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Different Market Research Techniques

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Hi, can you please help to review the characteristics, strengths and weaknesses of any two out of the several data collection methods choosing one from each one of the bigger categories, which are qualitative and quantitative research.

Review and evaluate different types of market research techniques, providing examples.

Please don't forget to provide references.

Thanks.

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Greetings,

Hope you are well.

Try and associate the main concepts in relations towards quantitative research as a way to measure in quantity that provides strength in numbers (i.e. market size, consumer demographics, and the frequencies in relating consumer buyer behavior). While the qualitative is more in focus on the valuable data of user needs and wants that enhances demographic particulars with regard to behaviors. In doing so, the qualitative is more adept to accomplishing the goal in learning from or predicting the participants / consumer base behavioral preferences.

The strengths and weaknesses of qualitative and quantitative research can relate to the following core ...

Solution Summary

Different market research techniques are provided. Bigger categories for collection methods are determined.

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Develop a research proposal presentation using power point...

Please see attached.Denise Chisholm Unit 1 IP Marketing

Research Topic: Enrollment of College of Business Administration at Texas A&M University- Kingsville

(General information about research topic)
Texas A&M University-Kingsville is located in historic Kingsville, a friendly, safe city
Of 25,000 that is the home of the legendary King Ranch. Corpus Christi and its beaches
Are just 40 miles to the northeast, and the border with Mexico is 120 miles to the south at
Brownsville or 119 miles to the west at Laredo. College of Business is noted for
Preparation of business professionals and offers a broad variety of undergraduate and
Graduate programs. College of Business Administration, is nationally accredited by the
Association of Collegiate Business Schools and Programs (ACBSP) to offer the Bachelor
Of Business Administration (B.B.A.) degree at the undergraduate level and the Master of
Business Administration (M.B.A.) and the Master of Professional Accountancy (M.P.A.)
Degrees at the graduate level.
The College of Business Administration is composed of the following three departments:
The Department of Accounting and Computer Information Systems
The Department of Economics and Finance
The Department of Management and Marketing

In addition, the College houses the J.R. Manning Center for Professional Ethics which
Serves as the location of the Philosophy program for the University.
Primary service area of the College of Business is the three counties of Kleberg, Nueces,
And Jim Wells. However, it has traditionally also drawn many students from the areas
Around San Antonio, Victoria, and the Rio Grande Valley.

(Research problem)
Since A&M-Kingsville became part of The Texas A&M University System, and changed
Its name in 1993, its identity has suffered. No external campaign promoted the name
Change or made it known. And, no marketing effort has attempted to re-establish the old
Identity, or create a new one. In this same environment, competition for students has
Increased from community colleges and universities throughout South Texas, while
Universities from other parts of the state and nation are increasing their recruitment
Efforts in South Texas as they attempt to increase their minority enrollments. From 1998
To 2002 the enrollment of College Of Business increased 38%, from 713 to 986.
However this is significantly lower than desired.

(Research approach)
The prior research objective is to prepare a marketing research to increase the enrollment of College Of Business Administration at Texas A&M University-Kingsville and offer
Strategic goals that support this objective. The other main objectives to do this are as
Follows:
1. Obtain information about the general characteristics and student demographics
Of College Of Business and use statistics of enrollment and retention rates to
Understand the current situation and define the target population.
2. Refer to the results of previous studies and marketing plans to determine the
Strengths and weaknesses to prepare a SWOT analysis.
3. Analyze the survey results of all external and internal audiences, learn the general
Opinions and focus on the biggest qualities and lacks of the school.
4. Search the rival institutions in South Texas, learn their superiorities and lacks
To compare with A&M-Kingsville
5. Evaluate performance and offer goals to implement a plan with resources
Available.

Denise Chisholm Unit 2 IP#1 Marketing

? Formulate a research design for research topic

Objective: To increase student enrollment.
Project: Texas A&M University-Kingsville improving the enrollment in the College of Business Administration at different levels that is (B.B.A), (M.B.A) and (M.P.A) levels.

Background and Objectives
DESIGN OF THE RESEARCH
1. First to use secondary information about the student preferences and choices industry in the business schools limited to the area around Kleberg, Nueces, Jim Wells, Corpus Christi, Houston, San Antonio, Victoria, and the Rio Grande Valley. The sources will range from publications related to different universities, news articles to internet sources (including student sites).
2. Second, to use syndicated sources to get a report on the business schools in the universities in the area around Kleberg, Nueces, Corpus Christi, Houston, Jim Wells, San Antonio, Victoria, and the Rio Grande Valley. This would give us an idea of the pricing, product, distribution and promotional strategies of other business schools.
3. Thirdly, survey- interviewing to be use to collect primary data including the use of observational methods and focus groups.
4. Fourthly, the use of sampling methods for the selection of sample for survey.
5. Fifth, measurement of the data collected.
6. Sixth, the tabulation of the data collected and data processing.
7. Seventh, preliminary data to be analyzed.
8. Eighth, data analysis and development of strategies for improvement of enrollment rate for the College of Business Administration...

METHODS TO BE USED
1. Mainly university publications, publications on student preferences and Internet sources, including student maintained sites of competing universities for secondary data.
2. We intend to use the CMS report on business schools for secondary data.
3. Observational methods will be through recording the behavior of our target students through indirect methods.
4. Sampling design given below will be used for sampling.
5. The data will be entered on a spreadsheet, and calculations and statistical analysis including multidimensional scaling will be done from the data collected.
SAMPLE
The target population will be all the potential applicants in the areas of Kleberg, Nueces, Jim Wells, San Antonio, Corpus Christi, Houston, Victoria, and the Rio Grande Valley. The sampling frame will be the list of all potential applicants to Business Schools from the areas of Kleberg, Nueces, Jim Wells, San Antonio, Corpus Christi, Houston, Victoria, and the Rio Grande Valley. The sampling unit will be each member of the designated area who may apply to a business school. To determine the sample size a small-scale pilot study would be carried out to determine an estimate of the standard deviation. Then we would take a sample size that would give us a 95 percent confidence level. Using a combination of referrals and prior notification would reduce non-response or refusal bias.

STATISTICS EMPLOYED
this would be a combination of means, standard deviation and a correlation coefficient matrix. This matrix would be used to do cluster analysis.

OBSERVATIONAL RESEARCH
this would be mainly through the observations made through the University application process. That is the items that are ticked as preferences by potential applicants to the business school. As a part of developing the list of preferences of our potential students, especially to the business school, the observations need to be made of the behavior of the students. For example, the rankings of the universities that are patronized by the target students, the pricing policies of the university selected and the placement potential of those universities that are preferred by our target students.

ESTIMATE OF THE MARKET SIZE/ FORECAST.
From the secondary data we have an estimate of the market size of the Business Schools which draw students from the areas of Kleberg, Nueces, Jim Wells, San Antonio, Corpus Christi, Houston, Victoria, and the Rio Grande Valley. This apart the secondary data will also provide reliable information on the pricing strategies, product positioning, distribution and promotional strategies of competing universities. From this data we can use moving averages or exponential smoothing to estimate the market size in the year of launching of the new strategy. Based on the percent of the market share that we will be targeting for the first year we will be able to get an estimate of the enrollment during the first year.

DATA COLLECTION design is created by specifying the time required for collecting the primary data in this case it is three weeks, the length of the interview is expected to last 20 minutes, the sample size will be determined by the pilot study done and the number of individuals working on the study especially the persons involved in the primary data collection is estimated to be five. The design will be enhanced through good communication between the field personnel and the research leader...

First setting up the rules will create measurement instrument design for assigning numbers to objects of represent quantities or attributes. There are three basic types of designs that will be used to measure potential student preferences, that is the affect referral design, that is the presupposition that the student's overall preferences drives the business school selection process. Second, we begin with a set of explicit perceptions of beliefs about business school characteristic or attributes and use them as a basis or predicting business school valuations. This would be our compositional design. And in the third case we begin with the measures of the overall evaluations of general attributes of business schools patronized by students from Kleberg, Nueces, Jim Wells, San Antonio, Victoria, and the Rio Grande Valley, and use the information to infer the weights of values attached to individual, underlying characteristics of business schools. That design would be our de compositional design.

DATA PREPARATION DESIGN: This includes the use of mark-sensed questionnaires, optical scanning, verification and cleaning. Cleaning includes a check of all internal consistencies including multi punches, frequency distribution and check for missing responses. The missing responses check procedures normally include leaving them blank, case wise deletion, pair wise deletion, the mean response and imputed responses. In case of computer-based survey, the cleaning would be carried out for the computer-generated data.

Denise Chisholm Unit 3 IP #1 Continued

Project: Texas A&M University-Kingsville improving the enrollment in the College of Business Administration at different levels that is (B.B.A), (M.B.A) and (M.P.A) levels.
Sampling Design
Cluster sampling will be used because "natural" groupings are evident in the population. The total population is divided into groups or clusters, namely the demarcated areas in Kleberg, Nueces, Jim Wells, Houston, San Antonio, Victoria, and the Rio Grande Valley. Elements within a cluster should be as heterogeneous as possible. But there should be homogeneity between clusters. We do not know of any reason to differentiate between the states. Each cluster will be a small scale version of the total population. Each cluster must be mutually exclusive and collectively exhaustive. A random sampling technique will be then used on any relevant clusters to choose which clusters to include in the study. We will use a two-stage cluster sampling, a random sampling technique that will be applied to the elements from each of the selected clusters.
Each cluster that is the demarcated area of Kleberg, Nueces, Jim Wells, Houston , San Antonio, Victoria, and the Rio Grande Valley will be treated as the sampling unit so analysis will be done on a population of clusters (at least in the first stage). 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.).
The version of cluster sampling we will be using is area sampling or geographical cluster sampling. Clusters consist of geographical areas. These are the demarcated areas of Kleberg, Nueces, Jim Wells, Houston, San Antonio, Victoria, and the Rio Grande Valley 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 the savings in cost may make that feasible.
In our study, cluster analysis will be 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 will be taken. In this method, the probability of selecting an element in any given cluster will vary inversely with the size of the cluster.
THE MEASUREMENT INSTRUMENT TO BE USED WOULD BE THE LIKERT SCALE
a Likert scale will be the questionnaire format. It will request respondents to specify their level of agreement to each of a list of statements. .
Likert scaling is a unidimensional scaling method. As in all scaling methods, the first step is to define what to measure. Because this is a unidimensional scaling method, it is assumed that the concept is one-dimensional in nature.
A typical question using a five-point Likert scale might make a statement, then ask the respondents to indicate whether they:
1. Strongly disagree
2. Disagree
3. Neither agree nor disagree
4. Agree
5. Strongly agree
the results show an ordinal level of preference; numbers have an inherent order or sequence but do not correspond to a precise mathematical value.
Our type of survey questions will be where respondents will be asked to rate the level at which they agree or disagree with a given statement. For example:
Texas A&M University-Kingsville is a top business school.
strongly disagree 1 2 3 4 5 6 7 strongly agree
A Likert scale is used to measure attitudes, preferences, and subjective reactions in the areas of Kleberg, Nueces, Jim Wells, Corpus Christi, Houston, San Antonio, Victoria, and the Rio Grande Valley. Using software evaluation, we will then objectively measure efficiency and effectiveness with performance metrics such as time taken or errors made. Likert scale will help get at the emotional and preferential responses people have to the Texas A&M University-Kingsville business school.

DATA COLLECTION AND PREPARATION DESIGN
In Texas A&M University-Kingsville project you may have data coming from a number of different sources at different times:
mail surveys returns
coded interview data
pretest or posttest data
observational data
In the Texas A&M University-Kingsville study, you are ready to do a comprehensive data analysis. Different researchers differ in how they prefer to keep track of incoming data. In most cases, you will want to set up a database that enables you to assess at any time what data is already in and what is still outstanding. You could do this with any standard computerized database program (e.g., Microsoft Access, Claris Filmmaker), although this requires familiarity with such programs. Or, you can accomplish this using standard statistical program (e.g., SPSS, SAS, Minitab, Data desk) and running simple descriptive analyses to get reports on data status. It is also critical that the Texas A&M University-Kingsville data analyst retain the original data records for a reasonable period of time -- returned surveys, field notes, test protocols, and so on. Most researchers will retain such records for at least 5-7 years. For important or expensive studies, the original data might be stored in a data archive. The data analyst of the Texas A&M University-Kingsville business school study should always be able to trace a result from a data analysis back to the original forms on which the data was collected. A database for logging incoming data is a critical component in good research record-keeping.
ACCURACY
as soon as data is received you should screen it for accuracy. In some circumstances doing this right away will allow you to go back to the sample to clarify any problems or errors. There are several questions you should ask as part of this initial data screening:
Are the responses legible/readable?
Are all important questions answered?
Are the responses complete?
Is all relevant contextual information included (e.g., data, time, place, researcher)?
In most marketing research, quality of measurement is a major issue. Assuring that the data collection process does not contribute inaccuracies will help assure the overall quality of subsequent analyses.
Database Structure
The database structure is the manner in which Texas A&M University-Kingsville data for the study so that it can be accessed in subsequent data analyses. You might use the same structure you used for logging in the data or, if your study is large and complex, you might have one structure for logging data and another for storing it. As mentioned above, there are generally two options for storing data on computer -- database programs and statistical programs. Usually a database program allows you greater flexibility in manipulating the data and should be selected.
In every research project, Texas A&M University-Kingsville team will generate a printed codebook that describes the data and indicates where and how it can be accessed. Minimally the codebook should include the following items for each variable:
variable name
variable description
variable format (number, data, text)
instrument/method of collection
date collected
respondent or group
variable location (in database)
notes
the codebook will be an indispensable tool for the analysis team. Together with the database, it should provide comprehensive documentation that will enable other researchers who might subsequently want to analyze the data to do so without any additional information.
Entering the Data
In order to assure a high level of data accuracy, the Texas A&M University-Kingsville team will use a procedure called double entry. In this procedure you enter the data once. Then, you use a special program that allows you to enter the data a second time and checks each second entry against the first. If there is a discrepancy, the program notifies the user and allows the user to determine the correct entry. This double entry procedure significantly reduces entry errors. However, these double entry programs are not widely available and require some training. An alternative is to enter the data once and set up a procedure for checking the data for accuracy. For instance, you might spot check records on a random basis. Once the data have been entered, you will use various programs to summarize the data that allow you to check that all the data are within acceptable limits and boundaries. For instance, such summaries will enable you to easily spot whether there are persons whose age is 601 or who have a 7 entered where you expect a 1-to-5 response.
Data Transformations
Once the data have been entered Texas A&M University-Kingsville team will transform the raw data into variables that are usable in the analyses. There are a wide variety of transformations that you might perform. Some of the more common are:
missing values
many analysis programs automatically treat blank values as missing. In others, you need to designate specific values to represent missing values. For instance, you might use a value of -99 to indicate that the item is missing. You need to check the specific program you are using to determine how to handle missing values.
Item reversals
on scales and surveys, we sometimes use reversal items to help reduce the possibility of a response set. When you analyze the data, you want all scores for scale items to be in the same direction where high scores mean the same thing and low scores mean the same thing. In these cases, you have to reverse the ratings for some of the scale items. For instance, let's say you had a five point response scale for a self esteem measure where 1 meant strongly disagree and 5 meant strongly agree. One item is "I generally feel good about myself." If the respondent strongly agrees with this item they will put a 5 and this value would be indicative of higher self esteem. Alternatively, consider an item like "Sometimes I feel like I'm not worth much as a person." Here, if a respondent strongly agrees by rating this 5 it would indicate low self esteem. To compare these two items, we would reverse the scores of one of them (probably we'd reverse the latter item so that high values will always indicate higher self esteem). We want a transformation where if the original value was 1 it's changed to 5, 2 is changed to 4, 3 remains the same, 4 is changed to 2 and 5 is changed to 1. While you could program these changes as separate statements in most program, it's easier to do this with a simple formula like:
New Value = (High Value + 1) - Original Value
In our example, the High Value for the scale is 5, so to get the new (transformed) scale value, we simply subtract each Original Value from 6 (i.e., 5 + 1).
Scale totals
once you've transformed any individual scale items you will often want to add or average across individual items to get a total score for the scale.
Categories
For many variables you will want to collapse them into categories. For instance, you may want to collapse income estimates (in dollar amounts) into income ranges.

References:
msdn.microsoft.com/library/ en-us/createdw/createdw_20wx.asp
/ documentation/sas_usg_3.1/USG/node96.html - 6k
www.startsampling.com/ - 29k - 14 Jun 2005
www.socialresearchmethods.net/kb/sampling.htm

Denise Chisholm Unit 4 IP#1 marketing

DATA ANALYSIS DESIGN
Project: Texas A&M University-Kingsville improving the enrollment in the College of Business Administration at different levels that is (B.B.A), (M.B.A) and (M.P.A) levels.
One analysis used will be "Discriminant analysis"

1. Texas A&M University-Kingsville marketing research team will formulate the problem and gather data - 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. The data collection stage is usually done by marketing research professionals. Survey questions ask the respondent to rate a product from one to five (or 1 to 5) on a range of attributes chosen by the researcher. Anywhere from five to twenty attributes will be chosen. They could include things like: reputation, placement after graduation, the quality of faculty and computing facilities. The attributes chosen will vary depending on the area being studied. The same question is asked about all the attributes in the study. The data for multiple dimensions is codified and input into a statistical program such as SPSS or SAS.
2. Texas A&M University-Kingsville marketing research team will estimate the Discriminant Function Coefficients and determine the statistical significance and validity - Texas A&M University-Kingsville marketing research team will choose the appropriate discriminant analysis method. The direct method involves estimating the discriminant function so that all the predictors are assessed simultaneously. The stepwise method enters the predictors sequentially. The two-group method will be used when the dependent variable has two categories or states. The multiple discriminant method is used when the dependent variable has three or more categorical states. Texas A&M University-Kingsville marketing research team will use Wilks's Lambda to test for significance in SPSS or F stat in SAS. This is the most common method used to test validity is to split the sample into an estimation or analysis sample, and a validation or holdout sample. The estimation sample will be used by the Texas A&M University-Kingsville marketing research team in constructing the discriminant function. The validation sample will be used to construct a classification matrix which contains the number of correctly classified and incorrectly classified cases. The percentage of correctly classified cases will be called the hit ratio.
3. Texas A&M University-Kingsville marketing research team will plot the results on a two dimensional map, define the dimensions, and interpret the results. The statistical program (or a related module) will map the results. The map will plot each product (usually in two dimensional space). The distance of products to each other will indicate either how different they are. The dimensions will be labelled by the Texas A&M University-Kingsville marketing research team. This requires subjective judgement and is likely to be very challenging.

Cluster analysis
In addition to discriminant analysis, the Texas A&M University-Kingsville marketing research team will use cluster analysis. Cluster analysis is a class of statistical techniques that can be applied to data that exhibits "natural" groupings. Cluster analysis will sort through the raw data and group it into clusters. A cluster is a group of relatively homogeneous cases or observations. Objects in a cluster are similar to each other. They are also dissimilar to objects outside the cluster, particularly objects in other clusters.
Cluster analysis, like factor analysis and multi dimensional scaling, is an interdependence technique : it makes no distinction between dependent and independent variables. The entire set of interdependent relationships will be examined. It is similar to multi dimensional scaling in that both examine inter-object similarity by examining the complete set of interdependent relationships. The difference is that multi dimensional scaling identifies underlying dimensions, while cluster analysis identifies clusters. Cluster analysis is the obverse of factor analysis. Whereas factor analysis reduces the number of variables by grouping them into a smaller set of factors, cluster analysis reduces the number of observations or cases by grouping them into a smaller set of clusters. The Texas A&M University-Kingsville marketing research team expects clusters like the "demographic factors that attract students" or "social issues" that push up the ranking of a business school will emerge.
cluster analysis will be used by the Texas A&M University-Kingsville marketing research team for
? Segmenting the university market and determining target markets
? Positioning of Texas A&M University-Kingsville and New Product Development
? Selecting test markets for pilot studies.

The basic procedure to be followed by the Texas A&M University-Kingsville marketing research team will be:
1. Formulate the problem - select the variables that Texas A&M University-Kingsville marketing research team wish to apply the clustering technique to
2. Select a distance measure - various ways of computing distance:
o Squared Euclidean distance - the square root of the sum of the squared differences in value for each variable
o Manhattan distance - the sum of the absolute differences in value for any variable
o Chebychev distance - the maximum absolute difference in values for any variable
3. Select a clustering procedure
4. Decide on the number of clusters
5. Texas A&M University-Kingsville marketing research team will map and interpret clusters - draw conclusions - illustrative techniques like perceptual maps, icicle plots, and dendrograms are useful
6. Texas A&M University-Kingsville marketing research team will assess reliability and validity - various methods:
o repeat analysis but use different distance measure
o repeat analysis but use different clustering technique
o split the data randomly into two halves and analyze each part separately
o repeat analysis several times, deleting one variable each time
o repeat analysis several times, using a different order each time

Texas A&M University-Kingsville marketing research team will do perceptual mapping

Texas A&M University-Kingsville marketing research team will use perceptual mapping for understanding and communicating the position of Texas A&M University-Kingsville business school on different dimensions in relation to competitors. Perceptual mapping is a graphics technique used by marketers that attempts to visually display the perceptions of customers or potential customers. Typically the position of a product, product line, brand, or company is displayed relative to their competition.
Texas A&M University-Kingsville marketing research team will prepare perceptual maps at different stages of the study and examine how they change as the study progresses. Perceptual maps need not come from a detailed study. There are also intuitive maps (also called judgmental maps or consensus maps) that are created by marketers based on their understanding of their industry. Management uses its best judgement. It is questionable how valuable this type of map is. Often they just give the appearance of credibility to management's preconceptions.
There is an assortment of statistical procedures that can be used to convert the raw data collected in a survey into a perceptual map. Texas A&M University-Kingsville marketing research team will use discriminant analysis and cluster analysis to do perceptual mapping. Some techniques are constructed from perceived differences between products, others are constructed from perceived similarities..
References:
www.statsoft.com/textbook/stdiscan.html
www2.chass.ncsu.edu/garson/pa765/discrim.htm
www.psychstat.smsu.edu/multibook/mlt03.htm -
www.resample.com/xlminer/help/DA/da_intro.htm
www.statsoft.com/textbook/stcluan.html
149.170.199.144/multivar/ca.htm - 21k
www.clustan.com/what_is_cluster_analysis.html -
www.psychstat.smsu.edu/multibook/mlt04.htm
www.populus.com/techpapers/map.php
www.marketingiq.com/software/pm/index.cfm
www.sdr-consulting.com/article11.html

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