# Statistics Allowable With Nominal, Ordinal and Interval Thesis

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¶ … statistics allowable with nominal, ordinal and interval scales.

Nominal is a counting operation and its descriptive statistics is "frequency in each category, percentage in each category mode." Ordinal is a rank ordering and its descriptive statistics is "median range, percentile ranking." Interval is an arithmetic operation on intervals between numbers and its descriptive statistics is "mean, standard deviation and variance." Understanding descriptive statistics necessitates specifically looking at the type of data that are being described. The nominal scales only place numeric labels on non-quantitative concepts, for example, dogs have the value of "1" and cats have the value "2." Many categories or groups are actually nominal, such as racial group and gender. In some research, for instance, the study counts the number of individuals who are in a specific category, such as living in a designated city. Ordinal scales are ranked in a way that compares one to another, with a highest and lowest. An example is the tallest and shortest children in the school. It is not possible to perform ordinal data with mathematical computations. Interval scales allocate specific values to something, so that the intervals are equal, such as a six-point attitudinal scale. In this case, mathematical operations can be performed. With ratio scales, there is the interval with a true zero point, such as weight or the number of something in a room. Then ratios can be determined.Buy full paper

for $19.77## Thesis on

Difference between validity and reliability. The purpose of conducting a study is to come up with accurate measurement results. This is why research must be both reliable and valid; the two are interrelated. Reliability is the consistency of the measurements, or how well the study can be repeated. Does the same measurement yield the same results when repeated? Reliability cannot be calculated, only estimated. Validity is whether the test is measuring what it expects to measure. if, for example, the researchers are measuring a table that is six feet wide, they measure the table with a measuring tape and find it is six feet. They measure it again and again and consistently get six feet. The tape measure is yielding reliable results. The tape measure includes inches and feet, so it should also yield valid results. If the researchers measure the table with the "right" tape measure, it should yield a correct measurement of the table's width. In other words, when conducting research, it is necessary to use measurement tools that yield consistent responses when asked time after time and that yield accurate responses from the participants.

Difference between conceptual and operational definition. Conceptual definitions define a concept with the use of other concepts, which makes measuring difficult. An operational definition specifically identifies at least one observable condition or event, so the researcher knows how to measure that condition or event. The operational definition must be reliable and valid. For instance, if a researcher wanted to know about a person's enjoyment for his or her job, the conceptual definition would reflect interest for an enjoyment and satisfaction in his or her job and the ability that he or she has to apply skills. The operational definition would ask this person how true each statement is about his or her job: very true, somewhat true or not very true at all. (e.g. 1) I find my work enjoyable; 2) I can use my own unique skills and abilities; 3) I gain satisfaction knowing that I have the opportunity of demonstrating my best abilities to others.

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Nominal is a counting operation and its descriptive statistics is "frequency in each category, percentage in each category mode." Ordinal is a rank ordering and its descriptive statistics is "median range, percentile ranking." Interval is an arithmetic operation on intervals between numbers and its descriptive statistics is "mean, standard deviation and variance." Understanding descriptive statistics necessitates specifically looking at the type of data that are being described. The nominal scales only place numeric labels on non-quantitative concepts, for example, dogs have the value of "1" and cats have the value "2." Many categories or groups are actually nominal, such as racial group and gender. In some research, for instance, the study counts the number of individuals who are in a specific category, such as living in a designated city. Ordinal scales are ranked in a way that compares one to another, with a highest and lowest. An example is the tallest and shortest children in the school. It is not possible to perform ordinal data with mathematical computations. Interval scales allocate specific values to something, so that the intervals are equal, such as a six-point attitudinal scale. In this case, mathematical operations can be performed. With ratio scales, there is the interval with a true zero point, such as weight or the number of something in a room. Then ratios can be determined.Buy full paper

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## Thesis on *Statistics Allowable With Nominal, Ordinal and Interval* Assignment

Difference between validity and reliability. The purpose of conducting a study is to come up with accurate measurement results. This is why research must be both reliable and valid; the two are interrelated. Reliability is the consistency of the measurements, or how well the study can be repeated. Does the same measurement yield the same results when repeated? Reliability cannot be calculated, only estimated. Validity is whether the test is measuring what it expects to measure. if, for example, the researchers are measuring a table that is six feet wide, they measure the table with a measuring tape and find it is six feet. They measure it again and again and consistently get six feet. The tape measure is yielding reliable results. The tape measure includes inches and feet, so it should also yield valid results. If the researchers measure the table with the "right" tape measure, it should yield a correct measurement of the table's width. In other words, when conducting research, it is necessary to use measurement tools that yield consistent responses when asked time after time and that yield accurate responses from the participants.Difference between conceptual and operational definition. Conceptual definitions define a concept with the use of other concepts, which makes measuring difficult. An operational definition specifically identifies at least one observable condition or event, so the researcher knows how to measure that condition or event. The operational definition must be reliable and valid. For instance, if a researcher wanted to know about a person's enjoyment for his or her job, the conceptual definition would reflect interest for an enjoyment and satisfaction in his or her job and the ability that he or she has to apply skills. The operational definition would ask this person how true each statement is about his or her job: very true, somewhat true or not very true at all. (e.g. 1) I find my work enjoyable; 2) I can use my own unique skills and abilities; 3) I gain satisfaction knowing that I have the opportunity of demonstrating my best abilities to others.

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