypothesis. It is the hypothesis that the researcher is trying to disprove. In the example, Susie's null hypothesis would be something like this: There is no statistically significant relationship between the type of water I feed the flowers and growth of the flowers. A researcher is challenged by the null hypothesis and usually wants to disprove it, to demonstrate that there is a statistically-significant relationship between the two variables in the hypothesis.

5) What is chi-square test? Write its formula.

A chi-square test for independence compares two variables in a contingency table to see if they are related. In a more general sense, it tests to see whether distributions of categorical variables differ from each another. A very small chi square test statistic means that your observed data fits your expected data extremely well. In other words, there is a relationship. A very large chi square test statistic means that the data does not fit very well. In other words, there isn’t a relationship.

Note: Answer any two questions. Each question carries 5 marks (Word limits 500)

Q. 2 State merits and demerits in collecting data of a questionnaire. Describe main aspects of a questionnaire.

ANS- In this method a questionnaire is sent (mailed) to the concerned respondents who are expected to read, understand and reply on their own and return the questionnaire. It consists of a number of questions printed on typed in a definite order on a form on set of forms. It is advisable to conduct a `Pilot study’ which is the rehearsal of the main survey by experts for testing the questionnaire for weaknesses of the questions and techniques used.

Advantages –

⦁ Free from bias of interviewer

⦁ Respondents have adequate time to give

⦁ Respondents have adequate time to give answers

⦁ Respondents are easily and conveniently approachable

⦁ Large samples can be used to be more reliable

Limitations –

⦁ Low rate of return of duly filled questionnaire

⦁ Control over questions is lost once it is sent

⦁ It is inflexible once sent

⦁ Possibility of ambiguous or omission of replies

⦁ Time taking and slow process

Q.3 Discuss the characteristics of hypothesis. Explain type I and type II errors in the context of testing of hypothesis.

ANS- Hypothesis is used as a step in the procedure of induction. Before the inductive generalization, usually a hypothesis is framed.

It is a stage of making a probable supposition. It is not the only use of hypothesis. Hypotheses are used very frequently by the natural scientists; it is also a probable supposition to explain certain facts or phenomena whose explanations are not known.

Characteristics of hypothesis:

i) Hypothesis is an attempt at an explanation of certain puzzled facts.

ii) It is a probable explanation or presupposition of a cause.

iii) No hypothesis is certain or definite at the stage of assumption.

iv) Through hypothesis facts are organized in a systematic manner.

It is possible through systematization of involved facts.

v) Formation of hypothesis is keenly connected with the verification of it.

No hypothesis test is 100% certain. Because the test is based on probabilities, there is always a chance of making an incorrect conclusion. When you do a hypothesis test, two types of errors are possible: type I and type II. The risks of these two errors are inversely related and determined by the level of significance and the power for the test. Therefore, you should determine which error has more severe consequences for your situation before you define their risks.

Type I error

When the null hypothesis is true and you reject it, you make a type I error. The probability of making a type I error is α, which is the level of significance you set for your hypothesis test. An α of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis. To lower this risk, you must use a lower value for α. However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists.

Type II error

When the null hypothesis is false and you fail to reject it, you make a type II error. The probability of making a type II error is β, which depends on the power of the test. You can decrease your risk of committing a type II error by ensuring your test has enough power. You can do this by ensuring your sample size is large enough to detect a practical difference when one truly exists.