MS 66 IGNOU MBA Solved Assignment -What are the different types of probability sampling methods? Explain with the help of suitable examples

What are the different types of probability sampling methods? Explain with the help of suitable examples 

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

In this sampling technique, the researcher must guarantee that every individual has an equal opportunity for selection and this can be achieved if the researcher utilizes randomization.
Types of Probability Sampling
Systematic Random Sampling

The advantage of using a random sample is the absence of both systematic and sampling bias. If random selection was done properly, the sample is therefore representative of the entire population.

The effect of this is a minimal or absent systematic bias which is the difference between the results from the sample and the results from the population. Sampling bias is also eliminated since the subjects are randomly chosen.

Simple Random Sampling

Simple random sampling is the easiest form of probability sampling. All the researcher needs to do is assure that all the members of the population are included in the list and then randomly select the desired number of subjects.

There are a lot of methods to do this. It can be as mechanical as picking strips of paper with names written on it from a hat while the researcher is blindfolded or it can be as easy as using a computer software to do the random selection for you.

Stratified Random Sampling

Stratified random sampling is also known as proportional random sampling. This is a probability sampling technique wherein the subjects are initially grouped into different classifications such as age, socioeconomic status or gender.

Then, the researcher randomly selects the final list of subjects from the different strata. It is important to note that all the strata must have no overlaps.

Researchers usually use stratified random sampling if they want to study a particular subgroup within the population. It is also preferred over the simple random sampling because it warrants more precise statistical outcomes.

Systematic random sampling can be likened to an arithmetic progression wherein the difference between any two consecutive numbers is the same. Say for example you are in a clinic and you have 100 patients.

1.    The first thing you do is pick an integer that is less than the total number of the population; this will be your first subject e.g. (3).

2.    Select another integer which will be the number of individuals between subjects e.g. (5).

3.    You subjects will be patients 3, 8, 13, 18, 23, and so on.

There is no clear advantage when using this technique.

Cluster Random Sampling

Cluster random sampling is done when simple random sampling is almost impossible because of the size of the population. Just imagine doing a simple random sampling when the population in question is the entire population of Asia.

1.    In cluster sampling, the research first identifies boundaries, in case of our example; it can be countries within Asia.

2.    The researcher randomly selects a number of identified areas. It is important that all areas (countries) within the population be given equal chances of being selected.

3.    The researcher can either include all the individuals within the selected areas or he can randomly select subjects from the identified areas.

Mixed/Multi-Stage Random Sampling

This probability sampling technique involves a combination of two or more sampling techniques enumerated above. In most of the complex researches done in the field or in the lab, it is not suited to use just a single type of probability sampling.

Most of the researches are done in different stages with each stage applying a different random sampling technique.

MS 66 IGNOU MBA Solved Assignment -What is conjoint analysis? Explain its application in marketing Research.

What is conjoint analysis? Explain its application in marketing Research.  

Conjoint (trade-off) analysis is one of the most widely-used quantitative methods in Marketing Research. It is used to measure preferences for product features, to learn how changes to price affect demand for products or service, and to forecast the likely acceptance of a product if brought to market.

With 30 years in business and thousands of users worldwide, Sawtooth Software is the consensus leading provider of conjoint analysis software. Companies like Procter & Gamble, Google, General Motors, General Electric, and Microsoft use our software and have presented case studies and success stories at our popular research conferences.

Over the last few decades, conjoint analysis has become the premier market research methodology for studying how buyers value the characteristics (attributes) of products/services and for predicting buyer behavior. Perhaps the most valuable aspect of conjoint analysis is the strategic what-if market simulator.

Conjoint analysis questionnaires ask respondents to evaluate realistic product profiles (described by multiple features) and to choose which they would buy. Such surveys are more realistic than traditional questionnaires that simply ask respondents which features they prefer or regarding the generic importance of attributes.

Common business applications include:

• Designing new products

• Re-designing existing products

• Product line extension research

• Estimating brand equity

• Measuring price sensitivity (elasticity)

• Optimizing employee compensation packages and workplace conditions

• Branding and packaging

• Respondents usually complete between 12 to 30 conjoint questions. The questions are designed carefully, using experimental design principles of independence and balance of the features. By independently varying the features that are shown to the respondents and observing the responses to the product profiles, the analyst can statistically deduce what product features are most desired and which attributes have the most impact on choice. In contrast to simpler survey research methods that directly ask respondents what they prefer or the importance of each attribute, these preferences are derived from these relatively realistic tradeoff situations.

• The result is usually a full set of preference scores (often called part-worth utilities) for each attribute level included in the study.

• Sometimes it can be challenging to decide which conjoint method is most appropriate for your particular research situation. We've developed an interactive advisor to help you decide which conjoint method might be best for your specific situation. We’ve also written a document entitledWhich Conjoint Method Should I Use? for further reference.

• Conjoint market simulators let the researcher define specific competitive contexts (specific products in competition with another) and project the share of choices (shares of preference), given respondent’s estimated part-worth scores. These simulators let researchers and managers test a variety of what-if scenarios.

• Market simulators can be taken one step further. Rather than using them to answer the question: "How good is this product?" they can be used to discover "Which product is best?". Computer search routines (such as Sawtooth Software's Advanced Simulation Module) can efficiently find optimal products, based on the criterion of utility, share, revenue or profit. Profit optimization is probably the most actionable and managerially useful application of conjoint analysis data, but requires that the costs of the different components of a product be known with reasonable accuracy.

Today, thousands of conjoint studies are conducted each year, over the internet, via computers not connected to the internet, using person-to-person interviews, or mailed paper surveys. Leading organizations are saving a great deal of money on research and development costs, successfully using the results to design new products or line extensions, reposition existing products, and make more profitable pricing decisions.

MS 66 IGNOU MBA Solved Assignment -Why and when multi-dimensional scaling technique can be applied in marketing research?

Why and when multi-dimensional scaling technique can be applied in marketing research?

           Discuss.  

Ans

• Which products do consumers see as similar to my product?

• Which products do consumers see as different from my product?

• Who are my customers?

• Who else should be my customers?

• What new products should I create?

Multidimensional scaling (MDS) can be considered to be an alternative to factor analysis. In general, the goal of the analysis is to detect meaningful underlying dimensions that allow the researcher to explain observed similarities or dissimilarities between the investigated objects. In factor analysis, the similarities between objects (e.g. variables) are expressed in the correlation matrix. With MDS one may analyse any kind of similarity or dissimilarity matrix, in addition to correlation matrices.

This outcome is visualised in a 2 dimensional map, which gives the researcher an immediate feel of how differentiating the questions were. Questions which are clustered together did get very similar scores by all respondents. This can be very useful when optimising a questionnaire or to differentiate consumers based on the most distinct questions.

Even though there are similarities in the type of research questions to which MDS and factor analysis can be applied, they are fundamentally different methods. Factor analysis requires that the underlying data is distributed as multivariate normal, and that the relationships are linear. MDS imposes no such restrictions. Just as long as the rank-ordering similarities in the matrix are meaningful, MDS can be used.

In terms of resultant differences, factor analysis tends to extract more factors (dimensions) than MDS; as a result, MDS often yields more readily, interpretable solutions. Most importantly, however, MDS can be applied to any kind of similarities, while factor analysis requires us to first compute a correlation matrix. MDS can be based on subjects' direct assessment of similarities between stimuli, while factor analysis requires subjects to rate those stimuli on some list of attributes (for which the factor analysis is performed).

In summary, MDS methods are applicable to a wide variety of research designs.

                                                                                                                             

Multidimensional scaling (MDS) analysis takes consumer judgments of similarity (or difference) of pairs of products and produces a map of the perceived relationship among the products. Each consumer evaluates the similarity (or difference) of each pair of products. MDS determines the relative similarity perceived by consumers among all the products. The results enable you to identify products that consumers see as similar. The following are some of the questions that can be answered with a multidimensional scaling analysis.

Multidimensional Preference Analysis

In a conjoint analysis, consumers indicate their preferences for products that are composed of attributes. Sometimes in market research, the available data consist of consumer preferences for products for which attributes are not defined. Multidimensional preference analysis (MDPREF) is used to analyze such data. MDPREF analysis is a principal component analysis of a data matrix with columns that correspond to consumers and with rows that correspond to products. The analysis results in a plot that reveals patterns of consumer preference for the products. The following are some of the questions that can be answered with a multidimensional preference analysis.

MS 66 IGNOU MBA Solved Assignment -Elaborate on Qualitative Vs Quantitative research approaches. Explain any two methods of conducting Qualitative research and their limitations.

Elaborate on Qualitative Vs Quantitative research approaches. Explain any two methods of conducting Qualitative research and their limitations. 

Qualitative and quantitative research are the two main schools of research, and although they are often used in tandem, the benefits and disadvantages of each are hotly debated. Particularly in the social sciences, the merits of both qualitative and quantitative research are fought over, with intense views held on both sides of the argument. It is generally agreed upon, however, that there are some phases of research where one or the other is clearly more useful than the other, and so few people completely dismiss either.

Quantitative research is probably the least contentious of the two schools, as it is more closely aligned with what is viewed as the classical scientific paradigm. Quantitative research involves gathering data that is absolute, such as numerical data, so that it can be examined in as unbiased a manner as possible. There are many principles that go along with quantitative research, which help promote its supposed neutrality. Quantitative research generally comes later in a research project, once the scope of the project is well understood.

Mixed methods research is an approach that combines quantitative and qualitative research methods in the same research inquiry. Such work can help develop rich insights into various phenomena of interest that cannot be fully understood using only a quantitative or a qualitative method. Notwithstanding the benefits and repeated calls for such work, there is a dearth of mixed methods research in information systems. Building on the literature on recent methodological advances in mixed methods research, we develop a set of guidelines for conducting mixed methods research in IS. We particularly elaborate on three important aspects of conducting mixed methods research: (1)　appropriateness of a mixed methods approach; (2)　development of meta-inferences (i.e., substantive theory) from mixed methods research; and (3)　assessment of the quality of meta-inferences (i.e., validation of mixed methods research). The applicability of these guidelines is illustrated using two published IS papers that used mixed methods.

v There are differences between the two research methods: Quantitative Research and Qualitative Research. Quantitative research uses deductive process as the study approach, investigates phenomena that lend themselves to precise measurement and quantification, often involving a rigorous and controlled design; it's more on objective and reductionist, most closely allied with the positivist tradition.Qualitative research, on the other hand, which is most often associated with naturalistic inquiry with inductive study approach, investigates phenomena through the collection of rich narrative materials using a flexible research design, broad,

subjective and holistic. The researchers' expectations in quantitative research are to test and verify hypothesis and theories, and predict physical or human events; role of the person being studied is the subject, its behavior as a means to the end. While in qualitative data - researchers' expectations are to discover information, to describe human experiences, to develop concepts and theories, and to explain and interpret human behavior or events; person being studied is an active participant and its behavior is both means and end. In addition, Data being collected inqualitative research reflects the perceptions of persons being studied in form of "words" for individual interpretation, and is collected throughout the study through unstructured interviews and observations in settings natural to person being studied with no manipulation of the environment by the researcher. On the contrary,quantitative data is independent on perceptions of person being studied in form of "numbers" for statistical analysis, and is collected in designated time frame through structured interviews, questionnaires, observations, scales, or physiologic instruments with controlled environment and researchers manipulates the environment to control for variables. Lastly, quantitative findings are generalized, accept or reject theoretical propositions with numerical accounts of data that are highly reliable. While qualitative findings are unique, dynamic, understands phenomena and new theory with written descriptions of findings that are highly valid.     

The need to understand how and why of any occurrence, has always intrigued the human mind. This curiosity gave birth to qualitative research in the 1950s. It is widely applied in many researches and studies in various disciplines such as education, social work, information, management, psychology, communication, etc. 
Methods Used for Conducting Qualitative Research
Qualitative research helps to gain insight into people's behavior, value systems, motivations, culture or lifestyles, and how these attributes have a role to play in decision and policy making, communicating, and conducting research. Following are some of the many approaches that are used for conducting qualitative research.

Case Studies: A case study is an intensive and in-depth study of a specific individual, an event, a group, or an institution in a specific context. There is no fixed criteria and way of conducting case studies, and it is done with a combination of other methods. This method studies in a systematic way events, collection of data, analyzes of information, and reporting the results. The end result is generally a clear perception and understanding of why and how things happen in the manner they do. Some social researchers like Sigmund Freud and Piaget have used case studies to study several individuals and child development respectively.

Disadvantages of Using Qualitative Research Methods

Because specific measurements and objective data are not the focus in qualitative research, biased information is a problem. The disadvantage of subjectivity is that results are dependent upon the researcher's interpretations and descriptions. Also, when surveys are used, the data collected can be inaccurate because respondents are often untruthful or biased. Also, unlike quantitative research that controls as many variables as possible, qualitative research has very little control; therefore, "confound" variables are a problem. Results found may be due to many other factors that researchers did not consider or even know. Lastly, the replication of results is much more difficult -- and in some cases even impossible -- in qualitative research. 

MS 66 IGNOU MBA Solved Assignment -Discuss the importance of developing a questionnaire in a research project. What are the do’s and dont’s that should be considered while designing the questionnaire.

Discuss the importance of developing a questionnaire in a research project. What are the do’s and dont’s that should be  considered while designing the questionnair

The questionnaire is a structured technique for collecting primary data in a marketing survey. It is a series of written or verbal questions for which the respondent provides answers. A well-designed questionnaire motivates the respondent to provide complete and accurate information.

The survey questionnaire should not be viewed as a stand-alone tool. Along with the questionnaire there is field work, rewards for the respondents, and communication aids, all of which are important components of the questionnaire process.

Steps to Developing a Questionnaire

The following are steps to developing a questionnaire - the exact order may vary somewhat.

·         Determine which information is being sought.

·         Choose a question type (structure and amount of disguise) and method of administration (for example, written form, email or web form, telephone interview, verbal interview).

·         Determine the general question content needed to obtain the desired information.

·         Determine the form of response.

·         Choose the exact question wording.

·         Arrange the questions into an effective sequence.

·         Specify the physical characteristics of the questionnaire (paper type, number of questions per page, etc.)

Test the questionnaire and revise it as needed.

Required Information

To determine exactly which information is needed, it is useful to construct tables into which the data will be placed once it is collected. The tables will help to define what data is needed and what is not needed.

Question Type and Administration Method

Some question types include fixed alternative, open ended, and projective:

·         Fixed-alternative questions provide multiple-choice answers. These types of questions are good when the possible replies are few and clear-cut, such as age, car ownership, etc.

·         Open-ended questions allow the respondent to better express his/her answer, but are more difficult to administer and analyze. Often, open-ended questions are administered in a depth interview. This technique is most appropriate for exploratory research.

Projective methods use a vague question or stimulus and attempt to project a person's attitudes from the response. The questionnaire could use techniques such as word associations and fill-in-the-blank sentences. Projective methods are difficult to analyze and are better suited for exploratory research than for descriptive or causal research.

There are three commonly used rating scales: graphic, itemized, and comparative.

·         Graphic - simply a line on which one marks an X anywhere between the extremes with an infinite number of places where the X can be placed.

·         Itemized - similar to graphic except there are a limited number of categories that can be marked.

·         Comparative - the respondent compares one attribute to others. Examples include the Q-sort technique and the constant sum method, which requires one to divide a fixed number of points among the alternatives.

Questionnaires typically are administered via a personal or telephone interview or via a mail questionnaire. Newer methods include e-mail and the Web.
· group or focus interviews
· mailed questionnaires
· telephone interviews.

Choose the method(s) of reaching target respondents

It may seem strange to be suggesting that the method of reaching the intended respondents should constitute part of the questionnaire design process. However, a moment's reflection is sufficient to conclude that the method of contact will influence not only the questions the researcher is able to ask but the phrasing of those questions. The main methods available in survey research are:

· personal interviews

Within this region the first two mentioned are used much more extensively than the second pair. However, each has its advantages and disadvantages. A general rule is that the more sensitive or personal the information, the more personal the form of data collection should be.

Decide on question content

Researchers must always be prepared to ask, "Is this question really needed?" The temptation to include questions without critically evaluating their contribution towards the achievement of the research objectives, as they are specified in the research proposal, is surprisingly strong. No question should be included unless the data it gives rise to is directly of use in testing one or more of the hypotheses established during the research design.

There are only two occasions when seemingly "redundant" questions might be included:

· Opening questions that are easy to answer and which are not perceived as being "threatening", and/or are perceived as being interesting, can greatly assist in gaining the respondent's involvement in the survey and help to establish a rapport.

This, however, should not be an approach that should be overly used. It is almost always the case that questions which are of use in testing hypotheses can also serve the same functions.

· "Dummy" questions can disguise the purpose of the survey and/or the sponsorship of a study. For example, if a manufacturer wanted to find out whether its distributors were giving the consumers or end-users of its products a reasonable level of service, the researcher would want to disguise the fact that the distributors' service level was being investigated. If he/she did not, then rumours would abound that there was something wrong with the distributor.

Question Content

Each question should have a specific purpose or should not be included in the questionnaire. The goal of the questions is to obtain the required information. This is not to say that all questions directly must ask for the desired data. In some cases questions can be used to establish rapport with the respondent, especially when sensitive information is being sought.

Sensitive questions can be posed in ways to increase response likelihood and to facilitate more honest responses. Some techniques are:

·         Place the question in a series of less personal questions.

·         State that the behavior or attitude is not so unusual.

·         Phrase the question in terms of other people, not the respondent.

·         Provide response choices that specify ranges, not exact numbers.

·         Use a randomized response model giving the respondent pairs of questions with a randomly assigned one to answer. The interviewer does not know which question the person is answering, but the overall percentage of people assigned to the sensitive question is known and statistics can be calculated.

Form of Question Response

Questions can be designed for open-ended, dichotomous, or multichotomous responses. 

·         Open-ended responses are difficult to evaluate, but are useful early in the research process for determining the possible range of responses.

·         Dichotomous questions have two possible opposing responses, for example, "Yes" and "No".

Multichotomous questions have a range of responses as in a multiple choice test.

The questionnaire designer should consider that respondents may not be able to answer some questions accurately. Two types of error are telescoping error and recall loss.

·         Telescoping error is an error resulting from the tendency of people to remember events as occurring more recently than they actually did.

Recall loss occurs when people forget that an event even occurred. For recent events, telescoping error dominates; for events that happened in the distant past, recall loss dominates.

Sequence the Questions

Some neutral questions should be placed at the beginning of the questionnaire in order to establish rapport and put the respondent at ease. Effective opening questions are simple and non-threatening.

When sequencing the questions, keep in mind that their order can affect the response. One way to correct for this effect is to distribute half of the questionnaires with one order, and the other half with another order. 

Physical Characteristics of the Questionnaire

Physical aspects such as the page layout, font type and size, question spacing, and type of paper should be considered. In order to eliminate the need to flip back and forth between pages, the layout should be designed so that a question at the bottom of the page does not need to be continued onto the next page. The font should be readable by respondents who have less-than-perfect visual acuity. The paper stock should be good quality to project the image that the questionnaire is important enough to warrant the respondents' time. Each questionnaire should have a unique number in order to better account for it and to know if any have been lost. 

Test and Revise the Questionnaire

The questionnaire should be pre-tested in two stages before distributing. In the first stage, it should be administered using personal interviews in order to get better feedback on problems such as ambiguous questions. Then, it should be tested in the same way it will be administered. The data from the test should be analyzed the same way the administered data is to be analyzed in order to uncover any unanticipated shortcomings.

Different respondents will answer the same questionnaire differently. One hopes that the differences are due to real differences in the measured characteristics, but that often is not the case. Some sources of the differences between scores of different respondents are:

·         True differences in the characteristic being measured.

·         Differences in other characteristics such as response styles.

·         Differences in transient personal factors such as fatigue, etc.

·         Differences in situation, such as whether spouse is present.

Differences in the administration, such as interviewer tone of voice.

·         Differences resulting from sampling of items relevant toward the characteristic being measured.

·         Differences resulting from lack of clarity of the question - may mean different things to different people.

Differences caused by mechanical factors such as space to answer, inadvertent check marks, etc.

MS 66 IGNOU MBA Solved Assignment -What is data? Discuss the various types of data and their sources available to the researcher. Evaluate each of there sources in terms of their merits, demerits and limitations.

What is data? Discuss the various types of data and their sources available to the   researcher. Evaluate each of there sources in terms of their merits, demerits and limitations.

(1) In computing, data is information that has been translated into a form that is more convenient to move or process. Relative to today's computers and transmission media, data is information converted into binarydigital form.

2) In computer component interconnection and network communication, data is often distinguished from "control information," "control bits," and similar terms to identify the main content of a transmission unit.

3) In telecommunications, data sometimes means digital-encoded information to distinguish it from analog-encoded information such as conventional telephone voice calls. In general, "analog" or voice transmission requires a dedicated continual connection for the duration of a related series of transmissions. Data transmission can often be sent with intermittent connections in packets that arrive in piecemeal fashion.

4) Generally and in science, data is a gathered body of facts.

Some authorities and publishers, cognizant of the word's Latin origin and as the plural form of "datum," use plural verb forms with "data". Others take the view that since "datum" is rarely used, it is more natural to treat "data" as a singular form.

Many potential researchers are unsure where they can obtain data to begin their research and analysis. We should first divide the different types of data into two major classifications.

Primary Data

When someone refers to "primary data" they are referring to data collected by the researcher himself/herself. This is data that has never been gathered before, whether in a particular way, or at a certain period of time. Researchers tend to gather this type of data when what they want cannot be find from outside sources. You can tailor your data questions and collection to fit the need of your research questions. This can be an extremely costly task and, if associated with a college or institute, requires permission and authorization to collect such data. Issues of consent and confidentiality are of extreme importance. Primary data actually follows behind secondary data because you should use current information and data before collecting more so you can be informed about what has already been discovered on a particular research topic.

Secondary Data

If the time or hassle of collecting your own data is too much, or the data collection has already been done, secondary data may be more appropriate for your research. This type of data typically comes from other studies done by other institutions or organizations. There is no less validity with secondary data, but you should be well informed about how it was collected. There are a number of free services online as well as many other made available through your current status as BYU students.

Data Sources

Below is a list of general data sources. For a more complete list vist our newest page by clicking here. 

Inter-University Consortium for Political and Social Research (ICPSR)

This online data archive provided by the Institute for Social Research at the University of Michigan is free to all current BYU students. With the help of over 700 academic institutions and research organizations, ICPSR has over 500,000 data files relating to social science fields including education, aging, criminal justice, substance abuse, and terrorism.

To register to download data from ICPSR, you first need to be at an on-campus computer because the IP address is what allows you access. Go to https://www.icpsr.umich.edu/ticketlogin and select Create Account. Fill out the information and then you can search for data that is relevant for your research.

The Institute for Quantitative Social Science (IQSS Dataverse Network)

An open-source service, this "dataverse" network is provided by The Institute for Quantitative Social Science (IQSS) at Harvard University with over 300 "dataverses" and nearly 650,000 data files available for download.

U.S. Census Bureau (DataFerrett)

Provided on behalf of the United States Census Bureau, patrons can download data from dozens of government surveys including the American Community Survey (ACS), the Decennial Census of Population and Housing (1990 and 2000 available), the National Health and Nutrition Examination Survey (NHANES), and the Survey of Income and Program Participation (SIPP). Pop-up blockers must be turned off to run the DataFerrett application. It also requires a download to your computer.

General Social Survey (GSS)

From their website: "The GSS contains a standard 'core' of demographic, behavioral, and attitudinal questions, plus topics of special interest. Many of the core questions have remained unchanged since 1972 to facilitate time-trend studies as well as replication of earlier findings. The GSS takes the pulse of America, and is a unique and valuable resource. It has tracked the opinions of Americans over the last four decades."

Integrated Public Use Microdata Series (IPUMS)

From Wikipedia: "Integrated Public Use Microdata Series (IPUMS) is the world's largest individual-level population database. IPUMS consists of microdata samples from United States (IPUMS-USA) and international (IPUMS-International) census records. The records are converted into a consistent format and made available to researchers through a web-based data dissemination system. Additional databases in the IPUMS family include: the North Atlantic Population Project, the National Historical Geographic Information System, the Integrated Health Interview Series (IHIS), and the Integrated Public Use Microdata Series-Current Population Survey (IPUMS-CPS)."

Simple Online Data Archive for Population Studies (SodaPop)

From their website: "SodaPop makes as much data as possible available to the research community. While we are unable to distribute some data to anyone except PRI-affiliated researchers, most of our data collections are accessible to the entire Penn State community, and many are also publicly available. Anyone can use our data description pages, resource links, and variable search."

The Association of Religion Data Archives (ARDA)

From their website: "The Association of Religion Data Archives (ARDA) strives to democratize access to the best data on religion. Founded as the American Religion Data Archive in 1997 and going online in 1998, the initial archive was targeted at researchers interested in American religion. The targeted audience and the data collection have both greatly expanded since 1998, now including American and international collections and developing features for educators, journalists, religious congregations, and researchers. Data included in the ARDA are submitted by the foremost religion scholars and research centers in the world."

Geocommons (Geocommons)

This unique website houses many independently posted data sets. The main feature of this website, however, is the ability to map geospatial data. Data sets with addresses, county, state, or country variables can be mapped and color-coded based on quantitative features.​

Common Core of Data (CCD)

• what types of data the proposed research will generate

• which data will have value to other research users and could be shared

• what data formats and quality standards will be applied to enable the data to be shared effectively.

• People and skills - is there sufficient expertise and resource in the research team to manage, preserve and share the data effectively?Is additional specialist expertise (or training for existing staff) required? If so, how will this be sourced?

• Infrastructure - are there appropriate computational facilities to manage, store and analyse the data generated by the research?

• Tools - will additional computational facilities and resources need to be accessed, and what will be the costs associated with this?

• Sources of Primary Data

• The sources of generating primary data are -

• Observation Method

• Survey Method

• Experimental Method

• Experimental Method

• There are number of experimental designs that are used in carrying out and experiment. However, Market researchers have used 4 experimental designs most frequently. These are -

• CRD - Completely Randomized Design

• RBD - Randomized Block Design - The term Randomized Block Design has originated from agricultural research. In this design several treatments of variables are applied to different blocks of land to ascertain their effect on the yield of the crop. Blocks are formed in such a manner that each block contains as many plots as a number of treatments so that one plot from each is selected at random for each treatment. The production of each plot is measured after the treatment is given. These data are then interpreted and inferences are drawn by using the analysis of Variance Technique so as to know the effect of various treatments like different dozes of fertilizers, different types of irrigation etc.

• LSD - Latin Square Design - A Latin square is one of the experimental designs which has a balanced two way classification scheme say for example - 4 X 4 arrangement. In this scheme each letter from A to D occurs only once in each row and also only once in each column. The balance arrangement, it may be noted that, will not get disturbed if any row gets changed with the other.

A	B	C	D
B	C	D	A
C	D	A	B
D	A	B	C

• The balance arrangement achieved in a Latin Square is its main strength. In this design, the comparisons among treatments, will be free from both differences between rows and columns. Thus the magnitude of error will be smaller than any other design.

• FD - Factorial Designs - This design allows the experimenter to test two or more variables simultaneously. It also measures interaction effects of the variables and analyzes the impacts of each of the variables.

• In a true experiment, randomization is essential so that the experimenter can infer cause and effect without any bias.

• Sources of Secondary Data

• While primary data can be collected through questionnaires, depth interview, focus group interviews, case studies, experimentation and observation; The secondary data can be obtained through

• Internal Sources - These are within the organization

• External Sources - These are outside the organization

• Internal Sources of Data

• If available, internal secondary data may be obtained with less time, effort and money than the external secondary data. In addition, they may also be more pertinent to the situation at hand since they are from within the organization. The internal sources include

• Accounting resources- This gives so much information which can be used by the marketing researcher. They give information about internal factors.

• Sales Force Report- It gives information about the sale of a product. The information provided is of outside the organization.

• Internal Experts- These are people who are heading the various departments. They can give an idea of how a particular thing is working

• Miscellaneous Reports- These are what information you are getting from operational reports.

• If the data available within the organization are unsuitable or inadequate, the marketer should extend the search to external secondary data sources.

• External Sources of Data

• External Sources are sources which are outside the company in a larger environment. Collection of external data is more difficult because the data have much greater variety and the sources are much more numerous.

• External data can be divided into following classes.

• Government Publications- Government sources provide an extremely rich pool of data for the researchers. In addition, many of these data are available free of cost on internet websites. There are number of government agencies generating data. These are:

• Registrar General of India- It is an office which generate demographic data. It includes details of gender, age, occupation etc.

• Central Statistical Organization- This organization publishes the national accounts statistics. It contains estimates of national income for several years, growth rate, and rate of major economic activities. Annual survey of Industries is also published by the CSO. It gives information about the total number of workers employed, production units, material used and value added by the manufacturer.

• Director General of Commercial Intelligence- This office operates from Kolkata. It gives information about foreign trade i.e. import and export. These figures are provided region-wise and country-wise.

• Ministry of Commerce and Industries- This ministry through the office of economic advisor provides information on wholesale price index. These indices may be related to a number of sectors like food, fuel, power, food grains etc. It also generates All India Consumer Price Index numbers for industrial workers, urban, non manual employees and cultural labourers.

• Planning Commission- It provides the basic statistics of Indian Economy.

• Reserve Bank of India- This provides information on Banking Savings and investment. RBI also prepares currency and finance reports.

• Labour Bureau- It provides information on skilled, unskilled, white collared jobs etc.

• National Sample Survey- This is done by the Ministry of Planning and it provides social, economic, demographic, industrial and agricultural statistics.

• Department of Economic Affairs- It conducts economic survey and it also generates information on income, consumption, expenditure, investment, savings and foreign trade.

• State Statistical Abstract- This gives information on various types of activities related to the state like - commercial activities, education, occupation etc.

• Non Government Publications- These includes publications of various industrial and trade associations, such as

• The Indian Cotton Mill Association

• Various chambers of commerce

• The Bombay Stock Exchange (it publishes a directory containing financial accounts, key profitability and other relevant matter)

• Various Associations of Press Media.

• Export Promotion Council.

• Confederation of Indian Industries ( CII )

• Small Industries Development Board of India

• Different Mills like - Woolen mills, Textile mills etc

• The only disadvantage of the above sources is that the data may be biased. They are likely to colour their negative points.

• Syndicate Services- These services are provided by certain organizations which collect and tabulate the marketing information on a regular basis for a number of clients who are the subscribers to these services. So the services are designed in such a way that the information suits the subscriber. These services are useful in television viewing, movement of consumer goods etc. These syndicate services provide information data from both household as well as institution.

• In collecting data from household they use three approaches

• Survey- They conduct surveys regarding - lifestyle, sociographic, general topics.

• Mail Diary Panel- It may be related to 2 fields - Purchase and Media.

• Electronic Scanner Services- These are used to generate data on volume.

• They collect data for Institutions from

• Whole sellers

• Retailers, and

• Industrial Firms

• Various syndicate services are Operations Research Group (ORG) and The Indian Marketing Research Bureau (IMRB).

• Importance of Syndicate Services

• Syndicate services are becoming popular since the constraints of decision making are changing and we need more of specific decision-making in the light of changing environment. Also Syndicate services are able to provide information to the industries at a low unit cost.

• Disadvantages of Syndicate Services

• The information provided is not exclusive. A number of research agencies provide customized services which suits the requirement of each individual organization.

• International Organization- These includes

• The International Labour Organization (ILO)- It publishes data on the total and active population, employment, unemployment, wages and consumer prices

• The Organization for Economic Co-operation and development (OECD)- It publishes data on foreign trade, industry, food, transport, and science and technology.

• The International Monetary Fund (IMA)- It publishes reports on national and international foreign exchange regulations.

From their website: "The Common Core of Data (CCD) is a program of the U.S. Department of Education's National Center for Education Statistics that annually collects fiscal and non-fiscal data about all public schools, public school districts and state education agencies in the United States. The data are supplied by state education agency officials and include information that describes schools and school districts, including name, address, and phone number; descriptive information about students and staff, including demographics; and fiscal data, including revenues and current expenditures."

EconData.net (EconData)

From their website: "We have 1,000 links to socioeconomic data sources, arranged by subject and provider, pointers to the Web's premiere data collections, and our own list of the ten best sites for finding regional economic data."

World Bank Data (WorldBank)

From their website: "At the World Bank, the Development Data Group coordinates statistical and data work and maintains a number of macro, financial and sector databases. These databases are used by teams to prepare Country Assistance Strategies, poverty assessments, research studies and other forms of economic and sector work. This site is meant to provide all users with improved access to World Bank data and to make that data easy to find and use."

Panel Study of Income Dynamics (PSID)

From their website: "The study began in 1968 with a nationally representative sample of over 18,000 individuals living in 5,000 families in the United States. Information on these individuals and their descendants has been collected continuously, including data covering employment, income, wealth, expenditures, health, marriage, childbearing, child development, philanthropy, education, and numerous other topics."

Statistics in Sports: Sports Data Resources (AMSTAT)

This site hosted by the American Statistical Association lists sports data resources around the web.

Researchers should maximise access to research datasets of value to the wider research community in a timely and responsible manner. Any data that is shared should be of a sufficiently high quality that it will have value to other researchers and should be provided in a format that enables it to be used effectively.

15. We recognise that in some cases it may not be appropriate for researchers to share their data. However, if your research meets the criteria for requiring a data management and sharing plan but you are intending not to share your data, the reasons for this must be clearly justified.

16. Data should be shared in accordance with recognised data standards where these exist, and in a way that maximises opportunities for data linkage and interoperability. Sufficient metadata must be provided to enable the dataset to be used by others. Agreed best practice standards for metadata provision should be adopted where these are in place.

17. When developing data management and sharing plans, researchers should therefore consider and briefly describe:

In preparing data management and sharing plans, researchers should consider carefully any resources they may need to deliver their plan.Where dedicated resources are required, these should be outlined and justified as part of the plan.

41. Issues to consider include: