MB0050 - Research Methodology Master of Business Administration- MBA Semester 3

MB0050 - Research Methodology Master of Business Administration- MBA Semester 3Heena Nigam R.No: 1305000095

Q1. How would you distinguish between a management decision problem and a management research problem? Do all decision problems require research? Explain and illustrate with examples.Ans:Management Decision Problem vs. Management Research ProblemS.No.Management Decision ProblemManagement Research Problem

1What should be done to increase the consumers of organic food products in the domestic market?What is the awareness and purchase intention of health conscious consumers for organic food products?

2How to reduce turnover rates in the BPO sector?What is the impact of shift duties on work exhaustion and turnover intentions of the BPO employees?

3Can the housing and real estate growth be accelerated?What is the current investment in real estate and housing? Can the demand in the sector be forecasted for the next six months?

Explaination:The problem recognition process starts when the decision maker faces some difficulty or decision dilemma.. Sometimes, this might be related to actual and immediate difficulties faced by the manager (applied research) or gaps experienced in the existing body of knowledge (basic research). The broad decision problem has to be narrowed down to information-oriented problem, which focuses on the data or information required to arrive at any meaningful conclusion.Example:In case the decision maker is a business manager, the management research problem requires that we look for an answer to to the problem faced by the manager, as in the above example of how to reduce the turnover rate in a BPO company. This problem has to be translated to a simpler form of research question. And as said earlier, there can be more than one research problem that can help the manager in taking a decision. It depends on the researcher how he looks at it. he may say that the research problem is: What are the management policies in other BPO companies? Why do the employees leave the company? What is the problem area? Are the shift duties creating a problem of work family conflict which is why they leave? How can the company work on employee engagement so that he stays with the company?Thus, as you can see we can have many questions. Finally, the research problem you think is likely to give the possible solution is the one you decide to take as your research problem.

Q1. Q2. How is research designs classified? What are the distinguishing features of each? Differentiate by giving appropriate examples.Ans: Research design as the specification of methods and procedures for acquiring the information needed. It is the overall operational pattern or framework of the project that stipulates what information is to be collected from which sources by what procedures. The formulated design must ensure three basic principles: Convert the research question and the stated assumptions/hypotheses into variables that can be measured. Specify the process to complete the above task. Specify the control mechanism(s) to follow so that the effect of other variables that could have an effect on the outcome of the study has been controlled.Designs are classified as per figure shown below:1) Exploratory research design: Loosely structured research design to explore and gain clarity about the research questions.a. Secondary Resource Analysisb. Case Study Methodc. Expert Opinion Surveyd. Focus Group Discussions2) Descriptive designs: Research designs that describe in detail the phenomena under study.a. Cross-sectional Studiesb. Longitudinal Studies3) Experimental designs are conducted to infer causality. There are four types of experimental designs a. Pre-experimental designsb. Quasi experimental designsc. True experimental designsd. Statistical designs

Q3. Discuss with the help of examples the four key levels of measurement. What mathematical operations/statistical techniques are and are not permissible on data from each type of scale?Ans: Key Levels of Measurement1. Single Item:In the single item scale, there is only one item to measure a given construct. For example: Consider the following question: How satisfied are you with your current job?a. Very Dissatisfiedb. Dissatisfiedc. Neutrald. Satisfiede. Very satisfiedThe problem with the above question is that there are many aspects to a job, like pay, work environment, rules and regulations, security of job and communication with the seniors. The respondent may be satisfied on some of the factors but may not on others. By asking a question as stated above, it will be difficult to analyses the problem areas. To overcome this problem, a multiple item scale is proposed.2. Multiple item scale: In multiple item scale, there are many items that play arole in forming the underlying construct that the researcher is trying to measure. This is because each of the item forms some part of the construct (satisfaction) which the researcher is trying to measure. For example: Consider the following question: How satisfied are you with the pay you are getting on your current job?a. Very dissatisfiedb. Dissatisfiedc. Neutrald. Satisfiede. Very satisfied How satisfied are you with the rules and regulations of your organization?a. Very dissatisfiedb. Dissatisfiedc. Neutrald. Satisfiede. Very satisfied3. Comparative scales: In comparative scales it is assumed that respondents make use of a standard frame of reference before answering the question. For example:A question like How do you rate Barista in comparison to Cafe Coffee Day on quality of beverages? is an example of the comparative rating scale. It involves the direct comparison of stimulus objects.Comparative scale data is interpreted generally in a relative kind. Below are discussed each of the scale under comparative rating scales in detail below: Paired comparison scaling: The child is offered to choose one out of the two from the six possible pairs, i.e., chocolate or burger, chocolate or ice cream, chocolate or pizza, burger or ice cream, burger or pizza and ice cream or pizza. In general, if there are n items, the number of paired comparison would be (n(n 1)/2). Rank order scaling: In the rank order scaling, respondents are presented with several objects simultaneously and asked to order or rank them according to some criterion. Consider, for example the following question:Rank the following soft drinks in order of your preference, the most preferred soft drink should be ranked one, the second most preferred should be ranked two and so on. Soft DrinksRank

Coke

Pepsi

Limca

Sprite

Constant sum rating scaling: Allocate a total of 100 points among the various schools into which you would like to admit your child. The points should be allocated in such a way that the sum total of the points allocated to various schools adds up to 100.Schools Points

DPS

Mothers International

DAV Public School

Laxman Public School

TOTAL POINTS 100

Q-sort technique: Suppose there are 100 statements and an individual is asked to pile them into five groups, in such a way, that the strongly agreed statements could be put in one pile, agreed statements could be put in another pile, neutral statement form the third pile, disagreed statements come in the fourth pile and strongly disagreed statements form the fifth pile, and so on.4. Non-comparative scales:In the non-comparative scales, the respondents do not make use of any frame of reference before answering the questions. The resulting data is generally assumed to be interval or ratio scale. Graphic rating scale: This is a continuous scale, also called graphic rating Scale. In the graphic rating scale the respondent is asked to tick his preference on a graph. Consider for example the following question:Please put a tick mark on the following line to indicate your preference for fast food.Least Preferred Most Preferred Itemized rating scale: In the itemized rating scale, the respondents are provided with a scale that has a number of brief descriptions associated with each of the response categories. The response categories are ordered in terms of the scale position and the respondents are supposed to select the specified category that describes in the best possible way an object is rated. 1) Likert, 2) Semantic Differential, 3) StapelQ4. Processing of data involves editing, coding, classifying and tabulating. Explain each of these steps by taking an appropriate example.Ans: 1. Data Editing: Data editing is the process that involves detecting and correcting errors (logical inconsistencies) in data. After collection, the data is subjected to processing. Once the validation process has been completed, the next step is the editing of the raw data obtained. The editing process is carried out at two levels, the first of these is field editing and the second is central editing.I. Field EditingUsually, the preliminary editing of the information obtained is done by the field investigators or supervisors who review the filled forms for any inconsistencies, non-response, illegible responses or incomplete questionnaires. Thus the errors can be corrected immediately and if need be the respondent who filled in the form, can be contacted again.II. Centralized in-house EditingThe second level of editing takes place at the researchers end. At this stage there are two kinds of typical problems that the researcher might encounter.a. First, one might detect an incorrect entry.b. The second and the major problem that most researchers face is that of armchair interviewing or a fudged interview.2. Coding: The process of identifying and denoting a numeral to the responses given by a respondent is called coding. This is essentially done in order to help the researchers in recording the data in a tabular form later.Codebook formulation: In order to manage the data entry process, it is best to prepare a method for entering the records. This coding scheme for all the variables under study is called a code book. Generally, while designing the rules, care must be taken to decide on some categories that are:a. Comprehensive: Should cover all the possible answer to the question that was asked.b. Mutually exclusive: The categories and codes devised must be exclusive or clearly different from each other.c. Single variable entry: The response that is being entered and the code for it should indicate only a single variable. For example, a working single mother might seem an apparently simple category which one could code as occupation. However, it needs three columnsoccupation, marital status and family life cycle. So, one needs to have three different codes to enter this information. 3. Classification and Tabulation of Data: Sometimes, the data obtained from the primary instrument is so huge that it becomes difficult to interpret. In such cases, the researcher might decide to reduce the information into homogenous categories. This method of arrangement is called classification of data. This can be done on the basis of class intervals.Classification by class intervals: Numerical data, like the ratio scale data, can be classified into class intervals. This is to assist the quantitative analysis of data.Once the categories and codes have been decided upon, the researcher needs to arrange the same according to some logical pattern. This is referred to as tabulation of data. This involves an orderly arrangement of data into an array that is suitable for a statistical analysis.

Q5. Distinguish between the following:Ans:a. Descriptive AnalysisInferential Analysis

DefinitionThe hypotheses that are proposed with the intent of receiving a rejection for them are called null hypotheses. This requires that we hypothesize the opposite of what is desired to be roved.Rejection of null hypotheses leads to theacceptance of alternative hypotheses. The rejection of null hypothesis indicates that the relationship between variables

ExampleIf we want to prove that the average wages of skilled workers in town 1 is greater than that of town 2, we formulate the null hypotheses that there is no difference in the average wages of the skilled workers in both the towns.The rejection of null hypothesis indicates that the relationship between variables (e.g., sales and advertisement expenditure) or the difference between means (e.g., wagesof skilled workers in town 1 and town 2) or the difference between proportions have statistical significance and the acceptance of the null hypotheses indicates that these differences are due to chance.

DenotationA null hypothesis - H0.The alternative hypotheses H1.

b. One TailedTwo Tailed

DefinitionA test is called one-sided (or one-tailed)only if the null hypothesis gets rejected when a value of the test statistic falls in one specified tail of the distributionthe test is called two sided (or two-tailed) if null hypothesis gets rejected when a value of the test statistic falls in either one or the other of the two tails of its sampling distribution.

Exampleit would prefer to test the hypothesis whether the mean content of the bottles is different from 300 ml. This hypothesis could be written as:H0 : = 300 ml.H1 : 300 mlThe hypotheses stated above are called two-tailed or two-sided hypotheses.if the concern is the overfilling of bottles, it could be stated as:H0 : = 300 ml.H1 : > 300 ml.Such hypotheses are called one-tailed or one-sided hypotheses and theresearcher would be interested in the upper tail (right hand tail) of the distribution.

c. Type I ErrorType II Error

Definitionif the hypothesis H0 is rejected when it is actually true, the researcher is committing an Type I errorif the null hypothesis H0 when false is accepted, the researcher is committing an error called Type II error.

DenotationThe probability of committing a Type I error is denoted by alpha (). Thisis termed as the level of significance.The probability of committing a Type II error is denoted by beta ().

d. One way ANOVATwo Way ANOVA

DefinitionCompletely randomized design involves the testing of the equality of means oftwo or more groups. In this design, there is one dependent variable and one independent variable. The dependent variable is metric (interval/ratio scale)whereas the independent variable is categorical (nominal scale). A sample isdrawn at random from each category of the independent variable. The size ofthe sample from each category could be equal or different.A statistical test used to determine the effect of two nominal predictor variables on a continuous outcome variable. A two-way ANOVA test analyzes the effect of the independent variables on the expected outcome along with their relationship to the outcome itself.

e.Descriptive AnalysisInferential Analysis

DefinitionDescriptive analysis refers to transformation of raw data into a form that will facilitate easy understanding and interpretation. Descriptive analysis deals with summary measures relating to the sample data. After descriptive analysis has been carried out, the tools of inferential statistic are applied. Under inferential statistics, inferences are drawn on populationparameters based on sample results.

QuestionsWhat is the average income of the sample?What is the standard deviation of ages in the sample?What percentage of sample respondents are married?What is the median age of the sample respondents?Which income group has the highest number of user of product in questionin the sample?Is there any association between the frequency of purchase of productand income level of the consumers?Is the average age of the population significantly different from 35?Is the job satisfaction of unskilled workers significantly related with theirpay packet?Do the users and non-users of a brand vary significantly with respect toage?Does the advertisement expenditure influences sale significantly?Are consumption expenditure and disposable income of householdssignificantly correlated?Is the proportion of satisfied workers significantly more for skilled workersthan for unskilled works?

Q6. a. What is Chi-square test of goodness of fit? What precautions are necessary while applying this test? Point out its role in business decision making.b. Two research workers classified some people in income groups on the basis of sampling studies. Their results are as follow:

Show that the sampling technique of atleast one research worker is defective.Ans: Chi-square test of goodness of fit : A goodness of fit test is a statistical test of how well the observed data supports the assumption about the distribution of a population. The test also examines that how well an assumed distribution fits the data.The principles are summarized in the following steps: State the null and the alternative hypothesis about a population. Specify a level of significance. Compute the expected frequencies of the occurrence of certain eventsunder the assumption that the null hypothesis is true. Make a note of the observed counts of the data points falling in differentcells Compute the chi-square value given by the formula.

b. Let us make the hypothesis that the techniques adopted by both the groups are similar and the data is similar also.Expected frequencies are

= 55.54

Degree of freedom = (3-1)*(2-1) = 2

Table value of 2 2for 2 degree of freedom at 5% level of significance is 5.991. Since the calculated value is bigger than the table value, we conclude the rejection of null hypothesis at 5% level of significance. Technique adopted by one of two groups indata collection is defective.

Hence, sampling technique of atleast one research worker is defective.

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