Consumers Preferences Regarding Shopping Malls Cultural Studies Essay

I want to measure the consumers preferences regarding the shopping malls .which method will be best suited for me to collect the data regarding their preferences and also give valid reasons to support your answer

ANSWER: To measure the consumer’s preference regarding the shopping malls different method which are available are as follows:

Observation

Questionnaire

The method which is best suited is questionnaire designing because observation method comprises of some negative points which are as follows:-

Inadequacies of our sense organs:- several studies show that the perception of a man depends on several factors such as :

freshness

interest

freedom from interruption

The more favourable the conditions, the more receptive the person will be outside the impressions.

Interdependence of observation and inference:- observation and inference are inseparable. This means that whatever an observer sees, he tries to explain or interpret it on the basis of his past experience. Thus the observer inference problem is the main difficulty in as much as the observer can draw wrong inferences from observations.

Get Help With Your Essay

If you need assistance with writing your essay, our professional essay writing service is here to help!
Find out more about our Essay Writing Service

Effects of interaction between the observer and the observed:- this may have two distinct dangers. First, person being observed may become self-conscious of the observation and this may influence their normal behavior. Second, observation may get distorted merely because one more person- the observer- is present and people are conscious of his presence.

While questionnaire has following advantages against limitations of observation:-

Structured questionnaire:- it facilitates the collection of information in a systematic and orderly manner as the question have been formulated in advance.

Since the question ask by the each interviewer happen to be identical and are asked in the same order.

It calls for a straight forward and simple approach on the part of interviewers.

It is far easier to edit, tabulate and interpret data it contains.

It can be conveniently pre-tested so that suitable modifications can be made in the questions or in their sequence or both.

QUESTION-2

How you will design a questionnaire to measure the perception of Lovely Professional University in the minds Punjab’s people

ANSWER:- this are the step to be measure the perception of the lovely professional university in minds of Punjab people:-

Types of information to be collected is primary data:- because in this data we have to find the information for ourselves while in secondary data we do search for someone else and provide him with the information collected from market or net or magazines or newspaper.

types of questions:- the second important aspect in the designing of a questionnaire is to decide which types of question are to be used.

Open-ended question:- this type of questions are preffered when the researcher is interested in knowing what is uppermost in the mind of the respondent. It gives them the complete freedom to decide the the form,length and detail of the answer.

Multiple choice question:- in this case the respondent is offered

two or more choices. In this case their are various choices offered

and the respondent has to indicate which one is applicable in his case.

3.order of questions:- the another aspect to receive the attention of the researcher is sequence or order of questions to be contained in the questionnaire. Though in the beginning the researcher has to establish some raaport with the respondent, it is necessary that question asked in the beginning are simple and thereby helpful in establishing the raaport. Difficulty question should be asked to the end of the questionnaire.

4. How many questions to be asked?

The researcher has also to decide how many question are to be asked. It should not be too lengthy that would obviously be a disadvantage and the response to it may be quite poor. We have to sustain the interest of the respondent until the last moment till it is complete and the required information has been obtained.

layout of the questionnaire

finally the layout of the questionnaire has to be decided. This implies that the document should be set in such a way that it leaves a favourable impression on the respondent. It should be neatly printed and the individual pages should not have too many questions so as to appear crowded.

Mail questionnaire

In fact the type of questionnaire to be designed depends on the type of survey. Brodly there are three types of survey:-

Personal

Mail and

Telephone.

As far as telephone survey is concerned,it is not commonly used in India. Mainly the personal interview and mail survey are the only two survey methods.

QUESTION -3

“A sample may be large yet worthless because it is not random ; or it may be random but unreliable because it is small.” Comment on the statement.

QUESTION :- 4

A president of a company is concerned about the declining motivational level of his employees. Assume that there are 750 employees in the organization (including all top managers to lower managers) and president decided to select sample of 100 employees. As a researcher what type of sampling technique you will suggest to choose the sample and why?

ANSWER:- as a researcher we will suggest cluster sampling to choose the sample because by this, we can divide the employees in different groups as per their post and departments. According to questions we have to take sample of 100 employees out of 750 and to select 100 employees, we need to divide 750 employees in different groups and out of these groups, their group representatives will be taken out who will be the sample for the research.

QUESTION -5

Intel want to conduct a survey to determine businesses selection criterion for choosing PC and network communication products. Develop a suitable sampling plan for conducting the survey

ANSWER:-

Sampling plan to conduct survey wil be 

Define the target population- under this firsty the target population is the collection of elements or objects that possess the information sought by the researcher and about inferences are to be made what will be the elements what will be the sample units what wil be time and place

Determining the sampling frame-a sampling frame is a representation of the elements of the target population it consist of a list or set of direction for identifying the target population examples include the telephone book,an association directory listing the firms in an industry a list from an organisation etc

Selecting a sampling technique-selecting a sampling technique involves several decisions .the researcher must decide whether to use or sample with or without replacement and to use non probability or probability sampling

Determining the sample size- it generally refers to the number of elements to be included in the study determining the sample size is complex and involve qualitative and quantitative consideration which include the importance of decision, the nature of the research the number of variables the nature of analysis etc

Execute the sampling process -this include a detail specifications of how the sampling design decisions with respect to the population, sampling frame, unit which are to be implemented…..

QUESTION :-6

If population size is very large and heterogeneous the how sample should collected so that errors can be minimized?

ANSWER:- If population size is very large and heterogeneous then we will use cluster sampling because Cluster sampling is a sampling technique used when “natural” groupings are evident in a statistical population. It is often used in marketing research. In this technique, the total population is divided into these groups (or clusters) and a sample of the groups is selected. Then the required information is collected from the elements within each selected group. This may be done for every element in these groups or a subsample of elements may be selected within each of these groups. The technique works best when most of the variation in the population is within the groups, not between them. Elements within a cluster should ideally be as heterogeneous as possible, but there should be homogeneity between cluster means. Each cluster should be a small scale representation of the total population. 

ANSWER :-7

: a) its LIKERT scale. 

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.

For eg:

Strongly Agree

Agree

Neither

Disagree

Strongly Disagree

If the price of raw materials fell firms would reduce the price of their food products.

1

2

3

4

5

Without government regulation the firms would exploit the consumer.

1

2

3

4

5

Most food companies are so concerned about making profits they do not care about quality.

1

2

3

4

5

The food industry spends a great deal of money making sure that its manufacturing is hygienic.

1

2

3

4

5

Food companies should charge the same price for their products throughout the country

1

2

3

4

5

Likert scales are treated as yielding Interval data by the majority of marketing researchers.

b) its ratio scale. 

Ratio scales

The highest level of measurement is a ratio scale. This has the properties of an interval scale together with a fixed origin or zero point. Examples of variables which are ratio scaled include weights, lengths and times. Ratio scales permit the researcher to compare both differences in scores and the relative magnitude of scores. For instance the difference between 5 and 10 minutes is the same as that between 10 and 15 minutes, and 10 minutes is twice as long as 5 minutes.

Given that sociological and management research seldom aspires beyond the interval level of measurement, it is not proposed that particular attention be given to this level of analysis. Suffice it to say that virtually all statistical operations can be performed on ratio scales.

c) its ordinal scale. 

Ordinal scales

Ordinal scales involve the ranking of individuals, attitudes or items along the continuum of the characteristic being scaled. For example, if a researcher asked farmers to rank 5 brands of pesticide in order of preference he/she might obtain responses like those in table 3.2 below.

Figure 3.2 An example of an ordinal scale used to determine farmers’ preferences among 5 brands of pesticide.

Order of preference

Brand

1

Rambo

2

R.I.P.

3

Killalot

4

D.O.A.

5

Bugdeath

From such a table the researcher knows the order of preference but nothing about how much more one brand is preferred to another, that is there is no information about the interval between any two brands. All of the information a nominal scale would have given is available from an ordinal scale. In addition, positional statistics such as the median, quartile and percentile can be determined.

It is possible to test for order correlation with ranked data. The two main methods are Spearman’s Ranked Correlation Coefficient and Kendall’s Coefficient of Concordance. Using either procedure one can, for example, ascertain the degree to which two or more survey respondents agree in their ranking of a set of items. Consider again the ranking of pesticides example in figure 3.2. The researcher might wish to measure similarities and differences in the rankings of pesticide brands according to whether the respondents’ farm enterprises were classified as “arable” or “mixed” (a combination of crops and livestock). The resultant coefficient takes a value in the range 0 to 1. A zero would mean that there was no agreement between the two groups, and 1 would indicate total agreement. It is more likely that an answer somewhere between these two extremes would be found.

d) it is a ratio scale. 

Ratio scales

The highest level of measurement is a ratio scale. This has the properties of an interval scale together with a fixed origin or zero point. Examples of variables which are ratio scaled include weights, lengths and times. Ratio scales permit the researcher to compare both differences in scores and the relative magnitude of scores. For instance the difference between 5 and 10 minutes is the same as that between 10 and 15 minutes, and 10 minutes is twice as long as 5 minutes.

Given that sociological and management research seldom aspires beyond the interval level of measurement, it is not proposed that particular attention be given to this level of analysis. Suffice it to say that virtually all statistical operations can be performed on ratio scales. 

QUESTION:- 8

How sampling errors are different from non sampling errors? Give at least two examples of each

ANSWER:- What is sampling error?

    The uncertainty associated with an estimate that is based on data gathered from a sample of the population rather than the full population. In most cases, the analyst can only state that, for example, the errors are probably relatively small and will not affect most conclusions drawn from the survey, or that the errors may be fairly large and inferences are to be made with caution. In rare instances, researchers may be able to say with some confidence in what direction the error might be. 

 What is non-sampling error?

Any error affecting a survey or census estimate apart from sampling error Occurs in complete censuses as well as in sample surveys . A particular example of sampling error is the difference between the sample mean and the population mean

QUESTION :- 9

Department of agriculture wishes to investigate the use of pesticides by farmers in Punjab. How cluster sampling can help them to conduct their research.

ANSWER:- Cluster sampling is a sampling technique in which the entire population of interest is divided into groups, or clusters, and a random sample of these clusters is selected. Each cluster must be mutually exclusive and together the clusters must include the entire population. After clusters are selected, then all units within the clusters are selected. No units from non-selected clusters are included in the sample. This differs from stratified sampling, in which some units are selected from each group. When all the units within a cluster are selected, the technique is referred to as one-stage cluster sampling. If a subset of units is selected randomly from each selected cluster, it is called two-stage cluster sampling. Cluster sampling can also be made in three or more stages: it is then referred to as multistage cluster sampling.

In cluster sampling, the clusters are the primary sampling unit (PSU’s) and the units within the clusters are the secondary sampling units (SSU’s). It is important to keep these two levels in mind when calculating standard errors from cluster samples. If a cluster sample is analysed as if it were a simple random sample, the reported standard errors would probably be smaller then they should be. That would give the impression that the survey results are more precise than they really are. Whereas stratification often increases precision of the estimation compared with simple random sampling, cluster sampling often decreases it. That is because units in a cluster tend to be more similar than elements selected at random from the whole population. When using cluster sampling, it is usually necessary to increase the total sample size to achieve the same precision as in simple random sampling. Nevertheless, there are cases where cluster sampling is useful.

The main reason for using cluster sampling is that it usually much cheaper and more convenient to sample the population in clusters rather than randomly. In some cases, constructing a sampling frame that identifies every population element is too expensive or impossible. Cluster sampling can also reduce cost when the population elements are scattered over a wide area. Suppose you want to survey school children of a certain age in a specific area. If you drew a simple random sampling of school children, you might have to visit all schools in the area to interview your sample. With cluster sampling you could first select the schools to be included in your sample, and then select school children within each of the selected schools. That would probably reduce the number of schools you have to visit and therefore reduce the cost of data collection. In this example, the schools are what are sometimes referred to as natural clusters. In other cases, the population may be widely distributed geographically, and then cluster sampling, where the clusters consists of geographical areas, could reduce the number of areas that need to be visited. A smaller number of areas that need to be visited could reduce travel expenses and also make possible more efficient supervision of the fieldwork.

Cluster sampling is a sampling technique used when “natural” groupings are evident in a statistical population. It is often used in marketing research. In this technique, the total population is divided into these groups (or clusters) and a sample of the groups is selected. Then the required information is collected from the elements within each selected group. This may be done for every element in these groups or a subsample of elements may be selected within each of these groups. The technique works best when most of the variation in the population is within the groups, not between them.

Cluster elements

Elements within a cluster should ideally be as heterogeneous as possible, but there should be homogeneity between cluster means. Each cluster should be a small scale representation of the total population. The clusters should be mutually exclusive and collectively exhaustive. A random sampling technique is then used on any relevant clusters to choose which clusters to include in the study. In single-stage cluster sampling, all the elements from each of the selected clusters are used. In two-stage cluster sampling, a random sampling technique is applied to the elements from each of the selected clusters.

The main difference between cluster sampling and stratified sampling is that in cluster sampling the cluster is treated as the sampling unit so analysis is done on a population of clusters (at least in the first stage). In stratified sampling, the analysis is done on elements within strata. In stratified sampling, a random sample is drawn from each of the strata, whereas in cluster sampling only the selected clusters are studied. The main objective of cluster sampling is to reduce costs by increasing sampling efficiency. This contrasts with stratified sampling where the main objective is to increase precision.

There also exists multistage sampling, where more than two steps are taken in selecting clusters from clusters.

Aspects of cluster sampling

One version of cluster sampling is area sampling or geographical cluster sampling. Clusters consist of geographical areas. Because a geographically dispersed population can be expensive to survey, greater economy than simple random sampling can be achieved by treating several respondents within a local area as a cluster. It is usually necessary to increase the total sample size to achieve equivalent precision in the estimators, but cost savings may make that feasible.

In some situations, cluster analysis is only appropriate when the clusters are approximately the same size. This can be achieved by combining clusters. If this is not possible, probability proportionate to size sampling is used. In this method, the probability of selecting any cluster varies with the size of the cluster, giving larger clusters a greater probability of selection and smaller clusters a lower probability. However, if clusters are selected with probability proportionate to size, the same number of interviews should be carried out in each sampled cluster so that each unit sampled has the same probability of selection.

QUESTION:- 10

What is the role of data analysis and report preparation in marketing research? After the collection of data how analysis is done by the researcher

ANSWER: the background information would be:

Age

Gender: male or female

Social networking sites in which you are the member

How many hours in a week do u serve them

How many friends do you have in social networking,

What all communities you hav joined

After collecting data analysis will be done through following steps:

Development of an approach to the problem

Research Design Formulation

Known Characteristics of the Data

Properties of Statistical Techniques

Background and philosophy of researcher

Data Analysis Strategy

Problem Definition