MB0050 – Research Methodology

 

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Summer 2013

Master of Business Administration- MBA Semester 3

MB0050 – Research Methodology – 4 Credits

(Book ID: B1700)

Note: Answer all questions. Kindly note that answers for 10 marks questions should be approximately of 400 words. Each question is followed by evaluation scheme.

 

Q1.Explain the process of problem identification with an example.

Answer :

Problem Identification – A Process :

One of the most important first tasks of research is to identify and define clearly the problem you wish to study. If you are uncertain about the research problem or if you are not clear in your own mind about what you want to study, others who read your proposal will also be uncertain. A well-defined research problem statement leads naturally to the statement of research objectives, to the hypotheses, to a definition of key variables, and to a selection of a methodology for measuring the variables. A poorly defined research problem leads to confusion. Given the fast pace of change in today’s market and the high volume of information that inundate leaders on a daily basis, it is essential to have an approach for identifying key organizational issues.

 

Q2. Interview method involves a dialogue between the Interviewee and the Interviewer. Explain the interview method of data collection. What are the uses of this technique? What are the different types of interviews?

Answer : Interview method of data collection :

Interviews are a systematic way of talking and listening to people  and are another way to collect data from individuals  through conversations. The researcher or the interviewer often uses open  questions. Data is collected from the interviewee. The researcher needs  to remember the interviewer’s views about the topic is not of importance.

Types of interviews :

Unstructured: There are no specifications in the wording of the questions or the order of the questions. The interviewer forms questions as and when required. The structure of the interview is flexible.

 

Q3. A study of different sampling methods is necessary because precision, accuracy, and efficiency of the sample results depend on the method employed for selecting the sample. Explain the different types of Probability and Non-Probability sampling designs.

Answer : Probability sampling designs :

Probability sampling is a sampling technique where the samples are gathered in a process that gives all the individuals in the population equal chances of being selected.

(1) Simple Random Sample. The simple random sample is the basic sampling method assumed in statistical methods and computations. To collect a simple random sample, each unit of the target population is assigned a number. A set of random numbers is then generated and the units having those numbers are included in the sample.

 

Non probability sampling designs :

Non-probability sampling is a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected.

(1)Reliance On Available Subjects. Relying on available subjects, such as stopping people on a street corner as they pass by, is one method of sampling, although it is extremely risky and comes with many cautions. This method, sometimes referred to as a convenience sample, does not allow the researcher to have any control over the representativeness of the sample.

 

 

 

Q4. a. Differentiate between descriptive and inferential analysis of data.

Answer : Descriptive Statistics :

Descriptive statistics includes statistical procedures that we use to describe the population we are studying. The data could be collected from either a sample or a population, but the results help us organize and describe data. Descriptive statistics can only be used to describe the group that is being studying. That is, the results cannot be generalized to any larger group.

b. Explain with examples various measures of Central Tendency.

Answer : The three most commonly-used measures of central tendency are the following.

(1) Mean :

The sum of the values divided by the number of values–often called the “average.”

Add all of the values together.

Divide by the number of values to obtain the mean.

Example: The mean of 7, 12, 24, 20, 19 is (7 + 12 + 24 + 20 + 19) / 5 = 16.4.

 

Q5. The chi-square test is widely used in research. Discuss the various applications of chi-square test. Under what conditions is this test applicable?

Answer :  Chi -square test :

Chi-square is a statistical test commonly used to compare observed data with data we would expect to obtain according to a specific hypothesis. For example, if, according to Mendel’s laws, you expected 10 of 20 offspring from a cross to be male and the actual observed number was 8 males, then you might want to know about the “goodness to fit” between the observed and expected. Were the deviations (differences between observed and expected) the result of chance, or were they due to other factors.

Applications of chi-square test :

1.In business : No matter the business analytics problem, the chi-square test will find uses when you are trying to establish or invalidate that a relationship exists between two given business parameters that are categorical (or nominal) data types.

2.In biological statistics : Use the chi-square test for goodness-of-fit when you have one nominal variable with two or more values (such as red, pink and white flowers).

 

 

Q6. What is analysis of variance? What are the assumptions of the technique? Give a few examples where this technique could be used.

Answer : Analysis of variance :

Analysis of variance (ANOVA) is a collection of statistical models used to analyze the differences between group means and their associated procedures (such as “variation” among and between groups). In ANOVA setting, the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are equal, and therefore generalizes t-test to more than two groups. Doing multiple two-sample t-tests would result in an increased chance of committing a type I error. For this reason, ANOVAs are useful in comparing (testing) three or more means (groups or variables) for statistical significance.

Assumptions :

ANOVA models are parametric, relying on assumptions about the distribution of the dependent variables (DVs) for each level of the independent variable(s) (IVs).

 

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