Course Title:Research Methodology for Management Decisions Assignment Code:MS-95/SEM – II /2011 Coverage:All Blocks Note : Answer all the questions and submit this assignment on or before 31st October 2011, to the coordinator of your study center. 1. Under the circumstances stratified random sampling design is considered appropriate? How would you select such sample? Explain by means of an example.

2. “Experimental method of research is not suitable in management field. ” Discuss, what are the problems in the introduction of this research design in business organisation? 3.

What is the meaning of measurement in research? What difference does it make whether we measure in terms of a nominal, ordinal, interval or ratio scale? 4. “Interpretation is a fundamental component of research Process”. Explain. Why so? Describe the precautions that the researcher should take while interpreting his findings. 5. Write shot notes on a) Criterion of good research. b) Dependent and Independent variable. c) Casestudy method. d) Components of a Research Problem. 1. Under the circumstances stratified random sampling design is considered appropriate? How would you select such sample? Explain by means of an example.

Stratified sampling is commonly used probability method that is superior to random sampling because it reduces sampling error. A stratum is a subset of the population that share at least one common characteristic. Examples of stratums might be males and females, or managers and non-managers. The researcher first identifies the relevant stratums and their actual representation in the population. Random sampling is then used to select a sufficient number of subjects from each stratum. “Sufficient” refers to a sample size large enough for us to be reasonably confident that the stratum represents the population.

Stratified sampling is often used when one or more of the stratums in the population have a low incidence relative to the other stratums. Stratified sampling strategies Proportionate allocation uses a sampling fraction in each of the strata that is proportional to that of the total population. If the population consists of 60% in the male stratum and 40% in the female stratum, then the relative size of the two samples (three males, two females) should reflect this proportion. Optimum allocation (or Disproportionate allocation) – Each stratum is proportionate to the standard deviation of the distribution of the variable.

Larger samples are taken in the strata with the greatest variability to generate the least possible sampling variance. A real-world example of using stratified sampling would be for a US political survey. If we wanted the respondents to reflect the diversity of the population of the United States, the researcher would specifically seek to include participants of various minority groups such as race or religion, based on their proportionality to the total population as mentioned above. A stratified survey could thus claim to be more representative of the US population than a survey of simple random sampling or systematic sampling.

Similarly, if population density varies greatly within a region, stratified sampling will ensure that estimates can be made with equal accuracy in different parts of the region, and that comparisons of sub-regions can be made with equal statistical power. For example, in Ontario a survey taken throughout the province might use a larger sampling fraction in the less populated north, since the disparity in population between north and south is so great that a sampling fraction based on the provincial sample as a whole might result in the collection of only a handful of data from the north.

Randomized stratification can also be used to improve population representativeness in a study. Advantages over other sampling methods • focuses on important subpopulations and ignores irrelevant ones • improves the accuracy of estimation • efficient • sampling equal numbers from strata varying widely in size may be used to equate the statistical power of tests of differences between strata. Disadvantages • can be difficult to select relevant stratification variables • not useful when there are no homogeneous subgroups can be expensive • requires accurate information about the population, or introduces bias. • looks randomly within specific sub headings. =========================== There may often be factors which divide up the population into sub-populations (groups / strata) and we may expect the measurement of interest to vary among the different sub-populations. This has to be accounted for when we select a sample from the population in order that we obtain a sample that is representative of the population. This is achieved by stratified sampling.

A stratified sample is obtained by taking samples from each stratum or sub-group of a population. When we sample a population with several strata, we generally require that the proportion of each stratum in the sample should be the same as in the population. Stratified sampling techniques are generally used when the population is heterogeneous, or dissimilar, where certain homogeneous, or similar, sub-populations can be isolated (strata). Simple random sampling is most appropriate when the entire population from which the sample is taken is homogeneous.