Ann Harris ONLY
marnique1032
MGMT600Unit1IP1.pptxList at least 3 qualitative attributes of outdoor sporting goods about which they might want to ask consumers.
Attributes of outdoor sporting goods can either be ordinal or nominal in nature and the difference between these will be explored later on in the discussion.
One nominal attribute whose information consumers can be asked is the gender that is whether the consumer is male or female.
Ordinal attributes for which the consumer can provide information include consumer satisfaction and consumers love for the product.
For these two ordinal attributes the end point on the five point rating scale is 5 which will represent highly satisfied and a lot of love for the goods.
The level of love for the outdoor sporting goods and the level to which the consumers are satisfied with the products or how well the goods are able to meet their need are the ordinal attributes which the consumers can provide information to help in making decisions. This information can help to create a clear picture of how well the product is able to meet the need in the market, create value for consumers and how consumers feel about the product and how they value it. Gender in this case is the nominal attribute which can help the organization know whether their consumers are either male or female. This information is important since males and females have different needs and different preferences and understanding the numbers can help tailor their goods to the gender being targeted.
The five point rating scale runs from 1 to 5 and in this case the level of satisfaction and also love for the product will rank up from 1 to 5 in an ascending order for example one representing lowest level of satisfaction and 5 the highest.
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ORDINAL DATA VS NOMINAL DATA
There is a very distinct difference between nominal and ordinal data.
Nominal data on one hand is one which can be counted, where values are used as mere labels and one that cannot be measured (Diez, Barr & Rundel, 2016).
Ordinal data on the other hand is data that can be counted, cannot be measured and one where numbers are not only used as labels but can be used to put values or data in order.
It becomes very hard to put order in nominal data mainly because it is data that cannot be ranked but for ordinal data order can be attained because for this form of data ranking is possible and ranking can be done for example using the five point rating scale.
Nominal and ordinal data are similar in the fact that both can be counted but none of them can be measured. The main difference between the two come in the fact that for nominal data, ranking cannot be done while for ordinal data ranking can be done. For nominal data numbers are only used as a label for a name for example for the gender attribute mentioned in the first slide 1 can represent male and 2 can represent female. However in ordinal data numbers are not mere values since they help to provide information about the order choices. Scales are mainly used to capture the ordinal data. The main difference between ordinal and nominal data is the fact that ordinal data can be placed in order while nominal data cannot
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Continuation
Explain how nominal and ordinal data relate to a rating scale.
From the comprehensive differentiation offered for the nominal and ordinal scale, their relationship with the rating scale can be drawn easily.
The relationship boils down to the fact that there is no relationship between nominal data and the rating scale mainly because nominal data does not utilise the scale while for the ordinal data rating scales are the tools used to bring order to information (Stine & Foster, 2018).
List at least 2 quantitative attributes of outdoor sporting goods that market researchers might want to measure.
The researcher needs some quantitative market data to be able to inform the expansion move.
Example of qualitative attributes the researcher can measure include the level of demand in the market for the goods and the number of other brands available in the market and their market presence.
The relationship between nominal and ordinal data and the rating scale is the simple fact that the nominal data does not utilise it hence no relation since numbers in this case are only used as label as while the scales are used for ordinal data to help in ranking or bringing order.
Qualitative data is data that includes number which can be analysed and conclusions deduced from the analysis. In this case such data can include the levels of demand to gauge the need in the market and also the number of other brands and their market presence as a way of understanding the competition.
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INTERVAL DATA VS. RATIO DATA
Both interval and ratio data share a commonality in the fact they are both interval data.
The difference however between these two comes in the fact that there is no natural zero point for interval data but there s a very clear zero point for ratio data (Diez, Barr & Rundel, 2016).
Interval data is rather defined as data with equally split intervals between the values while the ratio data is data with a clear zero point.
Considering that they are both interval data it is easy to understand what a zero point is from the scale. A zero point is the point of the scale which has a zero value on it which symbolises absence of the data and from which positive value to the right start and negative values to the left. A good common example of interval data is the temperature scale on the Fahrenheit thermometer where a zero denotes no temperature. A good example of the ratio data is time since there is n single point when time will be absent or when it will be zero time. Time must always have a value. Tis is what it means to have no zero point natural. For interval data values can be either positive or negative meaning that values can go below zero whereas for ration data values are all positive. The interval data and ratio data are both similar in the fact that they are both ordinal data that is data that van be order end and ranked as discussed in the slides above.
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Population vs. Sample and target market definition
When an individual is conducting any form of research, there are certain individuals they are interested in depending on the topic they are studying. Their target population are the individuals who have certain characteristics that can help provide information about a given subject.
The population refers to all the targeted individuals who are of interest to the researcher a sample however refers to some part of the population which the researcher picks to represent the entire population. The information collected from the sample is generalized for the entire population (Mirakhor, 2014).
For the business in this case, the target market are the individuals they consider as consumers to their products. They are the individuals to whom they intend to sell their products to.
A researcher usually defined the group of interest for any research project. Every individual belonging to that group of interest form the population. In research however most due to constraints such as time and resources, it may not be possible to study every individual in the interested group and the researcher can choose to pick a part of the population to study and then generalise the conclusions drawn for the entire population. When a part of the population is picked, the part is referred to as the sample and they serve as representatives of the population in the research.
For a business a target market are the individuals that a business sees as their potential consumers and whom they aim at meeting their needs through their products.
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Avoiding Bias in research and possible population
Personal bias is often present in research and most especially in collection of qualitative data since it becomes hard for one to separate themselves from the data most of the times.
However as hard as it may be it is important to ensure as much as possible that bias is avoided.
This is important because bias in data sees to it that data collected is not accurate and that the quality of data is undermined (Black, 2013).
Where bias is allowed, the objectives of the research can fail to be met effectively.
A possible population in this case could be school sports teams and community sports teams.
In research bias can come in at any phase of the process whether it is the planning phase or the implementation phase. Presence of bias at any stage in the process goes against the whole purpose of the research which is to get a clear picture of what is happening on the ground. When personal bias sets in the ability for respondents to give actual data or for actual data to be collected is undermined and this consequently undermines the whole significance of research since the quality and accuracy of data is compromised (Black, 2013). For objectives of a research to be effectively achieved bias needs to be avoided as much as possible.
The possible population picked in this case is sports team from the community and from schools. These have been picked because they have a direct need for the sorts goods.
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References
Black, K. (2013). Applied business statistics : making better business decisions. Singapore: Wiley.
Diez, D., Barr, C. & Rundel, M. (2016). OpenIntro statistics. United States: publisher not identified.
Mirakhor, A. (2014). Introductory Mathematics and Statistics for Islamic Finance. Singapore: Wiley.
Stine, R. & Foster, D. (2018). Statistics for business : decision making and analysis. Boston: Pearson.
