Management Consulting/Few questions

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Question
1.     Discuss the significance and importance of the elements that make up the complete research problem
2.      How would you use a Likert scale to ascertain the image of a leading Computer Brand among some consumers?
3.      A group of 11 students selected at random secured the grade points: 1.5, 2.2, 0.9, 1.3, 2.0, 1.6, 1.8, 1.5, 2.0, 1.2 and 1.7 (out of 3). Use the sign test to test the hypothesis that intelligence is a random function (with a median of 1.8) at 5% level of significance.
4.      Write shot notes on
a.      Regression Analysis
b.      Discriminant Analysis
c.      Factor Analysis

5.       Write a brief note on the important elements of communication dimensions namely Purpose, Audience, Media, Message, Time, Place and Cost."

Answer
1.]Discuss the significance and importance of the elements that make up the complete research problem
Elements of a Good Proposal
A good proposal has nine elements, and each is important in an effective presentation. The specific format and content of the elements may vary, depending on requirements in the solicitation or announcement. It is also important to remember the elements will not always appear as separate sections or in the order listed here.
Statement of the Problem
This section should include a clear and concise statement of the purpose or goal of the project. In a grant proposal, it consists of (1) the specific question(s) to be answered, (2) a brief explanation of the need for or significance of the study, and (3) an explanation of how the results will contribute to the existing body of knowledge. In a response to an RFP, this section consists of the offerer's interpretation of the government's requirements. Proposals that restate or paraphrase the RFP suggest that the offerer has not really thought much about the issues.
Literature Review
A proposal should reflect the offerer's understanding of relevant bodies of literature and where his/her study fits in that context. This section need not be lengthy, however it should be comprehensive. It should trace the central themes in the literature, highlight major areas of disagreement, and reflect a critical stance toward the materials reviewed. Citing weak research or poorly articulated theory does not help. RFP's frequently contain hints or directions for the literature review. Grant announcements usually do not, so offerers have considerable autonomy in identifying relevant bodies of literature. That autonomy requires careful thought and creativity in identifying appropriate sources.
Conceptual Framework
In this section, the offerer provides his/her own perspective. What theories or concepts will guide the study? How or why do they suggest the specific hypotheses or research questions? What are the strengths and weaknesses of the proposed framework? RFP's sometimes specify a particular theoretical perspective that should guide the study. If so, the proposal should contain clear evidence that the offerer understands the theoretical perspective and can work with it. Grant programs usually do not specify a conceptual orientation.
Hypotheses or Research Questions
Following the description of the conceptual framework, there should be a clear, crisp statement of the research hypotheses, or, in the case of some qualitative studies, a concise description of the phenomena to be examined. Depending on the requirements of the solicitation, the hypotheses may be stated informally or formally. Finally, an explanation of why testing the hypotheses or answering the questions is appropriate for elucidating the research problems and is consistent with the conceptual framework should be included.
Methodology
This section consists of a description of plans for collecting and analyzing the data. What instruments will be used? Why are they appropriate for this study? Is there evidence of the instruments' reliability and validity? How and to whom will they be administered? What procedures will be followed in the data analysis? For qualitative studies, there should be an explanation of the purpose of observations and interviews, and, if possible, some indication of their content and format. The description of the proposed methodology should contain enough detail to indicate that the offerer knows what he/she is doing. Proposals that include the formula for a statistical test as the only information about plans for data analysis don't lend confidence that the study will yield robust findings or rich insights. Similarly, proposals that simply offer to use the newest research procedures may suggest that the investigator is attentive or attracted to fads, but may not be familiar with that particular research approach.
Task Structure (Scope of Work)
This section indicates exactly what will be done, the sequence of the various activities, and the products of deliverables that will be prepared. RFP's specify the tasks, deliverables, and schedule in some detail, although there is usually some latitude for offerers. In preparing grant proposals, there is more freedom to define the tasks. In both cases, it is important that the proposed task structure includes all of the activities necessary for completing the project. Planning a viable schedule for carrying out the tasks is often as important as developing a comprehensive list of tasks.
Management Plan
A crucial part of the plan is a creative and effective approach to project management. The approach should indicate who will be responsible for each part of the work, and who will be responsible for overall coordination. The management plan should also be carefully tailored to the unique nature of the individual project.
Staff and Institutional Qualifications
This section includes a full discussion of the qualifications and experience of the proposed staff. Sometimes it is useful to include brief summaries of the staff experience in the management plan and to attach complete resumes for each member of the team as appendices to the proposal. This information is essential should be presented in a way that demonstrates that the staff has the necessary qualifications and experience to conduct the research. This section should also include complete information about the relevant qualifications of the institution where the project will be located. Research projects often require a variety of hardware or software and there should be clear evidence that adequate facilities are available to support the project.
Budget
The project budget should include clear and reasonable estimates of the costs of each element of the project, and there should be enough supporting information to indicate how the estimates were developed. Base salaries for all staff, standard charges for computer use, and allowable travel costs are a few examples of useful background information. In preparing the budget, remember that the budget is a reflection of the offerer's understanding of the project and his/her ability to plan and manage effectively. A budget that is too low may indicate failure to grasp how much work is really necessary to do a good job. A budget that is too high may also suggest a lack of understanding of what is required, or it may reflect careless management. Both make the proposal unattractive. The grant programs and RFP's require that certain forms be used in preparing budgets, although background information can usually be presented in a number of ways. These forms should be filled out carefully and completely, since they are usually examined first when business sections of the proposal are reviewed. Even if a budget is not required, it is a good idea to have a budget for internal purposes.
A final note about good proposals
Quality writing is critical in all good proposals. It should be clear, concise, and free of jargon. There should be no spelling or grammatical errors, and the proposal should be easy to read. Sloppy proposals and proposals laden with jargon do not provide a positive image of the offerer, nor do they lend confidence that solid research will follow. Proposals that are well-written and attractive are a pleasure to read, and they make a good impression with reviewers.
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2.]How would you use a Likert scale to ascertain the image of a leading Computer Brand  
LIKEHART  TECHNIQUE
a.   Likert Scale
It was developed Rensis Likert. Here the respondents are asked to indicate a degree of agreement and disagreement with each of a series of statement. Each scale item has 5 response categories ranging from strongly agree and strongly disagree.
5
Strongly agree   4
Agree   3
Indifferent   2
Disagree   1
Strongly disagree
Each statement is assigned a numerical score ranging from 1 to 5. It can also be scaled as -2 to +2.
-2   -1   0   1   2
For example quality of Mother Diary ice-cream is poor then Not Good is a negative statement and Strongly Agree with this means the quality is not good.
Each degree of agreement is given a numerical score and the respondents total score is computed by summing these scores. This total score of respondent reveals the particular opinion of a person.
Likert Scale are of ordinal type, they enable one to rank attitudes, but not to measure the difference between attitudes. They take about the same amount of efforts to create as Thurston scale and are considered more discriminating and reliable because of the larger range of responses typically given in Likert scale.
A typical Likert scale has 20 - 30 statements. While designing a good Likert Scale, first a large pool of statements relevant to the measurement of attitude has to be generated and then from the pool statements, the statements which are vague and non-discriminating have to be eliminated.
Thus, likert scale is a five point scale ranging from ’strongly agreement’to ’strongly disagreement’. No judging gap is involved in this method.


use a Likert scale to ascertain the image of a  Computer Brand   

•   Create over 100 different question types.
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•   Engage survey takers with rich media.
How you ask a question is just as important as what you ask. Our research suite offers the flexibility that comes from offering the most question types in the industry to engage respondents, increase response rates and accurately collect data.
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Likert scales: A Likert scale is what is termed a summated instrument scale. This means that the items making up a Liken scale are summed to produce a total score. In fact, a Likert scale is a composite of itemised scales. Typically, each scale item will have 5 categories, with scale values ranging from -2 to +2 with 0 as neutral response. This explanation may be clearer from the example in figure 3.12.
A questionnaire can help you collect information about what people do, what they have, and what
they think, know, feel or want.
Five different types of information may be distinguished. Any one or a combination of these types
of information may be included in a questionnaire.
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1.KNOWLEDGE - what people know; how well they understand something.

These questions ask what people know, are aware of, understand. Choices implied in knowledge
questions include correct/incorrect, accurate/inaccurate, what is accepted as true or factual. For
example:
What is the major cause of accidental deaths among children inside the home?
The most effective weight loss plan includes exercise. TRUE-FALSE
The ideal refrigerator temperature is .
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2.BELIEF - what people think is true; an opinion.

Beliefs are judgments of what people think is true or false, what one thinks exists or does not
exist. Choices implied in belief questions include what did or did not happen. Questions may seek
perceptions of past, present or future reality. For example:
In your opinion, does positive self-esteem among adolescents prevent drug abuse?
Do you think that lower beef prices would increase beef consumption?
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3.ATTITUDE - how people feel about something; a preference.

Such questions ask people to indicate whether they have a positive or a negative feeling about a
subject, what they value. Words typically used in attitude questions include: prefer/not prefer;
desirable/undesirable; favor/oppose; should/should not; satisfactory/unsatisfactory. For example:
Do you favor or oppose controlled calving for your operation?
Do you agree or disagree that eating beef causes heart disease?
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4.BEHAVIOR - what people do Ñ may be a physical/manual or mental behavior.

Questions about behavior ask people what they have done in the past, what they are doing now, or
what they plan to do in the future. For example:
Have you ever attended an Extension program about cotton production?
Do you treat your cotton for bollworms?
How are you currently using the information gained in the food storage workshop?
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5.ATTRIBUTES - what people are; what people have.

Attributes are a person’s personal or demographic characteristics such as age, education,
occupation, income. Questions on attributes ask people about who they are rather than what they
do. For example:
Where do you currently live?
How many children do you have?
What percentage of your household income comes from off-farm employment?

To write meaningful questions, be clear about the intended uses and type of information desired.
If questions are vague, the questionnaire may elicit attitudes and beliefs when the intent is to
document actual behavior.

Likewise, questions related to each type of information present different writing problems.
Questions concerning attitudes tend to be more difficult to phrase, given the complexity underlying
most attitudes. Careful attention should be given to the wording of such questions. In contrast,
questions about knowledge, behaviors and attributes tend to be more direct.

The response or information you obtain is only as good as the question. To get the type of
information you want, you must ask the right question!
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1.KNOWLEDGE - what people know; how well they understand something.

These questions ask what people know, are aware of, understand. Choices implied in knowledge
questions include correct/incorrect, accurate/inaccurate, what is accepted as true or factual. For
example:

-do you know who has produced the X COMPUTER
-have you seen the X advertisement
-have you seen the X  COMPUTER
-are you aware that the X  is a small COMPUTER.
-do you know the price of the car is 1  YYYYY.
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2.BELIEF - what people think is true; an opinion.

Beliefs are judgments of what people think is true or false, what one thinks exists or does not
exist. Choices implied in belief questions include what did or did not happen. Questions may seek
perceptions of past, present or future reality.

-do you think  X COMPUTER is the smallest in size in the indian market.
-do you think it is a comfortable COMPUTER.
-do you like the design of the COMPUTER
-what features attracts you most.
-is the X  the cheapest COMPUTER  in the indian market.
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3.ATTITUDE - how people feel about something; a preference.

Such questions ask people to indicate whether they have a positive or a negative feeling about a
subject, what they value. Words typically used in attitude questions include: prefer/not prefer;
desirable/undesirable; favor/oppose; should/should not; satisfactory/unsatisfactory.

-what is your personal feeling about this X.
-do you think a lot of people will buy this X.
-will you go for this X.
-if so, what will be the one major factor, which will make you buy the X.
-what other factor will push you to buy this X.
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4.BEHAVIOR - what people do Ñ may be a physical/manual or mental behavior.

Questions about behavior ask people what they have done in the past, what they are doing now, or
what they plan to do in the future.

-do you own a  COMPUTER.
-if, what model is it.
-do you drive your own COMPUTER .
-will you replace your COMPUTER  WITH   X.
-will you add  X  to your OFFICE  DESK.
-will you recommend this to your friend.
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5.ATTRIBUTES - what people are; what people have.

Attributes are a person’s personal or demographic characteristics such as age, education,
occupation, income. Questions on attributes ask people about who they are rather than what they
do. For example:

-can I ask your good name.
-can I ask your age,please.
-where do you currently live --just the location.
-are you married.
-if married, do you have children.
-can I ask --what is your education level.
-what is your profession.
-how will you describe your lifestyle
conservative/ modern / upmarket
-if employed, what will be your income bracket.

To write meaningful questions, be clear about the intended uses and type of information desired.
If questions are vague, the questionnaire may elicit attitudes and beliefs when the intent is to
document actual behavior.

Likewise, questions related to each type of information present different writing problems.
Questions concerning attitudes tend to be more difficult to phrase, given the complexity underlying
most attitudes. Careful attention should be given to the wording of such questions. In contrast,
questions about knowledge, behaviors and attributes tend to be more direct.

The response or information you obtain is only as good as the question. To get the type of
information you want, you must ask the right question!
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Figure 3.12 The Likert scale
  Strongly Agree   Agree   Neither   Disagree   Strongly Disagree
A   1   2   3   4   5
B   1   2   3   4   5
C   1   2   3   4   5
D   1   2   3   4   5
E   1   2   3   4   5
Likert scales are treated as yielding Interval data by the majority of marketing researchers.
The scales which have been described in this chapter are among the most commonly used in marketing research.
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3.]A group of 11 students selected at random secured the grade points: 1.5, 2.2, 0.9, 1.3, 2.0, 1.6, 1.8, 1.5, 2.0, 1.2 and 1.7 (out of 3). Use the sign test to test the hypothesis that intelligence is a random function (with a median of 1.8) at 5% level of significance.
NO EXPERTISE
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4]Write shot notes on
a.   Regression Analysis


Regression analysis is used when you want to predict a continuous dependent variable from a number of independent variables. If the dependent variable is dichotomous, then logistic regression should be used. (If the split between the two levels of the dependent variable is close to 50-50, then both logistic and linear regression will end up giving you similar results.) The independent variables used in regression can be either continuous or dichotomous. Independent variables with more than two levels can also be used in regression analyses, but they first must be converted into variables that have only two levels. This is called dummy coding and will be discussed later. Usually, regression analysis is used with naturally-occurring variables, as opposed to experimentally manipulated variables, although you can use regression with experimentally manipulated variables. One point to keep in mind with regression analysis is that causal relationships among the variables cannot be determined. While the terminology is such that we say that X "predicts" Y, we cannot say that X "causes" Y.
Assumptions of regression
Number of cases
When doing regression, the cases-to-Independent Variables (IVs) ratio should ideally be 20:1; that is 20 cases for every IV in the model. The lowest your ratio should be is 5:1 (i.e., 5 cases for every IV in the model).
Accuracy of data
If you have entered the data (rather than using an established dataset), it is a good idea to check the accuracy of the data entry. If you don't want to re-check each data point, you should at least check the minimum and maximum value for each variable to ensure that all values for each variable are "valid." For example, a variable that is measured using a 1 to 5 scale should not have a value of 8.
Missing data
You also want to look for missing data. If specific variables have a lot of missing values, you may decide not to include those variables in your analyses. If only a few cases have any missing values, then you might want to delete those cases. If there are missing values for several cases on different variables, then you probably don't want to delete those cases (because a lot of your data will be lost). If there are not too much missing data, and there does not seem to be any pattern in terms of what is missing, then you don't really need to worry. Just run your regression, and any cases that do not have values for the variables used in that regression will not be included. Although tempting, do not assume that there is no pattern; check for this. To do this, separate the dataset into two groups: those cases missing values for a certain variable, and those not missing a value for that variable. Using t-tests, you can determine if the two groups differ on other variables included in the sample. For example, you might find that the cases that are missing values for the "salary" variable are younger than those cases that have values for salary. You would want to do t-tests for each variable with a lot of missing values. If there is a systematic difference between the two groups (i.e., the group missing values vs. the group not missing values), then you would need to keep this in mind when interpreting your findings and not overgeneralize.
After examining your data, you may decide that you want to replace the missing values with some other value. The easiest thing to use as the replacement value is the mean of this variable. Some statistics programs have an option within regression where you can replace the missing value with the mean. Alternatively, you may want to substitute a group mean (e.g., the mean for females) rather than the overall mean.
The default option of statistics packages is to exclude cases that are missing values for any variable that is included in regression. (But that case could be included in another regression, as long as it was not missing values on any of the variables included in that analysis.) You can change this option so that your regression analysis does not exclude cases that are missing data for any variable included in the regression, but then you might have a different number of cases for each variable.
Outliers
You also need to check your data for outliers (i.e., an extreme value on a particular item) An outlier is often operationally defined as a value that is at least 3 standard deviations above or below the mean. If you feel that the cases that produced the outliers are not part of the same "population" as the other cases, then you might just want to delete those cases. Alternatively, you might want to count those extreme values as "missing," but retain the case for other variables. Alternatively, you could retain the outlier, but reduce how extreme it is. Specifically, you might want to recode the value so that it is the highest (or lowest) non-outlier value.
Normality
You also want to check that your data is normally distributed. To do this, you can construct histograms and "look" at the data to see its distribution. Often the histogram will include a line that depicts what the shape would look like if the distribution were truly normal (and you can "eyeball" how much the actual distribution deviates from this line). This histogram shows that age is normally distributed:

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b.   Discriminant Analysis
Discriminant Analysis may be used for two objectives: either   we want to assess the adequacy of classification, given the group memberships of the objects under study; or we wish to assign objects to one of a number of (known) groups of objects. Discriminant Analysis may thus have a descriptive or a predictive objective.
In both cases, some group assignments must be known before carrying out the Discriminant Analysis. Such group assignments, or labelling, may be arrived at in any way. Hence Discriminant Analysis can be employed as a useful complement to Cluster Analysis (in order to judge the results of the latter) or Principal Components Analysis. Alternatively, in star-galaxy separation, for instance, using digitised images, the analyst may define group (stars, galaxies) membership visually for a conveniently small training set or design set.     
Methods implemented in this area are Multiple Discriminant Analysis, Fisher's Linear Discriminant Analysis, and K-Nearest Neighbours Discriminant Analysis.
Multiple Discriminant Analysis
(MDA) is also termed Discriminant       Factor Analysis and Canonical Discriminant Analysis. It adopts a similar perspective to PCA: the rows of the data matrix to be examined constitute points in a multidimensional space, as also do the group mean vectors. Discriminating axes are determined in this space, in such a way that optimal separation of the predefined groups is attained. As with PCA, the problem becomes mathematically the eigenreduction of a real, symmetric matrix. The eigenvalues represent the discriminating power of the associated eigenvectors. The nYgroups lie in a space of dimension at most nY - 1. This will be the number of discriminant axes or factors obtainable in the most common practical case when n > m > nY (where n is the number of rows, and m the number of columns of the input data matrix).
Linear Discriminant Analysis
is the 2-group case of MDA.   It optimally separates two groups, using the Mahalanobis metric or generalized distance.     It also gives the same linear separating decision surface as Bayesian maximum likelihood discrimination in the case of equal class covariance matrices.
K-NNs Discriminant Analysis
: Non-parametric (distribution-free) methods dispense with the need for assumptions regarding the probability density function. They have become very popular especially in the image processing area. The K-NNs method assigns an object of unknown affiliation to the group to which the majority of its K nearest neighbours belongs.
There is no best discrimination method. A few remarks concerning the advantages and disadvantages of the methods studied are as follows.
•   Analytical simplicity or computational reasons may lead to initial consideration of linear discriminant analysis or the NN-rule.
•   Linear discrimination is the most widely used in practice. Often the 2-group method is used repeatedly for the analysis of pairs of multigroup data (yielding  decision surfaces for k groups).
•   To estimate the parameters required in quadratic discrimination more computation and data is required than in the case of linear discrimination. If there is not a great difference in the group covariance matrices, then the latter will perform as well as quadratic discrimination.
•   The k-NN rule is simply defined and implemented, especially if there is insufficient data to adequately define sample means and covariance matrices.
•   MDA is most appropriately used for feature selection. As in   the case of PCA, we may want to focus on the variables used in order to investigate the differences between groups; to create synthetic variables which improve the grouping ability of the data; to arrive at a similar objective by discarding irrelevant variables; or to determine the most parsimonious variables for graphical representational purposes.



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c.   Factor Analysis

Factor analysis is a statistical method used to explain variability among observed variables in terms of fewer unobserved variables called factors. The observed variables are modeled as linear combinations of the factors, plus "error" terms. The information gained about the interdependencies can be used later to reduce the set of variables in a dataset.
Factor analysis  is used in behavioral sciences, social sciences, marketing, product management, operations research, and other applied sciences that deal with large quantities of data.
Applications in psychology
Factor analysis is used to identify "factors" that explain a variety of results on different tests. For example, intelligence research found that people who get a high score on a test of verbal ability are also good on other tests that require verbal abilities. Researchers explained this by using factor analysis to isolate one factor, often called crystallized intelligence or verbal intelligence, that represents the degree in which someone is able to solve problems involving verbal skills.
Factor analysis in psychology is most often associated with intelligence research. However, it also has been used to find factors in a broad range of domains such as personality, attitudes, beliefs, etc. It is linked to psychometrics, as it can assess the validity of an instrument by finding if the instrument indeed measures the postulated factors.

Advantages
•   Reduction of number of variables, by combining two or more variables into a single factor. For example, performance at running, ball throwing, batting, jumping and weight lifting could be combined into a single factor such as general athletic ability. Usually, in an item by people matrix, factors are selected by grouping related items. In the Q factor analysis technique, the matrix is transposed and factors are created by grouping related people: For example, liberals, libertarians, conservatives and socialists, could form separate groups.
•   Identification of groups of inter-related variables, to see how they are related to each other. For example,  a factor called "broad visual perception" relates to how good an individual is at visual tasks  ----a "broad auditory perception" factor, relating to auditory task capability. Furthermore,  a global factor, called "g" or general intelligence, that relates to both "broad visual perception" and "broad auditory perception". This means someone with a high "g" is likely to have both a high "visual perception" capability and a high "auditory perception" capability, and that "g" therefore explains a good part of why someone is good or bad in both of those domains.

Disadvantages
•   "...each orientation is equally acceptable mathematically. But different factorial theories proved to differ as much in terms of the orientations of factorial axes for a given solution as in terms of anything else, so that model fitting did not prove to be useful in distinguishing among theories." . This means all rotations represent different underlying processes, but all rotations are equally valid outcomes of standard factor analysis optimization. Therefore, it is impossible to pick the proper rotation using factor analysis alone.
•   Factor analysis can be only as good as the data allows. In psychology, where researchers have to rely on more or less valid and reliable measures such as self-reports, this can be problematic.
•   Interpreting factor analysis is based on using a “heuristic”, which is a solution that is "convenient even if not absolutely true". More than one interpretation can be made of the same data factored the same way, and factor analysis cannot identify causality.

Factor analysis in marketing
The basic steps are:
•   Identify the salient attributes consumers use to evaluate products in this category.
•   Use quantitative marketing research techniques (such as surveys) to collect data from a sample of potential customers concerning their ratings of all the product attributes.
•   Input the data into a statistical program and run the factor analysis procedure. The computer will yield a set of underlying attributes (or factors).
•   Use these factors to construct perceptual maps and other product positioning devices.

Information collection
The data collection stage is usually done by marketing research professionals. Survey questions ask the respondent to rate a product sample or descriptions of product concepts on a range of attributes. Anywhere from five to twenty attributes are chosen. They could include things like: ease of use, weight, accuracy, durability, colourfulness, price, or size. The attributes chosen will vary depending on the product being studied. The same question is asked about all the products in the study. The data for multiple products is coded and input into a statistical program such as SPSS  and SYSTAT.

Analysis
The analysis will isolate the underlying factors that explain the data. Factor analysis is an interdependence technique. The complete set of interdependent relationships are examined. There is no specification of either dependent variables, independent variables, or causality. Factor analysis assumes that all the rating data on different attributes can be reduced down to a few important dimensions. This reduction is possible because the attributes are related. The rating given to any one attribute is partially the result of the influence of other attributes. The statistical algorithm deconstructs the rating (called a raw score) into its various components, and reconstructs the partial scores into underlying factor scores. The degree of correlation between the initial raw score and the final factor score is called a factor loading. There are two approaches to factor analysis: "principal component analysis" (the total variance in the data is considered); and "common factor analysis" (the common variance is considered).

How Many Cases and Variables?
The clearer the true factor structure, the smaller the sample size needed to discover it. But it would be very difficult to discover even a very clear and simple factor structure with fewer than about 50 cases, and 100 or more cases would be much preferable for a less clear structure.
The rules about number of variables are very different for factor analysis than for regression. In factor analysis it is perfectly okay to have many more variables than cases. In fact, generally speaking the more variables the better, so long as the variables remain relevant to the underlying factors.
How Many Factors?
This section describes two rules for choosing the number of factors. Readers familiar with factor analysis will be surprised to find no mention of Kaiser's familiar eigenvalue rule or Cattell's scree test. Both rules are mentioned later, though as explained at that time I consider both rules obsolescent. Also both use eigenvalues, which I have not yet introduced.
Of the two rules that are discussed in this section, the first uses a formal significance test to identify the number of common factors. Let N denote the sample size, p the number of variables, and m the number of factors. Also RU denotes the residual matrix U transformed into a correlation matrix, |RU| is its determinant, and ln(1/|RU|) is the natural logarithm of the reciprocal of that determinant.
To apply this rule, first compute G = N-1-(2p+5)/6-(2/3)m. Then compute
Chi-square = G ln(1/|RU|)
with
df = .5[(p-m)2-p-m]
If it is difficult to compute ln(1/|RU|), that expression is often well approximated by rU2, where the summation denotes the sum of all squared correlations above the diagonal in matrix RU.
To use this formula to choose the number of factors, start with m = 1 (or even with m = 0) and compute this test for successively increasing values of m, stopping when you find nonsignificance; that value of m is the smallest value of m that is not significantly contradicted by the data. The major difficulty with this rule is that in my experience, with moderately large samples it leads to more factors than can successfully be interpreted.

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5] Write a brief note on the important elements of communication dimensions namely Purpose, Audience, Media, Message, Time, Place and Cost.
IMPORTANCE  OF  Purpose IN  COMMUNICATION
The purpose of communication is to send your message effectively to the receiver/readers. Communication links people who believe in a common cause, together with a view to strengthen relationships
Communication allows people or groups to better understand each other and connect. Communication is the means in which information is disseminated.

Communication is also the transduction of emotions and or thoughts from one to another. The purpose is to intentionally create harmony or dissonance with the sender and receiver.
IMPORTANCE  OF  AUDIENCE   IN  COMMUNICATION
Audience is who the communication is targeted at. The recipient.
Every piece of communication has an audience. The audience is the person or persons who are to receive the communication and therefore interpret the purpose.
Here are some examples of audience:
•  Project manager
•  Senior manager
•  Developer
•  Tester
•  Test Manager
•  Friend
•  Partner
The list could continue. There may be more than one audience. These multiple audiences may be compatible, they may be at odds with each other. It can often be the case that the audience is confused or contradictory to the other; perfect material for a keen software tester. What is the audience of that function? Module? Feature? Button? Document?
If you know or can work out the audience then you are in a good place to make better decisions about the communication. You are also much better placed to find the real audience and find out more about them.
When you are communicating yourself, you also need to be wholly aware and clear of what the audience of the communication is. This will mean your communication is direct, powerful and reaches it's target audience in the first instance.
=======================
IMPORTANCE  OF  MEDIA  IN  COMMUNICATION
Media (singular medium) are the storage and transmission channels or tools used to store and deliver information or data. It is often referred to as synonymous with mass media or news media, but may refer to a single medium used to communicate any data for any purpose.
The communication media acts as a communication channel for linking various computing devices so that they may interact with each other.

Contemporary communication media facilitate communication and data exchange among a large number of individuals across long distances via teleportation, email, teleconferencing, Internet forums, etc. Traditional mass media channels such as TV, radio and magazines, on the other hand, promote one-to-many communication.


IMPORTANCE  OF  MESSAGE  IN  COMMUNICATION
A message (verbal or nonverbal--or both) is the content of the communication process.

•   Verbal and Nonverbal Content
"A message may include verbal content (i.e., written or spoken words, sign language, e-mail, text messages, phone calls, snail-mail, sky-writing, etc.) and will include nonverbal content (meaningful behavior beyond words: e.g., body movement and gestures, eye contact, artifacts and clothing, vocal variety, touch, timing, etc.). Intentionally or not, both verbal and nonverbal content is part of the information that is transferred in a message. If nonverbal cues do not align with the verbal message, ambiguity is introduced even as uncertainty is increased."

•   Communicating Messages
"Communication is the process of sending and receiving messages. However, communication is effective only when the message is understood and when it stimulates action or encourages the receiver to think in new ways."
  
IMPORTANCE  OF   TIMING  IN  COMMUNICATION
Workplace Communication: Timing is Everything
In workplace communications, timing can make or break the effectiveness of the communication process. Your message may be important, but if it is delivered too late, it will be irrelevant. Likewise, if you try to deliver a message at a time that the audience is not receptive, your efforts may be wasted.
 Since timing is such an important element of effective communication, be sure to time your communications so that they are delivered:
•   When you have the attention of the person to whom you are speaking. If you try to communicate with someone who is in the middle of doing something or surrounded by distractions, your message is unlikely to be heard.
•   When the person is most receptive. Trying to communicate with employees when they’re rushing out the door at the end of the day or on their way to lunch isn’t likely to produce positive results. Pick a time when they will be able to focus exclusively on your message.
•   When you are prepared to answer questions. Remember that good communication is interactive. You should always be prepared to answer questions and clear up any issues that are unclear to your listener. Timing matters for the communicator as well as the recipient.





IMPORTANCE  OF   PLACE IN  COMMUNICATION
The above components of communication promote shared meaning when they operate together to effectively deliver a message. The work environment in which those components take place, also affects the communication and whether the communication is received.
In a work environment that stresses open communication, employee involvement, and shared goals, communication more frequent and more effective. But, the expectation for significant communication sets the bar higher in these best workplaces. So, even in high morale, employee focused work environments, employees complain that they don’t know what is going on.
Because of all of the components and the overall environment of an individual workplace, communication remains challenging. The age old questions about who needs to know what and when do they need to know it, is never fully answered to just about anyone’s satisfaction.






IMPORTANCE  OF  COST   IN  COMMUNICATION

Poor communication costs business mil¬lions of dollars every single day. Most executives and managers understand this, yet they don’t realize how big a part they play in this miscommunication.
Communication is vital to the success of your organization. To be most effec¬tive, communication must circulate and reach all levels, not just the core.

Different forms of poor communication. Here are but a few:
Long, unproductive, numbing meet¬ings without a clear purpose or agen¬da, often reaching no conclusions, re¬sult in lost productivity as well as the collective time of everyone attending.

Uninspired selling skills and anemic sales presentations showing no inter¬est or understanding of a prospect’s needs, result in missed opportunities and lost sales.
Rambling, cryptic, and incoherent emails that are misunderstood or ig¬nored, result in wasted time. Often (up to 50% of the time) an email’s tenor is incorrectly perceived, simply because body language cannot be analyzed and tone of voice not perceived; this results in hurt feelings, ill will, and inaction.♦ ♦ ♦ ♦


Distracted managers who simply do not or cannot truly listen alienate staff and lower morale. Staff members who realize they are not being listened to and simply patronized, themselves stop communicating.

Poor communication squanders time, wastes effort, erodes loyalty, and loses business.
Squandering time. Poor communica¬tion simply takes longer to process and understand, if understanding can be at-tained. Unnecessary questions are asked, discussions are needlessly lengthy, the communication is recreated, only to be foisted again on a wary audience.
Here’s an example of an email re¬ceived by a colleague: “The company may need the more accurate methodology since it’s the standard approach employed of the more approximate method that may result in an estimate that underesti¬mates and not on-target estimates.” After a lengthy conversation with the sender, my colleague’s client rewrote the email. Final squandered time for one email: six hours.

Wasting effort. My bank’s CEO recent¬ly sent every customer a letter explain¬ing the bank’s checking account over¬draft policy: five dense paragraphs. The policy was more onerous than the cur¬rent overdraft protection plan. Many cus¬tomers didn’t appreciate the change and called to protest, inundating the bank. The customer service representatives explained why the letter was mislead¬ing and inaccurate. As a result, the CEO planned to rewrite and resend the letter. The CEO’s effort fell prey to the 30% of business letters that initially fail.♦ ♦
Eroding loyalty.
Losing business. The presentation was wonderful, beautiful slides, expertly delivered—all about the expertise of the company who was leading the proposal. Unfortunately, the state agency wanted to know how the company would solve the agency’s problem and support their budget. Instead, the agency got egotis¬tical fluff. The agency, rightly, awarded the contract to another firm; the compa¬ny came in “second”.

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Leo Lingham

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management consulting process, management consulting career, management development, human resource planning and development, strategic planning in human resources, marketing, careers in management, product management etc

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18 years working managerial experience covering business planning, strategic planning, corporate planning, management service, organization development, marketing, sales management etc

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24 years in management consulting which includes business planning, strategic planning, marketing , product management,
human resource management, management training, business coaching,
counseling etc

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PRINCIPAL -- BESTBUSICON Pty Ltd

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MASTERS IN SCIENCE

MASTERS IN BUSINESS ADMINSTRATION

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